Search

The intriguing network of antibody–antigen (Ab–Ag) interactions is highly governed by environmental perturbations and the nature of biomolecular interaction. Protein–protein interactions (PPIs) have potential applications in developing protein-adsorption-based sensors and nano-scale materials. Therefore, characterizing PPIs in the presence of a nanomaterial at the molecular level becomes imperative. The present work involves the investigation of antiferritin–ferritin (Ab–Ag) protein interactions under the influence of tungsten disulfide quantum dots (WS2 QDs). Isothermal calorimetry and contact angle measurements validated the strong influence of WS2 QDs on Ab–Ag interactions. The interfacial signatures of nano–bio-interactions were evaluated using sum frequency generation vibration spectroscopy (SFG-VS) at the air–water interface. Our SFG results reveal a variation in the tilt angle of methyl groups by ∼12° ± 2° for the Ab–Ag system in the presence of WS2 QDs. The results illustrated an enhanced ordering of water molecules in the presence of QDs, which underpins the active role of interfacial water molecules during nano–bio-interactions. We have also witnessed a differential impact of QDs on Ab–Ag by raising the concentration of the Ab–Ag combination, which showcased an increased inter-molecular interaction among the Ab and Ag molecules and a minimal influence on the methyl tilt angle. These findings suggest the formation of stronger and ordered Ab–Ag complexes upon introducing WS2 QDs in the aqueous medium and signify the potentiality of WS2 QDs relevant to protein-based sensing assays. [ABSTRACT FROM AUTHOR]

Most biological processes in living cells rely on interactions between proteins. Live-cell compatible approaches that can quantify to what extent a given protein participates in homo- and hetero-oligomeric complexes of different size and subunit composition are therefore critical to advance our understanding of how cellular physiology is governed by these molecular interactions. Biomolecular complex formation changes the diffusion coefficient of constituent proteins, and these changes can be measured using fluorescence microscopy-based approaches, such as single-molecule tracking, fluorescence correlation spectroscopy, and fluorescence recovery after photobleaching. In this review, we focus on the use of single-molecule tracking to identify, resolve, and quantify the presence of freely-diffusing proteins and protein complexes in living cells. We compare and contrast different data analysis methods that are currently employed in the field and discuss experimental designs that can aid the interpretation of the obtained results. Comparisons of diffusion rates for different proteins and protein complexes in intracellular aqueous environments reported in the recent literature reveal a clear and systematic deviation from the Stokes–Einstein diffusion theory. While a complete and quantitative theoretical explanation of why such deviations manifest is missing, the available data suggest the possibility of weighing freely-diffusing proteins and protein complexes in living cells by measuring their diffusion coefficients. Mapping individual diffusive states to protein complexes of defined molecular weight, subunit stoichiometry, and structure promises to provide key new insights into how protein–protein interactions regulate protein conformational, translational, and rotational dynamics, and ultimately protein function. [ABSTRACT FROM AUTHOR]

The adenosine triphosphate (ATP)–protein interactions have been of great interest since the recent experimental finding of ATP's role as a hydrotrope. The interaction between ATP and disordered proteins is fundamental to the dissolution of protein aggregates and the regulation of liquid–liquid phase separation by ATP. Molecular dynamics simulation is a powerful tool for analyzing these interactions in molecular detail but often suffers from inaccuracies in describing disordered proteins and ATPs in high concentrations. Recently, several water models have been proposed to improve the description of the protein-disordered states, yet how these models work with ATP has not been explored. To this end, here, we study how water models affect ATP and alter the ATP–ATP and ATP–protein interactions for the intrinsically disordered protein, α-Synuclein. Three water models, TIP4P-D, OPC, and TIP3P, are compared, while the protein force field is fixed to ff99SBildn. The results show that ATP over-aggregates into a single cluster in TIP3P water, but monomers and smaller clusters are found in TIP4P-D and OPC waters. ATP–protein interaction is also over-stabilized in TIP3P, whereas repeated binding/unbinding of ATP to α-Synuclein is observed in OPC and TIP4P-D waters, which is in line with the recent nuclear magnetic resonance experiment. The adenine ring-mediated interaction is found to play a major role in ATP–ATP and ATP–protein contacts. Interestingly, changing Mg2+ into Na+ strengthened the electrostatic interaction and promoted ATP oligomerization and ATP–α-Synuclein binding. Overall, this study shows that changing the water model can be an effective approach to improve the properties of ATP in high concentration. [ABSTRACT FROM AUTHOR]

Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fields. Currently, molecular mechanical (MM) force fields are mainly utilized in MD simulations because of their low computational cost. Quantum mechanical (QM) calculation has high accuracy, but it is exceedingly time consuming for protein simulations. Machine learning (ML) provides the capability for generating accurate potential at the QM level without increasing much computational effort for specific systems that can be studied at the QM level. However, the construction of general machine learned force fields, needed for broad applications and large and complex systems, is still challenging. Here, general and transferable neural network (NN) force fields based on CHARMM force fields, named CHARMM-NN, are constructed for proteins by training NN models on 27 fragments partitioned from the residue-based systematic molecular fragmentation (rSMF) method. The NN for each fragment is based on atom types and uses new input features that are similar to MM inputs, including bonds, angles, dihedrals, and non-bonded terms, which enhance the compatibility of CHARMM-NN to MM MD and enable the implementation of CHARMM-NN force fields in different MD programs. While the main part of the energy of the protein is based on rSMF and NN, the nonbonded interactions between the fragments and with water are taken from the CHARMM force field through mechanical embedding. The validations of the method for dipeptides on geometric data, relative potential energies, and structural reorganization energies demonstrate that the CHARMM-NN local minima on the potential energy surface are very accurate approximations to QM, showing the success of CHARMM-NN for bonded interactions. However, the MD simulations on peptides and proteins indicate that more accurate methods to represent protein–water interactions in fragments and non-bonded interactions between fragments should be considered in the future improvement of CHARMM-NN, which can increase the accuracy of approximation beyond the current mechanical embedding QM/MM level. [ABSTRACT FROM AUTHOR]

We present a hybrid, multi-method, computational scheme for protein/ligand systems well suited to be used on modern and forthcoming massively parallel computing systems. The scheme relies on a multi-scale polarizable molecular modeling, approach to perform molecular dynamics simulations, and on an efficient Density Functional Theory (DFT) linear scaling method to post-process simulation snapshots. We use this scheme to investigate recent α-ketoamide inhibitors targeting the main protease of the SARS-CoV-2 virus. We assessed the reliability and the coherence of the hybrid scheme, in particular, by checking the ability of MM and DFT to reproduce results from high-end ab initio computations regarding such inhibitors. The DFT approach enables an a posteriori fragmentation of the system and an investigation into the strength of interaction among identified fragment pairs. We show the necessity of accounting for a large set of plausible protease/inhibitor conformations to generate reliable interaction data. Finally, we point out ways to further improve α-ketoamide inhibitors to more strongly interact with particular protease domains neighboring the active site. [ABSTRACT FROM AUTHOR]

SUMMARY: Knotted1‐like homeodomain (KNOX) proteins are essential in regulating plant organ differentiation. Land plants, including tomato (Solanum lycopersicum), have two classes of the KNOX protein family, namely, class I (KNOX I) and class II KNOX (KNOX II). While tomato KNOX I proteins are known to stimulate chloroplast development in fruit, affecting fruit coloration, the role of KNOX II proteins in this context remains unclear. In this study, we employ CRISPR/Cas9 to generate knockout mutants of the KNOX II member, SlKN5. These mutants display increased leaf complexity, a phenotype commonly associated with reduced KNOX II activity, as well as enhanced accumulation of chloroplasts and chlorophylls in smaller cells within young, unripe fruit. RNA‐seq data analyses indicate that SlKN5 suppresses the transcriptions of genes involved in chloroplast biogenesis, chlorophyll biosynthesis, and gibberellin catabolism. Furthermore, protein–protein interaction assays reveal that SlKN5 physically interacts with three transcriptional repressors from the BLH1‐clade of BEL1‐like homeodomain (BLH) protein family, SlBLH4, SlBLH5, and SlBLH7, with SlBLH7 showing the strongest interaction. CRISPR/Cas9‐mediated knockout of these SlBLH genes confirmed their overlapping roles in suppressing chloroplast biogenesis, chlorophyll biosynthesis, and lycopene cyclization. Transient assays further demonstrate that the SlKN5–SlBLH7 interaction enhances binding capacity to regulatory regions of key chloroplast‐ and chlorophyll‐related genes, including SlAPRR2‐like1, SlCAB‐1C, and SlGUN4. Collectively, our findings elucidate that the KNOX II SlKN5‐SlBLH regulatory modules serve to inhibit fruit greening and subsequently promote lycopene accumulation, thereby fine‐tuning the color transition from immature green fruit to mature red fruit. Significance Statement: This study shed light on regulatory mechanisms of plastid development in fruits by demonstrating that the class II knotted‐like homeodomain protein SlKN5 negatively regulates chloroplast number and chlorophyll accumulation in tomato fruit development. SlKN5 interacts with BEL1‐like homeodomain protein SlBLH7 and its paralogs to suppress SlAPRR2‐like1 and chlorophyll biosynthetic genes, promoting the developmental shift from growth phase to onset of ripening. This highlights the crucial role of the SlKN5‐BLH module in the regulation of tomato fruit coloration. [ABSTRACT FROM AUTHOR]

