Approaching the CAPRI challenge with an efficient geometry-based docking

A Shortcut from Genome to Drug: The Employment of Bioinformatic Tools to Find New Targets for Gastric Cancer Treatment

Gastric cancer (GC) is a highly heterogeneous, complex disease and the fifth most common cancer worldwide (about 1 million cases and 784,000 deaths worldwide in 2018). GC has a poor prognosis (the 5-year survival rate is less than 20%), but there is an effort to find genes highly expressed during tumor establishment and use the related proteins as targets to find new anticancer molecules. Data were collected from the Gene Expression Omnibus (GEO) bank to obtain three dataset matrices analyzing gastric tumor tissue versus normal gastric tissue and involving microarray analysis performed using the GPL570 platform and different sources. The data were analyzed using the GEPIA tool for differential expression and KMPlot for survival analysis. For more robustness, GC data from the TCGA database were used to corroborate the analysis of data from GEO. The genes found in in silico analysis in both GEO and TCGA were confirmed in several lines of GC cells by RT-qPCR. The AlphaFold Protein Structure Database was used to find the corresponding proteins. Then, a structure-based virtual screening was performed to find molecules, and docking analysis was performed using the DockThor server. Our in silico and RT-qPCR analysis results confirmed the high expression of the AJUBA, CD80 and NOLC1 genes in GC lines. Thus, the corresponding proteins were used in SBVS analysis. There were three molecules, one molecule for each target, MCULE-2386589557-0-6, MCULE-9178344200-0-1 and MCULE-5881513100-0-29. All molecules had favorable pharmacokinetic, pharmacodynamic and toxicological properties. Molecular docking analysis revealed that the molecules interact with proteins in critical sites for their activity. Using a virtual screening approach, a molecular docking study was performed for proteins encoded by genes that play important roles in cellular functions for carcinogenesis. Combining a systematic collection of public microarray data with a comparative meta-profiling, RT-qPCR, SBVS and molecular docking analysis provided a suitable approach for finding genes involved in GC and working with the corresponding proteins to search for new molecules with anticancer properties.

A molecular docking study revealed that synthetic peptides induced conformational changes in the structure of SARS-CoV-2 spike glycoprotein, disrupting the interaction with human ACE2 receptor

The global outbreak of COVID-19 (Coronavirus Disease 2019) caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome caused by Coronavirus 2) began in December 2019. Its closest relative, SARS-CoV-1, has a slightly mutated Spike (S) protein, which interacts with ACE2 receptor in human cells to start the infection. So far, there are no vaccines or drugs to treat COVID-19. So, research groups worldwide are seeking new molecules targeting the S protein to prevent infection by SARS-CoV-2 and COVID-19 establishment. We performed molecular docking analysis of eight synthetic peptides against SARS-CoV-2 S protein. All interacted with the protein, but Mo-CBP₃-PepII and PepKAA had the highest affinity with it. By binding to the S protein, both peptides led to conformational alterations in the protein, resulting in incorrect interaction with ACE2. Therefore, given the importance of the S protein-ACE2 interaction for SARS-CoV-2 infection, synthetic peptides could block SARS-CoV-2 infection. Moreover, unlike other antiviral drugs, peptides have no toxicity to human cells. Thus, these peptides are potential molecules to be tested against SARS-CoV-2 and to develop new drugs to treat COVID-19.

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Synthetic peptides bind to SARS-CoV-2 Spike protein.

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Synthetic peptides induced conformational changes on SARS-CoV-2 spike protein structure.

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Synthetic peptides bind to ACE2 protein but did not affect its structure.

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Synthetic peptides induced the wrong interaction of SARS-CoV-2 with ACE2 receptor.

Homology modeling and docking studies of FabH (β-ketoacyl-ACP synthase III) enzyme involved in type II fatty acid biosynthesis of Chlorella variabilis: a potential algal feedstock for biofuel production

The concept of using microalgae as an alternative renewable source of biofuel has gained much importance in recent years. However, its commercial feasibility is still an area of concern for researchers. Unraveling the fatty acid metabolic pathway and understanding structural features of various key enzymes regulating the process will provide valuable insights to target microalgae for augmented oil content. FabH (β-ketoacyl-acyl carrier protein synthase; KAS III) is a condensing enzyme catalyzing the initial elongation step of type II fatty acid biosynthetic process and acyl carrier protein (ACP) facilitates the shuttling of the fatty acyl intermediates to the active site of the respective enzymes in the pathway. In the present study, a reliable three-dimensional structure of FabH from Chlorella variabilis, an oleaginous green microalga was modeled and subsequently the key residues involved in substrate binding were determined by employing protein-protein docking and molecular dynamics (MD) simulation protocols. The FabH-ACP complex having the lowest docking energy score showed the binding of ACP to the electropositive FabH surface with strong hydrogen bond interactions. The MD simulation results indicated that the substrate-complexed FabH adopted a more stable conformation than the free enzyme. Further, the FabH structure retained its stability throughout the simulation although noticeable displacements were observed in the loop regions. Molecular simulation studies suggested the importance of crucial hydrogen bonding of the conserved Arg(91) of FabH with Glu(53) and Asp(56) of ACP for exhibiting high affinity between the enzyme and substrate. The molecular modeling results are consistent with available experimental results on the flexibility of FabH and the present study provides first in silico insights into the structural and dynamical aspect of catalytic mechanism of FabH, which could be used for further site-specific mutagenic experiments to develop engineered high oil-yielding microalgal strains for biofuel production.

