Sequence based methods for the prediction and analysis of the structural topology of transmembrane beta barrel proteins

Transmembrane proteins play a major role in the normal functioning of the cell. Many transmembrane proteins act as a drug target and hence are of utmost importance to the pharmaceutical industry. In spite of the significance of transmembrane proteins, relatively few transmembrane 3D structures are available due to experimental bottlenecks. Due to this, it is imperative to develop novel computational methods to elucidate the structure and function of these proteins. The two major classes of transmembrane proteins are helical membrane proteins and transmembrane beta barrel proteins. Relatively more 3D structures of helical membrane proteins have been experimentally determined and in general, the majority of computational methods in the realm of transmembrane proteins deal with helical membrane proteins. However, in the recent years there has been an increased interest in the development of computational methods for the transmembrane beta barrel proteins. In this study, I focus on the transmembrane beta barrel proteins. More specifically, I present here computational methods for the prediction of the exposure status of the residues in the membrane spanning region of the transmembrane beta barrel proteins. To the best of our knowledge, the exposure status prediction is a novel problem in the realm of transmembrane beta barrel proteins. The knowledge about the exposure status of the membrane spanning residues is then used to analyse the structural properties of transmembrane beta strands. The exposure status information is also employed to identify relevant physico-chemical properties that are statistically significantly different in the transmembrane beta strands at the oligomeric interfaces and the rest of the protein surface. A method for the prediction of the beta strands in the membrane spanning regions of putative transmembrane beta barrel proteins from protein sequence has also been developed. The computational method for strand prediction is novel in the respect that it also gives the exposure status information of the residues predicted to be in the predicted transmembrane beta strands. The two computational methods developed in this study have been made available as web services. In the future, the information about the exposure status of the residues in the transmembrane beta strands can be used to identify putative transmembrane beta barrels from proteomic data. The exposure status prediction can also be extended to predict the pore region of transmembrane beta barrel proteins from sequence, which could in turn be used in the function prediction of putative transmembrane beta barrels.Die Klasse der Transmembranproteine übernimmt eine Reihe wesentlicher Funktionen innerhalb der Zelle. Daher eignen sich viele dieser Proteine als Ziele für medizinische Wirkstoffe und sind daher von außerordentlichem Interesse für die Pharmaindustrie. Trotz ihrer Wichtigkeit wurden bislang nur wenige drei-dimensionale Strukturen von Membranproteinen erfasst, denn deren experimentelle Bestimmung hat sich als ausgesprochen schwierig herausgestellt. Aus diesem Grund erweist sich die Entwicklung von in silico Methoden zur de novo Vorhersage von Struktur und Funktion dieser Proteine von als notwendige Strategie. Die beiden wesentlichen Klassen von Transmembranproteinen unterteilt man, basierend auf ihren charakteristischen Sekundärstrukturen, in alpha-helikale Proteine und beta-Barrels. Erstere machen den größeren Anteil an experimentell bestimmten Strukturen aus, und auch die meisten bislang vorgestellten in silico Methoden konzentrieren sich auf die Modellierung solch alpha-helikaler Strukturen. In den vergangenen Jahren stieg daher das Interesse an Methoden zur Modellierung von transmembranen beta-Barrels. Die vorliegende Disseration beschäftigt sich vorrangig mit dieser Klasse von Transmembranproteinen, insbesondere präsentieren wir ein Verfahren zur Vorhersage der Exposition ("Exposure\u27;) zur Lipidschicht einzelner Residuen innerhalb der Transmembranregion von beta-Barrels. Diese Vorhersage der Exposition stellt bislang ein neuartiges Problem im Feld der beta-Barrels dar. Die daraus gewonnenen Informationen wurden zur Analyse der strukturellen Eigenschaften von Transmembranketten verwendet. Darüber hinaus können die Exposure-Daten zur Identifikation bedeutender physikochemischer Eigenschaften verwendet werden. Unsere Untersuchungen ergaben, dass zwischen transmembranen beta-strands an Oligomer-Interfaces und dem Rest der Proteinoberfläche statistisch signifikante Unterschiede bezüglich dieser Eigenschaften auftreten. Darüber hinaus stellen wir ein Verfahren zur sequenzbasierten Vorhersage von Transmembran-Residuen mutmaßlicher beta-Barrels vor, welches in Kombination mit der Vorhersage des Exposure-Status in dieser Form neuartig ist. Die beiden in dieser Studie vorgestellten Methoden sind online als Webdienste verfügbar. Basierend auf den Exposure-Vorhersagen von beta-Faltblättern ist es möglich, in künftigen Studien mutmaßliche transmembrane beta-Barrels aus Proteomdatenzu identifizieren

