Proteins Feel More Than They See: Fine-Tuning of Binding Affinity by Properties of the Non-Interacting Surface

Structure-Directed Pan-Specific T-Cell Receptor-Peptide-Major Histocompatibility Complex Interaction Prediction

T-cell receptors (TCRs) play a pivotal role in the adaptive immune system, and understanding their antigen recognition mechanism remains a critical area of research. With the increasing availability of binding and interaction data between TCRs and peptide-major histocompatibility complexes (pMHCs), data-driven computational methods are emerging as powerful tools with significant potential for advancement. In this study, we collected and curated comprehensive sequence and structure data sets of TCRs from human CD8+ T-cells and cognate epitopes presented by MHC class I molecules. We developed two innovative computational frameworks: SG-TPMI, a lightweight, extensible, and structure-guided model for predicting TCR-pMHC binding specificity, and Seq/Struct-TCS, a pair of models (sequence-based and structure-based) for predicting contact sites within TCR-pMHC complexes. Notably, we directly integrated MHC-I alpha helices (or pseudosequences) and structural information on the protein complex into the prediction models. Our comprehensive modeling approach enabled quantitative investigations of TCR-pMHC interaction mechanisms, empowering SG-TPMI and Struct-TCS to achieve performances comparable to those of state-of-the-art methods. Furthermore, our results highlight the necessity of CDR1 and CDR2 loops as well as MHC restriction in pan-specific TCR-pMHC interaction prediction, providing new insights into TCR recognition. In summary, we not only propose SG-TPMI as an effective computational method for predicting TCR-pMHC binary interactions but also introduce the Seq/Struct-TCS design for predicting TCR interacting sites with peptide or MHC alpha helices.

DeepInterAware: Deep Interaction Interface-Aware Network for Improving Antigen-Antibody Interaction Prediction from Sequence Data

Identifying interactions between candidate antibodies and target antigens is a key step in developing effective human therapeutics. The antigen-antibody interaction (AAI) occurs at the structural level, but the limited structure data poses a significant challenge. However, recent studies revealed that structural information can be learned from the vast amount of sequence data, indicating that the interaction prediction can benefit from the abundance of antigen and antibody sequences. In this study, DeepInterAware (deep interaction interface-aware network) is proposed, a framework dynamically incorporating interaction interface information directly learned from sequence data, along with the inherent specificity information of the sequences. Experimental results in interaction prediction demonstrate that DeepInterAware outperforms existing methods and exhibits promising inductive capabilities for predicting interactions involving unseen antigens or antibodies, and transfer capabilities for similar tasks. More notably, DeepInterAware has unique advantages that existing methods lack. First, DeepInterAware can dive into the underlying mechanisms of AAIs, offering the ability to identify potential binding sites. Second, it is proficient in detecting mutations within antigens or antibodies, and can be extended for precise predictions of the binding free energy changes upon mutations. The HER2-targeting antibody screening experiment further underscores DeepInterAware's exceptional capability in identifying binding antibodies for target antigens, establishing it as an important tool for antibody screening.

The photoinduced β-carotene synthesis in Blakeslea trispora is dependent on WC-2A

β-Carotene, a high value-added natural pigment, is currently produced industrially in Blakeslea trispora. Although photoinduced carotenoid synthesis has been identified in some filamentous fungi, there are still relatively few studies focusing on B. trispora and its potential mechanisms. In this study, an integrated strategy-including correlation analysis of gene expression, bioinformatics analysis, protein interaction, and RNA interference-was adopted to elucidate photoinduced β-carotene synthesis in B. trispora. Light wavelength, intensity, and irradiation duration stimulated the transcription of photoreceptors [btwc-1 (a, b, c) and btwc-2 (a, b, c, d)] and carotenoid structural genes (carB and carRA). The transcription of photoreceptor genes showed significant or high correlation with carotenoid structural genes under continuous or short-term, high-intensity blue light irradiation. To elucidate the role of photoreceptors in carotenoid synthesis, the interaction between BTWC-1 and BTWC-2 was predicted. Furthermore, Glutathione S-Transferase (GST) pull-down assays showed that only BTWC-1C and BTWC-2A could interact to form complexes. Inhibition of btwc-2a expression under dark conditions did not affect β-carotene accumulation or the transcription of carB and carRA, but did reduce these parameters under blue light irradiation, indicating that btwc-2a mediates photoinduced β-carotene synthesis in B. trispora.

Assessment of Phage-Displayed Peptides Targeting Cancer Cell Surface Proteins: A Comprehensive Molecular Docking Study

Peptides binding overexpressed breast and cervical cancer cell surface proteins can be isolated by phage display technology, and their affinity to their potential receptors can be assessed by molecular docking. We isolated 44 phage clones displaying dodecapeptides with high affinity to HeLa cervical cancer and MDA-MB-231 (MDA) breast cancer cells by repeated biopanning of an MK13 phage library and explored their affinity to specific proteins by molecular docking. Six peptides appeared repeatedly during biopanning: two with affinity to HeLa (H5/H21), and four with affinity to MDA cells (M3/M7/M15/M17). Peptide pairs M3/H5 and H1/M17 had affinity to both cell lines. A systematic review identified Annexin A2, EGFR, CD44, CD146, and Integrin alpha V as potential protein targets in HeLa cells, and Vimentin, Galectin-1, and Annexins A1 and A5 in MDA cells. Via virtual screening, we selected six peptides with the highest total docking scores: H1 (-916.32), H6 (-979.21), H19 (-1093.24), M6 (-732.21), M16 (-745.5), and M19 (-739.64), and identified that docking scores were strengthened by the protein type, the interacting amino acid side chains, and the polarity of peptides. This approach facilitates the selection of relevant peptides that could be further explored for active targeting in cancer diagnosis and treatment.

Comprehensive mapping of mutations in TDP-43 and α-Synuclein that affect stability and binding

Abnormal aggregation and amyloid inclusions of TAR DNA-binding protein 43 (TDP-43) and α-Synuclein (α-Syn) are frequently co-observed in amyotrophic lateral sclerosis, Parkinson's disease, and Alzheimer's disease. Several reports showed TDP-43 C-terminal domain (CTD) and α-Syn interact with each other and the aggregates of these two proteins colocalized together in different cellular and animal models. Molecular dynamics simulation was conducted to elucidate the stability of the TDP-43 and Syn complex structure. The interfacial mutations in protein complexes changes the stability and binding affinity of the protein that may cause diseases. Here, we have utilized the computational saturation mutagenesis approach including structure-based stability and binding energy calculations to compute the systemic effects of missense mutations of TDP-43 CTD and α-Syn on protein stability and binding affinity. Most of the interfacial mutations of CTD and α-Syn were found to destabilize the protein and reduced the protein binding affinity. The results thus shed light on the functional consequences of missense mutations observed in TDP-43 associated proteinopathies and may provide the mechanisms of co-morbidities involving these two proteins.Communicated by Ramaswamy H. Sarma.

