On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

Advances and Challenges in Protein-Ligand Docking

Molecular docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular docking is protein-ligand docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions—the three important aspects in protein-ligand docking. Challenges and possible future directions are discussed in the Conclusion

Development of Computer-Aided Molecular Design Methods for Bioengineering Applications

Computer-aided molecular design (CAMD) offers a methodology for rational product design. The CAMD procedure consists of pre-design, design and post-design phases. CAMD was used to address two bioengineering problems: design of excipients for lyophilized protein formulations and design of ionic liquids for use in bioseparations. Protein stability remains a major concern during protein drug development. Lyophilization, or freeze-drying, is often sought to improve chemical stability. However, lyophilization can result in protein aggregation. Excipients, or additives, are included to stabilize proteins in lyophilized formulations. CAMD was used to rationally select or design excipients for lyophilized protein formulations. The use of solvents to aid separation is common in chemical processes. Ionic liquids offer a class of molecules with tunable properties that can be altered to find optimal solvents for a given application. CAMD was used to design ionic liquids for extractive distillation and in situ extractive fermentation processes. The pre-design phase involves experimental data gathering and problem formulation. When available, data was obtained from literature sources. For excipient design, data of percent protein monomer remaining post-lyophilization was measured for a variety of protein-excipient combinations. In problem formulation, the objective was to minimize the difference between the properties of the designed molecule and the target property values. Problem formulations resulted in either mixed-integer linear programs (MILPs) or mixed-integer non-linear programs (MINLPs). The design phase consists of the forward problem and the reverse problem. In the forward problem, linear quantitative structure-property relationships (QSPRs) were developed using connectivity indices. Chiral connectivity indices were used for excipient property models to improve fit and incorporate three-dimensional structural information. Descriptor selection methods were employed to find models that minimized Mallow's Cp statistic, obtaining models with good fit while avoiding overfitting. Cross-validation was performed to access predictive capabilities. Model development was also performed to develop group contribution models and non-linear QSPRs. A UNIFAC model was developed to predict the thermodynamic properties of ionic liquids. In the reverse problem of the design phase, molecules were proposed with optimal property values. Deterministic methods were used to design ionic liquids entrainers for azeotropic distillation. Tabu search, a stochastic optimization method, was applied to both ionic liquid and excipient design to provide novel molecular candidates. Tabu search was also compared to a genetic algorithm for CAMD applications. Tuning was performed using a test case to determine parameter values for both methods. After tuning, both stochastic methods were used with design cases to provide optimal excipient stabilizers for lyophilized protein formulations. Results suggested that the genetic algorithm provided a faster time to solution while the tabu search provides quality solutions more consistently. The post-design phase provides solution analysis and verification. Process simulation was used to evaluate the energy requirements of azeotropic separations using designed ionic liquids. Results demonstrated that less energy was required than processes using conventional entrainers or ionic liquids that were not optimally designed. Molecular simulation was used to guide protein formulation design and may prove to be a useful tool in post-design verification. Finally, prediction intervals were used for properties predicted from linear QSPRs to quantify the prediction error in the CAMD solutions. Overlapping prediction intervals indicate solutions with statistically similar property values. Prediction interval analysis showed that tabu search returns many results with statistically similar property values in the design of carbohydrate glass formers for lyophilized protein formulations. The best solutions from tabu search and the genetic algorithm were shown to be statistically similar for all design cases considered. Overall the CAMD method developed here provides a comprehensive framework for the design of novel molecules for bioengineering approaches

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Development of genetic algorithm for optimisation of predicted membrane protein structures

Due to the inherent problems with their structural elucidation in the laboratory, the computational prediction of membrane protein structure is an essential step toward understanding the function of these leading targets for drug discovery. In this work, the development of a genetic algorithm technique is described that is able to generate predictive 3D structures of membrane proteins in an ab initio fashion that possess high stability and similarity to the native structure. This is accomplished through optimisation of the distances between TM regions and the end-on rotation of each TM helix. The starting point for the genetic algorithm is from the model of general TM region arrangement predicted using the TMRelate program. From these approximate starting coordinates, the TMBuilder program is used to generate the helical backbone 3D coordinates. The amino acid side chains are constructed using the MaxSprout algorithm. The genetic algorithm is designed to represent a TM protein structure by encoding each alpha carbon atom starting position, the starting atom of the initial residue of each helix, and operates by manipulating these starting positions. To evaluate each predicted structure, the SwissPDBViewer software (incorporating the GROMOS force field software) is employed to calculate the free potential energy. For the first time, a GA has been successfully applied to the problem of predicting membrane protein structure. Comparison between newly predicted structures (tests) and the native structure (control) indicate that the developed GA approach represents an efficient and fast method for refinement of predicted TM protein structures. Further enhancement of the performance of the GA allows the TMGA system to generate predictive structures with comparable energetic stability and reasonable structural similarity to the native structure

