Protein Sequence Analysis Using the MPI Bioinformatics Toolkit

By Felix Gabler, Seung-Zin Nam, S. Till, Milot Mirdita, Martin Steinegger, Johannes Söding, Andrei N. Lupas, Vikram Alva. 2020

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The MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) provides interactive access to a wide range of the best‐performing bioinformatics tools and databases, including the state‐of‐the‐art protein sequence comparison methods HHblits and HHpred. The Toolkit currently includes 35 external and in‐house tools, covering functionalities such as sequence similarity searching, prediction of sequence features, and sequence classification. Due to this breadth of functionality, the tight interconnection of its constituent tools, and its ease of use, the Toolkit has become an important resource for biomedical research and for teaching protein sequence analysis to students in the life sciences. In this article, we provide detailed information on utilizing the three most widely accessed tools within the Toolkit: HHpred for the detection of homologs, HHpred in conjunction with MODELLER for structure prediction and homology modeling, and CLANS for the visualization of relationships in large sequence datasets. © 2020 The Authors.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Improved prediction of protein-protein interactions using AlphaFold2 (2021, methodology): “In addition, we extracted eight novel protein complexes deposited in PDB after 15 June 2021, which produced no results for at least one chain in each complex when submitted to the HHPRED web server (version 01-09-2021) 28,29 , see Table S1.”
Assessment of the CASP14 Assembly Predictions (2021, methodology): “44 These predictions came from a classical template-based approach (established by the consecutive use of HHPred 28,29 and Modeller 45 ).”
Target Classification in the 14th Round of the Critical Assessment of Protein Structure Prediction (CASP14). (2021, methodology): “To calculate the percentage, we divided the Neff by the theoretical maximum entropy of 20.(31) Finally, we included two scores from Dalilite: coverage (DaliCvg) calculated as the length of the alignment divided by the number of target residues and the DaliZ score (Dali%Max) expressed as a percentage”