Annual reports in computational chemistry

Annual Reports in COMPUTATIONAL CHEMISTRY VOLUME Edited by Ralph A Wheeler Department of Chemistry and Biochemistry, Duquesne University, 600 Forbes Avenue, Pittsburgh, PA 15282-1530 Sponsored by the Division of Computers in Chemistry of the American Chemical Society Amsterdam • Boston • Heidelberg • London • New York • Oxford Paris • San Diego • San Francisco • Singapore • Sydney • Tokyo Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands Linacre House, Jordan Hill, Oxford OX2 8DP, UK 32 Jamestown Road, London NW1 7BY, UK 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA First edition 2010 Copyright � 2010 Elsevier B V All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: permissions@elsevier.com Alternatively you can submit your request online by visiting the Elsevier web site at http://www.elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made Library of Congress Cataloging-in-Publication Data A catalogue record for this book is available from the Library of congress British Library Cataloging in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-444-53552-8 ISSN: 1574-1400 For information on all Elsevier publications visit our website at elsevierdirect.com Printed and bound in USA 10 11 12 13 10 Working together to grow libraries in developing countries www.elsevier.com | www.bookaid.org | www.sabre.org CONTRIBUTORS Orlando Acevedo Department of Chemistry and Biochemistry, Auburn University, Auburn, AL, USA Kristin S Alongi Dean’s Office and Department of Chemistry & Physics, College of Science & Technology, Armstrong Atlantic State University, Savannah, GA, USA Wei An Department of Chemical and Biological Engineering, The University of Alabama, Tuscaloosa, AL, USA Oshrit Arviv Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel Mauricio Cafiero Department of Chemistry, Rhodes College, Memphis, TN, USA Qiang Cui Department of Chemistry and Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, WI, USA Olga Dolgounitcheva Department of Chemistry and Biochemistry, Auburn University, Auburn, AL, USA Brett I Dunlap Chemistry Division, Naval Research Laboratory, Washington DC, USA George M Giambas¸u Biomedical Informatics and Computational Biology; Department of Chemistry, University of Minnesota, Minneapolis, MN, USA Andreas W Goătz San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, USA Tzachi Hagai Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel Daniel Harries Institute of Chemistry and the Fritz Haber Research Center, The Hebrew University of Jerusalem, Jerusalem, Israel ix x Contributors Sheng-You Huang Department of Physics and Astronomy, Department of Biochemistry, Dalton Cardiovascular Research Center, and Informatics Institute, University of Missouri, Columbia, MO, USA George Khelashvili Department of Physiology and Biophysics, Weill Medical College of Cornell University, New York, NY, USA Kah Chun Lau Department of Chemistry, George Washington University, Washington DC, USA Yaakov Levy Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel Hongzhi Li Institute of Molecular Biophysics, Florida State University, Tallahassee, FL, USA Yan Ling Department of Chemistry and Biochemistry, University of Southern Mississippi, Hattiesburg, MS, USA Maura Livengood Department of Chemistry, Rhodes College, Memphis, TN, USA Donghong Min Institute of Molecular Biophysics, Florida State University, Tallahassee, FL, USA J V Ortiz Department of Chemistry and Biochemistry, Auburn University, Auburn, AL, USA Dalit Shental-Bechor Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel Edward C Sherer Merck and Co., Inc., Rahway, NJ, USA George C Shields Dean’s Office & Department of Chemistry, College of Arts & Sciences, Bucknell University, Lewisburg, PA, USA Tai-Sung Lee Biomedical Informatics and Computational Biology; Department of Chemistry, University of Minnesota, Minneapolis, MN, USA C Heath Turner Department of Chemical and Biological Engineering, The University of Alabama, Tuscaloosa, AL, USA Hunter Utkov Department of Chemistry, Rhodes College, Memphis, TN, USA Contributors xi Jonah Z Vilseck Department of Chemistry and Biochemistry, Auburn University, Auburn, AL, USA Ross C Walker San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, USA Xian Wang Department of Chemical and Biological Engineering, The University of Alabama, Tuscaloosa, AL, USA Mark J Williamson San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, USA Thorsten Woălfle San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, USA; Lehrstuhl fuăr Theoretische Chemie, Universitaăt Erlangen, Erlangen, Germany Dong Xu San Diego Supercomputer Center; National Biomedical Computation Resource, University of California San Diego, La Jolla, CA, USA Wei Yang Institute of Molecular Biophysics; Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL, USA Darrin M York Department of Chemistry, University of Minnesota, Minneapolis, MN, USA Alexander V Zakjevskii Department of Chemistry and Biochemistry, Auburn University, Auburn, AL, USA Viatcheslav G Zakrzewski Department of Chemistry and Biochemistry, Auburn University, Auburn, AL, USA Yong Zhang Department of Chemistry, Chemical Biology, and Biomedical Engineering, Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ, USA Xiaoqin Zou Department of Physics and Astronomy, Department of Biochemistry, Dalton Cardiovascular Research Center, and Informatics Institute, University of Missouri, Columbia, MO, USA PREFACE Annual Reports in Computational Chemistry (ARCC) was instituted to provide timely reviews of topics important to researchers in Computational Chemistry ARCC is published and distributed by Elsevier and sponsored by the American Chemical Society’s Division of Computers in Chemistry (COMP) Members in good stand­ ing of the COMP Division receive a copy of the ARCC as part of their member benefits Since previous volumes have received such an enthusiastic response from our readers, the COMP Executive Committee expects to deliver future volumes of ARCC that build on the solid contributions in our first five volumes To ensure that you receive future installments of this series, please join the Division as described on the COMP website at http://www.acscomp.org Volume features 14 outstanding contributions in six sections and includes a new section devoted to Nanotechnology and the reemergence of the Chemical Education section Topics covered (and Section Editors) include Simulation Meth­ odologies (Carlos Simmerling), Quantum Chemistry (Gregory S Tschumper), Chemical Education (George C Shields), Nanotechnology (Luke E.K Achenie), Biological Modeling (Nathan Baker), and Bioinformatics (Wei Wang) Although individual