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Biomolecularmodeling and simulation: a field coming of age - PubMed

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Review

Biomolecularmodeling and simulation: a field coming of age

Tamar Schlick et al. Q Rev Biophys. 2011 May.

Abstract

We assess the progress in biomolecular modeling and simulation, focusing on structure prediction and dynamics, by presenting the field’s history, metrics for its rise in popularity, early expressed expectations, and current significant applications. The increases in computational power combined with improvements in algorithms and force fields have led to considerable success, especially in protein folding, specificity of ligand/biomolecule interactions, and interpretation of complex experimental phenomena (e.g. NMR relaxation, protein-folding kinetics and multiple conformational states) through the generation of structural hypotheses and pathway mechanisms. Although far from a general automated tool, structure prediction is notable for proteins and RNA that preceded the experiment, especially by knowledge-based approaches. Thus, despite early unrealistic expectations and the realization that computer technology alone will not quickly bridge the gap between experimental and theoretical time frames, ongoing improvements to enhance the accuracy and scope of modeling and simulation are propelling the field onto a productive trajectory to become full partner with experiment and a field on its own right.

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Figures

Fig. 1
Fig. 1

Examples of various modeling methods and applications. (a) QM/MM pathway (from top left to bottom right) of DNA repair enzyme polymerase β nucleotide incorporation (Radhakrishnan & Schlick, 2006); (b) superimposed MD configurations from a solvated dodecamer simulation ; (c) mesoscale oligonucleosome model with nucleosome cores in grey and tails and linker histones in color (see Arya & Schlick, 2009; Schlick, 2009a for details) ; (d) top three principal component motions in the thumb and finger subdomains of DNA polymerase β (Arora & Schlick, 2004); (e) representative Monte Carlo snapshots in the sampling of 48-unit oligonucleosomes at three different salt environments using the model shown in (c) ; and (f) minimized water clusters.

Fig. 2
Fig. 2

Metrics of the field’s rise in popularity and the evolution of computational performance. (a) CASP predictions (left-axis) and participants (right-axis) ; (b) modeling papers in peer-reviewed journals as found in the ISI Web of Science using the query words molecular dynamics, biomolecular simulation, molecular modeling, molecular simulation and/or biomolecular modeling; (c) papers from (b) appearing in high-impact-factor journals ; (d) papers from (b) decomposed by method; (e) citations to the original papers of four popular MD packages ; (f) computational systems ranked first, 500th, and highest- ranked academic facility assembled in the Top500 supercomputer lists (

http://www.top500.org

). The total speed for folding@home is also shown for 2007 and 2009. Some biomolecular modeling milestones are also connected to a specific computing system, assuming that the computations were performed about a year prior to publication, except for the two 1998 publications which we associate with computations started in 1996, for : 24-bp DNA system using NCSA SGI machines (Young & Beveridge, 1998), β-heptapeptide using SGI Power Challenge (Daura et al. 1998), villin using the Cray T3E900 (Duan & Kollman, 1998), bc1 membrane complex using the Cray T3E900 (Izrailev et al. 1999), B-DNA dodecamer using MareNostrum, Barcelona (Pérez et al. 2007) and fip35 protein ran on NCSA Dell clusters (Freddolino et al. 2008), with full details presented in Schlick (2010) ; also shown are future predictions (Duan et al. 2000) (see text).

Fig. 3
Fig. 3

Proposed expectation curve for the field of biomolecular modeling and simulation, with approximate timeline. The field started when comprehensive molecular mechanics efforts started, and it took off with the increasing availability of fast workstations and later supercomputers. Following unrealistically high expectations and disappointments, the field is expected to make more realistic progress so that eventually theory and experiment will be hand-in-hand partners.

Fig. 4
Fig. 4

Examples of folding predictions that preceded experiment. (a) HIV PR: tertiary structure of the homodimer (red), predicted by homology modeling (Pearl & Taylor, 1987), superimposed with the crystal structure 3HVP (blue), 198 residues shown (Wlodawer et al. 1989); (b) group-I intron : P4–P6 domain (red), predicted by a comparative modeling (Michel & Westhof, 1990), aligned with crystal structure 1GID (blue), 38 residues (Cate et al. 1996); (c) CASP protein prediction by the Baker group for target T0492 (Raman et al. 2009), kindly provided by Srivatsan Raman and David Baker; crystal structure, 73 residues (blue) of this all-β protein (SH3-like barrel fold) is shown against the best detectable template found by comparative modeling (green) and the predicted structure (red) built using Rosetta using the best template found.

Fig. 5
Fig. 5

Drug/inhibitor-binding pockets to HIV integrase as solved experimentally and as predicted by modeling. (a) Preferred (or primary) binding (yellow shading) of the inhibitor 5-CITEP (green) to the active site of the HIV-1 integrase core domain as determined by crystallography (Goldgur et al. 1999); (b) preferred binding (yellow shading) of the anti-HIV drug Raltegravir (purple) to the active site of the prototype foamy virus (PFV) integrase, a structural homologue of HIV-1 integrase, as determined by crystallography (Hare et al. 2010); (c) predicted preferred binding (yellow shading) for Raltegravir to the HIV-1 integrase catalytic core domain as determined by modeling (Perryman et al. 2010); (d) alternative, or flipped binding (turquoise shading) for Raltegravir to the HIV-1 integrase catalytic core domain as determined by modeling (Perryman et al. 2010). C and D are based on PDB entry 1QS4, with missing residues in panel (A) reconstructed by homology. The magnesium ions are shown as orange spheres.

Fig. 6
Fig. 6

G quadruplex architecture and drug binding. (a) Antiparallel and (b) parallel folds of human telomeric G-quadruplexes; (c) biological unit of the complex between the drug candidate molecule BRACO-19 and two bimolecular human telomeric quadruplexes (Campbell et al. 2008). Figure kindly provided by Stephen Neidle.

Fig. 7
Fig. 7

Chromatin organization : ideal models and simulation-generated model. (a) Ideal solenoid model for the chromatin fiber (side, top, and upper top layer views), in which DNA linkers are bent and neighboring nucleosomes (i±1) are in closest contact ; (b) ideal zigzag model for the chromatin fiber (side, top, and upper top-layer views), in which DNA linkers are straight and next-nearest neighbors (i±2) are in closest contact; (c) heteromorphic architecture obtained by modeling (Grigoryev et al. 2009) at divalent ion environments with the chromatin model shown in Fig. 1c in which mostly zigzag forms with straight linker DNAs are interspersed with bent DNA linkers. In all views, linker and wrapped DNA are colored red ; odd and even nucleosomes are white and blue, respectively ; and linker histones are turquoise.

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