Prediction of protein-protein interactions by docking methods

Improving protein-protein interaction prediction using protein language model and protein network features

Interactions between proteins are ubiquitous in a wide variety of biological processes. Accurately identifying the protein-protein interaction (PPI) is of significant importance for understanding the mechanisms of protein functions and facilitating drug discovery. Although the wet-lab technological methods are the best way to identify PPI, their major constraints are their time-consuming nature, high cost, and labor-intensiveness. Hence, lots of efforts have been made towards developing computational methods to improve the performance of PPI prediction. In this study, we propose a novel hybrid computational method (called KSGPPI) that aims at improving the prediction performance of PPI via extracting the discriminative information from protein sequences and interaction networks. The KSGPPI model comprises two feature extraction modules. In the first feature extraction module, a large protein language model, ESM-2, is employed to exploit the global complex patterns concealed within protein sequences. Subsequently, feature representations are further extracted through CKSAAP, and a two-dimensional convolutional neural network (CNN) is utilized to capture local information. In the second feature extraction module, the query protein acquires its similar protein from the STRING database via the sequence alignment tool NW-align and then captures the graph embedding feature for the query protein in the protein interaction network of the similar protein using the algorithm of Node2vec. Finally, the features of these two feature extraction modules are efficiently fused; the fused features are then fed into the multilayer perceptron to predict PPI. The results of five-fold cross-validation on the used benchmarked datasets demonstrate that KSGPPI achieves an average prediction accuracy of 88.96 %. Additionally, the average Matthews correlation coefficient value (0.781) of KSGPPI is significantly higher than that of those state-of-the-art PPI prediction methods. The standalone package of KSGPPI is freely downloaded at https://github.com/rickleezhe/KSGPPI.

A multi-source molecular network representation model for protein-protein interactions prediction

The prediction of potential protein-protein interactions (PPIs) is a critical step in decoding diseases and understanding cellular mechanisms. Traditional biological experiments have identified plenty of potential PPIs in recent years, but this problem is still far from being solved. Hence, there is urgent to develop computational models with good performance and high efficiency to predict potential PPIs. In this study, we propose a multi-source molecular network representation learning model (called MultiPPIs) to predict potential protein-protein interactions. Specifically, we first extract the protein sequence features according to the physicochemical properties of amino acids by utilizing the auto covariance method. Second, a multi-source association network is constructed by integrating the known associations among miRNAs, proteins, lncRNAs, drugs, and diseases. The graph representation learning method, DeepWalk, is adopted to extract the multisource association information of proteins with other biomolecules. In this way, the known protein-protein interaction pairs can be represented as a concatenation of the protein sequence and the multi-source association features of proteins. Finally, the Random Forest classifier and corresponding optimal parameters are used for training and prediction. In the results, MultiPPIs obtains an average 86.03% prediction accuracy with 82.69% sensitivity at the AUC of 93.03% under five-fold cross-validation. The experimental results indicate that MultiPPIs has a good prediction performance and provides valuable insights into the field of potential protein-protein interactions prediction. MultiPPIs is free available at https://github.com/jiboyalab/multiPPIs .

Mutational analysis of SARS-CoV-2 ORF6-KPNA2 binding interface and identification of potent small molecule inhibitors to recuse the host immune system

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surfaced on 31 December, 2019, and was identified as the causative agent of the global COVID-19 pandemic, leading to a pneumonia-like disease. One of its accessory proteins, ORF6, has been found to play a critical role in immune evasion by interacting with KPNA2 to antagonize IFN signaling and production pathways, resulting in the inhibition of IRF3 and STAT1 nuclear translocation. Since various mutations have been observed in ORF6, therefore, a comparative binding, biophysical, and structural analysis was used to reveal how these mutations affect the virus's ability to evade the human immune system. Among the identified mutations, the V9F, V24A, W27L, and I33T, were found to have a highly destabilizing effect on the protein structure of ORF6. Additionally, the molecular docking analysis of wildtype and mutant ORF6 and KPNA2 revealed the docking score of - 53.72 kcal/mol for wildtype while, -267.90 kcal/mol, -258.41kcal/mol, -254.51 kcal/mol and -268.79 kcal/mol for V9F, V24A, W27L, and I33T respectively. As compared to the wildtype the V9F showed a stronger binding affinity with KPNA2 which is further verified by the binding free energy (-42.28 kcal/mol) calculation. Furthermore, to halt the binding interface of the ORF6-KPNA2 complex, we used a computational molecular search of potential natural products. A multi-step virtual screening of the African natural database identified the top 5 compounds with best docking scores of -6.40 kcal/mol, -6.10 kcal/mol, -6.09 kcal/mol, -6.06 kcal/mol, and -6.03 kcal/mol for tophit1-5 respectively. Subsequent all-atoms simulations of these top hits revealed consistent dynamics, indicating their stability and their potential to interact effectively with the interface residues. In conclusion, our study represents the first attempt to establish a foundation for understanding the heightened infectivity of new SARS-CoV-2 variants and provides a strong impetus for the development of novel drugs against them.

Assessing Protein-Protein Docking Protocols: Case Studies of G-Protein-Coupled Receptor Interactions

The interactions of G-protein-coupled receptors (GPCRs) with other proteins are critical in several cellular processes but resolving their structural dynamics remains challenging. An increasing number of GPCR complexes have been experimentally resolved but others including receptor variants are yet to be characterized, necessitating computational predictions of their interactions. Although integrative approaches with multi-scale simulations would provide rigorous estimates of their conformational dynamics, protein-protein docking remains a first tool of choice of many researchers due to the availability of open-source programs and easy to use web servers with reasonable predictive power. Protein-protein docking algorithms have limited ability to consider protein flexibility, environment effects, and entropy contributions and are usually a first step towards more integrative approaches. The two critical steps of docking: the sampling and scoring algorithms have improved considerably and their performance has been validated against experimental data. In this chapter, we provide an overview and generalized protocol of a few docking protocols using GPCRs as test cases. In particular, we demonstrate the interactions of GPCRs with extracellular protein ligands and an intracellular protein effectors (G-protein) predicted from docking approaches and test their limitations. The current chapter will help researchers critically assess docking protocols and predict experimentally testable structures of GPCR complexes.

