Flexible protein folding by ant colony optimization

Protein structure prediction is one of the most challenging topics in bioinformatics. As the protein structure is found to be closely related to its functions, predicting the folding structure of a protein to judge its functions is meaningful to the humanity. This chapter proposes a flexible ant colony (FAC) algorithm for solving protein folding problems (PFPs) based on the hydrophobic-polar (HP) square lattice model. Different from the previous ant algorithms for PFPs, the pheromones in the proposed algorithm are placed on the arcs connecting adjacent squares in the lattice. Such pheromone placement model is similar to the one used in the traveling salesmen problems (TSPs), where pheromones are released on the arcs connecting the cities. Moreover, the collaboration of effective heuristic and pheromone strategies greatly enhances the performance of the algorithm so that the algorithm can achieve good results without local search methods. By testing some benchmark two-dimensional hydrophobic-polar (2D-HP) protein sequences, the performance shows that the proposed algorithm is quite competitive compared with some other well-known methods for solving the same protein folding problems

Soft Computing Techiniques for the Protein Folding Problem on High Performance Computing Architectures

The protein-folding problem has been extensively studied during the last fifty years. The understanding of the dynamics of global shape of a protein and the influence on its biological function can help us to discover new and more effective drugs to deal with diseases of pharmacological relevance. Different computational approaches have been developed by different researchers in order to foresee the threedimensional arrangement of atoms of proteins from their sequences. However, the computational complexity of this problem makes mandatory the search for new models, novel algorithmic strategies and hardware platforms that provide solutions in a reasonable time frame. We present in this revision work the past and last tendencies regarding protein folding simulations from both perspectives; hardware and software. Of particular interest to us are both the use of inexact solutions to this computationally hard problem as well as which hardware platforms have been used for running this kind of Soft Computing techniques.This work is jointly supported by the FundaciónSéneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under grants 15290/PI/2010 and 18946/JLI/13, by the Spanish MEC and European Commission FEDER under grant with reference TEC2012-37945-C02-02 and TIN2012-31345, by the Nils Coordinated Mobility under grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF). We also thank NVIDIA for hardware donation within UCAM GPU educational and research centers.Ingeniería, Industria y Construcció

3D Protein structure prediction with genetic tabu search algorithm

Abstract Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively

Genetic algorithm in ab initio protein structure prediction using low resolution model : a review

Proteins are sequences of amino acids bound into a linear chain that adopt a specific folded three-dimensional (3D) shape. This specific folded shape enables proteins to perform specific tasks. The protein structure prediction (PSP) by ab initio or de novo approach is promising amongst various available computational methods and can help to unravel the important relationship between sequence and its corresponding structure. This article presents the ab initio protein structure prediction as a conformational search problem in low resolution model using genetic algorithm. As a review, the essence of twin removal, intelligence in coding, the development and application of domain specific heuristics garnered from the properties of the resulting model and the protein core formation concept discussed are all highly relevant in attempting to secure the best solution

Novel memetic algorithm for protein structure prediction

A novel Memetic Algorithm (MA) is proposed for investigating the complex ab initio protein structure prediction problem. The proposed MA has a new fitness function incorporating domain knowledge in the form of two new measures (H-compliance and P-compliance) to indicate hydrophobic and hydrophilic nature of a residue. It also includes two novel techniques for dynamically preserving best fit schema and for providing a guided search. The algorithm performance is investigated with the aid of commonly studied 2D lattice hydrophobic polar (HP) model for the benchmark as well as non-benchmark sequences. Comparative studies with other search algorithms reveal superior performance of the proposed techniqu

A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction

Protein structure prediction is computationally a very challenging problem. A large number of existing search algorithms attempt to solve the problem by exploring possible structures and finding the one with the minimum free energy. However, these algorithms perform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multipoint spiral search framework that uses parallel processing techniques to expedite exploration by starting from different points. In our approach, a set of random initial solutions are generated and distributed to different threads. We allow each thread to run for a predefined period of time. The improved solutions are stored threadwise. When the threads finish, the solutions are merged together and the duplicates are removed. A selected distinct set of solutions are then split to different threads again. In our ab initio protein structure prediction method, we use the three-dimensional face-centred-cubic lattice for structure-backbone mapping. We use both the low resolution hydrophobic-polar energy model and the high-resolution 20×20 energy model for search guiding. The experimental results show that our new parallel framework significantly improves the results obtained by the state-of-the-art single-point search approaches for both energy models on three-dimensional face-centred-cubic lattice. We also experimentally show the effectiveness of mixing energy models within parallel threads

Computational approaches guiding for the design and optimization of novel chemo-types endowed with F508del-CFTR modulator ability

To date, monotherapy with VX-809 (Lumacaftor) or VX-770 (Ivacaftor) has not resulted in obvious clinical benefits for CF patients, while their combination regimen has provided positive results, stabilizing disease progress. Consequently, therapy combined with dual modulators or triple combination represents today the most promising prospect for developing new therapies. In this context, the research group in which I have been carrying out this thesis has dealt with rational design and computational studies of CFTR modulators during the past few years. The information obtained from our previous studies allowed us to proceed with the rational design and to predict the possible corrective activity of a new series of compounds with an aminoarylthiazole structure (AAT)1.,165. The previously proposed studies' reliability was supported by biological studies carried out on the newly synthesized molecules in collaboration with the research group led by Dr. Nicoletta Pedemonte (Istituto Giannina Gaslini, Genoa), verifying the corrective activity for F508del-CFTR of the newly designed derivatives. About the computational approaches so far applied, a QSAR model has been developed on the correctors available in literature guiding the following design and synthesis of hybrids compounds. This ligand-based method was used to overcome the paucity of information regarding a single and specific mechanism of action responsible for the corrective activity of VX-809. Indeed, as described in the literature, several hypotheses suggest multiple sites on the CFTR protein to which VX-809 could bind, first of all, the NBD1 domain. This thesis deepened the structure-based approach concerning various correctors described in the literature, including the hybrids developed by the present research group. In this context, experimental but partial data of the NBD1 domain of F508del-CFTR (PDB code: 4WZ6) were considered to perform molecular docking simulations of the compounds mentioned above. This research has been completed by molecular docking calculations performed on a whole model of the F508del-CFTR protein, which has been built in silico by our research group. Unlike what occurs for CFTR correctors, applying structure-based methods in the rational design of potentiators appears to be a more straightforward strategy since the experimental data concerning the binding mode of the VX-770 potentiator has recently become available (PDB code = 6O2P) and GLP1837 (PDB code = 6O1V). Starting from these assumptions, in this thesis, several libraries of compounds, described in the literature as CFTR potentiators, such as indoles, pyrazolquinolines, thienopyranes, cyanoquinolines, and AAT, have been studied to perform molecular docking studies and QSAR analysis activities. These approaches allowed us to obtain information to guide the rational design and future synthesis of new CFTR modulators. The research activity's further goal was to apply - in parallel to the studies just mentioned - ligand-based drug design analysis, using classical QSAR type analysis. This approach made it possible to overcome any limitation related to uniquely examining a single possible target for CFTR modulators and focusing on chemical scaffolds known today as correctors or potentiators