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Alzheimer's disease is the most common form of dementia, characterized by the pathological accumulation of amyloid-beta (Aβ) plaques and tau neurofibrillary tangles. Triggering receptor expressed on myeloid cells 2 (TREM2) is increasingly recognized as playing a central role in Aβ clearance and microglia activation in AD. The TREM2 gene transcriptional product is alternatively spliced to produce three different protein isoforms. The canonical TREM2 isoform binds to DAP12 to activate downstream pathways. However, little is known about the function or interaction partners of the alternative TREM2 isoforms. The present study utilized a computational approach in a systematic search for new interaction partners of the TREM2 isoforms by integrating several state-of-the-art structural bioinformatics tools from initial large-scale screening to one-on-one corroborative modeling and eventual all-atom visualization. CD9, a cell surface glycoprotein involved in cell–cell adhesion and migration, was identified as a new interaction partner for two TREM2 isoforms, and CALM, a calcium-binding protein involved in calcium signaling, was identified as an interaction partner for a third TREM2 isoform, highlighting the potential role of cell adhesion and calcium regulation in AD. [ABSTRACT FROM AUTHOR]

Reliable and accurate methods of estimating the accuracy of predicted protein models are vital to understanding their respective utility. Discerning how the quaternary structure conforms can significantly improve our collective understanding of cell biology, systems biology, disease formation, and disease treatment. Accurately determining the quality of multimeric protein models is still computationally challenging, as the space of possible conformations is significantly larger when proteins form in complex with one another. Here, we present EGG (energy and graph-based architectures) to assess the accuracy of predicted multimeric protein models. We implemented message-passing and transformer layers to infer the overall fold and interface accuracy scores of predicted multimeric protein models. When evaluated with CASP15 targets, our methods achieved promising results against single model predictors: fourth and third place for determining the highest-quality model when estimating overall fold accuracy and overall interface accuracy, respectively, and first place for determining the top three highest quality models when estimating both overall fold accuracy and overall interface accuracy. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) represents a problem for the healthcare system worldwide. DM has very serious complications such as blindness, kidney failure, and cardiovascular disease. In addition to the very bad socioeconomic impacts, it influences patients and their families and communities. The global costs of DM and its complications are huge and expected to rise by the year 2030. DM is caused by genetic and environmental risk factors. Genetic testing will aid in early diagnosis and identification of susceptible individuals or populations using ATP-sensitive potassium (KATP) channels present in different tissues such as the pancreas, myocardium, myocytes, and nervous tissues. The channels respond to different concentrations of blood sugar, stimulation by hormones, or ischemic conditions. In pancreatic cells, they regulate the secretion of insulin and glucagon. Mutations in the KCNJ11 gene that encodes the Kir6.2 protein (a major constituent of KATP channels) were reported to be associated with Type 2 DM, neonatal diabetes mellitus (NDM), and maturity-onset diabetes of the young (MODY). Kir6.2 harbors binding sites for ATP and phosphatidylinositol 4,5-diphosphate (PIP2). The ATP inhibits the KATP channel, while the (PIP2) activates it. A Kir6.2 mutation at tyrosine330 (Y330) was demonstrated to reduce ATP inhibition and predisposes to NDM. In this study, we examined the effect of mutations on the Kir6.2 structure using bioinformatics tools and molecular dynamic simulations (SIFT, PolyPhen, SNAP2, PANTHER, PhD&SNP, SNP&Go, I-Mutant, MuPro, MutPred, ConSurf, HOPE, and GROMACS). Our results indicated that M199R, R201H, R206H, and Y330H mutations influence Kir6.2 structure and function and therefore may cause DM. We conclude that MD simulations are useful techniques to predict the effects of mutations on protein structure. In addition, the M199R, R201H, R206H, and Y330H variant in the Kir6.2 protein may be associated with DM. These results require further verification in protein–protein interactions, Kir6.2 function, and case-control studies. [ABSTRACT FROM AUTHOR]

Tumor necrosis factor α (TNF-α) is a multifunctional cytokine protein acknowledged as a vital mediator in cell differentiation, proliferation, and survival. Additionally, TNF-α is a crucial component of the host's defense by mediating inflammatory and immune responses against various aggressive agents, including viruses, bacteria parasites, and tumors. However, excessive production can be detrimental to the body and is also implicated in developing several inflammatory and immune-mediated disorders. Therefore, there is great interest in studying its role and its modulation, in various diseases, both in in vitro, in vivo, and in silico experiments. In this review, we evaluated the structures of proteins related to TNF-α available in public databases. In addition, we described the main antibodies blocking this cytokine and its applications and commented on the potential of naturally produced binding molecules, such as TNF-α-binding proteins produced by ticks. We also discuss the role of structural bioinformatics techniques in understanding the mechanisms of chronic inflammatory diseases related to TNF-α. We hope that the data presented in this review will be useful for studies that aim to better understand the mechanisms of the interactions of TNF-α with other proteins and will lead to new drugs or treatments. [ABSTRACT FROM AUTHOR]

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields. [ABSTRACT FROM AUTHOR]

Object retrieval systems measure the degree of similarity of the shape of 3D models. They search for the elements of the 3D model databases that resemble the query model. In structural bioinformatics, the query model is a protein tertiary/quaternary structure and the objective is to find similarly shaped molecules in the Protein Data Bank. With the ever-growing size of the PDB, a direct atomic coordinate comparison with all its members is impractical. To overcome this problem, the shape of the molecules can be encoded by fixed-length feature vectors. The distance of a protein to the entire PDB can be measured in this low-dimensional domain in linear time. The state-of-the-art approaches utilize Zernike–Canterakis moments for the shape encoding and supply the retrieval process with geometric data of the input structures. The BioZernike descriptors are a standard utility of the PDB since 2020. However, when trying to calculate the ZC moments locally, the issue of the deficiency of libraries readily available for use in custom programs (i.e., without relying on external binaries) is encountered, in particular programs written in Python. Here, a fast and well-documented Python implementation of the Pozo–Koehl algorithm is presented. In contrast to the more popular algorithm by Novotni and Klein, which is based on the voxelized volume, the PK algorithm produces ZC moments directly from the triangular surface meshes of 3D models. In particular, it can accept the molecular surfaces of proteins as its input. In the presented PK-Zernike library, owing to Numba's just-in-time compilation, a mesh with 50,000 facets is processed by a single thread in a second at the moment order 20. Since this is the first time the PK algorithm is used in structural bioinformatics, it is employed in a novel, simple, but efficient protein structure retrieval pipeline. The elimination of the outlying chain fragments via a fast PCA-based subroutine improves the discrimination ability, allowing for this pipeline to achieve an 0.961 area under the ROC curve in the BioZernike validation suite (0.997 for the assemblies). The correlation between the results of the proposed approach and of the 3D Surfer program attains values up to 0.99. [ABSTRACT FROM AUTHOR]

