ArticleNucleic acids research2013
LISE: a server using ligand-interacting and site-enriched protein triangles for prediction of ligand-binding sites.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Machine Learning in Enhancing Protein Binding Sites Predictions - What Has Changed Since Then?Combinatorial chemistry & high throughput screening · 2025Review
- Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites.Briefings in bioinformatics · 2023Article
- In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets.Molecules (Basel, Switzerland) · 2022Review
- Pharmacological inhibition of noncanonical EED-EZH2 signaling overcomes chemoresistance in prostate cancer.Theranostics · 2021Article
- Spatiotemporal identification of druggable binding sites using deep learning.Communications biology · 2020Article
- Predicting binding sites from unbound versus bound protein structures.Scientific reports · 2020Article
- Exploring the computational methods for protein-ligand binding site prediction.Computational and structural biotechnology journal · 2020Review
- bSiteFinder, an improved protein-binding sites prediction server based on structural alignment: more accurate and less time-consuming.Journal of cheminformatics · 2016Article
- Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods.International journal of molecular sciences · 2015Review
- Inspecting the potential physiological and biomedical value of 44 conserved uncharacterised proteins of Streptococcus pneumoniae.BMC genomics · 2014Article
- pocketZebra: a web-server for automated selection and classification of subfamily-specific binding sites by bioinformatic analysis of diverse protein families.Nucleic acids research · 2014Article
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Authors and funding
5 authors.
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Abstract
LISE is a web server for a novel method for predicting small molecule binding sites on proteins. It differs from a number of servers currently available for such predictions in two aspects. First, rather than relying on knowledge of similar protein structures, identification of surface cavities or estimation of binding energy, LISE computes a score by counting geometric motifs extracted from sub-structures of interaction networks connecting protein and ligand atoms. These network motifs take into account spatial and physicochemical properties of ligand-interacting protein surface atoms. Second, LISE has now been more thoroughly tested, as, in addition to the evaluation we previously reported using two commonly used small benchmark test sets and targets of two community-based experiments on ligand-binding site predictions, we now report an evaluation using a large non-redundant data set containing >2000 protein-ligand complexes. This unprecedented test, the largest ever reported to our knowledge, demonstrates LISE's overall accuracy and robustness. Furthermore, we have identified some hard to predict protein classes and provided an estimate of the performance that can be expected from a state-of-the-art binding site prediction server, such as LISE, on a proteome scale. The server is freely available at http://lise.ibms.sinica.edu.tw.
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