Protein-protein docking: from interaction to interactome

Docking-based long timescale simulation of cell-size protein systems at atomic resolution

Computational methodologies are increasingly addressing modeling of the whole cell at the molecular level. Proteins and their interactions are the key component of cellular processes. Techniques for modeling protein interactions, thus far, have included protein docking and molecular simulation. The latter approaches account for the dynamics of the interactions but are relatively slow, if carried out at all-atom resolution, or are significantly coarse grained. Protein docking algorithms are far more efficient in sampling spatial coordinates. However, they do not account for the kinetics of the association (i.e., they do not involve the time coordinate). Our proof-of-concept study bridges the two modeling approaches, developing an approach that can reach unprecedented simulation timescales at all-atom resolution. The global intermolecular energy landscape of a large system of proteins was mapped by the pairwise fast Fourier transform docking and sampled in space and time by Monte Carlo simulations. The simulation protocol was parametrized on existing data and validated on a number of observations from experiments and molecular dynamics simulations. The simulation protocol performed consistently across very different systems of proteins at different protein concentrations. It recapitulated data on the previously observed protein diffusion rates and aggregation. The speed of calculation allows reaching second-long trajectories of protein systems that approach the size of the cells, at atomic resolution.

Mechanisms of Xiaochaihu Decoction on Treating Hepatic Fibrosis Explored by Network Pharmacology

Purpose. To explore the material basis and pharmacological mechanism of Xiaochaihu Decoction (XCHD), the classic Traditional Chinese Medicine (TCM) formula in inhibiting hepatic fibrosis (HF). Methods. The main components in XCHD were screened from the TCMSP database, ETCM database, and literature, and their potential targets were detected and predicted using the Swiss Target Prediction platform. The HF-related targets were retrieved and screened through GeneCard database and OMIM database, combined with GEO gene chips. The XCHD targets and HF targets were mapped to search common targets. The protein-protein interaction (PPI) network was acquired via the STRING11.0 database and analyzed visually using Cytoscape 3.8.0 software. The potential mechanisms of the common targets identified through GO and KEGG pathway enrichment analysis were analyzed by using Metascape database. The results were visualized through OmicShare Tools. The “XCHD compound-HF target” network was visually constructed by Cytoscape 3.8.0 software. AutoDockVina1.1.2 and PyMoL software were used to verify the molecular docking of XCHD main active compounds and HF key targets. Results. A total of 164 potential active compounds from XCHD were screened to act on 95 HF-related targets. Bioinformatics analysis revealed that quercetin, β-sitosterol, and kaempferol may be candidate agents, which acted on multiple targets like PTGS2, HSP90AA1, and PTGS1 and regulate multiple key biological pathways like IL-17 signaling pathway, TNF signaling pathway and PI3K-Akt signaling pathway to relieve HF. Moreover, molecular docking suggested that quercetin and PTGS2 could statically bind and interact with each other through amino acid residues val-349, LEU-352, PHE-381, etc. Conclusion. This work provides a systems perspective to study the relationship between Chinese medicines and diseases. The therapeutic efficacy of XCHD on HF was the sum of multitarget and multi-approach effects from the bioactive ingredients. This study could be one of the cornerstones for further research.

An in silico study of how histone tail conformation affects the binding affinity of ING family proteins

Background

Due to its intrinsically disordered nature, the histone tail is conformationally heterogenic. Therefore, it provides specific binding sites for different binding proteins or factors through reversible post-translational modifications (PTMs). For instance, experimental studies stated that the ING family binds with the histone tail that has methylation on the lysine in position 4. However, numerous complexes featuring a methylated fourth lysine residue of the histone tail can be found in the UniProt database. So the question arose if other factors like the conformation of the histone tail affect the binding affinity.

Methods

The crystal structure of the PHD finger domain from the proteins ING1, ING2, ING4, and ING5 are docked to four histone H3 tails with two different conformations using Haddock 2.4 and ClusPro. The best four models for each combination are selected and a two-sample t-test is performed to compare the binding affinities of helical conformations vs. linear conformations using Prodigy. The protein-protein interactions are examined using LigPlot.

Results

The linear histone conformations in predicted INGs-histone H3 complexes exhibit statistically significant higher binding affinity than their helical counterparts (confidence level of 99%). The outputs of predicted models generated by the molecular docking programs Haddock 2.4 and ClusPro are comparable, and the obtained protein-protein interaction patterns are consistent with experimentally confirmed binding patterns.

Conclusion

The results show that the conformation of the histone tail is significantly affecting the binding affinity of the docking protein. Herewith, this in silico study demonstrated in detail the binding preference of the ING protein family to histone H3 tail. Further research on the effect of certain PTMs on the final tail conformation and the interaction between those factors seem to be promising for a better understanding of epigenetics.

Protein-protein interaction prediction with deep learning: A comprehensive review

Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.

Precise Assembly of Proteins and Carbohydrates for Next-Generation Biomaterials

The complexity and diversity of biomacromolecules make them a unique class of building blocks for generating precise assemblies. They are particularly available to a new generation of biomaterials integrated with living systems due to their intrinsic properties such as accurate recognition, self-organization, and adaptability. Therefore, many excellent approaches have been developed, leading to a variety of quite practical outcomes. Here, we review recent advances in the fabrication and application of artificially precise assemblies by employing proteins and carbohydrates as building blocks, followed by our perspectives on some of new challenges, goals, and opportunities for the future research directions in this field.

