Computational Methods for Remote Homolog Identification

Wan, Xiu Feng; Xu, Dong

doi:10.2174/138920305774933231

Cited by 23 publications

(15 citation statements)

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“…Authors have reviewed [81], [82] and benchmarked [83] these strategies. Often the only way to find remote homologs to a query sequence is through structure links, so structure prediction and remote homolog detection often rely on the same strategies.…”

Section: Methodsmentioning

confidence: 99%

Cholera- and Anthrax-Like Toxins Are among Several New ADP-Ribosyltransferases

et al. 2010

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Chelt, a cholera-like toxin from Vibrio cholerae, and Certhrax, an anthrax-like toxin from Bacillus cereus, are among six new bacterial protein toxins we identified and characterized using in silico and cell-based techniques. We also uncovered medically relevant toxins from Mycobacterium avium and Enterococcus faecalis. We found agriculturally relevant toxins in Photorhabdus luminescens and Vibrio splendidus. These toxins belong to the ADP-ribosyltransferase family that has conserved structure despite low sequence identity. Therefore, our search for new toxins combined fold recognition with rules for filtering sequences – including a primary sequence pattern – to reduce reliance on sequence identity and identify toxins using structure. We used computers to build models and analyzed each new toxin to understand features including: structure, secretion, cell entry, activation, NAD+ substrate binding, intracellular target binding and the reaction mechanism. We confirmed activity using a yeast growth test. In this era where an expanding protein structure library complements abundant protein sequence data – and we need high-throughput validation – our approach provides insight into the newest toxin ADP-ribosyltransferases.

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Section: Methodsmentioning

confidence: 99%

Cholera- and Anthrax-Like Toxins Are among Several New ADP-Ribosyltransferases

et al. 2010

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“…Continual research is advancing methods available for each stage. Methods for sequence alignment and target identification include bioinformatics-based approaches (MUSCLE [11], DIALIGN [12, 13], MuSiC [14], MANGO [15]), machine learning methods (hidden Markov models [16, 17], neural networks [18], support vector machines [19]), and threading methods where target sequence is compared and matched against known three dimensional folds [20]. Model construction includes the techniques based on satisfaction of spatial restraints (Modeller [21]), Cα positions from conserved segments in template structure(s) [22, 23].…”

Section: Protein Modelingmentioning

confidence: 99%

From Laptop to Benchtop to Bedside: Structure-based Drug Design on Protein Targets

Chen

Morrow

Tran

et al. 2012

CPD

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“…Continual research is advancing methods available for each stage. Methods for sequence alignment and target identification include bioinformaticsbased approaches (MUSCLE [11], DIALIGN [12,13], MuSiC [14], MANGO [15]), machine learning methods (hidden Markov models [16,17], neural networks [18], support vector machines [19]), and threading methods where target sequence is compared and matched against known three dimensional folds [20]. Model construction includes the techniques based on satisfaction of spatial restraints (Modeller [21]), C positions from conserved segments in template structure(s) [22,23].…”

Section: Homology Modelingmentioning

confidence: 99%

From Laptop to Benchtop to Bedside: Structure-based Drug Design on Protein Targets

Chen¹,

Morrow²,

Tran³

et al. 2012

curr drug metab

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As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting protein-ligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches. KeywordsStructure-based drug design; protein modeling; focused library design; pharmacophore; flexible docking; high-throughput virtual screening; de novo design; protein-protein interaction; polypharmacology Modern computational-aided drug design established a novel platform by which researchers perform in-depth in silico simulation prior to labor-extensive wet-lab validation [1]. It comprises of two major parts corresponding to the information of molecular source it utilizes: structure-based (or receptor-based) drug design and ligand-based drug design. Structure-based drug design, which relies on the knowledge of biological target structures, aims to discover small molecules/peptides leads with desired chemistry properties, and orchestrate the following experimental validation and lead optimization. Structure-based approach provides mechanism-based basis, where potential ligands are excavated using receptor-dependent parameters, while ligand-based approaches bypass the consideration of complex biomolecular "black box" in a living cell. This in silico method permeates all aspects of drug discovery today [2], and we expect it will draw more attentions with the unprecedented advances of computational power and modeling accuracy in this decade.* Corresponding author: Shuxing Zhang, Ph.D., The University of Texas M. D. Anderson Cancer Center, Department of Experimental Therapeutics, Unit 1950, 1901 East Rd., Houston, TX 77054, Telephone: (713)-745-2958 794-5577, shuzhang@mdanderson.org. Conflict of interestThe authors declare that they do not have competing interests. NIH Public Access Author ManuscriptCurr Pharm Des. Author manuscript; available in PMC 2013 November 07.Published in final edited form as:Curr Pharm Des. 2012 ; 18(9): 1217-1239. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptIn this review, we will overview the state-of-the-art structure-based drug discovery techniques ranging from receptor modeling to lead identification and optimization. In each topic, we will also highlight the fruitful successes as well as ch...

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Computational Methods for Remote Homolog Identification

Cited by 23 publications

References 125 publications

Cholera- and Anthrax-Like Toxins Are among Several New ADP-Ribosyltransferases

Cholera- and Anthrax-Like Toxins Are among Several New ADP-Ribosyltransferases

From Laptop to Benchtop to Bedside: Structure-based Drug Design on Protein Targets

From Laptop to Benchtop to Bedside: Structure-based Drug Design on Protein Targets

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