Outer membrane β-barrel structure prediction through the lens of AlphaFold2

Most proteins found in the outer membrane of Gram-negative bacteria share a common domain: the transmembrane β-barrel. These outer membrane β-barrels (OMBBs) occur in multiple sizes, and different families with a wide range of functions evolved independently by amplification from a pool of homologous ancestral ββ-hairpins. This is part of the reason why predicting their three-dimensional (3D) structure, especially by homology modeling, is a major challenge. Recently, DeepMind's AlphaFold v2 (AF2) became the first structure prediction method to reach close-to-experimental atomic accuracy in CASP even for difficult targets. However, membrane proteins, especially OMBBs, were not abundant during its training, raising the question of how accurate the predictions are for these families. In this study, we assessed the performance of AF2 in the prediction of OMBBs of various topologies using an in-house-developed tool for the analysis of OMBB 3D structures, barrOs . In agreement with previous studies on other membrane protein classes, our results indicate that AF2 predicts OMBB structures at high accuracy independently of the use of templates, even for novel topologies absent from the training set. These results provide confidence on the models generated by AF2 and open the door to the structural elucidation of novel OMBB topologies identified in high-throughput OMBB annotation studies

Cascading classifier application for topology prediction of TMB proteins

This paper is concerned with the use of a cascading classifier for trans-membrane beta-barrel topology prediction analysis. Most of novel drug design requires the use of membrane proteins. Trans-membrane proteins have key roles such as active transport across the membrane and signal transduction among other functions. Given their key roles, understanding their structures mechanisms and regulation at the level of molecules with the use of computational modeling is essential. In the field of bioinformatics, many years have been spent on the trans-membrane protein structure prediction focusing on the alpha-helix membrane proteins. Technological developments have been increasingly utilized in order to understand in more details membrane protein function and structure. Various methodologies have been developed for the prediction of TMB proteins topology however the use of cascading classifier has not been fully explored. This research presents a novel approach for TMB topology prediction. The MATLAB computer simulation results show that the proposed methodology predicts transmembrane topologies with high accuracy for randomly selected proteins

Machine learning tools for protein annotation: the cases of transmembrane β-barrel and myristoylated proteins

Biology is now a “Big Data Science” thanks to technological advancements allowing the characterization of the whole macromolecular content of a cell or a collection of cells. This opens interesting perspectives, but only a small portion of this data may be experimentally characterized. From this derives the demand of accurate and efficient computational tools for automatic annotation of biological molecules. This is even more true when dealing with membrane proteins, on which my research project is focused leading to the development of two machine learning-based methods: BetAware-Deep and SVMyr. BetAware-Deep is a tool for the detection and topology prediction of transmembrane beta-barrel proteins found in Gram-negative bacteria. These proteins are involved in many biological processes and primary candidates as drug targets. BetAware-Deep exploits the combination of a deep learning framework (bidirectional long short-term memory) and a probabilistic graphical model (grammatical-restrained hidden conditional random field). Moreover, it introduced a modified formulation of the hydrophobic moment, designed to include the evolutionary information. BetAware-Deep outperformed all the available methods in topology prediction and reported high scores in the detection task. Glycine myristoylation in Eukaryotes is the binding of a myristic acid on an N-terminal glycine. SVMyr is a fast method based on support vector machines designed to predict this modification in dataset of proteomic scale. It uses as input octapeptides and exploits computational scores derived from experimental examples and mean physicochemical features. SVMyr outperformed all the available methods for co-translational myristoylation prediction. In addition, it allows (as a unique feature) the prediction of post-translational myristoylation. Both the tools here described are designed having in mind best practices for the development of machine learning-based tools outlined by the bioinformatics community. Moreover, they are made available via user-friendly web servers. All this make them valuable tools for filling the gap between sequential and annotated data

Cascading classifier application for topology prediction of transmembrane beta-barrel proteins

Membrane proteins are a major focus for new drug discovery. Transmembrane beta-barrel proteins play key roles in the translocation machinery, pore formation, membrane anchoring and ion exchange. Given their key roles and the difficulty in membrane protein structure determination, the use of computational modelling is essential. This paper focuses on the topology prediction of transmembrane beta-barrel proteins. In the field of bioinformatics, many years of research has been spent on the topology prediction of transmembrane alpha-helices. The efforts to TMB (transmembrane beta-barrel) proteins topology prediction have been overshadowed and the prediction accuracy could be improved with further research. Various methodologies have been developed in the past for the prediction of TMB proteins topology, however the use of cascading classifier has never been fully explored. This research presents a novel approach to TMB topology prediction with the use of a cascading classifier. The MATLAB computer simulation results show that the proposed methodology predicts transmembrane beta-barrel proteins topologies with high accuracy for randomly selected proteins. By using the cascading classifier approach the best overall accuracy is 76.3% with a precision of 0.831 and recall or probability of detection of 0.799 for TMB topology prediction. The accuracy of 76.3% is achieved using a two-layers cascading classifier

