Scyllatoxin-based peptide design for E. coli expression and HIV gp120 binding.

Targeting the hydrophobic Phe43 pocket of HIV's envelope glycoprotein gp120 is a critical strategy for antiviral interventions due to its role in interacting with the host cell's CD4. Previous inhibitors, including small molecules and CD4 mimetic peptides based on scyllatoxin, have demonstrated significant binding and neutralization capabilities but were often chemically synthesized or contained non-canonical amino acids. Microbial expression using natural amino acids offers advantages such as cost-effectiveness, scalability, and efficient production of fusion proteins. In this study, we enhanced the previous scyllatoxin-based synthetic peptide by substituting natural amino acids and successfully expressed it in E. coli. The peptide was optimized by mutating the C-terminal amidated valine to valine and glutamine, and by reducing the disulfide bonds from three to two. Circular dichroism confirmed proper secondary structure formation, and fluorescence polarization analysis revealed specific, concentration-dependent binding to HIV gp120, supported by molecular dynamics simulations. These findings indicate the potential for scalable microbial production of effective antiviral peptides, with significant applications in pharmaceutical development for HIV treatment.

Repurposing Navitoclax to block SARS-CoV-2 fusion and entry by targeting heptapeptide repeat sequence 1 in S2 protein.

Along with the long pandemic of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has come the dilemma of emerging viral variants of concern (VOC), particularly Omicron and its subvariants, able to deftly escape immune surveillance and the otherwise protective effect of current vaccines and antibody drugs. We previously identified a peptide-based pan-CoV fusion inhibitor, termed as EK1, able to bind the HR1 region in viral spike (S) protein S2 subunit. This effectively blocked formation of the six-helix bundle (6-HB) fusion core and, thus, showed efficacy against all human coronaviruses (HCoVs). EK1 is now in phase 3 clinical trials. However, the peptide drug generally lacks oral availability. Therefore, we herein performed a structure-based virtual screening of the libraries of biologically active molecules and identified nine candidate compounds. One is Navitoclax, an orally active anticancer drug by inhibition of Bcl-2. Like EK1 peptide, it could bind HR1 and block 6-HB formation, efficiently inhibiting fusion and infection of all SARS-CoV-2 variants tested, as well as SARS-CoV and MERS-CoV, with IC50 values ranging from 0.5 to 3.7 µM. These findings suggest that Navitoclax is a promising repurposed drug candidate for development as a safe and orally available broad-spectrum antiviral drug to combat the current SARS-CoV-2 and its variants, as well as other HCoVs.

AI-Driven De Novo Design and Molecular Modeling for Discovery of Small-Molecule Compounds as Potential Drug Candidates Targeting SARS-CoV-2 Main Protease.

Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the development of novel potent and broad therapeutic agents targeting different vulnerable spots of the viral proteins. In this study, two deep learning generative models were developed and used in combination with molecular modeling tools for de novo design of small molecule compounds that can inhibit the catalytic activity of SARS-CoV-2 main protease (Mpro), an enzyme critically important for mediating viral replication and transcription. As a result, the seven best scoring compounds that exhibited low values of binding free energy comparable with those calculated for two potent inhibitors of Mpro, via the same computational protocol, were selected as the most probable inhibitors of the enzyme catalytic site. In light of the data obtained, the identified compounds are assumed to present promising scaffolds for the development of new potent and broad-spectrum drugs inhibiting SARS-CoV-2 Mpro, an attractive therapeutic target for anti-COVID-19 agents.

Gene ontology improves template selection in comparative protein docking.

Structural characterization of protein-protein interactions is essential for our ability to study life processes at the molecular level. Computational modeling of protein complexes (protein docking) is important as the source of their structure and as a way to understand the principles of protein interaction. Rapidly evolving comparative docking approaches utilize target/template similarity metrics, which are often based on the protein structure. Although the structural similarity, generally, yields good performance, other characteristics of the interacting proteins (eg, function, biological process, and localization) may improve the prediction quality, especially in the case of weak target/template structural similarity. For the ranking of a pool of models for each target, we tested scoring functions that quantify similarity of Gene Ontology (GO) terms assigned to target and template proteins in three ontology domains-biological process, molecular function, and cellular component (GO-score). The scoring functions were tested in docking of bound, unbound, and modeled proteins. The results indicate that the combined structural and GO-terms functions improve the scoring, especially in the twilight zone of structural similarity, typical for protein models of limited accuracy.

