Application of quantitative structure-activity relationship to food-derived peptides: Methods, situations, challenges and prospects

[1]  M. Shu,et al.  Structural Parameter Characterization and Bioactivity Simulation Based on Peptide Sequence , 2009 .

[2]  B. Hernández-Ledesma,et al.  Chemopreventive role of food-derived proteins and peptides: A review , 2017, Critical reviews in food science and nutrition.

[3]  Riccardo Leardi,et al.  Genetic algorithms in chemometrics , 2012 .

[4]  Zheng Rong Yang,et al.  Biological applications of support vector machines , 2004, Briefings Bioinform..

[5]  Peter Gedeck,et al.  QSAR - How Good Is It in Practice? Comparison of Descriptor Sets on an Unbiased Cross Section of Corporate Data Sets , 2006, J. Chem. Inf. Model..

[6]  Gajendra P. S. Raghava,et al.  SATPdb: a database of structurally annotated therapeutic peptides , 2015, Nucleic Acids Res..

[7]  J. Sangshetti,et al.  Recent advances in multidimensional QSAR (4D-6D): a critical review. , 2014, Mini reviews in medicinal chemistry.

[8]  Igor I. Baskin,et al.  A Neural Device for Searching Direct Correlations between Structures and Properties of Chemical Compounds , 1997, J. Chem. Inf. Comput. Sci..

[9]  Xia Li,et al.  APD3: the antimicrobial peptide database as a tool for research and education , 2015, Nucleic Acids Res..

[10]  Jie Zheng,et al.  An Index for Characterization of Natural and Non-Natural Amino Acids for Peptidomimetics , 2013, PloS one.

[11]  G. Klebe,et al.  Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. , 1994, Journal of medicinal chemistry.

[12]  J Gasteiger Some solved and unsolved problems of chemoinformatics , 2014, SAR and QSAR in environmental research.

[13]  H. Chung,et al.  Quantitative Structure–Activity Relationship Study of Bitter Di-, Tri- and Tetrapeptides Using Integrated Descriptors , 2019, Molecules.

[14]  Zbigniew Grzonka,et al.  Prediction of high‐performance liquid chromatography retention of peptides with the use of quantitative structure‐retention relationships , 2005, Proteomics.

[15]  S. H. Davoodi,et al.  Techniques, perspectives, and challenges of bioactive peptide generation: A comprehensive systematic review. , 2020, Comprehensive reviews in food science and food safety.

[16]  Dong-Sheng Cao,et al.  ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database , 2018, Journal of Cheminformatics.

[17]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[18]  Chibuike C. Udenigwe Bioinformatics approaches, prospects and challenges of food bioactive peptide research , 2014 .

[19]  Gisbert Schneider,et al.  Recurrent Neural Network Model for Constructive Peptide Design , 2018, J. Chem. Inf. Model..

[20]  Z. Li,et al.  Three-Dimensional Quantitative Structure-Activity Relationship Analysis of the New Potent Sulfonylureas Using Comparative Molecular Similarity Indices Analysis , 2000, J. Chem. Inf. Comput. Sci..

[21]  D. S. Gill,et al.  Variable selection in multivariate multiple regression , 1985 .

[22]  A. Nongonierma,et al.  Strategies for the discovery and identification of food protein-derived biologically active peptides , 2017 .

[23]  Kumardeep Chaudhary,et al.  In Silico Models for Designing and Discovering Novel Anticancer Peptides , 2013, Scientific Reports.

[24]  Timur Shtatland,et al.  PepBank - a database of peptides based on sequence text mining and public peptide data sources , 2007, BMC Bioinformatics.

[25]  Chanin Nantasenamat,et al.  iDPPIV-SCM: A sequence-based predictor for identifying and analyzing dipeptidyl peptidase IV (DPP-IV) inhibitory peptides using a scoring card method. , 2020, Journal of proteome research.

[26]  Ying Xu,et al.  Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information. , 2006, Analytical chemistry.

[27]  R. Cramer,et al.  Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.

[28]  M. Akamatsu,et al.  Quantitative analysis of the relationship between structure and antioxidant activity of tripeptides , 2020, Journal of peptide science : an official publication of the European Peptide Society.

