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Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Updated: Jan 11, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Best Practices for Machine Learning-Assisted Protein Engineering.

Fabio Herrera-Rocha¹, David Medina-Ortiz^1,2, Fabian Mauz¹

¹Leibniz-Institute of Plant Biochemistry, Department of Bioorganic Chemistry, Weinberg 3, D-06120 Halle, Germany.

Journal of Chemical Information and Modeling

|November 17, 2025

Summary

This summary is machine-generated.

This perspective outlines guidelines for developing reliable machine learning (ML) models in protein engineering. It emphasizes software engineering best practices to enhance ML transparency and credibility in research.

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

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Area of Science:

Computational Biology
Biochemistry
Machine Learning

Background:

Machine learning (ML) is increasingly integral to protein engineering workflows.
Developing effective, reliable, and reproducible ML models is crucial for advancing the field.

Purpose of the Study:

To present essential elements and guidelines for robust ML model development in protein engineering.
To promote transparency and credibility in ML-based protein engineering research.

Main Methods:

Discussion of software engineering best practices for ML development and evaluation.
Emphasis on supervised learning approaches.
Guidance covering the entire ML development lifecycle, from data acquisition to deployment.

Main Results:

A comprehensive set of guidelines for ML development in protein engineering.
Practical resources for implementing these guidelines.
Recommendations for scientific journals and editors to enforce good practices.

Conclusions:

Adoption of these guidelines will improve ML transparency and credibility in protein engineering.
Standardized best practices will foster informed application of ML to real-world protein engineering challenges.