New scoring schemes for protein fold recognition based on Voronoi contacts

MOTIVATION: The genome projects produce a wealth of protein sequences. Theoretical methods to predict possible structures and functions are needed for screening purposes, large-scale comparisons and in-depth analysis to identify worthwhile targets for further experimental research. Sequence-structure alignment is a basic tool for the identification of model folds for protein sequences and the construction of crude structural models. Empirical contact potentials (potentials of mean force) are used to optimize and evaluate such alignments. RESULTS: We propose new scoring schemes based on a contact definition derived from Voronoi decompositions of the three-dimensional coordinates of protein structures. We demonstrate that Voronoi potentials are superior to pure distance-based contact potentials with respect to recognition rate and significance for native folds. Moreover, the scoring scheme has the potential to provide a reasonable balance of detail and ion such that it is also useful for the recognition of distantly related (both homologous and non-homologous) proteins. This is demonstrated here on a set of structural alignments showing much better correspondence of native and model scores for the Voronoi potentials as compared to conventional distance-based potentials

DISCRETIZED GEOMETRIC APPROACHES TO THE ANALYSIS OF PROTEIN STRUCTURES

Proteins play crucial roles in a variety of biological processes. While we know that their amino acid sequence determines their structure, which in turn determines their function, we do not know why particular sequences fold into particular structures. My work focuses on discretized geometric descriptions of protein structure—conceptualizing native structure space as composed of mostly discrete, geometrically defined fragments—to better understand the patterns underlying why particular sequence elements correspond to particular structure elements. This discretized geometric approach is applied to multiple levels of protein structure, from conceptualizing contacts between residues as interactions between discrete structural elements to treating protein structures as an assembly of discrete fragments. My earlier work focused on better understanding inter-residue contacts and estimating their energies statistically. By scoring structures with energies derived from a stricter notion of contact, I show that native protein structures can be identified out of a set of decoy structures more often than when using energies derived from traditional definitions of contact and how this has implications for the evaluation of predictions that rely on structurally defined contacts for validation. Demonstrating how useful simple geometric descriptors of structure can be, I then show that these energies identify native structures on par with well-validated, detailed, atomistic energy functions. Moving to a higher level of structure, in my later work I demonstrate that discretized, geometrically defined structural fragments make good objects for the interactive assembly of protein backbones and present a software application which lets users do so. Finally, I use these fragments to generate structure-conditioned statistical energies, generalizing the classic idea of contact energies by incorporating specific structural context, enabling these energies to reflect the interaction geometries they come from. These structure-conditioned energies contain more information about native sequence preferences, correlate more highly with experimentally determined energies, and show that pairwise sequence preferences are tightly coupled to their structural context. Considered jointly, these projects highlight the degree to which protein structures and the interactions they comprise can be understood as geometric elements coming together in finely tuned ways

HIV Drug Resistant Prediction and Featured Mutants Selection using Machine Learning Approaches

HIV/AIDS is widely spread and ranks as the sixth biggest killer all over the world. Moreover, due to the rapid replication rate and the lack of proofreading mechanism of HIV virus, drug resistance is commonly found and is one of the reasons causing the failure of the treatment. Even though the drug resistance tests are provided to the patients and help choose more efficient drugs, such experiments may take up to two weeks to finish and are expensive. Because of the fast development of the computer, drug resistance prediction using machine learning is feasible. In order to accurately predict the HIV drug resistance, two main tasks need to be solved: how to encode the protein structure, extracting the more useful information and feeding it into the machine learning tools; and which kinds of machine learning tools to choose. In our research, we first proposed a new protein encoding algorithm, which could convert various sizes of proteins into a fixed size vector. This algorithm enables feeding the protein structure information to most state of the art machine learning algorithms. In the next step, we also proposed a new classification algorithm based on sparse representation. Following that, mean shift and quantile regression were included to help extract the feature information from the data. Our results show that encoding protein structure using our newly proposed method is very efficient, and has consistently higher accuracy regardless of type of machine learning tools. Furthermore, our new classification algorithm based on sparse representation is the first application of sparse representation performed on biological data, and the result is comparable to other state of the art classification algorithms, for example ANN, SVM and multiple regression. Following that, the mean shift and quantile regression provided us with the potentially most important drug resistant mutants, and such results might help biologists/chemists to determine which mutants are the most representative candidates for further research