SUMMARY: Flavan‐3‐ols are prominent phenolic compounds found abundantly in the young leaves of tea plants. The enzymes involved in flavan‐3‐ol biosynthesis in tea plants have been extensively investigated. However, the localization and associations of these numerous functional enzymes within cells have been largely neglected. In this study, we aimed to investigate the synthesis of flavan‐3‐ols in tea plants, particularly focusing on epigallocatechin gallate. Our analysis involving the DESI‐MSI method to reveal a distinct distribution pattern of B‐ring trihydroxylated flavonoids, primarily concentrated in the outer layer of buds. Subcellular localization showed that CsC4H, CsF3′H, and CsF3′5′H localizes endoplasmic reticulum. Protein–protein interaction studies demonstrated direct associations between CsC4H, CsF3′H, and cytoplasmic enzymes (CHS, CHI, F3H, DFR, FLS, and ANR), highlighting their interactions within the biosynthetic pathway. Notably, CsF3′5′H, the enzyme for B‐ring trihydroxylation, did not directly interact with other enzymes. We identified cytochrome b5 isoform C serving as an essential redox partner, ensuring the proper functioning of CsF3′5′H. Our findings suggest the existence of distinct modules governing the synthesis of different B‐ring hydroxylation compounds. This study provides valuable insights into the mechanisms underlying flavonoid diversity and efficient synthesis and enhances our understanding of the substantial accumulation of B‐ring trihydroxylated flavan‐3‐ols in tea plants. Significance Statement: Our work reveals the mechanism of flavonoid diversity and efficient synthesis in tea plants from the perspective of metabolon. Different metabolons modules provide a new perspective on the scientific problem of why tea plants accumulate high levels of EGCG. The discovery of obligate electron shuttle proteins provides new directions for improving metabolic engineering. [ABSTRACT FROM AUTHOR]

Haemoglobin (Hb) is a vital oxygen carrier in vertebrates. Low blood Hb levels may indicate anaemia or genetic disorders, while its presence in the lower digestive system suggests colon cancer. Detecting and quantifying human Hb is essential for medical diagnostics. A nanobody‐based sandwich‐ELISA test was recently developed utilising llama‐derived nanobodies NbE11 and NbB9. These nanobodies specifically bind to human Hb without cross‐reacting with Hb from other vertebrates. Here, we determine the crystal structure of NbE11 in complex with human Hb. NbE11 binds Hb with high affinity, predominantly binding the β‐Hb subunit. Structural differences between human Hb and other vertebrates at the NbE11 binding interface likely explain the assay's lack of cross‐reactivity, providing insights for developing Hb binding diagnostics. [ABSTRACT FROM AUTHOR]

Summary: Seminal roots play a critical role in water and nutrient absorption, particularly in the early developmental stages of wheat. However, the genes responsible for controlling SRN in wheat remain largely unknown.Genetic mapping and functional analyses identified a candidate gene (TraesCS3D01G137200, TaSRN‐3D) encoding a Ser/Thr kinase glycogen synthase kinase 3 (STKc_GSK3) that regulated SRN in wheat. Additionally, experiments involving hormone treatment, nitrate absorption and protein interaction were conducted to explore the regulatory mechanism of TaSRN‐3D.Results showed that the TaSRN‐3D4332 allele inhibited seminal roots initiation and development, while loss‐of‐function mutants showed significantly higher seminal root number (SRN). Exogenous application of epi‐brassinolide could increase the SRN in a HS2‐allelic background. Furthermore, chlorate sensitivity and 15N uptake assays revealed that a higher number of seminal roots promoted nitrate accumulation. TaBSR1 (BIN2‐related SRN Regulator 1, orthologous to OsGRF4/GL2 in rice) acts as an interactor of TaSRN‐3D and promotes TaBSR1 degradation to reduce SRN.This study provides valuable insights into understanding the genetic basis and regulatory network of SRN in wheat, highlighting their roles as potential targets for root‐based improvement in wheat breeding. [ABSTRACT FROM AUTHOR]

Summary: Gibberellic acid (GA) plays a central role in many plant developmental processes and is crucial for crop improvement. DELLA proteins, the core suppressors in the GA signaling pathway, are degraded by GA via the 26S proteasomal pathway to release the GA response. However, little is known about the phosphorylation‐mediated regulation of DELLA proteins.In this study, we combined GA response assays with protein–protein interaction analysis to infer the connection between Arabidopsis thaliana DELLAs and the C‐TERMINAL DOMAIN PHOSPHATASE‐LIKE 3 (CPL3), a phosphatase involved in the dephosphorylation of RNA polymerase II.We show that CPL3 directly interacts with DELLA proteins and promotes DELLA protein stability by inhibiting its degradation by the 26S proteasome. Consequently, CPL3 negatively modulates multiple GA‐mediated processes of plant development, including hypocotyl elongation, flowering time, and anthocyanin accumulation.Taken together, our findings demonstrate that CPL3 serves as a novel regulator that could improve DELLA stability and thereby participate in GA signaling transduction. [ABSTRACT FROM AUTHOR]

Ameloblastoma is a non-cancerous but aggressive oral tumor emerging from odontogenic epithelial tissue involved during odontogenesis. Since there is lack in unravelling the complete molecular pathogenesis of ameloblastoma, chemotherapy is less attempted and a lot of disagreement over the optimal treatment option. Hence, till date, wide surgical resection is considered to be the reliable treatment for ameloblastoma. The Neurotrophin Signaling pathway plays an important role in neuron signaling and it is closely related with the MAPK pathway, which on the other hand regulated cell differentiation, apoptosis, proliferation, plasticity and survival. Protein- Protein Interaction analysis was analysed with STRING tool using WNL value, identified that CTNNB1, HRAS, NGFR, NGFR, and SORT1 having high interacting with BDNF, NT4, p75NTR, NGF, and NT3. The results of ontology analysis revealed that Neurotrophin signaling pathway is associated with Cell surface receptor signaling pathway, regulation of cell differentiation, regulation of development process, EGFR tyrosine kinase inhibitor resistance, MAPK signaling pathway, PI3K-Akt signaling pathway and Ras signaling pathway leading to pathogenesis involving genes. Further, clustering coefficient values of proteins BDNF, NT4, p75NTR, NGF & NT3 were identified as 0.627, 0.708, 0.367, 0.644 & 0.415. The results of molecular docking studies revealed among the selected ligands Methyl-ɣ-oresellinate, N-(4-Hydroxy-phenyl)-2-phenyl-N-phenylacetyl-acetamide, Atranorin and Oresellinate exhibited high binding affinity with selected protein. The key genes involved in Neurotrophin signaling pathway leading to ameloblastoma pathogenesis is revealed, which are closely associated with cell differentiation, cell proliferation, pro-apoptosis, and pro-survival regulations. Further it can be concluded that Neurotrophin signaling pathway could be one of the promising pathway to tailor the targeted drug therapy for Ameloblastoma treatment. [ABSTRACT FROM AUTHOR]

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High‐throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF‐CBA) to identify proteins that bind a target of interest from a pull‐down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide‐protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF‐CBA to help users identify scenarios where the approach will be most useful. Given the method's speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification. [ABSTRACT FROM AUTHOR]

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High‐throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF‐CBA) to identify proteins that bind a target of interest from a pull‐down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide‐protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF‐CBA to help users identify scenarios where the approach will be most useful. Given the method's speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification. [ABSTRACT FROM AUTHOR]