In eukaryotic organisms, genomic DNA associates with histone proteins to form nucleosomes. Nucleosomes provide a basis for genome compaction, epigenetic markup, and mediate interactions of nuclear proteins with their target DNA loci. A negatively charged (acidic) patch located on the H2A-H2B histone dimer is a characteristic feature of the nucleosomal surface. The acidic patch is a common site in the attachment of various chromatin proteins, including viral ones. Acidic patch-binding peptides present perspective compounds that can be used to modulate chromatin functioning by disrupting interactions of nucleosomes with natural proteins or alternatively targeting artificial moieties to the nucleosomes, which may be beneficial for the development of new therapeutics. In this work, we used several computational and experimental techniques to improve our understanding of how peptides may bind to the acidic patch and what are the consequences of their binding. Through extensive analysis of the PDB database, histone sequence analysis, and molecular dynamic simulations, we elucidated common binding patterns and key interactions that stabilize peptide–nucleosome complexes. Through MD simulations and FRET measurements, we characterized changes in nucleosome dynamics conferred by peptide binding. Using fluorescence polarization and gel electrophoresis, we evaluated the affinity and specificity of the LANA1-22 peptide to DNA and nucleosomes. Taken together, our study provides new insights into the different patterns of intermolecular interactions that can be employed by natural and designed peptides to bind to nucleosomes, and the effects of peptide binding on nucleosome dynamics and stability. [ABSTRACT FROM AUTHOR]

Access to the three-dimensional (3D) structural information of macromolecules is of major interest in both fundamental and applied research. Obtaining this experimental data can be complex, time consuming, and costly. Therefore, in silico computational approaches are an alternative of interest, and sometimes present a unique option. In this context, the Protein Structure Prediction method AlphaFold2 represented a revolutionary advance in structural bioinformatics. Named method of the year in 2021, and widely distributed by DeepMind and EBI, it was thought at this time that protein-folding issues had been resolved. However, the reality is slightly more complex. Due to a lack of input experimental data, related to crystallographic challenges, some targets have remained highly challenging or not feasible. This perspective exercise, dedicated to a non-expert audience, discusses and correctly places AlphaFold2 methodology in its context and, above all, highlights its use, limitations, and opportunities. After a review of the interest in the 3D structure and of the previous methods used in the field, AF2 is brought into its historical context. Its spatial interests are detailed before presenting precise quantifications showing some limitations of this approach and finishing with the perspectives in the field. [ABSTRACT FROM AUTHOR]

This study systematically investigated the differences in allergenicity of casein in cow milk (CM), goat milk (GM), camel milk (CAM), and mare milk (MM) from protein structures using bioinformatics. Primary structure sequence analysis reveals high sequence similarity between the α-casein of CM and GM, while all allergenic subtypes are likely to have good hydrophilicity and thermal stability. By analyzing linear B-cell epitope, T-cell epitope, and allergenic peptides, the strongest casein allergenicity is observed for CM, followed by GM, and the casein of MM has the weakest allergenicity. Meanwhile, 7, 9, and 16 similar or identical amino acid fragments in linear B-cell epitopes, T-cell epitopes, and allergenic peptides, respectively, were observed in different milks. Among these, the same T-cell epitope FLGAEVQNQ was shared by κ-CN in all four different species' milk. Epitope results may provide targets of allergenic fragments for reducing milk allergenicity through physical or/and chemical methods. This study explained the underlying secrets for the high allergenicity of CM to some extent from the perspective of casein and provided new insights for the dairy industry to reduce milk allergy. Furthermore, it provides a new idea and method for comparing the allergenicity of homologous proteins from different species. [ABSTRACT FROM AUTHOR]

Invasive fungal infections represent a public health problem that worsens over the years with the increasing resistance to current antimycotic agents. Therefore, there is a compelling medical need of widening the antifungal drug repertoire, following different methods such as drug repositioning, identification and validation of new molecular targets and developing new inhibitors against these targets. In this work we developed a structure-based strategy for drug repositioning and new drug design, which can be applied to infectious fungi and other pathogens. Instead of applying the commonly accepted off-target criterion to discard fungal proteins with close homologues in humans, the core of our approach consists in identifying fungal proteins with active sites that are structurally similar, but preferably not identical to binding sites of proteins from the so-called "human pharmacolome". Using structural information from thousands of human protein target-inhibitor complexes, we identified dozens of proteins in fungal species of the genera Histoplasma, Candida, Cryptococcus, Aspergillus and Fusarium, which might be exploited for drug repositioning and, more importantly, also for the design of new fungus-specific inhibitors. As a case study, we present the in vitro experiments performed with a set of selected inhibitors of the human mitogen-activated protein kinases 1/2 (MEK1/2), several of which showed a marked cytotoxic activity in different fungal species. [ABSTRACT FROM AUTHOR]

Over the past few decades, the number of available structural bioinformatics pipelines, libraries, plugins, web resources and software has increased exponentially and become accessible to the broad realm of life scientists. This expansion has shaped the field as a tangled network of methods, algorithms and user interfaces. In recent years PyMOL, widely used software for biomolecules visualization and analysis, has started to play a key role in providing an open platform for the successful implementation of expert knowledge into an easy-to-use molecular graphics tool. This review outlines the plugins and features that make PyMOL an eligible environment for supporting structural bioinformatics analyses. [ABSTRACT FROM AUTHOR]

Alternative splicing is an important means of generating the protein diversity necessary for cellular functions. Hence, there is a growing interest in assessing the structural and functional impact of alternative protein isoforms. Typically, experimental studies are used to determine the structures of the canonical proteins ignoring the other isoforms. Therefore, there is still a large gap between abundant sequence information and meager structural data on these isoforms. During the last decade, significant progress has been achieved in the development of bioinformatics tools for structural and functional annotations of proteins. Moreover, the appearance of the AlphaFold program opened up the possibility to model a large number of high-confidence structures of the isoforms. In this study, using state-of-the-art tools, we performed in silico analysis of 58 eukaryotic proteomes. The evaluated structural states included structured domains, intrinsically disordered regions, aggregation-prone regions, and tandem repeats. Among other things, we found that the isoforms have fewer signal peptides, transmembrane regions, or tandem repeat regions in comparison with their canonical counterparts. This could change protein function and/or cellular localization. The AlphaFold modeling demonstrated that frequently isoforms, having differences with the canonical sequences, still can fold in similar structures though with significant structural rearrangements which can lead to changes of their functions. Based on the modeling, we suggested classification of the structural differences between canonical proteins and isoforms. Altogether, we can conclude that a majority of isoforms, similarly to the canonical proteins are under selective pressure for the functional roles. [ABSTRACT FROM AUTHOR]

