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Structure-based prediction of protein–protein binding affinity with consideration of allosteric effect

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Abstract

The conformational change upon protein–protein binding is largely ignored for a long time in the affinity prediction community. However, it is widely recognized that allosteric effect does play an important role in biomolecular recognition and association. In this article, we describe a new quantitative structure–activity relationship (QSAR)-based strategy to capture the structural and nonbonding information relating to not only the direct noncovalent interactions between protein binding partners, but also the indirect allosteric effect associated with binding. This method is then employed to quantitatively model and predict the protein–protein binding affinities compiled in a recently published benchmark consisting of 144 functionally diverse protein complexes with their structures available in both bound and unbound states (Kastritis et al. Protein Sci 20:482–491, 2011). With incorporating genetic algorithm and partial least squares regression (GA-PLS) into this method, a significant linear relationship between structural information descriptors and experimentally measured affinities is readily emerged and, on this basis, detailed discussions of physicochemical properties and structural implications underlying protein binding process, as well as the contribution of allosteric effect to the binding are addressed. We also give an empirical estimation of the prediction limit r 2pred  = 0.80 for structure-based method used to determine protein–protein binding affinity.

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References

  • Acharya KR, Lloyd MD (2005) The advantages and limitations of protein crystal structures. Trends Pharm Sci 26:10–14

    Article  PubMed  CAS  Google Scholar 

  • Audie J, Scarlata S (2007) A novel empirical free energy function that explains and predicts protein–protein binding affinities. Biophys Chem 129:198–211

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • Blobel J, Bernadó P, Svergun DI, Tauler R, Pons M (2009) Low-resolution structures of transient protein–protein complexes using small-angle X-ray scattering. J Am Chem Soc 131:4378–4386

    Article  PubMed  CAS  Google Scholar 

  • Bogan AA, Thorn KS (1998) Anatomy of hot spots in protein interfaces. J Mol Biol 1998(280):1–9

    Article  Google Scholar 

  • Bondi A (1964) van der Waals volumes and radii. J Phys Chem 68:441–451

    Google Scholar 

  • Boobbyer DNA, Goodford PJ, McWhinnie PM, Wade RC (1989) New hydrogen-bond potentials for use in determining energetically favorable binding sites on molecules of known structure. J Med Chem 32:1083–1094

    Article  PubMed  CAS  Google Scholar 

  • Brandsdal BO, Smalås AO (2000) Evaluation of protein–protein association energies by free energy perturbation calculations. Protein Eng 13:239–245

    Article  PubMed  CAS  Google Scholar 

  • Cho SJ, Hermsmeier MA (2002) Genetic algorithm guided selection: variable selection and subset selection. J Chem Inf Comput Sci 42:927–936

    Article  PubMed  CAS  Google Scholar 

  • Clackson T, Wells JA (1995) A hot spot of binding energy in a hormone–receptor interface. Science 267:383–386

    Article  PubMed  CAS  Google Scholar 

  • Cole DJ, Skylaris CK, Rajendra E, Venkitaraman AR, Payne MC (2010) Protein–protein interactions from linear-scaling first-principles quantum-mechanical calculations. Europhys Lett 91:37004

    Article  Google Scholar 

  • Conte LL, Chothia C, Janin L (1999) The atomic structure of protein–protein recognition sites. J Mol Biol 285:2177–2198

    Article  PubMed  Google Scholar 

  • Cramer RD III, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967

    Article  PubMed  CAS  Google Scholar 

  • Du QS, Huang RB, Chou KC (2008) Recent advances in QSAR and their applications in predicting the activities of chemical molecules, peptides and proteins for drug design. Curr Protein Pept Sci 9:248–259

    Article  PubMed  CAS  Google Scholar 

  • Duan Y, Wu C, Chowdhury S, Lee MC, Xiong G, Zhang W, Yang R, Cieplak P, Luo R, Lee T, Caldwell J, Wang J, Kollman P (2003) A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J Comput Chem 24:1999–2012

    Article  PubMed  CAS  Google Scholar 

  • Eisenberg D, McLachlan AD (1986) Solvation energy in protein folding and binding. Nature 319:199–203

    Article  PubMed  CAS  Google Scholar 

  • Fields BA, Bartsch HH, Bartunik HD, Cordes F, Guss JM, Freeman HC (1994) Accuracy and precision in protein crystal structure analysis: two independent refinements of the structure of poplar plastocyanin at 173 K. Acta Crystallogr D 50:709–730

