Reconstructing protein structure from solvent exposure using tabu search
- Martin Paluszewski^{1}Email author,
- Thomas Hamelryck^{2} and
- Pawel Winter^{1}
DOI: 10.1186/1748-7188-1-20
© Paluszewski et al; licensee BioMed Central Ltd. 2006
Received: 30 March 2006
Accepted: 27 October 2006
Published: 27 October 2006
Abstract
Background
A new, promising solvent exposure measure, called half-sphere-exposure (HSE), has recently been proposed. Here, we study the reconstruction of a protein's C_{ α }trace solely from structure-derived HSE information. This problem is of relevance for de novo structure prediction using predicted HSE measure. For comparison, we also consider the well-established contact number (CN) measure. We define energy functions based on the HSE- or CN-vectors and minimize them using two conformational search heuristics: Monte Carlo simulation (MCS) and tabu search (TS). While MCS has been the dominant conformational search heuristic in literature, TS has been applied only a few times. To discretize the conformational space, we use lattice models with various complexity.
Results
The proposed TS heuristic with a novel tabu definition generally performs better than MCS for this problem. Our experiments show that, at least for small proteins (up to 35 amino acids), it is possible to reconstruct the protein backbone solely from the HSE or CN information. In general, the HSE measure leads to better models than the CN measure, as judged by the RMSD and the angle correlation with the native structure. The angle correlation, a measure of structural similarity, evaluates whether equivalent residues in two structures have the same general orientation. Our results indicate that the HSE measure is potentially very useful to represent solvent exposure in protein structure prediction, design and simulation.
Background
Here, we study if it is possible to reconstruct a protein's C_{ α }trace solely from a CN vector or an HSE vector. These vectors are obtained from the protein's known native state and our goal is therefore to evaluate the information contents of these measures. This problem could become important for de novo structure prediction, for example if predicted HSE values are used as restraints. Preliminary results show that the HSE measure can be predicted with reasonably high accuracy[11]. Reconstruction of a protein structure from a predicted HSE vector might thus be an attractive way of approaching the sequence-to-structure problem. Predicted CN-/HSE vectors are expected to have errors compared to the exact vectors. The results presented in this paper are based on exact vectors and therefore provide an upper bound on the information contents of predicted CN-/HSE vectors. If protein structure prediction was carried out on a predicted HSE vector only, it is expected that the results would not be better than the results presented in this paper. It would therefore be natural to add other predictable information such as secondary structure, radius of gyration etc. to a structure prediction system using predicted HSE vectors. The problem of reconstructing protein structure from vectors of one-dimensional structural information has been studied before. Kinjo et al.[12] used exact vectors of secondary structure (SS), CN and residue-wise contact order (RWCO) together with refinement using the AMBER force field to reconstruct native like structures. Their results show that SS and CN information without the use of RWCO is not enough to reconstruct native like structures. Unfortunately, prediction methods for the RWCO measure only have moderate performance as compared to SS and CN[12].
Porto et al.[13] described an algorithm for reconstructing the contact map (CM) from its principal (one-dimensional) eigenvector. However, methods for predicting a high quality eigenvector are not likely to exist. Here, we only consider measures that potentially can be predicted with high accuracy. Furthermore we only use one type of measure (either CN or HSE), which is important for evaluating the information content of a measure. To this end, we compare structure reconstruction using an energy function based on the HSE measure with an energy function that uses the well-established CN measure.
If an approximate CN-/HSE vector is obtained from a prediction method, there might be no structure that exactly realizes the vector. In that case, we are interested in finding a structure with a CN- or HSE-vector similar to the predicted vector. Therefore we define energy functions based on the HSE- or CN-vectors and minimize them using two conformational search heuristics: Monte Carlo simulation (MCS) and tabu search (TS). MCS has been widely used for protein structure prediction, and TS has been applied with great success to many optimization problems, but has rarely been used for protein structure prediction [14–16].
In this article, the radius of the HSE sphere is chosen to be 12 Å for all experiments. The optimal radius has yet to be determined, both in terms of predictability and reconstructability. If the radius is too small, important residue pairs might be overlooked. On the other hand, if the radius is too large, many irrelevant residues are considered. In this respect, 12 Å seems to be a good compromise [9].
The rest of the article is organized as follows. In the next section we describe the energy function based on the HSE measure. Then the protein abstraction and lattice model are discussed. In section Heuristics, we present the two conformational search heuristics, MCS and TS. In section Lattice experiments, MCS and TS are evaluated in lattices of different complexity. Finally, we evaluate the information content (that is, to what extent they can be used to reconstruct a protein structure) of the HSE and CN measures using TS and a high complexity lattice.
