MeMotif: a database of linear motifs in alpha-helical transmembrane proteins

The evolutionary history of Saccharomyces species inferred from completed mitochondrial genomes and revision in the 'yeast mitochondrial genetic code'

The yeast Saccharomyces are widely used to test ecological and evolutionary hypotheses. A large number of nuclear genomic DNA sequences are available, but mitochondrial genomic data are insufficient. We completed mitochondrial DNA (mtDNA) sequencing from Illumina MiSeq reads for all Saccharomyces species. All are circularly mapped molecules decreasing in size with phylogenetic distance from Saccharomyces cerevisiae but with similar gene content including regulatory and selfish elements like origins of replication, introns, free-standing open reading frames or GC clusters. Their most profound feature is species-specific alteration in gene order. The genetic code slightly differs from well-established yeast mitochondrial code as GUG is used rarely as the translation start and CGA and CGC code for arginine. The multilocus phylogeny, inferred from mtDNA, does not correlate with the trees derived from nuclear genes. mtDNA data demonstrate that Saccharomyces cariocanus should be assigned as a separate species and Saccharomyces bayanus CBS 380T should not be considered as a distinct species due to mtDNA nearly identical to Saccharomyces uvarum mtDNA. Apparently, comparison of mtDNAs should not be neglected in genomic studies as it is an important tool to understand the origin and evolutionary history of some yeast species.

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

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.

Computational modeling of membrane proteins

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade.

Expediting topology data gathering for the TOPDB database

The Topology Data Bank of Transmembrane Proteins (TOPDB, http://topdb.enzim.ttk.mta.hu) contains experimentally determined topology data of transmembrane proteins. Recently, we have updated TOPDB from several sources and utilized a newly developed topology prediction algorithm to determine the most reliable topology using the results of experiments as constraints. In addition to collecting the experimentally determined topology data published in the last couple of years, we gathered topographies defined by the TMDET algorithm using 3D structures from the PDBTM. Results of global topology analysis of various organisms as well as topology data generated by high throughput techniques, like the sequential positions of N- or O-glycosylations were incorporated into the TOPDB database. Moreover, a new algorithm was developed to integrate scattered topology data from various publicly available databases and a new method was introduced to measure the reliability of predicted topologies. We show that reliability values highly correlate with the per protein topology accuracy of the utilized prediction method. Altogether, more than 52 000 new topology data and more than 2600 new transmembrane proteins have been collected since the last public release of the TOPDB database.

Fast and accurate discovery of degenerate linear motifs in protein sequences

Linear motifs mediate a wide variety of cellular functions, which makes their characterization in protein sequences crucial to understanding cellular systems. However, the short length and degenerate nature of linear motifs make their discovery a difficult problem. Here, we introduce MotifHound, an algorithm particularly suited for the discovery of small and degenerate linear motifs. MotifHound performs an exact and exhaustive enumeration of all motifs present in proteins of interest, including all of their degenerate forms, and scores the overrepresentation of each motif based on its occurrence in proteins of interest relative to a background (e.g., proteome) using the hypergeometric distribution. To assess MotifHound, we benchmarked it together with state-of-the-art algorithms. The benchmark consists of 11,880 sets of proteins from S. cerevisiae; in each set, we artificially spiked-in one motif varying in terms of three key parameters, (i) number of occurrences, (ii) length and (iii) the number of degenerate or “wildcard” positions. The benchmark enabled the evaluation of the impact of these three properties on the performance of the different algorithms. The results showed that MotifHound and SLiMFinder were the most accurate in detecting degenerate linear motifs. Interestingly, MotifHound was 15 to 20 times faster at comparable accuracy and performed best in the discovery of highly degenerate motifs. We complemented the benchmark by an analysis of proteins experimentally shown to bind the FUS1 SH3 domain from S. cerevisiae. Using the full-length protein partners as sole information, MotifHound recapitulated most experimentally determined motifs binding to the FUS1 SH3 domain. Moreover, these motifs exhibited properties typical of SH3 binding peptides, e.g., high intrinsic disorder and evolutionary conservation, despite the fact that none of these properties were used as prior information. MotifHound is available (http://michnick.bcm.umontreal.ca or http://tinyurl.com/motifhound) together with the benchmark that can be used as a reference to assess future developments in motif discovery.