HNSPPI: a hybrid computational model combing network and sequence information for predicting protein-protein interaction

Most life activities in organisms are regulated through protein complexes, which are mainly controlled via Protein-Protein Interactions (PPIs). Discovering new interactions between proteins and revealing their biological functions are of great significance for understanding the molecular mechanisms of biological processes and identifying the potential targets in drug discovery. Current experimental methods only capture stable protein interactions, which lead to limited coverage. In addition, expensive cost and time consuming are also the obvious shortcomings. In recent years, various computational methods have been successfully developed for predicting PPIs based only on protein homology, primary sequences of protein or gene ontology information. Computational efficiency and data complexity are still the main bottlenecks for the algorithm generalization. In this study, we proposed a novel computational framework, HNSPPI, to predict PPIs. As a hybrid supervised learning model, HNSPPI comprehensively characterizes the intrinsic relationship between two proteins by integrating amino acid sequence information and connection properties of PPI network. The experimental results show that HNSPPI works very well on six benchmark datasets. Moreover, the comparison analysis proved that our model significantly outperforms other five existing algorithms. Finally, we used the HNSPPI model to explore the SARS-CoV-2-Human interaction system and found several potential regulations. In summary, HNSPPI is a promising model for predicting new protein interactions from known PPI data.

A New Tool to Study the Binding Behavior of Intrinsically Disordered Proteins

Understanding the binding behavior and conformational dynamics of intrinsically disordered proteins (IDPs) is crucial for unraveling their regulatory roles in biological processes. However, their lack of stable 3D structures poses challenges for analysis. To address this, we propose an algorithm that explores IDP binding behavior with protein complexes by extracting topological and geometric features from the protein surface model. Our algorithm identifies a geometrically favorable binding pose for the IDP and plans a feasible trajectory to evaluate its transition to the docking position. We focus on IDPs from Homo sapiens and Mus-musculus, investigating their interaction with the Plasmodium falciparum (PF) pathogen associated with malaria-related deaths. We compare our algorithm with HawkDock and HDOCK docking tools for quantitative (computation time) and qualitative (binding affinity) measures. Our results indicated that our method outperformed the compared methods in computation performance and binding affinity in experimental conformations.

Structural insights into the effect of mutations in the spike protein of SARS-CoV-2 on the binding with human furin protein

The SARS COV-2 and its variants are spreading around the world at an alarming speed, due to its higher transmissibility and the conformational changes caused by mutations. The resulting COVID-19 pandemic has imposed severe health consequences on human health. Several countries of the world including Pakistan have studied its genome extensively and provided productive findings. In the current study, the mCSM, DynaMut2, and I-Mutant servers were used to analyze the effect of identified mutations on the structural stability of spike protein however, the molecular docking and simulations approaches were used to evaluate the dynamics of the bonding network between the wild-type and mutant spike proteins with furin. We addressed the mutational modifications that have occurred in the spike protein of SARS-COV-2 that were found in 215 Pakistani's isolates of COVID-19 patients to study the influence of mutations on the stability of the protein and its interaction with the host cell. We found 7 single amino acid substitute mutations in various domains that reside in spike protein. The H49Y, N74K, G181V, and G446V were found in the S1 domain while the D614A, V622F, and Q677H mutations were found in the central helices of the spike protein. Based on the observation, G181V, G446V, D614A, and V622F mutants were found highly destabilizing and responsible for structural perturbation. Protein-protein docking and molecular simulation analysis with that of furin have predicted that all the mutants enhanced the binding efficiency however, the V622F mutant has greatly altered the binding capacity which is further verified by the K_D value (7.1 E^-14) and therefore may enhance the spike protein cleavage by Furin and increase the rate of infectivity by SARS-CoV-2. On the other hand, the total binding energy for each complex was calculated which revealed -50.57 kcal/mol for the wild type, for G181V -52.69 kcal/mol, for G446V -56.44 kcal/mol, for D614A -59.78 kcal/mol while for V622F the TBE was calculated to be -85.84 kcal/mol. Overall, the current finding shows that these mutations have increased the binding of Furin for spike protein and shows that D614A and V622F have significant effects on the binding and infectivity.

Protein-protein interaction prediction methods: from docking-based to AI-based approaches

Protein-protein interactions (PPIs), such as protein-protein inhibitor, antibody-antigen complex, and supercomplexes play diverse and important roles in cells. Recent advances in structural analysis methods, including cryo-EM, for the determination of protein complex structures are remarkable. Nevertheless, much room remains for improvement and utilization of computational methods to predict PPIs because of the large number and great diversity of unresolved complex structures. This review introduces a wide array of computational methods, including our own, for estimating PPIs including antibody-antigen interactions, offering both historical and forward-looking perspectives.

Characterization of the binding interaction between atrazine and human serum albumin: Fluorescence spectroscopy, molecular dynamics and quantum biochemistry

Atrazine (ATR), one of the most used herbicides worldwide, causes persistent contamination of water and soil due to its high resistance to degradation. ATR is associated with low fertility and increased risk of prostate cancer in humans, as well as birth defects, low birth weight and premature delivery. Describing ATR binding to human serum albumin (HSA) is clinically relevant to future studies about pharmacokinetics, pharmacodynamics and toxicity of ATR, as albumin is the most abundant carrier protein in plasma and binds important small biological molecules. In this work we characterize, for the first time, the binding of ATR to HSA by using fluorescence spectroscopy and performing simulations using molecular docking, classical molecular dynamics and quantum biochemistry based on density functional theory (DFT). We determine the most likely binding sites of ATR to HSA, highlighting the fatty acid binding site FA8 (located between subdomains IA-IB-IIA and IIB-IIIA-IIIB) as the most important one, and evaluate each nearby amino acid residue contribution to the binding interactions explaining the fluorescence quenching due to ATR complexation with HSA. The stabilization of the ATR/FA8 complex was also aided by the interaction between the atrazine ring and SER454 (hydrogen bond) and LEU481(alkyl interaction).

