Development of computational methods for predicting structural characteristics of helical membrane proteins

Helical membrane proteins (HMPs) play a crucial role in diverse cellular processes. Given the difficulty in determining their structures by experimental techniques, it is desired to develop computational methods for predicting their structural characteristics. In addition, computational analysis can provide interesting insights into their structure and function that experimental work can not provide. This thesis summarizes years of such computational endeavours, comprising 4 published papers (Paper I ~ IV). In Paper I, it was attempted to model low-resolution tertiary structures of HMPs with a modest number of transmembrane (TM) helices from packing constraints and sequence conservation patterns. In Paper II, a fundamental investigation was undertaken to analyze the degree of correlation between exposure patterns of TM helices to the membrane and their properties such as their hydrophobicities and conservation patterns. In Paper III, on the basis of the work presented in Paper II, an optimal way of deriving the propensity scales of the 20 amino acids to preferentially interact with the membrane as reflected in known HMP structures was presented, which revealed a surprising fact that the architectural principle of HMPs is best captured by the partial specific volumes of the amino acids. In Paper IV, the development of TMX (TransMembrane eXposure), a novel computational method for predicting the lipid accessibility of TM residues of HMPs, was described, which significantly outperforms other existing methods. A web interface for TMX is available at http://service.bioinformatik.uni-saarland.de/tmx.Helikale Membranproteine (HMPs) spielen in diversen zellulären Prozessen eine bedeutende Rolle. In Anbetracht der Schwierigkeit, die Struktur dieser Proteine mittels experimenteller Techniken aufzuklären, ist es erstrebenswert, computerunterstützte Methoden für ihre Strukturaufklärung zu entwickeln. Zusätzlich könnten computerunterstützte Analysen interessante Aspekte ihrer Struktur und Funktion aufzeigen, die experimentelle Studien nicht aufzeigen können. Meine Doktorarbeit fasst jahrelange Anstrengungen zusammen, die sich auf 4 publizierte Artikel (Artikel I ~ IV) aufteilen. In Artikel I wurde der Versuch unternommen, mittelmäßig aufgelöste HMP-Strukturen mit einer geringen Zahl von Transmembranprotein (TM) Helices mit Hilfe von Packungsregeln und konservierten Sequenzmotiven zu modellieren. In Artikel II wurde eine grundlegende Untersuchung durchgeführt, um den Grad der Korrelation zwischen exponierten Motiven in den TM Helices zur Doppelmembranschicht und ihren Eigenschaften wie Hydrophobizität und konservierten Motiven zu analysieren. Darauf aufbauend wurde in Artikel III ein optimaler Weg zur Generierung von Skalen vorgestellt, die Paarungspräferenzen der 20 Aminosäuren mit der Doppelmembranschicht auf Basis von bekannten HMP Strukturen zu bewerten. Die Ergebnisse zeigen überraschenderweise, dass das architektonische Prinzip von HMPs am besten durch die partiellen spezifischen Volumina der Aminosäuren beschrieben werden kann. Artikel IV präsentiert TMX (TransMembrane eXposure), eine neue computerunterstützte Methode zur Vorhersage der Lipidzugänglichkeit der TM-Aminosäuren in HMPs, die bisherige Methoden deutlich an Genauigkeit übertrifft. Unter http://service.bioinformatik.uni-saarland.de/tmx ist eine Web-Schnittstelle für TMX aufrufbar

Prediction of the burial status of transmembrane residues of helical membrane proteins