chapters in ARCC are now indexed by the major abstracting services, we plan to continue the practice of cumulative indexing of both the current and past editions to provide an easy identification of past reports As was the case with our previous volumes, the current volume of Annual Reports in Computational Chemistry has been assembled entirely by volunteers to produce a high-quality scientific publication at the lowest possible cost The Editor and the COMP Executive Committee extend our gratitude to the many people who have given their time to make this edition of Annual Reports in Computational Chemistry possible The authors of each of this year’s contributions and the Section Editors have graciously dedicated significant amounts of their time to make this volume successful This year’s edition could not have been assembled without the help of Clare Caruana of Elsevier Thank you one and all for your hard work, your time, and your contributions We trust that you will find this edition to be interesting and valuable We are actively planning the seventh volume and anticipate that it will restore one or more previously popular sections, including Materials and/or Emerging Technologies In addition, we are actively soliciting input from our readers about future topics, so please contact the editor to make suggestions and/or to volunteer as a contributor Sincerely, Ralph A Wheeler, Editor xiii Section Simulation Methodologies Section Editor: Carlos Simmerling Department of Chemistry, State University of New York, Stony Brook, NY 11794, USA CHAPTER Advancements in Molecular Dynamics Simulations of Biomolecules on Graphical Processing Units Dong Xu1,2, Mark J Williamson1, and Ross C Walker1 Contents Introduction An Overview of GPU Programming 2.1 GPU/CPU hardware differences 2.2 The emergence of GPU programming languages 2.3 GPU programming considerations GPU-Based Implementations of Classical Molecular Dynamics 3.1 Early GPU-based MD code development 3.2 Production GPU-based MD codes Performance and Accuracy 4.1 Performance and scaling 4.2 Validation Applications 5.1 Protein folding Conclusions and Future Directions Acknowledgments References Abstract Over the past few years competition within the computer game market coupled with the emergence of application programming interfaces to support general purpose computation on graphics processing units (GPUs) has led to an explosion in the use of GPUs for acceleration of scientific applications Here we explore the use of GPUs within the context of condensed phase molecular dynamics (MD) simulations We discuss the algorithmic differences that the GPU architecture imposes on MD codes, an overview of the challenges involved in using GPUs for MD, followed by a San Diego Supercomputer Center, University of California San Diego, La Jolla, CA, USA National Biomedical Computation Resource, University of California San Diego, La Jolla, CA, USA Annual Reports in Computational Chemistry, Volume ISSN: 1574-1400, DOI 10.1016/S1574-1400(10)06001-9 6 9 11 13 13 14 15 15 16 17 17 � 2010 Elsevier B.V All rights reserved Ross C Walker et al critical survey of contemporary MD simulation packages that are attempting to utilize GPUs Finally we discuss the possible outlook for this field Keywords: GPU; CUDA; stream; NVIDIA; ATI; molecular dynamics; accelerator; OpenMM; ACEMD; NAMD; AMBER INTRODUCTION Since the first molecular dynamics (MD) simulation of an enzyme was described by McCammon et al [1] MD simulations have evolved to become an important tool in understanding the behavior of biomolecules Since that first 10 ps long simulation of merely 500 atoms in 1977, the field has grown to where small enzymes can be routinely simulated on the microsecond timescale [2—4] Simula­ tions containing millions of atoms are now also considered routine [5,6] Such simulations are numerically intensive requiring access to large-scale supercom­ puters or well-designed clusters with expensive interconnects that are beyond the reach of many research groups Many attempts have been made over the years to accelerate classical MD simulation of condensed-phase biological systems by exploiting alternative hardware technologies Some notable examples include ATOMS by AT&T Bell Laboratories [7], FASTRUN designed by Columbia University in 1984 and con­ structed by Brookhaven National Laboratory in 1989 [8], the MDGRAPE system by RIKEN [9] which used custom hardware—accelerated lookup tables to accel­ erate the direct space nonbond calculations, Clearspeed Inc which developed an implicit solvent version of the AMBER PMEMD engine [10,11] that ran on their custom designed Advance X620 and e620 acceleration cards [12], and most recently DE Shaw Research LLC who developed their own specialized architec­ ture for classical MD simulations code-named Anton [13] All of these approaches have, however, failed to make an impact on main­ stream research because of their excessive cost Table provides estimates of the original acquisition or development costs of several accelerator technologies These costs have posed a significant barrier to widespread development within the academic research community Additionally these technologies not form Table Example cost estimates for a range of hardware MD acceleration projects Accelerator technology Manufacturer Estimated cost per node CX600 MDGRAPE-3 ATOMS FASTRUN ClearSpeed Riken AT&T Bell Laboratories Columbia University and Brookhaven National Laboratory NVIDIA/ATI ~$10,000 ~$9,000,000a ~$186,000 (1990) ~$17,000 (1989) GPU a Total development cost: $15 million [14] $200—800 290 Sheng-You Huang and Xiaoqin Zou ij ¼ Nij nij rị ; ij rị ẳ 4r2 Dr 4R =3 ð16Þ where Nij and nij ðrÞ are the numbers of protein—ligand atom pair occurrences of type ij in the reference sphere with a radius R and in the spherical shell between radii r and r ỵ Dr, respectively The calculation for the ligand volume j correction factor f Vol corr ðrÞ is more complicated [47], the basic principle of À which is that the spherical shell centered at a ligand atom j is not completely occupied by the protein due to the excluded volume effect of the ligand The shell volume may also be occupied by the ligand or the solvent Therefore, the shell volume for each shell centered at a ligand atom should be properly partitioned to the protein, ligand, and/or solvent in the derivation of the PMF potentials, by introducing the ligand volume correction factor j f VolÀ corr ðrÞ By adjusting the size R of the reference sphere to include the different volume proportions of the protein/ligand/solvent in the sphere, the PMF potentials attempted to account for the effects of desolvation and entropy PMF has 34 ligand atom types