[29]  Manoj Kumar,et al.  AVPpred: collection and prediction of highly effective antiviral peptides , 2012, Nucleic Acids Res..

[30]  M. Shu,et al.  ST-scale as a novel amino acid descriptor and its application in QSAM of peptides and analogues , 2010, Amino Acids.

[31]  Marina Cocchi,et al.  AMINO-ACIDS CHARACTERIZATION BY GRID AND MULTIVARIATE DATA-ANALYSIS , 1993 .

[32]  William Stafford Noble,et al.  Support vector machine , 2013 .

[33]  Paola Gramatica,et al.  The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models , 2003 .

[34]  Julio Caballero,et al.  Quantitative structure-activity relationship of rubiscolin analogues as delta opioid peptides using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). , 2007, Journal of agricultural and food chemistry.

[35]  S. Henikoff,et al.  Amino acid substitution matrices from protein blocks. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[36]  A. Iwaniak,et al.  BIOPEP-UWM Database of Bioactive Peptides: Current Opportunities , 2019, International journal of molecular sciences.

[37]  I. Sagardia,et al.  A new QSAR model, for angiotensin I-converting enzyme inhibitory oligopeptides. , 2013, Food chemistry.

[38]  G. Liang,et al.  Comprehensive comparison of twenty structural characterization scales applied as QSAM of antimicrobial dodecapeptides derived from Bac2A against P. aeruginosa. , 2017, Journal of molecular graphics & modelling.

[39]  Chunyan Qi,et al.  Studies on the Bioactivities of ACE‐inhibitory Peptides with Phenylalanine C‐terminus Using 3D‐QSAR, Molecular Docking and in vitro Evaluation , 2017, Molecular informatics.

[40]  A. Pihlanto-Leppälä Bioactive peptides derived from bovine whey proteins: opioid and ace-inhibitory peptides. , 2000 .

[41]  S. Sural,et al.  Hydrophobicity versus electrophilicity: A new protocol toward quantitative structure–toxicity relationship , 2018, Chemical biology & drug design.

[42]  C. Fjell,et al.  Identification of novel antibacterial peptides by chemoinformatics and machine learning. , 2009, Journal of medicinal chemistry.

[43]  Minoru Kanehisa,et al.  AAindex: amino acid index database, progress report 2008 , 2007, Nucleic Acids Res..

[44]  Bo Li,et al.  Characterization of structure-antioxidant activity relationship of peptides in free radical systems using QSAR models: key sequence positions and their amino acid properties. , 2013, Journal of theoretical biology.

[45]  Gordon A Anderson,et al.  Use of artificial neural networks for the accurate prediction of peptide liquid chromatography elution times in proteome analyses. , 2003, Analytical chemistry.

[46]  S. Nakai,et al.  Recent advances in structure and function of food proteins: QSAR approach. , 1993, Critical reviews in food science and nutrition.

[47]  S. Wold,et al.  Some recent developments in PLS modeling , 2001 .

[48]  P. Martín-Alvarez,et al.  Comparative prediction of the retention behaviour of small peptides in several reversed-phase high-performance liquid chromatography columns by using partial least squares and multiple linear regression , 1996 .

[49]  R. M. Muir,et al.  Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients , 1962, Nature.

[50]  Artem Cherkasov,et al.  QSAR without borders. , 2020, Chemical Society reviews.

[51]  N. Oulahal,et al.  Antibacterial Properties of Polyphenols: Characterization and QSAR (Quantitative Structure–Activity Relationship) Models , 2019, Front. Microbiol..

[52]  Jianping Wu,et al.  LC-MS/MS coupled with QSAR modeling in characterising of angiotensin I-converting enzyme inhibitory peptides from soybean proteins. , 2013, Food chemistry.

[53]  A. Nongonierma,et al.  Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review , 2016 .

[54]  Mark T. D. Cronin,et al.  The better predictive model: High q2 for the training set or low root mean square error of prediction for the test set? , 2005 .

[55]  A. Iwaniak,et al.  Food protein-originating peptides as tastants - Physiological, technological, sensory, and bioinformatic approaches. , 2016, Food research international.

[56]  Kumardeep Chaudhary,et al.  An in silico platform for predicting, screening and designing of antihypertensive peptides , 2015, Scientific Reports.

