Analysis and Ranking of Protein-Protein Docking Models Using Inter-Residue Contacts and Inter-Molecular Contact Maps

T cell epitope based vaccine design while targeting outer capsid proteins of rotavirus strains infecting neonates: an immunoinformatics approach

Gastrointestinal diarrhea is majorly caused by the rotavirus (RV) in the children who generally are under the age group of 5 years. WHO estimates that ∼95% of the children contract RV infection, by this age. The disease is highly contagious; notably in many cases, it is proven fatal with high mortality rates especially in the developing countries. In India alone, an estimated 145,000 yearly deaths occurs due to RV related gastrointestinal diarrhea. WHO pre-qualified vaccines that are available for RV are all live attenuated vaccines with modest efficacy range between 40 and 60%. Further, the risk of intussusceptions has been reported in some children on RV vaccination. Thus, in a quest to develop alternative candidate to overcome challenges associated with these oral vaccines, we chose immunoinformatics approach to design a multi-epitope vaccine (MEV) while targeting the outer capsid viral proteinsVP4 and VP7 of the neonatal strains of rotavirus. Interestingly, ten epitopes, that is, six CD8+T-cells and four CD4+T-cell epitopes were identified which were predicted to be antigenic, non-allergic, non-toxic and stable. These epitopes were then linked to adjuvants, linkers, and PADRE sequences to create a multi-epitope vaccine for RV. The in silico designed RV-MEV and human TLR5 complex displayed stable interactions during molecular dynamics simulations. Further, the immune simulation studies of RV-MEV corroborated that the vaccine candidate emerges as a promising immunogen. Future investigations while performing in vitro and in vivo analyses with designed RV-MEV construct are highly desirable to warrant the potential of this vaccine candidate in protective immunity against different strains of RVs infecting neonates.Communicated by Ramaswamy H. Sarma.

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

Motivation

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

Results

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

Availability and implementation

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

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Modeling Protein Complexes and Molecular Assemblies Using Computational Methods

Many biological molecules are assembled into supramolecular complexes that are necessary to perform functions in the cell. Better understanding and characterization of these molecular assemblies are thus essential to further elucidate molecular mechanisms and key protein-protein interactions that could be targeted to modulate the protein binding affinity or develop new binders. Experimental access to structural information on these supramolecular assemblies is often hampered by the size of these systems that make their recombinant production and characterization rather difficult. Computational methods combining both structural data, molecular modeling techniques, and sequence coevolution information can thus offer a good alternative to gain access to the structural organization of protein complexes and assemblies. Herein, we present some computational methods to predict structural models of the protein partners, to search for interacting regions using coevolution information, and to build molecular assemblies. The approach is exemplified using a case study to model the succinate-quinone oxidoreductase heterocomplex.

Immunoinformatics-Aided Design of a Peptide Based Multiepitope Vaccine Targeting Glycoproteins and Membrane Proteins against Monkeypox Virus

Monkeypox is a self-limiting zoonotic viral disease and causes smallpox-like symptoms. The disease has a case fatality ratio of 3-6% and, recently, a multi-country outbreak of the disease has occurred. The currently available vaccines that have provided immunization against monkeypox are classified as live attenuated vaccinia virus-based vaccines, which pose challenges of safety and efficacy in chronic infections. In this study, we have used an immunoinformatics-aided design of a multi-epitope vaccine (MEV) candidate by targeting monkeypox virus (MPXV) glycoproteins and membrane proteins. From these proteins, seven epitopes (two T-helper cell epitopes, four T-cytotoxic cell epitopes and one linear B cell epitopes) were finally selected and predicted as antigenic, non-allergic, interferon-γ activating and non-toxic. These epitopes were linked to adjuvants to design a non-allergic and antigenic candidate MPXV-MEV. Further, molecular docking and molecular dynamics simulations predicted stable interactions between predicted MEV and human receptor TLR5. Finally, the immune-simulation analysis showed that the candidate MPXV-MEV could elicit a human immune response. The results obtained from these in silico experiments are promising but require further validation through additional in vivo experiments.

Confrontation of AlphaFold models with experimental structures enlightens conformational dynamics supporting CYP102A1 functions

Conformational dynamics plays a critical role for the function of multidomain electron transfer complexes. While crystallographic or NMR approaches allow detailed insight into structures, lower resolution methods like cryo-electron microscopy can provide more information on dynamics. In silico structure modelling using AlphaFold was recently successfully extended to the prediction of protein complexes but its capability to address large conformational changes involved in catalysis remained obscure. We used bacterial CYP102A1 monooxygenase homodimer as a test case to design a competitive modelling approach (CMA) for assessing alternate conformations of multi-domain complexes. Predictions were confronted with published crystallographic and cryo-EM data, evidencing consistencies but also permitting some reinterpretation of experimental data. Structural determinants stabilising the new type of domain connectivity evidenced in this bacterial self-sufficient monooxygenase were analysed by CMA and used for in silico retro-engineering applied to its eukaryotic bi-component counterparts.