    Article  PubMed  CAS  Google Scholar 

  • Ford RC (1987) Investigation of highly stable Photosystem I chlorophyll–protein complexes from the thermophilic cyanobacterium Phormidium laminosum. Biochim Biophys Acta 893:115–125

    Article  CAS  Google Scholar 

  • Fry DC (2006) Fry protein–protein interactions as targets for small molecule drug discovery. Biopolymers (Pep Sci) 84:535–552

    Article  CAS  Google Scholar 

  • Gandhi NS, Mancera RL (2009) Free energy calculations of glycosaminoglycan–protein interactions. Glycobiology 19:1103–1115

    Article  PubMed  CAS  Google Scholar 

  • Golbraikh A, Tropsha A (2002) Beware of q 2! J Mol Graph Model 20:269–276

    Article  PubMed  CAS  Google Scholar 

  • Hartmann-Petersen R, Gordon C (2005) Quantifying protein–protein interactions in the ubiquitin pathway by surface plasmon resonance. Methods Enzymol 399:164–177

    Article  PubMed  CAS  Google Scholar 

  • Horton N, Lewis M (1992) Calculation of the free energy of association for protein complexes. Protein Sci 1:169–181

    Article  PubMed  CAS  Google Scholar 

  • Jenssen H, Fjell CD, Cherkasov A, Hancock RE (2008) QSAR modeling and computer-aided design of antimicrobial peptides. J Pept Sci 14:110–114

    Article  PubMed  CAS  Google Scholar 

  • Jiang L, Gao Y, Mao F, Liu Z, Lai L (2002) Potential of mean force for protein–protein interaction studies. Proteins 46:190–196

    Article  PubMed  CAS  Google Scholar 

  • Jones S, Thornton JM (1996) Principles of protein–protein interactions. Proc Natl Acad Sci USA 93:13–20

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • Kini RM, Evans HJ (1996) Prediction of potential protein–protein interaction sites from amino acid sequence. Identification of a fibrin polymerization site. FEBS Lett 385:81–86

    Article  PubMed  CAS  Google Scholar 

  • Krivov GG, Shapovalov MV, Dunbrack RL Jr (2009) Improved prediction of protein side-chain conformations with SCWRL4. Proteins 77:778–795

    Article  PubMed  CAS  Google Scholar 

  • Leavitt S, Freire E (2001) Direct measurement of protein binding energetics by isothermal titration calorimetry. Curr Opin Struct Biol 11:560–566

    Article  PubMed  CAS  Google Scholar 

  • Lim JH, Bustin M, Ogryzko VV, Postnikov YV (2002) Metastable macromolecular complexes containing high mobility group nucleosome-binding chromosomal proteins in HeLa nuclei. J Biol Chem 277:20774–20782

    Article  PubMed  CAS  Google Scholar 

  • Ma XH, Wang CX, Li CH, Chen WZ (2002) A fast empirical approach to binding free energy calculations based on protein interface information. Protein Eng 15:677–681

    Article  PubMed  CAS  Google Scholar 

  • Madden DR (1995) The three-dimensional structure of peptide–MHC complexes. Annu Rev Immunol 13:587–622

    Article  PubMed  CAS  Google Scholar 

  • McDonald IK, Thornton JM (1994) Satisfying hydrogen bonding potential in proteins. J Mol Biol 238:777–793

    Article  PubMed  CAS  Google Scholar 

  • Neduva V, Linding R, Su Angrand I, Stark A, de Masi F, Gibson TJ, Lewis J, Serrano L, Russell RB (2005) Systematic discovery of new recognition peptides mediating protein interaction networks. PLoS Biol 3:e405

    Article  PubMed  Google Scholar 

  • Otlewski J, Apostoluk W (1997) Structural and energetic aspects of protein–protein recognition. Acta Biochim Pol 44:367–387

    PubMed  CAS  Google Scholar 

  • Petsalaki E, Russell RB (2008) Peptide mediated interactions in biological systems: new discoveries and applications. Curr Opin Biotechnol 19:344–350

    Article  PubMed  CAS  Google Scholar 

  • Pripp AH, Ardö Y (2007) QSAR modeling and computer-aided design of antimicrobial peptides. 102:880–888

