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

Faraz Shaikh,Nirali Desai,Hio Kuan Tai,Shirley W I Siu

doi:10.1186/s13321-021-00523-1

Faraz Shaikh, Nirali Desai + Show 2 more

Open Access

https://doi.org/10.1186/s13321-021-00523-1

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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.

Highlights

In recent years, the number of small natural and synthetic molecules, both real and virtual, has significantly increased [1]
Target prediction of small molecules is a crucial step in drug discovery and study of disease mechanisms
We present LigTMap, a new target prediction method developed to predict 17 therapeutic protein classes, including human and nonhuman protein targets

Summary

Introduction

The number of small natural and synthetic molecules, both real and virtual, has significantly increased [1]. One way to evaluate their potential for therapeutic applications is to identify their molecular targets related to diseases. Compared with traditional methods, finding new targets for existing drugs, that is, drug repurposing, can disclose new clinical applications of known drugs in a shorter time and at a lower cost [2]. Various in silico approaches have been developed to provide solutions to the target prediction problem [5]. Additional file 1: Table S1 presents a list of some of these computational target prediction methods, highlighting their methodological strategies, employed datasets, and availability of online servers.

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Results

Conclusion