and 16 protein atom types, extracted from a training set of 697 protein—ligand complex structures With a test set of 77 protein—ligand complexes, the PMF scoring function outperforms the Bohm’s score (LUDI) [31] and small molecule growth (SMoG) [59], yielding a high correlation coefficient (R2 ¼ 0:61) between the calculated scores and the experimental binding constants The PMF scoring function was also successfully applied to docking/scoring studies of weak ligands for the FK506 binding protein [65] and inhibitors for matrix metalloprotease MMP3 [66] Recently, a newer version of PMF (PMF04) has been developed on a much larger database of 7152 protein—ligand complexes from the PDB [67] In this work, Muegge showed that the larger size of the training structural database does not improve the scoring results much and so for the inclusion of metal ions Based on 19 atom types and 200 protein—ligand complexes, Zhou and cow­ orkers used a distance-scale finite ideal-gas reference (DFIRE) state to develop a mean-force energy function for protein—ligand, protein—protein, and protein—DNA complexes [56] The idea of DFIRE is that unlike the ideal gas system in which the available volume is 4r2 Dr for a shell between radii r and r ỵ Dr, the available volume for a shell from the radius r to r ỵ Dr centered at a protein/ ligand atom is not proportional to 4r2 for the protein—ligand complex due to the effects of excluded volume of the protein/ligand An appropriate correction factor should be applied to include the effect of the excluded volume in the computation of the mean-force potentials Specifically, the DFIRE potentials between atom types i and j at distance r is given by 6 wij rị ẳ kB Tln nij rị  r Dr nij ðrcut Þ Drcut rcut ð17Þ Mean-Force Scoring Functions for Ligand Binding 291 where nij ðrÞ is the number of atom pairs ij in the shell from the radius r Dr=2 to r ỵ Dr=2 observed in a given structure database, and rcut is the cutoff for the ˚ The exponent  is a scale factor for considering potentials and was set to 14.5 A the effect of excluded volume and was found to be 1.61 on the basis of a state of uniformly distributed points in finite spheres [68] Dr=Drcut was introduced so that the interaction potentials wij ðrÞ ! when r ! rcut Using the test set of 100 protein—ligand complexes by Wang et al [69], the DFIRE scoring function obtained a correlation coefficient of 0.63 between the calculated scores and the experimental data for binding affinity prediction and a success rate of 58% for identifying native or near-native binding modes WITHOUT THE USE OF THE REFERENCE STATE No matter whether the reference state is approximated with an atom-randomized state or corrected with a volume factor, the derived mean-force/knowledge-based potentials are not the true interaction potentials according to Eqs (2)—(5) Due to the complexity of proteins, the reference state is inaccessible [45] Attempting to address this problem, Huang and Zou have recently developed a mean-force scoring func­ tion (ITScore) using an iterative method to circumvent the accurate calculation of the reference state [49,50,70—72] The basic idea of the iterative method is to improve the effective pair potentials uij ðrÞ by iteration until the mean-force potentials converge to a set of stable effective potentials that can reproduce the pair distribu­ tion functions in experimentally determined protein-ligand complex structures The method uses the following iterative function: kỵ1ị uij h i kị kị rị ẳ uij rị ỵ lkB T gij ðrÞ À gobs ðrÞ ij ð18Þ where i and j represent the atom types for a pair of atoms in the protein and the ligand, respectively; k stands for the iterative step; and l is a parameter to control the convergence rate Without loss of generality, kB T was set to unit one in the calculation gobs ij ðrÞ is the experimentally observed pair distribution function reflecting the structural characteristics of experimentally determined pro­ ðkÞ tein—ligand structures, and gij ðrÞ is the predicted pair distribution function for ðkÞ all the sampled ligand poses using the trial potentials uij ðrÞ at the k-th step ðkÞ The predicted pair distribution function gij ðrÞ can be calculated by using the following formula: ðkÞ gij rị kị kị kị ẳ ij rị kị ij;bulk ð19Þ where ij ðrÞ and ij;bulk are the number densities of atom pair type ij occurring in a spherical shell Dr and in a reference sphere of radius Rmax , respectively Given a dataset of M protein—ligand complexes and L decoys for each complex, ðkÞ ðkÞ ij ðrÞ and ij;bulk were calculated as 292 Sheng-You Huang and Xiaoqin Zou ðkÞ ij rị ẳ M X L nml rịe Uml X ij MÁL m l 4r2 Dr ðkÞ and ij;bulk ¼ M X L N ml e À Uml X ij M Á L m l VðRmax Þ ð20Þ where nijml ðrÞ and Nijml are the numbers of atom pair type ij in the spherical shell from r Dr=2 to r ỵ Dr=2 and the reference sphere for the l-th decoy ligand pose of the m-th complex, respectively Uml is the energy score of this ligand ðkÞ pose calculated by the potentials uij ðrÞ L is the total number of putative ligand poses for each complex (including the native binding mode, i.e., l ẳ 0) VRmax ị ¼ 4R3max =3 is the volume of the reference sphere Obviously, max m Nijm ẳ ặrẳR nij rị rẳ0 Similarly, the experimentally observed pair distribution function gobs ij ðrÞ can be obtained from the native structures of the complexes by using Eq (19) and setting L ¼ in Eq (20) ð0Þ ð1Þ Thus, given a guess of initial potentials uij ðrÞ, an improved potential uij ðrÞ can be obtained using Eq (18) Repeating this cycle, the potentials will converge kỵ1ị kị to relatively stable values [i.e., uij rịằuij rị] It can be seen from Eq (18) that the improvement for the potentials uij ðrÞ depends only on the difference between the predicted and experimentally observed pair distribution functions instead of any properties related to the reference state Therefore, the iterative method does not face the reference state problem encountered by traditional mean-force/knowledge-based scoring functions ITScore has been extensively assessed with diverse test sets [50] It yielded a success rate of 82% on binding mode identification for Wang et al.’s test set [69] of 100 protein—ligand complexes It achieved a correlation (R) of 0.64 and 0.81 on binding affinity prediction for Wang et al.’s test set and the PMF validation set [47] of 77 complexes, respectively ITScore was also validated on virtual screen­ ing using compound databases for four protein targets [73]: ER, MMP3, fXa, and AChE Very recently, Huang and Zou have also included the solvation effect and configurational entropy in ITScore by using a similar iterative method The resulted scoring function, referred to as ITScore/SE, significantly