Helix kinks are equally prevalent in soluble and membrane proteins

Helix kinks are a common feature of α-helical membrane proteins, but are thought to be rare in soluble proteins. In this study we find that kinks are a feature of long α-helices in both soluble and membrane proteins, rather than just transmembrane α-helices. The apparent rarity of kinks in soluble proteins is due to the relative infrequency of long helices (≥20 residues) in these proteins. We compare length-matched sets of soluble and membrane helices, and find that the frequency of kinks, the role of Proline, the patterns of other amino acid around kinks (allowing for the expected differences in amino acid distributions between the two types of protein), and the effects of hydrogen bonds are the same for the two types of helices. In both types of protein, helices that contain Proline in the second and subsequent turns are very frequently kinked. However, there are a sizeable proportion of kinked helices that do not contain a Proline in either their sequence or sequence homolog. Moreover, we observe that in soluble proteins, kinked helices have a structural preference in that they typically point into the solvent.

Deorphanizing the human transmembrane genome: A landscape of uncharacterized membrane proteins

The sequencing of the human genome has fueled the last decade of work to functionally characterize genome content. An important subset of genes encodes membrane proteins, which are the targets of many drugs. They reside in lipid bilayers, restricting their endogenous activity to a relatively specialized biochemical environment. Without a reference phenotype, the application of systematic screens to profile candidate membrane proteins is not immediately possible. Bioinformatics has begun to show its effectiveness in focusing the functional characterization of orphan proteins of a particular functional class, such as channels or receptors. Here we discuss integration of experimental and bioinformatics approaches for characterizing the orphan membrane proteome. By analyzing the human genome, a landscape reference for the human transmembrane genome is provided.

Transferring functional annotations of membrane transporters on the basis of sequence similarity and sequence motifs

Background

Membrane transporters catalyze the transport of small solute molecules across biological barriers such as lipid bilayer membranes. Experimental identification of the transported substrates is very tedious. Once a particular transport mechanism has been identified in one organism, it is thus highly desirable to transfer this information to related transporter sequences in different organisms based on bioinformatics evidence.

Results

We present a thorough benchmark at which level of sequence identity membrane transporters from Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana belong to the same families of the Transporter Classification (TC) system, and at what level these membrane transporters mediate the transport of the same substrate. We found that two membrane transporter sequences from different organisms that are aligned with normalized BLAST expectation value better than E-value 1e-8 are highly likely to belong to the same TC family (F-measure around 90%). Enriched sequence motifs identified by MEME at thresholds below 1e-12 support accurate classification into TC families for about two thirds of the sequences (F-measure 80% and higher). For the comparison of transported substrates, we focused on the four largest substrate classes of amino acids, sugars, metal ions, and phosphate. At similar identity thresholds, the nature of the transported substrates was more divergent (F-measure 40 - 75% at the same thresholds) than the TC family membership.

Conclusions

We suggest an acceptable threshold of 1e-8 for BLAST and HMMER where at least three quarters of the sequences are classified according to the TC system with a reasonably high accuracy. Researchers who wish to apply these thresholds in their studies should multiply these thresholds by the size of the database they search against. Our findings should be useful to those who wish to transfer transporter functional annotations across species.

Dead Sea rhodopsins revisited

The Dead Sea is a unique hypersaline ecosystem with near toxic magnesium levels (∼2 M), dominance of divalent cations and a slightly acidic pH. Previously, we reported a haloarchaeon related to Halobacterium salinarum to dominate in a microbial bloom that developed in 1992 in the upper water layers of the lake following massive freshwater runoff. Whether this clade also dominated an earlier bloom in 1980-1982 cannot be ascertained as no samples for cultivation-independent analysis were preserved. The presence of the light-driven proton pump bacteriorhodopsin was reported in the 1980-1982 bloom of prokaryotes that had developed in the Dead Sea. To test the hypothesis that bacteriorhodopsin proton pumping may play a major role in determining what type of haloarchaea may dominate in specific bloom conditions, we compared rhodopsin genes recovered from Dead Sea biomass collected in different periods with genes coding for retinal proteins in isolated haloarchaea. Novel bacteriorhodopsin and sensory rhodopsin genes were found in samples collected in 2007 and 2010. The fact that no rhodopsin genes were recovered from samples collected during the 1992 bloom, which was dominated by a single species, suggests that different clades were present in the 1980-1982 and 1992 blooms, and that bacteriorhodopsin proton pumping did not necessarily play a determinative role in the dominance of specific halophiles in the blooms.

