kl800.com省心范文网

FastContact rapid estimate of contact and binding free energies. Bioinformatics

BIOINFORMATICS APPLICATIONS NOTE
Structural bioinformatics

Vol. 21 no. 10 2005, pages 2534–2536 doi:10.1093/bioinformatics/bti322

FastContact: rapid estimate of contact and binding free energies
Carlos J. Camacho1,? and Chao Zhang2
1 Department

of Computational Biology, University of Pittsburgh, Pittsburgh, PA 15261, USA and 2 Plexxikon Inc., 91 Bolivar Drive, Berkeley, CA 94710, USA

Received on October 17, 2004; revised on February 4, 2005; accepted on February 9, 2005 Advance Access publication February 15, 2005

ABSTRACT Summary: Interaction free energies are crucial for analyzing binding propensities in proteins. Although the problem of computing binding free energies remains open, approximate estimates have become very useful for ?ltering potential binding complexes. We report on the implementation of a fast computational estimate of the binding free energy based on a statistically determined desolvation contact potential and Coulomb electrostatics with a distance-dependent dielectric constant, and validated in the Critical Assessment of PRotein Interactions experiment. The application also reports residue contact free energies that rapidly highlight the hotspots of the interaction. Availability: The program was written in Fortran. The executable and full documentation is freely available at http://structure.pitt.edu/ software/FastContact Contact: ccamacho@pitt.edu

proteins is estimated as

Gbind , where Eelec + Gdes . (1)

Gbind =

Today, nearly all docking methods use some type of scoring function to differentiate between near-native complexes and non-speci?c encounter complexes. In the First Critical Assessment of PRedicted Interaction meeting, CAPRI (Janin et al., 2003), computational scoring functions involved free energy-like terms adjusted by free parameters that optimized the discrimination of bound crystal structures (Fernández-Recio et al., 2003; Gray et al., 2003; Ritchie, 2003; Smith and Sternberg, 2003) or more geometrical discriminators, e.g., buried surface area (Gardiner et al., 2003; Krippahl et al., 2003; Law et al., 2003; Schneidman-Duhovny et al., 2003), or hybrids of these two approaches (Ben-Zeev et al., 2003; Chen et al., 2003). At the same time, in the literature one ?nds several more sophisticated, and perhaps more accurate, approaches for estimating different free energy contributions—e.g., free energy perturbation (Kollman, 1993), Poisson–Boltzman (Honig and Nicholls, 1995), atomic continuum electrostatic (Schaefer and Karplus, 1996) and generalized-Born solvation (see, e.g., Qiu et al., 1997). However, since protein docking requires ?ltering or sampling millions of plausible complex structures and these methods are computationally expensive, they are not used for free energy screening. Finally, a somewhat different approach to screen protein binding interactions has been developed by Camacho et al. (2000). These authors use a free energy scoring function developed independently of the bound crystal structures present in the Protein Data Bank (PDB) (Berman et al., 2000). Namely, the interaction between two

? To

whom correspondence should be addressed.

