Methods for Sequence–Structure Alignment

Venclovas, C̆eslovas

doi:10.1007/978-1-61779-588-6_3

Cited by 10 publications

(7 citation statements)

References 88 publications

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“…Profile HMMs require more computations and experience, but they are more useful with low-homology cases since it is possible to detect gaps in the alignment. As a general rule, it is recommended to use profile HMMs with identities below 35% (also where alignment gaps are obvious) while PSSM alignments and sequence-to-sequence alignments can be tolerated with identities above 35% [36]. We believe that it is very important to detect alignment gaps or "shifts" in homology modeling early [37].…”

Section: Tip 5: Mind the Gaps In The Alignmentmentioning

confidence: 99%

Ten quick tips for homology modeling of high-resolution protein 3D structures

2020

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The purpose of this quick guide is to help new modelers who have little or no background in comparative modeling yet are keen to produce high-resolution protein 3D structures for their study by following systematic good modeling practices, using affordable personal computers or online computational resources. Through the available experimental 3Dstructure repositories, the modeler should be able to access and use the atomic coordinates for building homology models. We also aim to provide the modeler with a rationale behind making a simple list of atomic coordinates suitable for computational analysis abiding to principles of physics (e.g., molecular mechanics). Keeping that objective in mind, these quick tips cover the process of homology modeling and some postmodeling computations such as molecular docking and molecular dynamics (MD). A brief section was left for modeling nonprotein molecules, and a short case study of homology modeling is discussed.

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Section: Tip 5: Mind the Gaps In The Alignmentmentioning

confidence: 99%

Ten quick tips for homology modeling of high-resolution protein 3D structures

2020

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“…A standard HHsearch protocol [18,19] is then used to find structurally related templates from remotely homologous sequences. Given that in some cases heuristic algorithms are not accurate enough to obtain a reliable HMM for the target [20], a good alignment is recommended for the generation of a robust HMM profile. For the generation of the HMM, the users can (i) choose to input an alignment of the target with relevant sequences from the same subfamily, (ii) let the server calculate such alignment, or (iii) in the case of human GPCRs, use one of the pre-generated alignment present in the server.…”

Section: The Gomodo Pipelinementioning

confidence: 99%

GOMoDo: A GPCRs Online Modeling and Docking Webserver

et al. 2013

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G-protein coupled receptors (GPCRs) are a superfamily of cell signaling membrane proteins that include >750 members in the human genome alone. They are the largest family of drug targets. The vast diversity and relevance of GPCRs contrasts with the paucity of structures available: only 21 unique GPCR structures have been experimentally determined as of the beginning of 2013. User-friendly modeling and small molecule docking tools are thus in great demand. While both GPCR structural predictions and docking servers exist separately, with GOMoDo (GPCR Online M odeling and D ocking), we provide a web server to seamlessly model GPCR structures and dock ligands to the models in a single consistent pipeline. GOMoDo can automatically perform template choice, homology modeling and either blind or information-driven docking by combining together proven, state of the art bioinformatic tools. The web server gives the user the possibility of guiding the whole procedure. The GOMoDo server is freely accessible at http://molsim.sci.univr.it/gomodo.

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“…After choosing sequences of templates that will be used in the second stage, the optimal alignment between this sequence and the target is required to build three-dimensional models (Centeno et al, 2005 ). The main strategies used are: progressive alignment between the sequences using the software Clustal W (Larkin et al, 2007 ), the sequence-profile alignment, HMM-based method (HIDDEN MARKOV MODEL) between query and profile of families of templates, using up the database profiles Pfam (Finn et al, 2010 ) and HMMER web server (Finn et al, 2011 ) one can still perform profile-profile alignment, from building a profile for the target and matching with the profile templates in a database of profiles (Centeno et al, 2005 ; Ramachandran and Dokholyan, 2012 ; Venclovas, 2012 ).…”

Section: Prediction Of Antimicrobial Peptide From Dna/rna Librarymentioning

confidence: 99%

Strategies and molecular tools to fight antimicrobial resistance: resistome, transcriptome, and antimicrobial peptides

et al. 2013

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The increasing number of antibiotic resistant bacteria motivates prospective research toward discovery of new antimicrobial active substances. There are, however, controversies concerning the cost-effectiveness of such research with regards to the description of new substances with novel cellular interactions, or description of new uses of existing substances to overcome resistance. Although examination of bacteria isolated from remote locations with limited exposure to humans has revealed an absence of antibiotic resistance genes, it is accepted that these genes were both abundant and diverse in ancient living organisms, as detected in DNA recovered from Pleistocene deposits (30,000 years ago). Indeed, even before the first clinical use of antibiotics more than 60 years ago, resistant organisms had been isolated. Bacteria can exhibit different strategies for resistance against antibiotics. New genetic information may lead to the modification of protein structure affecting the antibiotic carriage into the cell, enzymatic inactivation of drugs, or even modification of cellular structure interfering in the drug-bacteria interaction. There are still plenty of new genes out there in the environment that can be appropriated by putative pathogenic bacteria to resist antimicrobial agents. On the other hand, there are several natural compounds with antibiotic activity that may be used to oppose them. Antimicrobial peptides (AMPs) are molecules which are wide-spread in all forms of life, from multi-cellular organisms to bacterial cells used to interfere with microbial growth. Several AMPs have been shown to be effective against multi-drug resistant bacteria and have low propensity to resistance development, probably due to their unique mode of action, different from well-known antimicrobial drugs. These substances may interact in different ways with bacterial cell membrane, protein synthesis, protein modulation, and protein folding. The analysis of bacterial transcriptome may contribute to the understanding of microbial strategies under different environmental stresses and allows the understanding of their interaction with novel AMPs.

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Methods for Sequence–Structure Alignment

Cited by 10 publications

References 88 publications

Ten quick tips for homology modeling of high-resolution protein 3D structures

Ten quick tips for homology modeling of high-resolution protein 3D structures

GOMoDo: A GPCRs Online Modeling and Docking Webserver

Strategies and molecular tools to fight antimicrobial resistance: resistome, transcriptome, and antimicrobial peptides

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