Activated GPCRs are phosphorylated and internalized mostly via clathrin-mediated endocytosis (CME), which are then sorted for recycling or degradation. We investigated how differential activation of the same GPCR affects its endocytic trafficking in vivo using rhodopsin as a model in pupal photoreceptors of flies expressing mCherry-tagged rhodopsin 1 (Rh1-mC) or GFP-tagged arrestin 1 (Arr1-GFP). Upon blue light stimulation, activated Rh1 recruited Arr1-GFP to the rhabdomere, which became co-internalized and accumulated in cytoplasmic vesicles of photoreceptors. This internalization was eliminated in shits1 mutants affecting dynamin. Moreover, it was blocked by either rdgA or rdgB mutations affecting the PIP2 biosynthesis. Together, the blue light-initiated internalization of Rh1 and Arr1 belongs to CME. Green light stimulation also triggered the internalization and accumulation of activated Rh1-mC in the cytoplasm but with faster kinetics. Importantly, Arr1-GFP was also recruited to the rhabdomere but not co-internalized with Rh1-mC. This endocytosis was not affected in shits1 nor rdgA mutants, indicating it is not CME. We explored the fate of internalized Rh1-mC following CME and observed it remained in cytoplasmic vesicles following 30 min of dark adaptation. In contrast, in the non-CME Rh1-mC appeared readily recycled back to the rhabdomere within five min of dark treatment. This faster recycling may be regulated by rhodopsin phosphatase, RdgC. Together, we demonstrate two distinct endocytic and recycling mechanisms of Rh1 via two light stimulations. It appears that each stimulation triggers a distinct conformation leading to different phosphorylation patterns of Rh1 capable of recruiting Arr1 to rhabdomeres. However, a more stable interaction leads to the co-internalization of Arr1 that orchestrates CME. A stronger Arr1 association appears to impede the recycling of the phosphorylated Rh1 by preventing the recruitment of RdgC. We conclude that conformations of activated rhodopsin determine the downstream outputs upon phosphorylation that confers differential protein-protein interactions. [ABSTRACT FROM AUTHOR]

Polymeric IgMs are secreted from plasma cells abundantly despite their structural complexity and intricate multimerization steps. To gain insights into IgM's assembly mechanics that underwrite such high-level secretion, we characterized the biosynthetic process of a natural human IgM, SAM-6, using a heterologous HEK293(6E) cell platform that allowed the production of IgMs both in hexameric and pentameric forms in a controlled fashion. By creating a series of mutant subunits that differentially disrupt secretion, folding, and specific inter-chain disulfide bond formation, we assessed their effects on various aspects of IgM biosynthesis in 57 different subunit chain combinations, both in hexameric and pentameric formats. The mutations caused a spectrum of changes in steady-state subcellular subunit distribution, ER-associated inclusion body formation, intracellular subunit detergent solubility, covalent assembly, secreted IgM product quality, and secretion output. Some mutations produced differential effects on product quality depending on whether the mutation was introduced to hexameric IgM or pentameric IgM. Through this systematic combinatorial approach, we consolidate diverse overlapping knowledge on IgM biosynthesis for both hexamers and pentamers, while unexpectedly revealing that the loss of certain inter-chain disulfide bonds, including the one between μHC and λLC, is tolerated in polymeric IgM assembly and secretion. The findings highlight the differential roles of underlying non-covalent protein-protein interactions in hexamers and pentamers when orchestrating the initial subunit interactions and maintaining the polymeric IgM product integrity during ER quality control steps, secretory pathway trafficking, and secretion. [ABSTRACT FROM AUTHOR]

The physical interactome of a protein can be altered upon perturbation, modulating cell physiology and contributing to disease. Identifying interactome differences of normal and disease states of proteins could help understand disease mechanisms, but current methods do not pinpoint structure-specific PPIs and interaction interfaces proteome-wide. We used limited proteolysis–mass spectrometry (LiP–MS) to screen for structure-specific PPIs by probing for protease susceptibility changes of proteins in cellular extracts upon treatment with specific structural states of a protein. We first demonstrated that LiP–MS detects well-characterized PPIs, including antibody–target protein interactions and interactions with membrane proteins, and that it pinpoints interfaces, including epitopes. We then applied the approach to study conformation-specific interactors of the Parkinson's disease hallmark protein alpha-synuclein (aSyn). We identified known interactors of aSyn monomer and amyloid fibrils and provide a resource of novel putative conformation-specific aSyn interactors for validation in further studies. We also used our approach on GDP- and GTP-bound forms of two Rab GTPases, showing detection of differential candidate interactors of conformationally similar proteins. This approach is applicable to screen for structure-specific interactomes of any protein, including posttranslationally modified and unmodified, or metabolite-bound and unbound protein states. Synopsis: A structural proteomics approach for identifying candidate interactors of any protein within a complex lysate is presented. It is applied to identify conformation-specific interactors of Rab GTPases and of the monomeric and fibrillar forms of the disease-associated protein alpha-synuclein. A method is presented for identifying candidate interactors of any bait protein within a complex lysate. It is applicable to the identification of conformation-specific interactors and pinpoints interaction sites. A resource of candidate interactors of monomeric and fibrillar forms of alpha-synuclein is presented. Candidate interactors of GTP- and GDP-bound forms of Rab GTPases are identified. A structural proteomics approach for identifying candidate interactors of any protein within a complex lysate is presented. It is applied to identify conformation-specific interactors of Rab GTPases and of the monomeric and fibrillar forms of the disease-associated protein alpha-synuclein. [ABSTRACT FROM AUTHOR]

The type VI secretion system (T6SS) is an important mediator of microbe–microbe and microbe–host interactions. Gram-negative bacteria use the T6SS to inject T6SS effectors (T6Es), which are usually proteins with toxic activity, into neighboring cells. Antibacterial effectors have cognate immunity proteins that neutralize self-intoxication. Here, we applied novel structural bioinformatic tools to perform systematic discovery and functional annotation of T6Es and their cognate immunity proteins from a dataset of 17,920 T6SS-encoding bacterial genomes. Using structural clustering, we identified 517 putative T6E families, outperforming sequence-based clustering. We developed a logistic regression model to reliably quantify protein–protein interaction of new T6E-immunity pairs, yielding candidate immunity proteins for 231 out of the 517 T6E families. We used sensitive structure-based annotation which yielded functional annotations for 51% of the T6E families, again outperforming sequence-based annotation. Next, we validated four novel T6E-immunity pairs using basic experiments in E. coli. In particular, we showed that the Pfam domain DUF3289 is a homolog of Colicin M and that DUF943 acts as its cognate immunity protein. Furthermore, we discovered a novel T6E that is a structural homolog of SleB, a lytic transglycosylase, and identified a specific glutamate that acts as its putative catalytic residue. Overall, this study applies novel structural bioinformatic tools to T6E-immunity pair discovery, and provides an extensive database of annotated T6E-immunity pairs. Synopsis: Structural bioinformatic tools were utilized for the discovery of novel specialized Type VI Secretion System (T6SS) effectors and their cognate immunity proteins, highlighting their utility over standard sequence-based tools. The effector predictions were supported by experimental results. Structural clustering provided better compression of effectors than sequence-based methods, with 517 structural clusters representing the structure space of specialized effectors in Proteobacteria. The ipTM score from Alphafold-multimer was used as a reliable and quantitative measure for predicting candidate immunity proteins in 231 out of 517 effector clusters. Annotations were provided for 265 out of the 517 specialized effector domain families using fast and sensitive searches with Foldseek, expanding capabilities beyond Pfam-based annotation alone. Four putative effectors were demonstrated to be toxic to Escherichia coli, with co-expression of cognate immunity proteins neutralizing their toxicity. Structural bioinformatic tools were utilized for the discovery of novel specialized Type VI Secretion System (T6SS) effectors and their cognate immunity proteins, highlighting their utility over standard sequence-based tools. The effector predictions were supported by experimental results. [ABSTRACT FROM AUTHOR]

Computational methods have significantly transformed biomedical research, offering a comprehensive exploration of disease mechanisms and molecular protein functions. This article reviews a spectrum of computational tools and network analysis databases that play a crucial role in identifying potential interactions and signaling networks contributing to the onset of disease states. The utilization of protein/gene interaction and genetic variation databases, coupled with pathway analysis can facilitate the identification of potential drug targets. By bridging the gap between molecular-level information and disease understanding, this review contributes insights into the impactful utilization of computational methods, paving the way for targeted interventions and therapeutic advancements in biomedical research. [ABSTRACT FROM AUTHOR]

All mitochondria import >95% of their proteins from the cytosol. This process is mediated by protein translocases in the mitochondrial membranes, whose subunits are generally highly conserved. Most eukaryotes have two inner membrane protein translocases (TIMs) that are specialized to import either presequence‐containing or mitochondrial carrier proteins. In contrast, the parasitic protozoan Trypanosoma brucei has a single TIM complex consisting of one conserved and five unique subunits. Here, we identify candidates for new subunits of the TIM or the presequence translocase‐associated motor (PAM) using a protein–protein interaction network of previously characterized TIM and PAM subunits. This analysis reveals that the trypanosomal TIM complex contains an additional trypanosomatid‐specific subunit, designated TbTim15. TbTim15 is associated with the TIM complex, lacks transmembrane domains, and localizes to the intermembrane space. TbTim15 is essential for procyclic and bloodstream forms of trypanosomes. It contains two twin CX9C motifs and mediates import of both presequence‐containing and mitochondrial carrier proteins. While the precise function of TbTim15 in mitochondrial protein import is unknown, our results are consistent with the notion that it may function as an import receptor for the non‐canonical trypanosomal TIM complex. [ABSTRACT FROM AUTHOR]