Mycoplasma hyopneumoniae membrane protein Mhp271 interacts with host UPR protein GRP78 to facilitate infection

The unfolded protein response (UPR) plays a crucial role in Mycoplasma hyopneumoniae (M. hyopneumoniae) pathogenesis. We previously demonstrated that M. hyopneumoniae interferes with the host UPR to foster bacterial adhesion and infection. However, the underlying molecular mechanism of this UPR modulation is unclear. Here, we report that M. hyopneumoniae membrane protein Mhp271 interacts with host GRP78, a master regulator of UPR localized to the porcine tracheal epithelial cells (PTECs) surface. The interaction of Mhp271 with GRP78 reduces the porcine beta‐defensin 2 (PBD‐2) production, thereby facilitating M. hyopneumoniae adherence and infection. Furthermore, the R1‐2 repeat region of Mhp271 is crucial for GRP78 binding and the regulation of PBD‐2 expression. Intriguingly, a coimmunoprecipitation (Co‐IP) assay and molecular docking prediction indicated that the ATP, rather than the substrate‐binding domain of GRP78, is targeted by Mhp271 R1‐2. Overall, our findings identify host GRP78 as a target for M. hyopneumoniae Mhp271 modulating the host UPR to facilitate M. hyopneumoniae adherence and infection.

Designing of disruptor molecules to restrain the protein-protein interaction network of VANG1/SCRIB/NOS1AP using fragment-based drug discovery techniques

Governing protein-protein interaction networks are the cynosure of cell signaling and oncogenic networks. Multifarious processes when aligned with one another can result in a dysregulated output which can result in cancer progression. In the current research, one such network of proteins comprising VANG1/SCRIB/NOS1AP, which is responsible for cell migration, is targeted. The proteins are modeled using in-silico approaches, and the interaction is visualized utilizing protein-protein docking. Designing drugs for the convoluted protein network can serve as a challenging task that can be overcome by fragment-based drug designing, a recent game-changer in the computational drug discovery strategy for protein interaction networks. The model is exposed to the extraction of hotspots, also known as the restrained regions for small molecular hits. The hotspot regions are subjected to a library of generated fragments, which are then recombined and rejoined to develop small molecular disruptors of the macromolecular assemblage. Rapid screening methods using pharmacokinetic tools and 2D interaction studies resulted in four molecules that could serve the purpose of a disruptor. The final validation is executed by long-range simulations of 100 ns and exploring the stability of the complex using several parameters leading to the emergence of two novel molecules VNS003 and VNS005 that could be used as the disruptors of the protein assembly VANG1/SCRIB/NOS1AP. Also, the molecules were explored as single protein targets approbated via molecular docking and 100 ns molecular dynamics simulation. This concluded VNS003 as the most suitable inhibitor module capable of acting as a disruptor of a macromolecular assembly as well as acting on individual protein chains, thus leading to the primary hindrance in the formation of the protein interaction complex.

Shape Complementarity Optimization of Antibody–Antigen Interfaces: The Application to SARS-CoV-2 Spike Protein

Many factors influence biomolecule binding, and its assessment constitutes an elusive challenge in computational structural biology. In this aspect, the evaluation of shape complementarity at molecular interfaces is one of the main factors to be considered. We focus on the particular case of antibody–antigen complexes to quantify the complementarities occurring at molecular interfaces. We relied on a method we recently developed, which employs the 2D Zernike descriptors, to characterize the investigated regions with an ordered set of numbers summarizing the local shape properties. Collecting a structural dataset of antibody–antigen complexes, we applied this method and we statistically distinguished, in terms of shape complementarity, pairs of the interacting regions from the non-interacting ones. Thus, we set up a novel computational strategy based on in silico mutagenesis of antibody-binding site residues. We developed a Monte Carlo procedure to increase the shape complementarity between the antibody paratope and a given epitope on a target protein surface. We applied our protocol against several molecular targets in SARS-CoV-2 spike protein, known to be indispensable for viral cell invasion. We, therefore, optimized the shape of template antibodies for the interaction with such regions. As the last step of our procedure, we performed an independent molecular docking validation of the results of our Monte Carlo simulations.

Anti-PCSK9 monoclonal antibody attenuates high-fat diet and zymosan-induced vascular inflammation in C57BL/6 mice by modulating TLR2/NF-ƙB signaling pathway

Objectives

Excess intake of a high-fatty diet (HFD) together with zymosan administration mediates vasculitis response which leads to impaired serum lipid levels and causes arterial stiffness. In the development of new cholesterol-lowering medications, PCSK9 inhibitor (proprotein convertase subtilisin/kexin type 9) is an emerging therapeutic. The goal of the present study was to see whether anti-PCSK9 mAb1 might prevent vasculitis in C57BL/6 mice by blocking TLR2/NF-B activation in HFD and Zymosan-induced vasculitis.

Materials and Methods

Protein-protein molecular docking was performed to validate the binding affinity of anti-PCSK9 mAb1 against TLR2. Under the experimental study, mice were randomly allocated to the following groups: Group I: standard mice diet (30 days) + Zymosan vehicle (sterile PBS solution of 5mg/ml on 8^th day); Group II: HFD (30 days) + Zymosan ( single IP dose 80 mg/kg on day 8^th); Group III: HFD+Zymosan + anti-PCSK9 mAb1 (6 mg/kg, s.c. on 10^th and 20^th days); Group IV: HFD+Zymosan+anti-PCSK9 mAb1 (10 mg/kg, s.c. on 10^th and 20^th days).

Results

In comparison with the low dose of anti-PCSK9 mAb1 (6 mg/kg), the high dose of anti-PCSK9 mAb1 (10 mg/kg) together with HFD and Zymosan inhibited vasculitis more effectively by decreasing aortic TLR2 and NF-B levels, reducing serum TNF- and IL-6, and up-regulating liver LDLR levels, which down-regulated serum LDL-C and improved serum lipids levels. Histopathological studies showed that anti-PCSK9 mAb1 treatment reduced plaque accumulation in the aorta of mice.