NN approach and its comparison with NN-SVM to beta-barrel prediction

This paper is concerned with applications of a dual Neural Network (NN) and Support Vector Machine (SVM) to prediction and analysis of beta barrel trans membrane proteins. The prediction and analysis of beta barrel proteins usually offer a host of challenges to the research community, because of their low presence in genomes. Current beta barrel prediction methodologies present intermittent misclassifications resulting in mismatch in the number of membrane spanning regions within amino-acid sequences. To address the problem, this research embarks upon a NN technique and its comparison with hybrid- two-level NN-SVM methodology to classify inter-class and intra-class transitions to predict the number and range of beta membrane spanning regions. The methodology utilizes a sliding-window-based feature extraction to train two different class transitions entitled symmetric and asymmetric models. In symmet- ric modelling, the NN and SVM frameworks train for sliding window over the same intra-class areas such as inner-to-inner, membrane(beta)-to-membrane and outer-to-outer. In contrast, the asymmetric transi- tion trains a NN-SVM classifier for inter-class transition such as outer-to-membrane (beta) and membrane (beta)-to-inner, inner-to-membrane and membrane-to-outer. For the NN and NN-SVM to generate robust outcomes, the prediction methodologies are analysed by jack-knife tests and single protein tests. The computer simulation results demonstrate a significant impact and a superior performance of NN-SVM tests with a 5 residue overlap for signal protein over NN with and without redundant proteins for pre- diction of trans membrane beta barrel spanning regions

Ranking models of transmembrane β-barrel proteins using Z-coordinate predictions

Motivation: Transmembrane β-barrels exist in the outer membrane of gram-negative bacteria as well as in chloroplast and mitochondria. They are often involved in transport processes and are promising antimicrobial drug targets. Structures of only a few β-barrel protein families are known. Therefore, a method that could automatically generate such models would be valuable. The symmetrical arrangement of the barrels suggests that an approach based on idealized geometries may be successful

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

Transmembrane protein structure prediction using machine learning

This thesis describes the development and application of machine learning-based methods for the prediction of alpha-helical transmembrane protein structure from sequence alone. It is divided into six chapters. Chapter 1 provides an introduction to membrane structure and dynamics, membrane protein classes and families, and membrane protein structure prediction. Chapter 2 describes a topological study of the transmembrane protein CLN3 using a consensus of bioinformatic approaches constrained by experimental data. Mutations in CLN3 can cause juvenile neuronal ceroid lipofuscinosis, or Batten disease, an inherited neurodegenerative lysosomal storage disease affecting children, therefore such studies are important for directing further experimental work into this incurable illness. Chapter 3 explores the possibility of using biologically meaningful signatures described as regular expressions to influence the assignment of inside and outside loop locations during transmembrane topology prediction. Using this approach, it was possilbe to modify a recent topology prediction method leading to an improvement of 6% prediction accuracy using a standard data set. Chapter 4 describes the development of a novel support vector machine-based topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of sequences with known crystal structures. The method achieves state-of-the-art performance in predicting topology and discriminating between globular and transmembrane proteins. We also present the results of applying these tools to a number of complete genomes. Chapter 5 describes a novel approach to predict lipid exposure, residue contacts, helix-helix interactions and finally the optimal helical packing arrangement of transmembrane proteins. It is based on two support vector machine classifiers that predict per residue lipid exposure and residue contacts, which are used to determine helix-helix interaction with up to 65% accuracy. The method is also able to discriminate native from decoy helical packing arrangements with up to 70% accuracy. Finally, a force-directed algorithm is employed to construct the optimal helical packing arrangement which demonstrates success for proteins containing up to 13 transmembrane helices. The final chapter summarises the major contributions of this thesis to biology, before future perspectives for TM protein structure prediction are discussed

