Zhiliang Li

Quantitative Sequence-Activity Model (QSAM): Applying QSAR Strategy to Model and Predict Bioactivity and Function of Peptides, Proteins and Nucleic Acids

Traditional quantitative structure-activity relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. Quantitative sequence-activity model (QSAM), applying QSAR strategy to explore sequence-activity/function relationship for biosystems, is greatly meaningful but meanwhile extremely difficult. For biomolecules, high molecular weight, diverse structural morphology and intricate interaction network all bring in traditional QSAR methodologies unprecedented challenges. This article comprehensively reviewed developing process, current state and future perspective of QSAM, concerning its applications into fields of pharmacy, food science, immunology and molecular biology. Besides, discipline-crossing and amalgamation of QSAM with QSAR, bioinformatics and computational biology were also discussed.

Molecular structural vector description and retention index of polycyclic aromatic hydrocarbons

Abstract A molecular electronegativity–distance vector (MEDV) has been proposed to describe the structure of polycyclic aromatic hydrocarbons (PAHs) and relate to their retention indices (RI) for programmed temperature SE-52 capillary-column gas chromatography. The 209 PAHs investigated contain not only one or two heteroatoms such as nitrogen, oxygen and sulfur but also one, two or more conjugated rings. Applying multiple linear regression (MLR) in combination with the cross-validation (CV) technique, a four-parameter quantitative structure–retention relationship (QSRR) of 209 PAHs is developed with the correlation coefficient ( R ) of 0.9812 and the root mean square error (RMS) of 15.533 between the estimated and experimental retention indices (RI). It was found that the errors for oxygen-containing PAHs are often positive and for sulfur-containing PAHs, negative. Thus, MEDV is modified by lengthening the Cue5f8O or Cue5fbO bond and by shortening the Cue5f8S bond. Leaving out several outliers, a better QSRR is described with R =0.9936 and RMS=9.212 for 172 PAHs.

ST-scale as a novel amino acid descriptor and its application in QSAM of peptides and analogues.

In this study, structural topology scale (ST-scale) was recruited as a novel structural topological descriptor derived from principal component analysis on 827 structural variables of 167 amino acids. By using partial least squares (PLS), we applied ST-scale for the study of quantitative sequence-activity models (QSAMs) on three peptide datasets (58 angiotensin-converting enzyme (ACE) inhibitors, 34 antimicrobial peptides (AMPs) and 89 elastase substrates (ES)). The results of QSAMs were superior to that of the earlier studies, with determination coefficient (r2) and cross-validated (q2) equal to 0.855, 0.774; 0.79, 0.371 (OSC-PLS: 0.995, 0.848) and 0.846, 0.747, respectively. Therefore, ST-scale descriptors were considered to be competent to extract information from 827 structural variables and relate with their bioactivities.

A Structure‐based, Quantitative Structure–Activity Relationship Approach for Predicting HLA‐A*0201‐restricted Cytotoxic T Lymphocyte Epitopes

In this investigation, we first constructed four types of non‐bonding interaction matrixes by defining direct contacting residue types for HLA‐A*0201 protein in interaction with each position of HLA‐A*0201‐restricted cytotoxic T lymphocyte epitope as well as several formulae calculating ligand/receptor non‐bonding interactions. Relative to these studies, a method which we refer to as structure‐based, quantitative structure–activity relationship is proposed and utilized for studies on 266 HLA‐A*0201‐restricted cytotoxic T lymphocyte epitopes. The resulting genetic algorithm‐partial least square regression model is consistent with both published studies and molecular graphics analysis. Two non‐bonding interactions (i.e. hydrophobic and hydrogen bonding), are found to play important roles in antigen recognition and presentation, especially exerting effects at positions of anchor residues in antigen peptides.

Factor Analysis Scales of Generalized Amino Acid Information as Applied in Predicting Interactions between the Human Amphiphysin-1 SH3 Domains and Their Peptide Ligands

Factor analysis scales of generalized amino acid information (FASGAI) involving hydrophobicity, alpha‐helix and beta‐turn propensities, bulky properties, compositional characteristics, local flexibility, and electronic properties, was proposed to represent the structures of the decapeptides binding the human amphiphysin‐1 SH3 domains. Parameters being responsible for the binding affinities were selected by genetic algorithm, and a quantitative structure–affinity relationship (QSAR) model by partial least square was established to predict the peptide–SH3 domain interactions. Diversified properties of the residues between P2 and P−3 (including P2 and P−3) of the decapeptide (P4P3P2P1P0P−1P−2P−3P−4P−5) may contribute remarkable effect to the interactions between the SH3 domain and the decapeptide. Particularly, electronic properties of P2 may provide relatively large positive contributions to the interactions, and reversely, hydrophobicity of P2 may be largely negative to the interactions. These results showed that FASGAI vectors can well represent the structural characteristics of the decapeptides. Furthermore, the model obtained, which showed low computational complexity, correlated FASGAI descriptors with the binding affinities as well as that FASGAI vectors may also be applied in QSAR studies of peptides.