Conclusion

These findings indicate that anti-PCSK9 mAb1 has therapeutic potential in reducing HFD and Zymosan-induced vascular inflammation.

Network Pharmacology Analysis of Huangqi Jianzhong Tang Targets in Gastric Cancer

Background: The Chinese medicine, Huangqi Jianzhong Tang (HJT), is widely used to treat gastric cancer (GC). In this study, network pharmacological methods were used to analyze the potential therapeutic targets and pharmacological mechanisms of HJT in GC.

Methods: Bioactive components and targets of HJT and GC-related targets were identified using public databases. The protein-protein interaction network of potential targets of HJT in GC was constructed using the Cytoscape plug-in (v3.8.0), CytoHubba. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, in addition to molecular docking and animal experiments to verify the results of network pharmacology analysis.

Results: A total of 538 GC-related targets were identified. The bioactive components of HJT were selected for drug-likeness evaluation and binomial statistical model screening, which revealed 63 bioactive components and 72 targets. Based on GO enrichment analysis, all targets in the protein-protein interaction network were mainly involved in the response to oxidative stress and neuronal death. Further, KEGG enrichment analysis suggested that the treatment of GC with HJT mainly involved the Wnt signaling pathway, PI3K-Akt signaling pathway, TGF-β signaling pathway, and MAPK signaling pathway, thereby providing insights into the mechanism of the effects of HJT on GC.

Conclusion: This study revealed the potential bioactive components and molecular mechanisms of HJT, which may be useful for the treatment of GC, and provided insights into the development of new drugs for GC.

A novel computational strategy for defining the minimal protein molecular surface representation

Most proteins perform their biological function by interacting with one or more molecular partners. In this respect, characterizing local features of the molecular surface, that can potentially be involved in the interaction with other molecules, represents a step forward in the investigation of the mechanisms of recognition and binding between molecules. Predictive methods often rely on extensive samplings of molecular patches with the aim to identify hot spots on the surface. In this framework, analysis of large proteins and/or many molecular dynamics frames is often unfeasible due to the high computational cost. Thus, finding optimal ways to reduce the number of points to be sampled maintaining the biological information (including the surface shape) carried by the molecular surface is pivotal. In this perspective, we here present a new theoretical and computational algorithm with the aim of defining a set of molecular surfaces composed of points not uniformly distributed in space, in such a way as to maximize the information of the overall shape of the molecule by minimizing the number of total points. We test our procedure’s ability in recognizing hot-spots by describing the local shape properties of portions of molecular surfaces through a recently developed method based on the formalism of 2D Zernike polynomials. The results of this work show the ability of the proposed algorithm to preserve the key information of the molecular surface using a reduced number of points compared to the complete surface, where all points of the surface are used for the description. In fact, the methodology shows a significant gain of the information stored in the sampling procedure compared to uniform random sampling.

GWYRE: A resource for mapping variants onto experimental and modeled structures of human protein complexes

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Structure of protein complexes is important for interpreting genetic variation.

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Data on single amino acid variants is available from high-throughput sequencing.

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Integrated modeling approach was applied to proteins and their complexes.

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GWYRE resource incorporates predicted protein complexes with mapped mutations.

Rapid progress in structural modeling of proteins and their interactions is powered by advances in knowledge-based methodologies along with better understanding of physical principles of protein structure and function. The pool of structural data for modeling of proteins and protein–protein complexes is constantly increasing due to the rapid growth of protein interaction databases and Protein Data Bank. The GWYRE (Genome Wide PhYRE) project capitalizes on these developments by advancing and applying new powerful modeling methodologies to structural modeling of protein–protein interactions and genetic variation. The methods integrate knowledge-based tertiary structure prediction using Phyre2 and quaternary structure prediction using template-based docking by a full-structure alignment protocol to generate models for binary complexes. The predictions are incorporated in a comprehensive public resource for structural characterization of the human interactome and the location of human genetic variants. The GWYRE resource facilitates better understanding of principles of protein interaction and structure/function relationships. The resource is available at http://www.gwyre.org.

AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins. Moreover, we introduce metrics for predicting direct protein-protein interactions between arbitrary protein pairs and validate AF2Complex on some challenging benchmark sets and the E. coli proteome. Lastly, using the cytochrome c biogenesis system I as an example, we present high-confidence models of three sought-after assemblies formed by eight members of this system.

Computational prediction of the effect of mutations in the receptor-binding domain on the interaction between SARS-CoV-2 and human ACE2

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19 continues to mutate. Numerous studies have indicated that this viral mutation, particularly in the receptor-binding domain area, may increase the viral affinity for human angiotensin-converting enzyme 2 (hACE2), the receptor for viral entry into host cells, thereby increasing viral virulence and transmission. In this study, we investigated the binding affinity of SARS-CoV-2 variants (Delta plus, Iota, Kappa, Mu, Lambda, and C.1.2) on hACE2 using computational modeling with a protein-protein docking approach. The simulation results indicated that there were differences in the interactions between the RBD and hACE2, including hydrogen bonding, salt bridge interactions, non-bonded interactions, and binding free energy differences among these variants. Molecular dynamics simulations revealed that mutations in the RBD increase the stability of the hACE2-spike protein complex relative to the wild type, following the global stability trend and increasing the binding affinity. The value of binding-free energy calculated using molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) indicated that all mutations in the spike protein increased the contagiousness of SARS-CoV-2 variants. The findings of this study provide a foundation for developing effective interventions against these variants. Computational modeling elucidates that the spike protein of SARS-CoV-2 variants binds considerably stronger than the wild-type to hACE2.

