Biomacromolecular quantitative structure-activity relationship (BioQSAR): A proof-of-concept study on the modeling, prediction and interpretation of protein-protein binding affinity

Journal of Computer-Aided Molecular Design

Volumn 27, Issue 1, 2013, Pages 67-78

  Zhou, Peng ^a   Wang, Congcong ^a   Tian, Feifei ^b   Ren, Yanrong ^c   Yang, Chao ^a   Huang, Jian ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b SOUTHWEST JIAOTONG UNIVERSITY   (China)

^c CHONGQING UNIVERSITY OF EDUCATION   (China)

Author keywords

Affinity prediction; Biomacromolecular quantitative structure activity relationship; Protein protein interaction; Regression modeling

Indexed keywords

BINDING ENERGY; BIOCHEMISTRY; COMPUTATIONAL CHEMISTRY; KNOWLEDGE BASED SYSTEMS; NUCLEIC ACIDS; PROTEINS;

AFFINITY PREDICTION; BIOMACROMOLECULAR QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; BIOMACROMOLECULE; CONCEPT STUDIES; MODEL INTERPRETATIONS; MODEL PREDICTION; PROOF OF CONCEPT; PROTEIN-PROTEIN INTERACTIONS; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; REGRESSION MODELLING;

FORECASTING;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; BINDING AFFINITY; BIOMACROMOLECULAR QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; CONTROLLED STUDY; INTERMETHOD COMPARISON; PREDICTION; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REGRESSION ANALYSIS; STATISTICAL MODEL;

EID: 84874112905     PISSN: 0920654X     EISSN: 15734951     Source Type: Journal    
DOI: 10.1007/s10822-012-9625-3     Document Type: Article

References (78)

