The development of new drugs depends on the ability of scientists to understand the biological details of the disease, as well as the way to design new molecular drugs to cure the disease or reduce its symptoms. A very important biological mechanism is the way a protein recognizes and binds to another protein to regulate its function. This functional regulation of protein-protein interactions is the basis of most biological activities in living cells, but we don’t know what properties of a protein enable it to bind to another protein, or how to design molecules to prevent or enhance This interaction. Therefore, it is necessary to study the protein interaction network in order to better understand the research and development of new drugs.
Figure 1. PPI network. (From wikipedia)
Computational chemistry methods and artificial intelligence (AI) methods can help us predict protein interaction networks, and protein-protein interaction (PPI) may increase drug development plans for months or even years. Designing drug discovery programs around new targets or modulating the function of target proteins in unconventional ways can be challenging. Computer and AI-based prediction methods can reduce these risks and provide a more fluid path for the clinic.In recent years, the quantity and quality of omics data have continued to increase, leading to the assembly of biological networks, the ultimate goal of which is to reveal underlying cellular processes. In this case, protein-protein interaction (producer price index) is the most important and extensive research network. In PPI networks, biological systems are described in terms of proteins (ie nodes) and their relationships (physical/functional interactions) (ie edges). The extensiveness of the PPI network is demonstrated by its versatility, which can promote its applications, such as in omics data integration, protein function discovery, molecular mechanism understanding, and drug discovery or drug repositioning.
Like in many real-world networks, protein-protein interaction networks in various organisms have common topological characteristics, which makes these networks different from random networks. These topological features have been used as evidence to discern the difference between interactions that indicate true positives and false positives. These enable researchers to assign a higher confidence score to each interaction. Analyzing the PPI network from a topological perspective is essential to better understand the basic evolutionary mechanism and network dynamics that constitute the network. Since network theory is a relatively new field, in order to determine the importance of topological attributes in a given PPI network, compare it with the topological attributes in a random network, and then assign a confidence score to the PPI. Finally based on these scores some intearctions can be eliminated and others can be added to the network.
|Protein-protein interaction network prediction
|Product delivery mode
|The simulation results provide you with the raw data and analysis results of molecular dynamics.
The protein-protein interaction (PPI) network is a viable tool for understanding cell function, disease mechanisms, and drug design/repositioning. However, explaining PPI, due to the complexity of the network, is a particularly challenging task. We can provide several algorithms for automatic PPI interpretation, first by considering only the network topology, and then integrating gene ontology (GO) terms as node similarity attributes.
The protein-protein interaction (PPI) network predictions we provided by CD ComputaBio can help you obtain many important information in biology, because they can regulate almost all cellular processes, including metabolic cycles, DNA transcription and replication, different signal cascades and mny other processes.
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