GADP-align: A genetic algorithm and dynamic programming-based method for structural alignment of proteins

Introduction: Similarity analysis of protein structure is considered as a fundamental step to give insight into the relationships between proteins. The primary step in structural alignment is looking for the optimal correspondence between residues of two structures to optimize the scoring function. An exhaustive search for finding such a correspondence between two structures is intractable. Methods: In this paper, a hybrid method is proposed, namely GADP-align, for pairwise protein structure alignment. The proposed method looks for an optimal alignment using a hybrid method based on a genetic algorithm and an iterative dynamic programming technique. To this end, the method first creates an initial map of correspondence between secondary structure elements (SSEs) of two proteins. Then, a genetic algorithm combined with an iterative dynamic programming algorithm is employed to optimize the alignment. Results: The GADP-align algorithm was employed to align 10 ‘difficult to align’ protein pairs in order to evaluate its performance. The experimental study shows that the proposed hybrid method produces highly accurate alignments in comparison with the methods using exactly the dynamic programming technique. Furthermore, the proposed method prevents the local optimal traps caused by the unsuitable initial guess of the corresponding residues. Conclusion: The findings of this paper demonstrate that employing the genetic algorithm along with the dynamic programming technique yields highly accurate alignments between a protein pair by exploring the global alignment and avoiding trapping in local alignments.

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OPTIMAL PAIRWISE ALIGNMENT OF FIXED PROTEIN STRUCTURES IN SUBQUADRATIC TIME

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720011005562 ◽

2011 ◽

Vol 09 (03) ◽

pp. 367-382 ◽

Cited By ~ 7

Author(s):

ALEKSANDAR POLEKSIC

Keyword(s):

Protein Structure ◽

Structural Alignment ◽

Protein Structures ◽

Dynamic Programming Algorithm ◽

Pairwise Alignment ◽

Structure Alignment ◽

Programming Algorithm ◽

Protein Structure Alignment ◽

Running Time ◽

Speed Accuracy

The problem of finding an optimal structural alignment for a pair of superimposed proteins is often amenable to the Smith–Waterman dynamic programming algorithm, which runs in time proportional to the product of lengths of the sequences being aligned. While the quadratic running time is acceptable for computing a single alignment of two fixed protein structures, the time complexity becomes a bottleneck when running the Smith–Waterman routine multiple times in order to find a globally optimal superposition and alignment of the input proteins. We present a subquadratic running time algorithm capable of computing an alignment that optimizes one of the most widely used measures of protein structure similarity, defined as the number of pairs of residues in two proteins that can be superimposed under a predefined distance cutoff. The algorithm presented in this article can be used to significantly improve the speed–accuracy tradeoff in a number of popular protein structure alignment methods.

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Neural network model-based on-line re-optimisation control of fed-batch processes using a modified iterative dynamic programming algorithm

Chemical Engineering and Processing - Process Intensification ◽

10.1016/j.cep.2004.07.001 ◽

2005 ◽

Vol 44 (4) ◽

pp. 477-484 ◽

Cited By ~ 37

Author(s):

Zhihua Xiong ◽

Jie Zhang

Keyword(s):

Neural Network ◽

Dynamic Programming ◽

Network Model ◽

Neural Network Model ◽

Dynamic Programming Algorithm ◽

Batch Processes ◽

Programming Algorithm ◽

Fed Batch ◽

On Line ◽

Iterative Dynamic Programming

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Tree Diet: Reducing the Treewidth to Unlock FPT Algorithms in RNA Bioinformatics

10.21203/rs.3.rs-1082732/v1 ◽

2021 ◽

Author(s):

Bertrand Marchand ◽

Yann Ponty ◽

Laurent Bulteau

Keyword(s):

Dynamic Programming ◽

Dynamic Programming Algorithm ◽

Tree Decomposition ◽

Structure Alignment ◽

Programming Algorithm ◽

Fixed Parameter Tractable ◽

Fpt Algorithms ◽

Graph Problems ◽

Time Space ◽

Diet Problem

Abstract Hard graph problems are ubiquitous in Bioinformatics, inspiring the design of specialized Fixed-Parameter Tractable algorithms, many of which rely on a combination of tree-decomposition and dynamic programming. The time/space complexities of such approaches hinge critically on low values for the treewidth tw of the input graph. In order to extend their scope of applicability, we introduce the Tree-Diet problem, i.e. the removal of a minimal set of edges such that a given tree-decomposition can be slimmed down to a prescribed treewidth tw. Our rationale is that the time gained thanks to a smaller treewidth in a parameterized algorithm compensates the extra post-processing needed to take deleted edges into account. Our core result is an FPT dynamic programming algorithm for Tree-Diet, using 2^O(tw)n time and space. We complement this result with parameterized complexity lower-bounds for stronger variants (e.g., NP-hardness when tw or tw − tw is constant). We propose a prototype implementation for our approach which we apply on difficult instances of selected RNA-based problems: RNA design, sequence-structure alignment, and search of pseudoknotted RNAs in genomes, revealing very encouraging results. This work paves the way for a wider adoption of tree-decomposition-based algorithms in Bioinformatics.

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Optimizing motion trajectories of dextrous fingers by dynamic programming technique

Robotica ◽

10.1017/s0263574700010626 ◽

1992 ◽

Vol 10 (5) ◽

pp. 419-426 ◽

Cited By ~ 3

Author(s):

Ali Meghdari ◽

Hassan Sayyaadi

Keyword(s):

Dynamic Programming ◽

Motion Control ◽

Degrees Of Freedom ◽

Optimization Technique ◽

Dynamic Programming Algorithm ◽

Programming Algorithm ◽

Programming Technique ◽

Motion Trajectories ◽

Feasible Solutions ◽

Dextrous Hand

SUMMARYAn optimization technique based on the well known Dynamic Programming Algorithm is applied to the motion control trajectories and path planning of multi-jointed fingers in dextrous hand designs. A three-fingered hand with each finger containing four degrees of freedom is considered for analysis. After generating the kinematics and dynamics equations of such a hand, optimum values of the joints torques and velocities are computed such that the finger-tips of the hand are moved through their prescribed trajectories with the least time or/and energy to reach the object being grasped. Finally, optimal as well as feasible solutions for the multi-jointed fingers are identified and the results are presented.

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