This paper presents HBcompare, a method that classifies protein structures according to ligand binding preference categories by analyzing hydrogen bond topology. HBcompare excludes other characteristics of protein structure so that, in the event of accurate classification, it can implicate the involvement of hydrogen bonds in selective binding. This approach contrasts from methods that represent many aspects of protein structure because holistic representations cannot associate classification with just one characteristic. To our knowledge, HBcompare is the first technique with this capability. On five datasets of proteins that catalyze similar reactions with different preferred ligands, HBcompare correctly categorized proteins with similar ligand binding preferences 89.5% of the time. Using only hydrogen bond topology, classification accuracy with HBcompare surpassed standard structure-based comparison algorithms that use atomic coordinates. As a tool for implicating the role of hydrogen bonds in protein function categories, HBcompare represents a first step towards the automatic explanation of biochemical mechanisms. [ABSTRACT FROM AUTHOR]

NH–π interactions between polar and aromatic residues are well distributed in proteins whose stabilizing effects have been investigated in globular and fibrous proteins. In order to gain structural insights into side chain NH–π interactions, we solved a crystal structure of a collagen-like peptide containing Gln-Phe pairs. The Gln-Phe NH–π interactions were further characterized by quantum calculations, molecular simulations, and structural bioinformatics. The analyses indicated that the NH–π interactions are robust under various solvent conditions, can be distributed either on the protein surface or in its hydrophobic core and can form at a wide range of distances between residues. This study suggested that NH–π interactions can play a versatile role in protein design, including engineering hydrophobic cores, solvent accessible surfaces, and protein–protein interfaces. [ABSTRACT FROM AUTHOR]

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the United States National Science Foundation, National Institutes of Health, and Department of Energy, supports structural biologists and Protein Data Bank (PDB) data users around the world. The RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, serves as the US data center for the global PDB archive housing experimentally-determined three-dimensional (3D) structure data for biological macromolecules. As the wwPDB-designated Archive Keeper, RCSB PDB is also responsible for the security of PDB data and weekly update of the archive. RCSB PDB serves tens of thousands of data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) annually working on all permanently inhabited continents. RCSB PDB makes PDB data available from its research-focused web portal at no charge and without usage restrictions to many millions of PDB data consumers around the globe. It also provides educators, students, and the general public with an introduction to the PDB and related training materials through its outreach and education-focused web portal. This review article describes growth of the PDB, examines evolution of experimental methods for structure determination viewed through the lens of the PDB archive, and provides a detailed accounting of PDB archival holdings and their utilization by researchers, educators, and students worldwide. [ABSTRACT FROM AUTHOR]

One of the major drawbacks of the industrial implementation of enzymatic processes is the low operational stability of the enzymes under tough industrial conditions. In this respect, the use of thermostable enzymes in the industry is gaining ground during the last decades. Herein, we report a structure-guided approach for the development of novel and thermostable 2′-deoxyribosyltransferases (NDTs) based on the computational design of disulfide bonds on hot spot positions. To this end, a small library of NDT variants from Lactobacillus delbrueckii (LdNDT) with introduced cysteine pairs was created. Among them, LdNDTS104C (100% retained activity) was chosen as the most thermostable variant, displaying a six- and two-fold enhanced long-term stability when stored at 55 °C (t1/255 °C ≈ 24 h) and 60 °C (t1/260 °C ≈ 4 h), respectively. Moreover, the biochemical characterization revealed that LdNDTS104C showed >60% relative activity across a broad range of temperature (30–90 °C) and pH (5–7). Finally, to study the potential application of LdNDTS104C as an industrial catalyst, the enzymatic synthesis of nelarabine was successfully carried out under different substrate conditions (1:1 and 3:1) at different reaction times. Under these experimental conditions, the production of nelarabine was increased up to 2.8-fold (72% conversion) compared with wild-type LdNDT. [ABSTRACT FROM AUTHOR]

The protein core of the nucleosome is composed of an H3-H4 histone tetramer and two H2A-H2B histone dimers. The tetramer organizes the central 60 DNA bp, while H2A-H2B dimers lock the flanking DNA segments. Being positioned at the sides of the nucleosome, H2A-H2B dimers stabilize the overall structure of the nucleosome and modulate its dynamics, such as DNA unwrapping, sliding, etc. Such modulation at the epigenetic level is achieved through post-translational modifications and the incorporation of histone variants. However, the detailed connection between the sequence of H2A-H2B histones and their structure, dynamics and implications for nucleosome functioning remains elusive. In this work, we present a detailed study of H2A-H2B dimer dynamics in the free form and in the context of nucleosomes via atomistic molecular dynamics simulations (based on X. laevis histones). We supplement simulation results by comparative analysis of information in the structural databases. Particularly, we describe a major dynamical mode corresponding to the bending movement of the longest H2A and H2B α-helices. This overall bending dynamics of the H2A-H2B dimer were found to be modulated by its interactions with DNA, H3-H4 tetramer, the presence of DNA twist-defects with nucleosomal DNA and the amino acid sequence of histones. Taken together, our results shed new light on the dynamical mechanisms of nucleosome functioning, such as nucleosome sliding, DNA-unwrapping and their epigenetic modulation. [ABSTRACT FROM AUTHOR]

Objectives: The reprogramming of lipid metabolism is a new trait of cancers. However, the role of lipid metabolism in the tumor immune microenvironment (TIME) and the prognosis of gastric cancer remains unclear. Methods: Consensus clustering was applied to identify novel subgroups. ESTIMATE, TIMER, and MCPcounter algorithms were used to determine the TIME of the subgroups. The underlying mechanisms were elucidated using functional analysis. The prognostic model was established using the LASSO algorithm and multivariate Cox regression analysis. Results: Three molecular subgroups with significantly different survival were identified. The subgroup with relatively low lipid metabolic expression had a lower immune score and immune cells. The differentially expressed genes (DEGs) were concentrated in immune biological processes and cell migration via GO and KEGG analyses. GSEA analysis showed that the subgroups were mainly enriched in arachidonic acid metabolism. Gastric cancer survival can be predicted using risk models based on lipid metabolism genes. Conclusions: The TIME of gastric cancer patients is related to the expression of lipid metabolism genes and could be used to predict cancer prognosis accurately. [ABSTRACT FROM AUTHOR]