Molecular docking analysis of two bioactive molecules KLUF10 and KLUF13 isolated from the marine bacteria Micrococcus sp. OUS9 with TNF alpha

Tumor necrosis factor-alpha (TNF-α) is known to be linked with tumor. Therefore, it is of interest to document the Molecular docking analysis of two bioactive molecules KLUF10 and KLUF13 isolated from the marine bacteria Micrococcus sp. OUS9 with TNF alpha. We report the molecular interactions of KLUF10 and KLUF13 with TNF alpha.

A SARS-CoV-2 neutralizing antibody discovery by single cell sequencing and molecular modeling

Although vaccines have been developed, mutations of SARS-CoV-2, especially the dominant B.1.617.2 (delta) and B.1.529 (omicron) strains with more than 30 mutations on their spike protein, have caused a significant decline in prophylaxis, calling for the need for drug improvement. Antibodies are drugs preferentially used in infectious diseases and are easy to get from immunized organisms. The current study combined molecular modeling and single memory B cell sequencing to assess candidate sequences before experiments, providing a strategy for the fabrication of SARS-CoV-2 neutralizing antibodies. A total of 128 sequences were obtained after sequencing 196 memory B cells, and 42 sequences were left after merging extremely similar ones and discarding incomplete ones, followed by homology modeling of the antibody variable region. Thirteen candidate sequences were expressed, of which three were tested positive for receptor binding domain recognition but only one was confirmed as having broad neutralization against several SARS-CoV-2 variants. The current study successfully obtained a SARS-CoV-2 antibody with broad neutralizing abilities and provided a strategy for antibody development in emerging infectious diseases using single memory B cell BCR sequencing and computer assistance in antibody fabrication.

UDock2: interactive real-time multi-body protein-protein docking software

MOTIVATION

Protein-protein docking aims at predicting the geometry of protein interactions to gain insights into the mechanisms underlying these processes and develop new strategies for drug discovery. Interactive and user-oriented manipulation tools can support this task complementary to automated software.

RESULTS

This article presents an interactive multi-body protein-protein docking software, UDock2, designed for research but also usable for teaching and popularization of science purposes due to its high usability. In UDock2, the users tackle the conformational space of protein interfaces using an intuitive real-time docking procedure with on-the-fly scoring. UDock2 integrates traditional computer graphics methods to facilitate the visualization and to provide better insight into protein surfaces, interfaces, and properties.

AVAILABILITY AND IMPLEMENTATION

UDock2 is open-source, cross-platform (Windows and Linux), and available at http://udock.fr. The code can be accessed at https://gitlab.com/Udock/Udock2.

Insights into the Binding of Receptor-Binding Domain (RBD) of SARS-CoV-2 Wild Type and B.1.620 Variant with hACE2 Using Molecular Docking and Simulation Approaches

Recently, a new variant, B.1620, with mutations (S477N-E484K) in the spike protein's receptor-binding domain (RBD) has been reported in Europe. In order to design therapeutic strategies suitable for B.1.620, further studies are required. A detailed investigation of the structural features and variations caused by these substitutions, that is, a molecular level investigation, is essential to uncover the role of these changes. To determine whether and how the binding affinity of ACE2-RBD is affected, we used protein-protein docking and all-atom simulation approaches. Our analysis revealed that B.1.620 binds more strongly than the wild type and alters the hydrogen bonding network. The docking score for the wild type was reported to be -122.6 +/- 0.7 kcal/mol, while for B.1.620, the docking score was -124.9 +/- 3.8 kcal/mol. A comparative binding investigation showed that the wild-type complex has 11 hydrogen bonds and one salt bridge, while the B.1.620 complex has 14 hydrogen bonds and one salt bridge, among which most of the interactions are preserved between the wild type and B.1.620. A dynamic analysis of the two complexes revealed stable dynamics, which corroborated the global stability trend, compactness, and flexibility of the three essential loops, providing a better conformational optimization opportunity and binding. Furthermore, binding free energy revealed that the wild type had a total binding energy of -51.14 kcal/mol, while for B.1.628, the total binding energy was -68.25 kcal/mol. The current findings based on protein complex modeling and bio-simulation methods revealed the atomic features of the B.1.620 variant harboring S477N and E484K mutations in the RBD and the basis for infectivity. In conclusion, the current study presents distinguishing features of B.1.620, which can be used to design structure-based drugs against the B.1.620 variant.

Critical assessment of methods of protein structure prediction (CASP)-Round XIV

Critical assessment of structure prediction (CASP) is a community experiment to advance methods of computing three-dimensional protein structure from amino acid sequence. Core components are rigorous blind testing of methods and evaluation of the results by independent assessors. In the most recent experiment (CASP14), deep-learning methods from one research group consistently delivered computed structures rivaling the corresponding experimental ones in accuracy. In this sense, the results represent a solution to the classical protein-folding problem, at least for single proteins. The models have already been shown to be capable of providing solutions for problematic crystal structures, and there are broad implications for the rest of structural biology. Other research groups also substantially improved performance. Here, we describe these results and outline some of the many implications. Other related areas of CASP, including modeling of protein complexes, structure refinement, estimation of model accuracy, and prediction of inter-residue contacts and distances, are also described.

Protein Assembly by Design

Proteins are nature's primary building blocks for the construction of sophisticated molecular machines and dynamic materials, ranging from protein complexes such as photosystem II and nitrogenase that drive biogeochemical cycles to cytoskeletal assemblies and muscle fibers for motion. Such natural systems have inspired extensive efforts in the rational design of artificial protein assemblies in the last two decades. As molecular building blocks, proteins are highly complex, in terms of both their three-dimensional structures and chemical compositions. To enable control over the self-assembly of such complex molecules, scientists have devised many creative strategies by combining tools and principles of experimental and computational biophysics, supramolecular chemistry, inorganic chemistry, materials science, and polymer chemistry, among others. Owing to these innovative strategies, what started as a purely structure-building exercise two decades ago has, in short order, led to artificial protein assemblies with unprecedented structures and functions and protein-based materials with unusual properties. Our goal in this review is to give an overview of this exciting and highly interdisciplinary area of research, first outlining the design strategies and tools that have been devised for controlling protein self-assembly, then describing the diverse structures of artificial protein assemblies, and finally highlighting the emergent properties and functions of these assemblies.

