Volume 14 Supplement 9
Selected articles from the 8th International Symposium on Bioinformatics Research and Applications (ISBRA'12)
Solving the molecular distance geometry problem with inaccurate distance data
- Michael Souza^{1}Email author,
- Carlile Lavor^{2},
- Albert Muritiba^{1} and
- Nelson Maculan^{3}
https://doi.org/10.1186/1471-2105-14-S9-S7
© Souza et al.; licensee BioMed Central Ltd. 2013
Published: 28 June 2013
Abstract
We present a new iterative algorithm for the molecular distance geometry problem with inaccurate and sparse data, which is based on the solution of linear systems, maximum cliques, and a minimization of nonlinear least-squares function. Computational results with real protein structures are presented in order to validate our approach.
Background
The knowledge of the protein structure is very important to understand its function and to analyze possible interactions with other proteins. Different methods can be applied to acquire protein structural information. Until 1984, the X-ray crystallography was the ultimate tool for obtaining information about protein structures, but the introduction of nuclear magnetic resonance (NMR) as a technique to obtain protein structures made it possible to obtain data with high precision in an aqueous environment much closer to the natural surroundings of living organism than the crystals used in crystallography [1].
The NMR technique provides a set of inter-atomic distances for certain pairs of atoms of a given protein. The molecular distance geometry problem (MDGP) arises in NMR analysis context. The MDGP consists of finding one set of atomic coordinates such that a given list of geometric constraints are satisfied [2]. Formally, the molecular distance geometry problem can be defined as the problem of finding Cartesian coordinates ${x}_{\mathsf{\text{1}}},...,{x}_{n}\in {\mathbb{R}}^{\mathsf{\text{3}}}$ of atoms of a molecule such that l_{ ij } ≤ ||x_{ i } - x_{ j }|| ≤ u_{ ij }, ∀(i, j) ∈ E, where the bounds l_{ ij } and u_{ ij } for the Euclidean distances of pairs of atoms (i, j) ∈ E are given a priori [3].
Clearly, $x=\left({x}_{\mathsf{\text{1}}},...,{x}_{n}\right)\in {\mathbb{R}}^{\mathsf{\text{3}}n}$ solves the MDGP if, and only if, x is a global minimizer of f and f(x) = 0.
An overview on methods applied to the MDGP is given in [4] and a very recent survey on distance geometry is given in [5].
where $i\in \left\{{i}_{1},{i}_{2},{i}_{3},{i}_{4}\right\}\subset \mathcal{B}$ and d_{ ij } = ||x_{ j } - x_{ i }||. The indexes i_{1}, i_{2}, i_{3}, i_{4} can be chosen in an arbitrarily way, allowing us to choose another base subset when calculating the coordinate of the next x_{ j }. At each iteration, the index j of the new coordinate x_{ j } is inserted in the set $\mathcal{B}$ increasing the number of subsets {i_{1}, i_{2}, i_{3}, i_{4}} used as anchors to fix the remaining unset coordinates.
Unfortunately, in practice, the NMR experiments just provide a subset of distances between atoms spatially close and the data accuracy is limited. Thus in the real scenario, the set E is sparse and l_{ ij } < u_{ ij }. So, we just have bounds to some of the entries of the distance matrix D. In this situation, neither the singular value decomposition nor the buildup algorithm can be applied directly because they are both designed to deal with exact distances. In fact, the inaccurate and sparse instances of MDGP, where l_{ ij } <u_{ ij }, are much harder to solve as pointed by Moré and Wu who showed that the MDGP with inaccurate distances belongs to the NP-hard class of problems [7].
