Ivan Anishchenko

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 HIVs spread.

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 Cα 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. Proteins 2015; 83:891–897.

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 Cα 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. Proteins 2014; 82:278–287.

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. Proteins 2015; 83:1563–1570.

Modeling Complexes of Modeled Proteins

Structural characterization of proteins is essential for understanding life processes at the molecular level. However, only a fraction of known proteins have experimentally determined structures. This fraction is even smaller for protein–protein complexes. Thus, structural modeling of protein–protein interactions (docking) primarily has to rely on modeled structures of the individual proteins, which typically are less accurate than the experimentally determined ones. Such “double” modeling is the Grand Challenge of structural reconstruction of the interactome. Yet it remains so far largely untested in a systematic way. We present a comprehensive validation of template‐based and free docking on a set of 165 complexes, where each protein model has six levels of structural accuracy, from 1 to 6 Å Cα RMSD. Many template‐based docking predictions fall into acceptable quality category, according to the CAPRI criteria, even for highly inaccurate proteins (5–6 Å RMSD), although the number of such models (and, consequently, the docking success rate) drops significantly for models with RMSD > 4 Å. The results show that the existing docking methodologies can be successfully applied to protein models with a broad range of structural accuracy, and the template‐based docking is much less sensitive to inaccuracies of protein models than the free docking. Proteins 2017; 85:470–478.

Modeling CAPRI Targets 110 – 120 by Template-Based and Free Docking Using Contact Potential and Combined Scoring Function

The paper presents analysis of our template‐based and free docking predictions in the joint CASP12/CAPRI37 round. A new scoring function for template‐based docking was developed, benchmarked on the Dockground resource, and applied to the targets. The results showed that the function successfully discriminates the incorrect docking predictions. In correctly predicted targets, the scoring function was complemented by other considerations, such as consistency of the oligomeric states among templates, similarity of the biological functions, biological interface relevance, etc. The scoring function still does not distinguish well biological from crystal packing interfaces, and needs further development for the docking of bundles of α‐helices. In the case of the trimeric targets, sequence‐based methods did not find common templates, despite similarity of the structures, suggesting complementary use of structure‐ and sequence‐based alignments in comparative docking. The results showed that if a good docking template is found, an accurate model of the interface can be built even from largely inaccurate models of individual subunits. Free docking however is very sensitive to the quality of the individual models. However, our newly developed contact potential detected approximate locations of the binding sites.

Structural quality of unrefined models in protein docking

Structural characterization of protein‐protein interactions is essential for understanding life processes at the molecular level. However, only a fraction of protein interactions have experimentally resolved structures. Thus, reliable computational methods for structural modeling of protein interactions (protein docking) are important for generating such structures and understanding the principles of protein recognition. Template‐based docking techniques that utilize structural similarity between target protein‐protein interaction and cocrystallized protein‐protein complexes (templates) are gaining popularity due to generally higher reliability than that of the template‐free docking. However, the template‐based approach lacks explicit penalties for intermolecular penetration, as opposed to the typical free docking where such penalty is inherent due to the shape complementarity paradigm. Thus, template‐based docking models are commonly assumed to require special treatment to remove large structural penetrations. In this study, we compared clashes in the template‐based and free docking of the same proteins, with crystallographically determined and modeled structures. The results show that for the less accurate protein models, free docking produces fewer clashes than the template‐based approach. However, contrary to the common expectation, in acceptable and better quality docking models of unbound crystallographically determined proteins, the clashes in the template‐based docking are comparable to those in the free docking, due to the overall higher quality of the template‐based docking predictions. This suggests that the free docking refinement protocols can in principle be applied to the template‐based docking predictions as well. Proteins 2016; 85:39–45.

Dockground: A comprehensive data resource for modeling of protein complexes

Characterization of life processes at the molecular level requires structural details of protein interactions. The number of experimentally determined structures of protein–protein complexes accounts only for a fraction of known protein interactions. This gap in structural description of the interactome has to be bridged by modeling. An essential part of the development of structural modeling/docking techniques for protein interactions is databases of protein–protein complexes. They are necessary for studying protein interfaces, providing a knowledge base for docking algorithms, and developing intermolecular potentials, search procedures, and scoring functions. Development of protein–protein docking techniques requires thorough benchmarking of different parts of the docking protocols on carefully curated sets of protein–protein complexes. We present a comprehensive description of the Dockground resource (http://dockground.compbio.ku.edu) for structural modeling of protein interactions, including previously unpublished unbound docking benchmark set 4, and the X‐ray docking decoy set 2. The resource offers a variety of interconnected datasets of protein–protein complexes and other data for the development and testing of different aspects of protein docking methodologies. Based on protein–protein complexes extracted from the PDB biounit files, Dockground offers sets of X‐ray unbound, simulated unbound, model, and docking decoy structures. All datasets are freely available for download, as a whole or selecting specific structures, through a user‐friendly interface on one integrated website.

Genome-Wide Structural Modeling of Protein-Protein Interactions

Structural characterization of protein-protein interactions is essential for fundamental understanding of biomolecular processes and applications in biology and medicine. The number of protein interactions in a genome is significantly larger than the number of individual proteins. Most protein structures have to be models of limited accuracy. The structure-based methods for building the network of protein interactions have to be fast and insensitive to the inaccuracies of the modeled structures. This paper describes our latest development of the docking methodology, including global docking search, scoring and refinement of the predictions, its systematic benchmarking on comprehensive sets of protein structures of different accuracy, and application to the genome-wide networks of protein interactions.

Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model

The energy function is the key component of protein modeling methodology. This work presents a semianalytical approach to the development of contact potentials for protein structure modeling. Residue-residue and atom-atom contact energies were derived by maximizing the probability of observing native sequences in a nonredundant set of protein structures. The optimization task was formulated as an inverse statistical mechanics problem applied to the Potts model. Its solution by pseudolikelihood maximization provides consistent estimates of coupling constants at atomic and residue levels. The best performance was achieved when interacting atoms were grouped according to their physicochemical properties. For individual protein structures, the performance of the contact potentials in distinguishing near-native structures from the decoys is similar to the top-performing scoring functions. The potentials also yielded significant improvement in the protein docking success rates. The potentials recapitulated experimentally determined protein stability changes upon point mutations and protein-protein binding affinities. The approach offers a different perspective on knowledge-based potentials and may serve as the basis for their further development.