<p>Abstract</p> <p>Background</p> <p>Helical membrane proteins (HMPs) play a crucial role in diverse cellular processes, yet it still remains extremely difficult to determine their structures by experimental techniques. Given this situation, it is highly desirable to develop sequence-based computational methods for predicting structural characteristics of HMPs.</p> <p>Results</p> <p>We have developed TMX (TransMembrane eXposure), a novel method for predicting the burial status (i.e. buried in the protein structure vs. exposed to the membrane) of transmembrane (TM) residues of HMPs. TMX derives positional scores of TM residues based on their profiles and conservation indices. Then, a support vector classifier is used for predicting their burial status. Its prediction accuracy is 78.71% on a benchmark data set, representing considerable improvements over 68.67% and 71.06% of previously proposed methods. Importantly, unlike the previous methods, TMX automatically yields confidence scores for the predictions made. In addition, a feature selection incorporated in TMX reveals interesting insights into the structural organization of HMPs.</p> <p>Conclusion</p> <p>A novel computational method, TMX, has been developed for predicting the burial status of TM residues of HMPs. Its prediction accuracy is much higher than that of previously proposed methods. It will be useful in elucidating structural characteristics of HMPs as an inexpensive, auxiliary tool. A web server for TMX is established at http://service.bioinformatik.uni-saarland.de/tmx and freely available to academic users, along with the data set used.</p

Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions underlie many important biological processes. Computational prediction methods can nicely complement experimental approaches for identifying protein-protein interactions. Recently, a unique category of sequence-based prediction methods has been put forward - unique in the sense that it does not require homologous protein sequences. This enables it to be universally applicable to all protein sequences unlike many of previous sequence-based prediction methods. If effective as claimed, these new sequence-based, universally applicable prediction methods would have far-reaching utilities in many areas of biology research.</p> <p>Results</p> <p>Upon close survey, I realized that many of these new methods were ill-tested. In addition, newer methods were often published without performance comparison with previous ones. Thus, it is not clear how good they are and whether there are significant performance differences among them. In this study, I have implemented and thoroughly tested 4 different methods on large-scale, non-redundant data sets. It reveals several important points. First, significant performance differences are noted among different methods. Second, data sets typically used for training prediction methods appear significantly biased, limiting the general applicability of prediction methods trained with them. Third, there is still ample room for further developments. In addition, my analysis illustrates the importance of complementary performance measures coupled with right-sized data sets for meaningful benchmark tests.</p> <p>Conclusions</p> <p>The current study reveals the potentials and limits of the new category of sequence-based protein-protein interaction prediction methods, which in turn provides a firm ground for future endeavours in this important area of contemporary bioinformatics.</p

Entwicklung von Computermethoden zur Vorhersage struktureller Charakteristiken von helikalen Membranproteinen

Helical membrane proteins (HMPs) play a crucial role in diverse cellular processes. Given the difficulty in determining their structures by experimental techniques, it is desired to develop computational methods for predicting their structural characteristics. In addition, computational analysis can provide interesting insights into their structure and function that experimental work can not provide. This thesis summarizes years of such computational endeavours, comprising 4 published papers (Paper I ~ IV). In Paper I, it was attempted to model low-resolution tertiary structures of HMPs with a modest number of transmembrane (TM) helices from packing constraints and sequence conservation patterns. In Paper II, a fundamental investigation was undertaken to analyze the degree of correlation between exposure patterns of TM helices to the membrane and their properties such as their hydrophobicities and conservation patterns. In Paper III, on the basis of the work presented in Paper II, an optimal way of deriving the propensity scales of the 20 amino acids to preferentially interact with the membrane as reflected in known HMP structures was presented, which revealed a surprising fact that the architectural principle of HMPs is best captured by the partial specific volumes of the amino acids. In Paper IV, the development of TMX (TransMembrane eXposure), a novel computational method for predicting the lipid accessibility of TM residues of HMPs, was described, which significantly outperforms other existing methods. A web interface for TMX is available at http://service.bioinformatik.uni-saarland.de/tmx.Helikale Membranproteine (HMPs) spielen in diversen zellulären Prozessen eine bedeutende Rolle. In Anbetracht der Schwierigkeit, die Struktur dieser Proteine mittels experimenteller Techniken aufzuklären, ist es erstrebenswert, computerunterstützte Methoden für ihre Strukturaufklärung zu entwickeln. Zusätzlich könnten computerunterstützte Analysen interessante Aspekte ihrer Struktur und Funktion aufzeigen, die experimentelle Studien nicht aufzeigen können. Meine Doktorarbeit fasst jahrelange Anstrengungen zusammen, die sich auf 4 publizierte Artikel (Artikel I ~ IV) aufteilen. In Artikel I wurde der Versuch unternommen, mittelmäßig aufgelöste HMP-Strukturen mit einer geringen Zahl von Transmembranprotein (TM) Helices mit Hilfe von Packungsregeln und konservierten Sequenzmotiven zu modellieren. In Artikel II wurde eine grundlegende Untersuchung durchgeführt, um den Grad der Korrelation zwischen exponierten Motiven in den TM Helices zur Doppelmembranschicht und ihren Eigenschaften wie Hydrophobizität und konservierten Motiven zu analysieren. Darauf aufbauend wurde in Artikel III ein optimaler Weg zur Generierung von Skalen vorgestellt, die Paarungspräferenzen der 20 Aminosäuren mit der Doppelmembranschicht auf Basis von bekannten HMP Strukturen zu bewerten. Die Ergebnisse zeigen überraschenderweise, dass das architektonische Prinzip von HMPs am besten durch die partiellen spezifischen Volumina der Aminosäuren beschrieben werden kann. Artikel IV präsentiert TMX (TransMembrane eXposure), eine neue computerunterstützte Methode zur Vorhersage der Lipidzugänglichkeit der TM-Aminosäuren in HMPs, die bisherige Methoden deutlich an Genauigkeit übertrifft. Unter http://service.bioinformatik.uni-saarland.de/tmx ist eine Web-Schnittstelle für TMX aufrufbar

