T-scale as a novel vector of topological descriptors for amino acids and its application in QSARs of peptides

Tian, Fengchun; Zhou, Peng; Li, Zhiliang

doi:10.1016/j.molstruc.2006.07.004

Cited by 130 publications

(86 citation statements)

References 42 publications

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“…It has been shown that several potent ACE inhibitory or antioxidant short peptides (2 to 3 amino acid residues) were also bitter. 23,40,48,82 In contrast, in another study, 24 no direct correlation was seen between ACE inhibitory properties and bitterness of di-or tripeptides. The number of possible dipeptide combinations (20 2 ¼ 400) is lower than that of larger peptides, increasing the structural diversity of larger peptides.…”

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confidence: 80%

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Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review

Nongonierma

Fitzgerald

2016

RSC Adv.

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confidence: 80%

“…The T-scale has been developed by Tian et al 40 from a PCA of 67 structural and topological variables of 135 amino acids. A PCA was carried out on the hydrogen bonding (5), electronic (23), steric (37) and hydrophobic (54) properties of the 20 conventional amino acids, yielding 10 descriptors termed as divided physicochemical property scores (DPPS).…”

mentioning

confidence: 99%

Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review

Nongonierma

Fitzgerald

2016

RSC Adv.

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“…[12] In recent years, with the rapidly increasing number of biological sequences and structures, prediction of B-factor values and other properties from these data has become as an attractive domain for bioinformatics researchers. [13][14][15][16][17] Previously, numerous works have been addressed on theoretical and computational studies of protein B-factors. [18][19][20][21][22][23][24][25][26] However, the methods used for modeling and predicting Bfactor values of atoms in RNA crystals, particularly in the complicated ribosome crystals, to our knowledge, still remain unexploited.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Flexibility Profile of Ribosomal RNAs

Tian

Zhang

Xia

et al. 2010

Molecular Informatics

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Flexibility in biomolecules is an important determinant of biological functionality, which can be measured quantitatively by atomic Debye–Waller factor or B‐factor. Although numerous works have been addressed on theoretical and computational studies of the B‐factor profiles of proteins, the methods used for predicting B‐factor values of nucleic acids, especially the complicated ribosomal RNAs (rRNAs), which are very functionally similar to proteins in providing matrix structures and in catalyzing biochemical reactions, still remain unexploited. In this article, we present a quantitative structure–flexibility relationship (QSFR) study with the aim at the quantitative prediction of rRNA B‐factor based on primary sequences (sequence‐based) and advanced structures (structure‐based) by using both linear and nonlinear machine learning approaches, including partial least squares regression (PLS), least squares support vector machine (LSSVM), and Gaussian process (GP). By rigorously examining the performance and reliability of constructed statistical models and by comparing our models in detail to those developed previously for protein B‐factors, we demonstrate that (i) rRNA B‐factors could be predicted at a similar level of accuracy with that of protein, (ii) a structure‐based approach performed much better as compared to sequence‐based methods in modeling of rRNA B‐factors, and (iii) rRNA flexibility is primarily governed by the local features of nonbonding potential landscapes, such as electrostatic and van der Waals forces.

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“…Since Kidera et al (1985) first coded 10 orthogonal factors from 188 reported physicochemical properties through factor analysis, a series of inductive descriptors have been constructed and applied in peptide computational study. Some examples are Z-scales (Hellberg et al 1991;Sandberg et al 1998), ISA-ECI (Collantes and Dunn 1995), SZOTT (Liang et al 2006), T-scales (Tian et al 2007), ATS-QTMS (Yousefinejad et al 2012), etc. However, these inductive descriptors are the linear combinations of the multiple physicochemical property parameters selected for the amino acids and hence, the QSAR models established using these descriptors could not clearly elucidate the correlation between the initial physicochemical properties and the bioactivity of peptides.…”

Section: Introductionmentioning

confidence: 99%

A pipeline for improved QSAR analysis of peptides: physiochemical property parameter selection via BMSF, near-neighbor sample selection via semivariogram, and weighted SVR regression and prediction

et al. 2014

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Received: / Accepted: / Published:Abstract: In this paper, we present a pipeline to perform improved QSAR analysis of peptides. The modeling involves a double selection procedure that first performs feature selection and then conducts sample selection before the final regression analysis. Five hundred and thirty-one physicochemical property parameters of amino acids were used as descriptors to characterize the structure of peptides. These high-dimensional descriptors then go through a feature selection process given by the Binary Matrix Shuffling Filter (BMSF) to obtain a set of important low dimensional features. Each descriptor that passed the BMSF filtering also receives a weight defined through its contribution to reduce the estimation error. These selected features were served as the predictors for subsequent sample selection and modeling. Based on the weighted Euclidean distances between samples, a common range was determined with high-dimensional semivariogram and then used as a threshold to select the near-neighbor samples from the training set. For each sample to be predicted, the QSAR model was established using SVR with the weighted, selected features based on the exclusive set of near-neighbor training samples. Prediction was conducted for each test sample accordingly. The performances of this pipeline are tested with the QSAR analysis of angiotensin-converting enzyme (ACE)inhibitors and HLA-A*0201 data sets. Improved prediction accuracy was obtained in both applications. This pipeline can optimize the QSAR modeling from both the feature selection and sample selection perspectives.This leads to improved accuracy over single selection methods. We expect this pipeline to have extensive application prospect in the field of regression prediction.

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T-scale as a novel vector of topological descriptors for amino acids and its application in QSARs of peptides

Cited by 130 publications

References 42 publications

Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review

Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review

Predicting the Flexibility Profile of Ribosomal RNAs

A pipeline for improved QSAR analysis of peptides: physiochemical property parameter selection via BMSF, near-neighbor sample selection via semivariogram, and weighted SVR regression and prediction

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