Jan 3, 2007

2007 week 01: Articles in Proteins


Locating missing water molecules in protein cavities by the three-dimensional reference interaction site model theory of molecular solvation

Water molecules confined in protein cavities are of great importance in understanding the protein structure and functions. However, it is a nontrivial task to locate such water molecules in protein by the ordinary molecular simulation and modeling techniques as well as experimental methods. The present study proves that the three-dimensional reference interaction site model (3D-RISM) theory, a recently developed statistical-mechanical theory of molecular solvation, has an outstanding advantage in locating such water molecules. In this paper, we demonstrate that the 3D-RISM theory is able to reproduce the structure and the number of water molecules in cavities of hen egg-white lysozyme observed commonly in the X-ray structures of different resolutions and conditions. Furthermore, we show that the theory successfully identified a water molecule in a cavity, the existence of which has been ambiguous even from the X-ray results. In contrast, we confirmed that molecular dynamics simulation is helpless at present to find such water molecules because the results substantially depend on the initial coordinates of water molecules. Possible applications of the theory to problems in the fields of biochemistry and biophysics are also discussed. Proteins 2007. © 2006 Wiley-Liss, Inc.

Protein-RNA interactions: Structural analysis and functional classes

A data set of 89 protein-RNA complexes has been extracted from the Protein Data Bank, and the nucleic acid recognition sites characterized through direct contacts, accessible surface area, and secondary structure motifs.
However, the analysis of hydrogen bond and van der Waal contacts showed that in general proteins complexed with messenger RNA, transfer RNA and viral RNA have more base specific contacts and less backbone contacts than expected, while proteins complexed with ribosomal RNA have less base-specific contacts than the expected. Hence, whilst the types of amino acids involved in the interfaces are similar, the distribution of specific contacts is dependent upon the functional class of the RNA bound. Proteins 2007. © 2006 Wiley-Liss, Inc.

valuating protein structures determined by structural genomics consortia

Structural genomics projects are providing large quantities of new 3D structural data for proteins. To monitor the quality of these data, we have developed the protein structure validation software suite (PSVS), for assessment of protein structures generated by NMR or X-ray crystallographic methods. PSVS is broadly applicable for structure quality assessment in structural biology projects.
PSVS is particularly useful in assessing protein structures determined by NMR methods, but is also valuable for assessing X-ray crystal structures or homology models. Using these tools, we assessed protein structures generated by the Northeast Structural Genomics Consortium and other international structural genomics projects, over a 5-year period. Protein structures produced from structural genomics projects exhibit quality score distributions similar to those of structures produced in traditional structural biology projects during the same time period. However, while some NMR structures have structure quality scores similar to those seen in higher-resolution X-ray crystal structures, the majority of NMR structures have lower scores. Potential reasons for this "structure quality score gap" between NMR and X-ray crystal structures are discussed. Proteins 2007. © 2006 Wiley-Liss, Inc.

Achieving 80% ten-fold cross-validated accuracy for secondary structure prediction by large-scale training

An integrated system of neural networks, called SPINE, is established and optimized for predicting structural properties of proteins. SPINE is applied to three-state secondary-structure and residue-solvent-accessibility (RSA) prediction in this paper. The integrated neural networks are carefully trained with a large dataset of 2640 chains, sequence profiles generated from multiple sequence alignment, representative amino acid properties, a slow learning rate, overfitting protection, and an optimized sliding-widow size. Proteins 2007. © 2006 Wiley-Liss, Inc.

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