improves the performance compared to ITScore [48] CONCLUSION In this chapter, we have reviewed the current types of mean-force scoring func­ tions according to how they treat the reference state Despite considerable pro­ gress, several lines remain open for future development First, as demonstrated and discussed in our previous study [48], explicit inclusion of the entropic effects may significantly improve the accuracy of a mean-force scoring function Second, the appropriate categorization of atom types with a good balance of the statistics Mean-Force Scoring Functions for Ligand Binding 293 of the atom pair occurrences and the number of atom types is a common issue for mean-force scoring functions Third, the current pairwise potentials are obviously a simplification; how to incorporate many-body interactions and whether this can significantly improve the scoring performance remain unknown because of the introduction of many more parameters to be determined Finally, binding mode prediction and thus virtual screening are challenges for many mean-force scoring functions, particularly if they involve inaccurate reference state calculations The physics behind it is that conventional mean-force scoring functions use only information embedded in native structures; the derived potentials are thus not very useful for noncanonical interactions presented in decoys and thereby for binding mode prediction One way to solve the problem is the iterative approach for ITScore [49], which considers the information embedded in the whole energy landscape (i.e., energies for both native structures and decoys) The derived potentials would contain the gradient force to separate native structures from decoy structures As mean-force potentials become increasingly accurate, they will serve as a valuable tool for structure-based drug design Detailed analysis of the mean-force potentials will provide insightful hints on how to improve other physics-based scoring functions Mean-force potentials that perform well on binding mode predictions may also be implemented for molecular dynamics simulations of protein—ligand interactions ACKNOWLEDGMENTS We thank Sam G Grinter for critical reading of the manuscript Support to XZ from OpenEye Scientific Software, Inc (Santa Fe, NM, USA) and Tripos, Inc (St Louis, MO, USA) is gratefully acknowledged XZ is supported by NIH grant GM088517, Cystic Fibrosis Foundation grant ZOU07I0, the Research Board Award RB-07-32, and the Research Council Grant URC 09-004 of the University of Missouri This work is also supported by Federal Earmark NASA Funds for Bioinformatics Consortium Equipment and additional financial support from Dell, SGI, Sun Microsystems, TimeLogic, and Intel REFERENCES Brooijmans, N., Kuntz, I.D Molecular recognition and docking algorithms Annu Rev Biophys Biomol Struct 2003, 32, 33573 Boă hm, H.J., Stahl, M The use of scoring functions in drug discovery applications Rev Comput Chem 2002, 18, 41—87 Wang, W., Donini, O., Reyes, C.M., Kollman, P.A Biomolecular simulations: Recent developments in force fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and proteinnucleic acid noncovalent interactions Annu Rev Biophys Biomol Struct 2001, 30, 211—43 Shoichet, B.K., McGovern, S.L., Wei, B., Irwin, J.J Lead discovery using molecular docking Curr Opin Chem Biol 2002, 6, 439—46 Reddy, M.R., Erion, M.D Free Energy Calculations in Rational Drug Design, Kluwer Academic, New York, 2001 Seifert, M.H.J., Kraus, J., Kramer, B Virtual high-throughput screening of molecular databases Curr Opin Drug Discov Devel 2007, 10, 298—307 Jain, A.N Scoring functions for protein-ligand docking Curr Protein Pept Sci 2006, 7, 407—20 294 Sheng-You Huang and Xiaoqin Zou Schulz-Gasch, T., Stahl, M Scoring functions for protein-ligand interactions: A critical perspective Drug Discov Today Technol 2004, 1, 231—9 Rajamani, R., Good, A.C Ranking poses in structure-based lead discovery and optimization: Current trends in scoring function development Curr Opin Drug Discov Devel 2007, 10, 308—15 10 Gohlke, H., Klebe, G Statistical potentials and scoring functions applied to protein-ligand bind­ ing Curr Opin Struct Biol 2001, 11, 231—5 11 Gilson, M.K., Zhou, H.X Calculation of protein-ligand binding affinities Annu Rev Biophys Biomol Struct 2007, 36, 21—42 12 Meng, E.C., Shoichet, B.K., Kuntz, I.D Automated docking with grid-based energy approach to macromolecule-ligand interactions J Comput Chem 1992, 13, 505—24 13 Goodsell, D.S., Olson, A.J Automated docking of substrates to proteins by simulated annealing Proteins 1990, 8, 195—202 14 Zou, X., Sun, Y., Kuntz, I.D Inclusion of solvation in ligand binding free energy calculations using the generalized-Born model J Am Chem Soc 1999, 121, 8033—43 15 Wei, B.Q., Baase, W.A., Weaver, L.H., Matthews, B.W., Shoichet, B.K A model binding site for testing scoring functions in molecular docking J Mol Biol 2002, 322, 339—55 16 Liu, H.-Y., Kuntz, I.D., Zou, X Pairwise GB/SA scoring function for structure-based drug design J Phys Chem B 2004, 108, 5453—62 17 Kuhn, B., Gerber, P., Schulz-Gasch, T., Stahl, M Validation and use of the MM-PBSA approach for drug discovery J Med Chem 2005, 48, 4040—8 18 Liu, H.-Y., Zou, X Electrostatics of ligand binding: Parametrization of the generalized born model and comparison with the Poisson-Boltzmann approach J Phys Chem B 2006, 110, 9304—13 19 Liu, H.-Y., Grinter, S.Z., Zou, X Multiscale generalized Born modeling of ligand binding energies for virtual database screening J Phys Chem B 2009, 113, 11793—9 20 Lyne, P.D., Lamb, M.L., Saeh, J.C Accurate prediction of the relative potencies of members of a series of kinase inhibitors using molecular docking and MM-GBSA scoring J Med Chem 2006, 49, 4805—8 21 Guimaraes, C.R.W., Cardozo, M MM-GB/SA rescoring of docking poses in structure-based lead optimization J Chem Inf Model 2008, 48, 958—70 22 Thompson, D.C., Humblet, C., Joseph-McCarthy, D Investigation of MM-PBSA rescoring of docking poses J Chem Inf Model 2008, 48, 1081—91 23 Weiner, P.K., Kollman, P.A AMBER–assisted model building with energy refinement¡aa general program for modeling molecules and their interactions J Comput Chem 1981, 2, 287—303 24 Weiner, S.J., Kollman, P.A., Case, D.A., Singh, U.C., Ghio, C., Alagona, G., Profeta, S., Weiner, P A new force-field for molecular mechanical simulation of nucleic-acids and proteins J Am Chem Soc 1984, 106, 765—84 25 Weiner, S.J., Kollman, P.A., Nguyen, D.T., Case, D.A An all-atom force field for simulations of protein and nucleic acids J Comput Chem 1986, 7, 230—52 26 Nilsson, L., Karplus, M Empirical energy functions for energy minimization and dynamics of nucleic acids J Comput Chem 1986, 7, 