Eelec corresponds to the standard intermolecular Coulombic electrostatic potential with a distance-dependent dielectric constant equal to 4r (Pickersgill, 1988). Gdes captures the most essential features of the desolvation free energy in proteins, including hydrophobic interactions, the self-energy change upon desolvating charge on polar atom groups and side-chain entropy loss. Gdes is calculated by an empirical contact potential of the form Gdes = g(r) eij , where eij denotes the atomic contact potential (ACP) between atom i of the receptor and j of the ligand. The double sum is taken over all atom pairs and g(r) is 0 for atoms that are more than 7 ? apart, 1 if less than 5 ? apart and in between g(r) is a smooth function varying between these two limits (Zhang et al., 1997b). The ACPs have been de?ned for a total of 18 atom types, and obtained from a diverse set of close to 90 protein structures by converting frequencies of structural factors into atom–atom contacts. This free energy estimates reasonably well experimental binding af?nities from complex crystal structures (Zhang et al., 1997a,b; Kimura et al., 2001). However, ?ltering decoys with less than optimal side chain packing and structural/charge overlap is not as straightforward. Two problems are the sensitivity of the electrostatic energy to charge overlaps, and the overextended contribution of the desolvation term arising from overlapping contacts that are not at the protein surface. It is worth mentioning that although one could easily remove most overlaps by energy minimization, this is not computationally feasible for a million or so structures. We address these problems by ?rst not allowing two atoms to be any closer than the sum of their van der Waals radii, preventing arti?cial spikes on the electrostatic term (Vasmatzis et al., 1996; Zhang et al., 1999); and second, we provide an option to always require that at least one of the interacting atoms be exposed to the solvent by at least 1 ?2 in the unbound state (Camacho et al., 1999). The latter is done by computing the solvent accessible surface area of each individual protein using (Lee and Richards 1971) algorithm. It is worth mentioning that the problem of over-counting desolvation contact energies is worst when the receptor–ligand overlap is more than around 300 ?3 ; for a minimum overlap the range of the contact potential is suf?cient to constrain the interactions within the surfaces. This free energy was the main ?lter of potential binding sites used by Camacho and Gatchell (2003) in the ?rst CAPRI experiment. These authors produced some of the best predictions at

2534

? The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org

FastContact: fast estimate of binding free energy

CAPRI1–2 (Méndez et al., 2003), appropriately ranking the nativelike models. We have also implemented our method as a fully automated public server named ClusPro (Comeau et al., 2004). ClusPro was the only server validated in the second CAPRI meeting (Gaeta, Italy, 2004), where for 5 (out of 10) targets, nativelike structures were submitted. Moreover, for two of the targets, the models predicted using Equation (1) were some of the most accurate among all submissions (Comeau, Vajda and Camacho, Proteins 2005). It is also worth mentioning that, after adding the van der Waals interactions and Equation (1) to the scoring function, the native-like structures submitted after ?exible re?nement (Camacho, Proteins, 2005). In order to share this utility with the research community, we have implemented this fast scoring function in a program called FastContact. The input of FastContact is as follows: FastContact RTF receptor.pdb ligand.pdb Num_extra_ligands Contacts SASA The RTF_?le de?nes the united atom composition of each amino acid and it is provided together with the executable. The RTF ?le includes a list of residue types and their atomic makeup, partial charges and van der Waals radii; the data is consistent with CHARMM19 parameters. The user is free to modify this ?le provided the format of the data remains the same. The proteins should be in standard CHARMM (Brooks et al., 1983) or CONGEN (Bruccoleri et al., 1997) format with polar hydrogens only. Since in rigid-body docking one often is interested in scoring several ligand conformations against the same receptor, we provide an option that allows computing the binding free energy for as many extra ligands as needed. The program will read from standard input Num_extra_ligands ?le names of new ligand structures. The main output of the program is directed to the screen and consists of the total electrostatic and desolvation energy. If Contacts = 1, the output details the top 20 residues that have the minimum and maximum contribution to the different free energy components; the residues are renumbered starting with number 1 and the program creates two PDB ?les, fort.19 and fort.20, with the new numbers. If Contacts = 1 no contact energy information is produced. SASA = 1 will check that at least one of the contact residues is at the surface. If this constraint is not deemed necessary then SASA = 1. Computing the solvent accessible surface area (SASA = 1) is the most computationally by expensive step of the algorithm. Using a single Pentium 4 processor, FastContact takes less than 0.1 seconds to compute Gbind for two single domain proteins and SASA = 1, and 3 s if SASA = 1. However, once SASA is computed for one receptor and ligand, extra runs using different orientations of the same ligand structure take less than 0.1 s. The maximum number of residues is 1500. If the residue name is not in the RTF ?le, the program stops; if an atom is not in the RTF then its contribution is made equal to zero and a warning message is spooled to the screen. The contact information (Contacts = 1) is very useful in model re?nement of rigid-body docked conformations because in the output list one can read the residues and pair of residues that provide both the most attractive and repulsive free energy. While the former immediately highlights the hot spots of the binding interaction, the latter often suggest side chains that might need to be re?ned. Also, the residue contact free energies should prove useful in selecting interesting residues for mutagenesis experiments. FastContact provides a fast estimate of the interaction free energy between two proteins. Because it is based on folding data, the