Several syndromic forms of digestive cancers are known to predispose to early-onset gastric tumors such as Hereditary Diffuse Gastric Cancer (HDGC) and Lynch Syndrome (LS). LSII is an extracolonic cancer syndrome characterized by a tumor spectrum including gastric cancer (GC). In the current work, our main aim was to identify the mutational spectrum underlying the genetic predisposition to diffuse gastric tumors occurring in a Tunisian family suspected of both HDGC and LS II syndromes. We selected the index case "JI-021", which was a woman diagnosed with a Diffuse Gastric Carcinoma and fulfilling the international guidelines for both HDGC and LSII syndromes. For DNA repair, a custom panel targeting 87 candidate genes recovering the four DNA repair pathways was used. Structural bioinformatics analysis was conducted to predict the effect of the revealed variants on the functional properties of the proteins. DNA repair genes panel screening identified two variants: a rare MSH2 c.728G>A classified as a variant with uncertain significance (VUS) and a novel FANCD2 variant c.1879G>T. The structural prediction model of the MSH2 variant and electrostatic potential calculation showed for the first time that MSH2 c.728G>A is likely pathogenic and is involved in the MSH2-MLH1 complex stability. It appears to affect the MSH2-MLH1 complex as well as DNA-complex stability. The c.1879G>T FANCD2 variant was predicted to destabilize the protein structure. Our results showed that the MSH2 p.R243Q variant is likely pathogenic and is involved in the MSH2-MLH1 complex stability, and molecular modeling analysis highlights a putative impact on the binding with MLH1 by disrupting the electrostatic potential, suggesting the revision of its status from VUS to likely pathogenic. This variant seems to be a shared variant in the Mediterranean region. These findings emphasize the importance of testing DNA repair genes for patients diagnosed with diffuse GC with suspicion of LSII and colorectal cancer allowing better clinical surveillance for more personalized medicine. [ABSTRACT FROM AUTHOR]

All living organisms require metal ions for their energy production and metabolic and biosynthetic processes. Within cells, the metal ions involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids). The proteins that require binding to one or more metal ions in order to be able to carry out their physiological function are called metalloproteins. About one third of all protein structures in the Protein Data Bank involve metalloproteins. Over the past few years there has been tremendous progress in the number of computational tools and techniques making use of 3D structural information to support the investigation of metalloproteins. This trend has been boosted by the successful applications of neural networks and machine/deep learning approaches in molecular and structural biology at large. In this review, we discuss recent advances in the development and availability of resources dealing with metalloproteins from a structure-based perspective. We start by addressing tools for the prediction of metal-binding sites (MBSs) using structural information on apo-proteins. Then, we provide an overview of the methods for and lessons learned from the structural comparison of MBSs in a fold-independent manner. We then move to describing databases of metalloprotein/MBS structures. Finally, we summarizing recent ML/DL applications enhancing the functional interpretation of metalloprotein structures. [ABSTRACT FROM AUTHOR]

Neuronal calcium sensors (NCSs) are the family of EF-hand proteins mediating Ca2+-dependent signaling pathways in healthy neurons and neurodegenerative diseases. It was hypothesized that the calcium sensor activity of NCSs can be complemented by sensing fluctuation of intracellular zinc, which could further diversify their function. Here, using a set of biophysical techniques, we analyzed the Zn2+-binding properties of five proteins belonging to three different subgroups of the NCS family, namely, VILIP1 and neurocalcin-δ/NCLD (subgroup B), recoverin (subgroup C), as well as GCAP1 and GCAP2 (subgroup D). We demonstrate that each of these proteins is capable of coordinating Zn2+ with a different affinity, stoichiometry, and structural outcome. In the absence of calcium, recoverin and VILIP1 bind two zinc ions with submicromolar affinity, and the binding induces pronounced conformational changes and regulates the dimeric state of these proteins without significant destabilization of their structure. In the presence of calcium, recoverin binds zinc with slightly decreased affinity and moderate conformational outcome, whereas VILIP1 becomes insensitive to Zn2+. NCALD binds Zn2+ with micromolar affinity, but the binding induces dramatic destabilization and aggregation of the protein. In contrast, both GCAPs demonstrate low-affinity binding of zinc independent of calcium, remaining relatively stable even at submillimolar Zn2+ concentrations. Based on these data, and the results of structural bioinformatics analysis, NCSs can be divided into three categories: (1) physiological Ca2+/Zn2+ sensor proteins capable of binding exchangeable (signaling) zinc (recoverin and VILIP1), (2) pathological Ca2+/Zn2+ sensors responding only to aberrantly high free zinc concentrations by denaturation and aggregation (NCALD), and (3) Zn2+-resistant, Ca2+ sensor proteins (GCAP1, GCAP2). We suggest that NCS proteins may therefore govern the interconnection between Ca2+-dependent and Zn2+-dependent signaling pathways in healthy neurons and zinc cytotoxicity-related neurodegenerative diseases, such as Alzheimer's disease and glaucoma. [ABSTRACT FROM AUTHOR]

Computational structural biology has demonstrated a key role in improving human health. Their analysis showed that the same ligand can bind to GPCR molecules with diverse amino acid sequences and different overall structures, but the binding pockets of these GPCRs are locally similar in their electrostatic as well as their three-dimensional structural properties. Eight articles were published in this Special Issue with topics relevant to protein structure, protein-ligand interactions, protein mutations, and molecular dynamics simulation. [Extracted from the article]

The development of AlphaFold2 marked a paradigm-shift in the structural biology community. Herein, we assess the ability of AlphaFold2 to predict disordered regions against traditional sequence-based disorder predictors. We find that AlphaFold2 performs well at discriminating disordered regions, but also note that the disorder predictor one constructs from an AlphaFold2 structure determines accuracy. In particular, a naïve, but non-trivial assumption that residues assigned to helices, strands, and H-bond stabilized turns are likely ordered and all other residues are disordered results in a dramatic overestimation in disorder; conversely, the predicted local distance difference test (pLDDT) provides an excellent measure of residue-wise disorder. Furthermore, by employing molecular dynamics (MD) simulations, we note an interesting relationship between the pLDDT and secondary structure, that may explain our observations and suggests a broader application of the pLDDT for characterizing the local dynamics of intrinsically disordered proteins and regions (IDPs/IDRs). [ABSTRACT FROM AUTHOR]

