Popular bioinformatics approaches for studying protein functional dynamics include comparisons of crystallographic structures, molecular dynamics simulations and normal mode analysis. However, determining how observed displacements and predicted motions from these traditionally separate analyses relate to each other, as well as to the evolution of sequence, structure and function within large protein families, remains a considerable challenge. This is in part due to the general lack of tools that integrate information of molecular structure, dynamics and evolution. Here, we describe the integration of new methodologies for evolutionary sequence, structure and simulation analysis into the Bio3D package. This major update includes unique high-throughput normal mode analysis for examining and contrasting the dynamics of related proteins with non-identical sequences and structures, as well as new methods for quantifying dynamical couplings and their residue-wise dissection from correlation network analysis. These new methodologies are integrated with major biomolecular databases as well as established methods for evolutionary sequence and comparative structural analysis. New functionality for directly comparing results derived from normal modes, molecular dynamics and principal component analysis of heterogeneous experimental structure distributions is also included. We demonstrate these integrated capabilities with example applications to dihydrofolate reductase and heterotrimeric G-protein families along with a discussion of the mechanistic insight provided in each case. The integration of structural dynamics and evolutionary analysis in Bio3D enables researchers to go beyond a prediction of single protein dynamics to investigate dynamical features across large protein families. The Bio3D package is distributed with full source code and extensive documentation as a platform independent R package under a GPL2 license from http://thegrantlab.org/bio3d/ .
19 May 2011 in Journal of Chemical Theory and Computation
Multidrug resistance is a serious problem in current chemotherapy. The efflux system largely responsible for resistance in Escherichia coli contains the drug transporter, AcrB. The structures of AcrB were solved in 2002 as the symmetric homo-trimer, and then in 2006 as the asymmetric homo-trimer. The latter suggested a functionally rotating mechanism. Here, by molecular simulations of the AcrB porter domain, we uncovered allosteric coupling and the drug export mechanism in the AcrB trimer. Allosteric coupling stabilized the asymmetric structure with one drug molecule bound, which validated the modelling. Drug dissociation caused a conformational change and stabilized the symmetric structure, providing a unified view of the structures reported in 2002 and 2006. A dynamic study suggested that, among the three potential driving processes, only protonation of the drug-bound protomer can drive the functional rotation and simultaneously export the drug.
1 February 2016 in Journal of Biological Chemistry
G protein α subunits cycle between active and inactive conformations to regulate a multitude of intracellular signaling cascades. Important structural transitions occurring during this cycle have been characterized from extensive crystallographic studies. However, the link between observed conformations and the allosteric regulation of binding events at distal sites critical for signaling through G proteins remain unclear. Here we describe molecular dynamics simulations, bioinformatics analysis, and experimental mutagenesis that identifies residues involved in mediating the allosteric coupling of receptor, nucleotide, and helical domain interfaces of Gαi. Most notably, we predict and characterize novel allosteric decoupling mutants, which display enhanced helical domain opening, increased rates of nucleotide exchange, and constitutive activity in the absence of receptor activation. Collectively, our results provide a framework for explaining how binding events and mutations can alter internal dynamic couplings critical for G protein function.