Search

Inspired by the success of deep learning in predicting static protein structures, researchers are now actively exploring other deep learning algorithms aimed at predicting the conformational changes of proteins. Currently, a major challenge in the development of such models lies in the limited training data characterizing different conformational transitions. To address this issue, molecular dynamics simulations is combined with enhanced sampling methods to create a large-scale database. To this end, the study simulates the conformational changes of 2635 proteins featuring two known stable states, and collects the structural information along each transition pathway. Utilizing this database, a general deep learning model capable of predicting the transition pathway for a given protein is developed. The model exhibits general robustness across proteins with varying sequence lengths (ranging from 44 to 704 amino acids) and accommodates different types of conformational changes. Great agreement is shown between predictions and experimental data in several systems and successfully apply this model to identify a novel allosteric regulation in an important biological system, the human β-cardiac myosin. These results demonstrate the effectiveness of the model in revealing the nature of protein conformational changes., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the scarcity of extensively labeled data sets, a challenge compounded by the high costs and complexities of accurate protein annotation. In this study, we introduce GraphPI, a novel framework that treats protein inference as a node classification problem. We treat proteins as interconnected nodes within a protein-peptide-PSM graph, utilizing a graph neural network-based architecture to elucidate their interrelations. To address label scarcity, we train the model on a set of unlabeled public protein data sets with pseudolabels derived from an existing protein inference algorithm, enhanced by self-training to iteratively refine labels based on confidence scores. Contrary to prevalent methodologies necessitating data set-specific training, our research illustrates that GraphPI, due to the well-normalized nature of Percolator features, exhibits universal applicability without data set-specific fine-tuning, a feature that not only mitigates the risk of overfitting but also enhances computational efficiency. Our empirical experiments reveal notable performance on various test data sets and deliver significantly reduced computation times compared to common protein inference algorithms.

Dynamic nuclear polarization (DNP) enhanced magic angle spinning (MAS) solid-state NMR carried out at 25 K enables rapid acquisition of multi-dimensional 13 C- 15 N correlation spectra for protein structure studies and resonance assignment. Under commonly used DNP conditions, solvent deuteration reduces 1 H- 15 N cross polarization (CP) efficiencies, necessitates more careful optimization, and requires longer high-power 15 N radio-frequency pulses. The sensitivity of 2D heteronuclear correlation experiments is potentially impaired. Here we show that 2D 15 N- 13 C experiments based on 13 C- 15 N transferred echo double resonance (TEDOR) methods outperform 2D experiments based on CP transfers in a fully deuterated solvent, and are competitive with CP-based experiments when the solvent is only partially deuterated. Additionally, we show that optimization of TEDOR-based 2D experiments is simpler than optimization of CP-based experiments under 25 K MAS conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)

The recent success of RFdiffusion, a method for protein structure design with a denoising diffusion probabilistic model, has relied on fine-tuning the RoseTTAFold structure prediction network for protein backbone denoising. Here, we introduce SCUBA-diffusion (SCUBA-D), a protein backbone denoising diffusion probabilistic model freshly trained by considering co-diffusion of sequence representation to enhance model regularization and adversarial losses to minimize data-out-of-distribution errors. While matching the performance of the pretrained RoseTTAFold-based RFdiffusion in generating experimentally realizable protein structures, SCUBA-D readily generates protein structures with not-yet-observed overall folds that are different from those predictable with RoseTTAFold. The accuracy of SCUBA-D was confirmed by the X-ray structures of 16 designed proteins and a protein complex, and by experiments validating designed heme-binding proteins and Ras-binding proteins. Our work shows that deep generative models of images or texts can be fruitfully extended to complex physical objects like protein structures by addressing outstanding issues such as the data-out-of-distribution errors., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

A large fraction of soluble protein within the interior of living cells may reversibly associate with structural elements, including proteinaceous fibers and phospholipid membranes. In this opinion, we present theoretical and experimental evidence that many of these associations are due to nonspecific attraction between the protein and the surface of the fiber or membrane, and that such associations may lead to substantial changes in the association state of the adsorbed proteins, the biological function of the adsorbed proteins, and the distribution of these proteins between the many microenvironments existing within the cell., Competing Interests: Declaration of interests None declared by authors., (Published by Elsevier Ltd.)

Macromolecular crowding experiments bridge the gap between in-vivo and in-vitro studies by mimicking some of the cellular complexities like high viscosity and limited space, while still manageable for experiments and analysis. Macromolecular crowding impacts all biological processes and is a focus of contemporary research. Recent reviews have highlighted the effect of crowding on various protein properties. One of the essential characteristics of protein is its dynamic nature; however, how protein dynamics get modulated in the crowded milieu has been largely ignored. This article discusses how protein translational, rotational, conformational, and solvation dynamics change under crowded conditions, summarizing key observations in the literature. We emphasize our research on microsecond conformational and water dynamics in crowded milieus and their impact on enzymatic activity and stability. Lastly, we provided our outlook on how this field might move forward in the future., Competing Interests: Declaration of competing interest There are no conflicts to declare., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Cryptic sites can expand the space of druggable proteins, but the potential usefulness of such sites needs to be investigated before any major effort. Given that the binding pockets are not formed, the druggability of such sites is not well understood. The analysis of proteins and their ligands shows that cryptic sites that are formed primarily by the motion of side chains moving out of the pocket to enable ligand binding generally do not bind drug-sized molecules with sufficient potency. By contrast, sites that are formed by loop or hinge motion are potentially valuable drug targets. Arguments are provided to explain the underlying causes in terms of classical enzyme inhibition theory and the kinetics of side chain motion and ligand binding., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The availability of highly accurate protein structure predictions from AlphaFold2 (AF2) and similar tools has hugely expanded the applicability of molecular replacement (MR) for crystal structure solution. Many structures can be solved routinely using raw models, structures processed to remove unreliable parts or models split into distinct structural units. There is therefore an open question around how many and which cases still require experimental phasing methods such as single-wavelength anomalous diffraction (SAD). Here, this question is addressed using a large set of PDB depositions that were solved by SAD. A large majority (87%) could be solved using unedited or minimally edited AF2 predictions. A further 18 (4%) yield straightforwardly to MR after splitting of the AF2 prediction using Slice'N'Dice, although different splitting methods succeeded on slightly different sets of cases. It is also found that further unique targets can be solved by alternative modelling approaches such as ESMFold (four cases), alternative MR approaches such as ARCIMBOLDO and AMPLE (two cases each), and multimeric model building with AlphaFold-Multimer or UniFold (three cases). Ultimately, only 12 cases, or 3% of the SAD-phased set, did not yield to any form of MR tested here, offering valuable hints as to the number and the characteristics of cases where experimental phasing remains essential for macromolecular structure solution., (open access.)