Protein structure prediction and modelling

The prediction of protein structures from their amino acid sequence alone is a very challenging problem. Using the variety of methods available, it is often possible to achieve good models or at least to gain some more information, to aid scientists in their research. This thesis uses many of the widely available methods for the prediction and modelling of protein structures and proposes some new ideas for aiding the process. A new method for measuring the buriedness (or exposure) of residues is discussed which may lead to a potential way of assessing proteins' individual amino acid placement and whether they have a standard profile. This may become useful in assessing predicted models. Threading analysis and modelling of structures for the Critical Assessment of Techniques for Protein Structure Prediction (CASP2) highlights inaccuracies in the current state of protein prediction, particularly with the alignment predictions of sequence on structure. An in depth analysis of the placement of gaps within a multiple sequence threading method is discussed, with ideas for the improvement of threading predictions by the construction of an improved gap penalty. A threading based homology model was constructed with an RMSD of 6.2A, showing how combinations of methods can give usable results. Using a distance geometry method, DRAGON, the ab initio prediction of a protein (NK Lysin) for the CASP2 assessment was achieved with an accuracy of 4.6Å. This highlighted several ideas in disulphide prediction and a novel method for predicting which cysteine residues might form disulphide bonds in proteins. Using a combination of all the methods, with some like threading and homology modelling proving inadequate, an ab initio model of the N-terminal domain of a GPCR was built based on secondary structure and predictions of disulphide bonds. Use of multiple sequences in comparing sequences to structures in threading should give enough information to enable the improvements required before threading can be-come a major way of building homology models. Furthermore, with the ability to predict disulphide bonds: restraints can be placed when building models, ab initio or otherwise

Alternative Splicing and Protein Structure Evolution

In den letzten Jahren gab es in verschiedensten Bereichen der Biologie einen dramatischen Anstieg verfügbarer, experimenteller Daten. Diese erlauben zum ersten Mal eine detailierte Analyse der Funktionsweisen von zellulären Komponenten wie Genen und Proteinen, die Analyse ihrer Verknüpfung in zellulären Netzwerken sowie der Geschichte ihrer Evolution. Insbesondere der Bioinformatik kommt hier eine wichtige Rolle in der Datenaufbereitung und ihrer biologischen Interpretation zu. In der vorliegenden Doktorarbeit werden zwei wichtige Bereiche der aktuellen bioinformatischen Forschung untersucht, nämlich die Analyse von Proteinstrukturevolution und Ähnlichkeiten zwischen Proteinstrukturen, sowie die Analyse von alternativem Splicing, einem integralen Prozess in eukaryotischen Zellen, der zur funktionellen Diversität beiträgt. Insbesondere führen wir mit dieser Arbeit die Idee einer kombinierten Analyse der beiden Mechanismen (Strukturevolution und Splicing) ein. Wir zeigen, dass sich durch eine kombinierte Betrachtung neue Einsichten gewinnen lassen, wie Strukturevolution und alternatives Splicing sowie eine Kopplung beider Mechanismen zu funktioneller und struktureller Komplexität in höheren Organismen beitragen. Die in der Arbeit vorgestellten Methoden, Hypothesen und Ergebnisse können dabei einen Beitrag zu unserem Verständnis der Funktionsweise von Strukturevolution und alternativem Splicing bei der Entstehung komplexer Organismen leisten wodurch beide, traditionell getrennte Bereiche der Bioinformatik in Zukunft voneinander profitieren können