591—616 27 Brooks, B.R., Bruccoleri, R.E., Olafson, B.D., States, D.J., Swaminathan, S., Karplus, M CHARMM a program for macromolecular energy, minimization, and dynamics calculations J Comput Chem 1983, 4, 187—217 28 Jain, A.N Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities J Comput Aided Mol Des 1996, 10, 427—40 29 Head, R.D., Smythe, M.L., Oprea, T.I., Waller, C.L., Green, S.M., Marshall, G.R Validate a new method for the receptor-based prediction of binding affinities of novel ligands J Am Chem Soc 1996, 118, 3959—69 30 Eldridge, M.D., Murray, C.W., Auton, T.R., Paolini, G.V., Mee, R.P Empirical scoring functions: I The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes J Comput Aided Mol Des 1997, 11, 42545 31 Boă hm, H.J The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure J Comput Aided Mol Des 1994, 8, 243—56 Mean-Force Scoring Functions for Ligand Binding 295 32 Wang, R., Liu, L., Lai, L., Tang, Y SCORE: A new empirical method for estimating the binding affinity of a protein-ligand complex J Mol Model 1998, 4, 37994 33 Boă hm, H.J Prediction of binding constants of ptotein ligands: A fast method for the polarization of hits obtained from de novo design or 3D database search programs J Comput Aided Mol Des 1998, 12, 309—23 34 Gehlhaar, D.K., Verkhivker, G.M., Rejto, P.A., Sherman, C.J., Fogel, D.B., Freer, S.T Molecular recognition of the inhibitor AG-1343 by HIV-1 Protease: Conformationally flexible docking by evolutionary programming Chem Biol 1995, 2, 317—24 35 Gehlhaar, D.K., Bouzida, D., Rejto, P.A In Rational Drug Design: Novel Methodology and Practical Applications (eds L Parrill; M.R Reddy), American Chemical Society, Washington, DC, 1999, pp 292—311 36 Wang, R., Lai, L., Wang, S Further development and validation of empirical scoring functions for structure-based binding affinity prediction J Comput Aided Mol Des 2002, 16, 11—26 37 Tanaka, S., Scheraga, H.A Medium- and long-range interaction parameters between amino acids for predicting three-dimensional structures of proteins Macromolecules 1976, 9, 945—50 38 Miyazawa, S., Jernigan, R.L Estimation of effective interresidue contact energies from protein crystal structures: Quasi-chemical approximation Macromolecules 1985, 18, 534—52 39 Sippl, M.J Calculation of conformational ensembles from potentials of mean force J Mol Biol 1990, 213, 859—83 40 Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E The Protein Data Bank Nucleic Acids Res 2000, 28, 235—42 41 McQuarrie, D.A Statistical Mechanics, Harper Collins Publishers, New York, 1976 42 Hendlich, M., Lackner, P., Weitckus, S., Floeckner, H., Froschauer, R., Gottsbacher, K., Casari, G., Sippl, M.J Identification of native protein folds amongst a large number of incorrect models The calculation of low energy conformations from potentials of mean force J Mol Biol 1990, 216, 167—80 43 Jones, D.T., Taylor, W.R., Thornton, J.M A new approach to protein fold recognition Nature 1992, 358, 86—9 44 Thomas, P.D., Dill, K.A An iterative method for extracting energy-like quantities from protein structures Proc Natl Acad Sci U.S.A 1996, 93, 11628—33 45 Thomas, P.D., Dill, K.A Statistical potentials extracted from protein structures: How accurate are they? J Mol Biol 1996, 257, 457—69 46 Li, X., Liang, J In Computational Methods for Protein Structure Prediction and Modeling (eds Y Xu, D Xu and J Liang), Springer, New York, Vol 1, 2006, pp 71—124 47 Muegge, I., Martin, Y.C A general and fast scoring function for protein-ligand interactions: A simplified potential approach J Med Chem 1999, 42, 791—804 48 Huang, S.-Y., Zou, X Inclusion of solvation and entropy in the knowledge-based scoring function for protein-ligand interactions J Chem Inf Model 2010, 50, 262—73 49 Huang, S.-Y., Zou, X An iterative knowledge-based scoring function to predict protein-ligand interactions: I Derivation of interaction potentials J Comput Chem 2006, 27, 1865—75 50 Huang, S.-Y., Zou, X An iterative knowledge-based scoring function to predict protein-ligand interactions: II Validation of the scoring function J Comput Chem 2006, 27, 1876—82 51 Velec, H.F.G., Gohlke, H., Klebe, G DrugScoreCSD-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction J Med Chem 2005, 48, 6296—303 52 Gohlke, H., Hendlich, M., Klebe, G Knowledge-based scoring function to predict protein-ligand interactions J Mol Biol 2000, 295, 337—56 53 Ishchenko, A.V., Shakhnovich Small, E.I molecule growth 2001 (SMoG2001): An improved knowledge-based scoring function for protein-ligand interactions J Med Chem 2002, 45, 2770—80 54 Mitchell, J.B.O., Laskowski, R.A., Alex, A., Thornton, J.M BLEEP—Potential of mean force describ­ ing protein-ligand interactions: I Generating potential J Comput Chem 1999, 20, 1165—76 55 Mitchell, J.B.O., Laskowski, R.A., Alex, A., Forster, M.J., Thornton, J.M BLEEP-Potential of mean force describing protein-ligand interactions: II Calculation of binding energies and comparison with experimental data J Comput Chem 1999, 20, 1177—85 296 Sheng-You Huang and Xiaoqin Zou 56 Zhang, C., Liu, S., Zhu, Q., Zhou, Y A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes J Med Chem 2005, 48, 2325—35 57 Zhao, X., Liu, X., Wang, Y., Chen, Z., Kang, L., Zhang, H., Luo, X., Zhu, W., Chen, K., Li, H., Wang, X., Jiang, H An improved PMF scoring function for universally predicting the interactions of a ligand with protein, DNA, and RNA J Chem Inf Model 2008, 48, 1438—47 58 Verkhivker, G., Appelt, K., Freer, S.T., Villafranca, J.E Empirical free eenrgy calculations of ligandprotein crystallographic complexes I Knowledge-based ligand-protein interaction potentials applied to the prediction of human immunodeficiency virus protease binding affinity Protein Eng 1995, 8, 677—91 59 DeWitte, R.S., Shakhnovich, E.I SMoG: de Novo design method based on simple, fast, and accutate free energy estimate Methodology and supporting evidence J Am Chem Soc 1996, 118, 11733—44 60 Nobeli, I., Mitchell, J.B.O., Alex, A., Thornton, J.M Evaluation of a knowledge-based potential of mean force for scoring docked protein-ligand complexes J Comput Chem 2001, 22, 673—88 61 Sotriffer, C.A., Gohlke, H., Klebe, G Docking into knowledge-based potential fields: A compara­ tive evaluation of DrugScore J Med Chem 2002, 45, 1967—70 62 Allen, F.H The Cambridge Structural Database: A quarter of a million crystal structures and rising Acta Crystallogr 2002, B58, 380—8 63 Yang, C.