An integrated suite of fast docking algorithms

The CAPRI experiment (Critical Assessment of Predicted Interactions) simulates realistic and diverse docking challenges, each case having specific properties that may be exploited by docking algorithms. Motivated by the different CAPRI challenges, we developed and implemented a comprehensive suite of docking algorithms. These were incorporated into a dynamic docking protocol, consisting of four main stages: (1) Biological and bioinformatics research aiming to predict the binding site residues, to define distance constraints between interface atoms and to analyze the flexibility of molecules; (2) Rigid or flexible docking, performed by the PatchDock or FlexDock method, which utilizes the information gathered in the previous step. Symmetric complexes are predicted by the SymmDock method; (3) Flexible refinement and reranking of the rigid docking solution candidates, performed by FiberDock; and finally, (4) clustering and filtering the results based on energy funnels. We analyzed the performance of our docking protocol on a large benchmark and on recent CAPRI targets. The analysis has demonstrated the importance of biological information gathering prior to docking, which significantly increased the docking success rate, and of the refinement and rescoring stage that significantly improved the ranking of the rigid docking solutions. Our failures were mostly a result of mishandling backbone flexibility, inaccurate homology modeling, or incorrect biological assumptions. Most of the methods are available at http://bioinfo3d.cs.tau.ac.il/.

Comparison of PGH2 binding site in prostaglandin synthases

BACKGROUND

Prostaglandin H2 (PGH2) is a common precursor for the synthesis of five different Prostanoids via specific Prostanoid Synthases. The binding of this substrate with these Synthases is not properly understood. Moreover, currently no crystal structure of complexes bound with PGH2 has been reported. Hence, understanding the interactions of PGH2 and characterizing its binding sites in these synthases is crucial for developing novel therapeutics based on these proteins as targets.

RESULTS

Shape and physico-chemical properties of the PGH2 binding sites of the four prostanoid synthases were analyzed and compared in order to understand the molecular basis of the specificity. This study provides models with predicted pockets for the binding of PGH2 with PGD, PGE, PGF and PGI Synthases. The results closely match with available experimental data. The comparison showed seven physico-chemical features that are common to the four PGH2 binding sites. However this common pattern is not statistically unique and is not specific enough to distinguish between proteins that can or cannot bind PGH2. A large scale search in ASTRAL data bank, a non redundant Protein Data Bank, for a similar pattern showed the uniqueness of each of the PGH2 binding site in these Synthases.

CONCLUSION

The binding pockets in PGDS, PGES, PGFS and PGIS are unique and do not share significant commonality which can be characterized as a PGH2 binding site. Local comparison of these protein structures highlights a case of convergent evolution in analogous functional sites.

Rapid structural characterization of human antibody-antigen complexes through experimentally validated computational docking

If we understand the structural rules governing antibody (Ab)-antigen (Ag) interactions in a given virus, then we have the molecular basis to attempt to design and synthesize new epitopes to be used as vaccines or optimize the antibodies themselves for passive immunization. Comparing the binding of several different antibodies to related Ags should also further our understanding of general principles of recognition. To obtain and compare the three-dimensional structure of a large number of different complexes, however, we need a faster method than traditional experimental techniques. While biocomputational docking is fast, its results might not be accurate. Combining experimental validation with computational prediction may be a solution. As a proof of concept, here we isolated a monoclonal Ab from the blood of a human donor recovered from dengue virus infection, characterized its immunological properties, and identified its epitope on domain III of dengue virus E protein through simple and rapid NMR chemical shift mapping experiments. We then obtained the three-dimensional structure of the Ab/Ag complex by computational docking, using the NMR data to drive and validate the results. In an attempt to represent the multiple conformations available to flexible Ab loops, we docked several different starting models and present the result as an ensemble of models equally agreeing with the experimental data. The Ab was shown to bind a region accessible only in part on the viral surface, explaining why it cannot effectively neutralize the virus.