Post-translational modifications (PTMs) of proteins are crucial for regulating biological processes and their dysregulation is linked to various diseases, highlighting PTM regulation as a significant target for drug development. Traditional drug targets often interact with multiple proteins, resulting in lower selectivity and inevitable adverse effects, which limits their clinical applicability. Recent advancements in bifunctional molecules, such as proteolysis-targeting chimeras (PROTACs), have shown promise in targeting PTMs precisely. However, regulatory mechanisms for many of the >600 known PTMs remain underexplored. This review examines current progress and challenges in designing bifunctional molecules for PTM regulation, focusing on effector selection and ligand design strategies, aiming to propel the utilization and advancement of bifunctional molecules to the forefront of PTM research., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Water is essential for life as we know it, but it has paradoxically been considered inimical to the emergence of life. Proteins and nucleic acids have sustained evolution and life for billions of years, but both are condensation polymers, suggesting that their formation requires the elimination of water. This presents intrinsic challenges at the origins of life, including how condensation polymer synthesis can overcome the thermodynamic pressure of hydrolysis in water and how nucleophiles can kinetically outcompete water to yield condensation products. The answers to these questions lie in balancing thermodynamic activation and kinetic stability. For peptides, an effective strategy is to directly harness the energy trapped in prebiotic molecules, such as nitriles, and avoid the formation of fully hydrolysed monomers. In this Review, we discuss how chemical energy can be built into precursors, retained, and released selectively for polymer synthesis. Looking to the future, the outstanding goals include how nucleic acids can be synthesized, avoiding the formation of fully hydrolysed monomers and what caused information to flow from nucleic acids to proteins., (© 2024. Springer Nature Limited.)

After the recent double revolutions in structural biology, which include the use of direct detectors for cryo-electron microscopy resulting in a significant improvement in the expected resolution of large macromolecule structures, and the advent of AlphaFold which allows for near-accurate prediction of any protein structures, the field of structural biology is now pursuing more ambitious targets, including several MDa assemblies. But complex target systems cannot be tackled using a single biophysical technique. The field of integrative structural biology has emerged as a global solution. The aim is to integrate data from multiple complementary techniques to produce a final three-dimensional model that cannot be obtained from any single technique. The absence of atomic force microscopy data from integrative structural biology platforms is not necessarily due to its nm resolution, as opposed to Å resolution for x-ray crystallography, nuclear magnetic resonance, or electron microscopy. Rather a significant issue was that the AFM topographic data lacked interpretability. Fortunately, with the introduction of the AFM-Assembly pipeline and other similar tools, it is now possible to integrate AFM topographic data into integrative modeling platforms. The advantages of single molecule techniques, such as AFM, include the ability to confirm experimentally any assembled molecular models or to produce alternative conformations that mimic the inherent flexibility of large proteins or complexes. The review begins with a brief overview of the historical developments of AFM data in structural biology, followed by an examination of the strengths and limitations of AFM imaging, which have hindered its integration into modern modeling platforms. This review discusses the correction and improvement of AFM topographic images, as well as the principles behind the AFM-Assembly pipeline. It also presents and discusses a series of challenges that need to be addressed in order to improve the incorporation of AFM data into integrative modeling platform., (© 2024 The Author(s). Journal of Molecular Recognition published by John Wiley & Sons Ltd.)

Accurate prediction of drug-target binding affinity (DTA) is essential in the field of drug discovery. Recently, scientists have been attempting to utilize artificial intelligence prediction to screen out a significant number of ineffective compounds, thereby mitigating labor and financial losses. While graph neural networks (GNNs) have been applied to DTA, existing GNNs have limitations in effectively extracting substructural features across various sizes. Functional groups play a crucial role in modulating molecular properties, but existing GNNs struggle with feature extraction from certain motifs due to scale mismatches. Additionally, sequence-based models for target proteins lack the integration of structural information. To address these limitations, we present SSR-DTA, a multi-layer graph network capable of adapting to diverse structural sizes, which can extract richer biological features, thereby improving the robustness and accuracy of predictions. Multi-layer GNNs enable the capture of molecular motifs across different scales, ranging from atomic to macrocyclic motifs. Furthermore, we introduce BiGNN to simultaneously learn sequence and structural information. Sequence information corresponds to the primary structure of proteins, while graph information represents the tertiary structure. BiGNN assimilates richer information compared to sequence-based methods while mitigating the impact of errors from predicted structures, resulting in more accurate predictions. Through rigorous experimental evaluations conducted on four benchmark datasets, we demonstrate the superiority of SSR-DTA over state-of-the-art models. Particularly, in comparison to state-of-the-art models, SSR-DTA demonstrates an impressive 20% reduction in mean squared error on the Davis dataset and a 5% reduction on the KIBA dataset, underscoring its potential as a valuable tool for advancing DTA prediction., Competing Interests: Declaration of competing interest No conflict of interest exists. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Purpose of Review: Respiratory allergies are non-communicable diseases caused by the hypersensitivity of the immune system to environmental aeroallergens. The culprits are aero-transported proteins eliciting respiratory symptoms in sensitized/allergic individuals. This review intends to provide a holistic overview on the categorization of aeroallergens into protein families (Part 1) and to exploit the impact of physicochemical properties on inhalant protein allergenicity (Part 2). This first part will focus particularly on aeroallergen organization into families and how this classification fits their physicochemical properties., Recent Findings: Aeroallergen classification into protein families facilitates the identification of common physicochemical properties, thus aiding a better comprehension of known allergens, while predicting the behavior of novel ones. The available online databases gathering important features of aeroallergens are currently scarce. Information on distinct aeroallergen classification is still lacking, as data is dispersed and often outdated, hampering an efficient evaluation of new aeroallergens., (© 2024. The Author(s).)

Purpose of Review: A holistic perspective on how physicochemical properties modulate the allergenicity of proteins has recently been performed for food allergens, launching the challenge of a similar analysis for aeroallergens. After a first review on aeroallergen classification into protein families (Part 1), this second part (Part 2) will exploit the impact of physicochemical properties (abundance/biological function, protein structure/presence of post-translational modifications, ligand/cofactor/lipid-binding) on inhalant protein allergenicity., Recent Findings: The abundance linked to biological function is correlated with increased allergenic risk for most protein families, while the loss of structural integrity with consequent destruction of conformational epitopes is well linked with decreased allergenicity. Ligand-binding effect totally depends on the ligand type being highly variable among aeroallergens. Knowledge about the physicochemical properties of aeroallergens is still scarce, which highlights the need for research using integrated approaches (in silico and experimental) to generate and analyze new data on known/new aeroallergens., (© 2024. The Author(s).)