Context based bioinformatics

The goal of bioinformatics is to develop innovative and practical methods and algorithms for bio- logical questions. In many cases, these questions are driven by new biotechnological techniques, especially by genome and cell wide high throughput experiment studies. In principle there are two approaches: 1. Reduction and abstraction of the question to a clearly defined optimization problem, which can be solved with appropriate and efficient algorithms. 2. Development of context based methods, incorporating as much contextual knowledge as possible in the algorithms, and derivation of practical solutions for relevant biological ques- tions on the high-throughput data. These methods can be often supported by appropriate software tools and visualizations, allowing for interactive evaluation of the results by ex- perts. Context based methods are often much more complex and require more involved algorithmic techniques to get practical relevant and efficient solutions for real world problems, as in many cases already the simplified abstraction of problems result in NP-hard problem instances. In many cases, to solve these complex problems, one needs to employ efficient data structures and heuristic search methods to solve clearly defined sub-problems using efficient (polynomial) op- timization (such as dynamic programming, greedy, path- or tree-algorithms). In this thesis, we present new methods and analyses addressing open questions of bioinformatics from different contexts by incorporating the corresponding contextual knowledge. The two main contexts in this thesis are the protein structure similarity context (Part I) and net- work based interpretation of high-throughput data (Part II). For the protein structure similarity context Part I we analyze the consistency of gold standard structure classification systems and derive a consistent benchmark set usable for different ap- plications. We introduce two methods (Vorolign, PPM) for the protein structure similarity recog- nition problem, based on different features of the structures. Derived from the idea and results of Vorolign, we introduce the concept of contact neighbor- hood potential, aiming to improve the results of protein fold recognition and threading. For the re-scoring problem of predicted structure models we introduce the method Vorescore, clearly improving the fold-recognition performance, and enabling the evaluation of the contact neighborhood potential for structure prediction methods in general. We introduce a contact consistent Vorolign variant ccVorolign further improving the structure based fold recognition performance, and enabling direct optimization of the neighborhood po- tential in the future. Due to the enforcement of contact-consistence, the ccVorolign method has much higher computational complexity than the polynomial Vorolign method - the cost of com- puting interpretable and consistent alignments. Finally, we introduce a novel structural alignment method (PPM) enabling the explicit modeling and handling of phenotypic plasticity in protein structures. We employ PPM for the analysis of effects of alternative splicing on protein structures. With the help of PPM we test the hypothesis, whether splice isoforms of the same protein can lead to protein structures with different folds (fold transitions). In Part II of the thesis we present methods generating and using context information for the interpretation of high-throughput experiments. For the generation of context information of molecular regulations we introduce novel textmin- ing approaches extracting relations automatically from scientific publications. In addition to the fast NER (named entity recognition) method (syngrep) we also present a novel, fully ontology-based context-sensitive method (SynTree) allowing for the context-specific dis- ambiguation of ambiguous synonyms and resulting in much better identification performance. This context information is important for the interpretation of high-throughput data, but often missing in current databases. Despite all improvements, the results of automated text-mining methods are error prone. The RelAnn application presented in this thesis helps to curate the automatically extracted regula- tions enabling manual and ontology based curation and annotation. For the usage of high-throughput data one needs additional methods for data processing, for example methods to map the hundreds of millions short DNA/RNA fragments (so called reads) on a reference genome or transcriptome. Such data (RNA-seq reads) are the output of next generation sequencing methods measured by sequencing machines, which are becoming more and more efficient and affordable. Other than current state-of-the-art methods, our novel read-mapping method ContextMap re- solves the occurring ambiguities at the final step of the mapping process, employing thereby the knowledge of the complete set of possible ambiguous mappings. This approach allows for higher precision, even if more nucleotide errors are tolerated in the read mappings in the first step. The consistence between context information of molecular regulations stored in databases and extracted from textmining against measured data can be used to identify and score consistent reg- ulations (GGEA). This method substantially extends the commonly used gene-set based methods such over-representation (ORA) and gene set enrichment analysis (GSEA). Finally we introduce the novel method RelExplain, which uses the extracted contextual knowl- edge and generates network-based and testable hypotheses