Preservation of protein clefts in comparative models

Molecular dynamics study of naturally existing cavity couplings in proteins

Couplings between protein sub-structures are a common property of protein dynamics. Some of these couplings are especially interesting since they relate to function and its regulation. In this article we have studied the case of cavity couplings because cavities can host functional sites, allosteric sites, and are the locus of interactions with the cell milieu. We have divided this problem into two parts. In the first part, we have explored the presence of cavity couplings in the natural dynamics of 75 proteins, using 20 ns molecular dynamics simulations. For each of these proteins, we have obtained two trajectories around their native state. After applying a stringent filtering procedure, we found significant cavity correlations in 60% of the proteins. We analyze and discuss the structure origins of these correlations, including neighbourhood, cavity distance, etc. In the second part of our study, we have used longer simulations (≥100 ns) from the MoDEL project, to obtain a broader view of cavity couplings, particularly about their dependence on time. Using moving window computations we explored the fluctuations of cavity couplings along time, finding that these couplings could fluctuate substantially during the trajectory, reaching in several cases correlations above 0.25/0.5. In summary, we describe the structural origin and the variations with time of cavity couplings. We complete our work with a brief discussion of the biological implications of these results.

In silico investigation and structural characterization of virulent factor and a metallo peptidase present in Helicobacter pylori strain J99

VacA is a high-molecular weight multimeric pore-forming protein encoded by the chromosomal gene vacA of Helicobacter pylori J99 strian. It plays a significant role in the development of gastric cancer in human by inducing the formation of vacuoles. Genomics and proteomics features of an organism have provided a plethora of potential drug targets. The crystal structure of VacA is not available in any structural database; hence a 3D structure is very essential for structural studies and discovery of potential inhibitors against proteins. In this study 3D structure of VacA is modelled a by using Bhageerath: an energy based web enabled computer software suite. According to our study VacA steriochemical validation shows 91.7% residues are in allowed region of Ramachandran plot. Further validation was done by WHAT CHECK to provide evidence that the distribution of the main chain bond lengths and omega bond angles were within limits with Z-score 1.0 and error values are negligible. The modelled protein was submitted to Protein Model Database and can be downloaded with PMDID PM0077963. Further we found that metallo peptidase "M3" cleaves VacA and helps in import mechanism in mitochondria. Structure of metallo peptidase is also not available in any structural database so we modelled and validated its structure. With the help of docking studies we blocked the active site of metallo peptidase by ligand LA3 and 294 with binding energy -5.9 and -5.2 KJ/mol respectively, thus prevented import mechanism of VacA in mitochondria. The inhibitors identified from our study were LA3 and 294 ligands. The investigation concluded that these drugs could be used as the potential inhibitors against the damage of stomach and duodenum, which ultimately reduces the likelihood of ulcer as well as gastric cancer.

Characterization of the impact of alternative splicing on protein dynamics: the cases of glutathione S-transferase and ectodysplasin-A isoforms

Recent studies have shown how alternative splicing (AS), the process by which eukaryotic genes express more than one product, affects protein sequence and structure. However, little information is available on the impact of AS on protein dynamics, a property fundamental for protein function. In this work, we have addressed this issue using molecular dynamics simulations of the isoforms of two model proteins: glutathione S-transferase and ectodysplasin-A. We have found that AS does not have a noticeable impact on global or local structure fluctuations. We have also found that, quite interestingly, AS has a significant effect on the coupling between key structural elements such as surface cavities. Our results provide the first atom-level view of the impact of AS on protein dynamics, as far as we know. They can contribute to refine our present view of the relationship between AS and protein disorder and, more importantly, they reveal how AS may modify structural dynamic couplings in proteins.

FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level

The rapid accumulation of gene sequences, many of which are hypothetical proteins with unknown function, has stimulated the development of accurate computational tools for protein function prediction with evolution/structure-based approaches showing considerable promise. In this article, we present FINDSITE-metal, a new threading-based method designed specifically to detect metal-binding sites in modeled protein structures. Comprehensive benchmarks using different quality protein structures show that weakly homologous protein models provide sufficient structural information for quite accurate annotation by FINDSITE-metal. Combining structure/evolutionary information with machine learning results in highly accurate metal-binding annotations; for protein models constructed by TASSER, whose average Cα RMSD from the native structure is 8.9 Å, 59.5% (71.9%) of the best of top five predicted metal locations are within 4 Å (8 Å) from a bound metal in the crystal structure. For most of the targets, multiple metal-binding sites are detected with the best predicted binding site at rank 1 and within the top two ranks in 65.6% and 83.1% of the cases, respectively. Furthermore, for iron, copper, zinc, calcium, and magnesium ions, the binding metal can be predicted with high, typically 70% to 90%, accuracy. FINDSITE-metal also provides a set of confidence indexes that help assess the reliability of predictions. Finally, we describe the proteome-wide application of FINDSITE-metal that quantifies the metal-binding complement of the human proteome. FINDSITE-metal is freely available to the academic community at http://cssb.biology.gatech.edu/findsite-metal/.

Cross-reactivity virtual profiling of the human kinome by X-react(KIN): a chemical systems biology approach

Many drug candidates fail in clinical development due to their insufficient selectivity that may cause undesired side effects. Therefore, modern drug discovery is routinely supported by computational techniques, which can identify alternate molecular targets with a significant potential for cross-reactivity. In particular, the development of highly selective kinase inhibitors is complicated by the strong conservation of the ATP-binding site across the kinase family. In this paper, we describe X-React(KIN), a new machine learning approach that extends the modeling and virtual screening of individual protein kinases to a system level in order to construct a cross-reactivity virtual profile for the human kinome. To maximize the coverage of the kinome, X-React(KIN) relies solely on the predicted target structures and employs state-of-the-art modeling techniques. Benchmark tests carried out against available selectivity data from high-throughput kinase profiling experiments demonstrate that, for almost 70% of the inhibitors, their alternate molecular targets can be effectively identified in the human kinome with a high (>0.5) sensitivity at the expense of a relatively low false positive rate (<0.5). Furthermore, in a case study, we demonstrate how X-React(KIN) can support the development of selective inhibitors by optimizing the selection of kinase targets for small-scale counter-screen experiments. The constructed cross-reactivity profiles for the human kinome are freely available to the academic community at http://cssb.biology.gatech.edu/kinomelhm/ .

The utility of geometrical and chemical restraint information extracted from predicted ligand-binding sites in protein structure refinement

Exhaustive exploration of molecular interactions at the level of complete proteomes requires efficient and reliable computational approaches to protein function inference. Ligand docking and ranking techniques show considerable promise in their ability to quantify the interactions between proteins and small molecules. Despite the advances in the development of docking approaches and scoring functions, the genome-wide application of many ligand docking/screening algorithms is limited by the quality of the binding sites in theoretical receptor models constructed by protein structure prediction. In this study, we describe a new template-based method for the local refinement of ligand-binding regions in protein models using remotely related templates identified by threading. We designed a Support Vector Regression (SVR) model that selects correct binding site geometries in a large ensemble of multiple receptor conformations. The SVR model employs several scoring functions that impose geometrical restraints on the Cα positions, account for the specific chemical environment within a binding site and optimize the interactions with putative ligands. The SVR score is well correlated with the RMSD from the native structure; in 47% (70%) of the cases, the Pearson's correlation coefficient is >0.5 (>0.3). When applied to weakly homologous models, the average heavy atom, local RMSD from the native structure of the top-ranked (best of top five) binding site geometries is 3.1Å (2.9Å) for roughly half of the targets; this represents a 0.1 (0.3)Å average improvement over the original predicted structure. Focusing on the subset of strongly conserved residues, the average heavy atom RMSD is 2.6Å (2.3Å). Furthermore, we estimate the upper bound of template-based binding site refinement using only weakly related proteins to be ∼2.6Å RMSD. This value also corresponds to the plasticity of the ligand-binding regions in distant homologues. The Binding Site Refinement (BSR) approach is available to the scientific community as a web server that can be accessed at http://cssb.biology.gatech.edu/bsr/.