The identification of tumor driver genes facilitates accurate cancer diagnosis and treatment, playing a key role in precision oncology, along with gene signaling, regulation, and their interaction with protein complexes. To tackle the challenge of distinguishing driver genes from a large number of genomic data, we construct a feature extraction framework for discovering pan‐cancer driver genes based on multi‐omics data (mutations, gene expression, copy number variants, and DNA methylation) combined with protein–protein interaction (PPI) networks. Using a network propagation algorithm, we mine functional information among nodes in the PPI network, focusing on genes with weak node information to represent specific cancer information. From these functional features, we extract distribution features of pan‐cancer data, pan‐cancer TOPSIS features of functional features using the ideal solution method, and SetExpan features of pan‐cancer data from the gene functional features, a method to rank pan‐cancer data based on the average inverse rank. These features represent the common message of pan‐cancer. Finally, we use the lightGBM classification algorithm for gene prediction. Experimental results show that our method outperforms existing methods in terms of the area under the check precision‐recall curve (AUPRC) and demonstrates better performance across different PPI networks. This indicates our framework's effectiveness in predicting potential cancer genes, offering valuable insights for the diagnosis and treatment of tumors. [ABSTRACT FROM AUTHOR]

Protein–protein interactions (PPIs) involve the physical or functional contact between two or more proteins. Generally, proteins that can interact with each other always have special relationships. Some previous studies have reported that gene ontology (GO) terms are related to the determination of PPIs, suggesting the special patterns on the GO terms of proteins in PPIs. In this study, we explored the special GO term patterns on human PPIs, trying to uncover the underlying functional mechanism of PPIs. The experimental validated human PPIs were retrieved from STRING database, which were termed as positive samples. Additionally, we randomly paired proteins occurring in positive samples, yielding lots of negative samples. A simple calculation was conducted to count the number of positive samples for each GO term pair, where proteins in samples were annotated by GO terms in the pair individually. The similar number for negative samples was also counted and further adjusted due to the great gap between the numbers of positive and negative samples. The difference of the above two numbers and the relative ratio compared with the number on positive samples were calculated. This ratio provided a precise evaluation of the occurrence of GO term pairs for positive samples and negative samples, indicating the latent GO term patterns for PPIs. Our analysis unveiled several nuclear biological processes, including gene transcription, cell proliferation, and nutrient metabolism, as key biological functions. Interactions between major proliferative or metabolic GO terms consistently correspond with significantly reported PPIs in recent literature. [ABSTRACT FROM AUTHOR]

Background: Codonopsis Radix (CR), a renowned traditional Chinese medicine (TCM) formula, has been widely applied for its immunomodulatory, antitumor, antioxidant, neuroprotective, and antiviral effects. However, the multitarget mechanism of CR in ovarian cancer (OC) remains to be elucidated. Objectives: We applied bioinformatics and molecular docking techniques to explore possible pharmacological targets, bioactivities, and molecular mechanisms of CR for OC treatment. Materials and Methods: We identified 40 common genes associated with CR and OC and obtained core genes through a protein–protein interaction network. Results: Enrichment analysis revealed that mitochondrial electron transport was the key biological process involved. Based on the analysis, we selected estrogen receptor 1 (ESR1) and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) as the key target genes for molecular docking. In the final verification analysis, we evaluated the effect of the PIK3CA mutation on the survival rate of patients with OC and determined that the phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) pathway was the key pathway in the OC treatment. Conclusion: These results suggest that CR inhibits the activity of mitochondrial complex II, reduces adenosine triphosphate (ATP) production by mitochondrial electron transport, inhibits PI3K/AKT phosphorylation, and promotes apoptosis in OC cells. [ABSTRACT FROM AUTHOR]

Forkhead box P2 (FOXP2) regulates expression of various genes and is associated with language, speech and neural development as well as cancer. Since there may be a putative link between sex and language and because transcription factors rarely function in isolation, this study aims to investigate whether FOXP2 directly associates with oestrogen receptor α (ER1), a nuclear receptor responsible for sexual differentiation that is also associated with cancer. Isothermal titration calorimetry and fluorescence anisotropy were used to investigate the interaction between the DNA-binding forkhead domain (FHD) of FOXP2, the N-terminal region (NT) of FOXP2, and the ligand-binding domain (LBD) of ER1. ER1 LBD does not interact with FOXP2 NT but associates with apo-FOXP2 FHD in an enthalpically favourable manner. The affinity of this interaction is inversely correlated to the salt concentration. Additionally, FOXP2 FHD that is bound to ER1 LBD, has reduced ability to interact with its cognate DNA. This research identifies a novel interaction between ER1 LBD and FOXP2 FHD and shows that the interaction is regulated by salt. Moreover, FOXP2 FHD cannot bind to both ER1 LBD and DNA simultaneously, suggesting that this interaction could be involved in regulating the transcriptional pathway of FOXP2 should the interaction be found in vivo. This study could serve as a foundation for uncovering the basis of sexual dimorphism in speech and language development and related disorders and potentially offers an alternate for targeted cancer therapies. • A novel interaction is identified in vitro between FOXP2 FHD and ER1 LBD. • This interaction has both an electrostatic and hydrophobic interface and involves the DNA-binding Helix 3 of FOXP2 FHD. • The ligand binding region and the LXXLL-binding AF-2 region of ER1 LBD is not involved in the protein-protein interaction. • FOXP2 FHD can bind either to DNA or to ER1 but not to both simultaneously. • The identified FOXP2-ER1 interaction could be involved in regulating FOXP2's transcriptional activity. [ABSTRACT FROM AUTHOR]

Summary: Seed dormancy governs germination timing, with both evolutionary and applied consequences. Despite extensive studies on the hormonal and genetic control of these processes, molecular mechanisms directly linking dormancy and germination remain poorly understood.By screening a collection of lines overexpressing Arabidopsis transcription factors, we identified ERF50 as a key gene to control dormancy and germination. To study its regulation, we measured seed‐related physiological parameters in loss‐of‐function mutants and carried out transactivation, protein interaction and ChIP‐PCR analyses.We found direct ERF50‐mediated repression of DOG1 and activation of EXPA2 transcription, which results in enhanced seed germination. Although ERF50 expression is increased by DOG1 in dormant seeds, ERF50 germination‐promoting activity is blocked by RGL2.The physiological, genetic and molecular evidence gathered here supports that ERF50 controls germination timing by regulating DOG1 levels to leverage its role as enhancer of seed germination, via RGL2 antagonism on EXPA2 expression. Our results highlight the central role of ERF50 as a feedback regulator to couple and fine‐tune seed dormancy and germination. [ABSTRACT FROM AUTHOR]

SUMMARY: Anthocyanin generation in apples (Malus domestica) and the pigmentation that results from it may be caused by irradiation and through administration of methyl jasmonate (MeJA). However, their regulatory interrelationships associated with fruit coloration are not well defined. To determine whether MdERF109, a transcription factor (TF) involved in light‐mediated coloration and anthocyanin biosynthesis, has synergistic effects with other proteins, we performed a yeast two‐hybrid assessment and identified another TF, MdWER. MdWER was induced by MeJA treatment, and although overexpression of MdWER alone did not promote anthocyanin accumulation co‐overexpression with MdERF109 resulted in significantly increase in anthocyanin biosynthesis. MdWER may form a protein complex with MdERF109 to promote anthocyanin accumulation by enhancing combinations between the proteins and their corresponding genes. In addition, MdWER, as a MeJA responsive protein, interacts with the anthocyanin repressor MdJAZ2. Transient co‐expression in apple fruit and protein interaction assays allowed us to conclude that MdERF109 and MdJAZ2 interact with MdWER and take part in the production of anthocyanins upon MeJA treatment and irradiation. Our findings validate a role for the MdERF109‐MdWER‐MdJAZ2 module in anthocyanin biosynthesis and uncover a novel mechanism for how light and MeJA signals are coordinated anthocyanin biosynthesis in apple fruit. Significance Statement: Our findings validate a role for the MdERF109‐MdWER‐MdJAZ2 module in anthocyanin biosynthesis and uncover a novel mechanism for how light and MeJA signals are coordinated anthocyanin biosynthesis in apple fruit. [ABSTRACT FROM AUTHOR]

