Prediction of Protein-Protein Interaction

Proteins rarely play a role alone. They are often combined into "molecular machines" and have complex physical and chemical dynamic connections to undertake biological functions at the cell and system level. The key step to reveal the complex molecular relationships in living systems is to map the physical "interactions" between proteins. The complete map of protein interactions that can occur in organisms is called an interaction group. Protein-protein interaction (PPI) is a highly specific physical contact established between two or more protein molecules, which is the result of biochemical events generated by interactions, including electrostatic forces, hydrogen bonding, and hydrophobicity effect. Many are physical contacts that are associated with molecules between chains occurring in cells or living organisms in a specific biomolecular environment. All this information can create large-scale protein interaction networks similar to metabolic or genetic/epigenetic networks, thereby enhancing existing knowledge of the biochemical cascade and molecular etiology of diseases, and discovering presumptions with therapeutic significance protein target.

Overall solutions

Protein interaction refers to a process in which two or more protein molecules combine to form a protein complex. Under normal circumstances, it is difficult for a protein to function alone, because multiple protein molecules coordinate with each other to realize complex cell functions. Therefore, research focusing on protein interactions is more conducive to exploring the occurrence and development of diseases, finding specific disease prediction treatment methods, and providing new ideas in the research of new drug development. CD ComputaBio can provide a variety of methods for protein interaction analysis:

• Database-based approaches
  The large-scale identification of PPI has generated hundreds of thousands of interactions, which are collected in a specialized biological database, which is constantly updated to provide a complete set of interactions.
• PPI network analysis method
  The identification of functional modules in PPI networks is an important challenge in bioinformatics. Functional modules refer to a group of proteins that are highly connected to each other in the PPI network.
• Machine learning methods
  These methods use machine learning to distinguish paired features, such as cell co-localization, gene co-expression, and how closely the genes encoding these two proteins are in DNA for analysis.
• AI-baed approaches Compared with traditional screening methods, AI can evaluate difficult, tricky, and atypical targets, such as protein interactions, more effectively and cost-effectively. CD ComputaBio can provide an AI platform for predicting the binding of small molecules to protein targets and protein-protein interactions. This AI-based analysis method can also be used for deep neural networks for structure-based drug discovery applications. Predictions are made by combining structural information about targets, ligands and their interaction modes, and artificial intelligence is applied to predict binding affinity. This platform's binding affinity prediction can successfully block protein-protein interactions and targets in vivo.

Why choose us?

• Effective large-scale techniques for measuring the physical connections between protein pairs within the proteome are essential to complete the comprehensive knowledge of the protein interactome.
• Our team members are researchers from famous universities or scientific research institutions, and have extensive experience in molecular dynamics simulation of drugs and biological systems. We have independently or assisted customers to complete a large number of related topics.
• The user can specify the special database that needs to be used, and we can prepare it on his behalf.
• Service price: We will tailor a cost-effective service plan according to the specific needs of customers.