Simulated unbound structures for benchmarking of protein docking in the DOCKGROUND resource.

BACKGROUND: Proteins play an important role in biological processes in living organisms. Many protein functions are based on interaction with other proteins. The structural information is important for adequate description of these interactions. Sets of protein structures determined in both bound and unbound states are essential for benchmarking of the docking procedures. However, the number of such proteins in PDB is relatively small. A radical expansion of such sets is possible if the unbound structures are computationally simulated. RESULTS: The DOCKGROUND public resource provides data to improve our understanding of protein-protein interactions and to assist in the development of better tools for structural modeling of protein complexes, such as docking algorithms and scoring functions. A large set of simulated unbound protein structures was generated from the bound structures. The modeling protocol was based on 1 ns Langevin dynamics simulation. The simulated structures were validated on the ensemble of experimentally determined unbound and bound structures. The set is intended for large scale benchmarking of docking algorithms and scoring functions. CONCLUSIONS: A radical expansion of the unbound protein docking benchmark set was achieved by simulating the unbound structures. The simulated unbound structures were selected according to criteria from systematic comparison of experimentally determined bound and unbound structures. The set is publicly available at http://dockground.compbio.ku.edu.

Structural templates for comparative protein docking.

Structural characterization of protein-protein interactions is important for understanding life processes. Because of the inherent limitations of experimental techniques, such characterization requires computational approaches. Along with the traditional protein-protein docking (free search for a match between two proteins), comparative (template-based) modeling of protein-protein complexes has been gaining popularity. Its development puts an emphasis on full and partial structural similarity between the target protein monomers and the protein-protein complexes previously determined by experimental techniques (templates). The template-based docking relies on the quality and diversity of the template set. We present a carefully curated, nonredundant library of templates containing 4950 full structures of binary complexes and 5936 protein-protein interfaces extracted from the full structures at 12 Å distance cut-off. Redundancy in the libraries was removed by clustering the PDB structures based on structural similarity. The value of the clustering threshold was determined from the analysis of the clusters and the docking performance on a benchmark set. High structural quality of the interfaces in the template and validation sets was achieved by automated procedures and manual curation. The library is included in the Dockground resource for molecular recognition studies at http://dockground.bioinformatics.ku.edu.

Protein models docking benchmark 2.

Structural characterization of protein-protein interactions is essential for our ability to understand life processes. However, only a fraction of known proteins have experimentally determined structures. Such structures provide templates for modeling of a large part of the proteome, where individual proteins can be docked by template-free or template-based techniques. Still, the sensitivity of the docking methods to the inherent inaccuracies of protein models, as opposed to the experimentally determined high-resolution structures, remains largely untested, primarily due to the absence of appropriate benchmark set(s). Structures in such a set should have predefined inaccuracy levels and, at the same time, resemble actual protein models in terms of structural motifs/packing. The set should also be large enough to ensure statistical reliability of the benchmarking results. We present a major update of the previously developed benchmark set of protein models. For each interactor, six models were generated with the model-to-native C(α) RMSD in the 1 to 6 Å range. The models in the set were generated by a new approach, which corresponds to the actual modeling of new protein structures in the "real case scenario," as opposed to the previous set, where a significant number of structures were model-like only. In addition, the larger number of complexes (165 vs. 63 in the previous set) increases the statistical reliability of the benchmarking. We estimated the highest accuracy of the predicted complexes (according to CAPRI criteria), which can be attained using the benchmark structures. The set is available at http://dockground.bioinformatics.ku.edu.

Protein models: the Grand Challenge of protein docking.