-Y., Wang, R., Wang, S M-Score: A new knowledge-based potential scoring function accounting for protein atom mobility J Med Chem 2006, 49, 5903—11 64 Mooij, W.T., Verdonk, M.L General and targeted statistical potentials for protein-ligand interac­ tions Proteins 2005, 61, 272—87 65 Muegge, I., Martin, Y.C., Hajduk, P.J., Fesik, S.W Evaluation of PMF scoring in docking weak ligands to the FK506 binding protein J Med Chem 1999, 42, 2498—503 66 Ha, S., Andreani, R., Robbins, A., Muegge, I Evaluation of docking/scoring approaches: A comparative study based on MMP3 inhibitors J Comput Aided Mol Des 2000, 14, 435—48 67 Muegge, I PMF scoring revisited J Med Chem 2006, 49, 5895—902 68 Zhou, H., Zhou, Y Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction Protein Sci 2002, 11, 2714—26 69 Wang, R., Lu, Y., Wang, S Comparative evaluation of 11 scoring functions for molecular docking J Med Chem 2003, 46, 2287—303 70 Huang, S.-Y., Zou, X Ensemble docking of multiple protein structures: Considering protein structural variations in molecular docking Proteins 2007, 66, 399—421 71 Huang, S.-Y., Zou, X Efficient molecular docking of NMR structures: Application to HIV-1 protease Protein Sci 2007, 16, 43—51 72 Huang, S.-Y., Zou, X An iterative knowledge-based scoring function for protein-protein recogni­ tion Proteins 2008, 72, 557—79 73 Jasobsson, M., Liden, P., Stjernschantz, E., Bostroăm, H., Norinder, U Improving structure-based virtual screening by multivariate analysis of scoring data J Med Chem 2003, 46, 5781—9 SUBJECT INDEX Note: The letters ‘f ’ and ‘t’ following locators refer to figures and tables respectively ab initio electron correlation methods, 30-32 resolution-of-identity MP2, 31-32 ab initio electron propagator methods applications buckminsterfullerene, C60, 86-87 oligonucleotides, 87-91 See also Applications of electron propagator methods electron propagator theory quasiparticle virtual orbital spaces, 84-86 self-energy approximations, 81-84 Green’s functions, 80 Accelerator, 4, 4t, 22, 27, 29, 30 ACEMD code of Harvey, 11, 12t, 13-16 PME approach, 11 Acid dissociation constants (pKa), theoretical calculations of background gas-phase free energy calculations, 120 solvation free energy calculations, 121-122 thermodynamic cycles, 114-120 calculating changes in free energy in solution, 125-130 calculating changes in free energy in the gas phase, 122-124 chemical accuracy, 114 thermodynamic cycles, 130-132 AFE simulations, See Alchemical free energy (AFE) simulations AIM2000 program, 70 AIM theory, See Atoms-in-molecules (AIM) theory Alchemical free energy (AFE) simulations, 51-59 See also Direct scheme AFE simulations; Indirect scheme AFE simulations; QM/MM AFE simulations AMBER, 4, 11, 12t, 13, 14, 152, 194, 282 AMBER 11 AMBER PMEMD engine, Aminoglycoside derivatives I structure-based antibiotic design antibacterial activity of, 151 antibiotics binding to 30S, 151 refinement of aminoglycoside antibiotics, 152 RiboTargets, focus on antibiotics binding to 30S, 151 Aminoglycoside derivatives II, 160-161, 160f Amphipathic helix insertions, 246, 247, 249 Antibiotics targeting the ribosome ribosome antibiotic complexes, 140-144 RNA as drug target, 144-147 structure-based antibiotic design, case study aminoglycoside derivatives I, 151-152 aminoglycoside derivatives II, 160-161 A-site scaffolds, 159 chloramphenicol derivatives, 154-155 designer macrolides, 149-151 designer oxazolidinones, 147-148 pleuromutilin derivatives, 152-154 RNA-directed fragment libraries, 156-158 thiostrepton derivatives, 155-156 Anton, APIs, See Application programming interfaces (APIs) Application programming interfaces (APIs), 5, 7, 9, 23 Applications of electron propagator methods buckminsterfullerene, C60 ADC calculations, 86-87 equilibrium structure of, 86 NR2 calculations, 87 OVGF calculations, 86-87 vertical ionization energies of C60 (eV), 87t calculations with Gaussian03, 86 oligonucleotides 2’,2’-deoxyribodithymidine-3’, 5’-monophosphate anion, 89f, 91 deoxyribothymidine monophosphate anion, 89-91 disk space requirements in computer resourse calculations, 89 electron transfer phenomena in DNA and RNA, 87-88 electrospray photodetachment photoelectron spectroscopy, 88 297 298 Subject Index Applications of electron propagator methods (Continued) P3 studies of the VDEs, 88-89 QVOS method, 89 A-site scaffolds, 160f structure-based antibiotic design docking by RiboDock, 159 screening of docked ligands, FRET and NMR analysis, 159 screening strategy, 159 ATI, 4t, 9, 11, 12t, 23, 24 Atomistic modeling of SOFCs kinetic parameters computational estimates of, 210-211 experimental estimates of, 206-210 KMC simulations challenges/issues, 205-206 kinetic parameters, importance, 206, 208t-209t, See also Kinetic parameters in atomistic modeling of SOFCs rare-event processes, 204 system placed on cubice lattice, events/ even rates, 204-205 system propogation through time, 204 timescale inversely proportional to fastest events, 204 simulations, See Atomistic simulations of SOFCs See also Solid oxide fuel cells (SOFCs) Atomistic simulations of SOFCs, 212-227 modeling SOFCs with KMC simulations anode/cathode, influence on electrochemical reactions, 216-217 Bode plots and Nyquist plots, 215-216, 216f comparison with analytic models, 212 consistency of KMC model, materials- independent parameters, 217-218, 218f 3-D KMC model of a YSZ, 214 one-dimensional (1-D) lattice model, 212-214, 213f Pt-island, Pt-strap catalysts on top of the YSZ supercell, 224f SOFC voltage and power density plot of a simulated SOFC, 225f two-step electron transfer process, DFT calculation, 223 SOFC performance, factors (categories), 212 Atom-randomization reference state ASP, 289 BLEEP (Mitchell), application to 77 complexes, 288 DrugScorePDB (Gohlke), 288-289 KScore, 289 MScore, 289 prediction of HIV-1 protease binding affinity, 285-287 SMoG for de novo lead design, 287-288 Atoms-in-molecules (AIM) theory, 70, 74 Azithromycin, 143t, 144f, 149-151 BAR domains, 238-240, 241f, 246, 248-250, 251f, 252, 256 Berendsen thermostat, 267, 268f BigDFT software, 30 See also Daubechies wavelets Blocking electrodes, 215 B3LYP/6-311ỵỵG** model, 88, 124, 127-129 Cache units in CPU, CADD, See Computer-aided drug design (CADD) Cahn-Hilliard (CH) equations, 240, 242, 244, 245 Cambridge Structural Database (CSD), 151, 289 Carbidopa, 106 Catalysis, 66, 99, 169-195 See also RNA catalysis, confirmational transitions/metal ion binding in CCdA-Puromycin cofactor, 154 Cellular signaling processes endocytosis, 247 enzyme activation, 247 ion-channel activation, 247 See also Modeling signaling processes across cellular membranes sss“Chaperone,” 54 Chaperoned strategy, 54 CHARMM code, 13, 146, 194, 282 Chemical footprinting, 152-155 Chloramphenicol derivatives structure-based antibiotic design antibacterial properties, modification of, 154 binbing, cocrystallization in Deinococcus vs Haloarcula, 154 molecular modeling by MacroModel, 154-155 Clarithromycin, 149-150 CM3 