Innovations in Biomolecular Modeling and Simulations: Volume 1: Volume 23 (RSC Biomolecular Sciences) - Hardcover

Hardcover

ISBN 10: 1849734615 ISBN 13: 9781849734615

Publisher: Royal Society of Chemistry, 2012

Synopsis

This two volume set describes innovations in biomolecular modeling and simulation, in both the algorithmic and application fronts.

About this title

From the Back Cover

The chemical and biological sciences face unprecedented opportunities in the 21st century. A confluence of factors from parallel universes - advances in experimental techniques in biomolecular structure determination, progress in theoretical modeling and simulation for large biological systems, and breakthroughs in computer technology - has opened new avenues of opportunity as never before. Now, experimental data can be interpreted and further analysed by modeling, and predictions from any approach can be tested and advanced through companion methodologies and technologies. This two volume set describes innovations in biomolecular modeling and simulation, in both the algorithmic and application fronts. With contributions from experts in the field, the books describe progress and innovation in areas including: simulation algorithms for dynamics and enhanced configurational sampling, force field development, implicit solvation models, coarse-grained models, quantum-mechanical simulations, protein folding, DNA polymerase mechanisms, nucleic acid complexes and simulations, RNA structure analysis and design and other important topics in structural biology modeling. The books are aimed at graduate students and experts in structural biology and chemistry and the emphasis is on reporting innovative new approaches rather than providing comprehensive reviews on each subject.

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Innovations in Biomolecular Modeling and Simulations Volume 1

By Tamar Schlick

The Royal Society of Chemistry

Copyright © 2012 Royal Society of Chemistry
All rights reserved.

