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Forecasting method for forecasting DNA-mutation-influenced protein-protein interaction based on multivariate data

A technology of protein interaction and multivariate data, applied in proteomics, electronic digital data processing, special data processing applications, etc., to achieve the effect of improving robustness and accuracy

Active Publication Date: 2017-05-31
TONGJI UNIV
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Problems solved by technology

[0009] In order to overcome the defects of existing algorithms for predicting DNA mutations affecting protein interactions and obtain more accurate prediction results, the present invention provides a method for predicting DNA mutations affecting protein interactions based on protein multivariate data and referring to the site information around mutant amino acids. prediction method

Method used

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  • Forecasting method for forecasting DNA-mutation-influenced protein-protein interaction based on multivariate data
  • Forecasting method for forecasting DNA-mutation-influenced protein-protein interaction based on multivariate data
  • Forecasting method for forecasting DNA-mutation-influenced protein-protein interaction based on multivariate data

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Embodiment

[0060] Step 1. Organize all non-redundant protein interaction pairs contained in the five protein interaction databases of HPRD, BioGrid, IntAct, MINT and DIP, a total of 233,461 pairs, which can be used to determine whether there is protein interaction between proteins.

[0061] Step 2. Combining the 161,456,298 dbSNP data provided by NCBI, use the software Polyphen2, SIFT, and MutationAsseso to calculate the database of amino acid mutations on protein sequences caused by SNPs, with a total of 33,306 records, which can be used to determine whether SNPs cause amino acid mutations on proteins.

[0062] Step 3. Based on the protein interaction structure information provided in the PDB database, a total of 260,182 pieces of protein interaction surface information were sorted out. Using the database in Step 2, it was possible to determine whether the amino acid mutation caused by the SNP occurred on the protein interaction surface.

[0063] Step 4. Apply the SNP-protein interaction...

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Abstract

The invention discloses a forecasting method for forecasting DNA-mutation-influenced protein-protein interaction (PPI) based on multivariate data. The forecasting method includes the steps that whether the influence of single nucleotide polymorphisms (SNP) on DNA on PPI is generated or not serves as a researching object, seven characteristics related to the structures and functions of protein and amino acid sequences are adopted, a support vector machine (SVM) and the ensemble learning algorithm are used as a classifying device, and whether PPI is damaged by the SNP or not is forecasted; meanwhile, whether the interaction effect exists between protein or not and whether amino acid variation caused by the SNP occurs on the PPI interface or not are judged.

Description

technical field [0001] The invention relates to an algorithm for predicting DNA mutations affecting protein interactions under the background of machine learning and bioinformatics knowledge, in particular to a prediction method for predicting DNA mutations affecting protein interactions based on multivariate data. Background technique [0002] DNA single point nucleotide mutations (Single Nucleotide Polymorphisms, SNP) lead to protein amino acid mutations and thus destroy protein interaction (Protein Protein Interaction, PPI) may cause a variety of diseases and pose a great threat to human health. For example, the amino acid mutation of the protein APOE caused by SNP rs17646665 destroys the protein interaction between APOE and SORT1, promotes the production of APOE / Aβ compounds, and increases the risk of Alzheimer's disease (AD). [0003] At present, there are mainly four algorithms for predicting the influence of SNP on PPI stability: [0004] 1. Algorithms based on prote...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/18
CPCG16B20/00
Inventor 赵兴明何峰
Owner TONGJI UNIV
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