Sam-Sam Association Between EphA2 and SASH1: In Silico Studies of Cancer-Linked Mutations

Recently, SASH1 has emerged as a novel protein interactor of a few Eph tyrosine kinase receptors like EphA2. These interactions involve the first N-terminal Sam (sterile alpha motif) domain of SASH1 (SASH1-Sam1) and the Sam domain of Eph receptors. Currently, the functional meaning of the SASH1-Sam1/EphA2-Sam complex is unknown, but EphA2 is a well-established and crucial player in cancer onset and progression. Thus, herein, to investigate a possible correlation between the formation of the SASH1-Sam1/EphA2-Sam complex and EphA2 activity in cancer, cancer-linked mutations in SASH1-Sam1 were deeply analyzed. Our research plan relied first on searching the COSMIC database for cancer-related SASH1 variants carrying missense mutations in the Sam1 domain and then, through a variety of bioinformatic tools and molecular dynamic simulations, studying how these mutations could affect the stability of SASH1-Sam1 alone, leading eventually to a defective fold. Next, through docking studies, with the support of AlphaFold2 structure predictions, we investigated if/how mutations in SASH1-Sam1 could affect binding to EphA2-Sam. Our study, apart from presenting a solid multistep research protocol to analyze structural consequences related to cancer-associated protein variants with the support of cutting-edge artificial intelligence tools, suggests a few mutations that could more likely modulate the interaction between SASH1-Sam1 and EphA2-Sam.

(R)evolution of Viruses: Introduction to biothermodynamics of viruses

As of 26 April 2024, the International Committee on Taxonomy of Viruses has registered 14690 virus species. Of these, only several dozen have been chemically and thermodynamically characterized. Every virus species is characterized by a specific empirical formula and thermodynamic properties - enthalpy, entropy and Gibbs energy. These physical properties are used in a mechanistic model of virus-host interactions at the cell membrane and in the cytoplasm. This review article presents empirical formulas and Gibbs energies for all major variants of SARS-CoV-2. This article also reports and suggests a mechanistic model of evolutionary changes, with the example of time evolution of SARS-CoV-2 from 2019 to 2024.

Protein folding dependence on selenoprotein M contributes to steady cartilage extracellular matrix repressing ferroptosis via PERK/ATF4/CHAC1 axis

OBJECTIVE

Initiation of endoplasmic reticulum (ER) stress is pivotal to the advancement of osteoarthritis (OA). We aimed to explore the function of ER-resident selenoprotein M (SELM) in cartilage-forming chondrocytes, investigating how SELM participates in cartilage extracellular matrix (ECM) metabolism and ER stress modulation.

METHODS

Articular cartilage samples with knee OA undergoing total knee arthroplasty were categorised into OA-smooth and OA-damaged groups, with primary chondrocytes extracted from smooth areas. Destabilization of the medial meniscus was induced in male C57BL6/J mice, with sham operations on the left knee as controls. After 8 weeks, knee joint tissues were collected for analysis. Histology and immunohistochemistry examined cartilage damage. Molecular biology techniques investigated how SELM affects ECM metabolism and ER stress regulation. RNA sequencing revealed the pathway changes after SELM intervention. AlphaFold demonstrated how SELM interacts with other molecules. Cultured cartilage explants helped determine the effects of SELM supplementation.

RESULTS

SELM expression was reduced in the damaged cartilage. Increasing SELM levels positively impacted ECM equilibrium. Decreasing SELM expression activated genes linked to degenerative ailments and impaired the cellular response to misfolded proteins, initiating the PERK/P-EIF2A/ATF4 pathway and exacerbating GSH/GSSG imbalance via the ATF4/CHAC1 axis. SELM likely participated in protein folding and modification by leveraging its thioredoxin domains. In vitro SELM supplementation mitigated IL-1β effects on damaged cartilage explants and suppressed beneficial chondrocyte phenotypes.

CONCLUSIONS

Our results confirm the involvement of SELM in ER stress-induced cartilage damage as well as protein folding, pointing to new directions in molecular therapy for degenerative diseases.

Apigenin alleviates Sjögren's syndrome-induced salivary gland epithelial cell ferroptosis via ERα signaling-mediated regulation of the ATF3/SLC7A1l axis

BACKGROUND

In Sjögren's syndrome (SS)-an autoimmune disease characterized by dry mouth and eyes-salivary gland epithelial cells (SGECs) undergo ferroptosis, which disrupts their integrity and impairs saliva secretion. Apigenin, a phytoestrogen, is known to activate estrogen signalling and alleviate xerostomia in ovariectomized mice; however, its effect on SGEC survival and function in SS remains unclear. We hypothesized that apigenin alleviates SS symptoms and progression by inhibiting ferroptosis in SGECs and aimed to elucidate the underlying mechanism.

METHODS

Apigenin (50 mg/kg) was orally gavaged to non-obese diabetic (NOD)/LtJ female mice (SS model); changes in SS functional indicators were analyzed using mRNA sequencing and bioinformatic analyses of submandibular glands. Interferon-gamma (IFN-γ)-stimulated SGECs were used to model SS in vitro; SGEC activity and aquaporin 5 (AQP5) expression were analyzed. Immunohistochemical staining, transmission electron microscopy, RT-qPCR, western blotting and other methods were used to verify the mechanisms.

RESULTS

Apigenin significantly increased salivary secretion and AQP5 expression while inhibiting ferroptosis and immune infiltration in NOD mouse submandibular glands. The oxidative stress gene ATF3 was upregulated and GPX4 was downregulated in NOD mice compared to that in control group (ICR mice); however, apigenin reversed this effect. IFN-γ treatment downregulated AQP5, SLC7A11, and GPX4 expression while promoting ATF3 expression and ferroptosis, which was mitigated by apigenin. ATF3 knockdown increased SLC7A11 and GPX4 expression, inhibiting SS and ferroptosis. Furthermore, apigenin inhibited ferroptosis in SGECs through ESR1 binding to ATF3.

CONCLUSION

Apigenin alleviates SS by regulating SGEC ferroptosis via the ERα-regulated ATF3/SLC7A11 axis, highlighting its therapeutic potential in SS.

ProAffinity-GNN: A Novel Approach to Structure-Based Protein-Protein Binding Affinity Prediction via a Curated Data Set and Graph Neural Networks

Protein-protein interactions (PPIs) are crucial for understanding biological processes and disease mechanisms, contributing significantly to advances in protein engineering and drug discovery. The accurate determination of binding affinities, essential for decoding PPIs, faces challenges due to the substantial time and financial costs involved in experimental and theoretical methods. This situation underscores the urgent need for more effective and precise methodologies for predicting binding affinity. Despite the abundance of research on PPI modeling, the field of quantitative binding affinity prediction remains underexplored, mainly due to a lack of comprehensive data. This study seeks to address these needs by manually curating pairwise interaction labels on available 3D structures of protein complexes, with experimentally determined binding affinities, creating the largest data set for structure-based pairwise protein interaction with binding affinity to date. Subsequently, we introduce ProAffinity-GNN, a novel deep learning framework using protein language model and graph neural network (GNN) to improve the accuracy of prediction of structure-based protein-protein binding affinities. The evaluation results across several benchmark test sets and an additional case study demonstrate that ProAffinity-GNN not only outperforms existing models in terms of accuracy but also shows strong generalization capabilities.