Computer-aided molecular modeling and structural analysis of the human centromere protein–HIKM complex

Background

Protein–peptide and protein–protein interactions play an essential role in different functional and structural cellular organizational aspects. While Cryo-EM and X-ray crystallography generate the most complete structural characterization, most biological interactions exist in biomolecular complexes that are neither compliant nor responsive to direct experimental analysis. The development of computational docking approaches is therefore necessary. This starts from component protein structures to the prediction of their complexes, preferentially with precision close to complex structures generated by X-ray crystallography.

Results

To guarantee faithful chromosomal segregation, there must be a proper assembling of the kinetochore (a protein complex with multiple subunits) at the centromere during the process of cell division. As an important member of the inner kinetochore, defects in any of the subunits making up the CENP-HIKM complex lead to kinetochore dysfunction and an eventual chromosomal mis-segregation and cell death. Previous studies in an attempt to understand the assembly and mechanism devised by the CENP-HIKM in promoting the functionality of the kinetochore have reconstituted the protein complex from different organisms including fungi and yeast. Here, we present a detailed computational model of the physical interactions that exist between each component of the human CENP-HIKM, while validating each modeled structure using orthologs with existing crystal structures from the protein data bank.

Conclusions

Results from this study substantiate the existing hypothesis that the human CENP-HIK complex shares a similar architecture with its fungal and yeast orthologs, and likewise validate the binding mode of CENP-M to the C-terminus of the human CENP-I based on existing experimental reports.

Graphical abstract

Bioinformatics strategies for studying the molecular mechanisms of fungal extracellular vesicles with a focus on infection and immune responses

Fungal extracellular vesicles (EVs) are released during pathogenesis and are found to be an opportunistic infection in most cases. EVs are immunocompetent with their host and have paved the way for new biomedical approaches to drug delivery and the treatment of complex diseases including cancer. With computing and processing advancements, the rise of bioinformatics tools for the evaluation of various parameters involved in fungal EVs has blossomed. In this review, we have complied and explored the bioinformatics tools to analyze the host-pathogen interaction, toxicity, omics and pathogenesis with an array of specific tools that have depicted the ability of EVs as vector/carrier for therapeutic agents and as a potential theme for immunotherapy. We have also discussed the generation and pathways involved in the production, transport, pathogenic action and immunological interactions of EVs in the host system. The incorporation of network pharmacology approaches has been discussed regarding fungal pathogens and their significance in drug discovery. To represent the overview, we have presented and demonstrated an in silico study model to portray the human Cryptococcal interactions.

API Development Increases Access to Shared Computing Resources at Boston University

Within the last few decades, increases in computational resources have contributed enormously to the progress of science and engineering (S & E). To continue making rapid advancements, the S & E community must be able to access computing resources. One way to provide such resources is through High-Performance Computing (HPC) centers. Many academic research institutions offer their own HPC Centers but struggle to make the computing resources easily accessible and user-friendly. Here we present SHABU, a RESTful Web API framework that enables S & E communities to access resources from Boston University's Shared Computing Center (SCC). The SHABU requirements are derived from the use cases described in this work.

Predicting epitopes for vaccine development using bioinformatics tools

Epitope-based DNA vaccine development is one application of bioinformatics or in silico studies, that is, computational methods, including mathematical, chemical, and biological approaches, which are widely used in drug development. Many in silico studies have been conducted to analyze the efficacy, safety, toxicity effects, and interactions of drugs. In the vaccine design process, in silico studies are performed to predict epitopes that could trigger T-cell and B-cell reactions that would produce both cellular and humoral immune responses. Immunoinformatics is the branch of bioinformatics used to study the relationship between immune responses and predicted epitopes. Progress in immunoinformatics has been rapid and has led to the development of a variety of tools that are used for the prediction of epitopes recognized by B cells or T cells as well as the antigenic responses. However, the in silico approach to vaccine design is still relatively new; thus, this review is aimed at increasing understanding of the importance of in silico studies in the design of vaccines and thereby facilitating future research in this field.

Computational Network Inference for Bacterial Interactomics

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

Macromolecular protein crystallisation with biotemplate of live cells

Macromolecular protein crystallisation was one of the potential tools to accelerate the biomanufacturing of biopharmaceuticals. In this work, it was the first time to investigate the roles of biotemplates, Saccharomyces cerevisiae live cells, in the crystallisation processes of lysozyme, with different concentrations from 20 to 2.5 mg/mL lysozyme and different concentrations from 0 to 5.0 × 10⁷ (cfu/mL) Saccharomyces cerevisiae cells, during a period of 96 h. During the crystallisation period, the nucleation possibility in droplets, crystal numbers, and cell growth and cell density were observed and analysed. The results indicated the strong interaction between the lysozyme molecules and the cell wall of the S. cerevisiae, proved by the crystallization of lysozyme with fluorescent labels. The biotemplates demonstrated positive influence or negative influence on the nucleation, i.e. shorter or longer induction time, dependent on the concentrations of the lysozyme and the S. cerevisiae cells, and ratios between them. In the biomanufacturing process, target proteins were various cells were commonly mixed with various cells, and this work provides novel insights of new design and application of live cells as biotemplates for purification of macromolecules.

