Literature DB >> 12117796

CX, an algorithm that identifies protruding atoms in proteins.

Alessandro Pintar1, Oliviero Carugo, Sándor Pongor.   

Abstract

MOTIVATION: A simple and fast algorithm is described that calculates a measure of protrusion (cx) for atoms in protein structures, directly useable with the common molecular graphics programs.
RESULTS: A sphere of predetermined radius is centered around each non-hydrogen atom, and the volume occupied by the protein and the free volume within the sphere (internal and external volumes, respectively) are calculated. Atoms in protruding regions have a high ratio (cx) between the external and the internal volume. The program reads a PDB file, and writes the output in the same format, with cx values in the B factor field. Output structure files can be directly displayed with standard molecular graphics programs like RASMOL, MOLMOL, Swiss-PDB Viewer and colored according to cx values. We show the potential use of this program in the analysis of two protein-protein complexes and in the prediction of limited proteolysis sites in native proteins. AVAILABILITY: The algorithm is implemented in a standalone program written in C and its source is freely available at ftp.icgeb.trieste.it/pub/CX or on request from the authors.

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Year:  2002        PMID: 12117796     DOI: 10.1093/bioinformatics/18.7.980

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  31 in total

1.  Atom depth as a descriptor of the protein interior.

Authors:  Alessandro Pintar; Oliviero Carugo; Sándor Pongor
Journal:  Biophys J       Date:  2003-04       Impact factor: 4.033

Review 2.  Face-time with TAR: Portraits of an HIV-1 RNA with diverse modes of effector recognition relevant for drug discovery.

Authors:  Sai Shashank Chavali; Rachel Bonn-Breach; Joseph E Wedekind
Journal:  J Biol Chem       Date:  2019-05-12       Impact factor: 5.157

3.  Structural determinants of limited proteolysis.

Authors:  Marat D Kazanov; Yoshinobu Igarashi; Alexey M Eroshkin; Piotr Cieplak; Boris Ratnikov; Ying Zhang; Zhanwen Li; Adam Godzik; Andrei L Osterman; Jeffrey W Smith
Journal:  J Proteome Res       Date:  2011-07-08       Impact factor: 4.466

4.  Struct-NB: predicting protein-RNA binding sites using structural features.

Authors:  Fadi Towfic; Cornelia Caragea; David C Gemperline; Drena Dobbs; Vasant Honavar
Journal:  Int J Data Min Bioinform       Date:  2010       Impact factor: 0.667

5.  APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility.

Authors:  Jun-Feng Xia; Xing-Ming Zhao; Jiangning Song; De-Shuang Huang
Journal:  BMC Bioinformatics       Date:  2010-04-08       Impact factor: 3.169

Review 6.  Prediction of RNA binding proteins comes of age from low resolution to high resolution.

Authors:  Huiying Zhao; Yuedong Yang; Yaoqi Zhou
Journal:  Mol Biosyst       Date:  2013-10

7.  PAIRpred: partner-specific prediction of interacting residues from sequence and structure.

Authors:  Fayyaz ul Amir Afsar Minhas; Brian J Geiss; Asa Ben-Hur
Journal:  Proteins       Date:  2013-12-06

Review 8.  Computational prediction of protein interfaces: A review of data driven methods.

Authors:  Li C Xue; Drena Dobbs; Alexandre M J J Bonvin; Vasant Honavar
Journal:  FEBS Lett       Date:  2015-10-13       Impact factor: 4.124

9.  The interwinding nature of protein-protein interfaces and its implication for protein complex formation.

Authors:  Kei Yura; Steven Hayward
Journal:  Bioinformatics       Date:  2009-09-29       Impact factor: 6.937

10.  Prediction of protein-protein interaction sites in sequences and 3D structures by random forests.

Authors:  Mile Sikić; Sanja Tomić; Kristian Vlahovicek
Journal:  PLoS Comput Biol       Date:  2009-01-30       Impact factor: 4.475

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