charge model, 39 Coarse-grained models, 267, 274 Combined QM and MM (QM/MM) potentials, 53 Computer-aided drug design (CADD), 99-100 Compute Unified Device Architecture (CUDA), 7-10, 15, 16, 23-24, 26, 28, 30, 32 Conductor-Like Polarizable Continuum Model (CPCM), 122, 125, 127t, 128-130, 132 Subject Index Confirmational transitions/metal ion binding in RNA catalysis HHR crystallographic studies, 172 drug design/discovery, 171 inhibitor of BCR-ABLi1 gene expression, 171 inhibitor of hepatitis-B virus gene expressions, 171 potential anti-HIV-1 therapeutic agent, 171 role in posttranscriptional gene regulation, 171 L1 ligase ribozyme crystal structure of, 172 dynamical hinge points, identification, 173 in vitro selection techniques, 172 methods, 194 molecular simulations of HHR metal binding modes, 173-181 simulations along the reaction coordinate, 181-183 simulations of mutations of key residues, 183-187 molecular simulations of L1 ligase anatomy of the ligation site and implications for catalysis, 191-193 conformational variation of L1L at dynamical hinge points, 188-190 U38 loop for allosteric control, 190-191 “multiscale models” combined QM/MM potential, 170-171 protein enzyme systems electrostatic interactions in, 171 microscopic in silico model, 171 semiempirical quantum models, need for, 171 RNA as a messenger, 170 Conformational sampling, 56, 58 Construction of multidimensional PES, factors, 44 ene reaction between singlet O2 and tetramethylethylene, 45f 3-D PMF calculations, 45-46 rate-limiting transition structure, 45f, 46 three reaction coordinates, 45 “two-step no-intermediate” mechanism, 44 modeling of larger molecules, problems, 44 Corrected reference state DFIRE, 290-291 ligand volume correction factor, 289-290 test set of 77 protein-ligand complexes for PMF scoring function, 290 Correlation states, 92 CPCM, See Conductor-Like Polarizable Continuum Model (CPCM) CPU units, 6, 6f 299 Crystallography, 66, 67, 68, 99, 139-140, 147, 150, 151, 159, 269 See also X-ray crystallography for detection of metalloproteins CSD, See Cambridge Structural Database (CSD) CUDA, See Compute Unified Device Architecture (CUDA) Daubechies wavelets, 30 Deinococcus, 141f, 143, 154 Density-fitted Poisson method, 29-30 Density functional theory (DFT), 22, 24, 26, 28-31, 33, 67, 69, 70, 97-111, 123-124, 128, 210-211, 214-215, 223 Deoxyribothymidine monophosphate anion, 89-91, 89f Designer macrolides structure-based antibiotic design antibacterial activity against resistant organisms, 150, 150f crystallography and molecular modeling, 150 positioned near linezolid binding to RNA, 149f Designer oxazolidinones structure-based antibiotic design linezolid binding to ribosomal RNA, 147 oxazolidinone ring and U2539, interaction, 147 proximity of linezolid, linking algorithm, 147 QSAR modeling, 147 R�-01 family of compounds, 148 sparsomycin/linezolid proximity, schemes, 147-148, 148f, 149f Development of algorithms on GPUs, strategies, DFIRE, See Distance-scale finite ideal-gas reference (DFIRE) DFT-D (DFT plus dispersion) method, 103, 106, 108 Dielectric Polarizable Continuum Model (DPCM), 121, 125, 126, 127, 128 Dihydrobutyrine, 155 Direct scheme AFE simulations, 53-54 “annihilated” atoms, extrapolation strategy for, 54 calculations, electronic switching, 53-54 chaperoned approach, 54 Distance-scale finite ideal-gas reference (DFIRE), 290-291 Divalent metal ions, 171, 172, 173 3-D KMC model of a YSZ comparison with SSZ, 214 300 Subject Index 3-D KMC model of a YSZ (Continued) DFT calculations generation of database of 42 migration energy barriers, 214-215 DFTỵKMC modeling, study, 215 EIS studies, 215 DOCK version 4.0, 151 Domain, definition, 266 “Dominant” (rate-limiting) events, 206 Dos, See Dyson orbitals (DOs) DPCM, See Dielectric Polarizable Continuum Model (DPCM) D radiodurans 50S, 153 DrugScoreCSD, 287t, 289 DrugScoreRNA, 146 Dynamic Monte Carlo (DMC) scheme, 245 Dyson orbitals (DOs), 81, 83 EIS, See Electrochemical impedance spectroscopy (EIS) Electrochemical impedance spectroscopy (EIS), 215, 216f Electrochemistry, 207, 210, 223 Electron detachment energies, 85, 88 Electron propagator theory quasiparticle virtual orbital spaces, 84-86 self-energy approximations canonical Hartree-Fock orbitals, choice of, 82 Davidson diagonalization procedure, 84 Dyson equation, FDAs and electron binding energies computation, 81-82 Hermitian superoperator Hamiltonian matrices, 83 pole strengths (PSs), evaluation, 84 quasiparticle approximations, approaches, 82-83 quasiparticle techniques, 82 renormalized methods, 84 Electron repulsion integrals (ERIs), 24-28 calculation of, CUDA implementation for (Asadchev), 28 McMurchie-Davidson scheme, 26-27 mixed-precision (MP) CPU/GPU scheme, 26 Rys quadrature scheme, 26 Electron spin resonance, 66 Electron transfer phenomena in DNA and RNA, 87-88 Electrospray photodetachment photoelectron spectroscopy, 88 Ellipticine, 107 Empirical scoring function, 155, 282 ERI calculation, CUDA implementation for (Asadchev), 28 ERIs, See Electron repulsion integrals (ERIs) Ewald summation-based methods, 55-56 FAAH, See Fatty acid amide hydrolase (FAAH) FASTRUN, 4, 4t Fatty acid amide hydrolase (FAAH), 40 FDAs, See Feynman-Dyson amplitudes (FDAs) FEP technique, See Free-energy perturbation (FEP) technique Feynman-Dyson amplitudes (FDAs), 81 First generation GPUs, 24 First-order generalized ensemble-based QM/ MM AFE simulations, 56-57 advantage, 57 replica exchange-based strategy, 57 simulated scaling-based strategy in direct/indirect scheme, 57 Wang-Landau recursion method/ metadynamics recursion method, 57 Folding of conjugated proteins chaperons, role, 264 disordered tails, effects on protein characteristics, 265 funnel theory of protein folding, 264 glycosylated SH3 protein, 264f glycosylation deciphering a glycosylation code, difficulties, 265 methods coarse-grained models, 267 inhomogeneous degrees of freedom, 267 native topology-based model, 267 thermostat effects on temperature of the conjugate, 267, 268f multidomain proteins, 264f, 266 myristoylation and palmytoylation, 264 phosphorylation, 264 PTMs, role glycosylation and effect of glycans on folding, 264-265 results and discussion folding of glycoproteins, 267-270 folding of multidomain proteins, 273-275 folding of proteins with flexible tails, 270-271 folding of ubiquitinated proteins, 271-273 tailed SH3 protein, 264f ubiquitinated Ubc7 protein (monomeric/ tetrameric ubiquitin), 264f ubiquitination, 264, 265-266 Folding of glycoproteins, 267-270 biophysical characteristics of glycoproteins effect of degree of glycosylation on protein biophysics, 269-270, 269f Subject Index number of native contacts at conjugation/ glycosylation site, 268f protein