  • Pripp AH, Isaksson T, Stepaniak L, Sørhaug T, Ardo Y (2005) Quantitative structure activity relationship modelling of peptides and proteins as a tool in food science. Trends Food Sci Tech 16:484–494

    Article  CAS  Google Scholar 

  • Ren Y, Chen G, Hu Z, Chen X, Yan B (2008) Applying novel three-dimensional holographic vector of atomic interaction field to QSAR studies of artemisinin derivatives. QSAR Comb Sci 27:198–207

    Article  CAS  Google Scholar 

  • Sanner MF, Olson AJ, Spehner JC (1996) Reduced surface: an efficient way to compute molecular surfaces. Biopolymers 38:305–320

    Article  PubMed  CAS  Google Scholar 

  • Shen J, Zhang J, Luo X, Zhu W, Yu K, Chen K, Li Y, Jiang H (2007) Predicting protein–protein interactions based only on sequences information. Proc Natl Acad Sci USA 104:4337–4341

    Article  PubMed  CAS  Google Scholar 

  • Shi L, Chen B, Wang Z, Elias DA, Mayer MU, Gorby YA, Ni S, Lower BH, Kennedy DW, Wunschel DS, Mottaz HM, Marshall MJ, Hill EA, Beliaev AS, Zachara JM, Fredrickson JK, Squier TC (2006) Isolation of a high-affinity functional protein complex between OmcA and MtrC: two outer membrane decaheme c-type cytochromes of Shewanella oneidensis MR-1. J Bacteriol 18:4705–4714

    Article  Google Scholar 

  • Song X, Zhao X (2005) The van der Waals interaction between protein molecules in an electrolyte solution. J Chem Phys 120:2005–2009

    Article  Google Scholar 

  • Stein A, Rueda M, Panjkovich A, Orozco M, Aloy P (2011) A systematic study of the energetics involved in structural changes upon association and connectivity in protein interaction networks. Structure 19:881–889

    Article  PubMed  CAS  Google Scholar 

  • Stevens J, Blixt O, Tumpey TM, Taubenberger JK, Paulson JC, Wilson IA (2006) Structure and receptor specificity of the hemagglutinin from an H5N1 influenza 312:404–410

  • Strynadka NC, Eisenstein M, Katchalski-Katzir E, Shoichet BK, Kuntz ID, Abagyan R, Totrov M, Janin J, Cherfils J, Zimmerman F, Olson A, Duncan B, Rao M, Jackson R, Sternberg M, James MN (1996) Molecular docking programs successfully predict the binding of a β-lactamase inhibitory protein to TEM-1 β-lactamase. Nat Struct Biol 3:233–239

    Article  PubMed  CAS  Google Scholar 

  • Su Y, Zhou A, Xia X, Li W, Sun Z (2009) Quantitative prediction of protein–protein binding affinity with a potential of mean force considering volume correction. Protein Sci 18:2550–2558

    Article  PubMed  CAS  Google Scholar 

  • Sun J, Cai S, Yan N, Mei H (2010) Docking and 3D-QSAR studies of influenza neuraminidase inhibitors using three-dimensional holographic vector of atomic interaction field analysis. Eur J Med Chem 45:1008–1014

    Article  PubMed  CAS  Google Scholar 

  • Tang C, Iwahara J, Clore GM (2006) Visualization of transient encounter complexes in protein–protein association. Nature 444:383–386

    Article  PubMed  CAS  Google Scholar 

  • Tian F, Zhou P, Lv F, Song R, Li Z (2007) Three-dimensional holograph vector of atomic interaction field (3D-HoVAIF): a novel rotation-translation invariant 3D structure descriptor and its applications to peptides. J Pept Sci 13:549–566

    Article  PubMed  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • Tian F, Yang L, Lv F, Luo X, Pan Y (2011) Why OppA protein can bind sequence-independent peptides? A combination of QM/MM, PB/SA, and structure-based QSAR analyses. Amino Acids 40:493–503

    Article  PubMed  CAS  Google Scholar 

  • Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77

    Article  CAS  Google Scholar 

  • Tsai CJ, Nussinov R (1997) Hydrophobic folding units at protein–protein interfaces: Implication to protein folding and to protein–protein association. Protein Sci 6:1426–1437

