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.

/pdf/scalable-molecular-dynamics-with-namd-51o1z7rwp8.pdf

Scalable molecular dynamics with NAMD

/pdf/scalable-molecular-dynamics-with-namd-3j7xyc70g4.pdf

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.

/pdf/an-introduction-to-genetic-algorithms-32faq13a56.pdf

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.

An Analysis of Selection Procedures with Particular Attention Paid to Proportional and Boltzmann Selection

An important goal in biology is to predict from sequence data the high-resolution structures of proteins and the interactions that occur between them. In this paper, we describe a computational approach that can make these types of predictions for a series of coiled-coil dimers. Our method comprises a dual strategy that augments extensive conformational sampling with molecular mechanics minimization. To test the performance of the method, we designed six heterodimeric coiled coils with a range of stabilities and solved x-ray crystal structures for three of them. The stabilities and structures predicted by the calculations agree very well with experimental data: the average error in unfolding free energies is <1 kcal/mol, and nonhydrogen atoms in the predicted structures superimpose onto the experimental structures with rms deviations <0.7 A. We have also tested the method on a series of homodimers derived from vitellogenin-binding protein. The predicted relative stabilities of the homodimers show excellent agreement with previously published experimental measurements. A critical step in our procedure is to use energy minimization to relax side-chain geometries initially selected from a rotamer library. Our results show that computational methods can predict interaction specificities that are in good agreement with experimental data.

https://www.pnas.org/content/pnas/98/26/14825.full.pdf

Side-chain repacking calculations for predicting structures and stabilities of heterodimeric coiled coils.

The design of a tight-binding molecular ligand involves a tradeoff between an unfavorable electrostatic desolvation penalty incurred when the ligand binds a receptor in aqueous solution and the generally favorable intermolecular interactions made in the bound state. Using continuum electrostatic models we have developed a theoretical framework for analyzing this problem and have shown that the ligand-charge distribution can be optimized to produce the most favorable balance of these opposing free energy contributions [L.-P. Lee and B. Tidor, J. Chem. Phys. 106, 8681 (1997)]. Herein the theoretical framework is extended and calculations are performed for a wide range of model receptors. We examine methods for computing optimal ligands (including cases where there is conformational change) and the resulting properties of optimized ligands. In particular, indicators are developed to aid in the determination of the deficiencies in a specific ligand or basis. A connection is established between the optimization problem here and a generalized image problem, from which an inverse-image basis set can be defined; this basis is shown to perform very well in optimization calculations. Furthermore, the optimized ligands are shown to have favorable electrostatic binding free energies (in contrast to many natural ligands), there is a strong correlation between the receptor desolvation penalty and the optimized binding free energy for fixed geometry, and the ligand and receptor cannot generally be mutually optimal. Additionally, we introduce the display of complementary desolvation and interaction potentials and the deviation of their relationship from ideal as a useful tool for judging effective complementarity. Scripts for computing and displaying these potentials with GRASP are available at http://mit.edu/tidor.

Optimizing electrostatic affinity in ligand-receptor binding: Theory, computation, and ligand properties

Combinatorial mutagenesis with an alphabet limited to alanine, glutamic acid, lysine, and threonine was used to probe the role of interactions involving surface residues in stabilizing a short alpha-helical coiled coil. The residues at eight e and g positions in the leucine zipper of the Saccharomyces cerevisiae transcription factor GCN4 were randomized to these four residues in a lambda repressor-leucine zipper fusion protein, resulting in 65,536 possible residue combinations. Roughly three-fourths of these combinations allowed stable coiled-coil formation as assayed by DNA binding by the fusion protein. To understand the basis for the activity differences, functional and non-functional mutants were sequenced and statistical tests were applied to identify structure/function correlations. Helix-forming propensity and favorable intrasubunit and intersubunit charge-charge interactions were positively correlated with activity. These studies suggest that the identities of surface side chains at the e and g positions of coiled coils contribute modestly to stability; by comparison with previous work, however, the e and g positions are far less critical than residues at the a and d positions, which form the hydrophobic core of the dimer interface.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1002/pro.5560020701

Probing the roles of residues at the e and g positions of the GCN4 leucine zipper by combinatorial mutagenesis.

Theoretical calculations (Hendsch ZS & Tidor B, 1994, Protein Sci 3:211-226) and experiments (Waldburger CD et al., 1995, Nat Struct Biol 2:122-128; Wimley WC et al., 1996, Proc Natl Acad Sci USA 93:2985-2990) suggest that hydrophobic interactions are more stabilizing than salt bridges in protein folding. The lack of apparent stability benefit for many salt bridges requires an alternative explanation for their occurrence within proteins. To examine the effect of salt bridges on protein structure and stability in more detail, we have developed an energy function for simple cubic lattice polymers based on continuum electrostatic calculations of a representative selection of salt bridges found in known protein crystal structures. There are only three types of residues in the model, with charges of -1, 0, or + 1. We have exhaustively enumerated conformational space and significant regions of sequence space for three-dimensional cubic lattice polymers of length 16. The results demonstrate that, while the more highly charged sequences are less stable, the loss of stability is accompanied by a substantial reduction in the degeneracy of the lowest-energy state. Moreover, the reduction in degeneracy is greater due to charges that pair than for lone charges that remain relatively exposed to solvent. We have also explored and illustrated the use of ion-pairing strategies for rational structural design using model lattice studies.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/pro.5560070906

Bruce Tidor

Papers

An Analysis of Selection Procedures with Particular Attention Paid to Proportional and Boltzmann Selection

Side-chain repacking calculations for predicting structures and stabilities of heterodimeric coiled coils.

Optimizing electrostatic affinity in ligand-receptor binding: Theory, computation, and ligand properties

Probing the roles of residues at the e and g positions of the GCN4 leucine zipper by combinatorial mutagenesis.

Effects of salt bridges on protein structure and design