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A New Descriptor of Amino Acids‐SVGER and its Applications in Peptide QSAR
Author(s) -
Tong Jianbo,
Li Lingxiao,
Bai Min,
Li Kangnan
Publication year - 2017
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201501023
Subject(s) - quantitative structure–activity relationship , loo , linear regression , chemistry , peptide , correlation coefficient , stepwise regression , coefficient of determination , regression analysis , multiple correlation , regression , mathematics , stereochemistry , correlation , biochemistry , statistics , geometry
In the study of peptide quantitative structure activity relationship (QSAR), a new descriptor of amino acids (SVGER) was calculated. It was applied in two peptides which are angiotensin converting enzyme inhibitors and bitter tasting threshold of di‐peptide. QSAR models were built by stepwise multiple regression‐multiple linear regression (SMR‐MLR) and stepwise multiple regression‐partial least square regression (SMR‐PLS). In the SMR‐MLR models for angiotensin converting enzyme inhibitors, the squared cross‐validation correlation coefficient ( Q LOO 2 ) was 0.907, squared correlation coefficient between predicted and observed activities ( R cum 2 ) was 0.977 and external multiple correlation coefficient ( Q ext 2 ) was 0.867. The corresponding data for the bitter tasting threshold of di‐peptide were 0.802, 0.966, 0.719. While in the SMR‐PLS model, Q LOO 2 , R cum 2 and Q ext 2 were 0.804, 0.915, 0.858 for angiotensin converting enzyme inhibitors and 0.782, 0.881, 0.747 for bitter tasting threshold of di‐peptide. Our results showed that descriptor SVGER can afford good account of relationships between activity and structure of peptide drugs.