DFS-generated pathways in GA crossover for protein structure prediction

Hoque, Tamjidul; Chetty, Madhu; Lewis, Andrew; Sattar, Abdul; Avery, Vicky M.

doi:10.1016/j.neucom.2010.02.021

Cited by 19 publications

(12 citation statements)

References 67 publications

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“…An initial population was generated randomly and initialized an n -1 dimensional space within a fixed range. This study applied the method of the random conformation generation by Depth-first search (Hoque et al, 2010) to produce the initial population.…”

Section: Initializationmentioning

confidence: 99%

A Memetic Algorithm for Protein Structure Prediction Based on the 2d Triangular Lattice Model

Tsay¹,

Su²

2012

Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms

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Proteins play fundamental and crucial roles in nearly all biological processes, such as, enzymatic catalysis, signaling transduction, embryonic development, and DNA and RNA synthesis. The main function of the protein is decided by its structure. Therefore, many researchers are interested in the prediction of protein structure. The HP model is one of the commonly used models. But most research on the HP lattice model focuses on how to solve the problem of optimization and ignores the purpose of protein structure prediction, namely the prediction of structure similarity between proteins. The 2D triangular lattice model used in this study can predicate protein structure more closely to its topology compared to the 2D square model commonly used in the past. Besides proposing an effective memetic algorithm (MA), this study also investigated structure similarity of natural proteins.

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Section: Initializationmentioning

confidence: 99%

A Memetic Algorithm for Protein Structure Prediction Based on the 2d Triangular Lattice Model

Tsay¹,

Su²

2012

Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms

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“…They used Depth First Search (DFS) to generate pathways [31] in GA crossover for PSP. They also introduced a twin-removal operator [32] to remove duplicates from the population and thus to prevent the search from stalling.…”

Section: Introductionmentioning

confidence: 99%

Spiral search: a hydrophobic-core directed local search for simplified PSP on 3D FCC lattice

et al. 2013

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BackgroundProtein structure prediction is an important but unsolved problem in biological science. Predicted structures vary much with energy functions and structure-mapping spaces. In our simplified ab initio protein structure prediction methods, we use hydrophobic-polar (HP) energy model for structure evaluation, and 3-dimensional face-centred-cubic lattice for structure mapping. For HP energy model, developing a compact hydrophobic-core (H-core) is essential for the progress of the search. The H-core helps find a stable structure with the lowest possible free energy.ResultsIn order to build H-cores, we present a new Spiral Search algorithm based on tabu-guided local search. Our algorithm uses a novel H-core directed guidance heuristic that squeezes the structure around a dynamic hydrophobic-core centre. We applied random walks to break premature H-cores and thus to avoid early convergence. We also used a novel relay-restart technique to handle stagnation.ConclusionsWe have tested our algorithms on a set of benchmark protein sequences. The experimental results show that our spiral search algorithm outperforms the state-of-the-art local search algorithms for simplified protein structure prediction. We also experimentally show the effectiveness of the relay-restart.

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“…It 344 contains a population of chromosomes where each chromosome represents a possible solution to the 345 problem under consideration. In general, a GA operates by initializing the population randomly, and by 346 iteratively updating the population through various operators including elitism, crossover, and mutation to 347 discover, prioritize and recombine good building blocks present in parent chromosomes to finally obtain 348 fitter chromosome (Hoque, et al, 2010;Hoque, et al, 2007;Hoque and Iqbal, 2017). scale_pos_weight, tree_method, and max_bin were set to 6, 0.1, 1, 'multi:softprob', 2, 100, 5, 0.9, 3, 'hist' 359 and 500, respectively and the rest of the parameters were set to their default value.…”

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confidence: 99%

AIRBP: Accurate identification of RNA-binding proteins using machine learning techniques

Mishra¹,

Khanal

Hoque

2020

Preprint

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11Motivation: Identification of RNA-binding proteins (RBPs) that bind to ribonucleic acid molecules, is an 12 important problem in Computational Biology and Bioinformatics. It becomes indispensable to identify 13 RBPs as they play crucial roles in post-transcriptional control of RNAs and RNA metabolism as well as 14 have diverse roles in various biological processes such as splicing, mRNA stabilization, mRNA 15 localization, and translation, RNA synthesis, folding-unfolding, modification, processing, and degradation. 16The existing experimental techniques for identifying RBPs are time-consuming and expensive. Therefore, 17identifying RBPs directly from the sequence using computational methods can be useful to efficiently 18 annotate RBPs and assist the experimental design. In this work, we present a method, called AIRBP, which 19 is designed using an advanced machine learning technique, called stacking, to effectively predict RBPs by 20 utilizing features extracted from evolutionary information, physiochemical properties, and disordered 2 21 properties. Moreover, our method, AIRBP is trained on the useful feature-subset identified by the 22 evolutionary algorithm (EA). 23 Results: The results show that AIRBP attains Accuracy (ACC), F1-score, and MCC of 95.38%, 0.917, and 24 0.885, respectively, based on the benchmark dataset, using 10-fold cross-validation (CV). Further 25 evaluation of AIRBP on independent test set reveals that it achieves ACC, F1-score, and MCC of 93.04%, 26 0.943, and 0.855, for Human test set; 91.60%, 0.942 and 0.789 for S. cerevisiae test set; and 91.67%, 0.953 27 and 0.594 for A. thaliana test set, respectively. These results indicate that AIRBP outperforms the current 28 state-of-the-art method. Therefore, the proposed top-performing AIRBP can be useful for accurate 29 identification and annotation of RBPs directly from the sequence and help gain valuable insight to treat 30 critical diseases. 50 and study of RBPs and RNP complexes date back to almost half a century ago where experimental methods 51 such as purification of mRNPs from in vitro UV-irradiated polysomal fractions (Greenberg, 1979), from 52 UV-irradiated intact cells (Wagenmakers, et al., 1980) and untreated cells (Lindberg and Sundquist, 1974) 53 revealed the association of a specific set of proteins with mRNA (Baltz, et al., 2012). Recently, cutting-54 edge experimental approaches are developed to recognize numerous RBPs, which include identification of 55 860 RBPs in human HeLa cells (Castello, et al., 2012) using UV crosslinking methods, 797 RBPs in human 56 embryonic kidney cell line (Baltz, et al., 2012) using photoreactive nucleotide-enhanced UV crosslinking 4 57 and oligo(dT) purification approach, 555 mRNA-binding proteins from mouse embryonic stem cells 58 (Kwon, et al., 2013) using UV crosslinking, oligo(dT) and Mass Spectrometry and 120 RBPs from S. 59 cerevisiae cells (Mitchell, et al., 2013) using UV crosslinking and purification methods. These experiments 60for identifying and analyzing of RBPs, have broadened ou...

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DFS-generated pathways in GA crossover for protein structure prediction

Cited by 19 publications

References 67 publications

A Memetic Algorithm for Protein Structure Prediction Based on the 2d Triangular Lattice Model

A Memetic Algorithm for Protein Structure Prediction Based on the 2d Triangular Lattice Model

Spiral search: a hydrophobic-core directed local search for simplified PSP on 3D FCC lattice

AIRBP: Accurate identification of RNA-binding proteins using machine learning techniques

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