LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds

Abstract

Target prediction is a crucial step in modern drug discovery. However, existing experimental approaches to target prediction are time-consuming and costly. Here, we introduce LigTMap, an online server with a fully automated workflow that can identify protein targets of chemical compounds among 17 classes of therapeutic proteins extracted from the PDBbind database. It combines ligand similarity search with docking and binding similarity analysis to predict putative targets. In the validation experiment of 1251 compounds, targets were successfully predicted for more than 70% of the compounds within the top-10 list. The performance of LigTMap is comparable to the current best servers SwissTargetPrediction and SEA. When testing with our newly compiled compounds from recent literature, we get improved top 10 success rate (66% ours vs. 60% SwissTargetPrediction and 64% SEA) and similar top 1 success rate (45% ours vs. 51% SwissTargetPrediction and 41% SEA). LigTMap directly provides ligand docking structures in PDB format, so that the results are ready for further structural studies in computer-aided drug design and drug repurposing projects. The LigTMap web server is freely accessible at https://cbbio.online/LigTMap. The source code is released on GitHub (https://github.com/ShirleyWISiu/LigTMap) under the BSD 3-Clause License to encourage re-use and further developments.

Computational studies on searching potential phytochemicals against DNA polymerase activity of the monkeypox virus

“… 67 In addition, the LigTMap ( https://cbbio.online/LigTMap ) was employed to predict the target specificity for GLA and A7G. 68 GLA showed high specificity towards tuberculosis whereas A7G for the targets of influenza, tuberculosis, HIV and Hpylori. Overall, despite some limitations, several in vivo, in vitro, clinical and our computational research supported the possible therapeutic applications of phytochemical like GLA and A7G against MPXV.…”

CACTI: An in-silicodrug-target prediction tool through the integration of chemogenomic data and clustering analysis

“…In contrast to most available tools that focus on one database for target-predictions (10,(18)(19)(20) we explored multiple chemogenomic databases as knowledge source. The solely criterion for database selection was the availability of REST API, a data request protocol for query and transfer.…”

TAIGET: A small-molecule target identification and annotation web server

“…We clustered the interaction pairs between ligand atoms and protein fragments extracted from the crystal structures of ligand-binding proteins in the PDB (released from January 1995 to April 2021) and found that the potential targets should meet with ≥600 significant interaction pairs, and meanwhile, ≥0.8 ratio of them to all the interaction pairs ( Wei et al, 2022 ). The advantage of our method was that we not only considered the major bonds between the ligand and protein, such as hydrogen bonds, salt bridges, hydrophobic contacts, halogen bonds, and pi-stacking ( Shaikh et al, 2021 ), but also summarized all the atomic contacts between the ligand and protein by defining an atomic contact between one ligand atom and the first atom of the protein fragment with an interatomic distance ≤5 Å. We proposed that the diverse characteristics of atomic contacts could accurately screen the targets of small molecules.…”