Characterization of life processes at the molecular level requires structural details of protein-protein interactions (PPIs). The number of experimentally determined protein structures accounts only for a fraction of known proteins. This gap has to be bridged by modeling, typically using experimentally determined structures as templates to model related proteins. The fraction of experimentally determined PPI structures is even smaller than that for the individual proteins, due to a larger number of interactions than the number of individual proteins, and a greater difficulty of crystallizing protein-protein complexes. The approaches to structural modeling of PPI (docking) often have to rely on modeled structures of the interactors, especially in the case of large PPI networks. Structures of modeled proteins are typically less accurate than the ones determined by X-ray crystallography or nuclear magnetic resonance. Thus the utility of approaches to dock these structures should be assessed by thorough benchmarking, specifically designed for protein models. To be credible, such benchmarking has to be based on carefully curated sets of structures with levels of distortion typical for modeled proteins. This article presents such a suite of models built for the benchmark set of the X-ray structures from the Dockground resource (http://dockground.bioinformatics.ku.edu) by a combination of homology modeling and Nudged Elastic Band method. For each monomer, six models were generated with predefined C(α) root mean square deviation from the native structure (1, 2, …, 6 Å). The sets and the accompanying data provide a comprehensive resource for the development of docking methodology for modeled proteins.

Virtual screening and identification of promising therapeutic compounds against drug-resistant Mycobacterium tuberculosis ß-ketoacyl-acyl carrier protein synthase I (KasA).

The emergence of new Mycobacterium tuberculosis (Mtb) strains resistant to the key drugs currently used in the clinic for tuberculosis treatment can substantially reduce the probability of therapy success, causing the relevance and importance of studies on the development of novel potent antibacterial agents targeting different vulnerable spots of Mtb. In this study, 28,860 compounds from the library of bioactive molecules were screened to identify novel potential inhibitors of ß-ketoacyl-acyl carrier protein synthase I (KasA), one of the key enzymes involved in the biosynthesis of mycolic acids of the Mtb cell wall. In doing so, we used a structure-based virtual screening approach to drug repurposing that included high-throughput docking of the C171Q KasA enzyme with compounds from the library of bioactive molecules including the FDA-approved drugs and investigational drug candidates, assessment of the binding affinity for the docked ligand/C171Q KasA complexes, and molecular dynamics simulations followed by binding free energy calculations. As a result, post-modeling analysis revealed 6 top-ranking compounds exhibiting a strong attachment to the malonyl binding site of the enzyme, as evidenced by the values of binding free energy which are significantly lower than those predicted for the KasA inhibitor TLM5 used in the calculations as a positive control. In light of the data obtained, the identified compounds are suggested to form a good basis for the development of new antitubercular molecules of clinical significance with activity against the KasA enzyme of Mtb.Communicated by Ramaswamy H. Sarma.

Correlation analysis of the side-chains conformational distribution in bound and unbound proteins.

BACKGROUND: Protein interactions play a key role in life processes. Characterization of conformational properties of protein-protein interactions is important for understanding the mechanisms of protein association. The rapidly increasing amount of experimentally determined structures of proteins and protein-protein complexes provides foundation for research on protein interactions and complex formation. The knowledge of the conformations of the surface side chains is essential for modeling of protein complexes. The purpose of this study was to analyze and compare dihedral angle distribution functions of the side chains at the interface and non-interface areas in bound and unbound proteins. RESULTS: To calculate the dihedral angle distribution functions, the configuration space was divided into grid cells. Statistical analysis showed that the similarity between bound and unbound interface and non-interface surface depends on the amino acid type and the grid resolution. The correlation coefficients between the distribution functions increased with the grid spacing increase for all amino acid types. The Manhattan distance showing the degree of dissimilarity between the distribution functions decreased accordingly. Short residues with one or two dihedral angles had higher correlations and smaller Manhattan distances than the longer residues. Met and Arg had the slowest growth of the correlation coefficient with the grid spacing increase. The correlations between the interface and non-interface distribution functions had a similar dependence on the grid resolution in both bound and unbound states. The interface and non-interface differences between bound and unbound distribution functions, caused by biological protein-protein interactions or crystal contacts, disappeared at the 70° grid spacing for interfaces and 30° for non-interface surface, which agrees with an average span of the side-chain rotamers. CONCLUSIONS: The two-fold difference in the critical grid spacing indicates larger conformational changes upon binding at the interface than at the rest of the surface. At the same time, transitions between rotamers induced by interactions across the interface or the crystal packing are rare, with most side chains having local readjustments that do not change the rotameric state. The analysis is important for better understanding of protein interactions and development of flexible docking approaches.