Inhibition of the EphA2-Sam/Ship2-Sam Association through Peptide Ligands: Studying the Combined Effect of Charge and Aromatic Character

The Sam (sterile alpha motif) domain from the lipid phosphatase Ship2 binds the Sam domain from the EphA2 receptor to negatively regulate receptor endocytosis and degradation. This interaction is primarily linked to pro-oncogenic effects. We report on the design and evaluation of EphA2-Sam/Ship2-Sam peptide inhibitors provided with positive charges and different aromatic characters. Starting from the sequence of previously identified Ship2-Sam targeting peptides, an in silico approach was set up to predict higher affinity peptide ligands. A few peptides were experimentally tested through an interdisciplinary approach. Interaction studies were performed by nuclear magnetic resonance spectroscopy and biolayer interferometry. 3D models of Ship2-Sam/peptide complexes were predicted by AlphaFold2. Cell-based assays were carried out to investigate whether such peptide sequences might have an influence on EphA2 signaling. The approach led to the identification of novel Ship2-Sam ligands and shed further light on original approaches to design inhibitors of the Ship2-Sam/EphA2-Sam interaction.

A Comparative Analysis of SARS-CoV-2 Variants of Concern (VOC) Spike Proteins Interacting with hACE2 Enzyme

In late 2019, the emergence of a novel coronavirus led to its identification as SARS-CoV-2, precipitating the onset of the COVID-19 pandemic. Many experimental and computational studies were performed on SARS-CoV-2 to understand its behavior and patterns. In this research, Molecular Dynamic (MD) simulation is utilized to compare the behaviors of SARS-CoV-2 and its Variants of Concern (VOC)-Alpha, Beta, Gamma, Delta, and Omicron-with the hACE2 protein. Protein structures from the Protein Data Bank (PDB) were aligned and trimmed for consistency using Chimera, focusing on the receptor-binding domain (RBD) responsible for ACE2 interaction. MD simulations were performed using Visual Molecular Dynamics (VMD) and Nanoscale Molecular Dynamics (NAMD2), and salt bridges and hydrogen bond data were extracted from the results of these simulations. The data extracted from the last 5 ns of the 10 ns simulations were visualized, providing insights into the comparative stability of each variant's interaction with ACE2. Moreover, electrostatics and hydrophobic protein surfaces were calculated, visualized, and analyzed. Our comprehensive computational results are helpful for drug discovery and future vaccine designs as they provide information regarding the vital amino acids in protein-protein interactions (PPIs). Our analysis reveals that the Original and Omicron variants are the two most structurally similar proteins. The Gamma variant forms the strongest interaction with hACE2 through hydrogen bonds, while Alpha and Delta form the most stable salt bridges; the Omicron is dominated by positive potential in the binding site, which makes it easy to attract the hACE2 receptor; meanwhile, the Original, Beta, Delta, and Omicron variants show varying levels of interaction stability through both hydrogen bonds and salt bridges, indicating that targeted therapeutic agents can disrupt these critical interactions to prevent SARS-CoV-2 infection.

Aortic disease and cardiomyopathy in patients with a novel DNMT3A gene variant causing Tatton-Brown-Rahman syndrome

Tatton-Brown-Rahman syndrome (TBRS) is a rare congenital genetic disorder caused by autosomal dominant pathogenic variants in the DNA methyltransferase DNMT3A gene. Typical TBRS clinical features are overgrowth, intellectual disability, and minor facial anomalies. However, since the syndrome was first described in 2014, a widening spectrum of abnormalities is being described. Cardiovascular abnormalities are less commonly reported but can be a major complication of the syndrome. This article describes a family of three individuals diagnosed with TBRS in adulthood and highlights the variable expression of cardiovascular features. A 34-year-old proband presented with progressive aortic dilatation, mitral valve (MV) regurgitation, left ventricular (LV) dilatation, and ventricular arrhythmias. The affected family members (mother and brother) were diagnosed with MV regurgitation, LV dilatation, and arrhythmias. Exome sequencing and computational protein analysis suggested that the novel familial DNMT3A mutation Ser775Tyr is located in the methyltransferase domain, however, distant from the active site or DNA-binding loops. Nevertheless, this bulky substitution may have a significant effect on DNMT3A protein structure, dynamics, and function. Analysis of peripheral blood cfDNA and transcriptome showed shortened mononucleosome fragments and altered gene expression in a number of genes related to cardiovascular health and of yet undescribed function, including several lncRNAs. This highlights the importance of epigenetic regulation by DNMT3A on cardiovascular system development and function. From the clinical perspective, we suggest that new patients diagnosed with congenital DNMT3A variants and TBRS require close examination and follow-up for aortic dilatation and valvular disease because these conditions can progress rapidly. Moreover, personalized treatments, based on the specific DNMT3A variants and the different pathways of their function loss, can be envisioned in the future.

Understanding the Reversible Binding of a Multichain Protein-Protein Complex through Free-Energy Calculations

We demonstrate that the binding affinity of a multichain protein-protein complex, insulin dimer, can be accurately predicted using a streamlined route of standard binding free-energy calculations. We find that chains A and C, which do not interact directly during binding, stabilize the insulin monomer structures and reduce the binding affinity of the two monomers, therefore enabling their reversible association. Notably, we confirm that although classical methods can estimate the binding affinity of the insulin dimer, conventional molecular dynamics, enhanced sampling algorithms, and classical geometrical routes of binding free-energy calculations may not fully capture certain aspects of the role played by the noninteracting chains in the binding dynamics. Therefore, this study not only elucidates the role of noninteracting chains in the reversible binding of the insulin dimer but also offers a methodological guide for investigating the reversible binding of multichain protein-protein complexes utilizing streamlined free-energy calculations.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Cancer-Related Mutations in the Sam Domains of EphA2 Receptor and Ship2 Lipid Phosphatase: A Computational Study

The lipid phosphatase Ship2 interacts with the EphA2 receptor by forming a heterotypic Sam (sterile alpha motif)-Sam complex. Ship2 works as a negative regulator of receptor endocytosis and consequent degradation, and anti-oncogenic effects in cancer cells should be induced by hindering its association with EphA2. Herein, a computational approach is presented to investigate the relationship between Ship2-Sam/EphA2-Sam interaction and cancer onset and further progression. A search was first conducted through the COSMIC (Catalogue of Somatic Mutations in Cancer) database to identify cancer-related missense mutations positioned inside or close to the EphA2-Sam and Ship2-Sam reciprocal binding interfaces. Next, potential differences in the chemical-physical properties of mutant and wild-type Sam domains were evaluated by bioinformatics tools based on analyses of primary sequences. Three-dimensional (3D) structural models of mutated EphA2-Sam and Ship2-Sam domains were built as well and deeply analysed with diverse computational instruments, including molecular dynamics, to classify potentially stabilizing and destabilizing mutations. In the end, the influence of mutations on the EphA2-Sam/Ship2-Sam interaction was studied through docking techniques. This in silico approach contributes to understanding, at the molecular level, the mutation/cancer relationship by predicting if amino acid substitutions could modulate EphA2 receptor endocytosis.

Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-Protein Binding Affinity

Protein-protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein-protein complex formation and defining the structure-function relationship. Despite advancements in understanding protein-protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein-protein interface. We found that the numbers of specific amino acids at the protein-protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry.

