Skittles: GNN-Assisted Pseudo-Ligands Generation and Its Application for Binding Sites Classification and Affinity Prediction.

IF 3.2 4区生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Proteins-Structure Function and Bioinformatics Pub Date : 2025-02-28 DOI:10.1002/prot.26816

Sergei Evteev, Alexey Ereshchenko, Denis Adjugim, Alexey Vyacheslavov, Anna Pastukhova, Alexander Malyshev, Victor Terentiev, Yan Ivanenkov

引用次数: 0

Abstract

Nowadays, multiple solutions are known for identifying ligand-protein binding sites. Another important task is labeling each point of a binding site with the appropriate atom type, a process known as pseudo-ligand generation. The number of solutions for pseudo-ligand generation is limited, and, to our knowledge, the influence of machine learning techniques has not been studied previously. Here, we describe Skittles, a new graph neural network-assisted pseudo-ligand generation approach, and compare it with known force-field-based methods. We also demonstrate the application of Skittles-based data for solving several important problems in structural biology, including ligand-protein binding site classification and ligand-protein affinity prediction.

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来源期刊

Proteins-Structure Function and Bioinformatics 生物-生化与分子生物学

CiteScore

5.90

自引率

3.40%

发文量

172

审稿时长

3 months

期刊介绍： PROTEINS : Structure, Function, and Bioinformatics publishes original reports of significant experimental and analytic research in all areas of protein research: structure, function, computation, genetics, and design. The journal encourages reports that present new experimental or computational approaches for interpreting and understanding data from biophysical chemistry, structural studies of proteins and macromolecular assemblies, alterations of protein structure and function engineered through techniques of molecular biology and genetics, functional analyses under physiologic conditions, as well as the interactions of proteins with receptors, nucleic acids, or other specific ligands or substrates. Research in protein and peptide biochemistry directed toward synthesizing or characterizing molecules that simulate aspects of the activity of proteins, or that act as inhibitors of protein function, is also within the scope of PROTEINS. In addition to full-length reports, short communications (usually not more than 4 printed pages) and prediction reports are welcome. Reviews are typically by invitation; authors are encouraged to submit proposed topics for consideration.