Comparative mutational analysis of SARS-CoV-2 isolates from Pakistan and structural-functional implications using computational modelling and simulation approaches

SARS-CoV-2, an RNA virus, has been prone to high mutations since its first emergence in Wuhan, China, and throughout its spread. Its genome has been sequenced continuously by many countries, including Pakistan, but the results vary. Understanding its genomic patterns and connecting them with phenotypic features will help in devising therapeutic strategies. Thus, in this study, we explored the mutation landscape of 250 Pakistani isolates of SARS-CoV-2 genomes to check the genome diversity and examine the impact of these mutations on protein stability and viral pathogenesis in comparison with a reference sequence (Wuhan NC 045512.2). Our results revealed that structural proteins mainly exhibit more mutations than others in the Pakistani isolates; in particular, the nucleocapsid protein is highly mutated. In comparison, the spike protein is the most mutated protein globally. Furthermore, nsp12 was found to be the most mutated NSP in the Pakistani isolates and worldwide. Regarding accessory proteins, ORF3A is the most mutated in the Pakistani isolates, whereas ORF8 is highly mutated in world isolates. These mutations decrease the structural stability of their proteins and alter different biological pathways. Molecular docking, the dissociation constant (KD), and MM/GBSA analysis showed that mutations in the S protein alter its binding with ACE2. The spike protein mutations D614G-S943T-V622F (-75.17 kcal/mol), D614G-Q677H (-75.78 kcal/mol), and N74K-D614G (-73.84 kcal/mol) exhibit stronger binding energy than the wild type (-66.34 kcal/mol), thus increasing infectivity. Furthermore, the simulation results strongly corroborated the predicted protein servers. Our analysis findings also showed that E, M, ORF6, ORF7A, ORF7B, and ORF10 are the most stable coding genes; they may be suitable targets for vaccine and drug development.

From complete cross-docking to partners identification and binding sites predictions

Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and for improving medicine and bioengineering. Over the past years, a large body of experimental data has been accumulated on who interacts with whom and in what manner. However, these data are highly heterogeneous and sometimes contradictory, noisy, and biased. Ab initio methods provide a means to a "blind" protein-protein interaction network reconstruction. Here, we report on a molecular cross-docking-based approach for the identification of protein partners. The docking algorithm uses a coarse-grained representation of the protein structures and treats them as rigid bodies. We applied the approach to a few hundred of proteins, in the unbound conformations, and we systematically investigated the influence of several key ingredients, such as the size and quality of the interfaces, and the scoring function. We achieved some significant improvement compared to previous works, and a very high discriminative power on some specific functional classes. We provide a readout of the contributions of shape and physico-chemical complementarity, interface matching, and specificity, in the predictions. In addition, we assessed the ability of the approach to account for protein surface multiple usages, and we compared it with a sequence-based deep learning method. This work may contribute to guiding the exploitation of the large amounts of protein structural models now available toward the discovery of unexpected partners and their complex structure characterisation.

Structural Analysis on the Severe Acute Respiratory Syndrome Coronavirus 2 Non-structural Protein 13 Mutants Revealed Altered Bonding Network With TANK Binding Kinase 1 to Evade Host Immune System

Mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have made this virus more infectious. Previous studies have confirmed that non-structural protein 13 (NSP13) plays an important role in immune evasion by physically interacting with TANK binding kinase 1 (TBK1) to inhibit IFNβ production. Mutations have been reported in NSP13; hence, in the current study, biophysical and structural modeling methodologies were adapted to dissect the influence of major mutations in NSP13, i.e., P77L, Q88H, D260Y, E341D, and M429I, on its binding to the TBK1 and to escape the human immune system. The results revealed that these mutations significantly affected the binding of NSP13 and TBK1 by altering the hydrogen bonding network and dynamic structural features. The stability, flexibility, and compactness of these mutants displayed different dynamic features, which are the basis for immune evasion. Moreover, the binding was further validated using the MM/GBSA approach, revealing that these mutations have higher binding energies than the wild-type (WT) NSP13 protein. These findings thus justify the basis of stronger interactions and evasion for these NSP13 mutants. In conclusion, the current findings explored the key features of the NSP13 WT and its mutant complexes, which can be used to design structure-based inhibitors against the SARS-CoV-2 new variants to rescue the host immune system.

Distance-based reconstruction of protein quaternary structures from inter-chain contacts

Predicting the quaternary structure of protein complex is an important problem. Inter‐chain residue‐residue contact prediction can provide useful information to guide the ab initio reconstruction of quaternary structures. However, few methods have been developed to build quaternary structures from predicted inter‐chain contacts. Here, we develop the first method based on gradient descent optimization (GD) to build quaternary structures of protein dimers utilizing inter‐chain contacts as distance restraints. We evaluate GD on several datasets of homodimers and heterodimers using true/predicted contacts and monomer structures as input. GD consistently performs better than both simulated annealing and Markov Chain Monte Carlo simulation. Starting from an arbitrarily quaternary structure randomly initialized from the tertiary structures of protein chains and using true inter‐chain contacts as input, GD can reconstruct high‐quality structural models for homodimers and heterodimers with average TM‐score ranging from 0.92 to 0.99 and average interface root mean square distance from 0.72 Å to 1.64 Å. On a dataset of 115 homodimers, using predicted inter‐chain contacts as restraints, the average TM‐score of the structural models built by GD is 0.76. For 46% of the homodimers, high‐quality structural models with TM‐score ≥ 0.9 are reconstructed from predicted contacts. There is a strong correlation between the quality of the reconstructed models and the precision and recall of predicted contacts. Only a moderate precision or recall of inter‐chain contact prediction is needed to build good structural models for most homodimers. Moreover, GD improves the quality of quaternary structures predicted by AlphaFold2 on a Critical Assessment of Techniques for Protein Structure Prediction–Critical Assessments of Predictions of Interactions dataset.