Rice serves as the main food source in many regions of the world. However, rice production is impacted by abiotic stress including drought, salt, temperature shifts, submersion, etc. Some rice accessions have a genetic potential to withstand in such stress conditions. Thus, identification and characterization of genes that are responsible to endure abiotic stress conditions are critical for developing new rice varieties with improved characteristics. In this respect, we studied the protein–protein interaction (PPI) network in abiotic stress-tolerant genes of rice cultivars. We explored various rice genome databases to retrieve abiotic stress-tolerant genes for this purpose. In total, 7984 genes were collected from different databases; out of these, 1408 were successfully mapped in PPI network of STRING. Subsequently, the network topology, hub protein identification, sub-network generation and functional enrichment analysis were performed using different plug-ins implemented in Cytoscape 3.9.0. We identified 17 key genes (MPK5, WRKY24, WRKY28, P5CS, DREB2A, MYB2, CLPB1, HSP81-2, HSP90, MCM2, NHX2, APX1, NYC1, CDKB1-1, CDKB2-1, ORR5 and HK4) after analysis of network using cytoHubba and MCODE tools. These identified key genes were effectively classified across diverse biological processes, encompassing hydrogen peroxide detoxification, cellular components like transport systems and molecular functions such as transcriptional activation and repression. Further, among identified key genes DREB2A, APX1 and NHX2, and MYB2, WRKY28 and WRKY24 were found to be upregulated and downregulated, respectively, in rice cultivars for different stress conditions (cold, salt and drought) in publicly available expression datasets. This information enables researchers and breeders for the development of novel abiotic stress-tolerant rice varieties. [ABSTRACT FROM AUTHOR]

In the intricate molecular world, post-translational modifications play pivotal roles in modulating protein activity and function. The Drosophilamelanogaster genome encodes an array of enzymes to mediate these processes. Here, we elucidate a previously unrecognized connection between the Drosophila transforming growth factor-β-activated kinase 1 (dTak1) and the deubiquitinase Ovarian tumor (Otu). We demonstrate that dTak1 mediates both the phase-separated condensation and the deubiquitinase enzymatical activity of Otu in a manner independent of its kinase function. Using a combination of biochemical assays and in vivo genetic interactions, our findings highlight the significance of dTak1 in orchestrating the assembly of Otu condensates, and consequently, in the regulation of Otu's biological functions. Our studies underscore the multifaceted nature of protein-protein interactions and open up avenues for exploring non-traditional roles of kinases in cellular processes. [ABSTRACT FROM AUTHOR]

Many drug formulations containing small active molecules are used for the treatment of coronary artery disease, which affects a significant part of the world's population. However, the inadequate profile of these molecules in terms of therapeutic efficacy has led to the therapeutic use of protein and peptide-based biomolecules with superior properties, such as target-specific affinity and low immunogenicity, in critical diseases. Protein‒protein interactions, as a consequence of advances in molecular techniques with strategies involving the combined use of in silico methods, have enabled the design of therapeutic peptides to reach an advanced dimension. In particular, with the advantages provided by protein/peptide structural modeling, molecular docking for the study of their interactions, molecular dynamics simulations for their interactions under physiological conditions and machine learning techniques that can work in combination with all these, significant progress has been made in approaches to developing therapeutic peptides that can modulate the development and progression of coronary artery diseases. In this scope, this review discusses in silico methods for the development of peptide therapeutics for the treatment of coronary artery disease and strategies for identifying the molecular mechanisms that can be modulated by these designs and provides a comprehensive perspective for future studies. [ABSTRACT FROM AUTHOR]

A web application, GTExome, is described that quickly identifies, classifies, and models missense mutations in commonly expressed human proteins. GTExome can be used to categorize genomic mutation data with tissue specific expression data from the Genotype-Tissue Expression (GTEx) project. Commonly expressed missense mutations in proteins from a wide range of tissue types can be selected and assessed for modeling suitability. Information about the consequences of each mutation is provided to the user including if disulfide bonds, hydrogen bonds, or salt bridges are broken, buried prolines introduced, buried charges are created or lost, charge is swapped, a buried glycine is replaced, or if the residue that would be removed is a proline in the cis configuration. Also, if the mutation site is in a binding pocket the number of pockets and their volumes are reported. The user can assess this information and then select from available experimental or computationally predicted structures of native proteins to create, visualize, and download a model of the mutated protein using Fast and Accurate Side-chain Protein Repacking (FASPR). For AlphaFold modeled proteins, confidence scores for native proteins are provided. Using this tool, we explored a set of 9,666 common missense mutations from a variety of tissues from GTEx and show that most mutations can be modeled using this tool to facilitate studies of protein-protein and protein-drug interactions. The open-source tool is freely available at https://pharmacogenomics.clas.ucdenver.edu/gtexome/. [ABSTRACT FROM AUTHOR]

Ebola virus (EBOV) matrix protein VP40 can assemble and bud as virus-like particles (VLPs) when expressed alone in mammalian cells. Nucleoprotein (NP) could be recruited to VLPs as inclusion body (IB) when co-expressed, and increase VLP production. However, the mechanism behind it remains unclear. Here, we use a computational approach to study NP-VP40 interactions. Our simulations indicate that NP may enhance VLP production through stabilizing VP40 filaments and accelerating the VLP budding step. Further, both the relative timing and amount of NP expression compared to VP40 are important for the effective production of IB-containing VLPs. We predict that relative NP/VP40 expression ratio and time are important for efficient production of IB-containing VLPs. We conclude that disrupting the expression timing and amount of NP and VP40 could provide new avenues to treat EBOV infection. This work provides quantitative insights into EBOV proteins interactions and how virion generation and drug efficacy could be influenced. A computational modeling study indicates that relative amount and timing of Ebola virus proteins VP40 and NP drive effective viral particle production. [ABSTRACT FROM AUTHOR]

Diabetic cardiomyopathy (DCM) is a prevalent complication of type 2 diabetes (T2D). 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) is a glycolysis regulator. However, the potential effects of PFKFB3 in the DCM remain unclear. In comparison to db/m mice, PFKFB3 levels decreased in the hearts of db/db mice. Cardiac-specific PFKFB3 overexpression inhibited myocardial oxidative stress and cardiomyocyte apoptosis, suppressed mitochondrial fragmentation, and partly restored mitochondrial function in db/db mice. Moreover, PFKFB3 overexpression stimulated glycolysis. Interestingly, based on the inhibition of glycolysis, PFKFB3 overexpression still suppressed oxidative stress and apoptosis of cardiomyocytes in vitro, which indicated that PFKFB3 overexpression could alleviate DCM independent of glycolysis. Using mass spectrometry combined with co-immunoprecipitation, we identified optic atrophy 1 (OPA1) interacting with PFKFB3. In db/db mice, the knockdown of OPA1 receded the effects of PFKFB3 overexpression in alleviating cardiac remodeling and dysfunction. Mechanistically, PFKFB3 stabilized OPA1 expression by promoting E3 ligase NEDD4L-mediated atypical K6-linked polyubiquitination and thus prevented the degradation of OPA1 by the proteasomal pathway. Our study indicates that PFKFB3/OPA1 could be potential therapeutic targets for DCM. [ABSTRACT FROM AUTHOR]

Aggregation of misfolded α-synuclein (α-syn) is a key characteristic feature of Parkinson's disease (PD) and related synucleinopathies. The nature of these aggregates and their contribution to cellular dysfunction is still not clearly elucidated. We employed mass spectrometry-based total and phospho-proteomics to characterize the underlying molecular and biological changes due to α-syn aggregation using the M83 mouse primary neuronal model of PD. We identified gross changes in the proteome that coincided with the formation of large Lewy body-like α-syn aggregates in these neurons. We used protein-protein interaction (PPI)-based network analysis to identify key protein clusters modulating specific biological pathways that may be dysregulated and identified several mechanisms that regulate protein homeostasis (proteostasis). The observed changes in the proteome may include both homeostatic compensation and dysregulation due to α-syn aggregation and a greater understanding of both processes and their role in α-syn-related proteostasis may lead to improved therapeutic options for patients with PD and related disorders. [ABSTRACT FROM AUTHOR]

