TMFoldRec: a statistical potential-based transmembrane protein fold recognition tool.

BACKGROUND: Transmembrane proteins (TMPs) are the key components of signal transduction, cell-cell adhesion and energy and material transport into and out from the cells. For the deep understanding of these processes, structure determination of transmembrane proteins is indispensable. However, due to technical difficulties, only a few transmembrane protein structures have been determined experimentally. Large-scale genomic sequencing provides increasing amounts of sequence information on the proteins and whole proteomes of living organisms resulting in the challenge of bioinformatics; how the structural information should be gained from a sequence. RESULTS: Here, we present a novel method, TMFoldRec, for fold prediction of membrane segments in transmembrane proteins. TMFoldRec based on statistical potentials was tested on a benchmark set containing 124 TMP chains from the PDBTM database. Using a 10-fold jackknife method, the native folds were correctly identified in 77 % of the cases. This accuracy overcomes the state-of-the-art methods. In addition, a key feature of TMFoldRec algorithm is the ability to estimate the reliability of the prediction and to decide with an accuracy of 70 %, whether the obtained, lowest energy structure is the native one. CONCLUSION: These results imply that the membrane embedded parts of TMPs dictate the TM structures rather than the soluble parts. Moreover, predictions with reliability scores make in this way our algorithm applicable for proteome-wide analyses. AVAILABILITY: The program is available upon request for academic use

CELLmicrocosmos 2.2: advancements and applications in modeling of three-dimensional PDB membranes

Sommer B, Dingersen T, Schneider S, Rubert S, Gamroth C. CELLmicrocosmos 2.2: advancements and applications in modeling of three-dimensional PDB membranes (Conference Abstract). In: Journal of Cheminformatics. Journal of Cheminformatics. Vol 2(Suppl 1):O21. Springer Science and Business Media LLC; 2010

Identification of Extracellular Segments by Mass Spectrometry Improves Topology Prediction of Transmembrane Proteins

Transmembrane proteins play crucial role in signaling, ion transport, nutrient uptake, as well as in maintaining the dynamic equilibrium between the internal and external environment of cells. Despite their important biological functions and abundance, less than 2% of all determined structures are transmembrane proteins. Given the persisting technical difficulties associated with high resolution structure determination of transmembrane proteins, additional methods, including computational and experimental techniques remain vital in promoting our understanding of their topologies, 3D structures, functions and interactions. Here we report a method for the high-throughput determination of extracellular segments of transmembrane proteins based on the identification of surface labeled and biotin captured peptide fragments by LC/MS/MS. We show that reliable identification of extracellular protein segments increases the accuracy and reliability of existing topology prediction algorithms. Using the experimental topology data as constraints, our improved prediction tool provides accurate and reliable topology models for hundreds of human transmembrane proteins

Transmembrane protein topology prediction using support vector machines

Background: Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated.Results: We present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from http://bioinf.cs.ucl.ac.uk/psipred/.Conclusion: The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins

Dihydrolipoic acid reduces cytochrome b561 proteins.

Cytochrome b561 (Cyt-b561) proteins constitute a family of trans-membrane proteins that are present in a wide variety of organisms. Two of their characteristic properties are the reducibility by ascorbate (ASC) and the presence of two distinct b-type hemes localized on two opposite sides of the membrane. Here we show that the tonoplast-localized and the putative tumor suppressor Cyt-b561 proteins can be reduced by other reductants than ASC and dithionite. A detailed spectral analysis of the ASC-dependent and dihydrolipoic acid (DHLA)-dependent reduction of these two Cyt-b561 proteins is also presented. Our results are discussed in relation to the known antioxidant capability of DHLA as well as its role in the regeneration of other antioxidant compounds of cells. These results allow us to speculate on new biological functions for the trans-membrane Cyt-b561 proteins

Fast and accurate mutation detection in whole genome sequences of multiple isogenic samples with IsoMut