stability, influence of glycan, 267 thermodynamic analyses of the simulations, results, 270 Folding of multidomain proteins, 273-275 FNfn9 domain, 274 thermal stability of multidomain FNfn9-FNfn10 protein, 274-275, 274f WHAM, study of thermodynamic properties, 274 Folding of proteins with flexible tails, 270-271 characteristics, 270f entropy of the protein, opposing factors, 271 longer tails, destabilization of protein, 270-271 repulsive interaction effects between the tail and protein, 270f, 271 Folding of ubiquitinated proteins, 271-273 protein degradation, 271-272 native-state simulation models, 272 thermostability of ubiquitinated proteins, 272f affecting factors, 273 Force field scoring functions AMBER/CHARMM force fields, 282 semiempirical weighting or scaling parameters, 282 van der Waals/electrostatic terms, energy components, 282 Free-energy perturbation (FEP) technique, 38, 39, 40, 41, 43, 46 Free-energy profiles, computation of methods FEP method, 39 MC sampling methods, 39 QM/MM calculations, methods, 39, See also Quantum and molecular mechanical (QM/MM) multidimensional potentials of mean force, 44-46 construction of PES, factors, 44 PMF calculation, approaches, 38-39 multidimensional computational technique, 39 polynomial quadrature method, applications, 39 Polynomial Quadrature Method, 40-44 Zwanzig expression for FEP technique, 38 Free energy simulations, types molecular dynamics (MD), 52 Monte Carlo (MC), 52 Frequency response characteristics, fuel cell, 220 Fullerene ionization energies, 86, 87t Fullerenes, 79-92 301 Gaussian 98 program, 69 Generalized ensemble simulation, 56-59 Generalized gradient approximation (GGA), 102 Generalized solvent boundary potential (GSBP) method, 55 GGA, See Generalized gradient approximation (GGA) Glycoproteins, folding of, 267-270 biophysical characteristics of glycoproteins effect of degree of glycosylation on protein biophysics, 269-270, 269f number of native contacts at conjugation/ glycosylation site, 268f protein stability, influence of glycan, 267 thermodynamic analyses of the simulations, results, 270 Glycosylation, 264-265, 267-268, 269f, 270 “Glycosylation code,” 265 Glycosylphosphatidylinositol (GPI)-anchored proteins, 239 GPCRs, See G-protein-coupled receptors (GPCRs) G-protein-coupled receptors (GPCRs), 238 GPU-based (early) MD code development, 9-10 cell-based list algorithm for neighbor list, 10 electrostatic model, simulations of liquid water (Davis), 10 HOOMD GPU implementation, 10 MD implementation for molecular modeling computations techniques for direct Coulomb summation, 10 MD implementation to use CUDA van der Waals potential, use of (Liu), 10 GPU programming languages AMD’s Stream, CUDA programming model from NVIDIA, 7-8 scientific use, extremes, GPU, See Graphics processing units (GPUs) GPUs, software development for first generation of GPUs, features, 24 high density of arithmetic units, example FFTW Fourier transform library, portable algorithm, 23 GPU kernels/streams, 23 GPU programming, problems, 23 NVIDIA GeForce 8800 GTX GPU, 22-23 parallel programming of memory, 23 second generation of GPUs, features, 24 third generation of GPUs, features, 24 GPU vs CPU, 6-7 CPU RAM, function of, 302 Subject Index GPU vs CPU (Continued) sequential code execution, Von Neumann architecture, SISD category, units of, 6, 6f GPU display of 3D graphics, strategy, larger memory bandwidth, larger number of ALUs, RAM, function of, SIMD category, units of, Graphics processing units (GPUs), 3-17 APIs, GPU hardware, applications, peak floating-point operations per second, 5f Voodoo graphics chip (1996), Green’s functions, 80 Grow-search-score algorithm (AnalogTM or BOMB), 147 GSBP method, See Generalized solvent boundary potential (GSBP) method Guoy-Chapman model, 212, 214 Half-cell SOFC model, 216-217 Haloracula, 154 Hammerhead ribozyme (HHR), 171-172 crystallographic studies, 172 drug design/discovery, 171 inhibitor of BCR-ABLi1 gene expression, 171 inhibitor of hepatitis-B virus gene expressions, 171 potential anti-HIV-1 therapeutic agent, 171 role in posttranscriptional gene regulation, 171 See also Molecular simulations of HHR Hartree-Fock (HF) theory, 22, 24, 82, 83-86, 90, 98, 122, 123, 124 HB (with distal His) models, 69 Heme systems, investigation approaches, 69-70 Bader’s AIM theory, 70 DFT method BPW91 with Wachters’ basis for Fe, 69 6-311G* for heavy atoms, 69 6-31G* for hydrogens in Gaussian 98 program, 69 HHR, See Hammerhead ribozyme (HHR) HMG-Co-A reductase, 110 HOOMD code, 10 Hybrid GGA methods, 102 Hydrogen bonding, 43, 46, 68, 71, 71f, 72-74, 106, 108, 109, 110, 145, 146, 151, 153, 159, 183, 186f, 187, 188 Hydrogen bonding in metalloprotein quantum chemical investigation of Moăssbauer properties, examples oxymyoglobin, MbO2, 68 Pauling-type closed-shell singlet 1FeII-1O2, 68 QM/S approach, results, 68 Weiss-type open-shell singlet 2FeIII"#2O2À, 68 Hygromycin (PDB 1HNZ), 143t, 151 Indirect scheme AFE simulations, 54-55 Induction/dispersion interactions in ligand-protein complexes applications binding of steroid hormones, examination, 108 development of anticancer drugs (DFT), 108 development of novel statin drugs, 110 DFT-QSAR approach, study, 109-110 hydrogen bonding on peptide structure, DFT study, 110 interactions in polypeptide structure, study, 109 modeling of coordinate chemistry of ligands bound to metal ions, 109 chemical accuracy, importance, 98 correct path to dispersion DFT-D method, augmentation of TPSS functional, 103 explicit treatment of dispersion, need for (Hobza), 104 interaction energies in JSCH-2005 database, evaluation, 103 interactions of DNA bases and ellipticine, study, 107 modeling of aromatic interactions, local functionals in, 105 M05-2X, DFT exchange and correlation energy, 104-105 parameterizing DFT methods, 105 PWB6K/M05-2X, dispersion-augmented functionals, 103 DFT GGA DFT methods, 102 hybrid/meta-GGA approach, improved GGA methods, 102 KS implementation of DFT, 100-101 LDA methods, 101 LSDA methods, 101-102 SVWN DFT method, 102 ligand-protein complexes CADD, ligand docking and pose scoring, 99-100 DFT, 100 ... Ever since 3DFX first introduced the Voodoo graphics chip in 1996, their development has been strongly influenced by the entertainment industry in order to meet the demands for ever increasing... complexities of the underlying algorithm Specifics include avoiding the need for FFTs and the use of infinite cutoffs which in turn remove the complexity of maintaining a neighbor list Friedrichs... performance improvements in sequential execution have been obtained by increasing CPU clock speeds and the introduction of more complex ALUs that perform increasingly composite operations in fewer clock