A random forest classifier for protein-protein docking models

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

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Software and data availability statement

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

D936Y and Other Mutations in the Fusion Core of the SARS-CoV-2 Spike Protein Heptad Repeat 1: Frequency, Geographical Distribution, and Structural Effect

The crown of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is constituted by its spike (S) glycoprotein. S protein mediates the SARS-CoV-2 entry into the host cells. The “fusion core” of the heptad repeat 1 (HR1) on S plays a crucial role in the virus infectivity, as it is part of a key membrane fusion architecture. While SARS-CoV-2 was becoming a global threat, scientists have been accumulating data on the virus at an impressive pace, both in terms of genomic sequences and of three-dimensional structures. On 15 February 2021, from the SARS-CoV-2 genomic sequences in the GISAID resource, we collected 415,673 complete S protein sequences and identified all the mutations occurring in the HR1 fusion core. This is a 21-residue segment, which, in the post-fusion conformation of the protein, gives many strong interactions with the heptad repeat 2, bringing viral and cellular membranes in proximity for fusion. We investigated the frequency and structural effect of novel mutations accumulated over time in such a crucial region for the virus infectivity. Three mutations were quite frequent, occurring in over 0.1% of the total sequences. These were S929T, D936Y, and S949F, all in the N-terminal half of the HR1 fusion core segment and particularly spread in Europe and USA. The most frequent of them, D936Y, was present in 17% of sequences from Finland and 12% of sequences from Sweden. In the post-fusion conformation of the unmutated S protein, D936 is involved in an inter-monomer salt bridge with R1185. We investigated the effect of the D936Y mutation on the pre-fusion and post-fusion state of the protein by using molecular dynamics, showing how it especially affects the latter one.

Evaluation of CONSRANK-Like Scoring Functions for Rescoring Ensembles of Protein–Protein Docking Poses

Scoring is a challenging step in protein–protein docking, where typically thousands of solutions are generated. In this study, we ought to investigate the contribution of consensus-rescoring, as introduced by Oliva et al. (2013) with the CONSRANK method, where the set of solutions is used to build statistics in order to identify recurrent solutions. We explore several ways to perform consensus-based rescoring on the ZDOCK decoy set for Benchmark 4. We show that the information of the interface size is critical for successful rescoring in this context, but that consensus rescoring in itself performs less well than traditional physics-based evaluation. The results of physics-based and consensus-based rescoring are partially overlapping, supporting the use of a combination of these approaches.

The CASP13-CAPRI targets as case studies to illustrate a novel scoring pipeline integrating CONSRANK with clustering and interface analyses

Background

Properly scoring protein-protein docking models to single out the correct ones is an open challenge, also object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), a community-wide blind docking experiment. We introduced in the field CONSRANK (CONSensus RANKing), the first pure consensus method. Also available as a web server, CONSRANK ranks docking models in an ensemble based on their ability to match the most frequent inter-residue contacts in it. We have been blindly testing CONSRANK in all the latest CAPRI rounds, where we showed it to perform competitively with the state-of-the-art energy and knowledge-based scoring functions. More recently, we developed Clust-CONSRANK, an algorithm introducing a contact-based clustering of the models as a preliminary step of the CONSRANK scoring process.

In the latest CASP13-CAPRI joint experiment, we participated as scorers with a novel pipeline, combining both our scoring tools, CONSRANK and Clust-CONSRANK, with our interface analysis tool COCOMAPS. Selection of the 10 models for submission was guided by the strength of the emerging consensus, and their final ranking was assisted by results of the interface analysis.

Results

As a result of the above approach, we were by far the first scorer in the CASP13-CAPRI top-1 ranking, having high/medium quality models ranked at the top-1 position for the majority of targets (11 out of the total 19). We were also the first scorer in the top-10 ranking, on a par with another group, and the second scorer in the top-5 ranking. Further, we topped the ranking relative to the prediction of binding interfaces, among all the scorers and predictors. Using the CASP13-CAPRI targets as case studies, we illustrate here in detail the approach we adopted.

Conclusions

Introducing some flexibility in the final model selection and ranking, as well as differentiating the adopted scoring approach depending on the targets were the key assets for our highly successful performance, as compared to previous CAPRI rounds. The approach we propose is entirely based on methods made available to the community and could thus be reproduced by any user.