estimate is robust and does not require to be re-parameterized as more complex structures become available. More importantly, as far as we know, it is the only scoring function validated in CAPRI that is made available to the community at large. FastContact can now be combined with the user favorite decoys generator and other scoring functions to further re?ne predictions of complex structures.

ACKNOWLEDGEMENTS
We are grateful to Charles DeLisi for his help and support while the authors were at Boston University. C.J.C. also acknowledges the help of S. Vajda and Z. Weng. C.J.C is grateful for the support of the University of Pittsburgh. We are also thankful to Christoph Champ for setting up the link to download. ACP and ACP-based binding energy function was developed by C. Zhang in collaboration with Drs. J. Cornette and G. Vasmatzis while working at Professor Charles DeLisi’s laboratory.

REFERENCES
Ben-Zeev,E. et al. (2003) Prediction of the unknown: Inspiring experience with the CAPRI experiment. Proteins, 52, 41–46. Berman,H.M. et al. (2000) The Protein Data Bank. Nucleic Acids Res., 28, 235–242. Brooks,B.R. et al. (1983) CHARMM: a program for macromolecular energy, minimization, and dynamic calculations. J. Comput. Chem., 4, 187–217. Bruccoleri,R.E. et al. (1997) Finite difference Poisson–Boltzmann electrostatic calculations: increased accuracy achieved by harmonic dielectric smoothing and charge antialiasing. J. Comp. Chem., 18, 268–276. Camacho,C.J. and Gatchell,D. (2003) Successful discrimination of protein interactions. Proteins, 52, 92–97. Camacho,C.J. et al. (1999) Free energy landscapes of encounter complexes in protein– protein association. Biophys. J., 76, 1166–1178. Camacho,C.J. et al. (2000) Scoring docked conformations generated by rigid-body protein-protein docking. Proteins, 40, 525–537. Camacho,C.J. (2005) Modeling side chains using molecular dynamics improve recognition of binding region in CAPRI targets. Proteins, in press. Chen,R. et al. (2003) ZDOCK: An initial-stage protein-docking algorithm. Proteins, 52, 80–87. Comeau,S.R. et al. (2004) ClusPro: An automated docking and discrimination method for the prediction of protein complexes. Bioinformatics, 20, 45–50. Comeau,S. et al. (2005) Performance of the ?rst protein docking server ClusPro in CAPRI rounds3-5. Proteins, in press. Fernández-Recio,J. et al. (2003) ICM-DISCO docking by global energy optimization with fully ?exible side-chains. Proteins, 52, 113–117. Gardiner,E.J. et al. (2003) GAPDOCK: a genetic algorithm approach to protein docking in CAPRI round 1. Proteins, 52, 10–14. Gray,J.J. et al. (2003) Protein-protein docking predictions for the CAPRI experiment. Proteins, 52, 118–122. Honig,B. and Nicholls,A. (1995) Classical electrostatics in biology and chemistry. Science, 268, 1144–1149. Janin,J. et al. (2003) CAPRI: A Critical Assessment of PRedicted Interactions. Proteins,52, 2–9. Kimura,S.R. et al. (2001) Dynamical view of the positions of key side chains in proteinprotein recognition. Biophys. J., 80, 635–642. Kollman,P.A. (1993) Free energy calculations: applications to chemical and biochemical phenomena. Chem. Rev., 93, 2395–2417. Krippahl,L. et al. (2003) Modeling protein complexes with BiGGER. Proteins, 52, 19–23. Law,D.S. et al. (2003) Finding needles in haystacks: Reranking DOT results by using shape complementarity, cluster analysis, and biological information. Proteins, 52, 33–40. Lee,B. and Richards,F.M. (1971) The interpretation of protein structures: estimation of static accessibility. J. Mol. Biol., 55, 379–400. Méndez,R. et al. (2003) Assessment of blind predictions of protein-protein interactions: Current status of docking methods. Proteins, 52, 51–67. Pickersgill,R.W. (1988) A rapid method of calculating charge-charge interaction energies in proteins. Protein Eng. 2, 247–248. Qiu,D. et al. (1997) The GB/SA continuum model for solvation. a fast analytical method for the calculation of approximate Born radii. J. Phys. Chem. A, 101, 3005–3014.