Background: Detection of somatic mutations is one of the main goals of next generation DNA sequencing. A wide range of experimental systems are available for the study of spontaneous or environmentally induced mutagenic processes. However, most of the routinely used mutation calling algorithms are not optimised for the simultaneous analysis of multiple samples, or for non-human experimental model systems with no reliable databases of common genetic variations. Most standard tools either require numerous in-house post filtering steps with scarce documentation or take an unpractically long time to run. To overcome these problems, we designed the streamlined IsoMut tool which can be readily adapted to experimental scenarios where the goal is the identification of experimentally induced mutations in multiple isogenic samples. Methods: Using 30 isogenic samples, reliable cohorts of validated mutations were created for testing purposes. Optimal values of the filtering parameters of IsoMut were determined in a thorough and strict optimization procedure based on these test sets. Results: We show that IsoMut, when tuned correctly, decreases the false positive rate compared to conventional tools in a 30 sample experimental setup; and detects not only single nucleotide variations, but short insertions and deletions as well. IsoMut can also be run more than a hundred times faster than the most precise state of art tool, due its straightforward and easily understandable filtering algorithm. Conclusions: IsoMut has already been successfully applied in multiple recent studies to find unique, treatment induced mutations in sets of isogenic samples with very low false positive rates. These types of studies provide an important contribution to determining the mutagenic effect of environmental agents or genetic defects, and IsoMut turned out to be an invaluable tool in the analysis of such data. © 2017 The Author(s)

IgTM: An algorithm to predict transmembrane domains and topology in proteins

<p>Abstract</p> <p>Background</p> <p>Due to their role of receptors or transporters, membrane proteins play a key role in many important biological functions. In our work we used Grammatical Inference (GI) to localize transmembrane segments. Our GI process is based specifically on the inference of Even Linear Languages.</p> <p>Results</p> <p>We obtained values close to 80% in both specificity and sensitivity. Six datasets have been used for the experiments, considering different encodings for the input sequences. An encoding that includes the topology changes in the sequence (from inside and outside the membrane to it and vice versa) allowed us to obtain the best results. This software is publicly available at: <url>http://www.dsic.upv.es/users/tlcc/bio/bio.html</url></p> <p>Conclusion</p> <p>We compared our results with other well-known methods, that obtain a slightly better precision. However, this work shows that it is possible to apply Grammatical Inference techniques in an effective way to bioinformatics problems.</p

Effects of Clinically Relevant MPL Mutations in the Transmembrane Domain Revealed at the Atomic Level through Computational Modeling

BACKGROUND: Mutations in the thrombopoietin receptor (MPL) may activate relevant pathways and lead to chronic myeloproliferative neoplasms (MPNs). The mechanisms of MPL activation remain elusive because of a lack of experimental structures. Modern computational biology techniques were utilized to explore the mechanisms of MPL protein activation due to various mutations. RESULTS: Transmembrane (TM) domain predictions, homology modeling, ab initio protein structure prediction, and molecular dynamics (MD) simulations were used to build structural dynamic models of wild-type and four clinically observed mutants of MPL. The simulation results suggest that S505 and W515 are important in keeping the TM domain in its correct position within the membrane. Mutations at either of these two positions cause movement of the TM domain, altering the conformation of the nearby intracellular domain in unexpected ways, and may cause the unwanted constitutive activation of MPL's kinase partner, JAK2. CONCLUSIONS: Our findings represent the first full-scale molecular dynamics simulations of the wild-type and clinically observed mutants of the MPL protein, a critical element of the MPL-JAK2-STAT signaling pathway. In contrast to usual explanations for the activation mechanism that are based on the relative translational movement between rigid domains of MPL, our results suggest that mutations within the TM region could result in conformational changes including tilt and rotation (azimuthal) angles along the membrane axis. Such changes may significantly alter the conformation of the adjacent and intrinsically flexible intracellular domain. Hence, caution should be exercised when interpreting experimental evidence based on rigid models of cytokine receptors or similar systems