modlAMP: Python for antimicrobial peptides
Open accessAbstract
Summary: We have implemented the molecular design laboratory’s antimicrobial peptides package (modlAMP), a Python-based software package for the design, classification and visual representation of peptide data. modlAMP offers functions for molecular descriptor calculation and the retrieval of amino acid sequences from public or local sequence databases, and provides instant access to precompiled datasets for machine learning. The package also contains methods for the analysis and representation of circular dichroism spectra. Availability and Implementation: The modlAMP Python package is available under the BSD license from URL http://doi.org/10.5905/ethz-1007-72 or via pip from the Python Package Index (PyPI). Supplementary information: Supplementary data are available at Bioinformatics online.
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The molecular weights and amino-acid distribution of the generated sequences from the mixed case are similar to the training set.
“The molecular weights and the amino-acid distribution of the generated sequences from the mixed case are also similar to the training set, as shown in Fig. 5e, f.”
From Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing (2022) · cites this paper as [35]
The Sequence generator section (p. 3) lists mixed peptide libraries, and the Descriptor calculation section (p. 3) says the software can calculate molecular weight. Neither reports a comparison of generated sequences with a training set for molecular weight or amino-acid distribution. Searches for the mixed case and those comparisons found no such result in our copy. (LLM, conf=0.76)
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Park, S.-O., Jeong, H., Park, J., Bae, J., & Choi, S. (2022) · Nature Communications · doi:10.1038/s41467-022-30539-6 · Cited by 331 · see citing works
“The result was evaluated by the generated output sequences ’ several features (probability of being AMP, global charge, molecular weights, and amino-acid distribution) using an AMP prediction tool 34 and a Python package for AMP analysis 35 .”
MethodRecurrent Neural Network Model for Constructive Peptide Design
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Self-citationPMIpred: a physics-informed web server for quantitative protein–membrane interaction prediction
van Hilten, N., Verwei, N., Methorst, J., Nase, C., Bernatavicius, A., & Risselada, H. J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae069 · Cited by 41 · see citing works
- Authors
- Müller, A. T., Gabernet, G., Hiss, J. A., & Schneider, G.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Schneider, Gisbert
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-05-04
- Volume
- 33
- Issue
- 17
- Pages
- 2753-2755
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2611281850
- Cited by
- 215
- References
- 35
- Citations by year
- 2026: 27 · 2025: 43 · 2024: 41 · 2023: 23 · 2022: 29 · 2021: 19 · 2020: 14 · 2019: 13 · 2018: 5 · 2017: 1
- FWCI
- 7.31
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- reroDoc Digital Library (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection
- Funders
- Swiss National Science Foundation (200021_157190, CRSII2_160699)
- Topics
- Life Sciences › Immunology and Microbiology › Microbiology › Antimicrobial Peptides and Activities
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- antimicrobial peptides, peptide design, peptide classification, molecular descriptors, circular dichroism spectra
- MeSH terms
- Machine Learning, Anti-Infective Agents, Peptides, Software, Computational Biology
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