In nature, protein chain interactions (Pro-ChInt) of single- / multi-protein, a common but complex system, refer to physical contacts established between two or more protein chains depending on the amino acid sequences, which contains tremendous information. Encoding amino acid sequence information of protein using complex networks or graphs of the peptides is a grateful solution to discover the communication information between different Pro-ChInt. We first constructed some python codes to directly download the specify protein sequences from the RCSB protein data bank (PDB). Then, we changed the FASTA format to S2SNet format to calculate the embedded / non-embedded parameters of protein chains according to the star graph topological indices of peptide sequences. Meanwhile, we numbered all protein chains, then used the chain numbers to get a random number for a given set of chain number or case number used for each protein. Then, we replaced all the random numbers with the corresponding parameters of each protein chain calculated with S2SNet application. After that, a machine learning classification model was constructed based on the combinatorial / combining interaction of different chains. This new method can be used to identify two or more protein chain interactions combined with machine learning technique.
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Pro-ChInt: Machine Learning Methods for Identifying Dual- / Multi- Protein Chain Interactions with Python
Published: 07 December 2015 by MDPI in MOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed. congress CHEMBIO.INFO-01: Cheminfo., Chemom., Comput. Quantum Chem. & Bioinfo. Congress, Cambridge, UK-Chapel Hill and Richmond, USA, 2015