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Huafeng Xu

Bio: Huafeng Xu is an academic researcher from D. E. Shaw Research. The author has contributed to research in topics: Molecular dynamics & Medicine. The author has an hindex of 27, co-authored 45 publications receiving 5545 citations. Previous affiliations of Huafeng Xu include University of California, San Francisco & Columbia University.


Papers
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Proceedings ArticleDOI
11 Nov 2006
TL;DR: This work presents several new algorithms and implementation techniques that significantly accelerate parallel MD simulations compared with current state-of-the-art codes, including a novel parallel decomposition method and message-passing techniques that reduce communication requirements, as well as novel communication primitives that further reduce communication time.
Abstract: Although molecular dynamics (MD) simulations of biomolecular systems often run for days to months, many events of great scientific interest and pharmaceutical relevance occur on long time scales that remain beyond reach. We present several new algorithms and implementation techniques that significantly accelerate parallel MD simulations compared with current stateof- the-art codes. These include a novel parallel decomposition method and message-passing techniques that reduce communication requirements, as well as novel communication primitives that further reduce communication time. We have also developed numerical techniques that maintain high accuracy while using single precision computation in order to exploit processor-level vector instructions. These methods are embodied in a newly developed MD code called Desmond that achieves unprecedented simulation throughput and parallel scalability on commodity clusters. Our results suggest that Desmond?s parallel performance substantially surpasses that of any previously described code. For example, on a standard benchmark, Desmond?s performance on a conventional Opteron cluster with 2K processors slightly exceeded the reported performance of IBM?s Blue Gene/L machine with 32K processors running its Blue Matter MD code.

2,035 citations

Journal ArticleDOI
TL;DR: The rapidly evolving state of the art for atomic-level biomolecular simulation is described, the types of biological discoveries that can now be made through simulation are illustrated, and challenges motivating continued innovation in this field are discussed.
Abstract: Molecular dynamics simulations capture the behavior of biological macromolecules in full atomic detail, but their computational demands, combined with the challenge of appropriately modeling the relevant physics, have historically restricted their length and accuracy. Dramatic recent improvements in achievable simulation speed and the underlying physical models have enabled atomic-level simulations on timescales as long as milliseconds that capture key biochemical processes such as protein folding, drug binding, membrane transport, and the conformational changes critical to protein function. Such simulation may serve as a computational microscope, revealing biomolecular mechanisms at spatial and temporal scales that are difficult to observe experimentally. We describe the rapidly evolving state of the art for atomic-level biomolecular simulation, illustrate the types of biological discoveries that can now be made through simulation, and discuss challenges motivating continued innovation in this field.

974 citations

Journal ArticleDOI
TL;DR: An atomic-level description of the binding process suggests opportunities for allosteric modulation and provides a structural foundation for future optimization of drug–receptor binding and unbinding rates.
Abstract: How drugs bind to their receptors--from initial association, through drug entry into the binding pocket, to adoption of the final bound conformation, or "pose"--has remained unknown, even for G-protein-coupled receptor modulators, which constitute one-third of all marketed drugs. We captured this pharmaceutically critical process in atomic detail using the first unbiased molecular dynamics simulations in which drug molecules spontaneously associate with G-protein-coupled receptors to achieve final poses matching those determined crystallographically. We found that several beta blockers and a beta agonist all traverse the same well-defined, dominant pathway as they bind to the β(1)- and β(2)-adrenergic receptors, initially making contact with a vestibule on each receptor's extracellular surface. Surprisingly, association with this vestibule, at a distance of 15 A from the binding pocket, often presents the largest energetic barrier to binding, despite the fact that subsequent entry into the binding pocket requires the receptor to deform and the drug to squeeze through a narrow passage. The early barrier appears to reflect the substantial dehydration that takes place as the drug associates with the vestibule. Our atomic-level description of the binding process suggests opportunities for allosteric modulation and provides a structural foundation for future optimization of drug-receptor binding and unbinding rates.

561 citations

Journal ArticleDOI
TL;DR: An activation mechanism for the β2-adrenergic receptor, a prototypical GPCR, is proposed based on atomic-level simulations in which an agonist-bound receptor transitions spontaneously from the active to the inactive crystallographically observed conformation.
Abstract: A third of marketed drugs act by binding to a G-protein-coupled receptor (GPCR) and either triggering or preventing receptor activation. Although recent crystal structures have provided snapshots of both active and inactive functional states of GPCRs, these structures do not reveal the mechanism by which GPCRs transition between these states. Here we propose an activation mechanism for the β2-adrenergic receptor, a prototypical GPCR, based on atomic-level simulations in which an agonist-bound receptor transitions spontaneously from the active to the inactive crystallographically observed conformation. A loosely coupled allosteric network, comprising three regions that can each switch individually between multiple distinct conformations, links small perturbations at the extracellular drug-binding site to large conformational changes at the intracellular G-protein-binding site. Our simulations also exhibit an intermediate that may represent a receptor conformation to which a G protein binds during activation, and suggest that the first structural changes during receptor activation often take place on the intracellular side of the receptor, far from the drug-binding site. By capturing this fundamental signaling process in atomic detail, our results may provide a foundation for the design of drugs that control receptor signaling more precisely by stabilizing specific receptor conformations.