2535

C.J.Camacho and C.Zhang

Ritchie,D.W. (2003) Evaluation of protein docking predictions using Hex 3.1 in CAPRI rounds 1 and 2. Proteins, 52, 98–106. Schaefer,M. and Karplus,M. (1996) A comprehensive analytical treatment of continuum electrostatics. J. Phys. Chem., 100, 1578–1599. Schneidman-Duhovny,D. et al. (2003) Taking geometry to its edge: Fast unbound rigid (and hinge-bent) docking. Proteins, 52, 107–112. Smith,G.R. and Sternberg,M.J.E. (2003) Evaluation of the 3D-Dock protein docking suite in rounds 1 and 2 of the CAPRI blind trial. Proteins, 52, 74–79.

Vasmatzis,G. et al. (1996) Computational determination of side chain speci?city for pockets in class I MHC molecules. Mol. Immunol., 33, 1231–1239. Zhang,C. et al. (1997a) Consistency in structural energetics of protein folding and peptide recognition. Protein Sci., 6, 1057–1064. Zhang,C. et al. (1997b) Determination of atomic desolvation energies from the structures of crystallized proteins. J. Mol. Biol., 267, 707–726. Zhang,C. et al. (1999) Protein-protein recognition: exploring the energy funnels near the binding sites. Proteins, 34, 255–267.

2536


FastContact rapid estimate of contact and binding free ....pdf

FastContact rapid estimate of contact and binding free energies. Bioinformatics - doi:10.1093/bio...

Rapid Estimation of Enthalpies of Formation from Hartree-Fock....pdf

Rapid Estimation of Enthalpies of Formation from Hartree-Fock Total Energy ...An estimate of the enthalpy of formation of any hydrocarbon may then be ...

bioinformatics-2010-larg....pdf

Bioinformatics-2010-Larget-2910-1_电子/电路_工程...Contact: ane@stat.wisc.edu Supplementary ...estimate of the population tree when discordance ...

A Simple Way to Estimate the Cost of Downtime David A ....pdf

A Simple Way to Estimate the Cost of Downtime ...The systems we have created are fast and cheap,...rapid increase in income as the deadline ...

rapid estimation of nutr....pdf

Rapid estimation of nutrients in chicken manure ...Also pH was used to estimate the nutrient ... Bio-Economic Model to ... 暂无评价 13页 免费...

bioinformatics orig....pdf

BIOINFORMATICS Data and text mining ORIGINAL PAPER...Contact: annette.molinaro@yale.edu Supplementary ...methods. For each r, an estimate of θn(1?p...

bioinformatics applic....pdf

BIOINFORMATICS APPLICATIONS NOTE Gene expression_专业...Contact: valentini@dsi.unimi.it Supplementary ...(as well as to estimate the associated P-value...

bioinformatics applic....pdf

BIOINFORMATICS APPLICATIONS NOTE Gene expression ...//www.broad.mit.edu/ genepattern Contact: j...nal p0 estimate. When a more conservative ...

Non-contact Eye Gaze Estimation System using Robust Feature ....pdf

Non-contact Eye Gaze Estimation System using ...estimate the eye-gaze direction of an user.The ...In this section, robust and fast feature ...