    Article  PubMed  CAS  Google Scholar 

  • Tsai CJ, Lin SL, Wolfson HJ, Nussinov R (1997) Studies of protein–protein interfaces: a statistical analysis of the hydrophobic effect. Protein Sci 6:53–64

    Article  PubMed  CAS  Google Scholar 

  • Vanhee P, Stricher F, Baeten L, Verschueren E, Lenaerts T, Serrano L, Rousseau F, Schymkowitz J (2009) Protein–peptide interactions adopt the same structural motifs as monomeric protein folds. Structure 17:1128–1136

    Article  PubMed  CAS  Google Scholar 

  • Wlodawer A, Miller M, Jaskolski M, Sathyanarayana BK, Baldwin E, Weber IT, Selk LM, Clawson L, Schneider J, Kent SB (1989) Conserved folding in retroviral proteases: crystal structure of a synthetic HIV-1 protease. Science 245:616–621

    Article  PubMed  CAS  Google Scholar 

  • Wold S, Sjöström M, Eriksson L (2001) PLS regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58:109–130

    Article  CAS  Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • Xu QS, Liang YZ (2001) Monte Carlo cross validation. Chemometr Intel Lab Syst 56:1–11

    Article  CAS  Google Scholar 

  • Xu D, Tsai CJ, Nussinov R (1997) Hydrogen bonds and salt bridges across protein–protein interfaces. Protein Eng 10:999–1012

    Google Scholar 

  • 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

    Article  PubMed  CAS  Google Scholar 

  • Zhao C, Zhang H, Luan F, Zhang R, Liu M, Hu Z, Fan B (2007) QSAR method for prediction of protein–peptide binding affinity: application to MHC class I molecule HLA-A*0201. J Mol Graph Model 26:246–254

    Article  PubMed  CAS  Google Scholar 

  • Zhou HX, Shan Y (2001) Prediction of protein interaction sites from sequence profile and residue neighbor list. Proteins 44:336–343

    Article  PubMed  CAS  Google Scholar 

  • Zhou P, Tian F, Li Z (2007a) Three dimensional holographic vector of atomic interaction field (3D-HoVAIF). Chemometr Intel Lab Syst 87:114–120

    Article  Google Scholar 

  • Zhou P, Tian F, Li Z (2007b) A structure-based, quantitative structure–activity relationship approach for predicting HLA-A*0201-restricted cytotoxic T lymphocyte epitopes. Chem Biol Drug Des 69:56–67

    Article  PubMed  CAS  Google Scholar 

  • Zhou P, Tian F, Shang Z (2009a) 2D depiction of nonbonding interactions for protein complexes. J Comput Chem 30:940–951

    Article  PubMed  CAS  Google Scholar 

  • Zhou P, Tian F, Lv F, Shang Z (2009b) 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

    Article  PubMed  CAS  Google Scholar 

  • Zhou P, Zou J, Tian F, Shang Z (2009c) Fluorine bonding—how does it work in protein–ligand interactions? J Chem Inf Model 49:2344–2355

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

The authors would like to express their gratitude to the anonymous reviewers and the editors for their professional, intensive, and useful comments. This work was supported by the Innovation and Attracting Talents Program for College and University (‘111’ Project) (Grant No. B06023), the National Natural Science Foundation of China (Grant Nos. 11032012 and 30870608), the Key Science and Technology Program of Chongqing CSTC (Grant No. 2009AA5045), and the Sharing Fund of Chongqing university’s Large-Scale Equipment and the Visiting Scholar Foundation of Key Laboratory of Biorheological Science and Technology Under Ministry of Education in Chongqing University.

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The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Key Laboratory of Biorheological Science and Technology Under Ministry of Education, ‘111’ Project Laboratory of Biomechanics and Tissue Repair, and Bioengineering College, Chongqing University, Chongqing, 400044, China

    Feifei Tian, Yonggang Lv & Li Yang

  2. School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Feifei Tian

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  1. Feifei Tian
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  2. Yonggang Lv
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  3. Li Yang
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Correspondence to Li Yang.

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F. Tian and Y. Lv contributed equally to this work.

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Tian, F., Lv, Y. & Yang, L. Structure-based prediction of protein–protein binding affinity with consideration of allosteric effect. Amino Acids 43, 531–543 (2012). https://doi.org/10.1007/s00726-011-1101-1

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