Our contribution is a new algorithm that can handle with inaccurate and sparse distance data. We propose an iterative method based on simple ideas: generate an approximated distance matrix D, take as base a clique in the graph that has D as a connectivity matrix, solve the system (1) and refine the solution using a nonlinear least-squares method. It needs to be pointed that the authors of the buildup algorithm and coworkers have done some modifications in the original form of the algorithm in order to handle inaccurate data [8, 9]. However, the main advantage of our proposal is its simplicity and robustness. We have been able to find solutions with acceptable quality to instances of MDGP with inaccurate and sparse data, considering up to thousands of atoms.
The new iterative method
Defining the initial base
The set E of pairs (i, j) and the set of indexes V = {1, 2, ..., n} can be considered as a set of edges and a set of vertexes of a graph G = (V, E), respectively. One may decide to use as base the biggest complete subgraph of G. The problem of calculating the biggest complete subgraph belongs to the NP-complete class and it has a large number of applications (for a review in this subject consult [10]). We decided to use the algorithm cliquer proposed by Östergård in [11, 12] mainly because its good behavior in graphs of moderately size and its availability on the Internet [13, 14]. The cliquer algorithm uses a branch-and-bound algorithm developed by Östergård [15], which is based on an algorithm proposed by Carraghan and Pardalos [16].
Setting the coordinates
for t_{ ij } ∈ [0, 1] for each (i, j) ∈ E. With this choice, we have l_{ ij } ≤ d_{ ij } ≤ u_{ ij }, but D may not be an EDM with appropriated embedding dimension (k = 3). This may happen because the entries d_{ ij } can violate the triangular inequality d_{ ij } ≤ d_{ ik } + d_{ jk } for some indexes i, j, k, or because the rank of D is greater than 3. With this in mind, instead of considering the solution given by singular value decomposition directly, we take the columns (eigenvectors) of U associated with the 3 largest eigenvalues, getting the best 3-approximation rank of the solution to xx^{ t } = D(t) [17].
Refinement process
The function φ_{ τ,λ } is infinitely differentiable with respect to x, and therefore allows the application of classical optimization methods. The function φ_{ τ,λ } is a variation of the hyperbolic penalty technique used in [18, 19]. In order to minimize the function φ_{ τ,λ }, we used the local minimization routine va35 encoded in FORTRAN and available at Harwell Subroutine Library. The routine va35 implements the method BFGS with limited memory [20] (For additional information on this routine, see [21]).
and then we refine the solution by minimizing the function φ_{ λ,τ }(x) restricted to the index j and to the indexes in the base (see eq. (3)). Each newly calculated coordinate is included in the base. In the end, some points may not be fixed because they have less than four constraints involving the points in the base. In this case, we just position these points solving an undetermined system defined by constraints with points in the base. Our presented ideas are compiled in the algorithm lsbuild (see Additional file 1).
Methods
We have implemented our algorithm lsbuild in Matlab and tested it with a set of model problems on an Intel Core 2 Quad CPU Q9550 2.83 GHz, 4GB of RAM and Linux OS-32 bits. In all experiments the parameters of the function φ_{ λ,τ } of the algorithm lsbuild were set at λ = 1.0 and at τ = 0.01.
The algorithm dgsol starts with an approximated solution and, given a sequence of smoothing parameters λ_{0} > λ_{1} > ... > λ_{ p } = 0, it determines a minimizer x_{k+1}of 〈f〉_{λ}. The algorithm dgsol uses the previous minimizer x_{ k } as the starting point for the search. In this manner a sequence of minimizers x_{1}, ..., x_{p+1}is generated, with the x_{p+1}a minimizer of f and the candidate for the global minimizer. In our experiments, we used the implementation of the algorithm dgsol encoded in language C and downloaded from [23].
with variables ${x}_{i}=\left({x}_{i}^{1},{x}_{i}^{2},{x}_{i}^{3}\right),{x}_{j}=\left({x}_{j}^{1},{x}_{j}^{2},{x}_{j}^{3}\right)\in {\mathbb{R}}^{3}$ and B being the set formed by the index k and the indexes of all neighbors of x_{ k } in the current base set. The parameters d_{ kj } are the given distances between the node x_{ k } and its neighbors x_{ j } in the base and, for the nodes x_{ j } and x_{ i } already in the base, if the distance between them is unknown, we consider d_{ ij } = ||x_{ i } - x_{ j }||. Once the substructure is obtained, it is inserted in the original structure by an appropriated rotation and translation and the point x_{ k } is included in the base. This process is repeated until all nodes are included in the base. We have implemented the buildup algorithm in Matlab.