Revisiting the negative example sampling problem for predicting protein–protein interactions

ABSTRACT Motivation: A number of computational methods have been proposed that predict protein-protein interactions (PPIs) based on protein sequence features. Since the number of potential non-interacting protein pairs (negative PPIs) is very high both in absolute terms and in comparison to that of interacting protein pairs (positive PPIs), computational prediction methods rely upon subsets of negative PPIs for training and validation. Hence, the need arises for subset sampling for negative PPIs. Results: We clarify that there are two fundamentally different types of subset sampling for negative PPIs. One is subset sampling for crossvalidated testing, where one desires unbiased subsets so that predictive performance estimated with them can be safely assumed to generalize to the population level. The other is subset sampling for training, where one desires the subsets that best train predictive algorithms, even if these subsets are biased. We show that confusion between these two fundamentally different types of subset sampling led one study recently published in Bioinformatics to the erroneous conclusion that predictive algorithms based on protein sequence features are hardly better than random in predicting PPIs. Rather, both protein sequence features and the &quot;hubbiness&quot; of interacting proteins contribute to effective prediction of PPIs. We provide guidance for appropriate use of random versus balanced sampling

A Bacteriophage Tailspike Domain Promotes Self-Cleavage of a Human Membrane-Bound Transcription Factor, the Myelin Regulatory Factor MYRF

