دورية أكاديمية
أعرض في EDS

Simultaneous prediction of protein secondary structure and transmembrane spans.

التفاصيل البيبلوغرافية
العنوان: Simultaneous prediction of protein secondary structure and transmembrane spans.
المؤلفون: Leman JK; Department of Chemistry, Vanderbilt University, Nashville, Tennessee; Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, USA., Mueller R, Karakas M, Woetzel N, Meiler J
المصدر: Proteins [Proteins] 2013 Jul; Vol. 81 (7), pp. 1127-40. Date of Electronic Publication: 2013 Apr 10.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.
اللغة: English
بيانات الدورية: Publisher: Wiley-Liss Country of Publication: United States NLM ID: 8700181 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0134 (Electronic) Linking ISSN: 08873585 NLM ISO Abbreviation: Proteins Subsets: MEDLINE
أسماء مطبوعة: Publication: New York, NY : Wiley-Liss
Original Publication: New York : Alan R. Liss, c1986-
مواضيع طبية MeSH: Neural Networks, Computer* , Protein Structure, Secondary*, Membrane Proteins/*chemistry , Proteins/*chemistry, Algorithms ; Amino Acid Sequence ; Databases, Protein ; Membrane Proteins/classification ; Sequence Alignment ; Software
مستخلص: Prediction of transmembrane spans and secondary structure from the protein sequence is generally the first step in the structural characterization of (membrane) proteins. Preference of a stretch of amino acids in a protein to form secondary structure and being placed in the membrane are correlated. Nevertheless, current methods predict either secondary structure or individual transmembrane states. We introduce a method that simultaneously predicts the secondary structure and transmembrane spans from the protein sequence. This approach not only eliminates the necessity to create a consensus prediction from possibly contradicting outputs of several predictors but bears the potential to predict conformational switches, i.e., sequence regions that have a high probability to change for example from a coil conformation in solution to an α-helical transmembrane state. An artificial neural network was trained on databases of 177 membrane proteins and 6048 soluble proteins. The output is a 3 × 3 dimensional probability matrix for each residue in the sequence that combines three secondary structure types (helix, strand, coil) and three environment types (membrane core, interface, solution). The prediction accuracies are 70.3% for nine possible states, 73.2% for three-state secondary structure prediction, and 94.8% for three-state transmembrane span prediction. These accuracies are comparable to state-of-the-art predictors of secondary structure (e.g., Psipred) or transmembrane placement (e.g., OCTOPUS). The method is available as web server and for download at www.meilerlab.org.
(Copyright © 2013 Wiley Periodicals, Inc.)
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معلومات مُعتمدة: R01 GM080403 United States GM NIGMS NIH HHS; R01 MH090192 United States MH NIMH NIH HHS
المشرفين على المادة: 0 (Membrane Proteins)
0 (Proteins)
تواريخ الأحداث: Date Created: 20130126 Date Completed: 20140221 Latest Revision: 20211021
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC5064873
DOI: 10.1002/prot.24258
PMID: 23349002
قاعدة البيانات: MEDLINE
الوصف
تدمد:1097-0134
DOI:10.1002/prot.24258