The identification of dynamical domains in proteins and the description of the low-frequency domain motions are one of the important applications of numerical simulation techniques. The application of these techniques to large proteins requires a substantial computational effort and therefore cannot be performed routinely, if at all. This article shows how physically motivated approximations permit the calculation of low-frequency normal modes in a few minutes on standard desktop computers. The technique is based on the observation that the low-frequency modes, which describe domain motions, are independent of force field details and can be obtained with simplified mechanical models. These models also provide a useful measure for rigidity in proteins, allowing the identification of quasi-rigid domains. The methods are validated by application to three well-studied proteins, crambin, lysozyme, and ATCase. In addition to being useful techniques for studying domain motions, the success of the approximations provides new insight into the relevance of normal mode calculations and the nature of the potential energy surface of proteins.

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The Molecular Modeling Toolkit is a library that implements common molecular simulation techniques, with an emphasis on biomolecular simulations. It uses modern software engineering techniques (object‐oriented design, a high‐level language) to overcome limitations associated with the large monolithic simulation programs that are commonly used for biomolecules. Its principal advantages are (1) easy extension and combination with other libraries due to modular library design; (2) a single high‐level general‐purpose programming language (Python) is used for library implementation as well as for application scripts; (3) use of documented and machine‐independent formats for all data files; and (4) interfaces to other simulation and visualization programs. © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 79–85, 2000

An efficient scheme is presented for the numerical calculation of hydrodynamic interactions of many spheres in Stokes flow. The spheres may have various sizes, and are freely moving or arranged in rigid arrays. Both the friction and mobility matrix are found from the solution of a set of coupled equations. The Stokesian dynamics of many spheres and the friction and mobility tensors of polymers and proteins may be calculated accurately at a modest expense of computer memory and time. The transport coefficients of suspensions can be evaluated by use of periodic boundary conditions.

We present a new approach for determining dynamical domains in large proteins, either based on a comparison of different experimental structures, or on a simplified normal mode calculation for a single conformation. In a first step, a deformation measure is evaluated for all residues in the protein; a high deformation indicates highly flexible interdomain regions. The sufficiently rigid parts of the protein are then classified into rigid domains and low-deformation interdomain regions on the basis of their global motion. We demonstrate the techniques on three proteins: citrate synthase, which has been the subject of earlier domain analyses, HIV-1 reverse transcriptase, which has a rather complex domain structure, and aspartate transcarbamylase as an example of a very large protein. These examples show that the comparison of conformations and the normal mode analysis lead to essentially the same domain identification, except for cases where the experimental conformations differ by the presence of a large ligand, such as a DNA strand. Normal mode analysis has the advantage of requiring only one experimental structure and of providing a more detailed picture of domain movements, e.g. the splitting of domains into subdomains at higher frequencies.

The programs developed for this work are available as supplementary material at Bioinformatics Online.

Elastic network models (ENMs) are valuable tools for investigating collective motions of proteins, and a rich variety of simple models have been proposed over the past decade. A good representation of the collective motions requires a good approximation of the covariances between the fluctuations of the individual atoms. Nevertheless, most studies have validated such models only by the magnitudes of the single-atom fluctuations they predict. In the present study, we have quantified the agreement between the covariance structure predicted by molecular dynamics (MD) simulations and those predicted by a representative selection of proposed coarse-grained ENMs. We then contrast this approach with the comparison to MD-predicted atomic fluctuations and comparison to crystallographic B-factors. While all the ENMs yield approximations to the MD-predicted covariance structure, we report large and consistent differences between proposed models. We also find that the ability of the ENMs to predict atomic fluctuations is correlated with their ability to capture the covariance structure. In contrast, we find that the models that agree best with B-factors model collective motions less reliably and recommend against using B-factors as a benchmark.

With the development of new experimental technologies, biologists are faced with an avalanche of data to be computationally analyzed for scientific advancements and discoveries to emerge. Faced with the complexity of analysis pipelines, the large number of computational tools, and the enormous amount of data to manage, there is compelling evidence that many if not most scientific discoveries will not stand the test of time: increasing the reproducibility of computed results is of paramount importance. The objective we set out in this paper is to place scientific workflows in the context of reproducibility. To do so, we define several kinds of reproducibility that can be reached when scientific workflows are used to perform experiments. We characterize and define the criteria that need to be catered for by reproducibility-friendly scientific workflow systems, and use such criteria to place several representative and widely used workflow systems and companion tools within such a framework. We also discuss the remaining challenges posed by reproducible scientific workflows in the life sciences. Our study was guided by three use cases from the life science domain involving in silico experiments. (Résumé d'auteur