Ligand binding is required for many proteins to function properly. A large number of bioinformatics tools have been developed to predict ligand binding sites as a first step in understanding a protein's function or facilitating docking computations in virtual screening-based drug design. Prediction usually only needs to search for the three-dimensional structure of the target protein of the ligand binding site (experimental determination or computational modeling) and the construction of a web server allows free and simple use of prediction tools. CD ComputaBio is proud to provide the most efficient protein-ligand binding site prediction services to our clients.
Figure 1. Protocol for Ligand Binding Site Prediction. (Ambrish Roy.; et al,2012)
Tools | Functions |
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cons-PPISP | A consensus neural network method for predicting interaction sites. |
BAPPL | computing binding free energy of a non-metallo protein-ligand complex using an all atom energy based empirical scoring function. |
BAPPL-Z | Binding affinity prediction of protein-ligand complex containing Zinc. |
DrugScore | Knowledge-based scoring functions. DrugScore enables you to score protein-ligand complexes of your interest and to visualize the per-atom score contributions. |
FoldX | Calculating binding energy. |
PEARLS | Computing small molecule ligand-protein, ligand-nucleic acid, protein-nucleic acid and ligand-protein-nucleic acid interaction energies. |
Project name | Ligand Binding Site Prediction |
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Samples requirement | Our ligand binding site prediction requires you to provide specific requirements. |
Timeline | Decide according to your needs. |
Deliverables | We provide you with raw data and analysis service. |
Price | Inquiry |
CD ComputaBio provides corresponding ligand binding site prediction services. Our service provides result that it is very useful for understanding the biochemical basis of physiological events. Our expert team can choose the most suitable analysis method for you, so you don't have to worry about technical issues. The CD ComputaBio team has been working in this field for more than ten years and has published his findings in top scientific journals.
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