[57]  S. Yousefinejad,et al.  Quantitative sequence-activity modeling of ACE peptide originated from milk using ACC–QTMS amino acid indices , 2019, Amino Acids.

[58]  A. Pripp Quantitative structure-activity relationship of prolyl oligopeptidase inhibitory peptides derived from beta-casein using simple amino acid descriptors. , 2006, Journal of agricultural and food chemistry.

[59]  Maolin Tu,et al.  Advancement and prospects of bioinformatics analysis for studying bioactive peptides from food-derived protein: Sequence, structure, and functions , 2018, TrAC Trends in Analytical Chemistry.

[60]  Bahram Hemmateenejad,et al.  Novel amino acids indices based on quantum topological molecular similarity and their application to QSAR study of peptides , 2011, Amino Acids.

[61]  S. Wold,et al.  Minimum analogue peptide sets (MAPS) for quantitative structure-activity relationships. , 2009, International journal of peptide and protein research.

[62]  Yiqun Huang,et al.  Applications of Artificial Neural Networks (ANNs) in Food Science , 2007, Critical reviews in food science and nutrition.

[63]  Yuanqiang Wang,et al.  New descriptors of amino acids and their application to peptide QSAR study , 2008, Peptides.

[64]  O. Krokhin,et al.  Sequence-specific retention calculator. Algorithm for peptide retention prediction in ion-pair RP-HPLC: application to 300- and 100-A pore size C18 sorbents. , 2006, Analytical chemistry.

[65]  F. Fdez-Riverola,et al.  In silico prediction reveals the existence of potential bioactive neuropeptides produced by the human gut microbiota. , 2019, Food research international.

[66]  M Daszykowski,et al.  Retention prediction of peptides based on uninformative variable elimination by partial least squares. , 2006, Journal of proteome research.

[67]  E. Li-Chan,et al.  Quantitative structure-activity relationship study of bitter peptides. , 2006, Journal of agricultural and food chemistry.

[68]  Chanin Nantasenamat,et al.  iUmami-SCM: A Novel Sequence-Based Predictor for Prediction and Analysis of Umami Peptides Using a Scoring Card Method with Propensity Scores of Dipeptides , 2020, J. Chem. Inf. Model..

[69]  Torbjörn Lundstedt,et al.  PREPROCESSING PEPTIDE SEQUENCES FOR MULTIVARIATE SEQUENCE-PROPERTY ANALYSIS , 1998 .

[70]  A. Peijnenburg,et al.  Development of a QSAR Model to Predict Hepatic Steatosis Using Freely Available Machine Learning Tools. , 2020, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.

[71]  S. Free,et al.  A MATHEMATICAL CONTRIBUTION TO STRUCTURE-ACTIVITY STUDIES. , 1964, Journal of medicinal chemistry.

[72]  Lanwei Zhang,et al.  Novel angiotensin I-converting enzyme inhibitory peptides from protease hydrolysates of Qula casein: Quantitative structure-activity relationship modeling and molecular docking study , 2017 .

[73]  A. Iwaniak,et al.  Chemometrics and cheminformatics in the analysis of biologically active peptides from food sources , 2015 .

[74]  Mouming Zhao,et al.  Structure–activity relationship of antioxidant dipeptides: Dominant role of Tyr, Trp, Cys and Met residues , 2016 .

[75]  CoMFA and CoMSIA analysis of ACE-inhibitory, antimicrobial and bitter-tasting peptides. , 2014, European journal of medicinal chemistry.

[76]  Hio Kuan Tai,et al.  Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning , 2020, Molecular therapy. Nucleic acids.

[77]  G. Liang,et al.  Interaction mechanism of flavonoids and zein in ethanol-water solution based on 3D-QSAR and spectrofluorimetry. , 2019, Food chemistry.

[78]  Jianping Wu,et al.  Structural requirements of Angiotensin I-converting enzyme inhibitory peptides: quantitative structure-activity relationship study of di- and tripeptides. , 2006, Journal of agricultural and food chemistry.

[79]  D. Dallas,et al.  Milk bioactive peptide database: A comprehensive database of milk protein-derived bioactive peptides and novel visualization. , 2017, Food chemistry.

[80]  S. Wold,et al.  Peptide quantitative structure-activity relationships, a multivariate approach. , 1987, Journal of medicinal chemistry.

