Protein Sequence Analysis Using the MPI Bioinformatics Toolkit
Open accessAbstract
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. Basic Protocol 1 : Sequence similarity searching using HHpred Alternate Protocol : Pairwise sequence comparison using HHpred Support Protocol : Building a custom multiple sequence alignment using PSI‐BLAST and forwarding it as input to HHpred Basic Protocol 2 : Calculation of homology models using HHpred and MODELLER Basic Protocol 3 : Cluster analysis using CLANS
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The MSA generation process can be sped up substantially at no performance loss (performance increase of 1% success rate) by fusing MSAs from two HHblits runs on Uniclust30 instead of using the MSAs from AF2.
“We find that the MSA generation process can be sped up substantially at no performance loss (performance increase of 1% SR) by simply fusing MSAs from two HHblits34 runs on Uniclust3035 instead of using the MSAs from AF2.”
From Improved prediction of protein-protein interactions using AlphaFold2 (2021) · cites this paper as [34]
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- Authors
- Gabler, F., Nam, S., Till, S., Mirdita, M., Steinegger, M., Söding, J., Lupas, A. N., & Alva, V.
- Affiliations
- Max Planck Institute for Developmental Biology (DE)
- Max Planck Institute for Biophysical Chemistry (DE)
- Seoul National University (KR)
- Corresponding author
- Alva, Vikram
- Venue
- Current Protocols in Bioinformatics
- Publisher
- Wiley
- Published
- 2020-12
- Volume
- 72
- Issue
- 1
- Pages
- e108-e108
- ISSN
- 1934-3396 (ISSN-L)
- 1934-340X
- OpenAlex
- W3113163651
- Cited by
- 831
- References
- 48
- Citations by year
- 2026: 101 · 2025: 141 · 2024: 179 · 2023: 165 · 2022: 150 · 2021: 95
- FWCI
- 33.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Protocols in Bioinformatics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Funders
- Max-Planck-Gesellschaft
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein sequence analysis, HHpred, MODELLER, homology modeling, protein structure prediction, sequence similarity searching, PSI-BLAST, multiple sequence alignment, clans, sequence clustering, protein sequence classification
- MeSH terms
- Protein Conformation, Software, User-Computer Interface, Sequence Alignment, Computational Biology, Sequence Analysis, Protein
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