510 citations

Journal ArticleDOI
TL;DR: This model suggests a possible explanation for the high degree of conservation of the DFG motif: that the flip, modulated by electrostatic changes inherent to the catalytic cycle, allows the kinase to access flexible conformations facilitating nucleotide binding and release.
Abstract: In many protein kinases, a characteristic conformational change (the “DFG flip”) connects catalytically active and inactive conformations. Many kinase inhibitors—including the cancer drug imatinib—selectively target a specific DFG conformation, but the function and mechanism of the flip remain unclear. Using long molecular dynamics simulations of the Abl kinase, we visualized the DFG flip in atomic-level detail and formulated an energetic model predicting that protonation of the DFG aspartate controls the flip. Consistent with our model's predictions, we demonstrated experimentally that the kinetics of imatinib binding to Abl kinase have a pH dependence that disappears when the DFG aspartate is mutated. Our model suggests a possible explanation for the high degree of conservation of the DFG motif: that the flip, modulated by electrostatic changes inherent to the catalytic cycle, allows the kinase to access flexible conformations facilitating nucleotide binding and release.

255 citations


Cited by
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01 May 1993
TL;DR: Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems.
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

29,323 citations

Journal ArticleDOI
TL;DR: A range of new simulation algorithms and features developed during the past 4 years are presented, leading up to the GROMACS 4.5 software package, which provides extremely high performance and cost efficiency for high-throughput as well as massively parallel simulations.
Abstract: Motivation: Molecular simulation has historically been a low-throughput technique, but faster computers and increasing amounts of genomic and structural data are changing this by enabling large-scale automated simulation of, for instance, many conformers or mutants of biomolecules with or without a range of ligands. At the same time, advances in performance and scaling now make it possible to model complex biomolecular interaction and function in a manner directly testable by experiment. These applications share a need for fast and efficient software that can be deployed on massive scale in clusters, web servers, distributed computing or cloud resources. Results: Here, we present a range of new simulation algorithms and features developed during the past 4 years, leading up to the GROMACS 4.5 software package. The software now automatically handles wide classes of biomolecules, such as proteins, nucleic acids and lipids, and comes with all commonly used force fields for these molecules built-in. GROMACS supports several implicit solvent models, as well as new free-energy algorithms, and the software now uses multithreading for efficient parallelization even on low-end systems, including windows-based workstations. Together with hand-tuned assembly kernels and state-of-the-art parallelization, this provides extremely high performance and cost efficiency for high-throughput as well as massively parallel simulations. Availability: GROMACS is an open source and free software available from http://www.gromacs.org. Contact: erik.lindahl@scilifelab.se Supplementary information:Supplementary data are available at Bioinformatics online.

6,029 citations

Journal ArticleDOI
01 Jun 2010-Proteins
TL;DR: A new force field, which is termed Amber ff99SB‐ILDN, exhibits considerably better agreement with the NMR data and is validated against a large set of experimental NMR measurements that directly probe side‐chain conformations.
Abstract: Recent advances in hardware and software have enabled increasingly long molecular dynamics (MD) simulations of biomolecules, exposing certain limitations in the accuracy of the force fields used for such simulations and spurring efforts to refine these force fields. Recent modifications to the Amber and CHARMM protein force fields, for example, have improved the backbone torsion potentials, remedying deficiencies in earlier versions. Here, we further advance simulation accuracy by improving the amino acid side-chain torsion potentials of the Amber ff99SB force field. First, we used simulations of model alpha-helical systems to identify the four residue types whose rotamer distribution differed the most from expectations based on Protein Data Bank statistics. Second, we optimized the side-chain torsion potentials of these residues to match new, high-level quantum-mechanical calculations. Finally, we used microsecond-timescale MD simulations in explicit solvent to validate the resulting force field against a large set of experimental NMR measurements that directly probe side-chain conformations. The new force field, which we have termed Amber ff99SB-ILDN, exhibits considerably better agreement with the NMR data. Proteins 2010. © 2010 Wiley-Liss, Inc.

4,590 citations