Distinct or Overlapping Areas of Mitochondrial Thioredoxin 2 May Be Used for Its Covalent and Strong Non-Covalent Interactions with Protein Ligands

In silico approaches were employed to examine the characteristics of interactions between human mitochondrial thioredoxin 2 (HsTrx2) and its 38 previously identified mitochondrial protein ligands. All interactions appeared driven mainly by electrostatic forces. The statistically significant residues of HsTrx2 for interactions were characterized as "contact hot spots". Since these were identical/adjacent to putative thermodynamic hot spots, an energy network approach identified their neighbors to highlight possible contact interfaces. Three distinct areas for binding emerged: (i) one around the active site for covalent interactions, (ii) another antipodal to the active site for strong non-covalent interactions, and (iii) a third area involved in both kinds of interactions. The contact interfaces of HsTrx2 were projected as respective interfaces for Escherichia coli Trx1 (EcoTrx1), 2, and HsTrx1. Comparison of the interfaces and contact hot spots of HsTrx2 to the contact residues of EcoTx1 and HsTrx1 from existing crystal complexes with protein ligands supported the hypothesis, except for a part of the cleft/groove adjacent to Trp30 preceding the active site. The outcomes of this study raise the possibility for the rational design of selective inhibitors for the interactions of HsTrx2 with specific protein ligands without affecting the entirety of the functions of the Trx system.

Circular RNA circARPC1B functions as a stabilisation enhancer of Vimentin to prevent high cholesterol-induced articular cartilage degeneration

BACKGROUND

Osteoarthritis (OA) is a prevalent and debilitating condition, that is, directly associated with cholesterol metabolism. Nevertheless, the molecular mechanisms of OA remain largely unknown, and the role of cholesterol in this process has not been thoroughly investigated. This study aimed to investigate the role of a novel circular RNA, circARPC1B in the relationship between cholesterol and OA progression.

METHODS

We measured total cholesterol (TC) levels in the synovial fluid of patients with or without OA to determine the diagnostic role of cholesterol in OA. The effects of cholesterol were explored in human and mouse chondrocytes in vitro. An in vivo OA model was also established in mice fed a high-cholesterol diet (HCD) to explore the role of cholesterol in OA. RNAseq analysis was used to study the influence of cholesterol on circRNAs in chondrocytes. The role of circARPC1B in the OA development was verified through circARPC1B overexpression and knockdown. Additionally, RNA pulldown assays and RNA binding protein immunoprecipitation were used to determine the interaction between circARPC1B and Vimentin. CircARPC1B adeno-associated virus (AAV) was used to determine the role of circARPC1B in cholesterol-induced OA.

RESULTS

TC levels in synovial fluid of OA patients were found to be elevated and exhibited high sensitivity and specificity as predictors of OA diagnosis. Moreover, elevated cholesterol accelerated OA progression. CircARPC1B was downregulated in chondrocytes treated with cholesterol and played a crucial role in preserving the extracellular matrix (ECM). Mechanistically, circARPC1B is competitively bound to the E3 ligase synoviolin 1 (SYVN1) binding site on Vimentin, inhibiting the proteasomal degradation of Vimentin. Furthermore, circARPC1B AAV infection alleviates Vimentin degradation and OA progression caused by high cholesterol.

CONCLUSIONS

These findings indicate that the cholesterol-circARPC1B-Vimentin axis plays a crucial role in OA progression, and circARPC1B gene therapy has the opportunity to provide a potential therapeutic approach for OA.

An updated dataset and a structure-based prediction model for protein-RNA binding affinity

Understanding the process of protein-RNA interaction is essential for structural biology. The thermodynamic process is an important part to uncover the protein-RNA interaction mechanism. The regulatory networks between protein and RNA in organisms are dominated by the binding or dissociation in the cells. Therefore, determining the binding affinity for protein-RNA complexes can help us to understand the regulation mechanism of protein-RNA interaction. Since it is time-consuming and labor-intensive to determine the binding affinity for protein-RNA complexes by experimental methods, it is necessary and urgent to develop computational methods to predict that. To develop a binding affinity prediction model, first we update the dataset of protein-RNA binding affinity benchmark (PRBAB), which includes 145 complexes now. Second, we extract the structural features based on complex structure, and then we analyze and select the representative structural features to train the regression model. Third, we random select the subset from the PRBAB2.0 to fit the protein-RNA binding affinity determined by experiment. In the end, we tested our model on the nonredundant PDBbind dataset, and the results showed that Pearson correlation coefficient r = .57 and RMSE = 2.51 kcal/mol. The Pearson correlation coefficient achieves 0.7 while removing 5 complex structures with modified residues/nucleotides and metal ions. While testing on ProNAB, the results showed that 71.60% of the prediction achieves Pearson correlation coefficient r = .61 and RMSE = 1.56 kcal/mol with experiment values.

Binding Affinity of Trastuzumab and Pertuzumab Monoclonal Antibodies to Extracellular HER2 Domain

The binding affinity of trastuzumab and pertuzumab to HER2 has been studied using both experimental and in silico methods. The experiments were conducted using the antibodies in their complete IgG form, as used in clinical therapy, and the extracellular domain of the HER2 protein in solution. This approach provides a precise, reproducible, and reliable view of the interaction between them in physicochemical conditions similar to those found in the tumoral environment. Dynamic light scattering and size exclusion chromatography coupled with tetra detection were utilized to characterize the protein complexes, measure their concentrations, and calculate the equilibrium-free binding energy, ΔGbind. In addition, PRODIGY, a QSAR-like model with excellent predictive ability, was employed to obtain in silico ΔGbind estimations. The results obtained indicate that pertuzumab exhibits a slightly higher binding affinity to HER2 than trastuzumab. The difference in binding affinity was explained based on the contribution of the different interfacial contact (IC) descriptors to the ΔGbind value estimated by the PRODIGY model. Furthermore, experiments revealed that the pertuzumab IgG antibody binds preferentially to two HER2 proteins, one per Fab fragment, while trastuzumab mainly forms a monovalent complex. This finding was interpreted based on a geometrical model that identified steric crowding in the trastuzumab-HER2 complex as compared with the pertuzumab-HER2 complex.

EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations

CONTEXT

Protein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis ('classic type functions' of proteins) or a signal transduction ('non-classic'), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such associations is inherent in the combined effect of shape and electrostatic complementarities (Sc, EC) of the interacting protein partners at their interface, which provides indirect probabilistic estimates of the stability and affinity of the interaction. While Sc is a necessary criterion for inter-protein associations, EC can be favorable as well as disfavored (e.g., in transient interactions). Estimating equilibrium thermodynamic parameters (∆G_binding, K_d) by experimental means is costly and time consuming, thereby opening windows for computational structural interventions. Attempts to empirically probe ∆G_binding from coarse-grain structural descriptors (primarily, surface area based terms) have lately been overtaken by physics-based, knowledge-based and their hybrid approaches (MM/PBSA, FoldX, etc.) that directly compute ∆G_binding without involving intermediate structural descriptors.

