Classical Monte Carlo simulations have been carried out for liquid water in the NPT ensemble at 25 °C and 1 atm using six of the simpler intermolecular potential functions for the water dimer: Bernal–Fowler (BF), SPC, ST2, TIPS2, TIP3P, and TIP4P. Comparisons are made with experimental thermodynamic and structural data including the recent neutron diffraction results of Thiessen and Narten. The computed densities and potential energies are in reasonable accord with experiment except for the original BF model, which yields an 18% overestimate of the density and poor structural results. The TIPS2 and TIP4P potentials yield oxygen–oxygen partial structure functions in good agreement with the neutron diffraction results. The accord with the experimental OH and HH partial structure functions is poorer; however, the computed results for these functions are similar for all the potential functions. Consequently, the discrepancy may be due to the correction terms needed in processing the neutron data or to an effect uniformly neglected in the computations. Comparisons are also made for self‐diffusion coefficients obtained from molecular dynamics simulations. Overall, the SPC, ST2, TIPS2, and TIP4P models give reasonable structural and thermodynamic descriptions of liquid water and they should be useful in simulations of aqueous solutions. The simplicity of the SPC, TIPS2, and TIP4P functions is also attractive from a computational standpoint.

Comparison of simple potential functions for simulating liquid water

Introduction Statistical mechanics Molecular dynamics Monte Carlo methods Some tricks of the trade How to analyse the results Advanced simulation techniques Non-equilibrium molecular dynamics Brownian dynamics Quantum simulations Some applications Appendix A: Computers and computer simulation Appendix B: Reduced units Appendix C: Calculation of forces and torques Appendix D: Fourier transforms Appendix E: The gear predictor - corrector Appendix F: Programs on microfiche Appendix G: Random numbers References Index.

Computer Simulation of Liquids

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

For a successful analysis of the relation between amino acid sequence and protein structure, an unambiguous and physically meaningful definition of secondary structure is essential. We have developed a set of simple and physically motivated criteria for secondary structure, programmed as a pattern-recognition process of hydrogen-bonded and geometrical features extracted from x-ray coordinates. Cooperative secondary structure is recognized as repeats of the elementary hydrogen-bonding patterns “turn” and “bridge.” Repeating turns are “helices,” repeating bridges are “ladders,” connected ladders are “sheets.” Geometric structure is defined in terms of the concepts torsion and curvature of differential geometry. Local chain “chirality” is the torsional handedness of four consecutive Cα positions and is positive for right-handed helices and negative for ideal twisted β-sheets. Curved pieces are defined as “bends.” Solvent “exposure” is given as the number of water molecules in possible contact with a residue. The end result is a compilation of the primary structure, including SS bonds, secondary structure, and solvent exposure of 62 different globular proteins. The presentation is in linear form: strip graphs for an overall view and strip tables for the details of each of 10.925 residues. The dictionary is also available in computer-readable form for protein structure prediction work.

Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features

CHARMM (Chemistry at HARvard Macromolecular Mechanics) is a highly flexible computer program which uses empirical energy functions to model macromolecular systems. The program can read or model build structures, energy minimize them by first- or second-derivative techniques, perform a normal mode or molecular dynamics simulation, and analyze the structural, equilibrium, and dynamic properties determined in these calculations. The operations that CHARMM can perform are described, and some implementation details are given. A set of parameters for the empirical energy function and a sample run are included.

CHARMM: A program for macromolecular energy, minimization, and dynamics calculations

Structural Studies of Ribonuclease. V. Reversible Change of Configuration1-3

The conformational potential energy of the normal hydrocarbon molecules is considered to be a function of the dihedral angles of internal rotation about the carbon—carbon single bonds, the bond lengths and valence angles being held fixed. The energy equation contains terms of two kinds, a sum over torsional or internal‐rotation terms, one for each carbon—carbon bond, which are attributed to exchange interactions of electrons in bonds adjacent to the bond about which internal rotation occurs, and a sum over all the pairwise nonbonded interactions between all of the atoms in the molecule. The torsional terms are dependent on the dihedral angles of internal rotation and on the choice of fixed valence angles, but each torsional term reduces to the form ½U0(1+cos3ωi) when all the fixed valence angles are selected to be tetrahedral. The nonbonded‐interaction terms are represented by Lennard‐Jones ``6–12'' potential functions, Uij=(dij/rij12) − (eij/rij6), the parameters eij being calculated from the Slater—Kirk...

Conformational Analysis of Macromolecules. II. The Rotational Isomeric States of the Normal Hydrocarbons

The distributions of side-chain conformations in 258 crystal structures of oligopeptides have been analyzed. The sample contains 321 residues having side chains that extend beyond the C beta atom. Statistically observed preferences of side-chain dihedral angles are summarized and correlated with stereochemical and energetic constraints. The distributions are compared with observed distributions in proteins of known X-ray structures and with computed minimum-energy conformations of amino acid derivatives. The distributions are similar in all three sets of data, and they appear to be governed primarily by intraresidue interactions. In side chains with no beta-branching, the most important interactions that determine chi 1 are those between the C gamma H2 group and atoms of the neighboring peptide groups. As a result, the g- conformation (chi 1 congruent to -60 degrees) occurs most frequently for rotation around the C alpha-C beta bond in oligopeptides, followed by the t conformation (chi 1 congruent to 180 degrees), while the g+ conformation (chi 1 congruent to 60 degrees) is least favored. In residues with beta-branching, steric repulsions between the C gamma H2 or C gamma H3 groups and backbone atoms govern the distribution of chi 1. The extended (t) conformation is highly favored for rotation around the C beta-C gamma and C gamma-C delta bonds in unbranched side chains, because the t conformer has a lower energy than the g+ and g- conformers in hydrocarbon chains. This study of the observed side-chain conformations has led to a refinement of one of the energy parameters used in empirical conformational energy computations.

Statistical and energetic analysis of side-chain conformations in oligopeptides.

Methods for describing a discrete number of conformational states of amino acid residues in proteins are presented and used to investigate the topography of chain folding. The relative importance of short-range, medium-range and long-range interactions is discussed in the light of an analysis of the conformational states for the different amino acid residues in eight proteins of known structure. A prediction algorithm, which assigns four states to each residue of a protein chain (α-helix, extended structure, bend, or coil), has been developed from a consideration of both short- and medium-range interactions and applied to thirteen proteins of known three-dimensional structure. The prediction algorithm is simple to apply, and the assignment of α-helix and extended structure is considerably better than in most other predictive schemes. The prediction of chain reversal or bend regions was also better than with previous algorithms, but these assignments were not as good as those for α-helix and extended structure. The motivation for the development of this algorithm is not only to demonstrate the relative importance of short- and longer-range interactions but, more important, to begin to develop procedures for obtaining an approximate starting conformation for subsequent energy minimization to predict the three-dimensional structure of a protein. This procedure, as well as various other methods for the prediction of the backbone topography and conformational states of residues in proteins from the amino acid sequence, have been reviewed and evaluated by comparing the success of the methods to the success expected from a random assignment of conformational states.

Analysis of Conformations of Amino Acid Residues and Prediction of Backbone Topography in Proteins

Computation of structures of homologous proteins. .alpha.-Lactalbumin from lysozyme

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