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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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Mapping membrane activity in undiscovered peptide sequence space using machine learning.

Ernest Y Lee¹, Benjamin M Fulan², Gerard C L Wong³

¹Department of Bioengineering, University of California, Los Angeles, CA 90095.

Proceedings of the National Academy of Sciences of the United States of America

|November 17, 2016

Summary

This summary is machine-generated.

We developed a machine learning model to discover new antimicrobial peptides (AMPs). The model identifies sequences with membrane-disrupting capabilities, revealing a topological basis for their function beyond simple sequence similarity.

Keywords:

antimicrobial peptides cell-penetrating peptides machine learning membrane curvature membrane permeation

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Peptide-based Identification of Functional Motifs and their Binding Partners

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

Area of Science:

Biophysics
Computational Biology
Biochemistry

Background:

Over 1,100 antimicrobial peptides (AMPs) are known, exhibiting diverse sequences but a common ability to permeabilize microbial membranes.
Understanding the relationship between sequence homology and functional commonality in AMPs is crucial for discovering new therapeutic agents.

Purpose of the Study:

To develop a machine learning classifier to identify novel ⍺-helical antimicrobial peptides (AMPs).
To investigate the link between sequence, membrane activity, and antimicrobial properties.
To explore the peptide sequence space for molecules with membrane-disrupting capabilities.

Main Methods:

Support vector machine (SVM) classifier trained on known AMP sequences.
Identification of Pareto-optimal peptide candidates maximizing ⍺-helicity and membrane activity while minimizing mutational distance to known AMPs.
Calibration of SVM predictions using killing assays and small-angle X-ray scattering (SAXS).

Main Results:

The SVM metric (σ) correlates with a peptide's ability to generate negative Gaussian membrane curvature, not its minimum inhibitory concentration (MIC).
A distinction was found between sequence recognizability by the classifier (membrane curvature) and maximal antimicrobial efficacy.
A diverse range of sequences, including neuropeptides and viral proteins, were identified as membrane-active.

Conclusions:

Membrane curvature generation provides a topological basis for the common membrane activity of AMPs.
The SVM classifier serves as a general tool for detecting membrane activity in peptide sequences.
Novel membrane-active peptides can be discovered through computational exploration of sequence space, independent of traditional AMP discovery methods.