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.

/pdf/charmm-the-biomolecular-simulation-program-4kexem0xkx.pdf

CHARMM: the biomolecular simulation program.

Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

/pdf/comparative-protein-structure-modeling-using-modeller-262v4shee8.pdf

Comparative Protein Structure Modeling Using MODELLER

■ Abstract Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more pro- teins of known structure (templates). The prediction process consists of fold assign- ment, target-template alignment, model building, and model evaluation. The number of protein sequences that can be modeled and the accuracy of the predictions are in- creasing steadily because of the growth in the number of known protein structures and because of the improvements in the modeling software. Further advances are nec- essary in recognizing weak sequence-structure similarities, aligning sequences with structures, modeling of rigid body shifts, distortions, loops and side chains, as well as detecting errors in a model. Despite these problems, it is currently possible to model with useful accuracy significant parts of approximately one third of all known protein sequences. The use of individual comparative models in biology is already rewarding and increasingly widespread. A major new challenge for comparative modeling is the integration of it with the torrents of data from genome sequencing projects as well as from functional and structural genomics. In particular, there is a need to develop an automated, rapid, robust, sensitive, and accurate comparative modeling pipeline applicable to whole genomes. Such large-scale modeling is likely to encourage new kinds of applications for the many resulting models, based on their large number and completeness at the level of the family, organism, or functional network.

Comparative protein structure modeling of genes and genomes

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

/pdf/comparative-protein-structure-modeling-using-modeller-1ye86wsiv7.pdf

Comparative protein structure modeling using Modeller.

Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes Here, we review key concepts and specific features of small-molecule-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches

/pdf/docking-and-scoring-in-virtual-screening-for-drug-discovery-4nrii464b0.pdf

Docking and scoring in virtual screening for drug discovery: methods and applications.

The application of all-atom force fields (and explicit or implicit solvent models) to protein homology-modeling tasks such as side-chain and loop prediction remains challenging both because of the expense of the individual energy calculations and because of the difficulty of sampling the rugged all-atom energy surface. Here we address this challenge for the problem of loop prediction through the development of numerous new algorithms, with an emphasis on multiscale and hierarchical techniques. As a first step in evaluating the performance of our loop prediction algorithm, we have applied it to the problem of reconstructing loops in native structures; we also explicitly include crystal packing to provide a fair comparison with crystal structures. In brief, large numbers of loops are generated by using a dihedral angle-based buildup procedure followed by iterative cycles of clustering, side-chain optimization, and complete energy minimization of selected loop structures. We evaluate this method by using the largest test set yet used for validation of a loop prediction method, with a total of 833 loops ranging from 4 to 12 residues in length. Average/median backbone root-mean-square deviations (RMSDs) to the native structures (superimposing the body of the protein, not the loop itself) are 0.42/0.24 A for 5 residue loops, 1.00/0.44 A for 8 residue loops, and 2.47/1.83 A for 11 residue loops. Median RMSDs are substantially lower than the averages because of a small number of outliers; the causes of these failures are examined in some detail, and many can be attributed to errors in assignment of protonation states of titratable residues, omission of ligands from the simulation, and, in a few cases, probable errors in the experimentally determined structures. When these obvious problems in the data sets are filtered out, average RMSDs to the native structures improve to 0.43 A for 5 residue loops, 0.84 A for 8 residue loops, and 1.63 A for 11 residue loops. In the vast majority of cases, the method locates energy minima that are lower than or equal to that of the minimized native loop, thus indicating that sampling rarely limits prediction accuracy. The overall results are, to our knowledge, the best reported to date, and we attribute this success to the combination of an accurate all-atom energy function, efficient methods for loop buildup and side-chain optimization, and, especially for the longer loops, the hierarchical refinement protocol.

A hierarchical approach to all-atom protein loop prediction.

We have derived a surface-area-based version of the generalized Born model (S-GB) as a well-defined approximation to the boundary element formulation of the Poisson−Boltzmann (PB) equation. The relationship of the surface area methodology to the volume-integration-based approach of Still and co-workers is elucidated. On the basis of insights obtained from these results, we then develop empirical correction schemes which yield significant improvements in accuracy, as compared to the uncorrected GB model, in reproducing accurate solutions of the Poisson−Boltzmann equation. A large suite of energetic comparisons of GB, corrected S-GB, and PB for multiple conformations of peptides and proteins is presented.

Generalized Born Model Based on a Surface Integral Formulation

The prediction of protein side chain conformations is used to evaluate the accuracy of force field parameters. Specifically, new torsional parameters have recently been reported for the OPLS-AA force field, which achieved substantially better accuracy with respect to high level gas-phase quantum chemical calculations [J. Phys. Chem. B 2001, 105, 6474]. Here we demonstrate that these new parameters also lead to qualitatively improved side chain prediction accuracy. The primary emphasis is on the prediction of single side chain conformations, with the rest of the protein held fixed at the native configuration. Errors due to incomplete sampling can thus be essentially eliminated, using a combination of rotamer search and energy minimization. In addition, the protein environment is modeled realistically using implicit solvation and an explicit representation of crystal packing effects. Aided by the development of new algorithms, these calculations have been performed with modest computational requirements (a ...

/pdf/force-field-validation-using-protein-side-chain-prediction-2n20ygadw9.pdf

Force Field Validation Using Protein Side Chain Prediction

Post-translational phosphorylation plays a key role in regulating protein function. Here, we provide a quantitative assessment of the relative strengths of hydrogen bonds involving phosphorylated amino acid side chains (pSer, pAsp) with several common donors (Arg, Lys, and backbone amide groups). We utilize multiple levels of theory, consisting of explicit solvent molecular dynamics, implicit solvent molecular mechanics, and quantum mechanics with a self-consistent reaction field treatment of solvent. Because the approximately 6 pKa of phosphate suggests that -1 and -2 charged species may coexist at physiological pH, hydrogen bonds involving both protonated and deprotonated phosphates for all donor-acceptor pairs are considered. Multiple bonding geometries for the charged-charged interactions are also considered. Arg is shown to be capable of substantially stronger salt bridges with phosphorylated side chains than Lys. A pSer hydrogen-bond acceptor tends to form more stable interactions than a pAsp acceptor. The effect of phosphate protonation state on the strengths of the hydrogen bonds is remarkably subtle, with a more pronounced effect on pAsp than on pSer.

Strengths of hydrogen bonds involving phosphorylated amino acid side chains.

We have carried out an extensive exploration of the possibility of predicting the structure of long loops in proteins, using an 8- and a 12-residue loop in ribonuclease A as models. The native X-ray structure is used as a template while allowing for template flexibility; this makes our work relevant to the problem of homology modeling in which the template is not precisely known. Energies are calculated with the AMBER* and AMBER94 molecular mechanics potentials and the generalized Born continuum solvation model; and conformational space is sampled by means of a combination of Monte Carlo and molecular dynamics methods. Our AMBER94 results demonstrate that we can successfully generate loops with low root-mean-square deviations from the native as well as excellent energetic rankings. Proteins 1999;35:173–183. © 1999 Wiley-Liss, Inc.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/%28SICI%291097-0134%2819990501%2935%3A2%3C173%3A%3AAID-PROT4%3E3.0.CO%3B2-2

Chaya S. Rapp

Papers

A hierarchical approach to all-atom protein loop prediction.

Generalized Born Model Based on a Surface Integral Formulation

Force Field Validation Using Protein Side Chain Prediction

Strengths of hydrogen bonds involving phosphorylated amino acid side chains.

Prediction of loop geometries using a generalized born model of solvation effects.