Rotamer libraries and probabilities of transition between rotamers for the side chains in protein-protein binding.

Conformational changes in the side chains are essential for protein-protein binding. Rotameric states and unbound- to-bound conformational changes in the surface residues were systematically studied on a representative set of protein complexes. The side-chain conformations were mapped onto dihedral angles space. The variable threshold algorithm was developed to cluster the dihedral angle distributions and to derive rotamers, defined as the most probable conformation in a cluster. Six rotamer libraries were generated: full surface, surface noninterface, and surface interface-each for bound and unbound states. The libraries were used to calculate the probabilities of the rotamer transitions upon binding. The stability of amino acids was quantified based on the transition maps. The noninterface residues' stability was higher than that of the interface. Long side chains with three or four dihedral angles were less stable than the shorter ones. The transitions between the rotamers at the interface occurred more frequently than on the noninterface surface. Most side chains changed conformation within the same rotamer or moved to an adjacent rotamer. The highest percentage of the transitions was observed primarily between the two most occupied rotamers. The probability of the transition between rotamers increased with the decrease of the rotamer stability. The analysis revealed characteristics of the surface side-chain conformational transitions that can be utilized in flexible docking protocols.

Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors.

A generative adversarial autoencoder for the rational design of potential HIV-1 entry inhibitors able to block CD4-binding site of the viral envelope protein gp120 was developed. To do this, the following studies were carried out: (i) an autoencoder architecture was constructed; (ii) a virtual compound library of potential anti-HIV-1 agents for training the neural network was formed by the concept of click chemistry allowing one to generate a large number of drug candidates by their assembly from small modular units; (iii) molecular docking of all compounds from this library with gp120 was made and calculations of the values of binding free energy were performed; (iv) molecular fingerprints of chemical compounds from the training dataset were generated; (v) training of the developed autoencoder was implemented followed by the validation of this neural network using more than 21 million molecules from the ZINC15 database. As a result, three small drug-like compounds that exhibited the high-affinity binding to gp120 were identified. According to the data from molecular docking, machine learning, quantum chemical calculations, and molecular dynamics simulations, these compounds show the low values of binding free energy in the complexes with gp120 similar to those calculated using the same computational protocols for the HIV-1 entry inhibitors NBD-11021 and NBD-14010, highly potent and broad anti-HIV-1 agents presenting a new generation of the viral CD4 antagonists. The identified CD4-mimetic candidates are suggested to present good scaffolds for the design of novel antiviral drugs inhibiting the early stages of HIV-1 infection.

Discovery of novel promising targets for anti-AIDS drug developments by computer modeling: application to the HIV-1 gp120 V3 loop.

The V3 loop on gp120 from HIV-1 is a focus of many research groups involved in anti-AIDS drug studies, because this region of the protein determines the preference of the virus for T-lymphocytes or primary macrophages. Although the V3 loop governs cell tropism and, for this reason, exhibits one of the most attractive targets for anti-HIV-1 drug developments, its high sequence variability is a major complicating factor. Nevertheless, the data on the spatial arrangement of V3 obtained here for different HIV-1 subtypes by computer modeling clearly show that, despite a wide range of 3D folds, this functionally important site of gp120 forms at least three structurally invariant segments, which contain residues critical for cell tropism. It is evident that these conserved V3 segments represent potential HIV-1 vulnerable spots and, therefore, provide a blueprint for the design of novel, potent and broad antiviral agents able to stop the HIV's spread.

Computational discovery of small drug-like compounds as potential inhibitors of SARS-CoV-2 main protease.

A computational approach to in silico drug discovery was carried out to identify small drug-like compounds able to show structural and functional mimicry of the high affinity ligand X77, potent non-covalent inhibitor of SARS-COV-2 main protease (MPro). In doing so, the X77-mimetic candidates were predicted based on the crystal X77-MPro structure by a public web-oriented virtual screening platform Pharmit. Models of these candidates bound to SARS-COV-2 MPro were generated by molecular docking, quantum chemical calculations and molecular dynamics simulations. At the final point, analysis of the interaction modes of the identified compounds with MPro and prediction of their binding affinity were carried out. Calculation revealed 5 top-ranking compounds that exhibited a high affinity to the active site of SARS-CoV-2 MPro. Insights into the ligand - MPro models indicate that all identified compounds may effectively block the binding pocket of SARS-CoV-2 MPro, in line with the low values ​​of binding free energy and dissociation constant. Mechanism of binding of these compounds to MPro is mainly provided by van der Waals interactions with the functionally important residues of the enzyme, such as His-41, Met-49, Cys-145, Met-165, and Gln-189 that play a role of the binding hot spots assisting the predicted molecules to effectively interact with the MPro active site. The data obtained show that the identified X77-mimetic candidates may serve as good scaffolds for the design of novel antiviral agents able to target the active site of SARS-CoV-2 MPro.Communicated by Ramaswamy H. Sarma.

