Protein-Ligand Interaction Modeling Service
Protein-ligand interaction modeling is a key tool for understanding the complexity of biological systems. CD ComputaBio, a professional organization that provides high-quality computational biology analysis services, is committed to providing comprehensive computational support for protein-ligand Interaction modeling for global researchers and pharmaceutical companies, covering molecular modeling, molecular dynamics simulation, drug design, bioinformatics analysis and other fields.
Introduction to Protein-Ligand Interaction Modeling
Protein-ligand interaction modeling is an important field in computational biology and structural biology. It involves the use of computational methods to predict and analyze the interactions between proteins, usually enzymes or receptors and ligands, such as nucleic acids, carbohydrates, peptides or other proteins. This complex interaction relationship is key to elucidating biological mechanisms and designing new therapeutic agents, helping researchers to:
- Understand Biological Functions - The interaction between proteins and macromolecular ligands is at the core of many biological processes, including gene expression regulation, signal transduction, cell cycle and immune response.
- Accelerate Drug Development - Many diseases are caused by abnormal protein-macromolecule interactions. Interaction modeling provides insights that help rationally design molecules with desired biological activities, reduce reliance on trial-and-error methods, and help design new drug intervention strategies.
- Advancing Enzyme Engineering - In fields such as enzyme engineering, protein design and synthetic biology, interaction modeling analysis can guide experimental design and improve efficiency.
Fig. 1 Overview of MD simulation and enhanced sampling methods utilized in the study of protein-protein (peptide) and protein-ligand complexes. (Lazim R, et al.; 2020)
Our Services
CD ComputaBio provides a comprehensive solution for protein-ligand interaction modeling. Our experienced team of experts first conducts homology modeling to obtain the three-dimensional structure of the target protein and the corresponding ligand, and then conducts detailed analysis through molecular docking and molecular dynamics simulation.
Protein-Protein Interaction Modeling Service
Our protein-protein interaction modeling service focuses on simulating and predicting the interaction relationship between proteins. Molecular docking and computational simulation methods are used to predict the possible interaction modes and binding sites between two or more proteins, helping you understand the structural basis and functional mechanism of protein interaction, thereby supporting signal transduction research and drug development.
Protein-DNA/RNA Interaction Modeling Service
Our scientists simulate the interaction between proteins and DNA or RNA. By constructing a three-dimensional model of the protein-nucleic acid complex, the binding sites and sequence specificity of proteins on DNA or RNA are predicted, and the spatial structure of their interaction is revealed.
Protein-Peptide Interaction Modeling Service
Peptides can act as signal molecules, receptor ligands, enzyme inhibitors, etc., and participate in regulating the function and activity of proteins. Our protein-peptide interaction modeling service aims to accurately predict the binding mode and affinity of proteins and peptides through computational simulation, helping you to deeply understand their interaction mechanism, and support new drug development and basic research.
Protein-Enzyme Interaction Modeling Service
Enzymes are key biomacromolecules that catalyze biochemical reactions. They interact with protein substrates, inhibitors, activators, etc., and regulate various biochemical reactions in life processes. Our scientists use advanced computational simulation technology to deeply analyze the binding mode between proteins and enzymes and identify active sites and key residues.
Methods for Protein-Ligand Interaction Analysis
Coarse-Grained Modeling
To reduce computational complexity, our scientists use coarse-grained models to simplify atomic-level details into larger particles for simulating large systems and long-time scale dynamic behaviors. A typical example is the use of this algorithm to predict the binding mode of peptides to proteins.
Molecular Docking
For macromolecular ligands, such as proteins, nucleic acids, and peptides, docking requires more complex interactions and a larger search space. Our scientists use advanced molecular docking tools such as HADDOCK, ClusPro and ZDOCK to predict the most likely binding mode between proteins and various ligands.
Molecular Dynamics Simulations
We perform thorough molecular dynamics simulations to provide detailed information about the dynamic behavior, conformational changes, and interaction mechanisms of protein-ligand systems. GROMACS is a high-performance molecular dynamics simulation software suitable for simulating large biological macromolecular systems and supports multiple force fields.
Machine Learning and Deep Learning Algorithms
CD ComputaBio uses graph neural networks and deep learning models to predict protein structures and protein-ligand interactions. AlphaFold Multimer is our deep learning model for predicting protein complex structures. It performs well in predicting the structures of multi-protein complexes.
CD ComputaBio's mission is to help you gain in-depth insights into the interaction mechanisms between proteins and ligand molecules using advanced computational techniques and expertise, thus accelerating the drug development process. Please don't hesitate to contact us to find out how our scientists can support your research program, if you are interested in our services,
References:
- Lazim R, et al. Advances in Molecular Dynamics Simulations and Enhanced Sampling Methods for the Study of Protein Systems. International Journal of Molecular Sciences. 2020; 21(17):6339.
- Liu Y, et al. Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning. Brief Bioinform. 2023;25(1):bbad488.
- Jespers W, et al. Gutiérrez-de-Terán H. Free Energy Calculations for Protein-Ligand Binding Prediction. Methods Mol Biol. 2021;2266:203-226.
* For Research Use Only.
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