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Analysis of protein dynamics using local-DME calculations.

ABSTRACT: Flexibility and dynamics of protein structures are reflected in the B-factors and order parameters obtained experimentally with X-ray crystallography and Nuclear Magnetic Resonance (NMR). Methods such as Normal Mode Analysis (NMA) and Elastic Network Models (ENM) can be used to predict the fluctuations of protein structures for either atomic level or coarse-grained structures. Here, we introduce the Local-Distance Matrix Error (DME), an efficient and simple analytic method to study the fluctuations of protein structures, especially for the ensembles of NMR-determined protein structures. Comparisons with the fluctuations obtained by experiments and other by computations show strong correlations.

SUBMITTER: Wu D

PROVIDER: S-EPMC3694616 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Analysis of protein dynamics using local-DME calculations.

Wu Di D Smith Stephen S Mahan Hannah H Jernigan Robert L RL

International journal of bioinformatics research and applications 20110101 2

Flexibility and dynamics of protein structures are reflected in the B-factors and order parameters obtained experimentally with X-ray crystallography and Nuclear Magnetic Resonance (NMR). Methods such as Normal Mode Analysis (NMA) and Elastic Network Models (ENM) can be used to predict the fluctuations of protein structures for either atomic level or coarse-grained structures. Here, we introduce the Local-Distance Matrix Error (DME), an efficient and simple analytic method to study the fluctuati ...[more]

PMID: 21576073

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Project description:Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small-molecule lead optimization. Relative free-energy perturbation (FEP) approaches are one of the most widely utilized for this goal but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to set up, execute, and analyze multisite lambda dynamics (MSLD) calculations run on GPUs with CHARMM implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse data set of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free-energy landscape of any MSLD system is developed, which enhances sampling and allows for efficient estimation of free-energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than 100 ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150 ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multisite systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore the chemical space around a lead compound and thus are of utility in lead optimization.

Dataset Information

Analysis of protein dynamics using local-DME calculations.

Publications

Analysis of protein dynamics using local-DME calculations.

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