Literature DB >> 32130758

Systematic and Comparative Evaluation of Software Programs for Template-Based Modeling of Protein Structures.

Woo Dae Jang1, Sang Mi Lee2, Hyun Uk Kim2,3,4, Sang Yup Lee1,3,4.   

Abstract

Modeling protein structures is critical for understanding protein functions in various biological and biotechnological studies. Among representative protein structure modeling approaches, template-based modeling (TBM) is by far the most reliable and most widely used approach to model protein structures. However, it still remains as a challenge to select appropriate software programs for pairwise alignments and model building, two major steps of the TBM. In this paper, pairwise alignment methods for TBM are first compared with respect to the quality of structure models built using these methods. This comparative study is conducted using comprehensive datasets, which cover 6185 domain sequences from Structural Classification of Proteins extended for soluble proteins, and 259 Protein Data Bank entries (whole protein sequences) from Orientations of Proteins in Membranes database for membrane proteins. Overall, a profile-based method, especially PSI-BLAST, consistently shows high performance across the datasets and model evaluation metrics used. Next, use of two model building programs, MODELLER and SWISS-MODEL, does not seem to significantly affect the quality of protein structure models built except for the Hard group (a group of relatively less homologous proteins) of membrane proteins. The results presented in this study will be useful for more accurate implementation of TBM.
© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  model building programs; pairwise alignment methods; protein structure modeling; soluble and membrane proteins; template-based modeling

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Year:  2020        PMID: 32130758     DOI: 10.1002/biot.201900343

Source DB:  PubMed          Journal:  Biotechnol J        ISSN: 1860-6768            Impact factor:   4.677


  1 in total

1.  Leveraging the Entirety of the Protein Data Bank to Enable Improved Structure Prediction Based on Cross-Link Data.

Authors:  Andrew Keller; Juan D Chavez; Xiaoting Tang; James E Bruce
Journal:  J Proteome Res       Date:  2020-12-02       Impact factor: 4.466

  1 in total

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