Development of a Novel Bioinformatics Tool for In Silico Validation of Protein Interactions

Protein interactions are crucial in most biological processes. Several in silico methods have been recently developed to predict them. This paper describes a bioinformatics method that combines sequence similarity and structural information to support experimental studies on protein interactions. Given a target protein, the approach selects the most likely interactors among the candidates revealed by experimental techniques, but not yet in vivo validated. The sequence and the structural information of the in vivo confirmed proteins and complexes are exploited to evaluate the candidate interactors. Finally, a score is calculated to suggest the most likely interactors of the target protein. As an example, we searched for GRB2 interactors. We ranked a set of 46 candidate interactors by the presented method. These candidates were then reduced to 21, through a score threshold chosen by means of a cross-validation strategy. Among them, the isoform 1 of MAPK14 was in silico confirmed as a GRB2 interactor. Finally, given a set of already confirmed interactors of GRB2, the accuracy and the precision of the approach were 75% and 86%, respectively. In conclusion, the proposed method can be conveniently exploited to select the proteins to be experimentally investigated within a set of potential interactors.

Computational modelling of protein interactions: energy minimization for the refinement and scoring of association decoys

The prediction of protein-protein interactions based on independently obtained structural information for each interacting partner remains an important challenge in computational chemistry. Procedures where hypothetical interaction models (or decoys) are generated, then ranked using a biochemically relevant scoring function have been garnering interest as an avenue for addressing such challenges. The program PatchDock has been shown to produce reasonable decoys for modeling the association between pig alpha-amylase and the VH-domains of camelide antibody raised against it. We designed a biochemically relevant method by which PatchDock decoys could be ranked in order to separate near-native structures from false positives. Several thousand steps of energy minimization were used to simulate induced fit within the otherwise rigid decoys and to rank them. We applied a partial free energy function to rank each of the binding modes, improving discrimination between near-native structures and false positives. Sorting decoys according to strain energy increased the proportion of near-native decoys near the bottom of the ranked list. Additionally, we propose a novel method which utilizes regression analysis for the selection of minimization convergence criteria and provides approximation of the partial free energy function as the number of algorithmic steps approaches infinity.

Classification of heterodimer interfaces using docking models and construction of scoring functions for the complex structure prediction

Protein–protein docking simulations can provide the predicted complex structural models. In a docking simulation, several putative structural models are selected by scoring functions from an ensemble of many complex models. Scoring functions based on statistical analyses of heterodimers are usually designed to select the complex model with the most abundant interaction mode found among the known complexes, as the correct model. However, because the formation schemes of heterodimers are extremely diverse, a single scoring function does not seem to be sufficient to describe the fitness of the predicted models other than the most abundant interaction mode. Thus, it is necessary to classify the heterodimers in terms of their individual interaction modes, and then to construct multiple scoring functions for each heterodimer type. In this study, we constructed the classification method of heterodimers based on the discriminative characters between near-native and decoy models, which were found in the comparison of the interfaces in terms of the complementarities for the hydrophobicity, the electrostatic potential and the shape. Consequently, we found four heterodimer clusters, and then constructed the multiple scoring functions, each of which was optimized for each cluster. Our multiple scoring functions were applied to the predictions in the unbound docking.

A simple reference state makes a significant improvement in near-native selections from structurally refined docking decoys

Near-native selections from docking decoys have proved challenging especially when unbound proteins are used in the molecular docking. One reason is that significant atomic clashes in docking decoys lead to poor predictions of binding affinities of near native decoys. Atomic clashes can be removed by structural refinement through energy minimization. Such an energy minimization, however, will lead to an unrealistic bias toward docked structures with large interfaces. Here, we extend an empirical energy function developed for protein design to protein-protein docking selection by introducing a simple reference state that removes the unrealistic dependence of binding affinity of docking decoys on the buried solvent accessible surface area of interface. The energy function called EMPIRE (EMpirical Protein-InteRaction Energy), when coupled with a refinement strategy, is found to provide a significantly improved success rate in near native selections when applied to RosettaDock and refined ZDOCK docking decoys. Our work underlines the importance of removing nonspecific interactions from specific ones in near native selections from docking decoys.

Automatic prediction of protein interactions with large scale motion

Proteins often change their conformation upon binding to other molecules. Taking these conformational changes into account in docking is an extremely difficult task: the larger the scale of the motion the harder it is to predict the structure of the association complex. Here, we present a fully automated method for flexible docking with large scale motion in one of the docked molecules. The method automatically identifies hinge regions and rigid parts and then docks the input molecules while explicitly considering the hinges and possible protein motions.