Proteins have emerged as promising therapeutics in oncology due to their great specificity. Many treatment strategies are developed based on protein biologics, such as immunotherapy, starvation therapy, and pro-apoptosis therapy, while some protein biologics have entered the clinics. However, clinical translation is severely impeded by instability, short circulation time, poor transmembrane transportation, and immunogenicity. Micro- and nano-particles-based drug delivery platforms are designed to solve those problems and enhance protein therapeutic efficacy. This review first summarizes the different types of therapeutic proteins in clinical and research stages, highlighting their administration limitations. Next, various types of micro- and nano-particles are described to demonstrate how they can overcome those limitations. The potential of micro- and nano-particles are then explored to enhance the therapeutic efficacy of proteins by combinational therapies. Finally, the challenges and future directions of protein biologics carriers are discussed for optimized protein delivery., (© 2024 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.)

The vastness of unexplored protein fold universe remains a significant question. Through systematic de novo design of proteins with novel αβ-folds, we demonstrated that nature has only explored a tiny portion of the possible folds. Numerous possible protein folds are still untouched by nature. This review outlines this study and discusses the prospects for design of functional proteins with novel folds., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Low-temperature cooking causes flavor weakness while improving the texture and digestive properties of meat. To enhance the flavor of low-temperature cooked Tibetan pork, samples were cooked at low-temperature with or without ultrasound-assisted (UBTP, BTP) for different times (30 min, 90 min) and then analyzed using GC-MS and LC-MS. The results showed that ultrasound-assisted cooking caused a significant increase in lipid oxidation by 9.10% in the early stage of the treatment. Additionally, at the later stage of ultrasound-assisted processing, proteins were oxidized and degraded, which resulted in a remarkable rise in the protein carbonyl content by 6.84%. With prolonged effects of ultrasound and low-temperature cooking, the formation of phenylacetaldehyde in UBTP-90 sample originated from the degradation of phenylalanine through multivariate statistics and correlation analysis. Meanwhile, trans, cis-2,6-nonadienal and 1-octen-3-one originated from the degradation of linolenic acid and arachidonic acid. This study clarified the mechanism of ultrasound-assisted treatment improving the flavor of low-temperature-cooked Tibetan pork based on the perspective of lipids and proteins oxidation, providing theoretical supports for flavor enhancement in Tibetan pork-related products., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Food gel is a kind of macromolecular biopolymer with viscoelasticity, which has good water retention and gelling ability, especially gels formed by protein and/or polysaccharide. The addition of calcium ions triggers gelation by interacting with the gel matrix, enhancing gels' textural and rheological properties like hardness, viscosity and elasticity. Thus calcium ions enrich the range of applications of food gels. This review focuses on forming a calcium-induced gel and improving the texture properties. It summarizes the mechanisms of gelation induced by calcium ions in polysaccharide, protein, and polysaccharide-protein systems and their gel properties. The effects of influencing factors in calcium ion concentration, types and mixing ratios of matrices, acid, and alkaline environments, as well as treatment methods on calcium-induced gel characteristics, are presented. Additionally, the current applications of calcium-induced gels in food industries and challenges are presented., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The real-time structural changes of the molecular space conformation of myofibrillar protein microgels (MPM) after heat treatment (90 °C, 30 min) were analyzed by molecular dynamics simulation, and the structural properties and changes of MPM at the oil-water interface were analyzed by the combination of Raman spectroscopy and molecular dynamics simulation. The shift in the oil ratio had a major impact on the transformation of disulfide bonds within the protein molecule. Simultaneously, it caused tryptophan and tyrosine residues (I 850 cm -1 / I 850 cm -1 > 1) to become exposed, increasing the locations of amino acid residues in the protein that interact with the oil phase. HIPE with different oil phases influenced the change in spatial structural conformation of MPM, and there was a flexible structural change in the molecular space. The HIPE system, which was stabilized by 3.0 wt% MPM and 0.75 oil phase, exhibited a thixotropic recovery of >70 % and the highest elastic modulus G' (822.14 Pa) based on the rheological behavior. It is expected to provide a theoretical basis for the development and utilization of high internal phase emulsion stabilized by microgel protein in food industry., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Histidine (His) photo-oxidation has been widely investigated with several transient and stable products characterized, especially for aerobic conditions. Due to its role and structure, His-side chain can be a key player in the quenching of excited states such as the triplet state of the photosensitizer 3-carboxybenzophenone ( 3 CB*). The capacity of His and its derivatives to quench 3 CB* under anaerobic conditions are characterized in the current study by laser flash photolysis, with the resulting oxidation products examined by mass spectrometry to determine the reaction mechanism. The latter include adducts of the 3-carboxybenzophenone ketyl radical (CBH • ) to the imidazole ring (Imid-CH 2 -CBH), His-His dimers, and other products with lower yields. The data obtained with model compounds are compared to those obtained with more complicated systems, including the peptide Exendin-4, and the protein MtHpt1. The data obtained from transient spectroscopy and product analyses indicate that two CB* quenching mechanisms occur: (i) proton-coupled electron transfer (as reported previously) yielding radicals that can recombine to give His-His dimers and CBH-adducts, and (ii) energy transfer yielding 3 His* undergoing further reaction leading to formation of Imidazyl-CH 2 -CBH adduct. The latter, unexpected process only occurs when His and its derivatives have a free α-amino group. This process yielded a novel adduct between the imidazole ring and the CBH • formed by sensitizer reduction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Protein assembly is an essential process in biological systems, where proteins self-assemble into complex structures with diverse functions. Inspired by the exquisite control over protein assembly in nature, scientists have been exploring ways to design and assemble protein structures with precise control over their topologies and functions. One promising approach for achieving this goal is through metal coordination, which utilizes metal-binding motifs to mediate protein-protein interactions and assemble protein complexes with controlled stoichiometry and geometry. Metal coordination provides a modular and tunable approach for protein assembly and de novo structure design, where the metal ion acts as a molecular glue that holds the protein subunits together in a specific orientation. Metal-coordinated protein assemblies have shown great potential for developing functional metalloproteinase, novel biomaterials and integrated drug delivery systems. In this review, an overview of the recent advances in protein assemblies benefited from metal coordination is provided, focusing on various protein arrangements in different dimensions including protein oligomers, protein nanocage and higher-order protein architectures. Moreover, the key metal-binding motifs and strategies used to assemble protein structures with precise control over their properties are highlighted. The potential applications of metal-mediated protein assemblies in biotechnology and biomedicine are also discussed., (© 2024 Wiley‐VCH GmbH.)