Protein Engineering in the Design of Protein-Protein Interactions: SARS-CoV-2 Inhibitors as a Test Case

The formation of specific protein-protein interactions (PPIs) drive most biological processes. Malfunction of such interactions is the molecular driver of many diseases. Our ability to engineer existing PPIs or create new ones has become a vital research tool. In addition, engineered proteins with new or altered interactions are among the most critical drugs that have been developed in recent years. These include antibodies, cytokines, inhibitors, and others. Here, we provide a perspective on the current status of the methods used to engineer new or altered PPIs. The emergence of the COVID-19 pandemic, which resulted in a worldwide quest to develop specific PPI inhibitors as drugs, provided an up-to-date and state-of-the-art status report on the methodologies for engineering PPIs targeting the interaction of the viral spike protein with its cellular target, ACE2. Multiple, very high affinity binders were generated within a few months using in vitro evolution by itself, or in combination with computational design. The different experimental and computational methods used to block this interaction provide a road map for the future of PPI engineering.

SARS-CoV-2 Variants, RBD Mutations, Binding Affinity, and Antibody Escape

Since 2020, the receptor-binding domain (RBD) of the spike protein of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been constantly mutating, producing most of the notable missense mutations in the context of "variants of concern", probably in response to the vaccine-driven alteration of immune profiles of the human population. The Delta variant, in particular, has become the most prevalent variant of the epidemic, and it is spreading in countries with the highest vaccination rates, causing the world to face the risk of a new wave of the contagion. Understanding the physical mechanism responsible for the mutation-induced changes in the RBD's binding affinity, its transmissibility, and its capacity to escape vaccine-induced immunity is the "urgent challenge" in the development of preventive measures, vaccines, and therapeutic antibodies against the coronavirus disease 2019 (COVID-19) pandemic. In this study, entropy-enthalpy compensation and the Gibbs free energy change were used to analyze the impact of the RBD mutations on the binding affinity of SARS-CoV-2 variants with the receptor angiotensin converting enzyme 2 (ACE2) and existing antibodies. Through the analysis, we found that the existing mutations have already covered almost all possible detrimental mutations that could result in an increase of transmissibility, and that a possible mutation in amino-acid position 498 of the RBD can potentially enhance its binding affinity. A new calculation method for the binding energies of protein-protein complexes is proposed based on the entropy-enthalpy compensation rule. All known structures of RBD-antibody complexes and the RBD-ACE2 complex comply with the entropy-enthalpy compensation rule in providing the driving force behind the spontaneous protein-protein docking. The variant-induced risk of breakthrough infections in vaccinated people is attributed to the L452R mutation's reduction of the binding affinity of many antibodies. Mutations reversing the hydrophobic or hydrophilic performance of residues in the spike RBD potentially cause breakthrough infections of coronaviruses due to the changes in geometric complementarity in the entropy-enthalpy compensations between antibodies and the virus at the binding sites.

Discovering the Ultimate Limits of Protein Secondary Structure Prediction

Secondary structure prediction (SSP) of proteins is an important structural biology technique with many applications. There have been ~300 algorithms published in the past seven decades with fierce competition in accuracy. In the first 60 years, the accuracy of three-state SSP rose from ~56% to 81%; after that, it has long stayed at 81-86%. In the 1990s, the theoretical limit of three-state SSP accuracy had been estimated to be 88%. Thus, SSP is now generally considered not challenging or too challenging to improve. However, we found that the limit of three-state SSP might be underestimated. Besides, there is still much room for improving segment-based and eight-state SSPs, but the limits of these emerging topics have not been determined. This work performs large-scale sequence and structural analyses to estimate SSP accuracy limits and assess state-of-the-art SSP methods. The limit of three-state SSP is re-estimated to be ~92%, 4-5% higher than previously expected, indicating that SSP is still challenging. The estimated limit of eight-state SSP is 84-87%. Several proposals for improving future SSP algorithms are made based on our results. We hope that these findings will help move forward the development of SSP and all its applications.

Part-II- in silico drug design: application and success

In silico tools have indeed reframed the steps involved in traditional drug discovery and development process and the term in silico has become a familiar term in pharmaceutical sector like the terms in vitro and in vivo. The successful design of HIV protease inhibitors, Saquinavir, Indinavir and other important medicinal agents, initiated interest of researchers in structure based drug design approaches (SBDD). The interactions between biomolecules and a ligand, binding energy, free energy and stability of biomolecule-ligand complex can be envisioned and predicted by applying molecular docking studies. Protein-ligand, protein-protein, DNA-ligand interactions etc. aid in elucidating molecular level mechanisms of drug molecules. In the Ligand based drug design (LBDD) approaches, QSAR studies have tremendously contributed to the development of antimicrobial, anticancer, antimalarial agents. In the recent years, multiQSAR (mt-QSAR) approaches have been successfully employed for designing drugs against multifactorial diseases. Output of a research in several instances is rewarding when both SBDD and LBDD approaches are combined. Application of in silico studies for prediction of pharmacokinetics was once a real challenge but one can see unlimited number publications comprising tools, data bases which can accurately predict almost all the pharmacokinetic parameters. Absorption, distribution, metabolism, transporters, blood brain barrier permeability, hERG toxicity, P-gp affinity and several toxicological end points can be accurately predicted for a candidate molecule before its synthesis. In silico approaches are greatly encouraged a result of growing limitations and new legislations related to the animal use for research. The combined use of in vitro data and in silico tools will definitely decrease the use of animal testing in the future.In this chapter, in silico approaches and their applications are reviewed and discussed giving suitable examples.