METHODS

Here, we present EnCPdock ( https://www.scinetmol.in/EnCPdock/ ), a user-friendly web-interface for the direct conjoint comparative analyses of complementarity and binding energetics in proteins. EnCPdock returns an AI-predicted ∆G_binding computed by combining complementarity (Sc, EC) and other high-level structural descriptors (input feature vectors), and renders a prediction accuracy comparable to the state-of-the-art. EnCPdock further locates a PPI complex in terms of its {Sc, EC} values (taken as an ordered pair) in the two-dimensional complementarity plot (CP). In addition, it also generates mobile molecular graphics of the interfacial atomic contact network for further analyses. EnCPdock also furnishes individual feature trends along with the relative probability estimates (Pr_fmax) of the obtained feature-scores with respect to the events of their highest observed frequencies. Together, these functionalities are of real practical use for structural tinkering and intervention as might be relevant in the design of targeted protein-interfaces. Combining all its features and applications, EnCPdock presents a unique online tool that should be beneficial to structural biologists and researchers across related fraternities.

AREA-AFFINITY: A Web Server for Machine Learning-Based Prediction of Protein-Protein and Antibody-Protein Antigen Binding Affinities

Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geometric characteristics such as area (both interface and surface areas) in the structure of a protein-protein complex play an important role in determining protein-protein interactions and their binding affinity. Here, we present a free web server for academic use, AREA-AFFINITY, for prediction of protein-protein or antibody-protein antigen binding affinity based on interface and surface areas in the structure of a protein-protein complex. AREA-AFFINITY implements 60 effective area-based protein-protein affinity predictive models and 37 effective area-based models specific for antibody-protein antigen binding affinity prediction developed in our recent studies. These models take into consideration the roles of interface and surface areas in binding affinity by using areas classified according to different amino acid types with different biophysical nature. The models with the best performances integrate machine learning methods such as neural network or random forest. These newly developed models have superior or comparable performance compared to the commonly used existing methods. AREA-AFFINITY is available for free at: https://affinity.cuhk.edu.cn/.

Structural analysis of an endogenous 4-megadalton succinyl-CoA-generating metabolon

The oxoglutarate dehydrogenase complex (OGDHc) participates in the tricarboxylic acid cycle and, in a multi-step reaction, decarboxylates α-ketoglutarate, transfers succinyl to CoA, and reduces NAD+. Due to its pivotal role in metabolism, OGDHc enzymatic components have been studied in isolation; however, their interactions within the endogenous OGDHc remain elusive. Here, we discern the organization of a thermophilic, eukaryotic, native OGDHc in its active state. By combining biochemical, biophysical, and bioinformatic methods, we resolve its composition, 3D architecture, and molecular function at 3.35 Å resolution. We further report the high-resolution cryo-EM structure of the OGDHc core (E2o), which displays various structural adaptations. These include hydrogen bonding patterns confining interactions of OGDHc participating enzymes (E1o-E2o-E3), electrostatic tunneling that drives inter-subunit communication, and the presence of a flexible subunit (E3BPo), connecting E2o and E3. This multi-scale analysis of a succinyl-CoA-producing native cell extract provides a blueprint for structure-function studies of complex mixtures of medical and biotechnological value.

WaterKit: Thermodynamic Profiling of Protein Hydration Sites

Water desolvation is one of the key components of the free energy of binding of small molecules to their receptors. Thus, understanding the energetic balance of solvation and desolvation resulting from individual water molecules can be crucial when estimating ligand binding, especially when evaluating different molecules and poses as done in High-Throughput Virtual Screening (HTVS). Over the most recent decades, several methods were developed to tackle this problem, ranging from fast approximate methods (usually empirical functions using either discrete atom-atom pairwise interactions or continuum solvent models) to more computationally expensive and accurate ones, mostly based on Molecular Dynamics (MD) simulations, such as Grid Inhomogeneous Solvation Theory (GIST) or Double Decoupling. On one hand, MD-based methods are prohibitive to use in HTVS to estimate the role of waters on the fly for each ligand. On the other hand, fast and approximate methods show an unsatisfactory level of accuracy, with low agreement with results obtained with the more expensive methods. Here we introduce WaterKit, a new grid-based sampling method with explicit water molecules to calculate thermodynamic properties using the GIST method. Our results show that the discrete placement of water molecules is successful in reproducing the position of crystallographic waters with very high accuracy, as well as providing thermodynamic estimates with accuracy comparable to more expensive MD simulations. Unlike these methods, WaterKit can be used to analyze specific regions on the protein surface, (such as the binding site of a receptor), without having to hydrate and simulate the whole receptor structure. The results show the feasibility of a general and fast method to compute thermodynamic properties of water molecules, making it well-suited to be integrated in high-throughput pipelines such as molecular docking.

Ensemble-based molecular docking and spectrofluorometric analysis of interaction between cytotoxin and tumor necrosis factor receptor 1

Cytotoxin (CTX) is a three-finger toxin presents predominantly in cobra venom. The functional site of the toxin is located at its three hydrophobic loop tips. Its actual mechanism of cytotoxicity remains inconclusive as few conflicting hypotheses have been proposed in addition to direct cytolytic effects. The present work investigated the interaction between CTX and death receptor families via ensemble-based molecular docking and fluorescence titration analysis. Multiple sequence alignments of different CTX isoforms obtained a conserved CTX sequence. The three-dimensional structure of the conserved CTX was later determined using homology modelling, and its quality was validated. Ensemble-based molecular docking of CTX was performed with different death receptors, such as Fas-ligand and tumor necrosis factor receptor families. Our results showed that tumor necrosis factor receptor 1 (TNFR1) was the best receptor interacting with CTX attributed to the interaction of all three functional loops and evinced with low HADDOCK, Z-score and RMSD value. The interaction between CTX and TNFR1 was also supported by a concentration-dependent reduction of fluorescence intensity with increasing binding affinity. The possible intermolecular interactions between CTX and TNFR1 were Van der Waals forces and hydrogen bonding. Our findings suggest a possibility that CTX triggers apoptosis cell death through non-covalent interactions with TNFR1.Communicated by Ramaswamy H. Sarma.

SARS-CoV-2 receptor-binding domain deep mutational AlphaFold2 structures

Leveraging recent advances in computational modeling of proteins with AlphaFold2 (AF2) we provide a complete curated data set of all single mutations from each of the 7 main SARS-CoV-2 lineages spike protein receptor binding domain (RBD) resulting in 3819X7 = 26733 PDB structures. We visualize the generated structures and show that AF2 pLDDT values are correlated with state-of-the-art disorder approximations, implying some internal protein dynamics are also captured by the model. Joint increasing mutational coverage of both structural and phenotype data coupled with advances in machine learning can be leveraged to accelerate virology research, specifically future variant prediction. We hope this data release can offer assistance into further understanding of the local and global mutational landscape of SARS-CoV-2 as well as provide insight into the biological understanding that 3D structure acts as a bridge between protein genotype and phenotype.

Improving classification of correct and incorrect protein-protein docking models by augmenting the training set

Motivation

Protein-protein interactions drive many relevant biological events, such as infection, replication and recognition. To control or engineer such events, we need to access the molecular details of the interaction provided by experimental 3D structures. However, such experiments take time and are expensive; moreover, the current technology cannot keep up with the high discovery rate of new interactions. Computational modeling, like protein-protein docking, can help to fill this gap by generating docking poses. Protein-protein docking generally consists of two parts, sampling and scoring. The sampling is an exhaustive search of the tridimensional space. The caveat of the sampling is that it generates a large number of incorrect poses, producing a highly unbalanced dataset. This limits the utility of the data to train machine learning classifiers.