Hydrogen sulfide (H₂S) has emerged as a significant signaling molecule involved in various physiological processes, including vasodilation, neurotransmission, and cytoprotection. Its interactions with biomolecules are critical to understand its roles in health and disease. Recent advances in biophysical characterization techniques have shed light on the complex interactions of H₂S with proteins, nucleic acids, and lipids. Proteins are primary targets for H₂S, which can modify cysteine residues through S-sulfhydration, impacting protein function and signaling pathways. Advanced spectroscopic techniques, such as mass spectrometry and NMR, have enabled the identification of specific sulfhydrated sites and provided insights into the structural and functional consequences of these modifications. Nucleic acids also interact with H₂S, although this area is less explored compared to proteins. Recent studies have demonstrated that H₂S can induce modifications in nucleic acids, affecting gene expression and stability. Techniques like gel electrophoresis and fluorescence spectroscopy have been utilized to investigate these interactions, revealing that H₂S can protect DNA from oxidative damage and modulate RNA stability and function. Lipids, being integral components of cell membranes, interact with H₂S, influencing membrane fluidity and signaling. Biophysical techniques such as electron paramagnetic resonance (EPR) and fluorescence microscopy have elucidated the effects of H₂S on lipid membranes. These studies have shown that H₂S can alter lipid packing and dynamics, which may impact membrane-associated signaling pathways and cellular responses to stress. In the current work we have integrated this with key scientific explainations to provide a comprehensive review., Competing Interests: Declaration of competing interest This work has no conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)

The biochemical and biophysical properties of the extracellular matrix (ECM) play a pivotal role in regulating cellular behaviors such as proliferation, migration, and differentiation. Engineered protein-based hydrogels, with highly tunable multifunctional properties, have the potential to replicate key features of the native ECM. Formed by self-assembly or crosslinking, engineered protein-based hydrogels can induce a range of cell behaviors through bioactive and functional domains incorporated into the polymer backbone. Using recombinant techniques, the amino acid sequence of the protein backbone can be designed with precise control over the chain-length, folded structure, and cell-interaction sites. In this review, the modular design of engineered protein-based hydrogels from both a molecular- and network-level perspective are discussed, and summarize recent progress and case studies to highlight the diverse strategies used to construct biomimetic scaffolds. This review focuses on amino acid sequences that form structural blocks, bioactive blocks, and stimuli-responsive blocks designed into the protein backbone for highly precise and tunable control of scaffold properties. Both physical and chemical methods to stabilize dynamic protein networks with defined structure and bioactivity for cell culture applications are discussed. Finally, a discussion of future directions of engineered protein-based hydrogels as biomimetic cellular scaffolds is concluded., (© 2024 Wiley‐VCH GmbH.)

Aims: Nonalcoholic steatohepatitis (NASH) is the severe subtype of nonalcoholic fatty diseases (NAFLD) with few options for treatment. Patients with NASH exhibit partial responses to the current therapeutics and adverse effects. Identification of the binding proteins for the drugs is essential to understanding the mechanism and adverse effects of the drugs and fuels the discovery of potent and safe drugs. This paper aims to critically discuss recent advances in covalent and noncovalent approaches for identifying binding proteins that mediate NASH progression, along with an in-depth analysis of the mechanisms by which these targets regulate NASH., Materials and Methods: A literature search was conducted to identify the relevant studies in the database of PubMed and the American Chemical Society. The search covered articles published from January 1990 to July 2024, using the search terms with keywords such as NASH, benzophenone, diazirine, photo-affinity labeling, thermal protein profiling, CETSA, target identification., Key Findings: The covalent approaches utilize drugs modified with diazirine and benzophenone to covalently crosslink with the target proteins, which facilitates the purification and identification of target proteins. In addition, they map the binding sites in the target proteins. By contrast, noncovalent approaches identify the binding targets of unmodified drugs in the intact cell proteome. The advantages and limitations of both approaches have been compared, along with a comprehensive analysis of recent innovations that further enhance the efficiency and specificity., Significance: The analyses of the applicability of these approaches provide novel tools to delineate NASH pathogenesis and promote drug discovery., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Proteins are classified as biopolymers which share similar structural features with semi-crystalline polymers. Although their unique biocompatibility facilitates the universal applications of protein-based hydrogels in the biomedical field, the mechanical performances of protein-based hydrogels fall short of practical requirements. Conventional strategies for enhancing mechanical properties focus on forming regularly folded secondary structures as analogs of crystalline regions. This concept is based on proteins as the analogy of semi-crystalline polymers, in which crystalline regions profoundly contribute to the mechanical performances. Even though the contribution of the amorphous region is equally weighted for semi-crystalline polymers, their capacity to improve the mechanical performances of protein-based structures is still undervalued. Herein, the potential of promoting the mechanical performances is explored by controlling the state of amorphous regions in protein-based hydrogels. A fibril protein is chosen, regenerated silk fibroin (RSF), as a model molecule for its similar viscoelasticity with a semi-crystalline polymer. The amorphous regions in the RSF hydrogels are transformed from extended to entangled states through a double-crosslinking method. The formation of entanglement integrates new physically crosslinked points for remarkable improvement in mechanical performances. A robust hydrogel is not only developed but also intended to provide new insights into the structural-property relationship of protein-based hydrogels., (© 2024 Wiley‐VCH GmbH.)

Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. Comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Understanding the sequence-structure relationship in protein is of fundamental interest, but has practical applications such as the rational design of peptides and proteins. This relationship in the Type I left-handed β-helix containing proteins is updated and revisited in this study. Analyzing the available experimental structures in the Protein Data Bank, we could describe, further in detail, the structural features that are important for the stability of this fold, as well as its nucleation and termination. This study is meant to complete previous work, as it provides a separate analysis of the N-terminal and C-terminal rungs of the helix. Particular sequence motifs of these rungs are described along with the structural element they form., (© 2024 The Author(s). PROTEINS: Structure, Function, and Bioinformatics published by Wiley Periodicals LLC.)