1. Hansch C, Fujita T (1964) ρ-σ-π analysis. A method for correlation of biological activity and chemical structure. J Am Chem Soc 86:1616-1626
2. Katritzky AR, Lobanov VS, Karelson M (1995) QSPR: the correlation and quantitative prediction of chemical and physical properties from structure. Chem Soc Rev 24:279-287
3. Siraki AG, Chevaldina T, Moridani MY, O'Brien PJ (2004) Quantitative structure-toxicity relationships by accelerated cytotoxicity mechanism screening. Curr Opin Drug Discov Devel 7:118-125
4. Mozrzymas A, Rózycka-Roszak B (2010) Prediction of critical micelle concentration of nonionic surfactants by a quantitative structure-property relationship. Comb Chem High Throughput Screen 13:39-44
5. Fourches D, Pu D, Tassa C, Weissleder R, Shaw SY, Mumper RJ, Tropsha A (2010) Quantitative nanostructure-activity relationship modeling. ACS Nano 4:5703-5712
6. Natesan S, Wang T, Lukacova V, Bartus V, Khandelwal A, Subramaniam R, Balaz S (2012) Cellular quantitative structure-activity relationship (Cell-QSAR): conceptual dissection of receptor binding and intracellular disposition in antifilarial activities of Selwood antimycins. J Med Chem 55:3699-3712
7. Martin E, Mukherjee P, Sullivan D, Jansen J (2011) Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity. J Chem Inf Model 51:1942-1956
8. Winkler DA (2002) The role of quantitative structure-activity relationships (QSAR) in biomolecular discovery. Brief. Bioinform. 3:73-86
9. Zhou P, Tian F, Wu Y, Li Z, Shang Z (2008) Quantitative sequence-activity model (QSAM): applying QSAR strategy to model and predict bioactivity and function of peptides, proteins and nucleic acids. Curr Comput Aided Drug Des 4:311-321
10. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235-242
11. Concu R, Podda G, González-Díaz H (2009) In quantitative structure-property relationships from bio-molecular to social networks. Nova Science Publisher, New York
12. González-Díaz H, Vilar S, Santana L, Uriarte E (2007) Medicinal chemistry and bioinformatics - current trends in drugs discovery with networks topological indices. Curr Top Med Chem 7:1025-1039
13. González-Díaz H, Prado-Prado F, Perez-Montoto LG, Duardo-Sanchez A, Lopez-Diaz A (2009) QSAR models for proteins of parasitic organisms, plants and human guests: theory, applications, legal protection, taxes, and regulatory issues. Curr Proteomics 6:214-227
14. Munteanu CR, González-Díaz H, Magalhaes AL (2008) Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices. J Theor Biol 254:476-482
15. González-Díaz H, Agüero-Chapin G, Varona J, Molina R, Delogu G, Santana L, Uriarte E, Gianni P (2007) 2D-RNAcoupling numbers: a new computational chemistry approach to link secondary structure topology with biological function. J Comput Chem 28:1049-1056
16. Munteanu CR, Vázquez JM, Dorado J, Pazos-Sierra A, Sánchez-González A, Prado-Prado FJ, González-Díaz H (2009) Complex network spectral moments for ATCUN motif DNA cleavage: first predictive study on proteins of human pathogen parasites. J Proteome Res 8:5219-5228
17. Neuvirth H, Raz R, Schreiber G (2004) ProMate: a structure based prediction program to identify the location of protein-protein binding sites. J Mol Biol 338:181-199
18. Tian F, Lv Y, Yang L (2012) Structure-based prediction of protein-protein binding affinity with consideration of allosteric effect. Amino Acids 43:531-543
19. Heuser P, Schomburg D (2007) Combination of scoring schemes for protein docking. BMC Bioinformatics 8:279
20. Kastritis PL, Bonvin AM (2010) Are scoring functions in protein-protein docking ready to predict interactomes? Clues from a novel binding affinity benchmark. J Proteome Res 9:2216-2225
21. Kastritis PL, Moal IH, Hwang H, Weng Z, Bates PA, Bonvin AM, Janin J (2011) A structure-based benchmark for protein-protein binding affinity. Protein Sci 20:482-491
22. Park C, Marqusee S (2004) Analysis of the stability of multimeric proteins by effective ΔG and effective m-values. Protein Sci 13:2553-2558
23. a values. Proteins 61:704-721
24. Word JM, Lovell SC, Richardson JS, Richardson DC (1999) Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J Mol Biol 285:1735-1747
25. Krivov GG, Shapovalov MV, Dunbrack RL (2009) Improved prediction of protein side-chain conformations with SCWRL4. Proteins 77:778-795
26. Zhou P, Zou J, Tian F, Shang Z (2009) Fluorine bonding: how does it work in protein-ligand interactions? J Chem Inf Model 49:2344-2355
27. Tian F, Lv Y, Zhou P, Yang L (2011) Characterization of PDZ domain-peptide interactions using an integrated protocol of QM/MM, PB/SA, and CFEA analyses. J Comput Aided Mol Des 25:947-958
28. Zhou P, Tian F, Ren Y, Shang Z (2010) Systematic classification and analysis of themes in protein-DNA recognition. J Chem Inf Model 50:1476-1488
29. Siggers TW, Silkov A, Honig B (2005) Structural alignment of protein-DNA interfaces: insights into the determinants of binding specificity. J Mol Biol 345:1027-1045
30. McDonald IK, Thornton JM (1994) Satisfying hydrogen bonding potential in proteins. J Mol Biol 238:777-793
31. Word JM, Lovell SC, LaBean TH, Taylor HC, Zalis ME, Presley BK, Richardson JS, Richardson DC (1999) Visualizing and quantifying molecular goodness-of-fit: small-probe contact dots with explicit hydrogen atoms. J Mol Biol 285:1711-1733
32. Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometr Intel Lab Syst 58:109-130
33. Stanton DT (2012) QSAR and QSPR model interpretation using partial least squares (PLS) analysis. Curr Comput Aided Drug Des 8:107-127
34. Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:273-293
35. Zhou P, Xiang C, Wu Y, Shang Z (2010) Gaussian process: an alternative approach for QSAM modeling of peptides. Amino Acids 38:199-212
36. Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, Cambridge
37. Ren Y, Chen X, Feng M, Wang Q, Zhou P (2011) Gaussian process: a promising approach for the modeling and prediction of peptide binding affinity to MHC proteins. Protein Pept Lett 18:670-678
38. Obrezanova O, Csanyi G, Gola JMR, Segall MD (2007) Gaussian processes: a method for automatic QSAR modeling of ADME properties. J Chem Inf Model 47:1847-1857