Results

Using weak supervision, we developed a data augmentation method that we named hAIkal. Using hAIkal, we increased the labeled training data to train several algorithms. We trained and obtained different classifiers; the best classifier has 81% accuracy and 0.51 Matthews' correlation coefficient on the test set, surpassing the state-of-the-art scoring functions.

Availability and implementation

Docking models from Benchmark 5 are available at https://doi.org/10.5281/zenodo.4012018. Processed tabular data are available at https://repository.kaust.edu.sa/handle/10754/666961. Google colab is available at https://colab.research.google.com/drive/1vbVrJcQSf6\_C3jOAmZzgQbTpuJ5zC1RP?usp=sharing.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

IRAA: A statistical tool for investigating a protein-protein interaction interface from multiple structures

Understanding protein-protein interactions (PPIs) is fundamental to infer how different molecular systems work. A major component to model molecular recognition is the buried surface area (BSA), that is, the area that becomes inaccessible to solvent upon complex formation. To date, many attempts tried to connect BSA to molecular recognition principles, and in particular, to the underlying binding affinity. However, the most popular approach to calculate BSA is to use a single (or in some cases few) bound structures, consequently neglecting a wealth of structural information of the interacting proteins derived from ensembles corresponding to their unbound and bound states. Moreover, the most popular method inherently assumes the component proteins to bind as rigid entities. To address the above shortcomings, we developed a Monte Carlo method-based Interface Residue Assessment Algorithm (IRAA), to calculate a combined distribution of BSA for a given complex. Further, we apply our algorithm to human ACE2 and SARS-CoV-2 Spike protein complex, a system of prime importance. Results show a much broader distribution of BSA compared to that obtained from only the bound structure or structures and extended residue members of the interface with implications to the underlying biomolecular recognition. We derive that specific interface residues of ACE2 and of S-protein are consistently highly flexible, whereas other residues systematically show minor conformational variations. In effect, IRAA facilitates the use of all available structural data for any biomolecular complex of interest, extracting quantitative parameters with statistical significance, thereby providing a deeper biophysical understanding of the molecular system under investigation.

Detailed Molecular Interactions between Respiratory Syncytial Virus Fusion Protein and the TLR4/MD-2 Complex In Silico

Molecular interactions between respiratory syncytial virus (RSV) fusion protein (F protein) and the cellular receptor Toll-like receptor 4 (TLR4) and myeloid differentiation factor-2 (MD-2) protein complex are unknown. Thus, to reveal the detailed molecular interactions between them, in silico analyses were performed using various bioinformatics techniques. The present simulation data showed that the neutralizing antibody (NT-Ab) binding sites in both prefusion and postfusion proteins at sites II and IV were involved in the interactions between them and the TLR4 molecule. Moreover, the binding affinity between postfusion proteins and the TLR4/MD-2 complex was higher than that between prefusion proteins and the TLR4/MD-2 complex. This increased binding affinity due to conformational changes in the F protein may be able to form syncytium in RSV-infected cells. These results may contribute to better understand the infectivity and pathogenicity (syncytium formation) of RSV.

Large protein complex interfaces have evolved to promote cotranslational assembly

Assembly pathways of protein complexes should be precise and efficient to minimise misfolding and unwanted interactions with other proteins in the cell. One way to achieve this efficiency is by seeding assembly pathways during translation via the cotranslational assembly of subunits. While recent evidence suggests that such cotranslational assembly is widespread, little is known about the properties of protein complexes associated with the phenomenon. Here, using a combination of proteome-specific protein complex structures and publicly available ribosome profiling data, we show that cotranslational assembly is particularly common between subunits that form large intermolecular interfaces. To test whether large interfaces have evolved to promote cotranslational assembly, as opposed to cotranslational assembly being a non-adaptive consequence of large interfaces, we compared the sizes of first and last translated interfaces of heteromeric subunits in bacterial, yeast, and human complexes. When considering all together, we observe the N-terminal interface to be larger than the C-terminal interface 54% of the time, increasing to 64% when we exclude subunits with only small interfaces, which are unlikely to cotranslationally assemble. This strongly suggests that large interfaces have evolved as a means to maximise the chance of successful cotranslational subunit binding.

An Electrostatically-steered Conformational Selection Mechanism Promotes SARS-CoV-2 Spike Protein Variation

After two years since the outbreak, the COVID-19 pandemic remains a global public health emergency. SARS-CoV-2 variants with substitutions on the spike (S) protein emerge increasing the risk of immune evasion and cross-species transmission. Here, we analyzed the evolution of the S protein as recorded in 276,712 samples collected before the start of vaccination efforts. Our analysis shows that most variants destabilize the S protein trimer, increase its conformational heterogeneity and improve the odds of the recognition by the host cell receptor. Most frequent substitutions promote overall hydrophobicity by replacing charged amino acids, reducing stabilizing local interactions in the unbound S protein trimer. Moreover, our results identify "forbidden" regions that rarely show any sequence variation, and which are related to conformational changes occurring upon fusion. These results are significant for understanding the structure and function of SARS-CoV-2 related proteins which is a critical step in vaccine development and epidemiological surveillance.

Picomolar-sensitive β-amyloid fibril fluorophores by tailoring the hydrophobicity of biannulated π-elongated dioxaborine-dyes

The pathological origin of Alzheimer's disease (AD) is still shrouded in mystery, despite intensive worldwide research efforts. The selective visualization of β-amyloid (Aβ), the most abundant proteinaceous deposit in AD, is pivotal to reveal AD pathology. To date, several small-molecule fluorophores for Aβ species have been developed, with increasing binding affinities. In the current work, two organic small-molecule dioxaborine-derived fluorophores were rationally designed through tailoring the hydrophobicity with the aim to enhance the binding affinity for Aβ1-42 fibrils -while concurrently preventing poor aqueous solubility-via biannulate donor motifs in D-π-A dyes. An unprecedented sub-nanomolar affinity was found (K d = 0.62 ± 0.33 nM) and applied to super-sensitive and red-emissive fluorescent staining of amyloid plaques in cortical brain tissue ex vivo. These fluorophores expand the dioxaborine-curcumin-based family of Aβ-sensitive fluorophores with a promising new imaging agent.

PPI-Affinity: A Web Tool for the Prediction and Optimization of Protein-Peptide and Protein-Protein Binding Affinity

Virtual screening of protein–protein and protein–peptide interactions is a challenging task that directly impacts the processes of hit identification and hit-to-lead optimization in drug design projects involving peptide-based pharmaceuticals. Although several screening tools designed to predict the binding affinity of protein–protein complexes have been proposed, methods specifically developed to predict protein–peptide binding affinity are comparatively scarce. Frequently, predictors trained to score the affinity of small molecules are used for peptides indistinctively, despite the larger complexity and heterogeneity of interactions rendered by peptide binders. To address this issue, we introduce PPI-Affinity, a tool that leverages support vector machine (SVM) predictors of binding affinity to screen datasets of protein–protein and protein–peptide complexes, as well as to generate and rank mutants of a given structure. The performance of the SVM models was assessed on four benchmark datasets, which include protein–protein and protein–peptide binding affinity data. In addition, we evaluated our model on a set of mutants of EPI-X4, an endogenous peptide inhibitor of the chemokine receptor CXCR4, and on complexes of the serine proteases HTRA1 and HTRA3 with peptides. PPI-Affinity is freely accessible at https://protdcal.zmb.uni-due.de/PPIAffinity.

