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Robles-loaiza AA, Pinos-tamayo EA, Mendes B, Ortega-pila JA, Proaño-bolaños C, Plisson F, Teixeira C, Gomes P, Almeida JR. Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity. Pharmaceuticals (Basel) 2022; 15:323. [PMID: 35337121 PMCID: PMC8953747 DOI: 10.3390/ph15030323] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/01/2022] [Accepted: 03/04/2022] [Indexed: 12/27/2022] Open
Abstract
Peptides have positively impacted the pharmaceutical industry as drugs, biomarkers, or diagnostic tools of high therapeutic value. However, only a handful have progressed to the market. Toxicity is one of the main obstacles to translating peptides into clinics. Hemolysis or hemotoxicity, the principal source of toxicity, is a natural or disease-induced event leading to the death of vital red blood cells. Initial screenings for toxicity have been widely evaluated using erythrocytes as the gold standard. More recently, many online databases filled with peptide sequences and their biological meta-data have paved the way toward hemolysis prediction using user-friendly, fast-access machine learning-driven programs. This review details the growing contributions of in silico approaches developed in the last decade for the large-scale prediction of erythrocyte lysis induced by peptides. After an overview of the pharmaceutical landscape of peptide therapeutics, we highlighted the relevance of early hemolysis studies in drug development. We emphasized the computational models and algorithms used to this end in light of historical and recent findings in this promising field. We benchmarked seven predictors using peptides from different data sets, having 7–35 amino acids in length. According to our predictions, the models have scored an accuracy over 50.42% and a minimal Matthew’s correlation coefficient over 0.11. The maximum values for these statistical parameters achieved 100.0% and 1.00, respectively. Finally, strategies for optimizing peptide selectivity were described, as well as prospects for future investigations. The development of in silico predictive approaches to peptide toxicity has just started, but their important contributions clearly demonstrate their potential for peptide science and computer-aided drug design. Methodology refinement and increasing use will motivate the timely and accurate in silico identification of selective, non-toxic peptide therapeutics.
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Timmons PB, Hewage CM. ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides. Brief Bioinform 2021; 22:bbab258. [PMID: 34297817 PMCID: PMC8575049 DOI: 10.1093/bib/bbab258] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/09/2021] [Accepted: 06/18/2021] [Indexed: 11/14/2022] Open
Abstract
Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore represent a promising field of novel antiviral drugs. As the quantity of sequencing data grows annually, the development of an accurate in silico method for the prediction of peptide antiviral activities is important. This study leverages advances in deep learning and cheminformatics to produce a novel sequence-based deep neural network classifier for the prediction of antiviral peptide activity. The method outperforms the existent best-in-class, with an external test accuracy of 93.9%, Matthews correlation coefficient of 0.87 and an Area Under the Curve of 0.93 on the dataset of experimentally validated peptide activities. This cutting-edge classifier is available as an online web server at https://research.timmons.eu/ennavia, facilitating in silico screening and design of peptide antiviral drugs by the wider research community.
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Affiliation(s)
- Patrick Brendan Timmons
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland
| | - Chandralal M Hewage
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland
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Timmons PB, Hewage CM. ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides. Biomed Pharmacother 2020; 133:111051. [PMID: 33254015 DOI: 10.1016/j.biopha.2020.111051] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/08/2020] [Accepted: 11/19/2020] [Indexed: 12/12/2022] Open
Abstract
The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact the human body unfavourably. Anticancer peptides represent a promising emerging field of anticancer therapeutics, which are characterized by favourable toxicity profile. The development of accurate in silico methods for anticancer peptide prediction is of paramount importance, as the amount of available sequence data is growing each year. This study leverages advances in machine learning research to produce a novel sequence-based deep neural network classifier for anticancer peptide activity. The classifier achieves performance comparable to the best-in-class, with a cross-validated accuracy of 98.3%, Matthews correlation coefficient of 0.91 and an Area Under the Curve of 0.95. This innovative classifier is available as a web server at https://research.timmons.eu/ennaact, facilitating in silico screening and design of new anticancer peptide chemotherapeutics by the research community.
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Affiliation(s)
- Patrick Brendan Timmons
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland
| | - Chandralal M Hewage
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland.