We have studied binding properties of three detergents, i.e., sodium dodecyl sulfate (SDS), Sarkosyl and sodium lauroyl glutamate (SLG), to model proteins based on their effects on electrophoretic mobilities of the proteins using agarose native gel electrophoresis and circular dichroism (CD). This simple technology can evaluate the dissociative properties of bound detergents from the proteins and their effects on protein structure. SDS influenced the electrophoretic mobilities of all model proteins more strongly than the other two detergents, implying a stronger inclination for protein binding and subsequent alterations in protein structure or reductions in activity, which are supported by CD analysis. On the contrary, Sarkosyl and SLG showed weaker binding and interfered less with the structure and biological activities, indicating that these detergents may be useful for protein purification and analysis. It appeared that SLG was weaker in protein binding than Sarkosyl, although the effects of these two detergents appeared to depend on the proteins., Competing Interests: Declaration of competing interest This work was supported by Kyokuto Pharmaceutical Industrial Co., Ltd. Employees T.A. (Teruo Akuta), T.O., and Y.T. are affiliated with Kyokuto Pharmaceuticals. T.U. is currently affiliated with the University of Tsukuba and formerlly with the National Institutes for Quantum Science and Technology. The authors declare no competing interests. T.A. (Tsutomu Arakawa) previously worked at Alliance Protein Laboratories but currently has no conflicts of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Protein nanoparticles are an attractive class of materials for nanomedicine applications due to the intrinsic biocompatibility, biodegradability, and intrinsic functionality of their constituent proteins. Despite the clinical success of select protein nanoparticles, this class of nanocarriers remains understudied and underdeveloped compared to lipid and polymer nanoparticles due to challenges related to formulation optimization, large design space, and their structural complexity. In this work, a modular strategy for protein nanoparticle preparation based on the concept of photoreactive jetting is introduced. The process relies on continuous ultraviolet irradiation during electrohydrodynamic (EHD) jetting of protein solutions that contain a homobifunctional photocrosslinker. Protein nanoparticles exhibit nanogel-like architectures comprised of proteins that are linked via synthetic moieties. Compared to conventional protein nanoparticles, this method reduces nanoparticle processing times to minutes, rather than hours to days. The inclusion of an emissive structural motif as the molecular scaffold of the photocrosslinker is used to study the supramolecular architecture of the stable nanoparticles via time-resolved fluorescence spectroscopy., (© 2024 The Author(s). Macromolecular Rapid Communications published by Wiley‐VCH GmbH.)

Protein-protein interactions (PPIs) play an essential role in life activities. Many artificial intelligence algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with various sequence lengths and suffer from low generalization and prediction accuracy. In this study, we proposed a novel end-to-end deep learning framework, RSPPI, combining residual neural network (ResNet) and spatial pyramid pooling (SPP), to predict PPIs based on the protein sequence physicochemistry properties and spatial structural information. In the RSPPI model, ResNet was employed to extract the structural and physicochemical information from the protein three-dimensional structure and primary sequence; the SPP layer was used to transform feature maps to a single vector and avoid the fixed-length requirement. The RSPPI model possessed excellent cross-species performance and outperformed several state-of-the-art methods based either on protein sequence or gene ontology in most evaluation metrics. The RSPPI model provides a novel strategy to develop an AI PPI prediction algorithm., (© 2024 Wiley Periodicals LLC.)

Polyphenols have a variety of physiological activities, including antioxidant, antimicrobial, and anti-inflammatory properties. However, their applications are often limited because due to the instability of polyphenols. Encapsulation technologies can be employed to overcome these problems and increase the utilization of polyphenols. In this article, the utilization of protein-based nanoparticles for encapsulating polyphenols is reviewed due to their good biocompatibility, biodegradability, and functional attributes. Initially, the various kinds of animal and plant proteins available for forming protein nanoparticles are discussed, as well as the fabrication methods that can be used to assemble these nanoparticles. The molecular interaction mechanisms between proteins and polyphenols are then summarized. Applications of protein-based nanoparticles for encapsulating polyphenols are then discussed, including as nutrient delivery systems, in food packaging materials, and in the creation of functional foods. Finally, areas where further research is need on the development, characterization, and application of protein-based polyphenol-loaded nanoparticles are highlighted.

A droplet microfluidic device to capture in real-time protein aggregation at liquid-liquid interfaces is described. In contrast to conventional methods, typically characterized by a lag time between the application of interfacial stress and the measurement of protein aggregation, here protein adsorption, the formation of a viscoelastic protein layer, aggregation, and shedding of protein particles into solution is simultaneously monitored. The device is applied to analyze the stability of antibody formulations over a wide range of concentrations (1-180 mg mL -1 ) at the silicone oil (SO)-water interface under controlled mechanical deformation. The adsorption onto oil droplets induces the formation of a viscoelastic protein layer on a subsecond timescale, which progressively restricts the relaxation of the droplets within the chip. Upon mechanical rupture, the protein layer releases particles in solution. The rate of particle formation increases strongly with concentration, similar to the bulk viscosity. Concentrations above 120 mg mL -1 lead to aggregation in seconds and drastically decrease the mechanical perturbations required to shed protein particles in solution. These results are important for the development of formulations at high-protein concentrations (>100 mg mL -1 ) and indicate that particular attention should be given to interface-induced particle formation in this concentration range. In this context, low-volume microfluidic platforms allow the assessment of protein physical instabilities early in development and represent attractive tools to optimize antibody stability and formulation design consuming limited amounts of material., (© 2024 The Author(s). Small published by Wiley‐VCH GmbH.)

In the burgeoning field of proteins, the effective analysis of intricate protein data remains a formidable challenge, necessitating advanced computational tools for data processing, feature extraction, and interpretation. This study introduces ProteinFlow, an innovative framework designed to revolutionize feature engineering in protein data analysis. ProteinFlow stands out by offering enhanced efficiency in data collection and preprocessing, along with advanced capabilities in feature extraction, directly addressing the complexities inherent in multidimensional protein data sets. Through a comparative analysis, ProteinFlow demonstrated a significant improvement over traditional methods, notably reducing data preprocessing time and expanding the scope of biologically significant features identified. The framework's parallel data processing strategy and advanced algorithms ensure not only rapid data handling but also the extraction of comprehensive, meaningful insights from protein sequences, structures, and interactions. Furthermore, ProteinFlow exhibits remarkable scalability, adeptly managing large-scale data sets without compromising performance, a crucial attribute in the era of big data., (© 2024 The Author(s). Biotechnology and Bioengineering published by Wiley Periodicals LLC.)