Background: While numerous allergy-related biomarkers and targeted treatment strategies have been developed and employed, there are still signifcant limitations and challenges in the early diagnosis and targeted treatment for allegic diseases. Our study aims to identify circulating proteins causally associated with allergic disease-related traits through Mendelian randomization (MR)-based analytical framework. Methods: Large-scale cis-MR was employed to estimate the effects of thousands of plasma proteins on five main allergic diseases. Additional analyses including MR Steiger analyzing and Bayesian colocalisation, were performed to test the robustness of the associations; These findings were further validated utilizing meta-analytical methods in the replication analysis. Both proteome- and transcriptome-wide association studies approach was applied, and then, a protein-protein interaction was conducted to examine the interplay between the identified proteins and the targets of existing medications. Results: Eleven plasma proteins were identified with links to atopic asthma (AA), atopic dermatitis (AD), and allergic rhinitis (AR). Subsequently, these proteins were classified into four distinct target groups, with a focus on tier 1 and 2 targets due to their higher potential to become drug targets. MR analysis and extra validation revealed STAT6 and TNFRSF6B to be Tier 1 and IL1RL2 and IL6R to be Tier 2 proteins with the potential for AA treatment. Two Tier 1 proteins, CRAT and TNFRSF6B, and five Tier 2 proteins, ERBB3, IL6R, MMP12, ICAM1, and IL1RL2, were linked to AD, and three Tier 2 proteins, MANF, STAT6, and TNFSF8, to AR. Conclusion: Eleven Tier 1 and 2 protein targets that are promising drug target candidates were identified for AA, AD, and AR, which influence the development of allergic diseases and expose new diagnostic and therapeutic targets. [ABSTRACT FROM AUTHOR]

Short amphipathic peptides are capable of binding to transcriptional coactivators, often targeting the same binding surfaces as native transcriptional activation domains. However, they do so with modest affinity and generally poor selectivity, limiting their utility as synthetic modulators. Here we show that incorporation of a medium‐chain, branched fatty acid to the N‐terminus of one such heptameric lipopeptidomimetic (LPPM‐8) increases the affinity for the coactivator Med25 >20‐fold (Ki >100 μM to 4 μM), rendering it an effective inhibitor of Med25 protein–protein interactions (PPIs). The lipid structure, the peptide sequence, and the C‐terminal functionalization of the lipopeptidomimetic each influence the structural propensity of LPPM‐8 and its effectiveness as an inhibitor. LPPM‐8 engages Med25 through interaction with the H2 face of its activator interaction domain and in doing so stabilizes full‐length protein in the cellular proteome. Further, genes regulated by Med25‐activator PPIs are inhibited in a cell model of triple‐negative breast cancer. Thus, LPPM‐8 is a useful tool for studying Med25 and mediator complex biology and the results indicate that lipopeptidomimetics may be a robust source of inhibitors for activator‐coactivator complexes. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is associated with the overall survival (OS) in patients with GBM. Methods: A systematic review was performed using PubMed, Scopus, Cochrane, and Web of Science up to Journey 2024. Two researchers independently extracted the data and assessed the study quality according to the New Castle Ottawa scale (NOS). The genes whose expression was found to be associated with survival were identified and considered in a subsequent bioinformatic study. The products of these genes were also analyzed considering protein-protein interaction (PPI) relationship analysis using STRING. Additionally, the most important genes associated with GBM patients' survival were also identified using the Cytoscape 3.9.0 software. For final validation, GEPIA and CGGA (mRNAseq_325 and mRNAseq_693) databases were used to conduct OS analyses. Gene set enrichment analysis was performed with GO Biological Process 2023. Results: From an initial search of 4104 articles, 255 studies were included from 24 countries. Studies described 613 unique genes whose mRNAs were significantly associated with OS in GBM patients, of which 107 were described in 2 or more studies. Based on the NOS, 131 studies were of high quality, while 124 were considered as low-quality studies. According to the PPI network, 31 key target genes were identified. Pathway analysis revealed five hub genes (IL6, NOTCH1, TGFB1, EGFR, and KDR). However, in the validation study, only, the FN1 gene was significant in three cohorts. Conclusion: We successfully identified the most important 31 genes whose products may be considered as potential prognosis biomarkers as well as candidate target genes for innovative therapy of GBM tumors. [ABSTRACT FROM AUTHOR]

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided. [ABSTRACT FROM AUTHOR]

The self-organization of cells relies on the profound complexity of protein-protein interactions. Challenges in directly observing these events have hindered progress toward understanding their diverse behaviors. One notable example is the interaction between molecular motors and cytoskeletal systems that combine to perform a variety of cellular functions. In this work, we leverage theory and experiments to identify and quantify the rate-limiting mechanism of the initial association between a cargo-bound kinesin motor and a microtubule track. Recent advances in optical tweezers provide binding times for several lengths of kinesin motors trapped at varying distances from a microtubule, empowering the investigation of competing models. We first explore a diffusion-limited model of binding. Through Brownian dynamics simulations and simulation-based inference, we find this simple diffusion model fails to explain the experimental binding times, but an extended model that accounts for the ADP state of the molecular motor agrees closely with the data, even under the scrutiny of penalizing for additional model complexity. We provide quantification of both kinetic rates and biophysical parameters underlying the proposed binding process. Our model suggests that a typical binding event is limited by ADP state rather than physical search. Lastly, we predict how these association rates can be modulated in distinct ways through variation of environmental concentrations and physical properties. Author summary: Cytoskeletal-motor assemblies self-organize to achieve cellular functions ranging from delivering intracellular cargoes to generating forces in mitosis. Advancements in single-molecule experiments have revealed immense detail about motor detachment and stepping, but relatively little regarding attachment. Newly available binding times for individual kinesin motors allow for the evaluation of mechanistic models of the process. We find that a model limited by both diffusive search and a weak-to-strong transition from ADP release best explains the data. The coupled chemo-mechanical nature of this interaction can be modulated more richly than either separately, possibly explaining the diversity and regulation observed in cells. More broadly, our study provides a timely vignette in leveraging computations with experiments to understand the mosaic of mechanisms underlying protein-protein interactions. [ABSTRACT FROM AUTHOR]

Apical-basal polarization in renal epithelial cells is crucial to renal function and an important trigger for tubule formation in kidney development. Loss of polarity can induce epithelial-to-mesenchymal transition (EMT), which can lead to kidney pathologies. Understanding the relative and combined roles of the involved proteins and their interactions that govern epithelial polarity may provide insights for controlling the process of polarization via chemical or mechanical manipulations in an in vitro or in vivo setting. Here, we developed a computational framework that integrates several known interactions between integrins, Rho-GTPases Rho, Rac and Cdc42, and polarity complexes Par and Scribble, to study their mutual roles in the emergence of polarization. The modeled protein interactions were shown to induce the emergence of polarized distributions of Rho-GTPases, which in turn led to the accumulation of apical and basal polarity complexes Par and Scribble at their respective poles, effectively recapitulating polarization. Our multiparametric sensitivity analysis suggested that polarization depends foremost on the mutual inhibition between Rac and Rho. Next, we used the computational framework to investigate the role of integrins and GTPases in the generation and disruption of polarization. We found that a minimum concentration of integrins is required to catalyze the process of polarization. Furthermore, loss of polarization was found to be only inducible via complete degradation of the Rho-GTPases Rho and Cdc42, suggesting that polarization is fairly stable once it is established. Comparison of our computational predictions against data from in vitro experiments in which we induced EMT in renal epithelial cells while quantifying the relative Rho-GTPase levels, displayed that EMT coincides with a large reduction in the Rho-GTPase Rho. Collectively, these results demonstrate the essential roles of integrins and Rho-GTPases in the establishment and disruption of apical-basal polarity and thereby provide handles for the in vitro or in vivo regulation of polarity. Author summary: Apical-basal polarization of renal epithelial cells lies at the foundation of the kidney's biological function and is the catalyst for tubulogenesis during kidney development. Although individual parts of the polarization pathway have been extensively studied before, a deep understanding of the combined and relative roles of the different proteins and/or protein interactions that lead to the emergence of polarization remains lacking due to the complexity of the various and integrated pathways. To dissect the complexity, we constructed a computational model that integrates known mechanisms of the polarization pathway. We found that the emergence of polarization is heavily dependent on a specific subset of the interactions between the involved proteins (i.e. integrins, Rho-GTPases, and polarity proteins Par and Scribble). Specifically, we concluded from our model that polarization could only be initiated when the number of integrins exceeded a threshold value, while polarization could only be disrupted in our model by complete degradation of certain Rho-GTPases. Collectively, these results indicate that apical-basal polarization is a stable process once it has emerged. Our findings help in deepening the understanding of the polarization pathway and provide new insights for regulating apical-basal polarization. [ABSTRACT FROM AUTHOR]

