Novel Peptide-Mediated Interactions Derived from High-Resolution 3-Dimensional Structures

Many biological responses to intra- and extracellular stimuli are regulated through complex networks of transient protein interactions where a globular domain in one protein recognizes a linear peptide from another, creating a relatively small contact interface. These peptide stretches are often found in unstructured regions of proteins, and contain a consensus motif complementary to the interaction surface displayed by their binding partners. While most current methods for the de novo discovery of such motifs exploit their tendency to occur in disordered regions, our work here focuses on another observation: upon binding to their partner domain, motifs adopt a well-defined structure. Indeed, through the analysis of all peptide-mediated interactions of known high-resolution three-dimensional (3D) structure, we found that the structure of the peptide may be as characteristic as the consensus motif, and help identify target peptides even though they do not match the established patterns. Our analyses of the structural features of known motifs reveal that they tend to have a particular stretched and elongated structure, unlike most other peptides of the same length. Accordingly, we have implemented a strategy based on a Support Vector Machine that uses this features, along with other structure-encoded information about binding interfaces, to search the set of protein interactions of known 3D structure and to identify unnoticed peptide-mediated interactions among them. We have also derived consensus patterns for these interactions, whenever enough information was available, and compared our results with established linear motif patterns and their binding domains. Finally, to cross-validate our identification strategy, we scanned interactome networks from four model organisms with our newly derived patterns to see if any of them occurred more often than expected. Indeed, we found significant over-representations for 64 domain-motif interactions, 46 of which had not been described before, involving over 6,000 interactions in total for which we could suggest the molecular details determining the binding

3did: identification and classification of domain-based interactions of known three-dimensional structure

The database of three-dimensional interacting domains (3did) is a collection of protein interactions for which high-resolution three-dimensional structures are known. 3did exploits the availability of structural data to provide molecular details on interactions between two globular domains as well as novel domain–peptide interactions, derived using a recently published method from our lab. The interface residues are presented for each interaction type individually, plus global domain interfaces at which one or more partners (domains or peptides) bind. The 3did web server at http://3did.irbbarcelona.org visualizes these interfaces along with atomic details of individual interactions using Jmol. The complete contents are also available for download

The identification of short linear motif-mediated interfaces within the human interactome

Motivation: Eukaryotic proteins are highly modular, containing multiple interaction interfaces that mediate binding to a network of regulators and effectors. Recent advances in high-throughput proteomics have rapidly expanded the number of known protein–protein interactions (PPIs); however, the molecular basis for the majority of these interactions remains to be elucidated. There has been a growing appreciation of the importance of a subset of these PPIs, namely those mediated by short linear motifs (SLiMs), particularly the canonical and ubiquitous SH2, SH3 and PDZ domain-binding motifs. However, these motif classes represent only a small fraction of known SLiMs and outside these examples little effort has been made, either bioinformatically or experimentally, to discover the full complement of motif instances

Sequence- and Interactome-Based Prediction of Viral Protein Hotspots Targeting Host Proteins: A Case Study for HIV Nef

Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk

DISCOVERING A NOVEL ANTIFUNGAL TARGET IN DOWNSTREAM STEROL BIOSYNTHESIS USING A SQUALENE SYNTHASE FUNCTIONAL MOTIF

The sterol biosynthetic pathway is essential for growth of all eukaryotic cells and the main target of antifungal agents. The emergence of resistance to these antifungals in an already ill patient population indicates a need to develop drugs that have a broad spectrum of activity among pathogenic fungi and have minimal patient toxicity. Squalene synthase is the first committed step in the sterol pathway and has been studied intensively for development of antifungal agents. While the overall architecture of this enzyme is identical throughout eukaryotes, it was shown that plant and animal genes cannot complement a squalene synthase knockout mutation in yeast unless the carboxy-terminal domain is swapped for one of fungal origin. This implies that there is a component of the fungal carboxy-terminal domain that is responsible for the complementation phenotype and that is unique to the fungal kingdom of life. To determine the role of the carboxy-terminal domain of squalene synthase in the sterol pathway, we used the yeast Saccharomyces cerevisiae with a squalene synthase knockout mutation and expressed squalene synthases originating from fungi, plants, and animals. In contrast to previous observations, all enzymes tested could partially complement the knockout mutation when the genes were weakly expressed. When induced, non-fungal squalene synthases could not complement the knockout mutation and instead led to the accumulation of carboxysterol intermediates, suggesting an interaction between squalene synthase and the downstream sterol C4-decarboxylase. Overexpression of a sterol C4-decarboxylase from any kingdom of life both decreased the accumulation of carboxysterol intermediates and allowed non-fungal squalene synthases to complement the squalene synthase knockout mutation. Using chimeric squalene synthases from each kingdom of life, the motif in the C-terminal domain responsible for preventing this toxicity was mapped to a kingdom-specific 26-amino acid hinge motif adjacent to the catalytic domain. Furthermore, over-expression of the carboxy-terminal domain alone containing a hinge motif from fungi, not from animals or plants, led to growth inhibition of wild-type yeast. Since this hinge region is unique to and highly conserved within each kingdom of life, this data provides evidence for the development of an antifungal therapeutic as well as for tools to develop an understanding of triterpene catalytic activity and identify similar motifs in other biosynthetic pathways

Machine Learning based Protein Sequence to (un)Structure Mapping and Interaction Prediction

Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs), and hence the involved dynamics, which help us explain complex interaction within a cell that is otherwise obscured. The objective of this dissertation is to develop machine learning based effective tools to predict disordered protein, its properties and dynamics, and interaction paradigm by systematically mining and analyzing large-scale biological data. In this dissertation, we propose a robust framework to predict disordered proteins given only sequence information, using an optimized SVM with RBF kernel. Through appropriate reasoning, we highlight the structure-like behavior of IDPs in disease-associated complexes. Further, we develop a fast and effective predictor of Accessible Surface Area (ASA) of protein residues, a useful structural property that defines protein’s exposure to partners, using regularized regression with 3rd-degree polynomial kernel function and genetic algorithm. As a key outcome of this research, we then introduce a novel method to extract position specific energy (PSEE) of protein residues by modeling the pairwise thermodynamic interactions and hydrophobic effect. PSEE is found to be an effective feature in identifying the enthalpy-gain of the folded state of a protein and otherwise the neutral state of the unstructured proteins. Moreover, we study the peptide-protein transient interactions that involve the induced folding of short peptides through disorder-to-order conformational changes to bind to an appropriate partner. A suite of predictors is developed to identify the residue-patterns of Peptide-Recognition Domains from protein sequence that can recognize and bind to the peptide-motifs and phospho-peptides with post-translational-modifications (PTMs) of amino acid, responsible for critical human diseases, using the stacked generalization ensemble technique. The involved biologically relevant case-studies demonstrate possibilities of discovering new knowledge using the developed tools

How codon choice determines evolvability and evolutionary robustness in short linear motifs

Short linear motifs, made up of 2-10 amino acids in linear sequence space, are a central component of cellular decision making through proteins. They form a modular system in cells where combinations of domains and motifs are used as basic functional building blocks through interactions. Functions mediated through these motifs include cellular localisation, post-translational modifications, degradation and general protein-protein interactions. Since motifs are made up of a small number of amino acids they have unusual evolutionary properties, for instance they can evolve de novo, or be lost, through a small number of substitutions. This is of particular importance in pathogens such as viruses. Many viruses evolve new host-like motifs to interact with the host and change the regulation and signalling landscape within host cells to mediate infection. In this body of work, I have used influenza as a model to elucidate aspects of the evolutionary properties of motifs. I have been able to leverage recent progress made in determining nucleotide mutation rates and have developed a model for motif evolution that is defined from the nucleotide and codon levels. Simulations using this methodology suggested that different codons have varying propensities to evolve into amino acids within a linear motif. In other words, some sequences have higher motif evolvability. The simulations also indicated a fitness benefit to use some codons over others to encode linear motifs, due to the varying propensity to evolve. These findings suggest that motifs that are encoded by specific codons have higher motif evolutionary robustness, i.e. they can tolerate more mutations without affecting function. I went on to investigate if these predicted properties have played a role in motif evolution in influenza. I found that conserved motifs in influenza use the codons inferred to have higher evolutionary robustness. This would lead to increased fitness, as motifs are less often lost through mutations. I also found that this mutational robustness acts on stop codon usage in influenza, suggesting an explanation for an old observation of predominant use of TAA in many organisms. Interestingly, it also appears that evolutionary robustness of a motif can be varied to tune the rate of motif change, which influenza utilises in glycosylation motifs that interface with the host immune system. Finally, I investigated whether the codon choice and evolvability at early stages of viral host shifts could be used to predict the emergence of functional motifs. I have found that motif evolvability can aid the prediction of motif emergence. For influenza strains H1N1 and H3N2, which were introduced in the human population from birds during the 1900s, the sequence of the early strains could be used to predict the majority of the glycosylation sites that would emerge the following decades. The predictability of motif emergence could have important implications for vaccination efforts. The methodologies developed here, and the observations made about how motif evolution is shaped by codon choices in a predictable way will be important for a better understanding of the evolution of complexity and regulation involving motifs. This may have implications for complex diseases such as cancers, and for our understanding of the evolution of pathogen innovations and functionality

