Time filter

Source Type

San Diego, CA, United States

Background: Neutralization sensitivity of HIV-1 virus to antibodies and anti-sera varies greatly between the isolates. Significant role of V1/V2 domain as a global neutralization sensitivity regulator has been suggested. Recent X-ray structures revealed presence of well-defined tertiary structure within this domain but also demonstrated partial disorder and conformational heterogeneity. Methods: Correlations of neutralization sensitivity with the conformational propensities for beta-strand and alpha-helix formation over the entire folded V1/V2 domain as well as within sliding 5-residue window were investigated. Analysis was based on a set of neutralization data for 106 HIV isolates for which consistent neutralization sensitivity measurements against multiple pools of human immune sera have been previously reported. Results: Significant correlation between beta-sheet formation propensity of the folded segments of V1/V2 domain and neutralization sensitivity was observed. Strongest correlation peaks localized to the beta-strands B and C. Correlation persisted when subsets of HIV isolates belonging to clades B, C and circulating recombinant form BC where analyzed individually or in combinations. Conclusions: Observed correlations suggest that stability of the beta-sheet structure and/or degree of structural disorder in the V1/V2 domain is an important determinant of the global neutralization sensitivity of HIV-1 virus. While specific mechanism is to yet to be investigated, plausible hypothesis is that less ordered V1/V2s may have stronger masking effect on various neutralizing epitopes, perhaps effectively occupying larger volume and thereby occluding antibody access. © 2014 Maxim Totrov. Source

Ferreira P.A.,Duke University | Orry A.,Molsoft LLC
Journal of Neurogenetics | Year: 2012

Despite remarkable advances in human genetics and other genetic model systems, the fruit fly, Drosophila melanogaster, remains a powerful experimental tool to probe with ease the inner workings of a myriad of biological and pathological processes, even when evolutionary forces impart apparent divergences to some of such processes. The understanding of such evolutionary differences provides mechanistic insights into genotype-phenotype correlations underpinning biological processes across metazoans. The pioneering work developed by the William Pak laboratory for the past four decades, and the work of others, epitomize the notion of how the Drosophila system breaks new fertile ground or complements research fields of high scientific and medical relevance. Among the three major genetic complementation groups produced by the Pak's laboratory and impairing distinct facets of photoreceptor neuronal function, the nina group (ninaA, ...., ninaJ) selectively affects the biogenesis of G protein-coupled receptors (GPCRs), mediating the photoconversion and transduction of light stimuli. Among the nina genes identified, ninaA arguably assumes heightened significance for several reasons. First, it presents unique physiological selectivity toward the biogenesis of a subset of GPCRs, a standalone biological manifestation yet to be discerned for most mammalian homologues of NinaA. Second, NinaA belongs to a family of proteins, immunophilins, which are the primary targets for immunosuppressive drugs at the therapeutic forefront of a multitude of medical conditions. Third, NinaA closest homologue, cyclophilin B (CyPB/PPIB), is an immunophilin whose loss-of-function was found recently to cause osteogenesis imperfecta in the human. This report highlights advances made by studies on some members of immunophilins, the cyclophilins. Finally, it reexamines critically data and dogmas derived from past and recent genetic, structural, biological, and pathological studies on NinaA and few other cyclophilins that support some of such paradigms to be less than definite and advance our understanding of the roles of cyclophilins in cell function, disease, and therapeutic interventions. Copyright © 2012 Informa Healthcare USA, Inc. Source

Rueda M.,University of California at San Diego | Totrov M.,Molsoft LLC | Abagyan R.,University of California at San Diego
Journal of Chemical Information and Modeling | Year: 2012

Docking and virtual screening (VS) reach maximum potential when the receptor displays the structural changes needed for accurate ligand binding. Unfortunately, these conformational changes are often poorly represented in experimental structures or homology models, debilitating their docking performance. Recently, we have shown that receptors optimized with our LiBERO method (Ligand-guided Backbone Ensemble Receptor Optimization) were able to better discriminate active ligands from inactives in flexible-ligand VS docking experiments. The LiBERO method relies on the use of ligand information for selecting the best performing individual pockets from ensembles derived from normal-mode analysis or Monte Carlo. Here we present ALiBERO, a new computational tool that has expanded the pocket selection from single to multiple, allowing for automatic iteration of the sampling-selection procedure. The selection of pockets is performed by a dual method that uses exhaustive combinatorial search plus individual addition of pockets, selecting only those that maximize the discrimination of known actives compounds from decoys. The resulting optimized pockets showed increased VS performance when later used in much larger unrelated test sets consisting of biologically active and inactive ligands. In this paper we will describe the design and implementation of the algorithm, using as a reference the human estrogen receptor alpha. © 2012 American Chemical Society. Source

Abagyan R.,Molsoft LLC | Bottegoni G.,Italian Institute of Technology
Journal of Chemical Information and Modeling | Year: 2010

The use of multiple X-ray protein structures has been reported to be an efficient alternative for the representation of the binding pocket flexibility needed for accurate small molecules docking. However, the docking performance of the individual single conformations varies widely, and adding certain conformations to an ensemble is even counterproductive. Here we used a very large and diverse benchmark of 1068 X-ray protein conformations of 99 therapeutically relevant proteins, first, to compare the performance of the ensemble and single-conformation docking and, second, to find the properties of the best-performing conformers that can be used to select a smaller set of conformers for ensemble docking. The conformer selection has been validated through retrospective virtual screening experiments aimed at separating known ligand binders from decoys. We found that the conformers cocrystallized with the largest ligands displayed high selectivity for binders, and when combined in ensembles they consistently provided better results than randomly chosen protein conformations. The use of ensembles encompassing between 3 and 5 experimental conformations consistently improved the docking accuracy and binders vs decoys separation. © 2010 American Chemical Society. Source

Orry A.J.W.,Molsoft LLC | Abagyan R.,University of California at San Diego
Methods in Molecular Biology | Year: 2012

The formation of ligand-protein complexes are critical for the correct functioning of a cell. The prediction of these interactions is important for our understanding of how the cell works and for the development of new drug molecules. Homology modeling is a method for predicting the structure of a protein based on a crystal structure template. Once a model of the protein is complete, a ligand-docking algorithm predicts the ligand-protein model interaction by searching for the best steric and energetically favorable fit. A refinement of the ligand-binding pocket improves the predicted interactions by considering the flexible nature of the ligand-binding pocket. In this chapter, we describe, from first principles, methods to identify and prepare the ligand-binding pocket in a protein model, to dock the ligand, and refine the resulting complex. © 2011 Springer Science+Business Media,LLC. Source

Discover hidden collaborations