Cellular maturation of mitochondrial molybdoenzymes

The molybdenum cofactor (Moco) is an essential component present in nearly all domains of life. In mammals, Moco is part of four currently known enzymes and constitutes a crucial redox-active center involved in a number of fundamental cellular reactions. Moco-dependent enzymes are present in the cytosol but also in or at mitochondria, where Moco is integrated into sulfite oxidase (SO) and the mitochondrial amidoxime-reducing component (mARC), respectively. The family of mitochondrial Moco-enzymes is of particular interest considering the cytosolic synthesis of enzymes and cofactor, which requires a coordinated mitochondrial transport and assembly process. In the current study, the mitochondrial maturations of SO and mARC1 were thus analyzed to obtain a mechanistic understanding of the processes starting with the cytosolic syntheses of apo-proteins all the way to the formation of the mature mitochondrial enzymes. The first part of this work uncovered the cellular assembly of SO, a soluble protein of the mitochondrial intermembrane space, and revealed a Moco-dependent mitochondrial targeting mechanism. In spite of its functional bipartite N-terminal targeting signal, about 70% of SO mislocalized to the cytosol if Moco was not present. Following the identification of SO processing by the inner membrane peptidase (IMP) complex, prevention of this cleavage and thus anchoring of SO in the inner mitochondrial membrane resulted in an efficient mitochondrial targeting even in absence of Moco. SO was thereby identified to undergo a reverse translocation to the cytosol in absence of Moco, which is required to trap SO in the intermembrane space and to constitute in addition a vectorial driving force for completion of SO translocation across the TOM complex. The integration of Moco is not only essential for correct sub-mitochondrial localization, but also a prerequisite for in vivo heme integration and homodimerization of SO. In conclusion, the identified molecular hierarchy of SO maturation represents a novel link between the canonical pre-sequence pathway and folding-trap mechanisms of mitochondrial import. The other mitochondrial Moco-enzyme mARC1 was recently discovered and its sub-mitochondrial localization had remained unclear. In the second part of this study, mARC1 was shown to be localized to the outer mitochondrial membrane. As a result of the translocation process, the C-terminal catalytic core of the protein remains exposed to the cytosol and confers an N(in)-C(out) membrane orientation of mARC1. This localization is mediated by the N-terminal domain of the enzyme, being composed of a classical but weak N-terminal targeting signal and a downstream transmembrane domain. Thereby, the transmembrane domain of mARC1 is sufficient for mitochondrial targeting, while the N-terminal targeting signal seems to function as a supportive receptor for the outer mitochondrial membrane. According to its localization and targeting mechanism, mARC1 is classified as a novel signal-anchored protein. Considering the membrane integration of mARC1, an SO-similar demand of Moco for mitochondrial retention of mARC1 is not required and its N-terminal targeting motifs are sufficient for adequate mitochondrial localization. During mitochondrial import, mARC1 is not processed and membrane integration proceeds membrane potential independently but requires external ATP, which finally results in the assembly of mARC1 into high-oligomeric protein complexes

Structural bioinformatics analysis of the SARS-COV-2 proteome evolution to characterize the emerging variants of the virus and to suggest possible therapeutic strategies

