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NCBI: db=pubmed; Term=(Vakser, Ilya[Author]) OR ((Miao, Yinglong[Author]) AND Kansas) OR (Deeds, Eric[Author]) OR (Ray, Christian[Author]) OR (Slusky, Joanna[Author]) OR (Ray JC[Author] AND Kansas)
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Ensemble Docking in Drug Discovery.

Fri, 06/14/2019 - 09:03
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Ensemble Docking in Drug Discovery.

Biophys J. 2018 05 22;114(10):2271-2278

Authors: Amaro RE, Baudry J, Chodera J, Demir Ö, McCammon JA, Miao Y, Smith JC

Abstract
Ensemble docking corresponds to the generation of an "ensemble" of drug target conformations in computational structure-based drug discovery, often obtained by using molecular dynamics simulation, that is used in docking candidate ligands. This approach is now well established in the field of early-stage drug discovery. This review gives a historical account of the development of ensemble docking and discusses some pertinent methodological advances in conformational sampling.

PMID: 29606412 [PubMed - indexed for MEDLINE]

PhyreRisk: A Dynamic Web Application to Bridge Genomics, Proteomics and 3D Structural Data to Guide Interpretation of Human Genetic Variants.

Sat, 05/11/2019 - 06:22
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PhyreRisk: A Dynamic Web Application to Bridge Genomics, Proteomics and 3D Structural Data to Guide Interpretation of Human Genetic Variants.

J Mol Biol. 2019 May 07;:

Authors: Ofoegbu TC, David A, Kelley LA, Mezulis S, Islam SA, Mersmann SF, Strömich L, Vakser IA, Houlston RS, Sternberg MJE

Abstract
PhyreRisk is an open-access, publicly-accessible web application for interactively bridging genomic, proteomic and structural data facilitating the mapping of human variants onto protein structures. A major advance over other tools for sequence-structure variant mapping is that PhyreRisk provides information on 20,214 human canonical proteins and an additional 22,271 alternative protein sequences (isoforms). Specifically, PhyreRisk provides structural coverage (partial or complete) for 70% (14,035 of 20,214 canonical proteins) of the human proteome, by storing 18,874 experimental structures and 84,818 pre-built models of canonical proteins and their isoforms generated using our in house Phyre2. PhyreRisk reports 55,732 experimentally, multi-validated protein interactions from IntAct and 24,260 experimental structures of protein complexes. Another major feature of PhyreRisk is that, rather than presenting a limited set of precomputed variant-structure mapping of known genetic variants, it allows the user to explore novel variants using, as input, genomic coordinates formats (Ensembl, VCF, reference SNP ID and HGVS notations) and Human Build GRCh37 and GRCh38. PhyreRisk also supports mapping variants using amino acid coordinates and searching for genes or proteins of interest. PhyreRisk is designed to empower researchers to translate genetic data into protein structural information thereby providing a more comprehensive appreciation of the functional impact of variants. PhyreRisk is freely available at http://phyrerisk.bc.ic.ac.uk.

PMID: 31075275 [PubMed - as supplied by publisher]

Deciphering Off-Target Effects in CRISPR-Cas9 through Accelerated Molecular Dynamics.

Thu, 05/02/2019 - 06:05
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Deciphering Off-Target Effects in CRISPR-Cas9 through Accelerated Molecular Dynamics.

ACS Cent Sci. 2019 Apr 24;5(4):651-662

Authors: Ricci CG, Chen JS, Miao Y, Jinek M, Doudna JA, McCammon JA, Palermo G

Abstract
CRISPR-Cas9 is the state-of-the-art technology for editing and manipulating nucleic acids. However, the occurrence of off-target mutations can limit its applicability. Here, all-atom enhanced molecular dynamics (MD) simulations-using Gaussian accelerated MD (GaMD)-are used to decipher the mechanism of off-target binding at the molecular level. GaMD reveals that base pair mismatches in the target DNA at distal sites with respect to the protospacer adjacent motif (PAM) can induce an extended opening of the RNA:DNA heteroduplex, which leads to newly formed interactions between the unwound DNA and the L2 loop of the catalytic HNH domain. These conserved interactions constitute a "lock" effectively decreasing the conformational freedom of the HNH domain and hampering its activation for cleavage. Remarkably, depending on their positions at PAM distal sites, DNA mismatches responsible for off-target cleavages are unable to "lock" the HNH domain, thereby leading to the unselective cleavage of DNA sequences. In consistency with the available experimental data, the ability to "lock" the catalytic HNH domain in an inactive "conformational checkpoint" is shown to be a key determinant in the onset of off-target effects. This mechanistic rationale contributes in clarifying a long lasting open issue in the CRISPR-Cas9 function and poses the foundation for designing novel and more specific Cas9 variants, which could be obtained by magnifying the "locking" interactions between HNH and the target DNA in the presence of any incorrect off-target sequence, thus preventing undesired cleavages.