HSE energy function
The similarity of two HSE vectors A and B of length N can be measured using the following RMS deviation:
$\text{RMSD}(A,B)=\sqrt{\frac{{\displaystyle {\sum}_{i=1}^{N}({({A}_{{u}_{i}}-{B}_{{u}_{i}})}^{2}+{({A}_{{d}_{i}}-{B}_{{d}_{i}})}^{2})}}{2N}},$
where ${\{A,B\}}_{{u}_{i}}$ and ${\{A,B\}}_{{d}_{i}}$ are the up and down values of the i'th index. RMSD (A, B) can be used to describe the energy of structure S_{ A }where A is the HSE vector of S_{ A }and B is the HSE vector of the native structure. These definitions are easily extended to the CN measure. The energy functions are the only optimization criteria used by the MCS and TS algorithms.
The protein model
Lattice models are widely used for studying the fundamental properties of protein structure[17]. Such models have for example provided invaluable insights on topics such as the validity of pairwise energy functions[18], the evolution of protein superfamilies[19] and the importance of local structural bias in the determination of a protein's fold[20]. Many lattice models have been proposed and evaluated in the literature. Not surprisingly, experiments show a high correlation between the number of basis vectors of a lattice and its ability to represent a protein backbone[21, 22]. When deciding on a lattice model, one must always consider the trade-off between the reduction of the conformational space and the quality of the structure representation. Therefore, in section Lattice experiments we evaluate four different lattices of various complexity: The SCC lattice, the FCC lattice and two high coordination (HC) lattices with 54 and 390 basis vectors, respectively.
A high coordination lattice has an underlying cubic lattice with unit length less than 3.8/N Å for some integer N > 1. Cubic lattice points are connected in the high coordination lattice if their Euclidean distance is between 3.8 ± ε for some ε > 0. The high coordination lattices used here are named HC4 and HC8 corresponding to their N value (4 and 8). The ε value is 0.2 for all HC lattices. Figure 3 shows an illustration of a 2D high coordination lattice with N = 3 and ε = 0.4. High coordination lattices have previously been used for protein structure prediction[23, 24]. Note that the SCC and FCC lattices both have the excluded volume property, meaning that atoms at two different lattice points will never collide. This property does not necessarily hold for high coordination lattices, and collisions must therefore be detected explicitly.
Heuristics
We apply two iterative search heuristics for minimization of the HSE energy. One of them is the tabu search metaheuristic proposed by F. Glover in 1989[25, 26]. A metaheuristic is a general framework that can be specialized to solve various optimization problems. For many problems in Operations Research (OR), tabu search is the metaheuristic of choice. However, for protein structure prediction, tabu search has only been given a modest amount of attention[14–16].
We have also applied standard Monte Carlo simulation (MCS) for minimizing the HSE energy. MCS heuristics are stochastic and therefore differ from TS by being nondetermistic. An MCS iteration consists of randomly choosing a protein conformation in the neighbourhood of a current conformation. For a fixed temperature T, the new protein conformation is accepted with the probability
p = e^{-ΔE/T},
where ΔE is the difference between the energy of the current conformation and the new conformation. A protein conformation is modelled as a list of N vectors, where N is the number of C_{ α }atoms of the protein. The neighbourhood of both MCS and TS consists of conformations resulting from changes of one, two or three consecutive indices. A single index change results in a new structure where one part of the structure is fixed and the other part is translated. Two or three indices are changed locally such that the parts of the structure before and after the changing indices are fixed. All local index changes between two lattice points can be stored in a table to speed up the computation time significantly.
Lattice experiments
Average HSE energy for Protegrin 1 using TS on various lattices and tabu differences.
Tabu difference | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Lattice | 0.0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | 1.0 |
SCC | 1.76 | 1.65 | 1.64 | 1.64 | 1.64 | 1.64 | 1.64 | 1.64 | 1.64 | 1.64 | 1.64 |
FCC | 1.52 | 1.12 | 1.12 | 1.11 | 1.07 | 1.07 | 1.04 | 1.05 | 1.03 | 1.03 | 1.03 |
HC4 | 1.13 | 0.41 | 0.36 | 0.30 | 0.28 | 0.27 | 0.29 | 0.32 | 0.32 | 0.36 | 0.38 |
HC8 | 1.21 | 0.46 | 0.08 | 0.01 | 0.00 | 0.00 | 0.01 | 0.07 | 0.15 | 0.22 | 0.30 |
Average HSE energy for Protegrin 1 using MCS on various lattices and temperatures.
