What are the ideal properties for functional food peptides with antihypertensive effect? A computational peptidology approach.

[1]  Li Yang,et al.  Integration of QSAR modelling and QM/MM analysis to investigate functional food peptides with antihypertensive activity , 2013 .

[2]  Chao Yang,et al.  Computational peptidology: a new and promising approach to therapeutic peptide design. , 2013, Current medicinal chemistry.

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

[4]  E. Sturrock,et al.  Molecular recognition and regulation of human angiotensin-I converting enzyme (ACE) activity by natural inhibitory peptides , 2012, Scientific Reports.

[5]  B. Miralles,et al.  Antihypertensive peptides from food proteins: a review. , 2012, Food & function.

[6]  P. Temussi The good taste of peptides , 2012, Journal of peptide science : an official publication of the European Peptide Society.

[7]  F. Tian,et al.  Why OppA protein can bind sequence-independent peptides? A combination of QM/MM, PB/SA, and structure-based QSAR analyses , 2011, Amino Acids.

[8]  Roland L. Dunbrack,et al.  proteins STRUCTURE O FUNCTION O BIOINFORMATICS Improved prediction of protein side-chain conformations with SCWRL4 , 2022 .

[9]  F. De Leo,et al.  Angiotensin converting enzyme (ACE) inhibitory peptides: production and implementation of functional food. , 2009, Current pharmaceutical design.

[10]  Peng Zhou,et al.  Fluorine Bonding - How Does It Work In Protein-Ligand Interactions? , 2009, J. Chem. Inf. Model..

[11]  F. Tian,et al.  Comprehensive comparison of eight statistical modelling methods used in quantitative structure-retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome. , 2009, Journal of chromatography. A.

[12]  J. Camp,et al.  Critical evaluation of the use of bioinformatics as a theoretical tool to find high-potential sources of ACE inhibitory peptides , 2009, Peptides.

[13]  F. Tian,et al.  Quantitative Sequence-Activity Model (QSAM): Applying QSAR Strategy to Model and Predict Bioactivity and Function of Peptides, Proteins and Nucleic Acids , 2008 .

[14]  Fang Hong,et al.  The antihypertensive effect of peptides: A novel alternative to drugs? , 2008, Peptides.

[15]  Fenglin Lv,et al.  Three‐dimensional holograph vector of atomic interaction field (3D‐HoVAIF): a novel rotation–translation invariant 3D structure descriptor and its applications to peptides , 2007, Journal of peptide science : an official publication of the European Peptide Society.

[16]  F. Tian,et al.  T-scale as a novel vector of topological descriptors for amino acids and its application in QSARs of peptides , 2007 .

[17]  Jianping Wu,et al.  Quantitative structure‐activity relationship study of bitter di‐ and tri‐peptides including relationship with angiotensin I‐converting enzyme inhibitory activity , 2007, Journal of peptide science : an official publication of the European Peptide Society.

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

[19]  Jianping Wu,et al.  Structural Requirements of Angiotensin I‐Converting Enzyme Inhibitory Peptides: Quantitative Structure‐Activity Relationship Modeling of Peptides Containing 4‐10 Amino Acid Residues , 2006 .

[20]  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.

[21]  Ken Chen,et al.  Prediction of binding affinities between the human amphiphysin-1 SH3 domain and its peptide ligands using homology modeling, molecular dynamics and molecular field analysis. , 2005, Journal of proteome research.

[22]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[23]  W. Verstraete,et al.  A quantitative in silico analysis calculates the angiotensin I converting enzyme (ACE) inhibitory activity in pea and whey protein digests. , 2004, Biochimie.

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

[25]  P. Kollman,et al.  Computational Alanine Scanning To Probe Protein−Protein Interactions: A Novel Approach To Evaluate Binding Free Energies , 1999 .

[26]  K. Morokuma,et al.  ONIOM: A Multilayered Integrated MO + MM Method for Geometry Optimizations and Single Point Energy Predictions. A Test for Diels−Alder Reactions and Pt(P(t-Bu)3)2 + H2 Oxidative Addition , 1996 .

[27]  J M Thornton,et al.  LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. , 1995, Protein engineering.

[28]  H. Chiba,et al.  Peptide inhibitors for angiotensin I-converting enzyme from thermolysin digest of dried bonito. , 1992, Bioscience, biotechnology, and biochemistry.

[29]  N. Nio,et al.  Inhibition of Angiotensin-converting Enzyme by Synthetic Peptides of Human β-Casein , 1989 .

[30]  H. Cheung,et al.  Binding of peptide substrates and inhibitors of angiotensin-converting enzyme. Importance of the COOH-terminal dipeptide sequence. , 1980, The Journal of biological chemistry.

[31]  Jian Huang,et al.  Computational Peptidology , 2015, Methods in Molecular Biology.

[32]  A. Pripp,et al.  Modelling relationship between angiotensin-(I)-converting enzyme inhibition and the bitter taste of peptides , 2007 .

[33]  M. Ferrone,et al.  Computational alanine scanning to probe DNA - Wild type and mutant p53 interactions. , 2003 .

[34]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[35]  J. Silva-Cardoso Os inibidores do sistema Renina-Angiotensina-Aldosterona e a epidemia Covid-19 , 2022, Revista Portuguesa de Cardiologia.

[36]  K. Maehashi,et al.  Bitter peptides and bitter taste receptors , 2009, Cellular and Molecular Life Sciences.