Immunoinformatics analysis and evaluation of recombinant chimeric triple antigen toxoid (r-HAB) against Staphylococcus aureus toxaemia in mouse model

Abstract

Staphylococcus aureus is a serious pathogen unleashing its virulence through several classes of exotoxins such as hemolysins and enterotoxins. In this study, we designed a novel multi-antigen subunit vaccine which can induce innate, humoral and cellular immune responses. Alpha hemolysin, enterotoxins A and B were selected as protective antigens for combining into a triple antigen chimeric protein (HAB). Immunoinformatics analysis predicted HAB protein as a suitable vaccine candidate for inducing both humoral and cellular immune responses. Tertiary structure of the HAB protein was predicted and validated through computational approaches. Docking studies were performed between the HAB protein and mice TLR2 receptor. Furthermore, we constructed and generated recombinant HAB (r-HAB) protein in E. coli and studied its toxicity, immunogenicity and protective efficacy in a mouse model. Triple antigen chimeric protein (r-HAB) was found to be highly immunogenic in mouse as the anti-r-HAB hyperimmune serum was strongly reactive to all three native exotoxins on Western blot. In vitro toxin neutralization assay using anti-r-HAB antibodies demonstrated > 75% neutralization of toxins on RAW 264.7 cell line. Active immunization with r-HAB toxoid gave ~ 83% protection against 2 × lethal dosage of secreted exotoxins. The protection was mediated by induction of strong antibody responses that neutralized the toxins. Passive immunization with anti-r-HAB antibodies gave ~ 50% protection from lethal challenge. In conclusion, in vitro and in vivo testing of r-HAB found the molecule to be nontoxic, highly immunogenic and induced excellent protection towards native toxins in actively immunized and partial protection to passively immunized mice groups.

Key points

• HAB protein was computationally designed to induce humoral and cellular responses.

• r-HAB protein was found to be nontoxic, immunogenic and protective in mouse model.

• r-HAB conferred protection against lethal challenge in active and passive immunization.

Modeling protein structures with the coarse-grained UNRES force field in the CASP14 experiment

The UNited RESidue (UNRES) force field was tested in the 14th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP14), in which larger oligomeric and multimeric targets were present compared to previous editions. Three prediction modes were tested (i) ab initio (the UNRES group), (ii) contact-assisted (the UNRES-contact group), and (iii) template-assisted (the UNRES-template group). For most of the targets, the contact restraints were derived from the server models top-ranked by the DeepQA method, while the DNCON2 method was used for 11 targets. Our consensus-fragment procedure was used to run template-assisted predictions. Each group also processed the Nuclear Magnetic Resonance (NMR)- and Small Angle X-Ray Scattering (SAXS)-data assisted targets. The average Global Distance Test Total Score (GDT_TS) of the 'Model 1' predictions were 29.17, 39.32, and 56.37 for the UNRES, UNRES-contact, and UNRES-template predictions, respectively, increasing by 0.53, 2.24, and 3.76, respectively, compared to CASP13. It was also found that the GDT_TS of the UNRES models obtained in ab initio mode and in the contact-assisted mode decreases with the square root of chain length, while the exponent in this relationship is 0.20 for the UNRES-template group models and 0.11 for the best performing AlphaFold2 models, which suggests that incorporation of database information, which stems from protein evolution, brings in long-range correlations, thus enabling the correction of force-field inaccuracies.

The role of complementary shape in protein dimerization

Shape guides colloidal nanoparticles to form complex assemblies, but its role in defining interfaces in biomolecular complexes is less clear. In this work, we isolate the role of shape in protein complexes by studying the reversible binding processes of 46 protein dimer pairs, and investigate when entropic effects from shape complementarity alone are sufficient to predict the native protein binding interface. We employ depletants using a generic, implicit depletion model to amplify the magnitude of the entropic forces arising from lock-and-key binding and isolate the effect of shape complementarity in protein dimerization. For 13% of the complexes studied here, protein shape is sufficient to predict native complexes as equilibrium assemblies. We elucidate the results by analyzing the importance of competing binding configurations and how it affects the assembly. A machine learning classifier, with a precision of 89.14% and a recall of 77.11%, is able to identify the cases where shape alone predicts the native protein interface.

Machine Learning in Drug Discovery: A Review

This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.

A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction

Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM.

Network Pharmacology Prediction and Molecular Docking-Based Strategy to Discover the Potential Pharmacological Mechanism of Huai Hua San Against Ulcerative Colitis

Background

Huai Hua San (HHS), a famous Traditional Chinese Medicine (TCM) formula, has been widely applied in treating ulcerative colitis (UC). However, the interaction of bioactives from HHS with the targets involved in UC has not been elucidated yet.

Aim

A network pharmacology-based approach combined with molecular docking and in vitro validation was performed to determine the bioactives, key targets, and potential pharmacological mechanism of HHS against UC.

Materials and Methods

Bioactives and potential targets of HHS, as well as UC-related targets, were retrieved from public databases. Crucial bioactive ingredients, potential targets, and signaling pathways were acquired through bioinformatics analysis, including protein-protein interaction (PPI), as well as the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Subsequently, molecular docking was carried out to predict the combination of active compounds with core targets. Lastly, in vitro experiments were conducted to further verify the findings.

Results

A total of 28 bioactive ingredients of HHS and 421 HHS-UC-related targets were screened. Bioinformatics analysis revealed that quercetin, luteolin, and nobiletin may be potential candidate agents. JUN, TP53, and ESR1 could become potential therapeutic targets. PI3K-AKT signaling pathway might play an important role in HHS against UC. Moreover, molecular docking suggested that quercetin, luteolin, and nobiletin combined well with JUN, TP53, and ESR1, respectively. Cell experiments showed that the most important ingredient of HHS, quercetin, could inhibit the levels of inflammatory factors and phosphorylated c-Jun, as well as PI3K-Akt signaling pathway in LPS-induced RAW264.7 cells, which further confirmed the prediction by network pharmacology strategy and molecular docking.