Neurexin-1 (NRXN1) is a membrane protein essential in synapse formation and cell signaling as a cell-adhesion molecule and cell-surface receptor. NRXN1 and its binding partner neuroligin have been associated with deficits in cognition. Recent genetics research has linked NRXN1 missense mutations to increased risk for brain disorders, including schizophrenia (SCZ) and autism spectrum disorder (ASD). Investigation of the structure–function relationship in NRXN1 has proven difficult due to a lack of the experimental full-length membrane protein structure. AlphaFold, a deep learning-based predictor, succeeds in high-quality protein structure prediction and offers a solution for membrane protein model construction. In the study, we applied a computational saturation mutagenesis method to analyze the systemic effects of missense mutations on protein functions in a human NRXN1 structure predicted from AlphaFold and an experimental Bos taurus structure. The folding energy changes were calculated to estimate the effects of the 29,540 mutations of AlphaFold model on protein stability. The comparative study on the experimental and computationally predicted structures shows that these energy changes are highly correlated, demonstrating the reliability of the AlphaFold structure for the downstream bioinformatics analysis. The energy calculation revealed that some target mutations associated with SCZ and ASD could make the protein unstable. The study can provide helpful information for characterizing the disease-causing mutations and elucidating the molecular mechanisms by which the variations cause SCZ and ASD. This methodology could provide the bioinformatics protocol to investigate the effects of target mutations on multiple AlphaFold structures. [ABSTRACT FROM AUTHOR]

Protein structure prediction is an important issue in structural bioinformatics. In this process, model quality assessment (MQA), which estimates the accuracy of the predicted structure, is also practically important. Currently, the most commonly used dataset to evaluate the performance of MQA is the critical assessment of the protein structure prediction (CASP) dataset. However, the CASP dataset does not contain enough targets with high-quality models, and thus cannot sufficiently evaluate the MQA performance in practical use. Additionally, most application studies employ homology modeling because of its reliability. However, the CASP dataset includes models generated by de novo methods, which may lead to the mis-estimation of MQA performance. In this study, we created new benchmark datasets, named a homology models dataset for model quality assessment (HMDM), that contain targets with high-quality models derived using homology modeling. We then benchmarked the performance of the MQA methods using the new datasets and compared their performance to that of the classical selection based on the sequence identity of the template proteins. The results showed that model selection by the latest MQA methods using deep learning is better than selection by template sequence identity and classical statistical potentials. Using HMDM, it is possible to verify the MQA performance for high-accuracy homology models. [ABSTRACT FROM AUTHOR]

The study of rare diseases is important not only for the individuals affected but also for the advancement of medical knowledge and a deeper understanding of human biology and genetics. The wide repertoire of structural information now available from reliable and accurate prediction methods provides the opportunity to investigate the molecular origins of most of the rare diseases reviewed in the Orpha.net database. Thus, it has been possible to analyze the topology of the pathogenic missense variants found in the 2515 proteins involved in Mendelian rare diseases (MRDs), which form the database for our structural bioinformatics study. The amino acid substitutions responsible for MRDs showed different mutation site distributions at different three-dimensional protein depths. We then highlighted the depth-dependent effects of pathogenic variants for the 20,061 pathogenic variants that are present in our database. The results of this structural bioinformatics investigation are relevant, as they provide additional clues to mitigate the damage caused by MRD.

This review aims to provide an overview of recent examples of lanthanoid-calixarene complexes incorporated into light-emitting materials. Background information on the antenna effect and early work on lanthanoid complexes on calixarenes is provided to set the context. Classes of materials discussed include polymers, nanoparticles, and metal clusters. [ABSTRACT FROM AUTHOR]

Recent trends in Ru complex chemistry are surveyed with emphasis on the development of anticancer drugs and applications in catalysis, polymers, materials science and nanotechnology. [ABSTRACT FROM AUTHOR]

A major challenge in molecular investigations at surfaces has been to image individual molecules, and the assemblies they form, with single-bond resolution. Scanning probe microscopy, with its exceptionally high resolution, is ideally suited to this goal. With the introduction of methods exploiting molecularly-terminated tips, where the apex of the probe is, for example, terminated with a single CO, Xe or H2 molecule, scanning probe methods can now achieve higher resolution than ever before. In this review, some of the landmark results related to attaining intramolecular resolution with non-contact atomic force microscopy (NC-AFM) are summarised before focussing on recent reports probing molecular assemblies where apparent intermolecular features have been observed. Several groups have now highlighted the critical role that flexure in the tip-sample junction plays in producing the exceptionally sharp images of both intra- and apparent inter-molecular structure. In the latter case, the features have been identified as imaging artefacts, rather than real intermolecular bonds. This review discusses the potential for NC-AFM to provide exceptional resolution of supramolecular assemblies stabilised via a variety of intermolecular forces and highlights the potential challenges and pitfalls involved in interpreting bonding interactions. [ABSTRACT FROM AUTHOR]

The role and function of a given protein is dependent on its structure. In recent years, however, numerous studies have highlighted the importance of unstructured, or disordered regions in governing a protein's function. Disordered proteins have been found to play important roles in pivotal cellular functions, such as DNA binding and signalling cascades. Studying proteins with extended disordered regions is often problematic as they can be challenging to express, purify and crystallise. This means that interpretable experimental data on protein disorder is hard to generate. As a result, predictive computational tools have been developed with the aim of predicting the level and location of disorder within a protein. Currently, over 60 prediction servers exist, utilizing different methods for classifying disorder and different training sets. Here we review several good performing, publicly available prediction methods, comparing their application and discussing how disorder prediction servers can be used to aid the experimental solution of protein structure. The use of disorder prediction methods allows us to adopt a more targeted approach to experimental studies by accurately identifying the boundaries of ordered protein domains so that they may be investigated separately, thereby increasing the likelihood of their successful experimental solution. [ABSTRACT FROM AUTHOR]

The crystal and molecular structures of the title compounds, phenyl quinoline-2-carboxylate and 2-methoxyphenyl quinoline-2-carboxylate, two new derivatives of quinolone-2-carboxylic acid, are reported and confirmed by single crystal X-ray diffraction and spectroscopic data. Compound (I), C16H11NO2, crystallizes in the monoclinic space group P21/c, with 8 molecules in the unit cell. The unit cell parameters are a = 14.7910(3) Å; b = 5.76446(12) Å; c = 28.4012(6) Å; β = 99.043(2)° V = 2391.45(9) °3. Compound (II), C17H13NO5, crystallizes in the monoclinic space group P21/n with 4 molecules in the unit cell. The unit cell parameters are a = 9.6095(3) Å; b = 10.8040(3) Å; c = 13.2427(4) Å; β = 102.012(3)°; V = 1344.76(7) ų . Density functional theory (DFT) geometry optimized molecular orbital calculations were performed and frontier molecular orbitals of each compound are displayed. Correlation between the calculated molecular orbital energies (eV) for the surfaces of the frontier molecular orbitals to the electronic excitation transitions from the absorption spectra of each compound has been proposed. Additionally, similar correlations observed among six closely related compounds examining small structural differences to their frontier molecular orbital surfaces and from their DFT molecular orbital energies, provide further support for the suggested assignments of the title compounds. [ABSTRACT FROM AUTHOR]