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Timmons PB, Hewage CM. HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks. Sci Rep 2020; 10:10869. [PMID: 32616760 PMCID: PMC7331684 DOI: 10.1038/s41598-020-67701-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 06/09/2020] [Indexed: 12/11/2022] Open
Abstract
The growing prevalence of resistance to antibiotics motivates the search for new antibacterial agents. Antimicrobial peptides are a diverse class of well-studied membrane-active peptides which function as part of the innate host defence system, and form a promising avenue in antibiotic drug research. Some antimicrobial peptides exhibit toxicity against eukaryotic membranes, typically characterised by hemolytic activity assays, but currently, the understanding of what differentiates hemolytic and non-hemolytic peptides is limited. This study leverages advances in machine learning research to produce a novel artificial neural network classifier for the prediction of hemolytic activity from a peptide's primary sequence. The classifier achieves best-in-class performance, with cross-validated accuracy of [Formula: see text] and Matthews correlation coefficient of 0.71. This innovative classifier is available as a web server at https://research.timmons.eu/happenn , allowing the research community to utilise it for in silico screening of peptide drug candidates for high therapeutic efficacies.
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Affiliation(s)
- Patrick Brendan Timmons
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland
| | - Chandralal M Hewage
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland.
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Bahadori M, Hemmateenejad B, Yousefinejad S. Quantitative sequence-activity modeling of ACE peptide originated from milk using ACC-QTMS amino acid indices. Amino Acids 2019; 51:1209-20. [PMID: 31321559 DOI: 10.1007/s00726-019-02761-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 07/05/2019] [Indexed: 01/06/2023]
Abstract
Up to now, numerous peptides/hydrolysates derived from casein and whey protein have shown angiotensin-I-converting enzyme (ACE) inhibitory. In this research, quantum topological molecular similarity (QTMS) indices of amino acids were utilized in quantitative sequence-activity modeling (QSAM) to predict the activity of a set of milk-driven peptides with ACE inhibition. Since the derived peptides have not the same number of residues, we overcame this issue by auto cross covariance (ACC) methodology. Then, some QSAMs were built to predict the pIC50 value of ACE peptides derived from Bovine Casein and Whey. The model established an acceptable relationship between the selected variables and the pIC50 of the peptides. To estimate the performance of the developed models, casein and whey proteins from human, goat, bovine and sheep were virtually broken by trypsin and chymotrypsin enzymes and the ACE activity of the resultant virtual peptides were predicted and some new ACE peptides were proposed.
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Müller AT, Gabernet G, Hiss JA, Schneider G. modlAMP: Python for antimicrobial peptides. Bioinformatics 2017; 33:2753-2755. [DOI: 10.1093/bioinformatics/btx285] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Accepted: 04/22/2017] [Indexed: 01/01/2023] Open
Affiliation(s)
- Alex T Müller
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
| | - Gisela Gabernet
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
| | - Jan A Hiss
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
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Yousefinejad S, Bagheri M, Moosavi-movahedi AA. Quantitative sequence–activity modeling of antimicrobial hexapeptides using a segmented principal component strategy: an approach to describe and predict activities of peptide drugs containing l/d and unnatural residues. Amino Acids 2015; 47:125-34. [DOI: 10.1007/s00726-014-1850-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Accepted: 10/03/2014] [Indexed: 12/20/2022]
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Li SZ, Fu B, Wang Y, Liu S. On Structural Parameterization and Molecular Modeling of Peptide Analogues by Molecular Electronegativity Edge Vector (VMEE): Estimation and Prediction for Biological Activity of Dipeptides. J CHIN CHEM SOC-TAIP 2013. [DOI: 10.1002/jccs.200100137] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Yousefinejad S, Hemmateenejad B, Mehdipour AR. New autocorrelation QTMS-based descriptors for use in QSAM of peptides. J IRAN CHEM SOC 2012; 9:569-77. [DOI: 10.1007/s13738-012-0070-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Abstract
A new amino acids descriptor E, which (E1~E5) has been introduced in bioactive peptides Quantitative Structure-Activity Relationship (QSAR) Study. It has been proved that correlate good with hydrophobicity, size, preference for amino acids to occur in -helices, composition and the net charge, respectively. They were then applied to construct characterization and QSAR analysis on 48 bitter tasting dipeptides and 30 bradykinin potentiating (BP) pentapeptides using multiple linear regression (MLR). The leave-one-out cross validation values (Q2(CV)) were 0.888 and 0.797, the multiple correlation coefficients (R2) were 0.940 and 0.891, respectively for bitter tasting dipeptides and BP pentapeptides. The results showed that, in comparison with the conventional descriptors, the descriptor (E) is a useful structure characterization method for peptide QSAR analysis. The importance of each property at each position in peptides is estimated by the regression coefficient value of the MLR model. The establishment of such methods will be a very meaningful work to peptide bioactive investigation in peptide drug design.