Conclusion

Our results comprehensively illustrated the bioactives, potential targets, and molecular mechanism of HHS against UC. It also provided a promising strategy to uncover the scientific basis and therapeutic mechanism of TCM formulae in treating diseases.

Potential activity of Linezolid against SARS-CoV-2 using electronic and molecular docking study

The crescent evolution of a global pandemic COVID-19 and its respiratory syndrome (SARS-Cov-2) has been a constant concern (Ghosh 2021; Khan et al. 2021; Alazmi and Motwalli 2020; Vargas et al. 2020). The absence of a proven and effective medication has compelled all the scientific community to search for a new drug. The use of known drugs is a faster way to develop new therapies. Molecular docking is a powerful tool (Gao et al. J Mol Model 10: 44-54, 2004; Singh et al. J Mol Model 18: 39-51, 2012; Schulz-Gasch and Stahl J Mol Model 9:47-57, 2003) to study the interaction of potential drugs with SARS-CoV-2, Alsalme et al. (2020) and Sanders et al. (2020) spike protein as a consequence the main goal of this article is to present the result of the study of an interaction between (R and S)-Linezolid with receptor-binding domain (RBD) of SARS-Cov-2 spike protein complexed with human Angiostensin-converting enzyme 2 (ACE2) (6vW1 - from PDB). The Linezolid enantiomers were optimized at B3LYP/6-311++G(2d,p) level of theory. Molecular docking of the system (S)-Linezolid⋯RBD⋯ACE2 and (R)-Linezolid⋯RBD⋯ACE2 was performed, the analysis was made using LigPlot+ and NCIplot software packages, to understand the intermolecular interactions. The UV-Vis and ECD of the complexes - (R and S)-Linezolid⋯RBD⋯ACE2 were performed in two layers with DFT/6-311++G(3df,2p) and DFT/6-31G(d), respectively. The results showed that only the (S)-Linezolid had a stable interaction with - 8.05 kcal.mol- 1, whereas all the R-enantiomeric configurations had positive values of binding energy. The (S)-Linezolid had the same interactions as in the (S)-Linezolid ⋯ Haluarcula morismortui Ribosomal system, where it is well-known the fact that the latter has biological activity. A specific interaction on the fluorine ring justified an attenuation on the ECD signal, in comparison to isolated species. Therefore, some biological activity of (S)-Linezolid with SARS-CoV-2 RBD was expected, indicated by the modification of its ECD signal and justified by a similar interaction in the S-Linezolid⋯Haluarcula marismortui Ribosomal system.

Identifying hydrophobic protein patches to inform protein interaction interfaces

Interactions between proteins lie at the heart of numerous biological processes and are essential for the proper functioning of the cell. Although the importance of hydrophobic residues in driving protein interactions is universally accepted, a characterization of protein hydrophobicity, which informs its interactions, has remained elusive. The challenge lies in capturing the collective response of the protein hydration waters to the nanoscale chemical and topographical protein patterns, which determine protein hydrophobicity. To address this challenge, here, we employ specialized molecular simulations wherein water molecules are systematically displaced from the protein hydration shell; by identifying protein regions that relinquish their waters more readily than others, we are then able to uncover the most hydrophobic protein patches. Surprisingly, such patches contain a large fraction of polar/charged atoms and have chemical compositions that are similar to the more hydrophilic protein patches. Importantly, we also find a striking correspondence between the most hydrophobic protein patches and regions that mediate protein interactions. Our work thus establishes a computational framework for characterizing the emergent hydrophobicity of amphiphilic solutes, such as proteins, which display nanoscale heterogeneity, and for uncovering their interaction interfaces.

Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review

Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data, computer-assisted drug design technology is playing a key role in drug discovery with its advantages of high efficiency, fast speed, and low cost. Over recent years, due to continuous progress in machine learning (ML) algorithms, AI has been extensively employed in various drug discovery stages. Very recently, drug design and discovery have entered the big data era. ML algorithms have progressively developed into a deep learning technique with potent generalization capability and more effectual big data handling, which further promotes the integration of AI technology and computer-assisted drug discovery technology, hence accelerating the design and discovery of the newest drugs. This review mainly summarizes the application progression of AI technology in the drug discovery process, and explores and compares its advantages over conventional methods. The challenges and limitations of AI in drug design and discovery have also been discussed.

Text mining for modeling of protein complexes enhanced by machine learning

MOTIVATION

Procedures for structural modeling of protein-protein complexes (protein docking) produce a number of models which need to be further analyzed and scored. Scoring can be based on independently determined constraints on the structure of the complex, such as knowledge of amino acids essential for the protein interaction. Previously, we showed that text mining of residues in freely available PubMed abstracts of papers on studies of protein-protein interactions may generate such constraints. However, absence of post-processing of the spotted residues reduced usability of the constraints, as a significant number of the residues were not relevant for the binding of the specific proteins.

RESULTS

We explored filtering of the irrelevant residues by two machine learning approaches, Deep Recursive Neural Network (DRNN) and Support Vector Machine (SVM) models with different training/testing schemes. The results showed that the DRNN model is superior to the SVM model when training is performed on the PMC-OA full-text articles and applied to classification (interface or non-interface) of the residues spotted in the PubMed abstracts. When both training and testing is performed on full-text articles or on abstracts, the performance of these models is similar. Thus, in such cases, there is no need to utilize computationally demanding DRNN approach, which is computationally expensive especially at the training stage. The reason is that SVM success is often determined by the similarity in data/text patterns in the training and the testing sets, whereas the sentence structures in the abstracts are, in general, different from those in the full text articles.