The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions. Author summary: Understanding how proteins interact with small molecules, such as drugs, is critical to advancing medical, biological, and biotechnological research. Our work introduces ProSmith, a machine learning framework that improves the prediction of protein-small molecule interactions. Protein-small molecule interactions can be predicted by using numerical representations of proteins and small molecules as input to machine learning prediction models. Previous methods typically generated separate numerical representations for the proteins and small molecules without considering their interactions. ProSmith, however, combines both protein sequence and small molecule structural information in the input of a single multimodal Transformer Network to generate a joint numerical representation. Unlike previous methods, this allows for a comprehensive exchange of information between protein and small molecule, capturing the complex relationships and interactions between these two types of molecules. ProSmith successfully predicts several biological interactions, including kinase inhibitions, potential enzyme-substrate pairs, and enzyme kinetic parameters KM. We provide Python code that can be easily adapted to improve predictions for any protein-small molecule interaction. [ABSTRACT FROM AUTHOR]

Short amphipathic peptides are capable of binding to transcriptional coactivators, often targeting the same binding surfaces as native transcriptional activation domains. However, they do so with modest affinity and generally poor selectivity, limiting their utility as synthetic modulators. Here we show that incorporation of a medium‐chain, branched fatty acid to the N‐terminus of one such heptameric lipopeptidomimetic (LPPM‐8) increases the affinity for the coactivator Med25 >20‐fold (Ki >100 μM to 4 μM), rendering it an effective inhibitor of Med25 protein–protein interactions (PPIs). The lipid structure, the peptide sequence, and the C‐terminal functionalization of the lipopeptidomimetic each influence the structural propensity of LPPM‐8 and its effectiveness as an inhibitor. LPPM‐8 engages Med25 through interaction with the H2 face of its activator interaction domain and in doing so stabilizes full‐length protein in the cellular proteome. Further, genes regulated by Med25‐activator PPIs are inhibited in a cell model of triple‐negative breast cancer. Thus, LPPM‐8 is a useful tool for studying Med25 and mediator complex biology and the results indicate that lipopeptidomimetics may be a robust source of inhibitors for activator‐coactivator complexes. [ABSTRACT FROM AUTHOR]

Acute myeloid leukemia (AML) driven by the activation of EVI1 due to chromosome 3q26/MECOM rearrangements is incurable. Because transcription factors such as EVI1 are notoriously hard to target, insight into the mechanism by which EVI1 drives myeloid transformation could provide alternative avenues for therapy. Applying protein folding predictions combined with proteomics technologies, we demonstrate that interaction of EVI1 with CTBP1 and CTBP2 via a single PLDLS motif is indispensable for leukemic transformation. A 4× PLDLS repeat construct outcompetes binding of EVI1 to CTBP1 and CTBP2 and inhibits proliferation of 3q26/MECOM rearranged AML in vitro and in xenotransplant models. This proof-of-concept study opens the possibility to target one of the most incurable forms of AML with specific EVI1-CTBP inhibitors. This has important implications for other tumor types with aberrant expression of EVI1 and for cancers transformed by different CTBP-dependent oncogenic transcription factors. [ABSTRACT FROM AUTHOR]

The endocytic adaptor protein 2 (AP-2) complex binds dynactin as part of its noncanonical function, which is necessary for dynein-driven autophagosome transport along microtubules in neuronal axons. The absence of this AP-2-dependent transport causes neuronal morphology simplification and neurodegeneration. The mechanisms that lead to formation of the AP-2-dynactin complex have not been studied to date. However, the inhibition of mammalian/mechanistic target of rapamycin complex 1 (mTORC1) enhances the transport of newly formed autophagosomes by influencing the biogenesis and protein interactions of Rab-interacting lysosomal protein (RILP), another dynein cargo adaptor. We tested effects of mTORC1 inhibition on interactions between the AP-2 and dynactin complexes, with a focus on their two essential subunits, AP-2β and p150Glued. We found that the mTORC1 inhibitor rapamycin enhanced p150Glued–AP-2β complex formation in both neurons and non-neuronal cells. Additional analysis revealed that the p150Glued–AP-2β interaction was indirect and required integrity of the dynactin complex. In non-neuronal cells rapamycin-driven enhancement of the p150Glued–AP-2β interaction also required the presence of cytoplasmic linker protein 170 (CLIP-170), the activation of autophagy, and an undisturbed endolysosomal system. The rapamycin-dependent p150Glued–AP-2β interaction occurred on lysosomal-associated membrane protein 1 (Lamp-1)-positive organelles but without the need for autolysosome formation. Rapamycin treatment also increased the acidification and number of acidic organelles and increased speed of the long-distance retrograde movement of Lamp-1-positive organelles. Altogether, our results indicate that autophagy regulates the p150Glued–AP-2β interaction, possibly to coordinate sufficient motor-adaptor complex availability for effective lysosome transport. [ABSTRACT FROM AUTHOR]

The interaction of Plasmodium falciparum—infected red blood cells (iRBCs) with the vascular endothelium plays a crucial role in malaria pathology and disease. KAHRP is an exported P. falciparum protein involved in iRBC remodelling, which is essential for the formation of protrusions or "knobs" on the iRBC surface. These knobs and the proteins that are concentrated within them allow the parasites to escape the immune response and host spleen clearance by mediating cytoadherence of the iRBC to the endothelial wall, but this also slows down blood circulation, leading in some cases to severe cerebral and placental complications. In this work, we have applied genetic and biochemical tools to identify proteins that interact with P. falciparum KAHRP using enhanced ascorbate peroxidase 2 (APEX2) proximity-dependent biotinylation and label-free shotgun proteomics. A total of 30 potential KAHRP-interacting candidates were identified, based on the assigned fragmented biotinylated ions. Several identified proteins have been previously reported to be part of the Maurer's clefts and knobs, where KAHRP resides. This study may contribute to a broader understanding of P. falciparum protein trafficking and knob architecture and shows for the first time the feasibility of using APEX2-proximity labelling in iRBCs. [ABSTRACT FROM AUTHOR]

HuaChanSu (HCS) or Cinobufacini injection is an aqueous extract of the dried skin of Bufo bufo gargarigans, and has anti-tumor effects. The aim of this study was to evaluate the possible therapeutic effect of HCS against breast cancer (BRCA) using cytology, network pharmacology, and molecular biology approaches. The half-inhibitory concentration (IC50) of HCS in the BRCA cells was determined by cytotoxicity assay, and were accordingly treated with high and low doses HCS in the TUNEL and scratch assays. The potential targets of HCS in the BRCA cells were identified through functional enrichment analysis and protein-protein interaction (PPI) networks, and verified by molecular docking. The expression levels of key signaling pathways-related proteins in HCS-treated BRCA cells by western blotting. HCS inhibited the proliferation and migration of MCF-7 and MDA-MB-231 cells, and induced apoptosis in a dose-dependent manner. Furthermore, we screened 289 core HCS targets against BRCA, which were primarily enriched in the PI3K-AKT, MAPK chemokines, and other. signaling pathways. In addition, PIK3CA, PIK3CD, and MTOR were confirmed as HCS targets by molecular docking. Consistent with this, we observed a reduction in the expression levels of phosphorylated PI3K, AKT, and MTOR in the HCS-treated BRCA cells. Taken together, our findings suggest that HCS inhibits the growth of BRCA cells by targeting the PI3K-AKT pathway, and warrants further investigation as a therapeutic agent for treating patients with BRCA. [ABSTRACT FROM AUTHOR]

Ischemic stroke causes a lack of oxygen and glucose supply to brain, eventually leads to severe neurological disorders. Retinoic acid is a major metabolic product of vitamin A and has various biological effects. The PI3K-Akt signaling pathway is an important survival pathway in brain. Phosphorylated Akt is important in regulating survival and apoptosis. We examined whether retinoic acid has neuroprotective effects in stroke model by regulating Akt and its downstream protein, Bad. Moreover, we investigated the relationship between retinoic acid and Bcl-2 family protein interactions. Animals were intraperitoneally administered vehicle or retinoic acid (5 mg/kg) for four days before surgery and ischemic stroke was induced by middle cerebral artery occlusion (MCAO) surgery. Neurobehavioral tests were performed 24 h after MCAO and cerebral cortical tissues were collected. Cresyl violet staining and TUNEL histochemistry were performed, Western blot and immunoprecipitation analysis were performed to elucidate the expression of various proteins. Retinoic acid reduced neurological deficits and histopathological changes, decreased the number of TUNEL-positive cells, and alleviated reduction of phospho-PDK1, phospho-Akt, and phospho-Bad expression caused by MCAO damage. Immunoprecipitation analysis showed that MCAO damage reduced the interaction between phospho-Bad and 14-3-3, which was attenuated by retinoic acid. Furthermore, retinoic acid mitigated the increase in Bcl-2/Bad and Bcl-xL/Bad binding levels and the reduction in Bcl-2/Bax and Bcl-xL/Bax binding levels caused by MCAO damage. Retinoic acid alleviated MCAO-induced increase of caspase-3 and cleaved caspase-3 expression. We demonstrate that retinoic acid prevented apoptosis against cerebral ischemia through phosphorylation of Akt and Bad, maintenance of phospho-Bad and 14-3-3 binding, and regulation of Bcl-2 family protein interactions. [ABSTRACT FROM AUTHOR]