Molecular Docking Studies of HIV-1 Resistance to Reverse Transcriptase Inhibitors: Mini-Review

Currently, millions of people are living with human immunodeficiency virus type 1 (HIV-1), which causes acquired immunodeficiency syndrome. However, the spread of the HIV-1 resistance to antiviral agents is the major problem in the antiretroviral therapy and medical management of HIV-infected patients. HIV-1 reverse transcriptase (RT) is one of the key viral targets for HIV-1 inhibition. Therefore, the studies on the combatting the HIV resistance that occurs due to the structural changes in RT, are in great demand. This work aims to provide an overview of the state-of-the-art molecular docking approaches applied to the studies of the HIV-1 resistance, associated with RT structure changes. We have reviewed recent studies using molecular docking with mutant forms of RT. The work discusses the modifications of molecular docking, which have been developed to find the novel molecules active against resistance mutants of RT and/or recombinant strains of HIV-1. The perspectives of the existing algorithms of molecular docking to the studies on molecular mechanisms of resistance and selection of the correct binding poses for the reverse transcriptase inhibitors are discussed.

Docking, steered molecular dynamics, and QSAR studies as strategies for studying isoflavonoids as 5-, 12-, and 15-lipoxygenase inhibitors

Lipoxygenases (LOX) are enzymes that catalyze polyunsaturated fatty acid peroxidation and have a non-heme iron atom located in their active site. They are implicated in the arachidonic acid pathway and involved in inflammation, fever, pain production, and in the origins of several diseases such as cancer, asthma, and psoriasis. The search for inhibitors of these enzymes has emerged in the last years, and isoflavonoids have a broad spectrum of biological activity with low cytotoxicity. Our previous results have shown that isoflavonoids inhibited different LOX isoforms in vitro. For this reason, we studied the most important interactions that govern the potency and selectivity of some isoflavones and isoflavans toward different LOX isoforms using computational methods. The docking results have shown that all the molecules can be located in different zones in the LOX active site. Steered molecular dynamics indicated that selectivity was present at the cavity entry, but not at its exit. We also observed the correlation between the potential mean force and the best (HIR-303) and worst inhibitors (IR-213) in 5-LOX. Finally, structure-activity relationship (QSAR) studies showed a good correlation between theoretical IC₅₀ values and experimental data for 5-LOX and 12-LOX with 96 and 95%, respectively, and a lower correlation for 15-LOX (79%). Conclusively, pharmacophore analysis showed that our proposed molecules should possess a donor-acceptor and aromatic centers to encourage interactions in the active site.

Meta-server for automatic analysis, scoring and ranking of docking models

MOTIVATION

Modelling with multiple servers that use different algorithms for docking results in more reliable predictions of interaction sites. However, the scoring and comparison of all models by an expert is time-consuming and is not feasible for large volumes of data generated by such modelling.

RESULTS

Quality ASsessment of DOcking Models (QASDOM) Server is a simple and efficient tool for real-time simultaneous analysis, scoring and ranking of data sets of receptor-ligand complexes built by a range of docking techniques. This meta-server is designed to analyse large data sets of docking models and rank them by scoring criteria developed in this study. It produces two types of output showing the likelihood of specific residues and clusters of residues to be involved in receptor-ligand interactions and the ranking of models. The server also allows visualizing residues that form interaction sites in the receptor and ligand sequence and displays 3D model structures of the receptor-ligand complexes.

AVAILABILITY

http://qasdom.eimb.ru.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models

Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers’ performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked models in native-like solutions. The best performing clustering approaches we tested indeed lead to more than double the number of cases for which at least one correct solution can be included within the top ten ranked models.

Analysis of the interface variability in NMR structure ensembles of protein-protein complexes

NMR structures consist in ensembles of conformers, all satisfying the experimental restraints, which exhibit a certain degree of structural variability. We analyzed here the interface in NMR ensembles of protein-protein heterodimeric complexes and found it to span a wide range of different conservations. The different exhibited conservations do not simply correlate with the size of the systems/interfaces, and are most probably the result of an interplay between different factors, including the quality of experimental data and the intrinsic complex flexibility. In any case, this information is not to be missed when NMR structures of protein-protein complexes are analyzed; especially considering that, as we also show here, the first NMR conformer is usually not the one which best reflects the overall interface. To quantify the interface conservation and to analyze it, we used an approach originally conceived for the analysis and ranking of ensembles of docking models, which has now been extended to directly deal with NMR ensembles. We propose this approach, based on the conservation of the inter-residue contacts at the interface, both for the analysis of the interface in whole ensembles of NMR complexes and for the possible selection of a single conformer as the best representative of the overall interface. In order to make the analyses automatic and fast, we made the protocol available as a web tool at: https://www.molnac.unisa.it/BioTools/consrank/consrank-nmr.html.