Structural motifs in protein cores and at protein-protein interfaces are different.

Structures of proteins and protein-protein complexes are determined by the same physical principles and thus share a number of similarities. At the same time, there could be differences because in order to function, proteins interact with other molecules, undergo conformations changes, and so forth, which might impose different restraints on the tertiary versus quaternary structures. This study focuses on structural properties of protein-protein interfaces in comparison with the protein core, based on the wealth of currently available structural data and new structure-based approaches. The results showed that physicochemical characteristics, such as amino acid composition, residue-residue contact preferences, and hydrophilicity/hydrophobicity distributions, are similar in protein core and protein-protein interfaces. On the other hand, characteristics that reflect the evolutionary pressure, such as structural composition and packing, are largely different. The results provide important insight into fundamental properties of protein structure and function. At the same time, the results contribute to better understanding of the ways to dock proteins. Recent progress in predicting structures of individual proteins follows the advancement of deep learning techniques and new approaches to residue coevolution data. Protein core could potentially provide large amounts of data for application of the deep learning to docking. However, our results showed that the core motifs are significantly different from those at protein-protein interfaces, and thus may not be directly useful for docking. At the same time, such difference may help to overcome a major obstacle in application of the coevolutionary data to docking-discrimination of the intramolecular information not directly relevant to docking.

Genome-Wide Analysis of MDR and XDR Tuberculosis from Belarus: Machine-Learning Approach.

Emergence of drug-resistant microorganisms has been recognized as a serious threat to public health worldwide. This problem is extensively discussed in the context of tuberculosis treatment. Alterations in pathogen genomes are among the main mechanisms by which microorganisms exhibit drug resistance. Analysis of 144 M. tuberculosis strains of different phenotypes including drug susceptible, MDR, and XDR isolated in Belarus was fulfilled in this paper. A wide range of machine learning methods that can discover SNPs related to drug-resistance in the whole bacteria genomes was investigated. Besides single-SNP testing approaches, methods that allow detecting joint effects from interacting SNPs were considered. We proposed a framework for automated selection of the best performing statistical model in terms of recall, precision, and accuracy to identify drug resistance-associated mutations. Analysis of whole-genome sequences often leads to situations where the number of treated features exceeds the number of available observations. For this reason, special attention is paid to fair evaluation of the model prediction quality and minimizing the risk of overfitting while estimating the underlying parameters. Results of our experiments aimed at identifying top-scoring resistance mutations to the major first-line and second-line anti-TB drugs are presented.

In Silico Identification of Novel Aromatic Compounds as Potential HIV-1 Entry Inhibitors Mimicking Cellular Receptor CD4.

Despite recent progress in the development of novel potent HIV-1 entry/fusion inhibitors, there are currently no licensed antiviral drugs based on inhibiting the critical interactions of the HIV-1 envelope gp120 protein with cellular receptor CD4. In this connection, studies on the design of new small-molecule compounds able to block the gp120-CD4 binding are still of great value. In this work, in silico design of drug-like compounds containing the moieties that make the ligand active towards gp120 was performed within the concept of click chemistry. Complexes of the designed molecules bound to gp120 were then generated by molecular docking and optimized using semiempirical quantum chemical method PM7. Finally, the binding affinity analysis of these ligand/gp120 complexes was performed by molecular dynamic simulations and binding free energy calculations. As a result, five top-ranking compounds that mimic the key interactions of CD4 with gp120 and show the high binding affinity were identified as the most promising CD4-mimemic candidates. Taken together, the data obtained suggest that these compounds may serve as promising scaffolds for the development of novel, highly potent and broad anti-HIV-1 therapeutics.

