TM-MOTIF: an alignment viewer to annotate predicted transmembrane helices and conserved motifs in aligned set of sequences

Nagarathnam, Balasubramanian; Sankar, Kalyanasundaram; Dharnidharka, Varadhan; Balakrishnan, V.; Archunan, Govindaraju; Sowdhamini, Ramanathan

doi:10.6026/97320630007214

Cited by 5 publications

(12 citation statements)

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“…In the all-α class, the proteins fold as α-helices bundles crossing the membrane (Fuchs et al 2009;Lo et al 2009;Nugent and Jones 2010;Wang et al 2011), the structural stability being governed by tight and specific interactions between residues (Marsico et al 2010a, b;Walters and DeGrado 2006;Nagarathnam et al 2011). In the all-α class, the proteins fold as α-helices bundles crossing the membrane (Fuchs et al 2009;Lo et al 2009;Nugent and Jones 2010;Wang et al 2011), the structural stability being governed by tight and specific interactions between residues (Marsico et al 2010a, b;Walters and DeGrado 2006;Nagarathnam et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Sequence–structure relationship study in all-α transmembrane proteins using an unsupervised learning approach

Esque¹,

Urbain²,

Etchebest³

et al. 2015

Amino Acids

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Transmembrane proteins (TMPs) are major drug targets, but the knowledge of their precise topology structure remains highly limited compared with globular proteins. In spite of the difficulties in obtaining their structures, an important effort has been made these last years to increase their number from an experimental and computational point of view. In view of this emerging challenge, the development of computational methods to extract knowledge from these data is crucial for the better understanding of their functions and in improving the quality of structural models. Here, we revisit an efficient unsupervised learning procedure, called Hybrid Protein Model (HPM), which is applied to the analysis of transmembrane proteins belonging to the all-α structural class. HPM method is an original classification procedure that efficiently combines sequence and structure learning. The procedure was initially applied to the analysis of globular proteins. In the present case, HPM classifies a set of overlapping protein fragments, extracted from a non-redundant databank of TMP 3D structure. After fine-tuning of the learning parameters, the optimal classification results in 65 clusters. They represent at best similar relationships between sequence and local structure properties of TMPs. Interestingly, HPM distinguishes among the resulting clusters two helical regions with distinct hydrophobic patterns. This underlines the complexity of the topology of these proteins. The HPM classification enlightens unusual relationship between amino acids in TMP fragments, which can be useful to elaborate new amino acids substitution matrices. Finally, two challenging applications are described: the first one aims at annotating protein functions (channel or not), the second one intends to assess the quality of the structures (X-ray or models) via a new scoring function deduced from the HPM classification.

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Section: Introductionmentioning

confidence: 99%

Sequence–structure relationship study in all-α transmembrane proteins using an unsupervised learning approach

Esque¹,

Urbain²,

Etchebest³

et al. 2015

Amino Acids

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“…The main menu provides three key features: "Sequences and Structures," "Alignments and Phylogeny," and "TM-MOTIF" (refer Figure 2A-C; HS51M1 from http:// caps.ncbs.res.in/DOR for an example). 36 OR sequences of target genomes. To retrieve sequences of ORs, users can download the respective sequences (in FASTA format) using the link provided for every sequence in every genome.…”

Section: Resultsmentioning

confidence: 99%

“…Also, MSA of 10 human OR clusters were used as an inbuilt dataset in the TM-MOTIF tool to observe the predicted TM-helices, conserved amino acids, and amino acid substitutions (AAS) at each position of the alignment. 36 Software and tools. TM-MOTIF is a downloadable software tool and an effective alignment viewer to map discovered motifs and predicted membrane topology on an aligned set of OR sequences in VIBGYOR coloring scheme (refer Figure 3I).…”

Section: Resultsmentioning

confidence: 99%

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DOR – a Database of Olfactory Receptors – Integrated Repository for Sequence and Secondary Structural Information of Olfactory Receptors in Selected Eukaryotic Genomes

