Conformational changes frequently occur when proteins interact with other proteins. How to detect such changes in silico is a major problem. Existing methods for docking with conformational changes remain time-consuming, and they solve only a small portion of protein complexes accurately. This work presents a more accurate method (FlexDoBi) for docking with conformational changes. FlexDoBi generates the possible conformational changes of the interface residues that transform the proteins from their unbound states to bound states. Based on the generated conformational changes, multidimensional scaling is performed to construct candidates for the bound structure. We develop a new energy item for determining the orientation of docking subunits and selecting of plausible conformational changes. Experimental results illustrate that FlexDoBi achieves better results. On 20 complexes, we obtained an average iRMSD of 1.55Å, which compares favorably with the average iRMSD of 1.94Å for FiberDock. Compared to ZDOCK, our results are of 0.27Å less in average iRMSD of the medium difficulty group.
Get full access to this article
View all access options for this article.
References
1.
AlcaroS., GasparriniF., IncaniO.et al.2007. “Quasi flexible” automatic docking processing for studying stereoselective recognition mechanisms, part 2: Prediction of deltadeltag of complexation and 1h-nmr noe correlation. Journal of Computational Chemistry, 28:1119–1128.
2.
Al-KhayyalF.1990. Jointly constrained bilinear programs and related problems: an overview. Computers and Mathematics with Applications, 19:53–62.
3.
BradfordJ.R., WestheadD.R.2005. Improved prediction of protein-protein binding sites using a support vector machines approach. Bioinformatics, 21:1487–1494.
4.
BrownJ.B., BahadurD., TomitaE., AkutsuT.2006. Multiple methods for protein side chain packing using maximum weight cliques. Genome Informatics, 3:191–200.
5.
ChenR., LiL., WengZ.2003. Zdock: an initial-stage protein-docking algorithm. Proteins, 52:80–87.
6.
de LeeuwJ.1977. Applications of convex analysis to multidimensional scaling. Recent Developments in Statistics, 133–146. North Holland Publishing Company: Amsterdam.
7.
DominguezC., BoelensR., BonvinA.M.J.J. 2003. Haddock: a protein-protein docking approach based on biochemical or biophysical information. Journal of the American Chemical Society, 125:1731–1737.
8.
EswarN., Marti-RenomM.A., WebbB.et al.2006. Comparative protein structure modeling with MODELLER. Current Protocols in BioinformaticsSupp:15.
9.
Fernández-RecioJ., TotrovM., AbagyanR.2004. Identification of protein-protein interaction sites from docking energy landscapes. Journal of Molecular Biology, 335:843–865.
10.
GuoF., LiS.C., WangL.2012. P-binder: A system for the protein-protein binding sites identification. ISBRA, Lecture Notes in Computer Science, 7292:127–138.
11.
HeifetzA., Katchalski-KatzirE., EisensteinM.2002. Electrostatics in protein-protein docking. Protein Science, 11:571–587.
12.
HoltbyD., LiS.C., LiM.2012. Loopweaver-loop modeling by the weighted scaling of verified proteins. RECOMB, Lecture Notes in Computer Science, 7262:113–126.
KabschW., SanderC.1983. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 22:2577–2637.
15.
KoncJ., JanežičD.2010. Probis algorithm for detection of structurally similar protein binding sites by local structural alignment. Bioinformatics, 26:1160–1168.
16.
KrivovG.G., ShapovalovM.V., DunbrackR.L.2009. Improved prediction of protein side-chain conformations with scwrl4. Proteins, 77:778–795.
17.
LindahlE., HessB., SpoelD.2001. Gromacs 3.0: a package for molecular simulation and trajectory analysis. Journal of Molecular Modeling, 7:306–317.
NeuvirthH., RazR., SchreiberG.2004. Promate: a structure based prediction program to identify the location of protein-protein binding sites. Journal of Molecular Biology, 338:181–199.
22.
Schneidman-DuhovnyD., InbarY., NussinovR., WolfsonH.J.2005. Geometry-based flexible and symmetric protein docking. Proteins, 60:224–231.
23.
Schneidman-DuhovnyD., NussinovR., WolfsonH.2007. Automatic prediction of protein interactions with large scale motion. Proteins, 69:764–773.
24.
Shulman-PelegA., NussinovR., WolfsonH.J.2005. Siteengines: recognition and comparison of binding sites and protein-protein interfaces. Nucleic Acids Research, 1:W337–W341.
25.
WangG., DunbrackR.L.2003. Pisces: a protein sequence culling server. Bioinformatics, 19:1589–1591.
26.
XuJ., BergerB.2006. Fast and accurate algorithms for protein side-chain packing. Journal of the ACM, 53:533–557.
27.
YangY., ZhouY.2008. Specific interactions for ab initio folding of protein terminal regions with secondary structures. Proteins, 72:793–803.
28.
ZhangC.1998. Extracting contact energies from protein structures: A study using a simplified model. Proteins, 31:299–308.
29.
ZhangC., VasmatzisG., CornetteJ.L., DeLisiC.1997. Determination of atomic desolvation energies from the structures of crystallized protein. Journal of Molecular Biology, 267:707–726.
30.
ZhangJ., WangQ., BarzB.et al.2010. Mufold: A new solution for protein 3d structure prediction. Proteins, 78:1137–1152.