ISBN: 978-1-84973-461-5

Contents

Volume 1, 
Beginnings, 
Chapter 1 Personal Perspective Harold A. Scheraga, 3, 
Chapter 2 Fashioning NAMD, a History of Risk and Reward: Klaus Schulten Reminisces Lisa Pollack, 8, 
Force Fields and Electrostatics, 
Chapter 3 Towards Biomolecular Simulations with Explicit Inclusion of Polarizability: Development of a CHARMM Polarizable Force Field based on the Classical Drude Oscillator Model C. M. Baker, E. Darian and A. D. MacKerell Jr, 23, 
Chapter 4 Integral Equation Theory of Biomolecules and Electrolytes Tyler Luchko, In Suk Joung and David A. Case, 51, 
Chapter 5 Molecular Simulation in the Energy Biosciences Xiaolin Cheng, Jerry M. Parks, Loukas Petridis, Benjamin Lindner, Roland Schulz, Hao-Bo Guo, Goundla Srinivas and Jeremy C. Smith, 87, 
Sampling and Rates, 
Chapter 6 Enhancing the Capacity of Molecular Dynamics Simulations with Trajectory Fragments Alfredo E. Cardenas and Ron Elber, 117, 
Chapter 7 Computing Reaction Rates in Bio-molecular Systems Using Discrete Macro-states Eric Darve and Ernest Ryu, 138, 
Chapter 8 Challenges in Applying Monte Carlo Sampling to Biomolecular Systems M. Mezei, 207, 
Coarse Graining and Multiscale Models, 
Chapter 9 Coarse-grain Protein Models N. Ceres and R. Lavery, 219, 
Chapter 10 Generalised Multi-level Coarse-grained Molecular Simulation and its Application to Myosin-V Movement William R. Taylor and Zoe Katsimitsoulia, 249, 
Chapter 11 Top-down Mesoscale Models and Free Energy Calculations of Multivalent Protein-Protein and Protein-Membrane, 272, 
Interactions in Nanocarrier Adhesion and Receptor Trafficking Jin Liu, Neeraj J. Agrawal, David M. Eckmann, Portonovo S. Ayyaswamy and Ravi Radhakrishnan, 272, 
Chapter 12 Studying Proteins and Peptides at Material Surfaces Jun Feng, Gillian C. Lynch and B. Montgomery Pettitt, 293, 
Chapter 13 Multiscale Design: From Theory to Practice J. Fish, V. Filonova and Z. Yuan, 321, 
Subject Index, 345, 
Volume 2, 
Atomistic Simulations of Nucleic Acids and Nucleic Acid Complexes, 
Chapter 1 Modelling Nucleic Acid Structure and Flexibility: From Atomic to Mesoscopic Scale Filip Lankas, 3, 
Chapter 2 Molecular Dynamics and Force Field Based Methods for Studying Quadruplex Nucleic Acids Shozeb M Haider and Stephen Neidle, 33, 
Chapter 3 Opposites Attract: Shape and Electrostatic Complementarity in Protein-DNA Complexes Robert C. Harris, Travis Mackoy, Ana Carolina Dantas Machado, Darui Xu, Remo Rohs and Marcia Oliveira Fenley, 53, 
Chapter 4 Intrinsic Motions of DNA Polymerases Underlie Their Remarkable Specificity and Selectivity and Suggest a Hybrid Substrate Binding Mechanism Meredith C. Foley, Karunesh Arora and Tamar Schlick, 81, 
Chapter 5 Molecular Dynamics Structure Prediction of a Novel Protein–DNA Complex: Two HU Proteins with a DNA Four-way Junction Elizabeth G. Wheatley, Susan N. Pieniazek, Iulia Vitoc, Ishita Mukerji and D.L. Beveridge, 111, 
Chapter 6 Molecular Dynamics Simulations of RNA Molecules J. Šponer, M. Otyepka, P. Banáš, K. Réblová and N. G. Walter, 129, 
Chapter 7 The Structure and Folding of Helical Junctions in RNA David M. J. Lilley, 156, 
DNA Folding, Knotting, Sliding and Hopping, 
Chapter 8 Structure and Dynamics of Supercoiled DNA Knots and Catenanes Guillaume Witz and Andrzej Stasiak, 179, 
Chapter 9 Monte Carlo Simulations of Nucleosome Chains to Identify Factors that Control DNA Compaction and Access Karsten Rippe, Rene Stehr and Gero Wedemann, 198, 
Chapter 10 Sliding Dynamics Along DNA: A Molecular Perspective Amir Marcovitz and Yaakov Levy, 236, 
Drug Design, 
Chapter 11 Structure-based Design Technology CONTOUR and its Application to Drug Discovery Zhijie Liu, Peter Lindblom, David A. Claremon and Suresh B. Singh, 265, 
Chapter 12 Molecular Simulation in Computer-aided Drug Design: Algorithms and Applications Robert V. Swift and Rommie E. Amaro, 281, 
Chapter 13 Computer-aided Drug Discovery: Two Antiviral Drugs for HIV/AIDS J. Andrew McCammon, 316, 
Subject Index, 320, 

CHAPTER 1

Personal Perspective

HAROLD A. SCHERAGA

Baker Lab of Chemistry, Cornell University, Ithaca, NY 14853-1301, US Email: has5@cornell.edu

My interest in biomolecular modeling and simulation has its origins in my graduate work at Duke University under the direction of Paul M. Gross and Marcus E. Hobbs, and in my year-long courses in quantum mechanics and statistical mechanics with Fritz London. Gross had previously spent a sabbatical leave with Peter Debye in Leipzig, and returned to Duke with an interest in the relation between molecular structure and dipole moments. Shortly before his arrival at Duke, London had formulated a quantum mechanical treatment of van der Waals forces, in which polarizability played an important role. In this atmosphere, I began graduate research using electrical birefringence (Kerr effect) to determine anisotropic polarizabilities of small organic molecules. This research was interrupted by the entry of the US into World War II, and my resulting participation in a war project at Duke.

