Amedeo Caflisch

CHARMM: The biomolecular simulation program

CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecular simulation program. It has been developed over the last three decades with a primary focus on molecules of biological 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 estimators, 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 numerous 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.

Evaluation of a fast implicit solvent model for molecular dynamics simulations

A solvation term based on the solvent accessible surface area (SASA) is combined with the CHARMM polar hydrogen force field for the efficient simulation of peptides and small proteins in aqueous solution. Only two atomic solvation parameters are used: one is negative for favoring the direct solvation of polar groups and the other positive for taking into account the hydrophobic effect on apolar groups. To approximate the water screening effects on the intrasolute electrostatic interactions, a distance‐dependent dielectric function is used and ionic side chains are neutralized. The use of an analytical approximation of the SASA renders the model extremely efficient (i.e., only about 50% slower than in vacuo simulations). The limitations and range of applicability of the SASA model are assessed by simulations of proteins and structured peptides. For the latter, the present study and results reported elsewhere show that with the SASA model it is possible to sample a significant amount of folding/unfolding transitions, which permit the study of the thermodynamics and kinetics of folding at an atomic level of detail. Proteins 2002;46:24–33.

The role of side-chain interactions in the early steps of aggregation: Molecular dynamics simulations of an amyloid-forming peptide from the yeast prion Sup35

Understanding the early steps of aggregation at atomic detail might be crucial for the rational design of therapeutics preventing diseases associated with amyloid deposits. In this paper, aggregation of the heptapeptide GNNQQNY, from the N-terminal prion-determining domain of the yeast protein Sup35, was studied by 20 molecular dynamics runs for a total simulation time of 20 μs. The simulations generate in-register parallel packing of GNNQQNY β-strands that is consistent with x-ray diffraction and Fourier transform infrared data. The statistically preferred aggregation pathway does not correspond to a purely downhill profile of the energy surface because of the presence of enthalpic barriers that originate from out-of-register interactions. The parallel β-sheet arrangement is favored over the antiparallel because of side-chain contacts; in particular, stacking interactions of the tyrosine rings and hydrogen bonds between amide groups. No ordered aggregation was found in control simulations with the mutant sequence SQNGNQQRG in accord with experimental data and the strong sequence dependence of aggregation.

Wordom: a program for efficient analysis of molecular dynamics simulations

Wordom is a versatile program for manipulation of molecular dynamics trajectories and efficient analysis of simulations. Original tools in Wordom include a procedure to evaluate significance of sampling for principal component analysis as well as modules for clustering multiple conformations and evaluation of order parameters for folding and aggregation. The program was developed with special emphasis on user-friendliness, effortless addition of new modules and efficient handling of large sets of trajectories.

Predicting free energy changes using structural ensembles

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PARP1 ADP-ribosylates lysine residues of the core histone tails

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Prediction of aggregation rate and aggregation-prone segments in polypeptide sequences

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FACTS: Fast analytical continuum treatment of solvation

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Macrodomain-containing proteins are new mono-ADP-ribosylhydrolases

The chromatin-associated enzyme PARP1 has previously been suggested to ADP-ribosylate histones, but the specific ADP-ribose acceptor sites have remained enigmatic. Here, we show that PARP1 covalently ADP-ribosylates the amino-terminal histone tails of all core histones. Using biochemical tools and novel electron transfer dissociation mass spectrometric protocols, we identify for the first time K13 of H2A, K30 of H2B, K27 and K37 of H3, as well as K16 of H4 as ADP-ribose acceptor sites. Multiple explicit water molecular dynamics simulations of the H4 tail peptide into the catalytic cleft of PARP1 indicate that two stable intermolecular salt bridges hold the peptide in an orientation that allows K16 ADP-ribosylation. Consistent with a functional cross-talk between ADP-ribosylation and other histone tail modifications, acetylation of H4K16 inhibits ADP-ribosylation by PARP1. Taken together, our computational and experimental results provide strong evidence that PARP1 modifies important regulatory lysines of the core histone tails.

Wordom: A user-friendly program for the analysis of molecular structures, trajectories, and free energy surfaces

The reliable identification of β‐aggregating stretches in protein sequences is essential for the development of therapeutic agents for Alzheimers and Parkinsons diseases, as well as other pathological conditions associated with protein deposition. Here, a model based on physicochemical properties and computational design of β‐aggregating peptide sequences is shown to be able to predict the aggregation rate over a large set of natural polypeptide sequences. Furthermore, the model identifies aggregation‐prone fragments within proteins and predicts the parallel or anti‐parallel β‐sheet organization in fibrils. The model recognizes different β‐aggregating segments in mammalian and nonmammalian prion proteins, providing insights into the species barrier for the transmission of the prion disease.