Our decision to compare the lsbuild with the algorithms dgsol and buildup is mainly motivated by theirs similarities with our proposal. In fact, the algorithm dgsol uses a smooth technique in order to avoid the local minimizers and the algorithm buildup solves a sequence of systems which produce partial solutions and iteratively try to construct a candidate to global solution. Our algorithm combines some variations of these two ideas. We use a hyperbolic smooth technique to insert differentiability in the problem and a divide-and-conquer approach based in sucessive solutions of overdetermined linear systems in order to construct a candidate to global solution.
In our experiments, the distance data were derived from the real structural data from the Protein Data Bank (PDB) [24]. It needs to be pointed that each of the algorithms considered has a level of randomness, the algorithm dgsol takes random start point and the algorithms lsbuild and buildup starts with an incomplete random matrix D = [d_{ ij }] where l_{ ij } ≤ d_{ ij } ≤ u_{ ij }. So, in order to do a fair comparison, we run each test 30 times.
where R(k) represents the k-th residue.
where ${\widehat{d}}_{ij}$ is the real distance between the nodes x_{ i } and x_{ j } in the known structure x* of protein PDB:1GPV. In this way, all distances between atoms in the same residue or neighboring residues were considered. We generated two instances for each fragment by taking ε equals to 0.00 and 0.08.
is the error associated to the constraint l_{ ij } ≤ ||x_{ i } - x_{ j }|| ≤ u_{ ij }: We also measured the deviation
with $h\in {\mathbb{R}}^{n\times 3}$ and $Q\in {\mathbb{R}}^{3\times 3}$ orthogonal.
where ${d}_{ij}^{*}$ is the true distance between atom i and atom j and ${\stackrel{\u0304}{\epsilon}}_{ij},{\underset{-}{\epsilon}}_{ij}~\mathcal{N}\left(0,{\sigma}_{ij}^{2}\right)$ (normal distribution). With this model, we generate a sparse set of constraints and introduce a noise in the distances that are not so simple as the one used in the instances proposed by Moré and Wu.
Results and discussion
RMSD, LDME and the CPU time in seconds for PDB:1GPV protein.
Fragment with 100 atoms | ||||||
---|---|---|---|---|---|---|
ε = 0.00 | ε = 0.08 | |||||
〈 LDME 〉 | 〈 RMSD 〉 | 〈 TIME 〉 | 〈 LDME 〉 | 〈 RMSD 〉 | 〈 TIME 〉 | |
dgsol | 8.29E-03 | 3.93E-01 | 3.61E+00 | 3.31E-03 | 8.25E-01 | 4.40E+00 |
buildup | 3.50E-15 | 1.46E-14 | 1.08E-01 | 0.00E+00 | 3.13E-01 | 1.08E-01 |
lsbuild | 6.47E-15 | 1.20E-14 | 1.51E-01 | 0.00E+00 | 7.77E-02 | 1.33E-01 |
Fragment with 200 atoms | ||||||
ε = 0.00 | ε = 0.08 | |||||
〈LDME〉 | 〈RMSD〉 | 〈TIME〉 | 〈LDME〉 | 〈RMSD〉 | 〈TIME〉 | |
dgsol | 3.18E-02 | 2.58E+00 | 1.48E+01 | 4.00E-03 | 2.45E+00 | 1.73E+01 |
buildup | 4.85E-15 | 2.45E-14 | 3.11E-01 | 0.00E+00 | 5.18E-01 | 3.11E-01 |
lsbuild | 1.90E-14 | 5.21E-14 | 6.01E-01 | 0.00E+00 | 4.21E-01 | 5.25E-01 |
RMSD and LDME for the larger instance set.