for the interpretation of high-throughput data.Bioinformatik befasst sich mit der Entwicklung innovativer und praktisch einsetzbarer Verfahren und Algorithmen für biologische Fragestellungen. Oft ergeben sich diese Fragestellungen aus neuen Beobachtungs- und Messverfahren, insbesondere neuen Hochdurchsatzverfahren und genom- und zellweiten Studien. Im Prinzip gibt es zwei Vorgehensweisen: Reduktion und Abstraktion der Fragestellung auf ein klar definiertes Optimierungsproblem, das dann mit geeigneten möglichst effizienten Algorithmen gelöst wird. Die Entwicklung von kontext-basierten Verfahren, die möglichst viel Kontextwissen und möglichst viele Randbedingungen in den Algorithmen nutzen, um praktisch relevante Lösungen für relvante biologische Fragestellungen und Hochdurchsatzdaten zu erhalten. Die Verfahren können oft durch geeignete Softwaretools und Visualisierungen unterstützt werden, um eine interaktive Auswertung der Ergebnisse durch Fachwissenschaftler zu ermöglichen. Kontext-basierte Verfahren sind oft wesentlich aufwändiger und erfordern involviertere algorithmische Techniken um für reale Probleme, deren simplifizierende Abstraktionen schon NP-hart sind, noch praktisch relevante und effiziente Lösungen zu ermöglichen. Oft werden effiziente Datenstrukturen und heuristische Suchverfahren benötigt, die für klar umrissene Teilprobleme auf effiziente (polynomielle) Optimierungsverfahren (z.B. dynamische Programmierung, Greedy, Wege- und Baumverfahren) zurückgreifen und sie entsprechend für das Gesamtverfahren einsetzen. In dieser Arbeit werden eine Reihe von neuen Methoden und Analysen vorgestellt um offene Fragen der Bioinformatik aus verschiedenen Kontexten durch Verwendung von entsprechendem Kontext-Wissen zu adressieren. Die zwei Hauptkontexte in dieser Arbeit sind (Teil 1) die Ähnlichkeiten von 3D Protein Strukturen und (Teil 2) auf die netzwerkbasierte Interpretation von Hochdurchsatzdaten. Im Proteinstrukturkontext Teil 1 analysieren wir die Konsistenz der heute verfügbaren Goldstandards für Proteinstruktur-Klassifikationen, und leiten ein vielseitig einsetzbares konsistentes Benchmark-Set ab. Für eine genauere Bestimmung der Ähnlichkeit von Proteinstrukturen beschreiben wir zwei Methoden (Vorolign, PPM), die unterschiedliche Strukturmerkmale nutzen. Ausgehend von den für Vorolign erzielten Ergebnissen, führen wir Kontakt-Umgebungs-Potentiale mit dem Ziel ein, Fold-Erkennung (auf Basis der vorhandenen Strukturen) und Threading (zur Proteinstrukturvorhersage) zu verbessern. Für das Problem des Re-scorings von vorhergesagten Strukturmodellen beschreiben wir das Vorescore Verfahren ein, mit dem die Fold-Erkennung deutlich verbessert, aber auch die Anwendbarkeit von Potentialen im Allgemeinen getested werden kann. Zur weiteren Verbesserung führen wir eine Kontakt-konsistente Vorolign Variante (ccVorolign) ein, die wegen der neuen Konsistenz-Randbedingung erheblich aufwändiger als das polynomielle Vorolignverfahren ist, aber eben auch interpretierbare konsistente Alignments liefert. Das neue Strukturalignment Verfahren (PPM) erlaubt es phänotypische Plastizität, explizit zu modellieren und zu berücksichtigen. PPM wird eingesetzt, um die Effekte von alternativem Splicing auf die Proteinstruktur zu untersuchen, insbesondere die Hypothese, ob Splice-Isoformen unterschiedliche Folds annehmen können (Fold-Transitionen). Im zweiten Teil der Arbeit werden Verfahren zur Generierung von Kontextinformationen und zu ihrer Verwendung für die Interpretation von Hochdurchsatz-Daten vorgestellt. Neue Textmining Verfahren extrahieren aus wissenschaftlichen Publikationen automatisch molekulare regulatorische Beziehungen und entsprechende Kontextinformation. Neben schnellen NER (named entity recognition) Verfahren (wie syngrep) wird auch ein vollständig Ontologie-basiertes kontext-sensitives Verfahren (SynTree) eingeführt, das es erlaubt, mehrdeutige Synonyme kontext-spezifisch und damit wesentlich genauer aufzulösen. Diese für die Interpretation von Hochdurchsatzdaten wichtige Kontextinformation fehlt häufig in heutigen Datenbanken. Automatische Verfahren produzieren aber trotz aller Verbesserungen noch viele Fehler. Mithilfe unserer Applikation RelAnn können aus Texten extrahierte regulatorische Beziehungen ontologiebasiert manuell annotiert und kuriert werden. Die Verwendung aktueller Hochdurchsatzdaten benötigt zusätzliche Ansätze für die Datenprozessierung, zum Beispiel für das Mapping von hunderten von Millionen kurzer DNA/RNA Fragmente (sog. reads) auf Genom oder Transkriptom. Diese Daten (RNA-seq) ergeben sich durch next generation sequencing Methoden, die derzeit mit immer leistungsfähigeren Geräten immer kostengünstiger gemessen werden können. In der ContextMap Methode werden im Gegensatz zu state-of-the-art Verfahren die auftretenden Mehrdeutigkeiten erst am Ende des Mappingprozesses aufgelöst, wenn die Gesamtheit der Mappinginformationen zur Verfügung steht. Dadurch könenn mehr Fehler beim Mapping zugelassen und trotzdem höhere Genauigkeit erreicht werden. Die Konsistenz zwischen der Kontextinformation aus Textmining und Datenbanken sowie den gemessenen Daten kann dann für das Auffinden und Bewerten von konsistente Regulationen (GGEA) genutzt werden. Dieses Verfahren stellt eine wesentliche Erweiterung der häufig verwendeten Mengen-orientierten Verfahren wie overrepresentation (ORA) und gene set enrichment analysis (GSEA) dar. Zuletzt stellen wir die Methode RelExplain vor, die aus dem extrahierten Kontextwissen netzwerk-basierte, testbare Hypothesen für die Erklärung von Hochdurchsatzdaten generiert