抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.

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Scalable molecular dynamics with NAMD

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Scalable Molecular Dynamics with NAMD

From the Publisher:

"This is the best general book on Genetic Algorithms written to date. It covers background, history, and motivation; it selects important, informative examples of applications and discusses the use of Genetic Algorithms in scientific models; and it gives a good account of the status of the theory of Genetic Algorithms. Best of all the book presents its material in clear, straightforward, felicitous prose, accessible to anyone with a college-level scientific background. If you want a broad, solid understanding of Genetic Algorithms -- where they came from, what's being done with them, and where they are going -- this is the book.
-- John H. Holland, Professor, Computer Science and Engineering, and Professor of Psychology, The University of Michigan; External Professor, the Santa Fe Institute. 
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues. 
The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. 
An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. 
The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

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An Introduction to Genetic Algorithms

CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecu- lar simulation program. It has been developed over the last three decades with a primary focus on molecules of bio- logical interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estima- tors, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numer- ous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983.

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CHARMM: the biomolecular simulation program.

A new molecular-modeling methodology has been applied to explain enzymatic enantioselectivity in water. This methodology, which combines vacuum molecular mechanics and the continuum solvation method, should provide a more realistic view of the solvent-enzyme and solvent-substrate interactions than the heretofore used approaches involving the vacuum molecular mechanics only. The methodology described herein has been validated using the experimental data on alpha-chymotrypsin's enantioselectivity in the hydrolysis of four chiral substrates. The reasons why the vacuum molecular mechanics, although not taking hydration into account, still in most cases provide a satisfactory approximation of reality are discussed.

Molecular-modeling calculations of enzymatic enantioselectivity taking hydration into account.

We explore the applicability of an additive treatment of substituent effects to the analysis and design of HIV protease inhibitors. Affinity data for a set of inhibitors with a common chemical framework were analyzed to provide estimates of the free energy contribution of each chemical substituent. These estimates were then used to design new inhibitors whose high affinities were confirmed by synthesis and experimental testing. Derivations of additive models by least-squares and ridge-regression methods were found to yield statistically similar results. The additivity approach was also compared with standard molecular descriptor-based QSAR; the latter was not found to provide superior predictions. Crystallographic studies of HIV protease−inhibitor complexes help explain the perhaps surprisingly high degree of substituent additivity in this system, and allow some of the additivity coefficients to be rationalized on a structural basis.

Additivity in the Analysis and Design of HIV Protease Inhibitors

The search for the global minimum energy conformation (GMEC) of protein side chains is an important computational challenge in protein structure prediction and design. Using rotamer models, the problem is formulated as a NP-hard optimization problem. Dead-end elimination (DEE) methods combined with systematic A ! search (DEE/A ! ) has proven useful, but may not be strong enough as we attempt to solve protein design problems where a large number of similar rotamers is eligible and the network of interactions between residues is dense. In this work, we present an exact solution method, named BroMAP (branch-and-bound rotamer optimization using MAP estimation), for such protein design problems. The design goal of BroMAP is to be able to expand smaller search trees than conventional branch-and- boundmethodswhileperformingonlyamoderateamountofcomputationineachnode,therebyreducingthetotalrunning time. To achieve that, BroMAP attempts reduction of the problem size within each node through DEE and elimination by lower bounds from approximate maximum-a-posteriori (MAP) estimation. The lower bounds are also exploited in branching and subproblem selection for fast discovery of strong upper bounds. Our computational results show that BroMAP tends to be faster than DEE/A ! for large protein design cases. BroMAP also solved cases that were not solved by DEE/A ! within the maximum allowed time, and did not incur significant disadvantage for cases where DEE/A ! performed well. Therefore, BroMAP is particularly applicable to large protein design problems where DEE/A ! struggles and can also substitute for DEE/A ! in general GMEC search.

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Rotamer Optimization for Protein Design through MAP Estimation and Problem-Size Reduction

Osmotic Pressure of Aqueous Chondroitin Sulfate Solution: A Molecular Modeling Investigation

The osmotic pressure of chondroitin sulfate (CS) solution in contact with an aqueous 1:1 salt reservoir of fixed ionic strength is studied using a recently developed coarse-grained molecular model. The effects of sulfation type (4- vs. 6-sulfation), sulfation pattern (statistical distribution of sulfate groups along a chain), ionic strength, CS intrinsic stiffness, and steric interactions on CS osmotic pressure are investigated. At physiological ionic strength (0.15 M NaCl), the sulfation type and pattern, as measured by a standard statistical description of copolymerization, are found to have a negligible influence on CS osmotic pressure, which depends principally on the mean volumetric fixed charge density. The intrinsic backbone stiffness characteristic of polysaccharides such as CS, however, is demonstrated to contribute significantly to its osmotic pressure behavior, which is similar to that of a solution of charged rods for the 20-disaccharide chains considered. Steric excluded volume is found to play a negligible role in determining CS osmotic pressure at physiological ionic strength due to the dominance of repulsive intermolecular electrostatic interactions that maintain chains maximally spaced in that regime, whereas at high ionic-strength steric interactions become dominant due to electrostatic screening. Osmotic pressure predictions are compared to experimental data and to well-established theoretical models including the Donnan theory and the Poisson-Boltzmann cylindrical cell model.

Bruce Tidor

Papers

Molecular-modeling calculations of enzymatic enantioselectivity taking hydration into account.

Additivity in the Analysis and Design of HIV Protease Inhibitors

Rotamer Optimization for Protein Design through MAP Estimation and Problem-Size Reduction

Osmotic Pressure of Aqueous Chondroitin Sulfate Solution: A Molecular Modeling Investigation

Osmotic Pressure of Aqueous Chondroitin Sulfate Solution: A Molecular Modeling Investigation