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Tong J, Che T, Li Y, Wang P, Xu X, Chen Y. A descriptor of amino acids: SVRG and its application to peptide quantitative structure-activity relationship. SAR QSAR Environ Res 2011; 22:611-620. [PMID: 21830880 DOI: 10.1080/1062936x.2011.604099] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
In this work, a descriptor, SVRG (principal component scores vector of radial distribution function descriptors and geometrical descriptors), was derived from principal component analysis (PCA) of a matrix of two structural variables of coded amino acids, including radial distribution function index (RDF) and geometrical index. SVRG scales were then applied in three panels of peptide quantitative structure-activity relationships (QSARs) which were modelled by partial least squares regression (PLS). The obtained models with the correlation coefficient (R²(cum)), cross-validation correlation coefficient (Q²(LOO)) were 0.910 and 0.863 for 48 bitter-tasting dipeptides; 0.968 and 0.931 for 21 oxytocin analogues; and 0.992 and 0.954 for 20 thromboplastin inhibitors. Satisfactory results showed that SVRG contained much chemical information relating to bioactivities. The approach may be a useful structural expression methodology for studies on peptide QSAR.
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Affiliation(s)
- J Tong
- College of Chemistry & Chemical Engineering, Shaanxi University of Science & Technology, Xi'an, PR China.
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Yin J, Diao Y, Wen Z, Wang Z, Li M. Studying Peptides Biological Activities Based on Multidimensional Descriptors (E) Using Support Vector Regression. Int J Pept Res Ther 2010. [DOI: 10.1007/s10989-010-9210-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Lin ZH, Long HX, Bo Z, Wang YQ, Wu YZ. New descriptors of amino acids and their application to peptide QSAR study. Peptides 2008; 29:1798-805. [PMID: 18606203 DOI: 10.1016/j.peptides.2008.06.004] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2008] [Revised: 06/09/2008] [Accepted: 06/10/2008] [Indexed: 11/18/2022]
Abstract
A new set of descriptors was derived from a matrix of three structural variables of the natural amino acid, including van der Waal's volume, net charge index and hydrophobic parameter of side residues. They were selected from many properties of amino acid residues, which have been validated being the key factors to influence the interaction between peptides and its protein receptor. They were then applied to structure characterization and QSAR analysis on bitter tasting di-peptide, agiotensin-converting enzyme inhibitor and bactericidal peptides by using multiple linear regression (MLR) method. The leave one out cross validation values (Q(2)) were 0.921, 0.943 and 0.773. The multiple correlation coefficients (R(2)) were 0.948, 0.970 and 0.926, the root mean square (RMS) error for estimated error were 0.165, 0.154 and 0.41, respectively for bitter tasting di-peptide, angiotensin-converting enzyme inhibitor and bactericidal peptides. Test sets of peptides were used to validate the quantitative model, and it was shown that all these QSAR models had good predictability for outside samples. The results showed that, in comparison with the conventional descriptors, the new set of descriptors is a useful structure characterization method for peptide QSAR analysis, which has multiple advantages, such as definite physical and chemical meaning, easy to get, and good structural characterization ability.
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Affiliation(s)
- Zhi-hua Lin
- College of Bioengineering, Chongqing Institute of Technology, Chongqing 400050, People's Republic of China.