Computational studies of membrane proteins: models and predictions for biological understanding

We discuss recent progresses in computational studies of membrane proteins based on physical models with parameters derived from bioinformatics analysis. We describe computational identification of membrane proteins and prediction of their topology from sequence, discovery of sequence and spatial motifs, and implications of these discoveries. The detection of evolutionary signal for understanding the substitution pattern of residues in the TM segments and for sequence alignment is also discussed. We further discuss empirical potential functions for energetics of inserting residues in the TM domain, for interactions between TM helices or strands, and their applications in predicting lipid-facing surfaces of the TM domain. Recent progresses in structure predictions of membrane proteins are also reviewed, with further discussions on calculation of ensemble properties such as melting temperature based on simplified state space model. Additional topics include prediction of oligomerization state of membrane proteins, identification of the interfaces for protein-protein interactions, and design of membrane proteins. This article is part of a Special Issue entitled: Protein Folding in Membranes.

TMPad: an integrated structural database for helix-packing folds in transmembrane proteins

α-Helical transmembrane (TM) proteins play an important role in many critical and diverse biological processes, and specific associations between TM helices are important determinants for membrane protein folding, dynamics and function. In order to gain insights into the above phenomena, it is necessary to investigate different types of helix-packing modes and interactions. However, such information is difficult to obtain because of the experimental impediment and a lack of a well-annotated source of helix-packing folds in TM proteins. We have developed the TMPad (TransMembrane Protein Helix-Packing Database) which addresses the above issues by integrating experimentally observed helix–helix interactions and related structural information of membrane proteins. Specifically, the TMPad offers pre-calculated geometric descriptors at the helix-packing interface including residue backbone/side-chain contacts, interhelical distances and crossing angles, helical translational shifts and rotational angles. The TMPad also includes the corresponding sequence, topology, lipid accessibility, ligand-binding information and supports structural classification, schematic diagrams and visualization of the above structural features of TM helix-packing. Through detailed annotations and visualizations of helix-packing, this online resource can serve as an information gateway for deciphering the relationship between helix–helix interactions and higher levels of organization in TM protein structure and function. The website of the TMPad is freely accessible to the public at http://bio-cluster.iis.sinica.edu.tw/TMPad.

Tryptophan residues promote membrane association for a plant lipid glycosyltransferase involved in phosphate stress

Chloroplast membranes contain a substantial excess of the nonbilayer-prone monogalactosyldiacylglycerol (GalDAG) over the biosynthetically consecutive, bilayer-forming digalactosyldiacylglycerol (GalGalDAG), yielding a high membrane curvature stress. During phosphate shortage, plants replace phospholipids with GalGalDAG to rescue phosphate while maintaining membrane homeostasis. Here we investigate how the activity of the corresponding glycosyltransferase (GT) in Arabidopsis thaliana (atDGD2) depends on local bilayer properties by analyzing structural and activity features of recombinant protein. Fold recognition and sequence analyses revealed a two-domain GT-B monotopic structure, present in other plant and bacterial glycolipid GTs, such as the major chloroplast GalGalDAG GT atDGD1. Modeling led to the identification of catalytically important residues in the active site of atDGD2 by site-directed mutagenesis. The DGD synthases share unique bilayer interface segments containing conserved tryptophan residues that are crucial for activity and for membrane association. More detailed localization studies and liposome binding analyses indicate differentiated anchor and substrate-binding functions for these separated enzyme interface regions. Anionic phospholipids, but not curvature-increasing nonbilayer lipids, strongly stimulate enzyme activity. From our studies, we propose a model for bilayer "control" of enzyme activity, where two tryptophan segments act as interface anchor points to keep the substrate region close to the membrane surface. Binding of the acceptor substrate is achieved by interaction of positive charges in a surface cluster of lysines, arginines, and histidines with the surrounding anionic phospholipids. The diminishing phospholipid fraction during phosphate shortage stress will then set the new GalGalDAG/phospholipid balance by decreasing stimulation of atDGD2.