Can ERAP1 and ERAP2 Form Functional Heterodimers? A Structural Dynamics Investigation

Endoplasmic reticulum aminopeptidases 1 and 2 (ERAP1 and ERAP2) play important roles in the generation of antigenic peptides presented by Major Histocompatibility Class I (MHCI) molecules and indirectly regulate adaptive immune responses. Although the discrete function of these enzymes has been extensively characterized, recent reports have suggested that they can also form heterodimers with functional consequences. However, lack of structural characterization of a putative ERAP1/ERAP2 dimer has limited our understanding of its biological role and significance. To address this, we employed computational molecular dynamics calculations to explore the topology of interactions between these two, based on experimentally determined homo-dimerization interfaces observed in crystal structures of ERAP2 or homologous enzymes. Our analysis of 8 possible dimerization models, suggested that the most likely ERAP1/ERAP2 heterodimerization topology involves the exon 10 loop, a non-conserved loop previously implicated in interactions between ERAP1 and the disulfide-bond shuffling chaperone ERp44. This dimerization topology allows access to the active site of both enzymes and is consistent with a previously reported construct in which ERAP1 and ERAP2 were linked by Fos/Jun zipper tags. The proposed model constitutes a tentative structural template to help understand the physiological role and significance of ERAP1/ERAP2 molecular interactions.

Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Here, we present a Gaussian-based method for estimation of protein-protein binding entropy to augment the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method for computational prediction of binding free energy (ΔG). The method is termed f5-MM/PBSA/E, where "E" stands for entropy and f5 for five adjustable parameters. The enthalpy components of ΔG (molecular mechanics, polar and non-polar solvation energies) are computed from a single implicit solvent generalized Born (GB) energy minimized structure of a protein-protein complex, while the binding entropy is computed using independently GB energy minimized unbound and bound structures. It should be emphasized that the f5-MM/PBSA/E method does not use snapshots, just energy minimized structures, and is thus very fast and computationally efficient. The method is trained and benchmarked in 5-fold validation test over a data set consisting of 46 protein-protein binding cases with experimentally determined dissociation constant K _d values. This data set has been used for benchmarking in recently published protein-protein binding studies that apply conventional MM/PBSA and MM/PBSA with an enhanced sampling method. The f5-MM/PBSA/E tested on the same data set achieves similar or better performance than these computationally demanding approaches, making it an excellent choice for high throughput protein-protein binding affinity prediction studies.

Theoretical Studies of Cyanophycin Dipeptides as Inhibitors of Tyrosinases

The three-dimensional structure of tyrosinase has been crystallized from many species but not from Homo sapiens. Tyrosinase is a key enzyme in melanin biosynthesis, being an important target for melanoma and skin-whitening cosmetics. Several studies employed the structure of tyrosinase from Agaricus bisporus as a model enzyme. Recently, 98% of human genome proteins were elucidated by AlphaFold. Herein, the AlphaFold structure of human tyrosinase and the previous model were compared. Moreover, tyrosinase-related proteins 1 and 2 were included, along with inhibition studies employing kojic and cinnamic acids. Peptides are widely studied for their inhibitory activity of skin-related enzymes. Cyanophycin is an amino acid polymer produced by cyanobacteria and is built of aspartic acid and arginine; arginine can be also replaced by other amino acids. A new set of cyanophycin-derived dipeptides was evaluated as potential inhibitors. Aspartate–glutamate showed the strongest interaction and was chosen as a leading compound for future studies.

Impact of new variants on SARS-CoV-2 infectivity and neutralization: A molecular assessment of the alterations in the spike-host protein interactions

The emergence of SARS-CoV-2 variants necessitates rational assessment of their impact on the recognition and neutralization of the virus by the host cell. We present a comparative analysis of the interactions of Alpha, Beta, Gamma, and Delta variants with cognate molecules (ACE2 and/or furin), neutralizing nanobodies (Nbs), and monoclonal antibodies (mAbs) using in silico methods, in addition to Nb-binding assays. Our study elucidates the molecular origin of the ability of Beta and Delta variants to evade selected antibodies, such as REGN10933, LY-CoV555, B38, C105, or H11-H4, while being insensitive to others including REGN10987. Experiments confirm that nanobody Nb20 retains neutralizing activity against the Delta variant. The substitutions T478K and L452R in the Delta variant enhance associations with ACE2, whereas P681R promotes recognition by proteases, thus facilitating viral entry. The Ab-specific responses of variants highlight how full-atomic structure and dynamics analyses are required for assessing the response to newly emerging variants.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.

A highly accurate metadynamics-based Dissociation Free Energy method to calculate protein-protein and protein-ligand binding potencies

Although seeking to develop a general and accurate binding free energy calculation method for protein-protein and protein-ligand interactions has been a continuous effort for decades, only limited successes have been obtained so far. Here, we report the development of a metadynamics-based procedure that calculates Dissociation Free Energy (DFE) and its application to 19 non-congeneric protein-protein complexes and hundreds of protein-ligand complexes covering eight targets. We achieved very high correlations in comparison to experimental binding free energies for these diverse sets of systems, demonstrating the generality and accuracy of the method. Since structures of most proteins are available owing to the recent success of prediction by artificial intelligence, a general free energy method such as DFE, combined with other methods, can make structure-based drug design a widely viable and reliable solution to develop both traditional small molecule drugs and biologic drugs as well as PROTACS.

Polydopamine nanospheres coated with bovine serum albumin permit enhanced cell differentiation: fundamental mechanism and practical application for protein coating formation

Protein coating is a strategy for modifying and improving the surface functional properties of nanomaterials. However, the underlying mechanism behind protein coating formation, which is essential for its practical applications, remains largely unknown. Herein, we investigate the fundamental molecular mechanism of protein coating formation. Polydopamine nanospheres (PDANS) coated with bovine serum albumin (BSA) are examined in this study due to their wide biomedical potential. Our results demonstrate that BSAs can flexibly bind to PDANS and maintain their structural dynamicity. Our findings unveil that regular structure formation arises from BSAs lateral interactions via electrostatic forces. Notably, the protein coating modified PDANS surface enhances cell adhesion and proliferation as well as osteogenic differentiation. Such an enhancement is attributed to complementary surface properties provided by the dynamic PDANS-BSA complex and regular structure caused by BSA-BSA interactions in protein coating formation. This study provides a fundamental understanding of the molecular mechanism of protein coating formation, which facilitates the further development of functional protein-coated nanomaterials and guides the bioengineering decision making for biomedical applications, especially in bone tissue engineering.