Zhihua Li, Yungki Park, Edward M. Marcotte, Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, United States of AmericaMyelination of the central nervous system (CNS) is critical to vertebrate nervous systems for efficient neural signaling. CNS myelination occurs as oligodendrocytes terminally differentiate, a process regulated in part by the myelin regulatory factor, MYRF. Using bioinformatics and extensive biochemical and functional assays, we find that MYRF is generated as an integral membrane protein that must be processed to release its transcription factor domain from the membrane. In contrast to most membrane-bound transcription factors, MYRF proteolysis seems constitutive and independent of cell- and tissue-type, as we demonstrate by reconstitution in E. coli and yeast. The apparent absence of physiological cues raises the question as to how and why MYRF is processed. By using computational methods capable of recognizing extremely divergent sequence homology, we identified a MYRF protein domain distantly related to bacteriophage tailspike proteins. Although occurring in otherwise unrelated proteins, the phage domains are known to chaperone the tailspike proteins' trimerization and auto-cleavage, raising the hypothesis that the MYRF domain might contribute to a novel activation method for a membrane-bound transcription factor. We find that the MYRF domain indeed serves as an intramolecular chaperone that facilitates MYRF trimerization and proteolysis. Functional assays confirm that the chaperone domain-mediated auto-proteolysis is essential both for MYRF's transcriptional activity and its ability to promote oligodendrocyte maturation. This work thus reveals a previously unknown key step in CNS myelination. These data also reconcile conflicting observations of this protein family, different members of which have been identified as transmembrane or nuclear proteins. Finally, our data illustrate a remarkable evolutionary repurposing between bacteriophages and eukaryotes, with a chaperone domain capable of catalyzing trimerization-dependent auto-proteolysis in two entirely distinct protein and cellular contexts, in one case participating in bacteriophage tailspike maturation and in the other activating a key transcription factor for CNS myelination.This work was supported by grants from the National Institutes of Health, National Science Foundation, Cancer Prevention Research Institute of Texas, and Welch Foundation (F1515) to EMM. YP acknowledges financial support from the Deutsche Forschungsgemeinschaft (DFG Forschungsstipendium). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Center for Systems and Synthetic BiologyInstitute for Cellular and Molecular BiologyEmail: [email protected] (YP)Email: [email protected] (EMM

Full-length MYRF is generated as a membrane protein.

<p>(A) Predicted sequence features of MYRF and sequence diagrams of various MYRF constructs used for IF microscopy. Stars in blue indicate predicted NLSs at K₂₄₅KRK₂₄₈ and K₄₈₂KGK₄₈₅. (B) IF images of GFP-MYRF, MYRF-GFP, MYRFΔTM-GFP, and MYRF-1:756-GFP in HeLa cells. (C) IF image of 3F-MYRF-GFP in HeLa cells. Scale bar, 10 µm.</p

The N-terminal trimer is formed by the ICA domain and enters the nucleus.

<p>(A) Predicted sequence features of MYRF and sequence diagrams of various MYRF constructs used for experiments. (B) Western blots showing co-immunoprecipitation results for the MYRF constructs. “Input” was incubated with FLAG antibody-coated beads and then spun down to separate “Sup” from “Bead” fractions. The failure of MYRF-1:577 to homo-oligomerize demonstrated the importance of the ICA domain for the N-terminal trimer formation. (C) When the NLSs (NLS1 and NLS2) were deleted, the nuclear translocation of the N-terminal trimer was partially blocked. Scale bar, 20 µm.</p

The ICA domain autonomously mediates the proteolytic processing of MYRF.

<p>(A) Multiple sequence alignment of the ICA domains from eukaryotes, a bacterium, and a phage, generated with ClustalW <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001624#pbio.1001624-Larkin1" target="_blank">[54]</a>. Strictly conserved residues are shown in red. The numbering system is based on MYRF. (B) The auto-processing mechanism for MYRF postulated based on the ICA domain and its known properties. (C) Western blots of HeLa cells transfected with various MYRF constructs, showing the effects of mutations in the ICA domain on the proteolytic processing of MYRF. (D) IF image of 3F-MYRF-S578A and 3F-MYRF-K583A in HeLa cells. (E) The amino acid sequence of MYRF (residues N567-R692) was mapped onto the crystal structure of an ICA domain (PDB ID: 3GW6) using the alignment shown in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001624#pbio.1001624.s003" target="_blank">Figure S3A</a>. In the zoomed active site are shown two key catalytic residues (S578 and K583, both belonging to the same subunit) in stick model and two strictly conserved residues (V670 of one subunit and G626 of a different subunit) in space filling model. Shown below are L683, I687, and L690 that were predicted to form a leucine zipper. For visual clarity, clipped images were generated when deemed necessary. (F) (Left) Western blot showing that the proteolytic processing of MYRF is independent of its membrane insertion. MYRF-1:756 is a mutant truncated before the TM domain at L756. (Middle) Western blot showing the proteolytic processing of MYRF-319:708 in HeLa cells and <i>E. coli</i>. (Right) Western blot showing the normal processing of full-length MYRF in budding yeast. Scale bar, 10 µm.</p