Ligand Binding Site Prediction Service
Ligand binding site prediction is a core research direction in bioinformatics and structural biology. It aims to identify functional regions on the surface or inside biological molecules such as proteins and nucleic acids that can specifically bind to ligands (such as small-molecule drugs, ions, other proteins, etc.). CD ComputaBio relies on cutting-edge algorithms and multi-dimensional technology integration to provide high-precision ligand binding site prediction services.
Introduction to Ligand Binding Site
Ligand binding sites, commonly characterized as cavities or pockets on protein surfaces, mediate ligand binding via non-covalent interactions (including hydrogen bonds and hydrophobic interactions) or, less frequently, through covalent bonds. These regions are usually located in depressions or hydrophobic surfaces of proteins, with conserved amino acid sequences and residues that have a clear tendency to bind to ligands. Ligand binding site prediction has important applications in drug discovery, protein engineering, protein-protein interaction research, and other fields. For example, predicting protein-ligand binding sites enables the design of inhibitors and antagonists, optimization of drug structures, and enhancement of drug affinity and selectivity.
Fig 1. The ligand binding site prediction model. (Ishitani R, et al., 2024)
Methods of Ligand Binding Site Prediction
Traditional Sequence-and Structure-Based Methods
The traditional approaches use amino acid composition preferences (such as polarity and charge distribution) to build a classification model, significantly improving prediction specificity. The approaches such as LIGSITE and CASTp identify potential pockets through geometric features (such as surface depressions and hydrophobicity).
Machine Learning and Deep Learning Fusion Methods
Tools such as GraSP-web model proteins as residue neighborhood graphs, integrating decision trees and resampling techniques to achieve accurate clustering and confidence scoring of binding sites. The COACH-D algorithm optimizes ligand postures by improving molecular docking (such as AutoDock Vina), solving stereo conflict problems in traditional methods, and improving AUC by 14.5%.
Homology Modeling and Database-Driven Methods
3DLigandSite uses AlphaFold predicted structures and ligand supermodels of PDB template libraries, combined with machine learning post-processing, to achieve cross-species binding site migration. ProBiS-Fold maps known PDB sites to the AlphaFold model through local structure comparison, predicting 3000+ new drug targets.
Our Services
CD ComputaBio offers a comprehensive suite of services to predict ligand binding sites with high accuracy. Our team of experts, state-of-the-art tools, and customized solutions ensure that we deliver high-quality results to our clients.
Structure-Based Ligand Binding Site Prediction
CD ComputaBio uses a variety of computational methods, including geometric analysis and energetics calculation, to conduct in-depth analysis of the three-dimensional structure of biological macromolecules and predict potential ligand binding sites.
Sequence-Based Ligand Binding Site Prediction
CD ComputaBio employs a range of computational methods, including pattern recognition and machine learning, to analyze biomacromolecule amino acid sequences and predict potential ligand binding sites, binding scores, and binding free energies.
Process of Ligand Binding Site Prediction
- 01Data Preparation and Preprocessing
Choosing whether to retain crystal water molecules, metal ions or cofactors according to project requirements to simulate the real binding environment. If the customer needs to predict a specific ligand, the structure file of the ligand must be provided.
- 02Multi-algorithm Integrated Prediction
CD ComputaBio's team uses the internal calculation module for targeted docking, calculate the binding free energy through MM/GBSA, and screen the energy-optimal site. For flexible targets (such as GPCRs), CD ComputaBio performs molecular dynamics simulations to capture conformational changes and identify dynamic pockets.
- 03Prediction Result Optimization
Quantum mechanics/molecular mechanics (QM/MM) optimization is conducted on candidate sites to refine geometric parameters of critical polar interactions, such as salt bridges and π-cation interactions.
- 04Result Delivery
CD ComputaBio delivers detailed binding site surface analysis, key residue identification, ligand docking poses, docking scores, and binding free energy decomposition results.
Our Tools
| Tools |
Description |
| BAPPL |
It calculates the binding free energies of nonmetallic protein-ligand complexes using an empirical scoring function based on all-atom energies. |
| FoldX |
It is suitable for calculating binding free energy |
| AutoDock Suite |
It enables comprehensive analysis, including docking cavity detection, ligand docking, and accurate binding site prediction. |
| Ledock |
It is primarily used for high-precision docking and may involve site prediction. |
Our Advantages
Virtual Screening Support
Build 3D pharmacophore models based on predicted sites (such as hydrogen bond donors, and aromatic rings), and perform high-throughput screening of compound libraries.
Interdisciplinary Expert Team
Customers can apply for online meetings with an interdisciplinary team of computational biologists and medicinal chemists to adjust parameters based on prediction results.
Cloud Computing Platform
It offers robust support for batch submissions, allowing researchers to process multiple datasets or execute numerous tasks simultaneously, streamlining workflows.
Partner with CD ComputaBio for full-chain drug development support, from target discovery to lead compound optimization. Our customer-centric service process leverages a modular technology stack and cross-platform collaboration. Contact our technical consultants to discuss your specific needs or explore customized solutions.
Reference:
- Ishitani R, Takemoto M, Tomii K. Protein ligand binding site prediction using graph transformer neural network[J]. Plos one, 2024, 19(8): e0308425.
* For Research Use Only.
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