A random forest classifier for protein-protein docking models

Herein, we present the results of a machine learning approach we developed to single out correct 3D docking models of protein-protein complexes obtained by popular docking software. To this aim, we generated 3 × 10 4 docking models for each of the 230 complexes in the protein-protein benchmark, version 5, using three different docking programs (HADDOCK, FTDock and ZDOCK), for a cumulative set of ≈ 7 × 10 6 docking models. Three different machine learning approaches (Random Forest, Supported Vector Machine and Perceptron) were used to train classifiers with 158 different scoring functions (features). The Random Forest algorithm outperformed the other two algorithms and was selected for further optimization. Using a features selection algorithm, and optimizing the random forest hyperparameters, allowed us to train and validate a random forest classifier, named COnservation Driven Expert System (CoDES). Testing of CoDES on independent datasets, as well as results of its comparative performance with machine learning methods recently developed in the field for the scoring of docking decoys, confirm its state-of-the-art ability to discriminate correct from incorrect decoys both in terms of global parameters and in terms of decoys ranked at the top positions.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Software and data availability statement

The docking models are available at https://doi.org/10.5281/zenodo.4012018. The programs underlying this article will be shared on request to the corresponding authors.

Structural Modelling of KCNQ1 and KCNH2 Double Mutant Proteins, Identified in Two Severe Long QT Syndrome Cases, Reveals New Insights into Cardiac Channelopathies

Congenital long QT syndrome (LQTS) is a cardiac channelopathy characterized by a prolongation of the QT interval and T-wave abnormalities, caused, in most cases, by mutations in KCNQ1, KCNH2, and SCN5A. Although the predominant pattern of LQTS inheritance is autosomal dominant, compound heterozygous mutations in genes encoding potassium channels have been reported, often with early disease onset and more severe phenotypes. Since the molecular mechanisms underlying severe phenotypes in carriers of compound heterozygous mutations are unknown, it is possible that these compound mutations lead to synergistic or additive alterations to channel structure and function. In this study, all-atom molecular dynamic simulations of KCNQ1 and hERG channels were carried out, including wild-type and channels with compound mutations found in two patients with severe LQTS phenotypes and limited family history of the disease. Because channels can likely incorporate different subunit combinations from different alleles, there are multiple possible configurations of ion channels in LQTS patients. This analysis allowed us to establish the structural impact of different configurations of mutant channels in the activated/open state. Our data suggest that channels with these mutations show moderate changes in folding energy (in most cases of stabilizing character) and changes in channel mobility and volume, differentiating them from each other and from WT. This would indicate possible alterations in K⁺ ion flow. Hetero-tetrameric mutant channels showed intermediate structural and volume alterations vis-à-vis homo-tetrameric channels. These findings support the hypothesis that hetero-tetrameric channels in patients with compound heterozygous mutations do not necessarily lead to synergistic structural alterations.

Cryo-EM snapshots of a native lysate provide structural insights into a metabolon-embedded transacetylase reaction

Found across all kingdoms of life, 2-keto acid dehydrogenase complexes possess prominent metabolic roles and form major regulatory sites. Although their component structures are known, their higher-order organization is highly heterogeneous, not only across species or tissues but also even within a single cell. Here, we report a cryo-EM structure of the fully active Chaetomium thermophilum pyruvate dehydrogenase complex (PDHc) core scaffold at 3.85 Å resolution (FSC = 0.143) from native cell extracts. By combining cryo-EM with macromolecular docking and molecular dynamics simulations, we resolve all PDHc core scaffold interfaces and dissect the residing transacetylase reaction. Electrostatics attract the lipoyl domain to the transacetylase active site and stabilize the coenzyme A, while apolar interactions position the lipoate in its binding cleft. Our results have direct implications on the structural determinants of the transacetylase reaction and the role of flexible regions in the context of the overall 10 MDa PDHc metabolon architecture.

Understanding the molecular interaction of SARS-CoV-2 spike mutants with ACE2 (angiotensin converting enzyme 2)

Covid-19 is a viral disease caused by the virus SARS-CoV-2 that spread worldwide and caused more than 4.3 million deaths. Moreover, SARS-CoV-2 still continues to evolve, and specifically the E484K, N501Y, and South Africa triple (K417N + E484K + N501Y) spike protein mutants remain as the 'escape' phenotypes. The aim of this study was to compare the interaction between the receptor binding domain (RBD) of the E484K, N501Y and South Africa triple spike variants and ACE2 with the interaction between wild-type spike RBD-ACE2 and to show whether the obtained binding affinities and conformations corraborate clinical findings. The structures of the RBDs of the E484K, N501Y and South Africa triple variants were generated with DS Studio v16 and energetically minimized using the CHARMM22 force field. Protein-protein dockings were performed in the HADDOCK server and the obtained wild-type and mutant spike-ACE2 complexes were submitted to 200-ns molecular dynamics simulations with subsequent free energy calculations using GROMACS. Based on docking binding affinities and free energy calculations the E484K, N501Y and triple mutant variants were found to interact stronger with the ACE2 than the wild-type spike. Interestingly, molecular dynamics and MM-PBSA results showed that E484K and spike triple mutant complexes were more stable than the N501Y one. Moreover, the E484K and South Africa triple mutants triggered greater conformational changes in the spike glycoprotein than N501Y. The E484K variant alone, or the combination of K417N + E484K + N501Y mutations induce significant conformational transitions in the spike glycoprotein, while increasing the spike-ACE2 binding affinity.Communicated by Ramaswamy H. Sarma.

Structural Biology in the Clouds: The WeNMR-EOSC Ecosystem

Structural biology aims at characterizing the structural and dynamic properties of biological macromolecules at atomic details. Gaining insight into three dimensional structures of biomolecules and their interactions is critical for understanding the vast majority of cellular processes, with direct applications in health and food sciences. Since 2010, the WeNMR project (www.wenmr.eu) has implemented numerous web-based services to facilitate the use of advanced computational tools by researchers in the field, using the high throughput computing infrastructure provided by EGI. These services have been further developed in subsequent initiatives under H2020 projects and are now operating as Thematic Services in the European Open Science Cloud portal (www.eosc-portal.eu), sending >12 millions of jobs and using around 4,000 CPU-years per year. Here we review 10 years of successful e-infrastructure solutions serving a large worldwide community of over 23,000 users to date, providing them with user-friendly, web-based solutions that run complex workflows in structural biology. The current set of active WeNMR portals are described, together with the complex backend machinery that allows distributed computing resources to be harvested efficiently.

Computational Assessment of Protein-Protein Binding Affinity by Reverse Engineering the Energetics in Protein Complexes

The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of binding affinity, and mutations that alter the binding affinity can cause various diseases such as cancer and diabetes. As a result, accurate estimation of the binding stability and the effects of mutations on changes of binding affinity is a crucial step to understanding the biological functions of proteins and their dysfunctional consequences. It has been hypothesized that the stability of a protein complex is dependent not only on the residues at its binding interface by pairwise interactions but also on all other remaining residues that do not appear at the binding interface. Here, we computationally reconstruct the binding affinity by decomposing it into the contributions of interfacial residues and other non-interfacial residues in a protein complex. We further assume that the contributions of both interfacial and non-interfacial residues to the binding affinity depend on their local structural environments such as solvent-accessible surfaces and secondary structural types. The weights of all corresponding parameters are optimized by Monte-Carlo simulations. After cross-validation against a large-scale dataset, we show that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinities, but can also be an effective approach to predict the relative changes of binding affinity from mutations. Moreover, we have found that the optimized weights of many parameters can capture the first-principle chemical and physical features of molecular recognition, therefore reversely engineering the energetics of protein complexes. These results suggest that our method can serve as a useful addition to current computational approaches for predicting binding affinity and understanding the molecular mechanism of protein–protein interactions.