Dataset Information

KINARI-Web: a server for protein rigidity analysis.

ABSTRACT: KINARI-Web is an interactive web server for performing rigidity analysis and visually exploring rigidity properties of proteins. It also provides tools for pre-processing the input data, such as selecting relevant chains from PDB files, adding hydrogen atoms and identifying stabilizing interactions. KINARI-Web offers a quick-start option for beginners, and highly customizable features for the experienced user. Chains, residues or atoms, as well as stabilizing constraints can be selected, removed or added, and the user can designate how different chemical interactions should be modeled during rigidity analysis. The enhanced Jmol-based visualizer allows for zooming in, highlighting or investigating different calculated rigidity properties of a molecular structure. KINARI-Web is freely available at http://kinari.cs.umass.edu.

SUBMITTER: Fox N

PROVIDER: S-EPMC3125808 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Publications

KINARI-Web: a server for protein rigidity analysis.

Fox Naomi N Jagodzinski Filip F Li Yang Y Streinu Ileana I

Nucleic acids research 20110621 Web Server issue

KINARI-Web is an interactive web server for performing rigidity analysis and visually exploring rigidity properties of proteins. It also provides tools for pre-processing the input data, such as selecting relevant chains from PDB files, adding hydrogen atoms and identifying stabilizing interactions. KINARI-Web offers a quick-start option for beginners, and highly customizable features for the experienced user. Chains, residues or atoms, as well as stabilizing constraints can be selected, removed ...[more]

PMID: 21693559

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Project description:BACKGROUND: Machine learning-based methods have been proven to be powerful in developing new fold recognition tools. In our previous work [Zhang, Kochhar and Grigorov (2005) Protein Science, 14: 431-444], a machine learning-based method called DescFold was established by using Support Vector Machines (SVMs) to combine the following four descriptors: a profile-sequence-alignment-based descriptor using Psi-blast e-values and bit scores, a sequence-profile-alignment-based descriptor using Rps-blast e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. In this work, we focus on the improvement of DescFold by incorporating more powerful descriptors and setting up a user-friendly web server. RESULTS: In seeking more powerful descriptors, the profile-profile alignment score generated from the COMPASS algorithm was first considered as a new descriptor (i.e., PPA). When considering a profile-profile alignment between two proteins in the context of fold recognition, one protein is regarded as a template (i.e., its 3D structure is known). Instead of a sequence profile derived from a Psi-blast search, a structure-seeded profile for the template protein was generated by searching its structural neighbors with the assistance of the TM-align structural alignment algorithm. Moreover, the COMPASS algorithm was used again to derive a profile-structural-profile-alignment-based descriptor (i.e., PSPA). We trained and tested the new DescFold in a total of 1,835 highly diverse proteins extracted from the SCOP 1.73 version. When the PPA and PSPA descriptors were introduced, the new DescFold boosts the performance of fold recognition substantially. Using the SCOP_1.73_40% dataset as the fold library, the DescFold web server based on the trained SVM models was further constructed. To provide a large-scale test for the new DescFold, a stringent test set of 1,866 proteins were selected from the SCOP 1.75 version. At a less than 5% false positive rate control, the new DescFold is able to correctly recognize structural homologs at the fold level for nearly 46% test proteins. Additionally, we also benchmarked the DescFold method against several well-established fold recognition algorithms through the LiveBench targets and Lindahl dataset. CONCLUSIONS: The new DescFold method was intensively benchmarked to have very competitive performance compared with some well-established fold recognition methods, suggesting that it can serve as a useful tool to assist in template-based protein structure prediction. The DescFold server is freely accessible at http://202.112.170.199/DescFold/index.html.

Dataset Information

KINARI-Web: a server for protein rigidity analysis.

Publications

KINARI-Web: a server for protein rigidity analysis.

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