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Li Z, Li G, Shu M, Sun J, Yang S, Mei H, Zhang M, Zhou P, Wu S, Chen G, Lu F, Lu T. A novel vector of topological and structural information for amino acids and its QSAR applications for peptides and analogues. ACTA ACUST UNITED AC 2008. [DOI: 10.1007/s11426-008-0040-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Tong J, Liu S, Zhou P, Wu B, Li Z. A novel descriptor of amino acids and its application in peptide QSAR. J Theor Biol 2008; 253:90-7. [DOI: 10.1016/j.jtbi.2008.02.030] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2007] [Revised: 02/21/2008] [Accepted: 02/21/2008] [Indexed: 11/23/2022]
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16
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Li Z, Wu S, Chen Z, Ye N, Yang S, Liao C, Zhang M, Yang L, Mei H, Yang Y, Zhao N, Zhou Y, Zhou P, Xiong Q, Xu H, Liu S, Ling Z, Chen G, Li G. Structural parameterization and functional prediction of antigenic polypeptome sequences with biological activity through quantitative sequence-activity models (QSAM) by molecular electronegativity edge-distance vector (VMED). Sci China C Life Sci 2007; 50:706-16. [PMID: 17879071 PMCID: PMC7089106 DOI: 10.1007/s11427-007-0080-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/30/2006] [Accepted: 06/14/2007] [Indexed: 11/18/2022]
Abstract
Only from the primary structures of peptides, a new set of descriptors called the molecular electronegativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the automated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1-14) A(d) and 14 other restricted activities assigned as "1"(+) and "0"(-), respectively. The latter comprised 6 A(b)(15-20), 3 A(k)(21-23), 2 E(k)(24-26), 2 H-2(k)(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction (none wrong for 8 testing samples); while contrastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross validations were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility antigen (MHC) epitope of human. It will be useful in immune identification and recognition of proteins and genes and in the design and development of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various oligopeptides and polypeptides including 58 dipeptides and 31 pentapeptides with angiotensin converting enzyme (ACE) inhibition by multiple linear regression (MLR) method. In order to explain the ability to characterize molecular structure of polypeptides, a molecular modeling investigation on QSAR was performed for functional prediction of polypeptide sequences with antigenic activity and heptapeptide sequences with tachykinin activity through quantitative sequence-activity models (QSAMs) by the molecular electronegativity edge-distance vector (VMED). The results showed that VMED exhibited both excellent structural selectivity and good activity prediction. Moreover, the results showed that VMED behaved quite well for both QSAR and QSAM of poly-and oligopeptides, which exhibited both good estimation ability and prediction power, equal to or better than those reported in the previous references. Finally, a preliminary conclusion was drawn: both classical and modified MED vectors were very useful structural descriptors. Some suggestions were proposed for further studies on QSAR/QSAM of proteins in various fields.
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Affiliation(s)
- ZhiLiang Li
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - ShiRong Wu
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - ZeCong Chen
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Nancy Ye
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - ShengXi Yang
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - ChunYang Liao
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - MengJun Zhang
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
- Department of Medical Analysis/PLA Center of Bioinformatics Immunology, Surgeon Third University, Chongqing, 400031 China
| | - Li Yang
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Hu Mei
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
- Technology Centre for Life Sciences, Singapore Polytechnic, 500 Dover Road, Singapore, 139651 Singapore
| | - Yan Yang
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Na Zhao
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Yuan Zhou
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Ping Zhou
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Qing Xiong
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Hong Xu
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - ShuShen Liu
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - ZiHua Ling
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Gang Chen
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
- Technology Centre for Life Sciences, Singapore Polytechnic, 500 Dover Road, Singapore, 139651 Singapore
| | - GenRong Li
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
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18
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Zhou P, Zhou Y, Wu S, Li B, Tian F, Li Z. A new descriptor of amino acids based on the three-dimensional vector of atomic interaction field. ACTA ACUST UNITED AC 2006; 51:524-9. [DOI: 10.1007/s11434-006-0524-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Zhihua L, Yuzhang W, Xuejun Q, Yuegang Z, Bing N, Ying W. Use of a novel electrotopological descriptor for the prediction of biological activity of peptide analogues. ACTA ACUST UNITED AC 2002. [DOI: 10.1007/bf02447552] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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