PMID: 31041385 [PubMed]

Evolutionary pathways of repeat protein topology in bacterial outer membrane proteins.

Thu, 05/02/2019 - 06:05
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Evolutionary pathways of repeat protein topology in bacterial outer membrane proteins.

Elife. 2018 11 29;7:

Authors: Franklin MW, Nepomnyachyi S, Feehan R, Ben-Tal N, Kolodny R, Slusky JS

Abstract
Outer membrane proteins (OMPs) are the proteins in the surface of Gram-negative bacteria. These proteins have diverse functions but a single topology: the β-barrel. Sequence analysis has suggested that this common fold is a β-hairpin repeat protein, and that amplification of the β-hairpin has resulted in 8-26-stranded barrels. Using an integrated approach that combines sequence and structural analyses, we find events in which non-amplification diversification also increases barrel strand number. Our network-based analysis reveals strand-number-based evolutionary pathways, including one that progresses from a primordial 8-stranded barrel to 16-strands and further, to 18-strands. Among these pathways are mechanisms of strand number accretion without domain duplication, like a loop-to-hairpin transition. These mechanisms illustrate perpetuation of repeat protein topology without genetic duplication, likely induced by the hydrophobic membrane. Finally, we find that the evolutionary trace is particularly prominent in the C-terminal half of OMPs, implicating this region in the nucleation of OMP folding.

PMID: 30489257 [PubMed - indexed for MEDLINE]

Gaussian accelerated molecular dynamics for elucidation of drug pathways.

Thu, 05/02/2019 - 06:05
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Gaussian accelerated molecular dynamics for elucidation of drug pathways.

Expert Opin Drug Discov. 2018 11;13(11):1055-1065

Authors: Bhattarai A, Miao Y

Abstract
INTRODUCTION: Understanding pathways and mechanisms of drug binding to receptors is important for rational drug design. Remarkable advances in supercomputing and methodological developments have opened a new era for application of computer simulations in predicting drug-receptor interactions at an atomistic level. Gaussian accelerated molecular dynamics (GaMD) is a computational enhanced sampling technique that works by adding a harmonic boost potential to reduce energy barriers. GaMD enables free energy calculations without the requirement of predefined collective variables. GaMD has proven useful in biomolecular simulations, in particular, the prediction of drug-receptor interactions. Areas covered: Herein, the authors review recent GaMD simulation studies that elucidated pathways of drug binding to proteins including the G-protein-coupled receptors and HIV protease. Expert opinion: GaMD is advantageous for enhanced simulations of, amongst many biological processes, drug binding to target receptors. Compared with conventional molecular dynamics, GaMD speeds up biomolecular simulations by orders of magnitude. GaMD enables routine drug binding simulations using personal computers with GPUs or common computing clusters. GaMD and, more broadly, enhanced sampling simulations are expected to dramatically increase our capabilities to determine the mechanisms of drug binding to a wide range of receptors in the near future. This will greatly facilitate computer-aided drug design.

PMID: 30371112 [PubMed - indexed for MEDLINE]

Recent advances in computational studies of GPCR-G protein interactions.

Wed, 05/01/2019 - 05:05
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Recent advances in computational studies of GPCR-G protein interactions.

Adv Protein Chem Struct Biol. 2019;116:397-419

Authors: Wang J, Miao Y

Abstract
Protein-protein interactions are key in cellular signaling. G protein-coupled receptors (GPCRs), the largest superfamily of human membrane proteins, are able to transduce extracellular signals (e.g., hormones and neurotransmitters) to intracellular proteins, in particular the G proteins. Since GPCRs serve as primary targets of ~1/3 of currently marketed drugs, it is important to understand mechanisms of GPCR signaling in order to design selective and potent drug molecules. This chapter focuses on recent advances in computational studies of the GPCR-G protein interactions using bioinformatics, protein-protein docking and molecular dynamics simulation approaches.

PMID: 31036298 [PubMed - in process]

Computational approaches to macromolecular interactions in the cell.

Fri, 04/19/2019 - 05:06

Computational approaches to macromolecular interactions in the cell.

Curr Opin Struct Biol. 2019 Apr 15;55:59-65

Authors: Vakser IA, Deeds EJ

Abstract
Structural modeling of a cell is an evolving strategic direction in computational structural biology. It takes advantage of new powerful modeling techniques, deeper understanding of fundamental principles of molecular structure and assembly, and rapid growth of the amount of structural data generated by experimental techniques. Key modeling approaches to principal types of macromolecular assemblies in a cell already exist. The main challenge, along with the further development of these modeling approaches, is putting them together in a consistent, unified whole cell model. This opinion piece addresses the fundamental aspects of modeling macromolecular assemblies in a cell, and the state-of-the-art in modeling of the principal types of such assemblies.

PMID: 30999240 [PubMed - as supplied by publisher]


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