Interaction Prediction Service
CD ComputaBio leverages computational technologies such as molecular docking, molecular dynamics simulations, and machine learning and deep learning models to provide peptide-biomolecule interaction prediction services. This not only accelerates the discovery and optimization process of peptide drugs but also offers a solid theoretical foundation for new drug design, side-effect prediction, and mechanism-of-action studies.
Introduction to Peptide and Biomolecule Interaction Prediction
Predicting interactions between peptides and biomolecules is crucial for drug discovery and practical applications in the biomedical field. Peptides, with their unique ability to modulate protein-protein interactions, have emerged as highly promising drug candidates. By predicting interactions between peptides and various biomolecules (such as proteins, nucleic acids, lipids, etc.), we can not only guide the design and discovery of novel peptide drugs and accelerate the exploration and development of new indications, but also gain deeper insights into drug mechanisms of action, thereby optimizing the drug development process.
Fig.1 Prediction of protein-protein interactions by AlphaFold. (MIYAZONO K, et al., 2022)
Our Services
CD ComputaBio specializes in predicting interactions between peptides and other biomolecules for clients, revealing the binding modes, binding affinities, and dynamic behaviors of peptides with their targets. Our services include, but are not limited to:
- Peptide-Protein Interaction Prediction
- Protein-Lipid Interaction Prediction
- Peptide-Nucleic Acid Interaction Prediction
- Peptide-Carbohydrate Interaction Prediction
Computational Approaches to Interaction Prediction
Molecular Docking
Molecular docking can predict the structural arrangement of ligands, as well as their orientation and position within the binding site. In the field of peptide-target interaction prediction, molecular docking methods have been widely applied for studying peptide drug design and mechanisms of action.
- Local Docking
- Global Docking
- Template-based Docking
- Rigid Docking
- Semi-flexible Docking
- Flexible Docking
Machine Learning Models
Machine learning models, such as support vector machines (SVM) and random forests (RF), manually extract features from sequence and structure datasets to predict peptide-protein interactions.
- Random Forest
- Ensemble Learning
- Support Vector Machine
- Other State-of-the-art Models
Molecular Dynamics Simulations
Molecular dynamics is a powerful computational simulation method that reveals the mechanisms of interaction between biological molecules (such as proteins and peptides) and their targets by simulating their dynamic behavior at the atomic level.
- Conventional Molecular Dynamics
- Accelerated Molecular Dynamics
- Replica Exchange Molecular Dynamics
- Gaussian Accelerated Molecular Dynamics
- Metadynamics
Deep Learning Models
Deep learning models, including convolutional neural networks (CNN), graph convolutional networks (GCN), and transformers, automatically extract multi-layer feature representations from data, providing valuable insights into residue-level contributions to peptide-protein binding.
- Attention Based Models
- Convolutional Neural Network
- Graph Convolutional Network
- AlphaFold/RoseTTAFold/OmegaFold/ESMFold
Additional Analysis Services
CD ComputaBio offers a variety of methods for predicting interactions between peptides and biomolecules, including protein-peptide interactions. Building upon this, we also provide the following series of analysis items to further advance your research:
- Peptide Conformation Sampling (Cluster Analysis, Dominant Conformation Identification)
- Interaction Analysis (Hydrogen Bond Network, Contact Map)
- Binding Free Energy Calculation (MM/GBSA, MM/PBSA, FEP, TI)
- Backbone Fluctuation Analysis (RMSD, RMSF)
- Hotspot Residue Analysis (Alanine Scanning, Energy Decomposition)
- Conformational Transition Analysis (PCA, Energy Landscape Plotting)
CD ComputaBio, with its advanced computational tools and professional expertise, predicts the interaction modes and binding affinities between peptides and various biomolecules, including proteins, nucleic acids, lipids, and polysaccharides. Contact our expert team to explore collaboration and advance peptide molecule research together.
References:
- MIYAZONO, K.; TANOKURA M. New era in structural biology with the AlphaFold program[J]. Translational and Regulatory Sciences. 2022, 4(2): 48-52.