Temperature | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Lattice | 0.000 | 0.002 | 0.004 | 0.006 | 0.008 | 0.010 | 0.012 | 0.014 | 0.016 | 0.018 | 0.020 | 0.022 |
SCC | 1.88 | 1.80 | 1.74 | 1.68 | 1.68 | 1.65 | 1.64 | 1.64 | 1.64 | 1.64 | 1.64 | 1.64 |
FCC | 1.57 | 1.39 | 1.25 | 1.15 | 1.08 | 1.05 | 1.03 | 1.03 | 1.03 | 1.03 | 1.03 | 1.03 |
HC4 | 1.48 | 1.09 | 0.85 | 0.63 | 0.54 | 0.48 | 0.37 | 0.32 | 0.31 | 0.29 | 0.27 | 0.34 |
HC8 | 1.29 | 0.46 | 0.37 | 0.25 | 0.17 | 0.06 | 0.07 | 0.07 | 0.04 | 0.08 | 0.15 | 0.28 |
Comparison of HSE and CN measures
In the previous section, experiments on a small protein show that minimization of the HSE energy in high coordination lattices leads to structures with HSE vectors that are very similar (or equal) to the native structure. In this section, experiments on proteins of varying size are done using the TS heuristic with tabu difference 0.4 and the HC8 lattice. The energy functions are based on the HSE vectors of native structures as described in section HSE energy function. In addition to the HSE energy, the CN energy is considered for comparison. The main purpose of the experiments is to examine the reconstructability of a protein's backbone solely from the information stored in the HSE-/CN vectors.
Each TS run is started from a random structure which is iteratively improved as described in section Heuristics. For these experiments we want to start TS on 100 random structures that are as different from each other as possible. Therefore, to effectively sample the search space, 10000 random conformations are initially generated. Ideally, from this set of 10000 conformations, we would like to choose the set of 100 conformations such that the minimum RMSD between any two conformations is maximized. This problem is generally known as the p-dispersion problem and is NP hard[27]. Solving this problem to optimality is therefore not feasible, so we use a greedy heuristic to find a good set of 100 different random conformations. The greedy heuristic works by first picking a random conformation. The following 99 conformations are then picked one at a time, such the minimimum RMSD to any of the already picked conformations is maximized.
For each protein, the energy function based on its native structure is minimized for each of the 100 random starting conformations and the structures with lowest energy are reported. The search is stopped after 12 hours or if the energy reaches zero. Zero energy means that a structure with exactly the same HSE- or CN vector as the native structure is found (but not necessarily identical structures).
To evaluate the quality of the structures with low energy, the RMSD with the native structure and angle correlation [28, 29] is used. Angle correlation is a measure with the following definition. For each C_{ α }, let $\overrightarrow{V}$_{ α }be the vector pointing in the side chain direction (see Figure 2). Let ${V}_{\alpha mc}^{\to}$ be the vector pointing in the direction of the mass center, and let θ_{ α }be the angle between $\overrightarrow{V}$_{ α }and ${V}_{\alpha mc}^{\to}$. The angle correlation measure is the average of the differences in θ_{ α }between the optimized structure and the native structure. Zero angle correlation is perfect correlation, 90° is random correlation and 180° is perfect 'anti'-correlation. Note that the CN- and HSE vectors of a structure are identical to the vectors of the mirror of the structure. Therefore, in the following results, if the RMSD between a structure and its native mirror image is smaller we report this value instead. All computations were performed on a 236 nodes Dell Optiplex GX260 cluster (2,4 GHz P4, 512 Mb RAM).
Results and discussion
Comparison of the HSE- and CN measures for various proteins.
Residues | Measure | < 7 Å RMSD | < 6 Å RMSD | < 5 Å RMSD | < 4 Å RMSD | < 3 Å RMSD | < 2 Å RMSD | lowest RMSD | lowest energy |
---|---|---|---|---|---|---|---|---|---|
Human Endothelin (1EDN) | |||||||||
21 | CN | 100 | 100 | 98 | 60 | 18 | 0 | 2.09 | 0.00 |
HSE | 100 | 100 | 100 | 93 | 65 | 37 | 0.88 | 0.00 | |
Tryptophan Zipper 1(1LE0) | |||||||||
13 | CN | 100 | 100 | 100 | 100 | 100 | 22 | 1.38 | 0.00 |
HSE | 100 | 100 | 100 | 100 | 100 | 67 | 0.95 | 0.00 | |
Third Zinc Finger (1SRK) | |||||||||
35 | CN | 60 | 42 | 17 | (1SRK) 1 | 0 | 0 | 3.52 | 0.00 |
HSE | 56 | 33 | 13 | 5 | 0 | 0 | 3.02 | 0.33 | |
Mu-Conotoxin GIIA (1TCH) | |||||||||
23 | CN | 100 | 100 | 97 | 63 | 23 | 5 | 1.58 | 0.00 |
HSE | 100 | 100 | 100 | 97 | 61 | 38 | 0.91 | 0.00 | |
Pandinus Toxin (2PTA) | |||||||||
35 | CN | 59 | 32 | 14 | 3 | 0 | 0 | 3.17 | 0.00 |
HSE | 58 | 44 | 17 | 11 | 2 | 0 | 2.66 | 0.33 |
Scatter plots show the angle correlation vs. RMSD. The Figures also show the best HSE- and CN-optimized structures superimposed on the native structure. The yellow backbone is the native structure, the red backbone is the best HSE optimized structure and the green backbone is the best CN optimized structure.