SARS-CoV-2 is a new coronavirus responsible for the global COVID-19 pandemic, detected in China in December 2019 and that has spread rapidly across the world. Our unit, with its specific expertise in structural bioinformatics and molecular modelling, has been involved in collaboration with epidemiology and molecular genetics groups to study SARS-CoV-2 proteome and to suggest possible molecular strategies able to inhibit virus infection. All coronaviruses, including SARS-CoV-2, evolve and adapt to the host through accumulation of mutations generated by characteristics of the virus RNA-polymerase. This work can be divided into two parts: the first part is focused onto the predictions of the potential effects of the mutations on the functions of the SARS-CoV-2 Spike glycoprotein, whereas the second part is focused at suggesting possible therapeutic strategies. In particular, I performed docking analyses to study the possible mode ad sites of interaction of inorganic polyphosphates with ACE2 and SARS-CoV-2 RNA dependent RNA polymerase (RdRp) because the molecular genetics group with whom we collaborate suggested that polyphosphates can enhance ACE2 proteasomal degradation and impair synthesis of viral RNA. In addition, I developed a pipeline to predict the most frequent sites of interaction between Spike glycoprotein and neutralizing monoclonal antibodies in order to propose therapeutic alternatives more specific and selective

The Roles of Gene Duplication, Gene Conversion and Positive Selection in Rodent \u3ci\u3eEsp\u3c/i\u3e and \u3ci\u3eMup\u3c/i\u3e Pheromone Gene Families with Comparison to the \u3ci\u3eAbp\u3c/i\u3e Family

Three proteinaceous pheromone families, the androgen-binding proteins (ABPs), the exocrine-gland secreting peptides (ESPs) and the major urinary proteins (MUPs) are encoded by large gene families in the genomes of Mus musculus and Rattus norvegicus. We studied the evolutionary histories of the Mup and Esp genes and compared them with what is known about the Abp genes. Apparently gene conversion has played little if any role in the expansion of the mouse Class A and Class B Mup genes and pseudogenes, and the rat Mups. By contrast, we found evidence of extensive gene conversion in many Esp genes although not in all of them. Our studies of selection identified at least two amino acid sites in β-sheets as having evolved under positive selection in the mouse Class A and Class B MUPs and in rat MUPs. We show that selection may have acted on the ESPs by determining Ka/Ks for Exon 3 sequences with and without the converted sequence segment. While it appears that purifying selection acted on the ESP signal peptides, the secreted portions of the ESPs probably have undergone much more rapid evolution. When the inner gene converted fragment sequences were removed, eleven Esp paralogs were present in two or more pairs with Ka/Ks \u3e1.0 and thus we propose that positive selection is detectable by this means in at least some mouse Esp paralogs. We compare and contrast the evolutionary histories of all three mouse pheromone gene families in light of their proposed functions in mouse communication

Structural biology of bacterial functional amyloid formation

Amyloids are proteinaceous aggregates best known for their role in degenerative diseases involving protein misfolding. Research into amyloid has intensified in recent times due to its prominence in many debilitating human diseases and limited understanding of the causes. The discovery of functional amyloids in a broad range of species has enhanced our understanding of amyloid, of these the curli system of E. coli has been extensively studied, in this system CsgC was identified as a potent inhibitor of amyloid. An additional protein was discovered in some curli operons in other species termed CsgH and warrants further study. A morphologically similar but genetically distinct bacterial functional amyloid system was identified in Pseudomonas encoded by the fapABCDEF operon and termed amyloid-like fibres (Alf). The study of functional amyloid has the potential to provide insights into how amyloid can be controlled. The aims of this thesis were to investigate the novel functional amyloid system of Pseudomonas with a view to structural and functional characterisation of the individual components. The structure and function of the CsgH protein were also studied by nuclear magnetic resonance (NMR) and the ThioflavinT (ThT) amyloid fibrillation assay. Constructs were produced for all the Alf proteins and the more structured components, FapD and FapF, were optimised to produce constructs for structural study. The structure of CsgH was solved successfully using NMR and showed that the protein shared a similar tertiary structure to CsgC. The function of the CsgH was shown to be similar to CsgC inhibiting amyloid formation by CsgA at substoichiometric concentrations. Mutagenesis, ThT assay and NMR were used to show that CsgH and CsgA interact and that several charged residues have an important role in function. It was also interesting to note that CsgH was capable of inhibiting amyloid formation by the FapC amyloid protein of Pseudomonas.Open Acces