Reductive amination of salicylaldehyde or 3,5-di-tert-butylsalicylaldehyde and 1-adamantylamine using NaBH4 gave the corresponding aminoalcohols in high yields. Subsequent addition of one equivalent of H3B.SMe2 to the aminoalcohols, with loss of two equivalents of dihydrogen, resulted in the formation of adamantanyl oxazaborinanes (1a,b). The molecular structure of 1b was studied by a single crystal X-ray diffraction study. Crystals were obtained from a saturated Et2O solution and belong to the triclinic space group P1 with unit cell parameters a = 9.1267(4) Å; b = 11.676(2) Å; c = 12.240(3) Å; a = 66.840(3)°; β = 78.529(3)°; and γ = 67.354(3)°. The molecular structure of the addition product (2a) arising from maltol and 1a was also confirmed by single crystal X-ray diffraction. Crystals were obtained from a saturated 1:2 mixture of toluene/Et2O and belong to the orthorhombic space group Pna2(1) with unit cell parameters a = 18.519(6) Å; b = 17.315(5) Å; and c = 12.680(4) Å. The asymmetric unit contains two molecules that differ slightly in some of the dihedral angles. [ABSTRACT FROM AUTHOR]

The crystal and molecular structure of 2-(diphenylphosphano)phenyl benzoate3borane adduct are reported. The title compound crystallizes from a petroleum ether/ethyl3acetate mixture in the triclinic space group P1 with two molecules in the unit cell. The unit3cell parameters are: a = 8.67(1) Å, b = 9.202(1) Å, c = 14.224(2) Å; a = 72.600(7)°, β = 73.577(7)°, γ = 84.349(7)° and V = 1039.5(2) ų. Bond lengths and angles are typical for this phosphane borane adduct. [ABSTRACT FROM AUTHOR]

Several efficient algorithms to conduct pairwise comparisons among large databases of protein structures have emerged in the recent literature. The central theme is the design of a measure between the Cα atoms of two protein chains, from which dynamic programming is used to compute an alignment. The efficiency and efficacy of these algorithms allows large-scale computational studies that would have been previously impractical. The computational study herein shows that the structural alignment algorithm eigen-decomposition alignment with the spectrum (EIGAs) is robust against both parametric and structural variation. [ABSTRACT FROM AUTHOR]

NONO and SFPQ are involved in multiple nuclear processes (e.g., pre-mRNA splicing, DNA repair, and transcriptional regulation). These proteins, along with NEAT1, enable paraspeckle formation, thus promoting multiple myeloma cell survival. In this paper, we investigate NONO and SFPQ dimer stability, highlighting the hetero- and homodimer structural differences, and model their interactions with RNA, simulating their binding to a polyG probe mimicking NEAT1guanine-rich regions. We demonstrated in silico that NONO::SFPQ heterodimerization is a more favorable process than homodimer formation. We also show that NONO and SFPQ RRM2 subunits are primarily required for protein-protein interactions with the other DBHS protomer. Simulation of RNA binding to NONO and SFPQ, beside validating RRM1 RNP signature importance, highlighted the role of β2 and β4 strand residues for RNA specific recognition. Moreover, we demonstrated the role of the NOPS region and other protomer's RRM2 β2/β3 loop in strengthening the interaction with RNA. Our results, having deepened RNA and DBHS dimer interactions, could contribute to the design of small molecules to modulate the activity of these proteins. RNA-mimetics, able to selectively bind to NONO and/or SFPQ RNA-recognition site, could impair paraspeckle formation, thus representing a first step towards the discovery of drugs for multiple myeloma treatment.

Unlike the secondary structure elements that connect in protein structures, loop fragments in protein chains are often highly mobile even in generally stable proteins. The structural variability of loops is often at the center of a protein's stability, folding, and even biological function. Loops are found to mediate important biological processes, such as signaling, protein-ligand binding, and protein-protein interactions. Modeling conformations of a loop under physiological conditions remains an open problem in computational biology. This article reviews computational research in loop modeling, highlighting progress and challenges. Important insight is obtained on potential directions for future research. [ABSTRACT FROM AUTHOR]

The notion of antifragility, an attribute of systems that makes them thrive under variable conditions, has recently been proposed by Nassim Taleb in a business context. This idea requires the ability of such systems to 'tinker', i.e., to creatively respond to changes in their environment. A fairly obvious example of this is natural selection-driven evolution. In this ubiquitous process, an original entity, challenged by an ever-changing environment, creates variants that evolve into novel entities. Analyzing functions that are essential during stationary-state life yield examples of entities that may be antifragile. One such example is proteins with flexible regions that can undergo functional alteration of their side residues or backbone and thus implement the tinkering that leads to antifragility. This in-built property of the cell chassis must be taken into account when considering construction of cell factories driven by engineering principles. [ABSTRACT FROM AUTHOR]

The functional repertoire of a cell is largely embodied in its proteome, the collection of proteins encoded in the genome of an organism. The molecular functions of proteins are the direct consequence of their structure and structure can be inferred from sequence using hidden Markov models of structural recognition. Here we analyze the functional annotation of protein domain structures in almost a thousand sequenced genomes, exploring the functional and structural diversity of proteomes. We find there is a remarkable conservation in the distribution of domains with respect to the molecular functions they perform in the three superkingdoms of life. In general, most of the protein repertoire is spent in functions related to metabolic processes but there are significant differences in the usage of domains for regulatory and extra-cellular processes both within and between superkingdoms. Our results support the hypotheses that the proteomes of superkingdom Eukarya evolved via genome expansion mechanisms that were directed towards innovating new domain architectures for regulatory and extra/intracellular process functions needed for example to maintain the integrity of multicellular structure or to interact with environmental biotic and abiotic factors (e.g., cell signaling and adhesion, immune responses, and toxin production). Proteomes of microbial superkingdoms Archaea and Bacteria retained fewer numbers of domains and maintained simple and smaller protein repertoires. Viruses appear to play an important role in the evolution of superkingdoms. We finally identify few genomic outliers that deviate significantly from the conserved functional design. These include Nanoarchaeum equitans, proteobacterial symbionts of insects with extremely reduced genomes, Tenericutes and Guillardia theta. These organisms spend most of their domains on information functions, including translation and transcription, rather than on metabolism and harbor a domain repertoire characteristic of parasitic organisms. In contrast, the functional repertoire of the proteomes of the Planctomycetes-Verrucomicrobia-Chlamydiae superphylum was no different than the rest of bacteria, failing to support claims of them representing a separate superkingdom. In turn, Protista and Bacteria shared similar functional distribution patterns suggesting an ancestral evolutionary link between these groups. [ABSTRACT FROM AUTHOR]