One day I had a chance encounter in the chemistry library with a then new book by Edwin Cohn and John Edsall, titled Proteins, Amino Acids and Peptides, which contained chapters by several authors besides Cohn and Edsall, namely John Kirkwood, George Scatchard, and Larry Oncley. Edsall described flow birefringence and Oncley described dielectric dispersion of proteins. This appealed to me as a chance to take up the birefringence work that I had to drop at Duke and, as an ACS postdoctoral fellow at Harvard Medical School, I applied flow birefringence to proteins under Edsall's guidance in an atmosphere devoted to the physical chemistry of blood plasma proteins.

Then, at Cornell, I began experimental work on the mechanism of the action of thrombin on fibrinogen to produce the fibrin clot. In a limited proteolytic reaction, thrombin releases peptides from fibrinogen, exposing a polymerization site on the resulting fibrin monomer. I used flow birefringence to elucidate the nature of the staggered-overlapped rod-like polymers formed from fibrin monomer on the pathway to the blood clot.

At the same time, Pauling and Corey had proposed the a and b structures of proteins, focusing on the backbone hydrogen bonds. With my first graduate student, Michael Laskowski, I examined the role of side-chain hydrogen bonds in proteins. Specifically, we demonstrated how side-chain hydrogen bonds are involved in the polymerization of fibrin monomer, and also influence the pK's of ionizable groups as well as limited proteolysis in which it is necessary to break hydrogen bonds (during the hydrolysis of a peptide bond) to liberate a fragment which had been connected to the rest of the molecule by such hydrogen bonds.

This led to an attempt to determine protein structure by acquisition of distance constraints by location of side-chain hydrogen bonds experimentally. Charles Tanford had used UV titration of ribonuclease A (RNase A) in the pH region near the pK° of tyrosine, viz. ~10, to demonstrate that three of the six tyrosines had abnormally large pK's and, with Jan Hermans, we used potentiometric titration to demonstrate that three of the eleven COOH groups had abnormally low pK's. During my sabbatical leave with Kai Linderstrøm-Lang at the Carlsberg Laboratory in Copenhagen in 1956–57, with UV difference spectroscopy (see Figure 1.1), I showed that the UV absorption spectrum of tyrosine varied with pH at low pH where COOH groups ionize, suggesting the proximity of COOH group(s) near tyrosine(s). Back at Cornell, I started a long series of experiments with graduate students and postdocs which ultimately located three Tyr–Asp interactions, viz., Tyr25 with Asp14, Tyr92 with Asp38, and Tyr97 with Asp83. These were subsequently verified by the crystal structure of RNase A.

Also, during my sabbatical leave at the Carlsberg Laboratory, Walter Kauzmann arrived in mid-year and, with Linderstrøm-Lang, we had many discussions about hydrophobic interactions. When Walter returned to Princeton, he wrote his famous article on hydrophobic interactions, and upon my return to Cornell, I started a new graduate student, George Némethy, on a statistical mechanical theory of hydrophobic interactions. Simultaneously, with Izchak Steinberg and George Némethy, we discussed the interactions between hydrogen bonds and hydrophobic interactions, and pointed out how the nonpolar portions of so-called polar side chains can be involved in hydrophobic interactions with nearby nonpolar side chains, as shown in Figure 1.2, providing increased strength to the hydrogen bond, and its consequent influence on protein structure and stability.

With George Némethy, we decided to formulate a theoretical approach to determine protein structure by making use of the distance constraints implied by the three Try–Asp interactions and the location of the four disulfide bonds in RNase A. This computational work evolved over the years, first by formulation of an all-atom force field, ECEPP, an Empirical Conformational Energy Program for Peptides, and subsequently, by development of UNRES, a united-residue coarse-grained force field.

In addition to the effort to determine the structure of RNase A, we also embarked on experimental work to determine its folding pathways by oxidation with two redox systems, GSSG/GSH and DTTox/DTTred. Later, with UNRES, we expanded our computational approach to simulate folding pathways and folding kinetics.