The CN and HSE comparisons show that low HSE-energy structures are generally closer to the native structure than low CN-energy structures, this both in terms of RMSD and angle correlation. A backbone structure with a good angle correlation implies that the general orientation of the residues is accurate. The plots show that this property is much more prevalent in HSE-optimized structures. Existing protein structure prediction methods that use the CN measure could therefore benefit from using the HSE measure instead of the CN measure.
Here we have developed a lattice model for protein structure prediction using the CN-/HSE energy functions. The search heuristic is based on TS with a novel tabu definition and the results indicate that TS performs better than MCS for this problem. TS with this new tabu definition might also be applied with success for other protein structure optimization problems.
Lattice experiments suggest that near zero energy structures only exists in high coordination lattices. Therefore, when using the HSE measure the model should have a high degree of freedom. All results are found using small proteins (the largest protein has 35 amino acids). When using larger proteins, it becomes very time consuming to find low energy structures and they are often not native like.
We have shown that it is possible to reconstruct the backbone of small proteins using the HSE vector of the native structure. Obviously, a predicted HSE vector would have some errors or noise as compared to the exact HSE vector. A future research project could therefore be to analyze the reconstructability of a protein backbone using HSE vectors with various degree of noise. Other directions could be to consider a more detailed energy function using other predictable information such as secondary structure. Another option could be to enforce protein-like geometry, using for example angular constraints.
In this article, we only considered lattice models. However, off-lattice models and other conformational search heuristics such as replica exchange MCMC[30] could be considered as well.
Declarations
Acknowledgements
Thomas Hamelryck is supported by a Marie Curie Intra-European Fellowship within the 6th European Community Framework Programme. Martin Paluszewski and Pawel Winter are partially supported by a grant from the Danish Research Council (51-00-0336).
Authors’ Affiliations
References
- Lee B, Richards F: The Interpretation of Protein Structures: Estimation of Static Accessibility. J Mol Biol. 1971, 55: 379-400.PubMedView ArticleGoogle Scholar
- Greer J, Bush BL: Macromolecular shape and surface maps by solvent exclusion. Proc Natl Acad Sci USA. 1978, 75: 303-7.PubMedPubMed CentralView ArticleGoogle Scholar
- Connolly ML: Solvent-accessible surfaces of proteins and nucleic acids. Science. 1983, 221 (4612): 709-13.PubMedView ArticleGoogle Scholar
- Chakravarty S, Varadarajan R: Residue depth: a novel parameter for the analysis of protein structure and stability. Structure. 1999, 7 (7): 723-32.PubMedView ArticleGoogle Scholar
- Pintar A, Carugo O, Pongor S: Atom depth in protein structure and function. Trends Biochem Sci. 2003, 28 (11): 593-7.PubMedView ArticleGoogle Scholar
- Pintar A, Carugo O, Pongor S: Atom depth as a descriptor of the protein interior. Biophys J. 2003, 84 (4): 2553-61.PubMedPubMed CentralView ArticleGoogle Scholar
- Pollastri G, Baldi P, Fariselli P, Casadio R: Prediction of coordination number and relative solvent accessibility in proteins. Proteins. 2002, 47 (2): 142-53.PubMedView ArticleGoogle Scholar
- Kinjo A, Horimoto K, Nishikawa K: Predicting absolute contact numbers of native protein structure from amino acid sequence. Proteins. 2005, 58: 158-65.PubMedView ArticleGoogle Scholar
- Hamelryck T: An amino acid has two sides: a new 2D measure provides a different view of solvent exposure. Proteins. 2005, 59: 38-48.PubMedView ArticleGoogle Scholar