In silico proteomic and phylogenetic analysis of the outer membrane protein repertoire of gastric Helicobacter species

Helicobacter (H.) pylori is an important risk factor for gastric malignancies worldwide. Its outer membrane proteome takes an important role in colonization of the human gastric mucosa. However, in zoonotic non-H. pylori helicobacters (NHPHs) also associated with human gastric disease, the composition of the outer membrane (OM) proteome and its relative contribution to disease remain largely unknown. By means of a comprehensive survey of the diversity and distribution of predicted outer membrane proteins (OMPs) identified in all known gastric Helicobacter species with fully annotated genome sequences, we found genus- and species-specific families known or thought to be implicated in virulence. Hop adhesins, part of the Helicobacter-specific family 13 (Hop, Hor and Horn) were restricted to the gastric species H. pylori, H. cetorum and H. acinonychis. Hof proteins (family 33) were putative adhesins with predicted Occ- or MOMP-family like 18-stranded beta-barrels. They were found to be widespread amongst all gastric Helicobacter species only sporadically detected in enterohepatic Helicobacter species. These latter are other members within the genus Helicobacter, although ecologically and genetically distinct. LpxR, a lipopolysaccharide remodeling factor, was also detected in all gastric Helicobacter species but lacking as well from the enterohepatic species H. cinaedi, H. equorum and H. hepaticus. In conclusion, our systemic survey of Helicobacter OMPs points to species and infection-site specific members that are interesting candidates for future virulence and colonization studies.Peer reviewe

Machine learning applications for the topology prediction of transmembrane beta-barrel proteins

The research topic for this PhD thesis focuses on the topology prediction of beta-barrel transmembrane proteins. Transmembrane proteins adopt various conformations that are about the functions that they provide. The two most predominant classes are alpha-helix bundles and beta-barrel transmembrane proteins. Alpha-helix proteins are present in larger numbers than beta-barrel transmembrane proteins in structure databases. Therefore, there is a need to find computational tools that can predict and detect the structure of beta-barrel transmembrane proteins. Transmembrane proteins are used for active transport across the membrane or signal transduction. Knowing the importance of their roles, it becomes essential to understand the structures of the proteins. Transmembrane proteins are also a significant focus for new drug discovery. Transmembrane beta-barrel proteins play critical roles in the translocation machinery, pore formation, membrane anchoring, and ion exchange. In bioinformatics, many years of research have been spent on the topology prediction of transmembrane alpha-helices. The efforts to TMB (transmembrane beta-barrel) proteins topology prediction have been overshadowed, and the prediction accuracy could be improved with further research. Various methodologies have been developed in the past to predict TMB proteins topology. Methods developed in the literature that are available include turn identification, hydrophobicity profiles, rule-based prediction, HMM (Hidden Markov model), ANN (Artificial Neural Networks), radial basis function networks, or combinations of methods. The use of cascading classifier has never been fully explored. This research presents and evaluates approaches such as ANN (Artificial Neural Networks), KNN (K-Nearest Neighbors, SVM (Support Vector Machines), and a novel approach to TMB topology prediction with the use of a cascading classifier. Computer simulations have been implemented in MATLAB, and the results have been evaluated. Data were collected from various datasets and pre-processed for each machine learning technique. A deep neural network was built with an input layer, hidden layers, and an output. Optimisation of the cascading classifier was mainly obtained by optimising each machine learning algorithm used and by starting using the parameters that gave the best results for each machine learning algorithm. The cascading classifier results show that the proposed methodology predicts transmembrane beta-barrel proteins topologies with high accuracy for randomly selected proteins. Using the cascading classifier approach, the best overall accuracy is 76.3%, with a precision of 0.831 and recall or probability of detection of 0.799 for TMB topology prediction. The accuracy of 76.3% is achieved using a two-layers cascading classifier. By constructing and using various machine-learning frameworks, systems were developed to analyse the TMB topologies with significant robustness. We have presented several experimental findings that may be useful for future research. Using the cascading classifier, we used a novel approach for the topology prediction of TMB proteins

Structural basis for chitin acquisition by marine <i>Vibrio </i>species

Chitin degrading bacteria are important for marine ecosystems. Here the authors structurally and functionally characterize the Vibrio harveyi outer membrane diffusion channel chitoporin and give mechanistic insights into chito-oligosaccharide uptake

Whole genome sequencing of \u3ci\u3eMoraxella bovis\u3c/i\u3e strains from North America reveals two genotypes with different genetic determinants

