ArticleProteins2025
CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces.
Article in Proteins, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Engineering and Partial Characterization of an Anti-IL-23/Anti-TNF-α Bispecific Domain Antibody Fused to Human Serum Albumin Domain for Inflammatory Disease Therapy.Applied biochemistry and biotechnology · 2026Article
- A Quantum Framework for Protein Binding-Site Structure Prediction on Utility-Level Quantum Processors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- An efficient ranking deep neural network algorithm for the prediction of Ca2+ binding sites of the protein.PloS one · 2026Article
- PIPENN-EMB ensemble net and protein embeddings generalise protein interface prediction beyond homology.Scientific reports · 2025Article
- CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces.Proteins · 2025Article
- Engineering G protein-coupled receptors for stabilization.Protein science : a publication of the Protein Society · 2024Article
- Discovering predisposing genes for hereditary breast cancer using deep learning.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Proteins are molecular machinery that participate in virtually all essential biological functions within the cell, which are tightly related to their 3D structure. The importance of understanding protein structure-function relationship is highlighted by the exponential growth of experimental structures, which has been greatly expanded by recent breakthroughs in protein structure prediction, most notably RosettaFold, and AlphaFold2. These advances have prompted the development of several computational approaches that leverage these data sources to explore potential biological interactions. However, most methods are generally limited to analysis of single types of interactions, such as protein-protein or protein-ligand interactions, and their complexity limits the usability to expert users. Here we report CSM-Potential2, a deep learning platform for the analysis of binding interfaces on protein structures. In addition to prediction of protein-protein interactions binding sites and classification of biological ligands, our new platform incorporates prediction of interactions with nucleic acids at the residue level and allows for ligand transplantation based on sequence and structure similarity to experimentally determined structures. We anticipate our platform to be a valuable resource that provides easy access to a range of state-of-the-art methods to expert and non-expert users for the study of biological interactions. Our tool is freely available as an easy-to-use web server and API available at https://biosig.lab.uq.edu.au/csm_potential.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.