Higher infectivity of the SARS-CoV-2 new variants is associated with K417N/T, E484K, and N501Y mutants: An insight from structural data

The evolution of the SARS-CoV-2 new variants reported to be 70% more contagious than the earlier one is now spreading fast worldwide. There is an instant need to discover how the new variants interact with the host receptor (ACE2). Among the reported mutations in the Spike glycoprotein of the new variants, three are specific to the receptor-binding domain (RBD) and required insightful scrutiny for new therapeutic options. These structural evolutions in the RBD domain may impart a critical role to the unique pathogenicity of the SARS-CoV-2 new variants. Herein, using structural and biophysical approaches, we explored that the specific mutations in the UK (N501Y), South African (K417N-E484K-N501Y), Brazilian (K417T-E484K-N501Y), and hypothetical (N501Y-E484K) variants alter the binding affinity, create new inter-protein contacts and changes the internal structural dynamics thereby increases the binding and eventually the infectivity. Our investigation highlighted that the South African (K417N-E484K-N501Y), Brazilian (K417T-E484K-N501Y) variants are more lethal than the UK variant (N501Y). The behavior of the wild type and N501Y is comparable. Free energy calculations further confirmed that increased binding of the spike RBD to the ACE2 is mainly due to the electrostatic contribution. Further, we find that the unusual virulence of this virus is potentially the consequence of Darwinian selection-driven epistasis in protein evolution. The triple mutants (South African and Brazilian) may pose a serious threat to the efficacy of the already developed vaccine. Our analysis would help to understand the binding and structural dynamics of the new mutations in the RBD domain of the Spike protein and demand further investigation in in vitro and in vivo models to design potential therapeutics against the new variants.

SARS-CoV-2 structural coverage map reveals viral protein assembly, mimicry, and hijacking mechanisms

We modeled 3D structures of all SARS-CoV-2 proteins, generating 2,060 models that span 69% of the viral proteome and provide details not available elsewhere. We found that ˜6% of the proteome mimicked human proteins, while ˜7% was implicated in hijacking mechanisms that reverse post-translational modifications, block host translation, and disable host defenses; a further ˜29% self-assembled into heteromeric states that provided insight into how the viral replication and translation complex forms. To make these 3D models more accessible, we devised a structural coverage map, a novel visualization method to show what is-and is not-known about the 3D structure of the viral proteome. We integrated the coverage map into an accompanying online resource (https://aquaria.ws/covid) that can be used to find and explore models corresponding to the 79 structural states identified in this work. The resulting Aquaria-COVID resource helps scientists use emerging structural data to understand the mechanisms underlying coronavirus infection and draws attention to the 31% of the viral proteome that remains structurally unknown or dark.

DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-chain Contact Prediction and Distance-Based Modelling

Proteins interact to form complexes. Predicting the quaternary structure of protein complexes is useful for protein function analysis, protein engineering, and drug design. However, few user-friendly tools leveraging the latest deep learning technology for inter-chain contact prediction and the distance-based modelling to predict protein quaternary structures are available. To address this gap, we develop DeepComplex, a web server for predicting structures of dimeric protein complexes. It uses deep learning to predict inter-chain contacts in a homodimer or heterodimer. The predicted contacts are then used to construct a quaternary structure of the dimer by the distance-based modelling, which can be interactively viewed and analysed. The web server is freely accessible and requires no registration. It can be easily used by providing a job name and an email address along with the tertiary structure for one chain of a homodimer or two chains of a heterodimer. The output webpage provides the multiple sequence alignment, predicted inter-chain residue-residue contact map, and predicted quaternary structure of the dimer. DeepComplex web server is freely available at http://tulip.rnet.missouri.edu/deepcomplex/web_index.html.

Mutations in SARS-CoV-2 ORF8 Altered the Bonding Network With Interferon Regulatory Factor 3 to Evade Host Immune System

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously mutating since its first emergence in early 2020. These alterations have led this virus to gain significant difference in infectivity, pathogenicity, and host immune evasion. We previously found that the open-reading frame 8 (ORF8) of SARS-CoV-2 can inhibit interferon production by decreasing the nuclear translocation of interferon regulatory factor 3 (IRF3). Since several mutations in ORF8 have been observed, therefore, in the present study, we adapted structural and biophysical analysis approaches to explore the impact of various mutations of ORF8, such as S24L, L84S, V62L, and W45L, the recently circulating mutant in Pakistan, on its ability to bind IRF3 and to evade the host immune system. We found that mutations in ORF8 could affect the binding efficiency with IRF3 based on molecular docking analysis, which was further supported by molecular dynamics simulations. Among all the reported mutations, W45L was found to bind most stringently to IRF3. Our analysis revealed that mutations in ORF8 may help the virus evade the immune system by changing its binding affinity with IRF3.

Structural Design and Analysis of the RHOA-ARHGEF1 Binding Mode: Challenges and Applications for Protein-Protein Interface Prediction

The interaction between two proteins may involve local movements, such as small side-chains re-positioning or more global allosteric movements, such as domain rearrangement. We studied how one can build a precise and detailed protein-protein interface using existing protein-protein docking methods, and how it can be possible to enhance the initial structures using molecular dynamics simulations and data-driven human inspection. We present how this strategy was applied to the modeling of RHOA-ARHGEF1 interaction using similar complexes of RHOA bound to other members of the Rho guanine nucleotide exchange factor family for comparative assessment. In parallel, a more crude approach based on structural superimposition and molecular replacement was also assessed. Both models were then successfully refined using molecular dynamics simulations leading to protein structures where the major data from scientific literature could be recovered. We expect that the detailed strategy used in this work will prove useful for other protein-protein interface design. The RHOA-ARHGEF1 interface modeled here will be extremely useful for the design of inhibitors targeting this protein-protein interaction (PPI).