[81]  A. Nongonierma,et al.  Strategies for the discovery, identification and validation of milk protein-derived bioactive peptides , 2016 .

[82]  Mati Karelson,et al.  Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework? , 2016, Expert opinion on drug discovery.

[83]  T. Isaksson,et al.  Quantitative structure activity relationship modelling of peptides and proteins as a tool in food science , 2005 .

[84]  D. Glossman-Mitnik,et al.  Computational prediction of bioactivity scores and chemical reactivity properties of the Parasin I therapeutic peptide of marine origin through the calculation of global and local conceptual DFT descriptors , 2019, Theoretical Chemistry Accounts.

[85]  P. Haddad,et al.  Chemometric-assisted method development in hydrophilic interaction liquid chromatography: A review. , 2018, Analytica chimica acta.

[86]  P. Pattynama,et al.  Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. , 1998, European journal of radiology.

[87]  Tao Wang,et al.  The advancement of multidimensional QSAR for novel drug discovery - where are we headed? , 2017, Expert opinion on drug discovery.

[88]  R. Cramer,et al.  Topomer CoMFA: a design methodology for rapid lead optimization. , 2003, Journal of medicinal chemistry.

[89]  Hu Mei,et al.  Molecular image-based convolutional neural network for the prediction of ADMET properties , 2019, Chemometrics and Intelligent Laboratory Systems.

[90]  P. Zhou,et al.  A novel descriptor of amino acids and its application in peptide QSAR. , 2008, Journal of theoretical biology.

[91]  Russ B Altman,et al.  Machine learning in chemoinformatics and drug discovery. , 2018, Drug discovery today.

[92]  Andrei Gabrielian,et al.  DBAASP v.2: an enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides , 2015, Nucleic acids research.

[93]  Shengshi Z. Li,et al.  A new set of amino acid descriptors and its application in peptide QSARs. , 2005, Biopolymers.

[94]  Chanin Nantasenamat,et al.  iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides. , 2020, Genomics.

[95]  R. P. Ross,et al.  New developments and applications of bacteriocins and peptides in foods. , 2011, Annual review of food science and technology.

[96]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[97]  E. Li-Chan,et al.  Application of Fourier Transform Raman Spectroscopy for Prediction of Bitterness of Peptides , 2006, Applied spectroscopy.

[98]  J. Topliss,et al.  Chance correlations in structure-activity studies using multiple regression analysis , 1972 .

[99]  Roberto Todeschini,et al.  Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors, 1. Theory of the Novel 3D Molecular Descriptors , 2002, J. Chem. Inf. Comput. Sci..

[100]  B. Rai,et al.  In-silico screening of database for finding potential sweet molecules: A combined data and structure based modeling approach. , 2020, Food chemistry.

[101]  Gerard J. P. van Westen,et al.  Benchmarking of protein descriptor sets in proteochemometric modeling (part 1): comparative study of 13 amino acid descriptor sets , 2013, Journal of Cheminformatics.

[102]  H. Mei,et al.  Using multidimensional patterns of amino acid attributes for QSAR analysis of peptides , 2009, Amino Acids.

[103]  S. Wold,et al.  New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. , 1998, Journal of medicinal chemistry.

[104]  Roman Kaliszan,et al.  Predictions of peptides' retention times in reversed‐phase liquid chromatography as a new supportive tool to improve protein identification in proteomics , 2009, Proteomics.

[105]  A. Nongonierma,et al.  Structure activity relationship modelling of milk protein-derived peptides with dipeptidyl peptidase IV (DPP-IV) inhibitory activity , 2016, Peptides.

[106]  Hugo Kubinyi,et al.  From Narcosis to Hyperspace: The History of QSAR , 2002 .

[107]  Roman Kaliszan,et al.  QSRR: quantitative structure-(chromatographic) retention relationships. , 2007, Chemical reviews.

[108]  J. Stadnik,et al.  Structure–activity relationships study on biological activity of peptides as dipeptidyl peptidase IV inhibitors by chemometric modeling , 2019, Chemical biology & drug design.

[109]  F. Tian,et al.  In silico quantitative prediction of peptides binding affinity to human MHC molecule: an intuitive quantitative structure–activity relationship approach , 2009, Amino Acids.