Potential HIV-1 fusion inhibitors mimicking gp41-specific broadly neutralizing antibody 10E8: In silico discovery and prediction of antiviral potency.

An integrated computational approach to in silico drug design was used to identify novel HIV-1 fusion inhibitor scaffolds mimicking broadly neutralizing antibody (bNab) 10E8 targeting the membrane proximal external region (MPER) of the HIV-1 gp41 protein. This computer-based approach included (i) generation of pharmacophore models representing 3D-arrangements of chemical functionalities that make bNAb 10E8 active towards the gp41 MPER segment, (ii) shape and pharmacophore-based identification of the 10E8-mimetic candidates by a web-oriented virtual screening platform pepMMsMIMIC, (iii) high-throughput docking of the identified compounds with the gp41 MPER peptide, and (iv) molecular dynamics simulations of the docked structures followed by binding free energy calculations. As a result, eight hits-able to mimic pharmacophore properties of bNAb 10E8 by specific and effective interactions with the MPER region of the HIV-1 protein gp41 were selected as the most probable 10E8-mimetic candidates. Similar to 10E8, the predicted compounds target the critically important residues of a highly conserved hinge region of the MPER peptide that provides a conformational flexibility necessary for its functioning in cell-virus membrane fusion process. In light of the data obtained, the identified small molecules may present promising HIV-1 fusion inhibitor scaffolds for the design of novel potent antiviral drugs.

Computational identification of novel entry inhibitor scaffolds mimicking primary receptor CD4 of HIV-1 gp120.

Virtual screening of novel entry inhibitor scaffolds mimicking primary receptor CD4 of HIV-1 gp120 was carried out in conjunction with evaluation of their potential inhibitory activity by molecular modeling. To do this, pharmacophore models presenting different sets of the hotspots of cellular receptor CD4 for its interaction with gp120 were generated. These models were used as the templates for identification of CD4-mimetic candidates by the pepMMsMIMIC screening platform. Complexes of these candidates with gp120 were built by high-throughput ligand docking and their stability was estimated by molecular dynamics simulations and binding free energy calculations. As a result, five top hits that exhibited strong attachment to the two well-conserved hotspots of the gp120 CD4-binding site were selected for the final analysis. In analogy to CD4, the identified compounds make hydrogen bonds with Asp-368gp120 and multiple van der Waals contacts with the gp120 residues that bind to Phe-43CD4, resulting in destruction of the critical interactions of gp120 with Phe-43CD4 and Arg-59CD4. The complexes of the CD4-mimetic candidates with gp120 show relative conformational stability within the molecular dynamics simulations and expose the high percentage occupancies of intermolecular hydrogen bonds, in line with the data on the binding free energy calculations. In light of these findings, the identified compounds are considered as good scaffolds for the development of new functional antagonists of viral entry with broad HIV-1 neutralization.

Computational discovery of novel HIV-1 entry inhibitors based on potent and broad neutralizing antibody VRC01.

Computational prediction of novel HIV-1 entry inhibitors presenting peptidomimetics of broadly neutralizing antibody (bNAb) VRC01 was carried out based on the analysis of the X-ray complex of this antibody antigen-binding fragment with the HIV envelope gp120 core. Using these empirical data, peptidomimetic candidates of bNAb VRC01 were identified by a public web-oriented virtual screening platform (pepMMsMIMIC) and models of these candidates bound to gp120 were generated by molecular docking. At the final point, the stability of the complexes of these molecules with gp120 was estimated by molecular dynamics and binding free energy calculations. The calculations identified six molecules exhibiting a high affinity to the HIV-1 gp120 protein. These molecules were selected as the most probable peptidomimetics of bNAb VRC01. In a mechanism similar to that of bNAb VRC01, these compounds were predicted to block the functionally conserved regions of gp120 critical for the HIV-1 binding to cellular receptor CD4. The docked structures of the identified molecules with gp120 do not undergo substantial rearrangements during the molecular dynamics simulations, in agreement with the low values of free energy of their formation. Based on these findings, the selected compounds are considered as promising basic structures for the rational design of novel, potent, and broad-spectrum anti-HIV-1 therapeutics.