The ultimate goal of all our research, beginning with our work on the thrombin–fibrinogen interaction, was to use physical chemistry to elucidate biological structure and function. With our coarse-grained force field, we have recently embarked on studies of protein–protein interactions, e.g., Aβ, PICK1 and Hsp 70, and have started to formulate a coarse-grained nucleic acid force field to be able to treat protein–nucleic acid interactions.

All of this work has been an excellent vehicle with which to train undergraduate, graduate, and postdoctoral students whose contributions to this research have been fundamental.

A more detailed description of some of the difficulties encountered and surmounted during the implementation of this research is provided in a prefatory chapter in Annual Reviews of Biophysics.

CHAPTER 2

Fashioning NAMD, a History of Risk and Reward: Klaus Schulten Reminisces

LISA POLLACK

Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, US

Email: lpollack@illinois.edu

2.1 Introduction

With a homemade parallel supercomputer in his backpack, Klaus Schulten waited patiently in the Chicago O'Hare airport, hoping for no trouble getting through customs after his flight from Germany. It was the summer of 1988 and Schulten was about to start a new job at the University of Illinois. It was also the height of the Cold War, the ultimate peak in US–Soviet tensions, and supercomputers were causing the Reagan administration much consternation. Although Reagan had escalated the arms race and all its accompanying technological advances while in office, he wanted to keep burgeoning super-computer developments out of the hands of the Soviets, for fear they would build better weapons. In 1986, the government announced a proposed policy to restrict Soviet scientists from using supercomputers housed in US universities. The policy objective was clear: no access to scientists from 20 Communist bloc countries. The implementation was not so clear, however. The universities were incensed about this policy, worrying about infringements on their academic freedom and research initiatives, and wondering how they would become enforcers of a government security policy.

The Reagan administration not only wanted to restrict Soviet scientists from running simulations on supercomputers, it also did not want Soviet scientists close to supercomputers for fear they might learn how to build their own. But in 1987, two young physics students in Munich embarked upon just such a mission to build their own parallel supercomputer, although neither had formal training in this field. Not only did they figure it out, but their project cost came in around $60 000, much less than the $17.6 million retail price tag of the Cray-2, a popular supercomputer in the late 1980s. Their advisor, Klaus Schulten, chose to risk all the grant money he had on the project, even though he had no guarantees it would succeed and he was not an expert in parallel computing. While the parallel computer would make possible very large simulations, Schulten sums up why he agreed to the precarious plan: "I believe in people, and when I believe in people I let them go."

This article charts the history behind the software program NAMD. It starts in the late 1980s, when Klaus Schulten and two students attempted to adopt a fundamentally new technology, parallel computing, for use in their own research on proteins. A student revolt precipitated a software code designed specifically for parallel computers, NAMD, and its continual improvement over time in describing the behavior of biological macromolecules led Schulten to regard it as a "computational microscope." The story behind the computational microscope looks at the hurdles some key scientists faced and specifically the risk these scientists assumed.

2.2 Early Influences of Molecular Dynamics

In the mid-1980s Klaus Schulten was a professor at the Technical University of Munich and he had a wish list. He wanted to simulate on a supercomputer the behavior of a membrane protein, the photosynthetic reaction center, using molecular dynamics. Earlier in the previous decade, Martin Karplus's group at Harvard had done the first molecular dynamics simulation of a biomolecule. Up to that time, molecular dynamics had been used to model hard spheres by Alder and Wainwright in the 1950s, and other liquids in the 1960s and early 1970s. But after much painstaking work, Andrew McCammon and Bruce Gelin, graduate students of Karplus's, prepared a computer program to model a small protein, the bovine pancreatic trypsin inhibitor. After locating the necessary computing power in Europe during a workshop at CECAM, (Center Européen Calcul Atomique et Moléculaire), the trio published their landmark paper in 1977.

