A critical assessment of information-guided protein-protein docking predictions

Prediction of Contact Residues in Anti-HIV Neutralizing Antibody by Deep Learning

The monoclonal antibody 1C10 targets the V3 loop of HIV-1 and neutralizes a broad range of clade B viruses. However, the mode of interaction between 1C10 and the V3 loop remains unclear because crystallization of 1C10 and the V3 peptide was unsuccessful due to the flexible regions present in both 1C10 and the V3 peptide. In this study, we predicted the 1C10 amino acid residues that make contact with the V3 loop using a deep learning (DL)-based method. Inputs from ROSIE for docking simulation and FastContact, Naccess, and PDBtools, to approximate interactions were processed by Chainer for DL, and outputs were obtained as probabilities of contact residues. Using this DL algorithm, D95, D97, P100a, and D100b of CDRH3; D53, and D56 of CDRH2; and D61 of FR3 were highly ranked as contact residues of 1C10. Substitution of these residues with alanine significantly decreased the affinity of 1C10 to the V3 peptide. Moreover, the higher the rank of the residue, the more the binding activity diminished. This study demonstrates that the prediction of contact residues using a DL-based approach is a precise and useful tool for the analysis of antibody-antigen interactions.

G-actin guides p53 nuclear transport: potential contribution of monomeric actin in altered localization of mutant p53

p53 preserves genomic integrity by restricting anomaly at the gene level. Till date, limited information is available for cytosol to nuclear shuttling of p53; except microtubule-based trafficking route, which utilizes minus-end directed motor dynein. The present study suggests that monomeric actin (G-actin) guides p53 traffic towards the nucleus. Histidine-tag pull-down assay using purified p53(1–393)-His and G-actin confirms direct physical association between p53 and monomeric G-actin. Co-immunoprecipitation data supports the same. Confocal imaging explores intense perinuclear colocalization between p53 and G-actin. To address atomistic details of the complex, constraint-based docked model of p53:G-actin complex was generated based on crystal structures. MD simulation reveals that p53 DNA-binding domain arrests very well the G-actin protein. Docking benchmark studies have been carried out for a known crystal structure, 1YCS (complex between p53DBD and BP2), which validates the docking protocol we adopted. Co-immunoprecipitation study using “hot-spot” p53 mutants suggested reduced G-actin association with cancer-associated p53 conformational mutants (R175H and R249S). Considering these findings, we hypothesized that point mutation in p53 structure, which diminishes p53:G-actin complexation results in mutant p53 altered subcellular localization. Our model suggests p53Arg249 form polar-contact with Arg357 of G-actin, which upon mutation, destabilizes p53:G-actin interaction and results in cytoplasmic retention of p53R249S.

Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures

Background

Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs.

Results

To address this problem, we developed eRank^PPI, an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRank^PPI employs multiple features including interface probability estimates calculated by eFindSite^PPI and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRank^PPI consistently outperforms state-of-the-art algorithms improving the success rate by ~10 %.

Conclusions

eRank^PPI was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/erankppi.

NPPD: A Protein-Protein Docking Scoring Function Based on Dyadic Differences in Networks of Hydrophobic and Hydrophilic Amino Acid Residues

Protein-protein docking (PPD) predictions usually rely on the use of a scoring function to rank docking models generated by exhaustive sampling. To rank good models higher than bad ones, a large number of scoring functions have been developed and evaluated, but the methods used for the computation of PPD predictions remain largely unsatisfactory. Here, we report a network-based PPD scoring function, the NPPD, in which the network consists of two types of network nodes, one for hydrophobic and the other for hydrophilic amino acid residues, and the nodes are connected when the residues they represent are within a certain contact distance. We showed that network parameters that compute dyadic interactions and those that compute heterophilic interactions of the amino acid networks thus constructed allowed NPPD to perform well in a benchmark evaluation of 115 PPD scoring functions, most of which, unlike NPPD, are based on some sort of protein-protein interaction energy. We also showed that NPPD was highly complementary to these energy-based scoring functions, suggesting that the combined use of conventional scoring functions and NPPD might significantly improve the accuracy of current PPD predictions.

Structural insights into the interaction of blood coagulation co-factor VIIIa with factor IXa: a computational protein-protein docking and molecular dynamics refinement study

Coagulation factor X (FX) zymogen activation by factor IXa (FIXa) enzyme plays a critical role in the middle-phase of coagulation cascade. The activation process is catalytically inert and requires FIXa binding and complex formation with co-factor VIIIa (FVIIIa). In order to understand the structural details of the FVIIIa:FIXa complex, we employed knowledge-driven protein-protein docking and aqueous-phase MD refinement methods to develop a stable structural complex between FVIIIa and FIXa. The model shows that all four domains of FIXa wrap across FVIIIa that spans the co-factor binding surface of A2, A3 and C1 domains. The region surrounding the 558-helix of the A2-domain of FVIIIa is predicted to be the key interaction site with the helical segments of Lys293-Lys301 and Asp332-Arg338 residues of the serine-protease domain of FIXa. The hydrophobic helical stack between the GLA and EGF1 domains of FIXa is predicted to be primary interacting region with the A3-C2 domain interface of FVIIIa.