Dataset Information

Matching Multiple Rigid Domain Decompositions of Proteins.

ABSTRACT: We describe efficient methods for consistently coloring and visualizing collections of rigid cluster decompositions obtained from variations of a protein structure, and lay the foundation for more complex setups, that may involve different computational and experimental methods. The focus here is on three biological applications: the conceptually simpler problems of visualizing results of dilution and mutation analyses, and the more complex task of matching decompositions of multiple Nucleic Magnetic Resonance (NMR) models of the same protein. Implemented into the KINematics And RIgidity (KINARI) web server application, the improved visualization techniques give useful information about protein folding cores, help examining the effect of mutations on protein flexibility and function, and provide insights into the structural motions of Protein Data Bank proteins solved with solution NMR. These tools have been developed with the goal of improving and validating rigidity analysis as a credible coarse-grained model capturing essential information about a protein's slow motions near the native state.

SUBMITTER: Flynn E

PROVIDER: S-EPMC5587209 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Publications

Matching Multiple Rigid Domain Decompositions of Proteins.

Flynn Emily E Streinu Ileana I

IEEE transactions on nanobioscience 20170127 2

We describe efficient methods for consistently coloring and visualizing collections of rigid cluster decompositions obtained from variations of a protein structure, and lay the foundation for more complex setups, that may involve different computational and experimental methods. The focus here is on three biological applications: the conceptually simpler problems of visualizing results of dilution and mutation analyses, and the more complex task of matching decompositions of multiple Nucleic Mag ...[more]

PMID: 28141528

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Project description:This work presents a novel detection method for three-dimensional domain swapping (DS), a mechanism for forming protein quaternary structures that can be visualized as if monomers had "opened" their "closed" structures and exchanged the opened portion to form intertwined oligomers. Since the first report of DS in the mid 1990s, an increasing number of identified cases has led to the postulation that DS might occur in a protein with an unconstrained terminus under appropriate conditions. DS may play important roles in the molecular evolution and functional regulation of proteins and the formation of depositions in Alzheimer's and prion diseases. Moreover, it is promising for designing auto-assembling biomaterials. Despite the increasing interest in DS, related bioinformatics methods are rarely available. Owing to a dramatic conformational difference between the monomeric/closed and oligomeric/open forms, conventional structural comparison methods are inadequate for detecting DS. Hence, there is also a lack of comprehensive datasets for studying DS. Based on angle-distance (A-D) image transformations of secondary structural elements (SSEs), specific patterns within A-D images can be recognized and classified for structural similarities. In this work, a matching algorithm to extract corresponding SSE pairs from A-D images and a novel DS score have been designed and demonstrated to be applicable to the detection of DS relationships. The Matthews correlation coefficient (MCC) and sensitivity of the proposed DS-detecting method were higher than 0.81 even when the sequence identities of the proteins examined were lower than 10%. On average, the alignment percentage and root-mean-square distance (RMSD) computed by the proposed method were 90% and 1.8Å for a set of 1,211 DS-related pairs of proteins. The performances of structural alignments remain high and stable for DS-related homologs with less than 10% sequence identities. In addition, the quality of its hinge loop determination is comparable to that of manual inspection. This method has been implemented as a web-based tool, which requires two protein structures as the input and then the type and/or existence of DS relationships between the input structures are determined according to the A-D image-based structural alignments and the DS score. The proposed method is expected to trigger large-scale studies of this interesting structural phenomenon and facilitate related applications.

Dataset Information

Matching Multiple Rigid Domain Decompositions of Proteins.

Publications

Matching Multiple Rigid Domain Decompositions of Proteins.

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