39. Wolfe P (1969) Convergence conditions for ascent methods. SIAM Rev 11:226-235
40. Zhou P, Tian F, Lv F, Shang Z (2009) 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. J Chromatogr A 1216:3107-3116
41. Cho SJ, Hermsmeier MA (2002) Genetic algorithm guided selection: variable selection and subset selection. J Chem Inf Comput Sci 42:927-936
42. Zhou P, Tian F, Chen X, Shang Z (2008) Modeling and prediction of binding affinities between the human amphiphysin SH3 domain and its peptide ligands using genetic algorithm-Gaussian processes. Biopolymers (Pept Sci) 90:792-802
43. Tian F, Yang L, Lv F, Yang Q, Zhou P (2009) In silico quantitative prediction of peptides binding affinity to human MHC molecule: an intuitive quantitative structure-activity relationship approach. Amino Acids 36:535-554
44. 2! J Mol Graph Model 20:269-276
45. Baroni M, Clementi S, Cruciani G, Kettaneh-Wold N, Wold S (1993) D-optimal designs in QSAR. Quant Struct Act Relat 12:225-231
46. Filzmoser P, Liebmann B, Varmuza K (2009) Repeated double cross validation. J Chemometr 23:160-171
47. Tian F, Zhang C, Fan X, Yang X, Wang X, Liang H (2010) Predicting the flexibility profile of ribosomal RNAs. Mol Inf 29:707-715
48. Ren Y, Wu B, Pan Y, Lv F, Kong X, Luo X, Li Y, Yang Q (2011) Characterization of the binding profile of peptide to transporter associated with antigen processing (TAP) using Gaussian process regression. Comput Biol Med 41:865-870
49. He P, Wu W, Wang HD, Yang K, Liao KL, Zhang W (2010) Toward quantitative characterization of the binding profile between the human amphiphysin-1 SH3 domain and its peptide ligands. Amino Acids 38:1209-1218
50. Acharya KR, Lloyd MD (2005) The advantages and limitations of protein crystal structures. Trends Pharm Sci 26:10-14
51. Dominguez C, Boelens R, Bonvin AM (2003) HADDOCK: a proteinprotein docking approach based on biochemical or biophysical information. J Am Chem Soc 125:1731-1737
52. Chen R, Li L, Weng Z (2003) ZDOCK: an initial-stage protein-docking algorithm. Proteins 52:80-87
53. Zhang C, Liu S, Zhu Q, Zhou Y (2005) A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes. J Med Chem 48:2325-2335
54. Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118:11225-11236
55. Ponder JW, Richards FM (1987) An efficient newton-like method for molecular mechanics energy minimization of large molecules. J Comput Chem 8:1016-1024
56. Qiu D, Shenkin PS, Hollinger FP, Still WC (1997) The GB/SA continuum model for solvation. A fast analytical method for the calculation of approximate Born radii. J Phys Chem 101:3005-3014
57. Almlöf M, Brandsdal BO, Aqvist J (2004) Binding affinity prediction with different force fields: examination of the linear interaction energy method. J Comput Chem 25:1242-1254
58. Khoruzhii O, Donchev AG, Galkin N, Illarionov A, Olevanov M, Ozrin V, Queen C, Tarasov V (2008) Application of a polarizable force field to calculations of relative protein-ligand binding affinities. Proc Natl Acad Sci USA 105:10378-10383
59. Liu S, Zhang C, Zhou H, Zhou Y (2004) A physical reference state unifies the structure-derived potential of mean force for protein folding and binding. Proteins 56:93-101
60. Zhou H, Zhou Y (2002) Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci 11:2714-2726
61. Biela A, Sielaff F, Terwesten F, Heine A, Steinmetzer T, Klebe G (2006) Ligand binding stepwise disrupts water network in thrombin: enthalpic and entropic changes reveal classical hydrophobic effect. J Med Chem 55:6094-6110
62. Freire E (2009) ITC: affinity is not everything. Eur Pharm Rev 14:44-47
63. Moreira IS, Fernandes PA, Ramos MJ (2007) Hot spots: a review of the protein-protein interface determinant amino-acid residues. Proteins 68:803-812
64. Kortemme T, Baker D (2002) A simple physical model for binding energy hot spots in protein-protein complexes. Proc Natl Acad Sci USA 99:14116-14121
65. Ofran Y, Rost B (2007) Protein-protein interaction hotspots carved into sequences. PLoS Comput Biol 3:e119
66. Xu D, Tsai CJ, Nussinov R (1997) Hydrogen bonds and salt bridges across protein-protein interfaces. Protein Eng 10:999-1012
67. Lo Conte L, Chothia C, Janin J (1999) The atomic structure of protein-protein recognition sites. J Mol Biol 285:2177-2198
68. Petsalaki E, Russell RB (2008) Peptide-mediated interactions in biological systems: new discoveries and applications. Curr. Opin. Biotech. 19:344-350
69. Tsai CJ, Nussinov R (1997) Hydrophobic folding units at protein-protein interfaces: implications to protein folding and to protein-protein association. Protein Sci 6:1426-1437
70. Young L, Jernigan RL, Covell DG (1994) A role for surface hydrophobicity in protein-protein recognition. Protein Sci 3:717-729
71. Tuffery P, Derreumaux P (2012) Flexibility and binding affinity in protein-ligand, protein-protein and multi-component protein interactions: limitations of current computational approaches. J R Soc Interface 9:20-33
72. Burnett JC, Kellogg GE, Abraham DJ (2000) Computational methodology for estimating changes in free energies of biomolecular association upon mutation. The importance of bound water in dimer-tetramer assembly for beta 37 mutant hemoglobins. Biochemistry 39:1622-1633
73. Jiang L, Kuhlman B, Kortemme T, Baker D (2005) A "solvated rotamer" approach to modeling water-mediated hydrogen bonds at protein-protein interfaces. Proteins 58:893-904
74. Kumar S, Nussinov R (2002) Close-range electrostatic interactions in proteins. ChemBioChem 3:604-617
75. Missimer JH, Steinmetz MO, Baron R, Winkler FK, Kammerer RA, Daura X, van Gunsteren WF (2007) Configurational entropy elucidates the role of salt-bridge networks in protein thermostability. Protein Sci 16:1349-1359
76. Kumar S, Wolfson HJ, Nussinov R (2001) Protein flexibility and electrostatic interactions. IBM J Res Dev 45:499-512
77. Marqusee S, Sauer RT (1994) Contributions of a hydrogen bond/salt bridge network to the stability of secondary and tertiary structure in lambda repressor. Protein Sci 3:2217-2225
78. Zhou P, Tian F, Shang Z (2009) 2D depiction of nonbonding interactions for protein complexes. J Comput Chem 30:940-951