BACKGROUND: It is known that N6-methyladenosine (m6A) plays a role in the pathogenesis of various diseases and studies have suggested its involvement in the pathologic changes of steroid-induced femoral head necrosis (SNFH). However, research on m6A methylation modifications in steroid-induced femoral head necrosis is limited. OBJECTIVE: Using bioinformatics methods to identify the differential expression of m6A-related genes in steroid-induced femoral head necrosis and to predict miRNAs associated with these genes to further elucidate the role and mechanism of m6A methylation in steroid-induced femoral head necrosis. METHODS: Differential gene expression between steroid-induced femoral head necrosis and control groups was analyzed using GSE123568 gene expression data and identified using the “limma” package in R. Functional enrichment analysis was performed on the differentially expressed genes. Differential analysis of the related genes was carried out using the “ggstatsplot” package in R. The differential genes were cross-validated using the GSE74089 dataset. An mRNA-miRNA regulatory network was constructed, and co-expression analysis was performed on the module genes followed by enrichment analysis. Differences in immune cell infiltration between steroid-induced femoral head necrosis and control groups were quantified using the ssGSEA method. RESULTS AND CONCLUSION: Correlation analysis revealed 13 m6A-related genes, and further analysis through the protein-protein interaction network identification and receiver operating characteristic curve analysis showed that YTHDF2 was expected to be a core differential gene as a potential early biomarker. Enrichment analysis indicated that differentially expressed genes were mainly involved in inflammation and immune response and were closely related to osteoclasts. Cross-validation analysis showed that differential gene expression results between the two datasets were consistent. mRNA-miRNA regulatory network analysis revealed that YTHDF2 was negatively correlated with miRNA-27a. Immune infiltration analysis revealed an increase in immune cell infiltration in steroid-induced femoral head necrosis, and YTHDF2 was positively correlated with the infiltration of CD4+ T cells. To conclude, m6A-related gene YTHDF2 can serve as a potential biomarker of steroid-induced femoral head necrosis and is valuable for the early clinical diagnosis and treatment of steroid-induced femoral head necrosis. The negative correlation between YTHDF2 and mir-27a and the positive correlation between YTHDF2 and CD4+ T cell infiltration provide new insights into the early diagnosis and treatment of steroid-induced femoral head necrosis and shed light on the mechanism of m6A in steroid-induced femoral head necrosis [ABSTRACT FROM AUTHOR]

Living systems rely on molecular building blocks with low structural symmetry. Therefore, constituent amino acids and nucleotides yield short-lived nuclear magnetic responses to electromagnetic radiation. Magnetic signals are at the basis of molecular imaging, structure determination and interaction studies. In solution state, as the molecular weight of analytes increases, coherences with long lifetimes are needed to yield advantageous through-space magnetisation transfers. Interactions between magnetic nuclei can only be detected provided the lifetimes of spin order are sufficient. In J-coupled pairs of nuclei, long-lived coherences (LLC's) connect states with different spin-permutation symmetry. Here in, we show sustained LLC's in protein Lysozyme, weighing 14.3 kDa, with lifetimes twice as long as those of classical magnetisation for the aliphatic protons of glycine residues. We found for the first time that, in a protein of significant molecular weight, LLC's yield substantial through-space magnetisation transfers: spin-order transfer stemming from LLC's overcame transfers from classical coherences by factors > 2. Furthermore, in agreement with theory, the permutation symmetry of LLC-based transfers allows mapping interacting atoms in the protein structure with respect to the molecular plane of glycine residues in a stereospecific manner. These findings can extend the scope of liquid-state high-resolution biomolecular spectroscopy. Solution NMR spectroscopy provides rich structural information on biomolecules, however, its resolution becomes limited when molecular size increases, due to short-lived nuclear magnetic responses to electromagnetic radiation. Here, the authors sustain long-lived coherences for the aliphatic protons of glycine residues within protein lysozyme, yielding substantial through-space magnetization transfers, and mapping interacting atoms in the protein structure. [ABSTRACT FROM AUTHOR]

Background: Recent Alzheimer's disease (AD) discoveries are increasingly based on studies from a variety of omics technologies on large cohorts. Currently, there is no easily accessible resource for neuroscientists to browse, query, and visualize these complex datasets in a harmonized manner. Objective: Create an online portal of public omics datasets for AD research. Methods: We developed Alzheimer DataLENS, a web-based portal, using the R Shiny platform to query and visualize publicly available transcriptomics and genetics studies of AD on human cohorts. To ensure consistent representation of AD findings, all datasets were processed through a uniform bioinformatics pipeline. Results: Alzheimer DataLENS currently houses 2 single-nucleus RNA sequencing datasets, over 30 bulk RNA sequencing datasets from 19 brain regions and 3 cohorts, and 2 genome-wide association studies (GWAS). Available visualizations for single-nucleus data include bubble plots, heatmaps, and UMAP plots; for bulk expression data include box plots and heatmaps; for pathways include protein-protein interaction network plots; and for GWAS results include Manhattan plots. Alzheimer DataLENS also links to two other knowledge resources: the AD Progression Atlas and the Astrocyte Atlas. Conclusions: Alzheimer DataLENS is a valuable resource for investigators to quickly and systematically explore omics datasets and is freely accessible at . [ABSTRACT FROM AUTHOR]

Understanding both the physicochemical and biological interactions of nanoparticles is mandatory for the biomedical application of nanomaterials. By binding proteins, nanoparticles acquire new surface identities in biological fluids, the protein corona. Various studies have revealed the dynamic structure and nano–bio interactions of the protein corona. The binding of proteins not only imparts new surface identities to nanoparticles in biological fluids but also significantly influences their bioactivity, stability, and targeting specificity. Interestingly, recent endeavors have been undertaken to harness the potential of the protein corona instead of evading its presence. Exploitation of this 'protein–nanoparticle alliance' has significant potential to change the field of nanomedicine. Here, we present a thorough examination of the latest research on protein corona, encompassing its formation, dynamics, recent developments, and diverse bioapplications. Furthermore, we also aim to explore the interactions at the nano–bio interface, paving the way for innovative strategies to advance the application potential of the protein corona. By addressing challenges and promises in controlling protein corona formation, this review provides insights into the evolving landscape of the 'protein–nanoparticle alliance' and highlights emerging. [ABSTRACT FROM AUTHOR]

SUMMARY: Living organisms possess mechanisms to safeguard genome integrity. To avoid spreading mutations, DNA lesions are detected and cell division is temporarily arrested to allow repair mechanisms. Afterward, cells either resume division or respond to unsuccessful repair by undergoing programmed cell death (PCD). How the success rate of DNA repair connects to later cell fate decisions remains incompletely known, particularly in plants. The Arabidopsis thaliana RETINOBLASTOMA‐RELATED1 (RBR) protein and its partner E2FA, play both structural and transcriptional functions in the DNA damage response (DDR). Here we provide evidence that distinct RBR protein interactions with LXCXE motif‐containing proteins guide these processes. Using the N849F substitution in the RBR B‐pocket domain, which specifically disrupts binding to the LXCXE motif, we show that these interactions are dispensable in unchallenging conditions. However, N849F substitution abolishes RBR nuclear foci and promotes PCD and growth arrest upon genotoxic stress. NAC044, which promotes growth arrest and PCD, accumulates after the initial recruitment of RBR to foci and can bind non‐focalized RBR through the LXCXE motif in a phosphorylation‐independent manner, allowing interaction at different cell cycle phases. Disrupting NAC044‐RBR interaction impairs PCD, but their genetic interaction points to opposite independent roles in the regulation of PCD. The LXCXE‐binding dependency of the roles of RBR in the DDR suggests a coordinating mechanism to translate DNA repair success to cell survival. We propose that RBR and NAC044 act in two distinct DDR pathways, but interact to integrate input from both DDR pathways to decide upon an irreversible cell fate decision. Significance Statement: RBR has recently emerged as a DNA damage response regulator in parallel to SOG1, which promotes growth arrest and Programmed Cell Death (PCD) upon genotoxic stress. We report that the ability to interact with the LXCXE motif is essential for RBR to form repair foci and to bind NAC044, the closest homolog, and target of SOG1, to regulate DNA damage‐induced PCD. [ABSTRACT FROM AUTHOR]