Research Article
An NMA-guided path planning approach for computing large-amplitude conformational changes in proteins
Svetlana Kirillova, Juan Cortés,
Alin Stefaniu, Thierry Siméon,
Corresponding Author
Juan Cortés
LAAS-CNRS, Toulouse, France
LAAS-CNRS, 7 Avenue du Colonel Roche, 31077 Toulouse, France===Search for more papers by this authorSvetlana Kirillova, Juan Cortés,
Alin Stefaniu, Thierry Siméon,
Corresponding Author
Juan Cortés
LAAS-CNRS, Toulouse, France
LAAS-CNRS, 7 Avenue du Colonel Roche, 31077 Toulouse, France===Search for more papers by this authorAbstract
This paper presents a new method for computing macromolecular motions based on the combination of path planning algorithms, originating from robotics research, and elastic network normal mode analysis. The low-frequency normal modes are regarded as the collective degrees of freedom of the molecule. Geometric path planning algorithms are used to explore these collective degrees of freedom in order to find possible large-amplitude conformational changes. To overcome the limits of the harmonic approximation, which is valid in the vicinity of the minimum energy structure, and to get larger conformational changes, normal mode calculations are iterated during the exploration. Initial results show the efficiency of our method, which requires a small number of normal mode calculations to compute large-amplitude conformational transitions in proteins. A detailed analysis is presented for the computed transition between the open and closed structures of adenylate kinase. This transition, important for its biological function, involves large-amplitude domain motions. The obtained motion correlates well with results presented in related works. Proteins 2008. © 2007 Wiley-Liss, Inc.
Supporting Information
| Filename | Description |
|---|---|
| jws-prot.21570.mov13.2 MB | Supporting Information file jws-prot.21570.mov |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
REFERENCES
- 1 Carlson HA. Protein flexibility is an important component of structure-based drug discovery. Curr Pharm Des 2002; 8: 1571–1578.
- 2 Cavasotto CN, Orry AJW, Abagyan RA. The challenge of considering receptor flexibility in ligand docking and virtual screening. Curr Comput Aided Drug Des 2005; 1: 423–440.
- 3 Janin J. Assessing predictions of protein–protein interaction: the CAPRI experiment. Protein Sci 2005; 14: 278–283.
- 4 Ehrlich LP, Nilges M, Wade RC. The impact of protein flexibility on protein–protein docking. Proteins 2005; 58: 126–133.
- 5 Floquet N, Marechal J-D, Badet-Denisot M-A, Robert CH, Dauchez M, Perahia D. Normal mode analysis as a prerequisite for drug design: application to matrix metalloproteinases inhibitors. FEBS Lett 2006; 580: 5130–5136.
- 6 Leach AR. Molecular modeling: principles and applications. Essex: Longman; 1996.
- 7
Schlick T.
Molecular modeling and simulation—an interdisciplinary guide.
New York:
Springer;
2002.
10.1007/978-0-387-22464-0 Google Scholar
- 8 Apaydin MS, Brutlag DL, Guestrin C, Hsu D, Latombe J-C, Varma C. Stochastic roadmap simulation: an efficient representation and algorithm for analyzing molecular motion. J Comput Biol 2003; 10: 257–281.
- 9 Cortés J, Siméon T, Ruiz-deAngulo V, Guieysse D, Remaud-Siméon M, Tran V. A path planning approach for computing large-amplitude motions of flexible molecules. Bioinformatics 2005; 21: i116–i125.
- 10 Cortés J, Siméon T, Remaud-Siméon M, Tran V. Geometric algorithms for the conformational analysis of long protein loops. J Comput Chem 2004; 25: 956–967.
- 11 Enosh A, Fleishman SJ, Ben-Tal N, Halperin D. Prediction and simulation of motion in pairs of transmembrane α-helices. Bioinformatics 2007; 23; e212–e218.
- 12 Cui Q, Bahar I. Normal mode analysis: theory and applications to biological and chemical systems. Boca Raton: CRC Press; 2006.
- 13 Brooks BR, Karplus M. Normal modes for specific motions of macromolecules: application to the hinge-bending mode of lysozyme. Proc Natl Acad Sci USA 1985; 82: 4995–4999.
- 14
Hinsen K.
Analysis of domain motions by approximate normal mode calculations.
Proteins
1998;
33:
417–429.
10.1002/(SICI)1097-0134(19981115)33:3<417::AID-PROT10>3.0.CO;2-8 CAS PubMed Web of Science® Google Scholar
- 15 Tama F, Sanejouand YH. Conformational change of proteins arising from normal mode analysis. Protein Eng 2001; 14: 1–6.
- 16 Alexandrov V, Lehnert U, Echols N, Milburn D, Engelman D, Gerstein M. Normal modes for predicting protein motions: a comprehensive database assessment and associated Web tool. Protein Sci 2005; 14: 633–643.