Cooperative Particle Swarm Optimization for Combinatorial Problems

A particularly successful line of research for numerical optimization is the well-known computational paradigm particle swarm optimization (PSO). In the PSO framework, candidate solutions are represented as particles that have a position and a velocity in a multidimensional search space. The direct representation of a candidate solution as a point that flies through hyperspace (i.e., Rn) seems to strongly predispose the PSO toward continuous optimization. However, while some attempts have been made towards developing PSO algorithms for combinatorial problems, these techniques usually encode candidate solutions as permutations instead of points in search space and rely on additional local search algorithms. In this dissertation, I present extensions to PSO that by, incorporating a cooperative strategy, allow the PSO to solve combinatorial problems. The central hypothesis is that by allowing a set of particles, rather than one single particle, to represent a candidate solution, combinatorial problems can be solved by collectively constructing solutions. The cooperative strategy partitions the problem into components where each component is optimized by an individual particle. Particles move in continuous space and communicate through a feedback mechanism. This feedback mechanism guides them in the assessment of their individual contribution to the overall solution. Three new PSO-based algorithms are proposed. Shared-space CCPSO and multispace CCPSO provide two new cooperative strategies to split the combinatorial problem, and both models are tested on proven NP-hard problems. Multimodal CCPSO extends these combinatorial PSO algorithms to efficiently sample the search space in problems with multiple global optima. Shared-space CCPSO was evaluated on an abductive problem-solving task: the construction of parsimonious set of independent hypothesis in diagnostic problems with direct causal links between disorders and manifestations. Multi-space CCPSO was used to solve a protein structure prediction subproblem, sidechain packing. Both models are evaluated against the provable optimal solutions and results show that both proposed PSO algorithms are able to find optimal or near-optimal solutions. The exploratory ability of multimodal CCPSO is assessed by evaluating both the quality and diversity of the solutions obtained in a protein sequence design problem, a highly multimodal problem. These results provide evidence that extended PSO algorithms are capable of dealing with combinatorial problems without having to hybridize the PSO with other local search techniques or sacrifice the concept of particles moving throughout a continuous search space

Multiple Biolgical Sequence Alignment: Scoring Functions, Algorithms, and Evaluations

Aligning multiple biological sequences such as protein sequences or DNA/RNA sequences is a fundamental task in bioinformatics and sequence analysis. These alignments may contain invaluable information that scientists need to predict the sequences\u27 structures, determine the evolutionary relationships between them, or discover drug-like compounds that can bind to the sequences. Unfortunately, multiple sequence alignment (MSA) is NP-Complete. In addition, the lack of a reliable scoring method makes it very hard to align the sequences reliably and to evaluate the alignment outcomes. In this dissertation, we have designed a new scoring method for use in multiple sequence alignment. Our scoring method encapsulates stereo-chemical properties of sequence residues and their substitution probabilities into a tree-structure scoring scheme. This new technique provides a reliable scoring scheme with low computational complexity. In addition to the new scoring scheme, we have designed an overlapping sequence clustering algorithm to use in our new three multiple sequence alignment algorithms. One of our alignment algorithms uses a dynamic weighted guidance tree to perform multiple sequence alignment in progressive fashion. The use of dynamic weighted tree allows errors in the early alignment stages to be corrected in the subsequence stages. Other two algorithms utilize sequence knowledge-bases and sequence consistency to produce biological meaningful sequence alignments. To improve the speed of the multiple sequence alignment, we have developed a parallel algorithm that can be deployed on reconfigurable computer models. Analytically, our parallel algorithm is the fastest progressive multiple sequence alignment algorithm