Charakterisierung und Optimierung von CD276 spezifischen Peptiden mittels Display Techniken

In dieser Arbeit sollten spezifisch bindende Peptide gegen die extrazelluläre Domäne der Zielstruktur CD276, ein in verschiedenen Tumorarten überexprimiertes Membranprotein, identifiziert werden. Tumor spezifische Peptide finden in der Bildgebung sowie zielgerichteten Therapien Anwendung. Die hier identifizierten Peptide sollen als Leitstrukturen für neue Tracer zum Einsatz in der Bildgebung dienen. Die Selektion der Peptide erfolgte mit Hilfe der Phagenbibliothek Ph.D12 sowie selbst hergestellten Peptidbibliotheken basierend auf Miniprotein-Scaffolds und zwei verschiedenen Display Systemen, dem Phage- und Ribosome Display. Peptide wurden anschließend durch Fmoc-Festphasensynthese hergestellt, wenn nötig entsprechend gefaltet sowie mit 125I radiomarkiert und in Kinetik-, Kompetitions- sowie Internalisierungsstudien charakterisiert. Vor dem Einsatz in vivo an Tumor tragenden Nacktmäusen wurden zudem Stabilitätsstudien in Serum vorgenommen. Das Peptid CD276.11, welches der Ph.D.12 Bibliothek entstammt und mit Phage Display nach vier Panningrunden isoliert wurde, zeigte eine mit der CD276 Expression korrelierende Bindungsstärke an fünf verschiedenen Tumorzelllinien. Kompetitionsexperimente mit radiomarkiertem Peptid und unmarkiertem Peptid wiesen auf eine spezifische Bindung an CD276 hin. Ein IC50 Wert von 750 nM konnte ermittelt werden. Stabilitätsexperimente in humanem Serum ergaben eine t1/2 von fünf Minuten. Der Versuch der Stabilisierung sowie Fragmentanalysen führten zu dem Schluss, dass es sich bei dem in der Serumanalyse entstehenden Metaboliten um einen 125I Tyrosinrest handelte. Diese Vermutung konnte durch Bindungsstudien von markiertem Tyrosin auf Zellen sowie Kompetitionsversuche mit 125I-CD276 und unmarkiertem Tyrosin bestätigt werden. Ein weiteres nach vier Panningrunden mit der Ph.D.12 Bibliothek und dem Phage Display System identifiziertes Peptid, das PDGFR-P1, wurde bereits zuvor in zwei unabhängigen Pannings gegen das Membranprotein PDGFRβ isoliert. Ziel war es hier, die Bindung an beide Zielproteine zu bestätigen und die Affinität des Peptids zu steigern. Mittels Peptid-Arrays konnte gezeigt werden, dass das Peptid sowohl an CD276 als auch an PDGFRβ, nicht aber an das Kontrollprotein FGFR band. Durch gezielten Austausch einzelner Aminosäuren sowie einer Fragmentanalyse auf dem Peptid-Array konnte ein Peptid G2 mit erhöhter Affinität identifiziert werden, welches anschließend in vitro sowie in vivo charakterisiert wurde