FastContact: a free energy scoring tool for protein-protein complex structures

‘FastContact’ is a server that estimates the direct electrostatic and desolvation interaction free energy between two proteins in units of kcal/mol. Users submit two proteins in PDB format, and the output is emailed back to the user in three files: one output file, and the two processed proteins. Besides the electrostatic and desolvation free energy, the server reports residue contact free energies that rapidly highlight the hotspots of the interaction and evaluates the van der Waals interaction using CHARMm. Response time is ∼1 min. The server has been successfully tested and validated, scoring refined complex structures and blind sets of docking decoys, as well as proven useful predicting protein interactions. ‘FastContact’ offers unique capabilities from biophysical insights to scoring and identifying important contacts.

Correlated mutations: advances and limitations. A study on fusion proteins and on the Cohesin-Dockerin families

Correlated mutations have been repeatedly exploited for intramolecular contact map prediction. Over the last decade these efforts yielded several methods for measuring correlated mutations. Nevertheless, the application of correlated mutations for the prediction of intermolecular interactions has not yet been explored. This gap is due to several obstacles, such as 3D complexes availability, paralog discrimination, and the availability of sequence pairs that are required for inter- but not intramolecular analyses. Here we selected for analysis fusion protein families that bypass some of these obstacles. We find that several correlated mutation measurements yield reasonable accuracy for intramolecular contact map prediction on the fusion dataset. However, the accuracy level drops sharply in intermolecular contacts prediction. This drop in accuracy does not occur always. In the Cohesin-Dockerin family, reasonable accuracy is achieved in the prediction of both intra- and intermolecular contacts. The Cohesin-Dockerin family is well suited for correlated mutation analysis. Because, however, this family constitutes a special case (it has radical mutations, has domain repeats, within each species each Dockerin domain interacts with each Cohesin domain, see below), the successful prediction in this family does not point to a general potential in using correlated mutations for predicting intermolecular contacts. Overall, the results of our study indicate that current methodologies of correlated mutations analysis are not suitable for large-scale intermolecular contact prediction, and thus cannot assist in docking. With current measurements, sequence availability, sequence annotations, and underdeveloped sequence pairing methods, correlated mutations can yield reasonable accuracy only for a handful of families.

Geometry-based flexible and symmetric protein docking

We present a set of geometric docking algorithms for rigid, flexible, and cyclic symmetry docking. The algorithms are highly efficient and have demonstrated very good performance in CAPRI Rounds 3-5. The flexible docking algorithm, FlexDock, is unique in its ability to handle any number of hinges in the flexible molecule, without degradation in run-time performance, as compared to rigid docking. The algorithm for reconstruction of cyclically symmetric complexes successfully assembles multimolecular complexes satisfying C(n) symmetry for any n in a matter of minutes on a desktop PC. Most of the algorithms presented here are available at the Tel Aviv University Structural Bioinformatics Web server (http://bioinfo3d.cs.tau.ac.il/).

Scoring a diverse set of high-quality docked conformations: A metascore based on electrostatic and desolvation interactions

Predicting protein-protein interactions involves sampling and scoring docked conformations. Barring some large structural rearrangement, rapidly sampling the space of docked conformations is now a real possibility, and the limiting step for the successful prediction of protein interactions is the scoring function used to reduce the space of conformations from billions to a few, and eventually one high affinity complex. An atomic level free-energy scoring function that estimates in units of kcal/mol both electrostatic and desolvation interactions (plus van der Waals if appropriate) of protein-protein docked conformations is used to rerank the blind predictions (860 in total) submitted for six targets to the community-wide Critical Assessment of PRediction of Interactions (CAPRI; http://capri.ebi.ac.uk). We found that native-like models often have varying intermolecular contacts and atom clashes, making unlikely that one can construct a universal function that would rank all these models as native-like. Nevertheless, our scoring function is able to consistently identify the native-like complexes as those with the lowest free energy for the individual models of 16 (out of 17) human predictors for five of the targets, while at the same time the modelers failed to do so in more than half of the cases. The scoring of high-quality models developed by a wide variety of methods and force fields confirms that electrostatic and desolvation forces are the dominant interactions determining the bound structure. The CAPRI experiment has shown that modelers can predict valuable models of protein-protein complexes, and improvements in scoring functions should soon solve the docking problem for complexes whose backbones do not change much upon binding. A scoring server and programs are available at http://structure.pitt.edu.

PatchDock and SymmDock: servers for rigid and symmetric docking

Here, we describe two freely available web servers for molecular docking. The PatchDock method performs structure prediction of protein–protein and protein–small molecule complexes. The SymmDock method predicts the structure of a homomultimer with cyclic symmetry given the structure of the monomeric unit. The inputs to the servers are either protein PDB codes or uploaded protein structures. The services are available at . The methods behind the servers are very efficient, allowing large-scale docking experiments.