Nagarathnam

Karpe

Krishnan

et al. 2014

Bioinform Biol Insights

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Olfaction is the response to odors and is mediated by a class of membrane-bound proteins called olfactory receptors (ORs). An understanding of these receptors serves as a good model for basic signal transduction mechanisms and also provides important clues for the strategies adopted by organisms for their ultimate survival using chemosensory perception in search of food or defense against predators. Prior research on cross-genome phylogenetic analyses from our group motivated the addressal of conserved evolutionary trends, clustering, and ortholog prediction of ORs. The database of olfactory receptors (DOR) is a repository that provides sequence and structural information on ORs of selected organisms (such as Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, and Homo sapiens). Users can download OR sequences, study predicted membrane topology, and obtain cross-genome sequence alignments and phylogeny, including three-dimensional (3D) structural models of 100 selected ORs and their predicted dimer interfaces. The database can be accessed from http://caps.ncbs.res.in/DOR. Such a database should be helpful in designing experiments on point mutations to probe into the possible dimerization modes of ORs and to even understand the evolutionary changes between different receptors.

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“…Moreover, crystal structures of several GPCRs, as well as experimental evidence, have shown the presence of a disulfide bond linking transmembrane helix 3 (TM3) to the second extracellular loop (ECL2) [60] and we used this additional information positioning this cysteine residue at the top of TM3. Finally, the position of the conserved motifs was also used for our selection process [61]. The homology modeling task was performed using the MODELLER program with its default settings [62].…”

Section: Methodsmentioning

confidence: 99%

GPCRs from fusarium graminearum detection, modeling and virtual screening - the search for new routes to control head blight disease

et al. 2016

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Backgound Fusarium graminearum (FG) is one of the major cereal infecting pathogens causing high economic losses worldwide and resulting in adverse effects on human and animal health. Therefore, the development of new fungicides against FG is an important issue to reduce cereal infection and economic impact. In the strategy for developing new fungicides, a critical step is the identification of new targets against which innovative chemicals weapons can be designed. As several G-protein coupled receptors (GPCRs) are implicated in signaling pathways critical for the fungi development and survival, such proteins could be valuable efficient targets to reduce Fusarium growth and therefore to prevent food contamination.ResultsIn this study, GPCRs were predicted in the FG proteome using a manually curated pipeline dedicated to the identification of GPCRs. Based on several successive filters, the most appropriate GPCR candidate target for developing new fungicides was selected. Searching for new compounds blocking this particular target requires the knowledge of its 3D-structure. As no experimental X-Ray structure of the selected protein was available, a 3D model was built by homology modeling. The model quality and stability was checked by 100 ns of molecular dynamics simulations. Two stable conformations representative of the conformational families of the protein were extracted from the 100 ns simulation and were used for an ensemble docking campaign. The model quality and stability was checked by 100 ns of molecular dynamics simulations previously to the virtual screening step. The virtual screening step comprised the exploration of a chemical library with 11,000 compounds that were docked to the GPCR model. Among these compounds, we selected the ten top-ranked nontoxic molecules proposed to be experimentally tested to validate the in silico simulation.ConclusionsThis study provides an integrated process merging genomics, structural bioinformatics and drug design for proposing innovative solutions to a world wide threat to grain producers and consumers.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1342-9) contains supplementary material, which is available to authorized users.

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TM-MOTIF: an alignment viewer to annotate predicted transmembrane helices and conserved motifs in aligned set of sequences

Cited by 5 publications

References 10 publications

Sequence–structure relationship study in all-α transmembrane proteins using an unsupervised learning approach

Sequence–structure relationship study in all-α transmembrane proteins using an unsupervised learning approach

DOR – a Database of Olfactory Receptors – Integrated Repository for Sequence and Secondary Structural Information of Olfactory Receptors in Selected Eukaryotic Genomes

GPCRs from fusarium graminearum detection, modeling and virtual screening - the search for new routes to control head blight disease

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