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Lethal Alleles

Lethal Alleles

Agouti: A Lethal Allele
Lucien Cuénot discovered lethal alleles in 1905 while studying the inheritance of coat color in mice. The agouti gene is responsible for the color of the coat in mice. This gene codes for an agouti-signaling protein, which is responsible for melanin distribution in mammals. The wild-type allele gives rise to gray-brown coat color in mice, while the mutant allele gives rise to yellow coat color. In addition to coat color, the agouti gene is associated with the yellow...

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,...

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

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Updated: Jan 18, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

Struct2SL: Synthetic lethality prediction based on AlphaFold2 structure information and Multilayer Perceptron.

Yurui Huang¹, Ruzhe Yuan¹, Yaxuan Li¹

¹School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guang Dong 518055, China.

Computational and Structural Biotechnology Journal

|January 16, 2026

Summary

Struct2SL accurately predicts synthetic lethal (SL) gene pairs by integrating protein structures and networks. This computational approach enhances cancer therapy precision and efficacy.

Keywords:

AlphaFold2 protein structure Multilayer perceptron Network link prediction Synthetic lethality prediction

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

Published on: January 26, 2024

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Synthetic lethality (SL) principles offer novel cancer treatment strategies.
Predicting SL gene pairs computationally can improve cancer therapy precision.
Existing methods often neglect crucial protein attributes like 3D structure and interaction networks.

Purpose of the Study:

To introduce Struct2SL, a novel computational framework for predicting SL gene pairs.
To integrate protein sequences, protein-protein interaction (PPI) networks, and 3D protein structures for enhanced prediction accuracy.
To refine feature representation of gene interactions for more accurate SL pair identification.

Main Methods:

Utilized AlphaFold2 for predicting protein tertiary structures, extracting sequence and network attributes.
Developed a gene embedding process by consolidating protein-gene mapping information.
Constructed a synthetic lethality graph for ultimate gene embedding.
Employed a multilayer perceptron for SL interaction prediction.

Main Results:

Struct2SL demonstrated superior performance compared to four state-of-the-art methods.
The framework achieved higher accuracy in predicting SL gene pairs.
The integration of structural and network features proved effective for SL prediction.

Conclusions:

Struct2SL provides a new, efficient computational approach for predicting SL gene pairs in cancer therapy.
The findings suggest Struct2SL can catalyze advancements in oncological treatment development.
A webserver, Synthetic Lethality Query Server, was developed to provide researchers with an accessible tool for SL pair prediction.