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      Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

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          Abstract

          Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

          Author Summary

          This paper provides an overview of recent advancements in computational methods for modeling macromolecular structure and dynamics. The focus is on methods aimed at providing efficient representations of macromolecular structure spaces for the purpose of characterizing equilibrium dynamics. The overview is meant to provide a summary of state-of-the-art capabilities of these methods from an application point of view, as well as highlight important algorithmic contributions responsible for recent advances in macromolecular structure and dynamics modeling.

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          Community structure in social and biological networks

          A number of recent studies have focused on the statistical properties of networked systems such as social networks and the World-Wide Web. Researchers have concentrated particularly on a few properties which seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this paper, we highlight another property which is found in many networks, the property of community structure, in which network nodes are joined together in tightly-knit groups between which there are only looser connections. We propose a new method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer generated and real-world graphs whose community structure is already known, and find that it detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well-known - a collaboration network and a food web - and find that it detects significant and informative community divisions in both cases.
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            Principles that govern the folding of protein chains.

            C ANFINSEN (1973)
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              ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules.

              We have recently completed a full re-architecturing of the ROSETTA molecular modeling program, generalizing and expanding its existing functionality. The new architecture enables the rapid prototyping of novel protocols by providing easy-to-use interfaces to powerful tools for molecular modeling. The source code of this rearchitecturing has been released as ROSETTA3 and is freely available for academic use. At the time of its release, it contained 470,000 lines of code. Counting currently unpublished protocols at the time of this writing, the source includes 1,285,000 lines. Its rapid growth is a testament to its ease of use. This chapter describes the requirements for our new architecture, justifies the design decisions, sketches out central classes, and highlights a few of the common tasks that the new software can perform. © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                28 April 2016
                April 2016
                : 12
                : 4
                Affiliations
                [1 ]Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America
                [2 ]Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland, United States of America
                [3 ]Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
                [4 ]Department of Biongineering, George Mason University, Fairfax, Virginia, United States of America
                [5 ]School of Systems Biology, George Mason University, Manassas, Virginia, United States of America
                Max Planck Institute for Biophysical Chemistry, GERMANY
                Author notes

                The authors have declared that no competing interests exist.

                Article
                PCOMPBIOL-D-15-00493
                10.1371/journal.pcbi.1004619
                4849799
                27124275
                9c7ac764-3e22-4d27-a29b-0a8afa19354c
                © 2016 Maximova et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 4, Tables: 0, Pages: 70
                Product
                Funding
                Funding for this work is provided in part by the National Science Foundation (Grant No. 1421001, Grant No. 1440581, and CAREER Award No. 1144106 to AS) and the Thomas F. and Kate Miller Jeffress Memorial Trust Award. This work has also been funded in whole or in part with Federal funds from the NCI, NIH, under contract number HHSN261200800001E to BM and RN. This study was supported (in part) by the Intramural Research Program of the NIH, NCI, Center for Cancer Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Review
                Biology and Life Sciences
                Molecular Biology
                Macromolecular Structure Analysis
                Protein Structure
                Protein Structure Prediction
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Structure
                Protein Structure Prediction
                Biology and Life Sciences
                Molecular Biology
                Macromolecular Structure Analysis
                Protein Structure
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Structure
                Biology and Life Sciences
                Biochemistry
                Biochemical Simulations
                Biology and Life Sciences
                Computational Biology
                Biochemical Simulations
                Physical Sciences
                Chemistry
                Polymer Chemistry
                Macromolecules
                Research and Analysis Methods
                Simulation and Modeling
                Biology and Life Sciences
                Biophysics
                Biophysical Simulations
                Physical Sciences
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                Biophysical Simulations
                Biology and Life Sciences
                Computational Biology
                Biophysical Simulations
                Physical Sciences
                Physics
                Thermodynamics
                Free Energy
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms

                Quantitative & Systems biology
                Quantitative & Systems biology

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