Protein sequence, structure, and function are inherently linked through evolution and population genetics. Our knowledge of protein structure comes from solved structures in the Protein Data Bank (PDB), our knowledge of sequence through sequences found in the NCBI sequence databases (http://www.ncbi.nlm.nih.gov/), and our knowledge of function through a limited set of in-vitro biochemical studies. How these intersect through evolution is described in the first part of the review. In the second part, our understanding of a series of questions is addressed. This includes how sequences evolve within structures, how evolutionary processes enable structural transitions, how the folding process can change through evolution and what the fitness impacts of this might be. Moving beyond static structures, the evolution of protein kinetics (including normal modes) is discussed, as is the evolution of conformational ensembles and structurally disordered proteins. This ties back to a question of the role of neostructuralization and how it relates to selection on sequences for functions. The relationship between metastability, the fitness landscape, sequence divergence, and organismal effective population size is explored. Lastly, a brief discussion of modeling the evolution of sequences of ordered and disordered proteins is entertained. [ABSTRACT FROM AUTHOR]

MGluR2 is G protein-coupled receptor that is targeted for diseases like anxiety, depression, Parkinson's disease and schizophrenia. Herein, we report the three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of a series of 1,3-dihydro-benzo[b][1,4]diazepin-2-one derivatives as mGluR2 antagonists. Two series of models using two different activities of the antagonists against rat mGluR2, which has been shown to be very similar to the human mGluR2, (activity I: inhibition of [3H]-LY354740; activity II: mGluR2 (1S,3R)-ACPD inhibition of forskolin stimulated cAMP.) were derived from datasets composed of 137 and 69 molecules respectively. For activity I study, the best predictive model obtained from CoMFA analysis yielded a Q2 of 0.513, R2 ncv of 0.868, R2 pred = 0.876, while the CoMSIA model yielded a Q2 of 0.450, R2 ncv = 0.899, R2 pred = 0.735. For activity II study, CoMFA model yielded statistics of Q2 = 0.5, R2 ncv = 0.715, R2 pred = 0.723. These results prove the high predictability of the models. Furthermore, a combined analysis between the CoMFA, CoMSIA contour maps shows that: (1) Bulky substituents in R7, R3 and position A benefit activity I of the antagonists, but decrease it when projected in R8 and position B; (2) Hydrophilic groups at position A and B increase both antagonistic activity I and II; (3) Electrostatic field plays an essential rule in the variance of activity II. In search for more potent mGluR2 antagonists, two pharmacophore models were developed separately for the two activities. The first model reveals six pharmacophoric features, namely an aromatic center, two hydrophobic centers, an H-donor atom, an H-acceptor atom and an H-donor site. The second model shares all features of the first one and has an additional acceptor site, a positive N and an aromatic center. These models can be used as guidance for the development of new mGluR2 antagonists of high activity and selectivity. This work is the first report on 3D-QSAR modeling of these mGluR2 antagonists. All the conclusions may lead to a better understanding of the mechanism of antagonism and be helpful in the design of new potent mGluR2 antagonists. [ABSTRACT FROM AUTHOR]

MGluR2 is G protein-coupled receptor that is targeted for diseases like anxiety, depression, Parkinson's disease and schizophrenia. Herein, we report the three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of a series of 1,3-dihydrobenzo[ b][1,4]diazepin-2-one derivatives as mGluR2 antagonists. Two series of models using two different activities of the antagonists against rat mGluR2, which has been shown to be very similar to the human mGluR2, (activity I: inhibition of [3H]-LY354740; activity II: mGluR2 (1S,3R)-ACPD inhibition of forskolin stimulated cAMP.) were derived from datasets composed of 137 and 69 molecules respectively. For activity I study, the best predictive model obtained from CoMFA analysis yielded a Q2 of 0.513, R2 ncv of 0.868, R2 pred = 0.876, while the CoMSIA model yielded a Q2 of 0.450, R2 ncv = 0.899, R2 pred = 0.735. For activity II study, CoMFA model yielded statistics of Q2 = 0.5, R2 ncv = 0.715, R2 pred = 0.723. These results prove the high predictability of the models. Furthermore, a combined analysis between the CoMFA, CoMSIA contour maps shows that: (1) Bulky substituents in R7, R3 and position A benefit activity I of the antagonists, but decrease it when projected in R8 and position B; (2) Hydrophilic groups at position A and B increase both antagonistic activity I and II; (3) Electrostatic field plays an essential rule in the variance of activity II. In search for more potent mGluR2 antagonists, two pharmacophore models were developed separately for the two activities. The first model reveals six pharmacophoric features, namely an aromatic center, two hydrophobic centers, an H-donor atom, an H-acceptor atom and an H-donor site. The second model shares all features of the first one and has an additional acceptor site, a positive N and an aromatic center. These models can be used as guidance for the development of new mGluR2 antagonists of high activity and selectivity. This work is the first report on 3D-QSAR modeling of these mGluR2 antagonists. All the conclusions may lead to a better understanding of the mechanism of antagonism and be helpful in the design of new potent mGluR2 antagonists. [ABSTRACT FROM AUTHOR]

Pen shell (Atrina pectinata) is a popular food source with a high commercial value in a number of Asian Pacific areas. The natural A. pectinata population has been declining continuously over the past several decades. Microsatellite DNA markers are a useful DNA-based tool for monitoring the genetic variation of pen shell populations. In this study, 20 polymorphic microsatellite (MS) DNA markers were identified from a partial genomic pen shell DNA library enriched in CA repeats, and used to compare allelic variation between wild and hatchery pen shell populations in Korea. A total of 438 alleles were detected at the 20 MS loci in the two populations. All loci were easily amplified and demonstrated allelic variability, with the number of alleles ranging from 5 to 35 in the wild population and from 5 to 22 in the farmed population. The average observed and expected heterozygosities were 0.69 and 0.82, respectively, in the hatchery samples and 0.69 and 0.83, respectively, in the wild samples. Statistical analysis of fixation index (FST) and analysis of molecular variance (AMOVA) showed minor, but significant, genetic differences between the wild and hatchery populations (FST = 0.0106, CI95% = 0.003-0.017). These microsatellite loci may be valuable for future aquaculture and population genetic studies for developing conservation and management plans. Further studies with additional pen shell samples are needed to conclusively determine the genetic diversity between the wild and hatchery populations. [ABSTRACT FROM AUTHOR]