A set of new amino acid descriptors applied in prediction of MHC class I binding peptides

A set of new amino acid descriptors, namely factor analysis scales of generalized amino acid information (FASGAI) involving hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, was proposed to resolve the representation of peptide structures. FASGAI vectors were then used to represent the structures of 152 HLA-A(*)0201 restrictive T-cell epitopes with 9 amino acid residues. The features that are closely related to binding affinities were selected by genetic arithmetic, and the model based on partial least squares was developed to predict binding affinities. The model revealed promising predictive power, giving relatively high predictions for training and test samples. Further, the PreMHCbinding program at significantly lower computational complexity was exploited to predict MHC class I binding peptides. Quantitative structure-affinity relationship analyses demonstrated the bulky properties and hydrophobicity of the 3rd residue, bulky properties of the 2nd residue, hydrophobicity of the 9th residue that provided high positive contribution to the binding affinities, and that the hydrophobicity of the 4th residue and local flexibility of the 3rd residue were negative to binding affinities. The results showed that FASGAI vectors can be further utilized to represent the structures of other functional peptides; moreover, it has thus showed us further direction into the potential applications on relationship between structures and functions of proteins.

Application of 'HESH' descriptors for the structure-activity relationships of antimicrobial peptides.

In this paper, HESH, which was a new set of amino acid descriptors including Hydrophobic, Electronic, Steric and Hydrogen bond contribution properties, were derived from multi-dimensional properties of 20 coded amino acids. The quantitative structure-activity relationship (QSAR) of 101 synthetic cationic antimicrobial polypeptides (CAMELs) was then characterized with HESH scales and studied by genetic algorithm-partial least square (GA-PLS) method. It was found that the robust QSAR model constructed with electronic and hydrophobic properties parameters of HESH descriptors was a better one. Through further analysis, electronic and hydrophobic properties of the 3rd, 6th, 7th, 11th and 12th residue of CAMELs sequence made high contribution to antimicrobial potencies. Based on this PLS model, a series of cationic antimicrobial peptides (AMPs), with relatively high antimicrobial activity was designed. Meanwhile, a robust QSAR model with favorable predictive capability for 34 antimicrobial peptides was constructed with HESH descriptors. The results showed that HESH descriptors had many obvious advantages, for it contains abundant information and its physico-chemical characteristics are clear and easily explained. The developed descriptors can be further expanded for the larger sets of biologically activities peptides and can serve as a useful quantitative tool for the rational design and discovery of antibiotics.

Atomic Electronegativity Interaction Vector and Atomic Hybrid State Index Utilized for Spectroscopic Simulation of 13C Nuclear Magnetic Resonance of Amino Acids

Abstract A new method of quantitative structure-spectroscopy relationship (QSSR) is developed for expression of local chemical microenvironment and atomic hybrid state based on both novel atomic electronegativity interaction vector (AEIV) and atomic hybrid state index (AHSI). By AEIV and AHSI, some QSSR models are successfully established for 103 13 C NMR chemical shift of 20 natural amino acids. The correlation coefficients of modeling fitting, leave-one-out (LOO) cross-validation-predicted value and leave-molecule-out (LMO) cross-validation-predicted value are 0.9948, 0.9940 and 0.9924, respectively. Then, this model is tested by 13 C NMR chemical shift of 4 non-natural amino acids with the prediction correlation coefficients being 0.9940.

Novel Molecular Electronegativity-interaction Vector and Its Application in Quantitative Prediction for Collision Cross-section of Singly Protonated Peptides

Abstract Based on two-dimensional topological characters, a novel method called the molecular electronegativity-interaction vector (MEIV) was proposed to parameterize molecular structures. By applying MEIV, ion mobility spectrometry collision cross-sections for 113 singly protonated peptides were successfully simulated and predicted. The resulting three models were strictly built with a cumulative multiple correlation coefficient R cum and a leave-one-out cross-validation Q of 0.983 and 0.979, 0.981 and 0.979, 0.980 and 0.978, respectively. It is confirmed that MEIV is largely dependent on the properties of the organic molecules.

Molecular graph fingerprint: a new molecular structural characterization method for the modelling and prediction of chromatographic retention behaviour of several persistent organic pollutants

How to extract and characterize information on molecular microstructures is deemed to be the key task to accurately simulate and predict molecular properties. In terms of atomic attributes, atoms in a molecule are divided into three levels. Based upon that, inter-atomic correlations are mapped to certain reasonable spatial coordinates in virtue of radial distribution function, generating the novel molecular graph fingerprint (MoGF), which essentially provides insight into molecular inner structures. MoGF, committing itself to transformation of molecular structures into characteristic graph curves, shows valuable advantages such as easy calculation, experimental parameters-free, rich information content, and structural significance and intuitive expressions. QSRR studies were performed for 115 polychlorinated dibenzofurans (PCDFs), 41 polychlorinated dibenzo-p-dioxins (PCDDs), 62 polychlorinated naphthalenes (PCNs), and 210 polychlorinated biphenyls (PCBs including the biphenyl)) tested for their retention behaviours on gas chromatographic column DB-5. The resulting PLS models showed good performances with correlation coefficients for both training and test sets above 0.97.