The cryoprotectant potential of 3-(1-(2-(2-methoxyethoxy)ethyl)imidazol-3-io)butane-1-carboxylate (OE 2 imC 3 C) for proteins necessitates assessment to elucidate its relationship with protein hydration. To reveal this relationship, we assessed the protein stability (pre-freezing and post-thawing) and melting behavior in dilute aqueous protein-OE 2 imC 3 C solutions containing varying mole fractions (x) of OE 2 imC 3 C (x = 0, 7.7 × 10 -3 , and 1.7 × 10 -2 ) using Fourier-transform infrared (FTIR) and near-UV circular dichroism (near-UV CD) spectroscopy and differential scanning calorimetry (DSC) techniques. Following freezing/thawing using a deep freezer, protein stability in aqueous OE 2 imC 3 C solutions (x = 1.7 × 10 -2 ) preserved the folded state owing to the protein-OE 2 imC 3 C interaction, whereas stability at x = 7.7 × 10 -3 was reduced. These results indicate that the protein cryoprotectant potential in aqueous OE 2 imC 3 C solutions at x = 1.7 × 10 -2 is higher than that at x = 7.7 × 10 -3 , owing to the preferential binding of OE 2 imC 3 C with proteins., (© 2024 Wiley Periodicals LLC.)

Protein-protein interactions (PPIs) regulate many cellular processes and engineered PPIs have cell and gene therapy applications. Here, we introduce massively parallel PPI measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Lastly, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics-based energy terms to predict MP3-seq values. We find that AF-M-based models could be valuable for prescreening interactions but experimentally measuring interactions remains necessary to rank their strengths quantitatively., (© 2024. The Author(s).)

Protein-protein and protein-peptide interactions (PPI and PPepI) belong to a similar category of interactions, yet seemingly subtle differences exist among them. To characterize differences between protein-protein (PP) and protein-peptide (PPep) interactions, we have focussed on two important classes of residues-hotspot and anchor residues. Using implicit solvation-based free energy calculations, a very large-scale alanine scanning has been performed on benchmark datasets, consisting of over 5700 interface residues. The differences in the two categories are more pronounced, if the data were divided into three distinct types, namely - weak hotspots (having binding free energy loss upon Ala mutation, ΔΔG, ∼2-10 kcal/mol), moderate hotspots (ΔΔG, ∼10-20 kcal/mol) and strong hotspots (ΔΔG ≥ ∼20 kcal/mol). The analysis suggests that for PPI, weak hotspots are predominantly populated by polar and hydrophobic residues. The distribution shifts towards charged and polar residues for moderate hotspot and charged residues (principally Arg) are overwhelmingly present in the strong hotspot. On the other hand, in the PPepI dataset, the distribution shifts from predominantly hydrophobic and polar (in the weak type) to almost similar preference for polar, hydrophobic and charged residues (in moderate type) and finally the charged residue (Arg) and Trp are mostly occupied in the strong type. The preferred anchor residues in both categories are Arg, Tyr and Leu, possessing bulky side chain and which also strike a delicate balance between side chain flexibility and rigidity. The present knowledge should aid in effective design of biologics, by augmentation or disruption of PPIs with peptides or peptidomimetics.Communicated by Ramaswamy H. Sarma.

Biomass-encapsulated liquid metals (LMs) composite gels have aroused tremendous attention as epidermal smart materials due to their biocompatibility and sustainability. However, they can still not simultaneously possess toughness, adhesion, and recoverability. In this work, the tough, sticky, and recyclable protein-encapsulated LMs organogels (GLM x ) are fabricated through the micro-interfacial stabilization of LMs by lignin and the following preparation of food-making inspired gels. With the help of lignin modification, the LMs micro-drops demonstrated uniform dispersion in the protein matrix, as well as dense non-covalent interactions (e.g., H─bond and hydrophobic interaction) with amino acid residues in peptide chains, which endowed the GLM x with high conductivity (≈5.4 S m -1 ), toughness (≈738.2 kJ m -3 ), self-adhesiveness (a maximal lap-shear strength of ≈58.3 kPa), and recoverability. By tightly adhering onto human skin, the GLM x can act as epidermal sensors to detect drastic (e.g., joint bending) and subtle body movements (e.g., swallowing) and even recognize handwriting and speaking in real-time. Moreover, the organogels can also harvest solar energy and convert it into heat and electricity, which is promising in self-powered intelligent devices. Thus, this work paves a facile way to prepare protein/LMs composite organogels that are suitable for multiple applications like healthcare, human-robot interactions, and solar energy conversion., (© 2024 Wiley‐VCH GmbH.)

RNA-dependent liquid-liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of these proteins is associated with various diseases, particularly neurodegenerative disorders like amyotrophic lateral sclerosis and frontotemporal dementia, making their identification crucial. However, conventional biochemistry-based methods for identifying these proteins are time-consuming and costly. Addressing this challenge, our study developed a robust computational model for their identification. We constructed a comprehensive dataset containing 137 RNA-dependent and 606 non-RNA-dependent LLPS protein sequences, which were then encoded using amino acid composition, composition of K-spaced amino acid pairs, Geary autocorrelation, and conjoined triad methods. Through a combination of correlation analysis, mutual information scoring, and incremental feature selection, we identified an optimal feature subset. This subset was used to train a random forest model, which achieved an accuracy of 90% when tested against an independent dataset. This study demonstrates the potential of computational methods as efficient alternatives for the identification of RNA-dependent LLPS proteins. To enhance the accessibility of the model, a user-centric web server has been established and can be accessed via the link: http://rpp.lin-group.cn., (© 2024 Wiley‐VCH GmbH.)

Polymer-mediated cytosolic protein delivery demonstrates a promising strategy for the development of protein therapeutics. Here, we propose a new designed diblock copolymer which realizes efficient cytosolic protein delivery both in vitro and in vivo. The polymer contains one protein-binding block composed of phenylboronic acid (PBA) and N-(3-dimethylaminopropyl) (DMAP) pendant units for protein binding and endosomal escape, respectively, followed by the response to ATP enriched in the cytosol which triggers the protein release. The other block is PEG designed to improve particle size control and circulation in vivo. By optimizing the block composition, sequence and length of the copolymer, the optimal one (BP 20 ) was identified with the binding block containing 20 units of both PBA and DMAP, randomly distributed along the chain. When mixed with proteins, the BP 20 forms stable nanoparticles and mediates efficient cytosolic delivery of a wide range of proteins including enzymes, toxic proteins and CRISPR/Cas9 ribonucleoproteins (RNP), to various cell lines. The PEG block, especially when further modified with folic acid (FA), enables tumor-targeted delivery of Saporin in vivo, which significantly suppresses the tumor growth. Our results shall inspire the design of novel polymeric vehicles with robust capability for cytosolic protein delivery, which holds great potential for both biological research and therapeutic applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Liquid chromatography-mass spectrometry (LC-MS) intact mass analysis and LC-MS/MS peptide mapping are decisional assays for developing biological drugs and other commercial protein products. Certain PTM types, such as truncation and oxidation, increase the difficulty of precise proteoform characterization owing to inherent limitations in peptide and intact protein analyses. Top-down MS (TDMS) can resolve this ambiguity via fragmentation of specific proteoforms. We leveraged the strengths of flow-programmed (fp) denaturing online buffer exchange (dOBE) chromatography, including robust automation, relatively high ESI sensitivity, and long MS/MS window time, to support a TDMS platform for industrial protein characterization. We tested data-dependent (DDA) and targeted strategies using 14 different MS/MS scan types featuring combinations of collisional- and electron-based fragmentation as well as proton transfer charge reduction. This large, focused dataset was processed using a new software platform, named TDAcquireX, that improves proteoform characterization through TDMS data aggregation. A DDA-based workflow provided objective identification of αLac truncation proteoforms with a two-termini clipping search. A targeted TDMS workflow facilitated the characterization of αLac oxidation positional isomers. This strategy relied on using sliding window-based fragment ion deconvolution to generate composite proteoform spectral match (cPrSM) results amenable to fragment noise filtering, which is a fundamental enhancement relevant to TDMS applications generally., (© 2024 Wiley‐VCH GmbH.)

