Predicting the accuracy of protein-ligand docking on homology models

Ligand-protein docking is increasingly used in Drug Discovery. The initial limitations imposed by a reduced availability of target protein structures have been overcome by the use of theoretical models, especially those derived by homology modeling techniques. While this greatly extended the use of docking simulations, it also introduced the need for general and robust criteria to estimate the reliability of docking results given the model quality. To this end, a large-scale experiment was performed on a diverse set including experimental structures and homology models for a group of representative ligand-protein complexes. A wide spectrum of model quality was sampled using templates at different evolutionary distances and different strategies for target-template alignment and modeling. The obtained models were scored by a selection of the most used model quality indices. The binding geometries were generated using AutoDock, one of the most common docking programs. An important result of this study is that indeed quantitative and robust correlations exist between the accuracy of docking results and the model quality, especially in the binding site. Moreover, state-of-the-art indices for model quality assessment are already an effective tool for an a priori prediction of the accuracy of docking experiments in the context of groups of proteins with conserved structural characteristics.Contract/grant sponsor: National Institutes of Health; contract/grant numbers: ES00768

A combined computational-experimental approach to define the structural origin of antibody recognition of sialyl-Tn, a tumor-associated carbohydrate antigen.

Anti-carbohydrate monoclonal antibodies (mAbs) hold great promise as cancer therapeutics and diagnostics. However, their specificity can be mixed, and detailed characterization is problematic, because antibody-glycan complexes are challenging to crystallize. Here, we developed a generalizable approach employing high-throughput techniques for characterizing the structure and specificity of such mAbs, and applied it to the mAb TKH2 developed against the tumor-associated carbohydrate antigen sialyl-Tn (STn). The mAb specificity was defined by apparent KD values determined by quantitative glycan microarray screening. Key residues in the antibody combining site were identified by site-directed mutagenesis, and the glycan-antigen contact surface was defined using saturation transfer difference NMR (STD-NMR). These features were then employed as metrics for selecting the optimal 3D-model of the antibody-glycan complex, out of thousands plausible options generated by automated docking and molecular dynamics simulation. STn-specificity was further validated by computationally screening of the selected antibody 3D-model against the human sialyl-Tn-glycome. This computational-experimental approach would allow rational design of potent antibodies targeting carbohydrates

Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening

This work introduces a number of algebraic topology approaches, such as multicomponent persistent homology, multi-level persistent homology and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. Multicomponent persistent homology retains critical chemical and biological information during the topological simplification of biomolecular geometric complexity. Multi-level persistent homology enables a tailored topological description of inter- and/or intra-molecular interactions of interest. Electrostatic persistence incorporates partial charge information into topological invariants. These topological methods are paired with Wasserstein distance to characterize similarities between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for chemical and biological problems. Extensive numerical experiments involving more than 4,000 protein-ligand complexes from the PDBBind database and near 100,000 ligands and decoys in the DUD database are performed to test respectively the scoring power and the virtual screening power of the proposed topological approaches. It is demonstrated that the present approaches outperform the modern machine learning based methods in protein-ligand binding affinity predictions and ligand-decoy discrimination

Structure and function prediction of human homologue hABH5 of _E. coli_ ALKB5 using in silico approach

Newly discovered human homologues of ALKB protein have shown the activity of DNA damaging drugs, used for cancer therapy. Little is known about the structure and function of hABH5, one of the members of this superfamily. Therefore, in the present study we intend to predict its structure and function using various bioinformatics tools. Modeling was done with modeler 9v7 to predict the 3D structure of the hABH5 protein. 3-D model of hABH5, ALKBH5.B99990005.pdb was predicted and evaluated. Validation results showed 96.8% residues in favor and an additional allowed region of the Ramachandran plot. Ligand binding residues prediction showed four ligand clusters, having 25 ligands in cluster 1. Importantly, conserved pattern of Pro158-X-Asp160-Xn-His266 in the functional domain was detected. DNA and RNA binding sites were also predicted in the model. The predicted and validated model of human homologue hABH5 resulting from this study may unveil the mechanism of DNA damage repair in humans and accelerate research on designing appropriate inhibitors, aiding in chemotherapy and cancer related diseases

Structure and function prediction of human homologue hABH5 of _E. coli_ ALKB5 using in silico approach

Newly discovered human homologues of ALKB protein have shown the activity of DNA damaging drugs, used for cancer therapy. Little is known about the structure and function of hABH5, one of the members of this superfamily. Therefore, in the present study we intend to predict its structure and function using various bioinformatics tools. Modeling was done with modeler 9v7 to predict the 3D structure of the hABH5 protein. 3-D model of hABH5, ALKBH5.B99990005.pdb was predicted and evaluated. Validation results showed 96.8% residues in favor and an additional allowed region of the Ramachandran plot. Ligand binding residues prediction showed four ligand clusters, having 25 ligands in cluster 1. Importantly, conserved pattern of Pro158-X-Asp160-Xn-His266 in the functional domain was detected. DNA and RNA binding sites were also predicted in the model. The predicted and validated model of human homologue hABH5 resulting from this study may unveil the mechanism of DNA damage repair in humans and accelerate research on designing appropriate inhibitors, aiding in chemotherapy and cancer related diseases

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Molecular modelling and Function Prediction of hABH7, human homologue of _E. coli_ ALKB7

Human homologues of ALKB protein have shown the prime role in DNA damaging drugs, used for cancer therapy. Little is known about structure and function of hABH7, one of the members of this superfamily. Therefore, in the present study we intend to predict its structure and function using various bioinformatics tools. Modeling was done with modeller 9v7 to predict the 3D structure of the hABH7 protein. The tertiary structure model of hABH7, ALKBH7.B99990002.pdb was predicted and evaluated. Validation results showed 97.8% residues in favored and additional allowed regions of Ramachandran plots. Ligand binding residues prediction showed four ligand clusters, having 25 ligands in cluster 1. Importantly, presence of a Phe120-Gly121-Gly122 conserved pattern in the functional domain was detected. In the predicted structural model of hABH7, amino acid residues, Arginine at 57, 58, 59 and 60 along with tyrosine at 61 were predicted in RNA binding sites of the model. The predicted and validated model of human homologue hABH7 resulting from this study may unveil the mechanism of DNA damage repair in humans and accelerate the research on designing appropriate inhibitors aiding in chemotherapy and cancer related diseases