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Types of Membrane Protrusions01:28

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The protrusion of the cell surface is an initial step for several cellular processes, including cell migration, phagocytosis, and neurite outgrowth. These membrane protrusions are a result of cytoskeletal rearrangement. The most  widely observed cell protrusions include lamellipodia, pseudopodia, filopodia, microvilli, invadopodia, and podosomes. These protrusions can be of two types — static or dynamic.
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Related Experiment Video

Updated: Jun 28, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

PepMCP: a graph-based membrane contact probability predictor for membrane-lytic antimicrobial peptides.

Ruihan Dong1,2, Tadsanee Awang1, Qiushi Cao1

  • 1Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.

Bioinformatics (Oxford, England)
|June 26, 2026
PubMed
Summary

Researchers developed PepMCP, a new tool to predict membrane-binding propensity in antimicrobial peptides (AMPs). This advances in silico discovery of membrane-lytic AMPs by improving accuracy for short peptides targeting bacterial membranes.

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Area of Science:

  • Computational biology
  • Biophysics
  • Drug discovery

Background:

  • The membrane-lytic mechanism of antimicrobial peptides (AMPs) is crucial but often overlooked in computational discovery due to limitations in predicting membrane-binding propensity.
  • Previous methods for predicting membrane contact probability (MCP) were not optimized for short peptides targeting bacterial membranes, limiting their effectiveness.

Purpose of the Study:

  • To develop a tailored model, PepMCP, for accurately predicting the membrane contact probability (MCP) of short antimicrobial peptides (AMPs).
  • To enhance the in silico discovery of membrane-lytic AMPs by providing a reliable metric for their membrane-binding propensity.

Main Methods:

  • Collected over 500 membrane-lytic AMPs from scientific literature.
  • Performed coarse-grained molecular dynamics (MD) simulations to determine residue-level MCP values.
  • Trained the PepMCP model using the GraphSAGE framework, representing peptide sequences as graphs.

Main Results:

  • PepMCP achieved high accuracy with a Pearson correlation coefficient of 0.883 and an RMSE of 0.123 on the test set.
  • The model effectively identifies membrane-lytic AMPs based on predicted MCP values.
  • A comprehensive database, MemAMPdb, and a user-friendly web server were established.

Conclusions:

  • PepMCP offers a significant advancement in predicting the membrane-binding propensity of short antimicrobial peptides.
  • This tool facilitates mechanism-driven discovery of novel membrane-lytic AMPs.
  • The availability of PepMCP and MemAMPdb supports further research in antimicrobial peptide development.