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Several Protein Structures (several + protein_structure)
Selected AbstractsIdentifying and reducing error in cluster-expansion approximations of protein energiesJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 16 2010Seungsoo Hahn Abstract Protein design involves searching a vast space for sequences that are compatible with a defined structure. This can pose significant computational challenges. Cluster expansion is a technique that can accelerate the evaluation of protein energies by generating a simple functional relationship between sequence and energy. The method consists of several steps. First, for a given protein structure, a training set of sequences with known energies is generated. Next, this training set is used to expand energy as a function of clusters consisting of single residues, residue pairs, and higher order terms, if required. The accuracy of the sequence-based expansion is monitored and improved using cross-validation testing and iterative inclusion of additional clusters. As a trade-off for evaluation speed, the cluster-expansion approximation causes prediction errors, which can be reduced by including more training sequences, including higher order terms in the expansion, and/or reducing the sequence space described by thecluster expansion. This article analyzes the sources of error and introduces a method whereby accuracy can be improved by judiciously reducing the described sequence space. The method is applied to describe the sequence,stability relationship for several protein structures: coiled-coil dimers and trimers, a PDZ domain, and T4 lysozyme as examples with computationally derived energies, and SH3 domains in amphiphysin-1 and endophilin-1 as examples where the expanded pseudo-energies are obtained from experiments. Our open-source software package Cluster Expansion Version 1.0 allows users to expand their own energy function of interest and thereby apply cluster expansion to custom problems in protein design. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010 [source] How to multiply a matrix of normal equations by an arbitrary vector using FFTACTA CRYSTALLOGRAPHICA SECTION A, Issue 6 2008Boris V. Strokopytov This paper describes a novel algorithm for multiplying a matrix of normal equations by an arbitrary real vector using the fast Fourier transform technique. The algorithm allows full-matrix least-squares refinement of macromolecular structures without explicit calculation of the normal matrix. The resulting equations have been implemented in a new computer program, FMLSQ. A preliminary version of the program has been tested on several protein structures. The consequences for crystallographic refinement of macromolecules are discussed in detail. [source] On a fast calculation of structure factors at a subatomic resolutionACTA CRYSTALLOGRAPHICA SECTION A, Issue 1 2004P. V. Afonine In the last decade, the progress of protein crystallography allowed several protein structures to be solved at a resolution higher than 0.9,Å. Such studies provide researchers with important new information reflecting very fine structural details. The signal from these details is very weak with respect to that corresponding to the whole structure. Its analysis requires high-quality data, which previously were available only for crystals of small molecules, and a high accuracy of calculations. The calculation of structure factors using direct formulae, traditional for `small-molecule' crystallography, allows a relatively simple accuracy control. For macromolecular crystals, diffraction data sets at a subatomic resolution contain hundreds of thousands of reflections, and the number of parameters used to describe the corresponding models may reach the same order. Therefore, the direct way of calculating structure factors becomes very time expensive when applied to large molecules. These problems of high accuracy and computational efficiency require a re-examination of computer tools and algorithms. The calculation of model structure factors through an intermediate generation of an electron density [Sayre (1951). Acta Cryst.4, 362,367; Ten Eyck (1977). Acta Cryst. A33, 486,492] may be much more computationally efficient, but contains some parameters (grid step, `effective' atom radii etc.) whose influence on the accuracy of the calculation is not straightforward. At the same time, the choice of parameters within safety margins that largely ensure a sufficient accuracy may result in a significant loss of the CPU time, making it close to the time for the direct-formulae calculations. The impact of the different parameters on the computer efficiency of structure-factor calculation is studied. It is shown that an appropriate choice of these parameters allows the structure factors to be obtained with a high accuracy and in a significantly shorter time than that required when using the direct formulae. Practical algorithms for the optimal choice of the parameters are suggested. [source] Development of an automated large-scale protein-crystallization and monitoring system for high-throughput protein-structure analysesACTA CRYSTALLOGRAPHICA SECTION D, Issue 9 2006Masahiko Hiraki Protein crystallization remains one of the bottlenecks in crystallographic analysis of macromolecules. An automated large-scale protein-crystallization system named PXS has been developed consisting of the following subsystems, which proceed in parallel under unified control software: dispensing precipitants and protein solutions, sealing crystallization plates, carrying robot, incubators, observation system and image-storage server. A sitting-drop crystallization plate specialized for PXS has also been designed and developed. PXS can set up 7680 drops for vapour diffusion per hour, which includes time for replenishing supplies such as disposable tips and crystallization plates. Images of the crystallization drops are automatically recorded according to a preprogrammed schedule and can be viewed by users remotely using web-based browser software. A number of protein crystals were successfully produced and several protein structures could be determined directly from crystals grown by PXS. In other cases, X-ray quality crystals were obtained by further optimization by manual screening based on the conditions found by PXS. [source] | ||||||||||