Accurate identifications of protein-peptide binding residues are essential for protein-peptide interactions and advancing drug discovery. To address this problem, extensive research efforts have been made to design more discriminative feature representations. However, extracting these explicit features usually depend on third-party tools, resulting in low computational efficacy and suffering from low predictive performance. In this study, we design an end-to-end deep learning-based method, E2EPep, for protein-peptide binding residue prediction using protein sequence only. E2EPep first employs and fine-tunes two state-of-the-art pre-trained protein language models that can extract two different high-latent feature representations from protein sequences relevant for protein structures and functions. A novel feature fusion module is then designed in E2EPep to fuse and optimize the above two feature representations of binding residues. In addition, we have also design E2EPep+, which integrates E2EPep and PepBCL models, to improve the prediction performance. Experimental results on two independent testing data sets demonstrate that E2EPep and E2EPep + could achieve the average AUC values of 0.846 and 0.842 while achieving an average Matthew's correlation coefficient value that is significantly higher than that of existing most of sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis shows that the primary strength of E2EPep lies in the effectiveness of feature representation using cross-attention mechanism to fuse the embeddings generated by two fine-tuned protein language models. The standalone package of E2EPep and E2EPep + can be obtained at https://github.com/ckx259/E2EPep.git for academic use only., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Protein-based drugs offer advantages, such as high specificity, low toxicity, and minimal side effects compared to small molecule drugs. However, delivery of proteins to target tissues or cells remains challenging due to the instability, diverse structures, charges, and molecular weights of proteins. Polymers have emerged as a leading choice for designing effective protein delivery systems, but identifying a suitable polymer for a given protein is complicated by the complexity of both proteins and polymers. To address this challenge, a fluorescence-based high-throughput screening platform called ProMatch to efficiently collect data on protein-polymer interactions, followed by in vivo and in vitro experiments with rational design is developed. Using this approach to streamline polymer selection for targeted protein delivery, candidate polymers from commercially available options are identified and a polyhexamethylene biguanide (PHMB)-based system for delivering proteins to white adipose tissue as a treatment for obesity is developed. A branched polyethylenimine (bPEI)-based system for neuron-specific protein delivery to stimulate optic nerve regeneration is also developed. The high-throughput screening methodology expedites identification of promising polymer candidates for tissue-specific protein delivery systems, thereby providing a platform to develop innovative protein-based therapeutics., (© 2024 Wiley‐VCH GmbH.)

Artificial bio-nanocoatings, primarily composed of proteins, offer a broad range of applications across various fields thanks to their unique properties. Proteins, as major components of these structures, enable a high degree of customization, such as mutations, conjugation with other molecules or nanoparticles, or the inclusion of an enzymatic activity. Their ability to self-assembly simplifies the production of bio-nanocoatings, making this process efficient and environment-friendly. Despite these advantages, a comprehensive understanding of the underlying self-assembly mechanism is lacking, and the reaction rates governing this process have not been characterized. In this article, we introduce a novel method to determine the key parameters describing the self-assembly mechanism of bio-nanostructures. For the first time, this approach enables an accurate calculation of the autocatalytic and self-inhibitory parameters controlling the process. Through mathematical modeling, our method enhances the understanding of how the protein-based nanocoatings form and opens new avenues for their application in nanotechnology and synthetic biology. Improved control over the self-assembly processes may enable the development of nanomaterials optimized for specific functions, such as drug delivery, biosensing, and bioactive surface fabrication.

Background: Deep learning-based drug-target affinity (DTA) prediction methods have shown impressive performance, despite a high number of training parameters relative to the available data. Previous studies have highlighted the presence of dataset bias by suggesting that models trained solely on protein or ligand structures may perform similarly to those trained on complex structures. However, these studies did not propose solutions and focused solely on analyzing complex structure-based models. Even when ligands are excluded, protein-only models trained on complex structures still incorporate some ligand information at the binding sites. Therefore, it is unclear whether binding affinity can be accurately predicted using only compound or protein features due to potential dataset bias. In this study, we expanded our analysis to comprehensive databases and investigated dataset bias through compound and protein feature-based methods using multilayer perceptron models. We assessed the impact of this bias on current prediction models and proposed the binding affinity similarity explorer (BASE) web service, which provides bias-reduced datasets., Results: By analyzing eight binding affinity databases using multilayer perceptron models, we confirmed a bias where the compound-protein binding affinity can be accurately predicted using compound features alone. This bias arises because most compounds show consistent binding affinities due to high sequence or functional similarity among their target proteins. Our Uniform Manifold Approximation and Projection analysis based on compound fingerprints further revealed that low and high variation compounds do not exhibit significant structural differences. This suggests that the primary factor driving the consistent binding affinities is protein similarity rather than compound structure. We addressed this bias by creating datasets with progressively reduced protein similarity between the training and test sets, observing significant changes in model performance. We developed the BASE web service to allow researchers to download and utilize these datasets. Feature importance analysis revealed that previous models heavily relied on protein features. However, using bias-reduced datasets increased the importance of compound and interaction features, enabling a more balanced extraction of key features., Conclusions: We propose the BASE web service, providing both the affinity prediction results of existing models and bias-reduced datasets. These resources contribute to the development of generalized and robust predictive models, enhancing the accuracy and reliability of DTA predictions in the drug discovery process. BASE is freely available online at https://synbi2024.kaist.ac.kr/base ., (© 2024. The Author(s).)