[110]  S. Guan,et al.  Prediction of LC-MS/MS Properties of Peptides from Sequence by Deep Learning. , 2019, Molecular & cellular proteomics : MCP.

[111]  Ş. Niculescu Artificial neural networks and genetic algorithms in QSAR , 2003 .

[112]  E. Daliri,et al.  Current trends and perspectives of bioactive peptides , 2018, Critical reviews in food science and nutrition.

[113]  Andrea Zaliani,et al.  MS-WHIM Scores for Amino Acids: A New 3D-Description for Peptide QSAR and QSPR Studies , 1999, J. Chem. Inf. Comput. Sci..

[114]  W. Dunn,et al.  Amino acid side chain descriptors for quantitative structure-activity relationship studies of peptide analogues. , 1995, Journal of medicinal chemistry.

[115]  Jianping Wu,et al.  QSAR-aided in silico approach in evaluation of food proteins as precursors of ACE inhibitory peptides , 2011 .

[116]  Jie Li,et al.  admetSAR 2.0: web‐service for prediction and optimization of chemical ADMET properties , 2018, Bioinform..

[117]  Lanwei Zhang,et al.  Quantitative Structure-Activity Relationship Modeling Coupled with Molecular Docking Analysis in Screening of Angiotensin I-Converting Enzyme Inhibitory Peptides from Qula Casein Hydrolysates Obtained by Two-Enzyme Combination Hydrolysis. , 2018, Journal of agricultural and food chemistry.

[118]  F. Ren,et al.  Structure-activity relationship of a series of antioxidant tripeptides derived from β-Lactoglobulin using QSAR modeling , 2015 .

[119]  M. Khalesi,et al.  Application of in silico approaches for the generation of milk protein-derived bioactive peptides , 2020 .

[120]  S. Muresan,et al.  Chemical predictive modelling to improve compound quality , 2013, Nature Reviews Drug Discovery.

[121]  J. Dearden,et al.  How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR) , 2009, SAR and QSAR in environmental research.

[122]  Kimito Funatsu,et al.  GA Strategy for Variable Selection in QSAR Studies: GA-Based PLS Analysis of Calcium Channel Antagonists , 1997, J. Chem. Inf. Comput. Sci..

[123]  R. Leardi,et al.  Genetic algorithms applied to feature selection in PLS regression: how and when to use them , 1998 .

[124]  Srikanta Sen,et al.  Predicting hERG activities of compounds from their 3D structures: development and evaluation of a global descriptors based QSAR model. , 2011, European journal of medicinal chemistry.

[125]  D. Flower,et al.  Toward the quantitative prediction of T-cell epitopes: coMFA and coMSIA studies of peptides with affinity for the class I MHC molecule HLA-A*0201. , 2001, Journal of medicinal chemistry.

[126]  A. Nongonierma,et al.  Improved short peptide identification using HILIC-MS/MS: retention time prediction model based on the impact of amino acid position in the peptide sequence. , 2015, Food chemistry.

[127]  Alexander A. Zamyatnin,et al.  The EROP-Moscow oligopeptide database , 2005, Nucleic Acids Res..

[128]  M. Shahlaei Descriptor selection methods in quantitative structure-activity relationship studies: a review study. , 2013, Chemical reviews.

[129]  Nora Khaldi,et al.  Bioinformatics: Current perspectives and future directions for food and nutritional research facilitated by a Food-Wiki database , 2013 .

[130]  Zhiliang Li,et al.  Factor Analysis Scale of Generalized Amino Acid Information as the Source of a New Set of Descriptors for Elucidating the Structure and Activity Relationships of Cationic Antimicrobial Peptides , 2007 .

[131]  Pierre Baldi,et al.  Influence Relevance Voting: An Accurate And Interpretable Virtual High Throughput Screening Method , 2009, J. Chem. Inf. Model..

[132]  David J. Livingstone,et al.  The Use of Artificial Neural Networks in QSAR , 1992 .

[133]  Gajendra P. S. Raghava,et al.  AHTPDB: a comprehensive platform for analysis and presentation of antihypertensive peptides , 2014, Nucleic Acids Res..

[134]  Roman Kaliszan,et al.  Quantitative structure-chromatographic retention relationships , 1987 .