Exploring effectiveness of ab-initio protein-protein docking methods on a novel antibacterial protein complex dataset

Diseases caused by bacterial infections become a critical problem in public heath. Antibiotic, the traditional treatment, gradually loses their effectiveness due to the resistance. Meanwhile, antibacterial proteins attract more attention because of broad spectrum and little harm to host cells. Therefore, exploring new effective antibacterial proteins is urgent and necessary. In this paper, we are committed to evaluating the effectiveness of ab-initio docking methods in antibacterial protein-protein docking. For this purpose, we constructed a three-dimensional (3D) structure dataset of antibacterial protein complex, called APCset, which contained $19$ protein complexes whose receptors or ligands are homologous to antibacterial peptides from Antimicrobial Peptide Database. Then we selected five representative ab-initio protein-protein docking tools including ZDOCK3.0.2, FRODOCK3.0, ATTRACT, PatchDock and Rosetta to identify these complexes' structure, whose performance differences were obtained by analyzing from five aspects, including top/best pose, first hit, success rate, average hit count and running time. Finally, according to different requirements, we assessed and recommended relatively efficient protein-protein docking tools. In terms of computational efficiency and performance, ZDOCK was more suitable as preferred computational tool, with average running time of $6.144$ minutes, average Fnat of best pose of $0.953$ and average rank of best pose of $4.158$. Meanwhile, ZDOCK still yielded better performance on Benchmark 5.0, which proved ZDOCK was effective in performing docking on large-scale dataset. Our survey can offer insights into the research on the treatment of bacterial infections by utilizing the appropriate docking methods.

The evolutionary analysis of complement component C5 and the gene co-expression network and putative interaction between C5a and C5a anaphylatoxin receptor (C5AR/CD88) in human and two Cyprinid fish

The complement system is a complex network of soluble and membrane-associated serum proteins that regulate immune response. Activation of the complement C5 generates C5a and C5b which generate chemoattractive effect on myeloid cells and initiate the membrane attack complex (MAC) assembly. However, the study of evolutionary process and systematic function of C5 are still limited. In this study, we performed an evolutionary analysis of C5. Phylogeny analysis indicated that C5 sequences underwent complete divergence in fish and non-fish vertebrate. It was found that codon usage bias improved and provided evolution evidence of C5 in species. Notably, the codon usage bias of grass carp was evolutionarily closer to the zebrafish genome compared with humans and stickleback. This suggested that the zebrafish cell line may provide an alternative environment for heterologous protein expression of grass carp. Sequence comparison showed a higher similarity between human and mouse, grass carp, and zebrafish. Moreover, selective pressure analysis revealed that the C5 genes in fish and non-fish vertebrates exhibited different evolutionary patterns. To study the function of C5, gene co-expression networks of human and zebrafish were built which revealed the complexity of C5 function networks in different species. The protein structure simulation of C5 indicated that grass carp and zebrafish are more similar than to human, however, differences between species in C5a proteins are extremely smaller. Spatial conformations of C5a-C5AR (CD88) protein complex were constructed, which showed that possible interaction may exist between C5a and CD88 proteins. Furthermore, the protein docking sites/residues were measured and calculated according to the minimum distance for all atoms from C5a and CD88 proteins. In summary, this study provides insights into the evolutionary history, function and potential regulatory mechanism of C5 in fish immune responses.

Residue-based pharmacophore approaches to study protein-protein interactions

This review focuses on pharmacophore approaches in researching protein interfaces that bind protein ligands. Pharmacophore descriptions of binding interfaces that employ molecular dynamics simulation can account for effects of solvation and conformational flexibility. In addition, these calculations provide an approximation to entropic considerations and as such, a better approximation of the free energy of binding. Residue-based pharmacophore approaches can facilitate a variety of drug discovery tasks such as the identification of receptor-ligand partners, identifying their binding poses, designing protein interfaces for selectivity, or defining a reduced mutational combinatorial exploration for subsequent experimental engineering techniques by orders of magnitudes.

Advances to tackle backbone flexibility in protein docking

Computational docking methods can provide structural models of protein-protein complexes, but protein backbone flexibility upon association often thwarts accurate predictions. In recent blind challenges, medium or high accuracy models were submitted in less than 20% of the 'difficult' targets (with significant backbone change or uncertainty). Here, we describe recent developments in protein-protein docking and highlight advances that tackle backbone flexibility. In molecular dynamics and Monte Carlo approaches, enhanced sampling techniques have reduced time-scale limitations. Internal coordinate formulations can now capture realistic motions of monomers and complexes using harmonic dynamics. And machine learning approaches adaptively guide docking trajectories or generate novel binding site predictions from deep neural networks trained on protein interfaces. These tools poise the field to break through the longstanding challenge of correctly predicting complex structures with significant conformational change.

Computational investigation for modeling the protein-protein interaction of TasA(28-261)-TapA(33-253): a decisive process in biofilm formation by Bacillus subtilis

Biofilms have a significant role in microbial persistence, antibiotic resistance, and chronic infections; consequently, there is a pressing need for development of novel "anti-biofilm strategies." One of the fundamental mechanisms involved in biofilm formation is protein-protein interactions of "amyloid-like proteins" (ALPs) in the extracellular matrix. Such interactions could be potential targets for development of novel anti-biofilm strategies; therefore, assessing the structural features of these interactions could be of great scientific value. Characterization of structural features the of protein-protein interaction with conventional structure biology tools including X-ray diffraction and nuclear magnetic resonance is technically challenging, expensive, and time-consuming. In contrast, modeling such interactions is time-efficient and economical, and might provide deeper understanding of structural basis of interactions. Although it is often acknowledged that molecular modeling methods have varying accuracy, their careful implementation with supplementary verification methods can provide valuable insight and directions for future studies. With this reasoning, during the present study, the protein-protein interaction of TasA_(28-261)-TapA_(33-253) (which is a decisive process for biofilm formation by Bacillus subtilis) was modeled using in silico approaches, viz., molecular modeling, protein-protein docking, and molecular dynamics simulations. Results obtained here identified amino acid residues present within intrinsically disordered regions of both proteins to be critical for interaction. These results were further supported with principal component analyses (PCA) and free energy landscape (FEL) analyses. Results presented here represent novel finding, and we hypothesize that amino acid residues identified during the present study could be targeted for inhibition of biofilm formation by B. subtilis.