Engineering proteins with desired functions and biochemical properties is pivotal for biotechnology and drug discovery. While computational methods based on evolutionary information are reducing the experimental burden by designing targeted libraries of functional variants, they still have a low success rate when the desired protein has few or very remote homologous sequences. Here we propose an autoregressive model, called Temporal Dirichlet Variational Autoencoder (TDVAE), which exploits the mathematical properties of the Dirichlet distribution and temporal convolution to efficiently learn high-order information from a functionally related, possibly remotely similar, set of sequences. TDVAE is highly accurate in predicting the effects of amino acid mutations, while being significantly 90% smaller than the other state-of-the-art models. We then use TDVAE to design variants of the human alpha galactosidase enzymes as potential treatment for Fabry disease. Our model builds a library of diverse variants which retain sequence, biochemical and structural properties of the wildtype protein, suggesting they could be suitable for enzyme replacement therapy. Taken together, our results show the importance of accurate sequence modelling and the potential of autoregressive models as protein engineering and analysis tools., (© 2024. The Author(s).)

Mass spectrometry-based methods can provide a global expression profile and structural readout of proteins in complex systems. Preserving the in vivo conformation of proteins in their innate state is challenging during proteomic experiments. Here, we introduce a whole animal in vivo protein footprinting method using perfusion of reagents to add dimethyl labels to exposed lysine residues on intact proteins which provides information about protein conformation. When this approach is used to measure dynamic structural changes during Alzheimer's disease (AD) progression in a mouse model, we detect 433 proteins that undergo structural changes attributed to AD, independent of aging, across 7 tissues. We identify structural changes of co-expressed proteins and link the communities of these proteins to their biological functions. Our findings show that structural alterations of proteins precede changes in expression, thereby demonstrating the value of in vivo protein conformation measurement. Our method represents a strategy for untangling mechanisms of proteostasis dysfunction caused by protein misfolding. In vivo whole-animal footprinting should have broad applicability for discovering conformational changes in systemic diseases and for the design of therapeutic interventions., (© 2024. The Author(s).)

Background: Accurate prediction of compound-protein interaction (CPI) plays a crucial role in drug discovery. Existing data-driven methods aim to learn from the chemical structures of compounds and proteins yet ignore the conceptual knowledge that is the interrelationships among the fundamental elements in the biomedical knowledge graph (KG). Knowledge graphs provide a comprehensive view of entities and relationships beyond individual compounds and proteins. They encompass a wealth of information like pathways, diseases, and biological processes, offering a richer context for CPI prediction. This contextual information can be used to identify indirect interactions, infer potential relationships, and improve prediction accuracy. In real-world applications, the prevalence of knowledge-missing compounds and proteins is a critical barrier for injecting knowledge into data-driven models., Results: Here, we propose BEACON, a data and knowledge dual-driven framework that bridges chemical structure and conceptual knowledge for CPI prediction. The proposed BEACON learns the consistent representations by maximizing the mutual information between chemical structure and conceptual knowledge and predicts the missing representations by minimizing their conditional entropy. BEACON achieves state-of-the-art performance on multiple datasets compared to competing methods, notably with 5.1% and 6.6% performance gain on the BIOSNAP and DrugBank datasets, respectively. Moreover, BEACON is the only approach capable of effectively predicting knowledge representations for knowledge-lacking compounds and proteins., Conclusions: Overall, our work provides a general approach for directly injecting conceptual knowledge to enhance the performance of CPI prediction., (© 2024. The Author(s).)

Mapping disulfide linkages is crucial for characterizing pharmaceutical proteins during drug development and quality control. Traditional bottom-up protein analysis workflows often suffer from incomplete mapping for tryptic peptides consisting of multiple disulfide bonds. Although the employment of a partial reduction of disulfide bonds can improve disulfide mapping, it becomes a bottleneck of analysis because individual tuning is often needed. Herein, we have developed an online disulfide-ene reaction system in which the composition of the reaction solvent can be programmed to achieve optimal partial reduction of tryptic disulfide peptides after liquid chromatography separation. By coupling this system onto a bottom-up protein analysis workflow, high coverage for sequencing (71-83%) and disulfide mapping (84-100%) was achieved for standard proteins consisting of 4-19 disulfide bonds. The analytical capability was further demonstrated by mapping 13 scrambled disulfide bonds in lysozyme and achieving compositional analysis of IgG isotypes (κ and λ) and subclasses (IgG1-IgG4) from human plasma.

In recent years, nanomaterials have been widely used in the biomedical field. The biphenylene network is a highly promising planar carbon nanomaterial. To better explore its biomedical applications, we need to understand the biological effects of the biphenylene network. To investigate the biological effects of the novel nanomaterial biphenylene network, we used molecular dynamics simulations to study the interactions of the novel planar carbon nanomaterial biphenylene network with α-helical and β-sheet proteins. We found that both types of proteins adsorb flatly on the surface of the biphenylene network; the strong van der Waals interaction is the main adsorption force, while π-π stacking also provides an auxiliary force for the adsorption. When the HP35 protein whose secondary structure is an α-helix was adsorbed on biphenylene network, the entire structure of α-helix 2 was disrupted and α-helix 3 partly recovered its helical structure after being disrupted. In contrast to the β-sheet YAP65 protein, only part of the structure of β-sheet 1 was disrupted. Therefore, the biocompatibility of the biphenylene network with the β-sheet YAP65 protein is better than that of the α-helical HP35 protein, which may be due to the different surface curvature of the protein's secondary structure. Our research promotes the application of the biphenylene network in biomedicine and provides a theoretical basis and experimental direction for practical experiments.

Chiral film-based sensors show great promise for discriminating between enantiomers due to their miniaturization and low power consumption. However, their practical use is hindered by the trade-off between enantioselectivity and mass transfer capability, especially concerning biomacromolecules such as proteins. In this work, we present an effective and straightforward method for creating highly organized macropores within crystalline chiral metal-organic framework (CMOF) films. This approach harnesses the shaping influence of a polystyrene nanosphere template and the crystallization induced by the liquid dielectric barrier discharge plasma. The resultant highly ordered macro-microporous structures improve mass diffusion and access to chiral active sites in the hierarchical CMOF films. Coupled with their inherent chirality, strong fluorescence emission, high crystallinity, and exceptional stability, these attributes endow these CMOF films with enhanced sensing capabilities for chiral molecules. Particularly, the macro-microporous structure facilitates efficient protein recognition, overcoming a significant challenge encountered by MOFs due to protein dimensions surpassing MOF pore sizes. These films exhibit increased enantioselectivity, better limits of detection, and wider linear ranges compared with purely microporous CMOF films. This study thus provides a powerful synthetic approach for hierarchical CMOF films, addressing the limitations of traditional thin film sensors and opening an avenue for efficient chiral sensing of large biomacromolecules.