Three-dimensional protein structure prediction: Methods and computational strategies

Dorn, Márcio; Silva, Mariel Barbachan e; Buriol, Luciana S.; Lamb, Luís C.

doi:10.1016/j.compbiolchem.2014.10.001

Cited by 163 publications

(110 citation statements)

References 394 publications

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“…Moreover, due to strict laboratory requirements and heavy operational burdens, it is not always feasible to determine the protein structure experimentally. Therefore, researchers focused on predicting protein structure from the given amino acid sequences by computational methods [6]. A successful study of computational methods in PSP reveals two facts.…”

Section: Introductionmentioning

confidence: 99%

Improved Bees Algorithm for Protein Structure Prediction Using AB Off-Lattice Model

Jana

Sil

Das

2015

Advances in Intelligent Systems and Computing

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Protein Structure Prediction (PSP) using sequence of amino acids is a multimodal optimization problem and belongs to NP hard class. Researchers and scientists put their efforts to design efficient computational intelligent algorithm for solving this kind of problem. Bees Algorithm (BA) is a swarm intelligence based algorithm inspired by the foraging behaviour of honey bees colony, already exhibits its potential ability for solving optimization problems. However, it may produce premature convergence when solving PSP like problems. To prevent this situation, Adaptive Polynomial Mutation based Bees Algorithm (APM-BA) has been proposed in this paper for predicting protein structure in 2D AB off-lattice model. In this strategy, each of best scout bees are mutated with adaptive polynomial mutation technique when their performances are no more improve during execution phase. The experiments are conducted on artificial and real protein sequences and numerical results show that the proposed algorithm has strong ability for solving PSP problem having minimum energy.

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Section: Introductionmentioning

confidence: 99%

Improved Bees Algorithm for Protein Structure Prediction Using AB Off-Lattice Model

Jana

Sil

Das

2015

Advances in Intelligent Systems and Computing

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“…Perhaps, the simplest conception is to use sequence homology based on comparisons to known structures to infer structural elements for proteins of interest [17–19]. Over the years other computational techniques have advanced significantly and protein structure prediction efforts can currently be grouped into those using ab initio methods (or ab initio guided approaches) and template-based methods (fold recognition and threading, comparative modelling) [20]. Currently, protein structure prediction remains a highly active and rapidly evolving area of research [21–23].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Peptide Ion Conformers Arising from Non-Helical and Helical Peptides Using Ion Mobility Spectrometry and Gas-Phase Hydrogen/Deuterium Exchange

Karanji

Khakinejad

Kondalaji

et al. 2018

J. Am. Soc. Mass Spectrom.

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The dominant gas-phase conformer of [M+3H]3+ ions of the model peptide Acetyl-PSSSSKSSSSKSSSSKSSSSK has been examined with ion mobility spectrometry (IMS), gas-phase hydrogen deuterium exchange (HDX), and mass spectrometry (MS) techniques. The [M+3H]3+ peptide ions are observed predominantly as a relatively compact conformer type. Upon subjecting these ions to electron transfer dissociation (ETD), the level of protection for each amino acid residue in the peptide sequence is assessed. The overall per-residue deuterium uptake is observed to be relatively more efficient for the neutral residues than for the model peptide Acetyl-PAAAAKAAAAKAAAAKAAAAK. In comparison, the N-terminal and C-terminal regions of the serine peptide show greater relative protection compared with interior residues. Molecular dynamics (MD) simulations have been used to generate candidate structures for collision cross section and HDX reactivity matching. Hydrogen accessibility scoring (HAS) for select structural candidates from MD simulations has been used to suggest conformer types that could contribute to the observed HDX patterns. The results are discussed with respect to recent studies employing extensive MD simulations of gas-phase structure establishment of a peptide system.

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“…If a BLAST search cannot safely indicate relatives, we apply different tools like dynamic programming [Smith & Waterman, 1981; also see UNIT 3.10 (Ropelewski, Nicholas, & Deerfield, 2004)], hidden Markov models (Krogh, Brown, Mian, Sjolander, & Haussler, 1994), and structural comparisons and predictions (Dorn, MB, Buriol, & Lamb, 2014). If a BLAST search cannot safely indicate relatives, we apply different tools like dynamic programming [Smith & Waterman, 1981; also see UNIT 3.10 (Ropelewski, Nicholas, & Deerfield, 2004)], hidden Markov models (Krogh, Brown, Mian, Sjolander, & Haussler, 1994), and structural comparisons and predictions (Dorn, MB, Buriol, & Lamb, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…BLAST is also the first tool to use when looking for homologs of a new protein, mRNA, or genomic DNA sequence. If a BLAST search cannot safely indicate relatives, we apply different tools like dynamic programming [Smith & Waterman, 1981; also see UNIT 3.10 (Ropelewski, Nicholas, & Deerfield, 2004)], hidden Markov models (Krogh, Brown, Mian, Sjolander, & Haussler, 1994), and structural comparisons and predictions (Dorn, MB, Buriol, & Lamb, 2014).…”

Section: Introductionmentioning

confidence: 99%

Finding Homologs in Amino Acid Sequences Using Network BLAST Searches

Ladunga

2017

CP in Bioinformatics

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BLAST, the Basic Local Alignment Search Tool, is used more frequently than any other biosequence database search program. We show how to run searches on the Web, and demonstrate how to increase performance by fine-tuning arguments for a specific research project. We offer guidance for interpreting results, statistical significance and biological relevance issues, and suggest complementary analyses. This unit covers both protein-to-protein (blastp) searches and translated searches (blastx, tblastn, tfastx). blastx conceptually translates the query sequence and tblastn translates all nucleotide sequences in a database, while tblastx translates both the query and the database sequences into amino acid sequences. © 2017 by John Wiley & Sons, Inc.

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Three-dimensional protein structure prediction: Methods and computational strategies

Cited by 163 publications

References 394 publications

Improved Bees Algorithm for Protein Structure Prediction Using AB Off-Lattice Model

Improved Bees Algorithm for Protein Structure Prediction Using AB Off-Lattice Model

Comparison of Peptide Ion Conformers Arising from Non-Helical and Helical Peptides Using Ion Mobility Spectrometry and Gas-Phase Hydrogen/Deuterium Exchange

Finding Homologs in Amino Acid Sequences Using Network BLAST Searches

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