Background: Moraxella bovis and Moraxella bovoculi both associate with infectious bovine keratoconjunctivitis (IBK), an economically significant and painful ocular disease that affects cattle worldwide. There are two genotypes of M. bovoculi (genotypes 1 and 2) that differ in their gene content and potential virulence factors, although neither have been experimentally shown to cause IBK. M. bovis is a causative IBK agent, however, not all strains carry a complete assortment of known virulence factors. The goals of this study were to determine the population structure and depth of M. bovis genomic diversity, and to compare core and accessory genes and predicted outer membrane protein profiles both within and between M. bovis and M. bovoculi. Results: Phylogenetic trees and bioinformatic analyses of 36 M. bovis chromosomes sequenced in this study and additional available chromosomes of M. bovis and both genotype 1 and 2 M. bovoculi, showed there are two genotypes (1 and 2) of M. bovis. The two M. bovis genotypes share a core of 2015 genes, with 121 and 186 genes specific to genotype 1 and 2, respectively. The two genotypes differ by their chromosome size and prophage content, encoded protein variants of the virulence factor hemolysin, and by their affiliation with different plasmids. Eight plasmid types were identified in this study, with types 1 and 6 observed in 88 and 56% of genotype 2 strains, respectively, and absent from genotype 1 strains. Only type 1 plasmids contained one or two gene copies encoding filamentous haemagglutinin- like proteins potentially involved with adhesion. A core of 1403 genes was shared between the genotype 1 and 2 strains of both M. bovis and M. bovoculi, which encoded a total of nine predicted outer membrane proteins. Conclusions: There are two genotypes of M. bovis that differ in both chromosome content and plasmid profiles and thus may not equally associate with IBK. Immunological reagents specifically targeting select genotypes of M. bovis, or all genotypes of M. bovis and M. bovoculi together could be designed from the outer membrane proteins identified in this study

Identifikation neuer Komponenten der dritten Plastidenmembran und Subkompartimentierung des endoplasmatischen Retikulums in Phaeodactylum tricornutum

Durch einen Prozess, der als sekundäre Endosymbiose bezeichnet wird, wurde eine Rotalge als Endosymbiont aufgenommen. Dies führte zur Entstehung der komplexen Plastiden von Cryptophyten, Haptophyten, Heterokontophyten und Apicomplexa. Das Genom dieser ehemaligen Rotalge wurde im Laufe der Evolution dramatisch reduziert und zum größten Teil in den Wirtskern transferiert. Dies führte dazu, dass die meisten plastidären Proteine nun im Zellkern des Wirtes kodiert sind und aus dem Cytosol in die Plastide importiert werden müssen. Es mussten entsprechende Translokationsmechanismen entwickelt werden. Im Rahmen dieser Arbeit wurde versucht neue Komponenten der dritten Plastidenmembran zu identifizieren, welche auf die äußere Chloroplastenmembran primärer Plastiden zurückgeht. Diese Membran unterscheidet sich von anderen plastidären Membranen durch die Präsenz von β-barrel Proteinen, die für die äußeren Membranen von Gram-negativen Bakterien, Mitochondrien und Chloroplasten charakteristisch sind. Mit Hilfe verschiedener Algorithmen wurden putative plastidäre β-barrel Proteine vorhergesagt und als Fusionsproteine in P. tricornutum lokalisiert. Es wurden von 23 Proteinen, vier in der Plasmamembran, vier im Cytosol, fünf im ER, fünf außerhalb der Plastide und fünf in der Plastide lokalisiert. Es konnte kein Protein in der dritten Plastidenmembran identifiziert werden. Die Vorhersagealgorithmen sind nicht dafür geeignet eukaryote Proteome auf β-barrel Proteine hin zu analysieren. Des Weiteren lag ein anderes Problem in der Beschaffenheit der Genmodelle, welche die Basis der Vorhersage bildeten. In einem zweiten Projekt wurde die Subkompartimentierung des ERs in P. tricornutum untersucht. Basierend auf der Erkenntnis, dass das ER in ein hostER, die Kernhülle (NE) und das cER eingeteilt werden kann, wurde untersucht, ob sich hER und cER in ihrer Funktion unterscheiden. Basierend auf der Annahme, dass hER und cER durch den Tag-Nacht-Zyklus der Photosynthese unterschiedlichen physiologischen Bedingungen ausgesetzt sind, wurde gefolgert, dass einige Funktionen wie zum Beispiel die Proteinqualitätskontrolle und -faltung auf das hER beschränkt sein könnten. Daher wurden Faktoren der UPR in P. tricornutum untersucht. Überraschenderweise konnten IRE1 und PERK in Heterokontophyten und Haptophyten identifiziert werden. Lokalisationsstudien der UPR-Faktoren zeigten, dass diese hauptsächlich auf das hER und den NE beschränkt sind, wohingegen hDer1-2 als Beispiel für Proteindegradation im gesamten ER und Transporterproteine wie Tpt1 hauptsächlich im cER vorhanden sind. Die Abwesenheit der UPR im cER lässt sich durch die Physiologie begründen. Außerdem wirft die Präsenz von PERK und IRE1 in Protisten ein neues Licht auf die Sichtweise der Evolution der UPR. So ist eine alternative Entstehungsgeschichte der UPR denkbar