Schulten was aware of this work of McCammon, Gelin, and Karplus because he was finishing his graduate studies at Harvard, where Karplus was one of his advisors, and McCammon and Gelin were fellow students who sat right down the hall. The 1977 paper had a definite impact on Schulten, who was trained to use the theoretical and mathematical methods employed in chemistry and physics. "I realized that this computational approach," he says of molecular dynamics, "opens new doors to describe problems that you couldn't do with a purely theoretical approach that I had taken until that time."

But embracing a computational method instead of staying exclusively with pure theory had its costs, and he soon became known as "Computing Schulten." "I paid a big price for it, because basically during much of my career people thought I was stupid," reminisces Schulten. "Although I continued publishing mathematically-oriented papers, for every computing-oriented paper I had to redeem myself with ten theoretical papers, and I couldn't possibly do that."

In fact, selling the usefulness of the computational microscope and the molecular dynamics approach in its very early stages was also a battle Schulten sometimes had to wage. In 1985, while still a professor in Munich, Schulten went to a supercomputing center in Illinois to run some calculations, and returned to Germany with a movie illustrating a protein in motion, based on molecular dynamics simulations. When Schulten showed the movie, one of his colleagues became quite enraged. "He got so upset when he saw it, he almost wanted to physically attack me," Schulten recounts. "He told everybody this is the greatest rubbish he'd ever seen in his life. He was a crystallographer who thought basically of proteins as some kind of Gothic cathedral that were cast in stone."

2.3 Building a Parallel Computer

Despite the many struggles Schulten had to face, he was intent on using molecular dynamics for his work; he was sure it would lead him to new discoveries that would be valuable for science. "My love of scientific discovery," Schulten confirms, "made me do the dirty business of computing."

This was exactly what motivated Schulten in 1987 to make an audacious judgment call. He wanted to simulate the photosynthetic reaction center, which is a large protein that sits within a membrane. Simulating the protein by itself and neglecting the membrane and the liquid that surrounds it was not highly desirable because such isolation is not a natural environment for the protein. This protein is about 12 000 atoms and the membrane and water that surrounds it in its natural environment adds another 100 000 atoms to the tally. In the late 1980s no supercomputer was even close to capable of handling that task. Schulten thus decided to focus on understanding and simulating just part of a membrane in water and managed to run a calculation on a Cray-XMP, but it only covered a few picoseconds of time and it taught him one thing: he needed a supercomputer all to himself, perhaps for a year or more, to really understand the mechanism.

While Schulten, teaching physics at the Technical University of Munich, was wondering how he could commandeer a supercomputer all for himself, a young physics student at the same university was wondering how he could build faster computers. Actually, Helmut Grubmüller knew in theory how to make a computer faster, but was really wondering if he could get someone else to pay for it. Enrolling at the university in Munich in 1985, barely twenty years old, Grubmüller was fascinated with how computers could elucidate nature. This fascination prompted him to begin soldering together a multi-processor computer in his second or third semester, one that would be much faster than what was available to regular students in those days. Although it was technically a parallel computer, Grubmüller just called it a multi-processor at that point. It was the mid-1980s and there was no World Wide Web to consult for details about how to construct his own. Instead, he read any books about microprocessors he could find, and talked to company representatives on the phone about technical details of the parts they sold. He cites as most important for making headway the data sheets of the chips he used as the processor, which were Motorola's 68 000 line. But he could only get so far on his own. "That very quickly blew my budget," recounts Grubmüller; "My private budget was a thousand dollars or so."

(Continues...)

Excerpted from Innovations in Biomolecular Modeling and Simulations Volume 1 by Tamar Schlick. Copyright © 2012 Royal Society of Chemistry. Excerpted by permission of The Royal Society of Chemistry.
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Edition details

• Publisher: Royal Society of Chemistry
• Publication date: 2012
• Language: English
• ISBN 10: 1849734615
• ISBN 13: 9781849734615
• Binding: Hardcover
• Number of pages: 380
• Editor: Tamar Schlick