〈LDME〉 | 〈RMSD〉 | ||||||
---|---|---|---|---|---|---|---|
PDB | |V| | lsbuild | buildup | dgsol | lsbuild | buildup | dgsol |
1PTQ | 402 | 2.61E-03 | 1.80E+00 | 5.41E-01 | 1.31E-02 | 9.49E+00 | 6.89E+00 |
1LFB | 641 | 2.03E-04 | 1.84E+00 | 3.91E-01 | 4.19E-03 | 1.23E+01 | 5.48E+00 |
1AX8 | 1003 | 2.00E-04 | 1.83E+00 | 4.33E-01 | 1.62E-02 | 1.35E+01 | 7.95E+00 |
1F39 | 1534 | 3.03E-02 | 1.89E+00 | 4.74E-01 | 4.22E-01 | 1.79E+01 | 1.28E+01 |
1RGS | 2015 | 1.08E-01 | 1.87E+00 | 4.73E-01 | 1.74E+00 | 1.92E+01 | 1.35E+01 |
1KDH | 2846 | 1.39E-02 | 1.86E+00 | 5.19E-01 | 9.43E-02 | 2.11E+01 | 1.61E+01 |
1BPM | 3671 | 2.20E-02 | 1.90E+00 | 5.14E-01 | 7.86E-02 | 2.29E+01 | 1.55E+01 |
1TOA | 4292 | 6.90E-03 | 1.89E+00 | 6.75E-01 | 2.56E-01 | 2.52E+01 | 2.39E+01 |
1MQQ | 5681 | 1.93E-02 | 1.91E+00 | 8.86E-01 | 1.89E-01 | 2.50E+01 | 2.50E+01 |
TIME for the larger instance set.
〈TIME〉 | |||
---|---|---|---|
PDB | lsbuild | buildup | dgsol |
1PTQ | 9.99E-01 | 5.34E-01 | 1.03E+01 |
1LFB | 1.86E+00 | 1.01E+00 | 2.55E+01 |
1AX8 | 2.98E+00 | 1.70E+00 | 4.36E+01 |
1F39 | 7.21E+00 | 3.57E+00 | 8.59E+01 |
1RGS | 1.43E+01 | 4.70E+00 | 1.33E+02 |
1KDH | 2.12E+01 | 7.28E+00 | 2.09E+02 |
1BPM | 2.47E+01 | 8.04E+00 | 2.99E+02 |
1TOA | 3.93E+01 | 1.14E+01 | 7.03E+02 |
1MQQ | 3.93E+01 | 1.82E+01 | 7.63E+02 |
Finally, the results of both set of instances indicate that our algorithm lsbuild based on the combination of the resolution of linear systems, derived from the approximated EDM matrices, and the refinement process based on hyperbolic smoothing penalty is a very effective strategy to solve MDGP instances with sparse and inaccurate data.
Conclusions
We presented a new algorithm to solve molecular distance geometry problems with inaccurate distance data. These problems are related to molecular structure calculations using data provided by NMR experiments which, in fact, are not precise. Our algorithm combines the divide-and-conquer framework and a variation of the hyperbolic smoothing technique. The computational results show that the proposed algorithm is an effective strategy to handle uncertainty in the data.
Declarations
Acknowledgements
We are grateful to the anonymous referees for improving this paper and the Brazilian Research Agencies FAPESP and CNPq by their support.
This article has been published as part of BMC Bioinformatics Volume 14 Supplement 9, 2013: Selected articles from the 8th International Symposium on Bioinformatics Research and Applications (ISBRA'12). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/14/S9.
Declarations
The publication of this article is supported by the Brazilian Research Agencies FAPESP and CNPq.
Authors’ Affiliations
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