- Simons KT, Kooperberg C, Huang E, Baker D: Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol. 1997, 268: 209-25.PubMedView ArticleGoogle Scholar
- Bjarni Vilhjalmsson, Thomas Hamelryck: Predicting a New Type of Solvent Exposure. ECCB Computational Biology Madrid 05 P-C35 Poster. 2005Google Scholar
- Kinjo AR, Nishikawa K: Recoverable one-dimensional encoding of three-dimensional protein structures. Bioinformatics. 2005, 21 (10): 2167-70.PubMedView ArticleGoogle Scholar
- Porto M, Bastolla U, Roman HE, Vendruscolo M: Reconstruction of protein structures from a vectorial representation. Phys Rev Lett. 2004, 92 (21):Google Scholar
- Pardalos PM, Liu X, Xue GL: Protein Conformation of a Lattice Model Using Tabu Search. Journal of Global Optimization. 1997, 11: 55-68. 10.1023/A:1008228509535.View ArticleGoogle Scholar
- Morales LB, Garduño-Juárez R, Aguilar-Alvarado JM, Riveros-Castro FJ: A parallel tabu search for conformational energy optimization of oligopeptides. Journal of Computational Chemistry. 2000, 21 (2): 147-156. 10.1002/(SICI)1096-987X(20000130)21:2<147::AID-JCC6>3.0.CO;2-6.View ArticleGoogle Scholar
- Oakley M, Garibaldi J, Hirst J: Lattice models of peptide aggregation: Evaluation of conformational search algorithms. J Comput Chem. 2005, 26 (15): 1638-46.PubMedView ArticleGoogle Scholar
- Dill KA, Bromberg S, Yue K, Fiebig KM, Yee DP, Thomas PD, Chan HS: Principles of protein folding-a perspective from simple exact models. Protein Sci. 1995, 4: 561-602.PubMedPubMed CentralView ArticleGoogle Scholar
- Thomas PD, Dill KA: Statistical potentials extracted from protein structures: how accurate are they?. J Mol Biol. 1996, 257: 457-69.PubMedView ArticleGoogle Scholar
- Zeldovich KB, Berezovsky IN, Shakhnovich EI: Physical origins of protein superfamilies. J Mol Biol. 2006, 357 (4): 1335-43.PubMedView ArticleGoogle Scholar
- Chikenji G, Fujitsuka Y, Takada S: Shaping up the protein folding funnel by local interaction: Lesson from a structure prediction study. Proc Natl Acad Sci USA. 2006, 103: 3141-3146.PubMedPubMed CentralView ArticleGoogle Scholar
- Adam Godzik, Andrzej Kolinski, Jeffrey Skolnick: Lattice Representations of Globular Proteins: How Good Are They. 1993, http://www3.interscience.wifey.com/cgi-bin/abstract/109582884/ABSTRACTGoogle Scholar
- Park B, Levitt M: The complexity and accuracy of discrete state models of protein structure. J Mol Biol. 1995, 249 (2): 493-507.PubMedView ArticleGoogle Scholar
- Kihara D, Lu H, Kolinski A, Skolnick J: TOUCHSTONE: an ab initio protein structure prediction method that uses threading-based tertiary restraints. Proc Natl Acad Sci USA. 2001, 98 (18): 10125-30.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhang Y, Kolinski A, Skolnick J: TOUCHSTONE II: a new approach to ab initio protein structure prediction. Biophys J. 2003, 85 (2): 1145-64.PubMedPubMed CentralView ArticleGoogle Scholar
- Glover F: Tabu Search, PART I. ORSA J Comput. 1989, 1: 190-206.View ArticleGoogle Scholar
- Glover F: Tabu Search, PART II. ORSA J Comput. 1990, 2: 4-32.View ArticleGoogle Scholar
- Erkut E: The Discrete P-Dispersion Problem. European Journal of Operational Research. 1990, 46: 48-60. 10.1016/0377-2217(90)90297-O.View ArticleGoogle Scholar
- Rackovsky S, Scheraga HA: Hydrophobicity, hydrophilicity, and the radial and orientational distributions of residues in native proteins. Proc Natl Acad Sci USA. 1977, 74 (12): 5248-51.PubMedPubMed CentralView ArticleGoogle Scholar
- Yan A, Jernigan RL: How do side chains orient globally in protein structures?. Proteins. 2005, 61: 513-22.PubMedPubMed CentralView ArticleGoogle Scholar
- Swendsen RH, Wang JS: Replica Monte Carlo simulation of spin glasses. PHYSICAL REVIEW LETTERS. 1986, 57 (21): 2607-2609.PubMedView ArticleGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.