Bayesian Active Learning for Optimization and Uncertainty Quantification in Protein Docking

Ab initio protein docking represents a major challenge for optimizing a noisy and costly "black box"-like function in a high-dimensional space. Despite progress in this field, there is a lack of rigorous uncertainty quantification (UQ). To fill the gap, we introduce a novel algorithm, Bayesian active learning (BAL), for optimization and UQ of such black-box functions with applications to flexible protein docking. BAL directly models the posterior distribution of the global optimum (i.e., native structures) with active sampling and posterior estimation iteratively feeding each other. Furthermore, it uses complex normal modes to span a homogeneous, Euclidean conformation space suitable for high-dimensional optimization and constructs funnel-like energy models for quality estimation of encounter complexes. Over a protein-docking benchmark set and a CAPRI set including homology docking, we establish that BAL significantly improves against starting points from rigid docking and refinements by particle swarm optimization, providing a top-3 near-native prediction for one third targets. Quality assessment empowered with UQ leads to tight quality intervals with half range around 25% of the actual interface root-mean-square deviation and confidence level at 85%. BAL's estimated probability of a prediction being near-native achieves binary classification AUROC at 0.93 and area under the precision recall curve over 0.60 (compared to 0.50 and 0.14, respectively, by chance), which also improves ranking predictions. This study represents the first UQ solution for protein docking, with rigorous theoretical frameworks and comprehensive empirical assessments.

ClusPro in rounds 38 to 45 of CAPRI: Toward combining template-based methods with free docking

Targets in the protein docking experiment CAPRI (Critical Assessment of Predicted Interactions) generally present new challenges and contribute to new developments in methodology. In rounds 38 to 45 of CAPRI, most targets could be effectively predicted using template-based methods. However, the server ClusPro required structures rather than sequences as input, and hence we had to generate and dock homology models. The available templates also provided distance restraints that were directly used as input to the server. We show here that such an approach has some advantages. Free docking with template-based restraints using ClusPro reproduced some interfaces suggested by weak or ambiguous templates while not reproducing others, resulting in correct server predicted models. More recently we developed the fully automated ClusPro TBM server that performs template-based modeling and thus can use sequences rather than structures of component proteins as input. The performance of the server, freely available for noncommercial use at https://tbm.cluspro.org, is demonstrated by predicting the protein-protein targets of rounds 38 to 45 of CAPRI.

Modeling Protein-Protein or Protein-DNA/RNA Complexes Using the HDOCK Webserver

Protein-protein and protein-DNA/RNA interactions are involved in many cellular processes. Therefore, determining their complex structures at the atomic level is valuable to gain insights into these interactions. Because of the technical difficulties and high cost in experimental methods, computational approaches like molecular docking have been developed to predict the structures of macromolecular complexes in the last decades. To automatically integrate the available binding information from the PDB, we have developed HDOCK, a protein-protein/nucleic acid docking web server by combining template-based and free docking. In this chapter, we first briefly introduce our HDOCK server and then give a step-by-step description of docking bovine chymotrypsinogen A against its inhibitor (PDB ID: 1CGI). Two case studies of realistic examples are also discussed. The HDOCK server is freely available at http://hdock.phys.hust.edu.cn/ .

Pushing the accuracy limit of shape complementarity for protein-protein docking

Background

Protein-protein docking is a valuable computational approach for investigating protein-protein interactions. Shape complementarity is the most basic component of a scoring function and plays an important role in protein-protein docking. Despite significant progresses, shape representation remains an open question in the development of protein-protein docking algorithms, especially for grid-based docking approaches.

Results

We have proposed a new pairwise shape-based scoring function (LSC) for protein-protein docking which adopts an exponential form to take into account long-range interactions between protein atoms. The LSC scoring function was incorporated into our FFT-based docking program and evaluated for both bound and unbound docking on the protein docking benchmark 4.0. It was shown that our LSC achieved a significantly better performance than four other similar docking methods, ZDOCK 2.1, MolFit/G, GRAMM, and FTDock/G, in both success rate and number of hits. When considering the top 10 predictions, LSC obtained a success rate of 51.71% and 6.82% for bound and unbound docking, respectively, compared to 42.61% and 4.55% for the second-best program ZDOCK 2.1. LSC also yielded an average of 8.38 and 3.94 hits per complex in the top 1000 predictions for bound and unbound docking, respectively, followed by 6.38 and 2.96 hits for the second-best ZDOCK 2.1.

Conclusions

The present LSC method will not only provide an initial-stage docking approach for post-docking processes but also have a general implementation for accurate representation of other energy terms on grids in protein-protein docking. The software has been implemented in our HDOCK web server at http://hdock.phys.hust.edu.cn/.

More efficient screening of protein-protein complex model structures for reducing the number of candidates

Rigid-body protein-protein docking is very efficient in generating tens of thousands of docked complex models (decoys) in a very short time without considering structure change upon binding, but typical docking scoring functions are not necessarily sufficiently accurate to narrow these decoys down to a small number of plausible candidates. Flexible refinements and sophisticated evaluation of the decoys are thus required to achieve more accurate prediction. Since this process is time-consuming, an efficient screening method to reduce the number of decoys is necessary immediately following rigid-body dockings. We attempted to develop an efficient screening method by clustering decoys generated by the rigid-body docking ZDOCK. We introduced the three metrics ligand-root-mean-square deviation (L-RMSD), interface-ligand-RMSD (iL-RMSD), and the fraction of common contacts (FCC), and examined various ranges of cut-offs for clusters to determine the best set of clustering parameters. Although the employed clustering algorithm is simple, it successfully reduced the number of decoys. Using iL-RMSD with a cut-off radius of 8 Å, the number of decoys that contain at least one near-native model with 90% probability decreased from 4,808 to 320, a 93% reduction in the original number of decoys. Using FCC for the clustering step, the top 1,000 success rates, defined as the probability that the top 1,000 models contain at least one near-native structure, reached 97%. We conclude that the proposed method is very efficient in selecting a small number of decoys that include near-native decoys.