AVAILABILITYAND IMPLEMENTATION

The code and the datasets generated in this study are available at https://gitlab.ku.edu/vakser-lab-public/text-mining/-/tree/2020-09-04.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational Approaches: An Underutilized Tool in the Quest to Elucidate Radical SAM Dynamics

Enzymes are biological catalysts whose dynamics enable their reactivity. Visualizing conformational changes, in particular, is technically challenging, and little is known about these crucial atomic motions. This is especially problematic for understanding the functional diversity associated with the radical S-adenosyl-L-methionine (SAM) superfamily whose members share a common radical mechanism but ultimately catalyze a broad range of challenging reactions. Computational chemistry approaches provide a readily accessible alternative to exploring the time-resolved behavior of these enzymes that is not limited by experimental logistics. Here, we review the application of molecular docking, molecular dynamics, and density functional theory, as well as hybrid quantum mechanics/molecular mechanics methods to the study of these enzymes, with a focus on understanding the mechanistic dynamics associated with turnover.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network.  Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target “ERBB4” and candidate drug “Wortmannin” provide insights on the possible personalized therapeutics for emerging COVID-19.

Integrating 3D structural information into systems biology

Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in systems biology and continuously extended and refined as new sources of evidence become available. Despite the vast amount of information about individual protein structures and protein complexes that has accumulated in the past 50 years in the Protein Data Bank, the data, computational tools, and language of structural biology are not an integral part of systems biology. However, increasing effort has been devoted to this integration, and the related literature is reviewed here. Relationships between proteins that are detected via structural similarity offer a rich source of information not available from sequence similarity, and homology modeling can be used to leverage Protein Data Bank structures to produce 3D models for a significant fraction of many proteomes. A number of structure-informed genomic and cross-species (i.e., virus–host) interactomes will be described, and the unique information they provide will be illustrated with a number of examples. Tissue- and tumor-specific interactomes have also been developed through computational strategies that exploit patient information and through genetic interactions available from increasingly sensitive screens. Strategies to integrate structural information with these alternate data sources will be described. Finally, efforts to link protein structure space with chemical compound space offer novel sources of information in drug design, off-target identification, and the identification of targets for compounds found to be effective in phenotypic screens.

Baseline Comparisons of Complementary Sampling Methods for Assembly Driven by Short-Ranged Pair Potentials toward Fast and Flexible Hybridization

This work measures baseline sampling characteristics that highlight fundamental differences between sampling methods for assembly driven by short-ranged pair potentials. Such granular comparison is essential for fast, flexible, and accurate hybridization of complementary methods. Besides sampling speed, efficiency, and accuracy of uniform grid coverage, other sampling characteristics measured are (i) accuracy of covering narrow low energy regions that have low effective dimension (ii) ability to localize sampling to specific basins, and (iii) flexibility in sampling distributions. As a proof of concept, we compare a recently developed geometric methodology EASAL (Efficient Atlasing and Search of Assembly Landscapes) and the traditional Monte Carlo (MC) method for sampling the energy landscape of two assembling trans-membrane helices, driven by short-range pair potentials. By measuring the above-mentioned sampling characteristics, we demonstrate that EASAL provides localized and accurate coverage of crucial regions of the energy landscape of low effective dimension, under flexible sampling distributions, with much fewer samples and computational resources than MC sampling. EASAL's empirically validated theoretical guarantees permit credible extrapolation of these measurements and comparisons to arbitrary number and size of assembling units. Promising avenues for hybridizing the complementary advantages of the two methods are discussed.

Decoding Protein-protein Interactions: An Overview

Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network.  Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target "ERBB4" and candidate drug "Wortmannin" provide insights on the possible personalized therapeutics for emerging COVID-19.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network.  Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target "ERBB4" and candidate drug "Wortmannin" provide insights on the possible personalized therapeutics for emerging COVID-19.

Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes

Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to distinguish accurate structural models from less accurate ones. This raises the question of whether it is possible to learn characteristics of accurate models directly from atomic coordinates of protein complexes, with no prior assumptions. Here we introduce a machine learning method that learns directly from the 3D positions of all atoms to identify accurate models of protein complexes, without using any precomputed physics-inspired or statistical terms. Our neural network architecture combines multiple ingredients that together enable end-to-end learning from molecular structures containing tens of thousands of atoms: a point-based representation of atoms, equivariance with respect to rotation and translation, local convolutions, and hierarchical subsampling operations. When used in combination with previously developed scoring functions, our network substantially improves the identification of accurate structural models among a large set of possible models. Our network can also be used to predict the accuracy of a given structural model in absolute terms. The architecture we present is readily applicable to other tasks involving learning on 3D structures of large atomic systems.

2D Zernike polynomial expansion: Finding the protein-protein binding regions

We present a method for efficiently and effectively assessing whether and where two proteins can interact with each other to form a complex. This is still largely an open problem, even for those relatively few cases where the 3D structure of both proteins is known. In fact, even if much of the information about the interaction is encoded in the chemical and geometric features of the structures, the set of possible contact patches and of their relative orientations are too large to be computationally affordable in a reasonable time, thus preventing the compilation of reliable interactome. Our method is able to rapidly and quantitatively measure the geometrical shape complementarity between interacting proteins, comparing their molecular iso-electron density surfaces expanding the surface patches in term of 2D Zernike polynomials. We first test the method against the real binding region of a large dataset of known protein complexes, reaching a success rate of 0.72. We then apply the method for the blind recognition of binding sites, identifying the real region of interaction in about 60% of the analyzed cases. Finally, we investigate how the efficiency in finding the right binding region depends on the surface roughness as a function of the expansion order.