Prediction of RNA-binding modules on proteins based on amino acid-nucleotide pairing preference information
A prediction method and amino acid technology, applied in the field of protein-RNA interaction and recognition, can solve the problems of limited, difficult protein acquisition, neglect of amino acid residue interactions and synergistic effects, etc., achieving high efficiency, low workload, and success. high rate effect
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Embodiment 1
[0054] The following takes a protein (Catalytic domain of E.coli RNase E) as an example, which interacts with 13-merRNA to form a complex 2COB (PDB ID), to introduce the implementation process of the PPQA method to predict the binding RNA module on the protein. Knowing the structure of the receptor protein in this complex, its RNA-binding interface module was obtained by carrying out this method.
[0055] (1) to (4) are completed under the Linux system.
[0056] (1) Protein structure pretreatment
[0057] First rename the protein receptor to 2c0b_r_b.pdb. When using this method package for the first time, you need to create a storage directory for the result file. Under the working directory of this program package, use the shell to create the directory as follows:
[0058] ...]$ mkdir structures
[0059] ...]$ mkdir data
[0060] ...]$ mkdir data / ReceptorModule
[0061] ...]$ mkdir data / Rsa
[0062] ...]$ mkdir data / Vor
[0063] After creating the above directory, move...
Embodiment 2
[0106] The system comes from the non-redundant non-ribosomal protein-RNA complex structure (Proteins,2012,80(1):14-24), remove the case where the receptor and the ligand are non-single-stranded, and the final research system is 69 complexes Object system (as shown in Table 6). The specific calculation process for each system is the same as in Example 1, and only the results will be described here.
[0107] Table 6 69 non-redundant non-ribosomes, and receptor ligands are single-stranded protein-RNA complexes
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[0110] We compared the results obtained using the method of the present invention (named after the parameter PPQA) with the results obtained by randomly picking interface modules and the PAMA method for protein-protein binding module prediction (see Table 7).
[0111] Table 7 The highest ranking results of the receptor protein interface module in 69 protein-RNA complexes obtained by different methods
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[0114]From the highe...
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