Intrinsically disordered proteins (IDPs) are proteins that usually do not adopt well-defined native structures when isolated in solution under physiological conditions. Numerous IDPs have close relationships with human diseases such as tumor, Parkinson disease, Alzheimer disease, diabetes, and so on. These disease-associated IDPs commonly play principal roles in the disease-associated protein-protein interaction networks. Most of them in the disease datasets have more interactants and hence the size of the disease-associated IDPs interaction network is simultaneously increased. For example, the tumor suppressor protein p53 is an intrinsically disordered protein and also a hub protein in the p53 interaction network; α-synuclein, an intrinsically disordered protein involved in Parkinson diseases, is also a hub of the protein network. The disease-associated IDPs may provide potential targets for drugs modulating protein-protein interaction networks. Therefore, novel strategies for drug discovery based on IDPs are in the ascendant. It is dependent on the features of IDPs to develop the novel strategies. It is found out that IDPs have unique structural features such as high flexibility and random coil-like conformations which enable them to participate in both the ―one to many‖ and ―many to one‖ interaction. Accordingly, in order to promote novel strategies for drug discovery, it is essential that more and more features of IDPs are revealed by experimental and computing methods. [ABSTRACT FROM AUTHOR]

A new aromaticity definition is advanced as the compactness formulation through the ratio between atoms-in-molecule and orbital molecular facets of the same chemical reactivity property around the pre- and post-bonding stabilization limit, respectively. Geometrical reactivity index of polarizability was assumed as providing the benchmark aromaticity scale, since due to its observable character; with this occasion new Hydrogenic polarizability quantum formula that recovers the exact value of 4.5 a03 for Hydrogen is provided, where a0 is the Bohr radius; a polarizability based-aromaticity scale enables the introduction of five referential aromatic rules (Aroma 1 to 5 Rules). With the help of these aromatic rules, the aromaticity scales based on energetic reactivity indices of electronegativity and chemical hardness were computed and analyzed within the major semi-empirical and ab initio quantum chemical methods. Results show that chemical hardness based-aromaticity is in better agreement with polarizability based-aromaticity than the electronegativity-based aromaticity scale, while the most favorable computational environment appears to be the quantum semi-empirical for the first and quantum ab initio for the last of them, respectively. [ABSTRACT FROM AUTHOR]

The entry of the SARS-CoV-2, a causative agent of COVID-19, into human host cells is mediated by the SARS-CoV-2 spike (S) glycoprotein, which critically depends on the formation of complexes involving the spike protein receptor-binding domain (RBD) and the human cellular membrane receptor angiotensin-converting enzyme 2 (hACE2). Using classical site density functional theory (SDFT) and structural bioinformatics methods, we investigate binding and conformational properties of these complexes and study the overlooked role of water-mediated interactions. Analysis of the three-dimensional reference interaction site model (3DRISM) of SDFT indicates that water mediated interactions in the form of additional water bridges strongly increases the binding between SARS-CoV-2 spike protein and hACE2 compared to SARS-CoV-1-hACE2 complex. By analyzing structures of SARS-CoV-2 and SARS-CoV-1, we find that the homotrimer SARS-CoV-2 S receptor-binding domain (RBD) has expanded in size, indicating large conformational change relative to SARS-CoV-1 S protein. Protomer with the up-conformational form of RBD, which binds with hACE2, exhibits stronger intermolecular interactions at the RBD-ACE2 interface, with differential distributions and the inclusion of specific H-bonds in the CoV-2 complex. Further interface analysis has shown that interfacial water promotes and stabilizes the formation of CoV-2/hACE2 complex. This interaction causes a significant structural rigidification of the spike protein, favoring proteolytic processing of the S protein for the fusion of the viral and cellular membrane. Moreover, conformational dynamics simulations of RBD motions in SARS-CoV-2 and SARS-CoV-1 point to the role in modification of the RBD dynamics and their impact on infectivity. [ABSTRACT FROM AUTHOR]

Glucosinolate transporters (GTRs) are part of the nitrate/peptide transporter (NPF) family, members of which also transport specialized secondary metabolites as substrates. Glucosinolates are defense compounds derived from amino acids. We selected 4-methylthiobutyl (4MTB) and indol-3-ylmethyl (I3M) glucosinolates to study how GTR1 from Arabidopsis thaliana transports these substrates in computational simulation approaches. The designed pipeline reported here includes massive docking of 4MTB and I3M in an ensemble of GTR1 conformations (in both inward and outward conformations) extracted from molecular dynamics simulations, followed by clustered and substrate–protein interactions profiling. The identified key residues were mutated, and their role in substrate transport was tested. We were able to identify key residues that integrate a major binding site of these substrates, which is critical for transport activity. In silico approaches employed here represent a breakthrough in the plant transportomics field, as the identification of key residues usually takes a long time if performed from a purely wet-lab experimental perspective. The inclusion of structural bioinformatics in the analyses of plant transporters significantly speeds up the knowledge-gaining process and optimizes valuable time and resources. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a neurodegenerative disorder incompatible with normal daily activity, affecting one in nine people. One of its potential targets is the apelin receptor (APJR), a G-protein coupled receptor, which presents considerably high expression levels in the central nervous system. In silico studies of APJR drug-like molecule binding are in small numbers while high throughput screenings (HTS) are already sufficiently many to devise efficient drug design strategies. This presents itself as an opportunity to optimize different steps in future large scale virtual screening endeavours. Here, we ran a first stage docking simulation against a library of 95 known binders and 3829 generated decoys in an effort to improve the rescoring stage. We then analyzed receptor binding site structure and ligands binding poses to describe their interactions. As a result, we devised a simple and straightforward virtual screening Stage II filtering score based on search space extension followed by a geometric estimation of the ligand—binding site fitness. Having this score, we used an ensemble of receptors generated by Hamiltonian Monte Carlo simulation and reported the results. The improvements shown herein prove that our ensemble docking protocol is suited for APJR and can be easily extrapolated to other GPCRs. [ABSTRACT FROM AUTHOR]