TCRpMHCmodels: Structural modelling of TCR-pMHC class I complexes

The interaction between the class I major histocompatibility complex (MHC), the peptide presented by the MHC and the T-cell receptor (TCR) is a key determinant of the cellular immune response. Here, we present TCRpMHCmodels, a method for accurate structural modelling of the TCR-peptide-MHC (TCR-pMHC) complex. This TCR-pMHC modelling pipeline takes as input the amino acid sequence and generates models of the TCR-pMHC complex, with a median Cα RMSD of 2.31 Å. TCRpMHCmodels significantly outperforms TCRFlexDock, a specialised method for docking pMHC and TCR structures. TCRpMHCmodels is simple to use and the modelling pipeline takes, on average, only two minutes. Thanks to its ease of use and high modelling accuracy, we expect TCRpMHCmodels to provide insights into the underlying mechanisms of TCR and pMHC interactions and aid in the development of advanced T-cell-based immunotherapies and rational design of vaccines. The TCRpMHCmodels tool is available at http://www.cbs.dtu.dk/services/TCRpMHCmodels/.

Toward rational antibody design: recent advancements in molecular dynamics simulations

Because antibodies have become an important therapeutic tool, rational antibody design is a challenging issue involving various science and technology fields. From the computational aspect, many types of design-assist methods have been developed, but their accuracy is not fully satisfactory. Because of recent advancements in computational power, molecular dynamics (MD) simulation has become a helpful tool to trace the motion of proteins and to characterize their properties. Thus, MD simulation has been applied to various systems involving antigen-antibody complexes and has been shown to provide accurate insight into antigen-antibody interactions and dynamics at an atomic resolution. Therefore, it is highly possible that MD simulation will play several roles complementing the conventional antibody design. In this review, we address several important features of MD simulation in the context of rational antibody design.

Exploring the binding mechanism between human profilin (PFN1) and polyproline-10 through binding mode screening

The large magnitude of protein-protein interaction (PPI) pairs within the human interactome necessitates the development of predictive models and screening tools to better understand this fundamental molecular communication. However, despite enormous efforts from various groups to develop predictive techniques in the last decade, PPI complex structures are in general still very challenging to predict due to the large number of degrees of freedom. In this study, we use the binding complex of human profilin (PFN1) and polyproline-10 (P10) as a model system to examine various approaches, with the aim of going beyond normal protein docking for PPI prediction and evaluation. The potential of mean force (PMF) was first obtained from the time-consuming umbrella sampling, which confirmed that the most stable binding structure identified by the maximal PMF difference is indeed the crystallographic binding structure. Moreover, crucial residues previously identified in experimental studies, W3, H133, and S137 of PFN1, were found to form favorable hydrogen bonds with P10, suggesting a zipping process during the binding between PFN1 and P10. We then explored both regular molecular dynamics (MD) and steered molecular dynamics (SMD) simulations, seeking for better criteria of ranking the PPI prediction. Despite valuable information obtained from conventional MD simulations, neither the commonly used interaction energy between the two binding parties nor the long-term root mean square displacement correlates well with the PMF results. On the other hand, with a sizable collection of trajectories, we demonstrated that the average and minimal rupture works calculated from SMD simulations correlate fairly well with the PMFs (R ² = 0.67), making this a promising PPI screening method.

Conversion of Protein Active Regions into Peptidomimetic Therapeutic Leads Using Backbone Cyclization and Cycloscan - How to Do it Yourself!

Protein-protein Interactions (PPIs) are particularly important for controlling both physiologic and pathologic biological processes but are difficult to target due to their large and/or shallow interaction surfaces unsuitable for small molecules. Linear peptides found in nature interact with some PPIs, and protein active regions can be used to design synthetic peptide compounds for inhibition of PPIs. However, linear peptides are limited therapeutically by poor metabolic and conformational stability, which can compromise their bioactivity and half-life. Cyclic peptidomimetics (modified peptides) can be used to overcome these challenges because they are more resistant to metabolic degradation and can be engineered to adopt desired conformations. Backbone cyclization is a strategy that we developed to improve drug-like properties of linear peptide leads without jeopardizing the integrity of functionally relevant side-chains. Here, we provide the first description of an entire approach for developing backbone cyclized peptide compounds, based upon two straightforward 'ABC' and 'DEF' processes. We present practical examples throughout our discussion of revealing active regions important for PPIs and identifying critical pharmacophores, as well as developing backbone cyclized peptide libraries and screening them using cycloscan. Finally, we review the impact of these advances and provide a summary of current ongoing work in the field.

Comparison of two docking methods for peptide-protein interactions

BACKGROUND

The importance of peptides in regulatory interactions has caused peptide-protein docking to attract the attention of many researchers. A variety of methods for molecular modeling of peptide-protein docking, such as local search and global search, are currently used.

RESULTS

The interactions of 11 peptides and CSFV E2 protein were evaluated by the GalaxyPepDock and FlexX/ SYBYL programs, respectively. The assessment scores of all the peptides were correlated with their K_D values. The final results showed that a moderate correlation coefficient was represented between K_D values and CScores of predicted models by FlexX/ SYBYL.

CONCLUSION

Our results demonstrate that considering the flexibility of the peptide is better than searching for more potential binding sites on the target protein surface while performing peptide-protein molecular docking. These data provide reasonable evidence for the molecular design of peptides and guidance for the functional assignment of target proteins. © 2018 Society of Chemical Industry.