- 17 Mouawad L, Perahia D. Motions in hemoglobin studied by normal mode analysis and energy minimization: evidence for the existence of tertiary T-like, quaternary R-like intermediate structures. J Mol Biol 1996; 258: 393–410.
- 18 Miyashita O, Onuchic JN, Wolynes PG. Nonlinear elasticity, proteinquakes, and the energy landscapes of functional transitions in proteins. Proc Natl Acad Sci USA 2003; 100: 12570–12575.
- 19 Tama F, Miyashita O, BrooksIIICL. Normal mode based flexible fitting of high-resolution structure into low-resolution experimental data from cryo-EM. J Struct Biol 2004; 147: 315–326.
- 20 Jeong JI, Lattman EE, Chirikjian GS. A method for finding candidate conformations for molecular replacement using relative rotation between domains of a known structure. Acta Cryst 2006; D62; 398–409.
- 21 Tirion MM. Large amplitude elastic motions in proteins from a single-parameter, atomic analysis. Phys Rev Lett 1996; 77: 1905–1908.
- 22 Tama F, BrooksIIICL. The mechanism and pathway of pH induced swelling in cowpea chlorotic mottle virus. J Mol Biol 2002; 318: 733–747.
- 23 Bahar I, Atilgan AR, Erman B. Direct evaluation of thermal fluctuations in proteins using a single-parameter harmonic potential. Fold Des 1997; 2: 173–181.
- 24 Cavasotto CN, Kovacs JA, Abagyan RA. Representing receptor flexibility in ligand docking through relevant normal modes. J Am Chem Soc 2005; 127: 9632–9640.
- 25 Kim MK, Jernigan RL, Chirikjian GS. Efficient generation of feasible pathways for protein conformational transitions. Biophys J 2002; 83: 1620–1630.
- 26 Maragakis P, Karplus M. Large amplitude conformational change in proteins explored with a plastic network model: adenylate kinase. J Mol Biol 2005; 352: 807–822.
- 27 Suhre K, Sanejouand YH. ElNémo: a normal mode web-server for protein movement analysis and the generation of templates for molecular replacement. Nucleic Acids Res 2004; 32: W610–W614.
- 28 Tama F, Gadea FX, Marques O, Sanejouand YH. Building-block approach for determining low-frequency normal modes of macromolecules. Proteins 2000; 41: 1–7.
- 29 Schuyler AD, Chirikjian GS. Efficient determination of low-frequency normal modes of large protein structures by cluster-NMA. J Mol Graph Model 2005; 24: 46–58.
- 30
Latombe J-C.
Robot motion planning.
Boston:
Kluwer Academic Publishers;
1991.
10.1007/978-1-4615-4022-9 Google Scholar
- 31
LaValle SM.
Planning algorithms.
New York:
Cambridge University Press;
2006.
10.1017/CBO9780511546877 Google Scholar
- 32 Amato NM, Dill KA, Song G. Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures. J Comput Biol 2003; 10: 239–255.
- 33 Ruiz de Angulo V, Cortés J, Siméon T. BioCD: an efficient algorithm for self-collision and distance computation between highly articulated molecular models. In: S Thrun, G Sukhatme, S Schaal, O Brock, editors. Robotics: science and systems I. Cambridge: MIT Press; 2005. p 6–11.
- 34 LaValle SM, Kuffner JJ. Rapidly-exploring random trees: progress and prospects. In: BR Donald, KM Lynch, D Rus, editors. Algorithmic and computational robotics: new directions (WAFR2000). Boston: AK Peters; 2001. p 293–308.
- 35 Lindorff-Larsen K, Best RB, DePristo MA, Dobson CM, Vendruscolo M. Simultaneous determination of protein structure and dynamics. Nature 2005; 433: 128–132.
- 36 Müller CW, Schulz GE. Structure of the complex between adenylate kinase from Escherichia coli and the inhibitor Ap5A refined at 1.9 Å resolution. A model for a catalytic transition state. J Mol Biol 1992; 224: 159–177.
- 37 Müller CW, Schlauderer GJ, Reinstein J, Schulz GE. Adenylate kinase motions during catalysis: an energetic counterweight balancing substrate binding. Structure 1996; 4: 147–156.
- 38 Dror O, Benyamini H, Nussinov R, Wolfson H. MASS: multiple structural alignment by secondary structures. Bioinformatics 2003; 19: i95–i104.
- 39 DePristo MA, de Bakker PIW, Lovell SC, Blundell TL. Ab initio construction of polypeptide fragments: efficient generation of accurate, representative ensembles. Proteins 2003; 51: 41–55.
- 40 Flores S, Echols N, Milburn D, Hespenheide B, Keating K, Lu J, Wells S, Yu EZ, Thorpe M, Gerstein M. The database of macromolecular motions: new features added at the decade mark. Nucleic Acids Res 2006; 34: D296–301.
- 41 Mouawad L, Perahia D. Diagonalization in a mixed basis: a method to compute low-frequency normal modes for large macromolecules. Biopolymers 1993; 33: 599–611.
Citing Literature
