Protein Tertiary Model Assessment Using Granular Machine Learning Techniques

The automatic prediction of protein three dimensional structures from its amino acid sequence has become one of the most important and researched fields in bioinformatics. As models are not experimental structures determined with known accuracy but rather with prediction it’s vital to determine estimates of models quality. We attempt to solve this problem using machine learning techniques and information from both the sequence and structure of the protein. The goal is to generate a machine that understands structures from PDB and when given a new model, predicts whether it belongs to the same class as the PDB structures (correct or incorrect protein models). Different subsets of PDB (protein data bank) are considered for evaluating the prediction potential of the machine learning methods. Here we show two such machines, one using SVM (support vector machines) and another using fuzzy decision trees (FDT). First using a preliminary encoding style SVM could get around 70% in protein model quality assessment accuracy, and improved Fuzzy Decision Tree (IFDT) could reach above 80% accuracy. For the purpose of reducing computational overhead multiprocessor environment and basic feature selection method is used in machine learning algorithm using SVM. Next an enhanced scheme is introduced using new encoding style. In the new style, information like amino acid substitution matrix, polarity, secondary structure information and relative distance between alpha carbon atoms etc is collected through spatial traversing of the 3D structure to form training vectors. This guarantees that the properties of alpha carbon atoms that are close together in 3D space and thus interacting are used in vector formation. With the use of fuzzy decision tree, we obtained a training accuracy around 90%. There is significant improvement compared to previous encoding technique in prediction accuracy and execution time. This outcome motivates to continue to explore effective machine learning algorithms for accurate protein model quality assessment. Finally these machines are tested using CASP8 and CASP9 templates and compared with other CASP competitors, with promising results. We further discuss the importance of model quality assessment and other information from proteins that could be considered for the same

New evolutionary approaches to protein structure prediction

Programa de doctorado en Biotecnología y Tecnología QuímicaThe problem of Protein Structure Prediction (PSP) is one of the principal topics in Bioinformatics. Multiple approaches have been developed in order to predict the protein structure of a protein. Determining the three dimensional structure of proteins is necessary to understand the functions of molecular protein level. An useful, and commonly used, representation for protein 3D structure is the protein contact map, which represents binary proximities (contact or non-contact) between each pair of amino acids of a protein. This thesis work, includes a compilation of the soft computing techniques for the protein structure prediction problem (secondary and tertiary structures). A novel evolutionary secondary structure predictor is also widely described in this work. Results obtained confirm the validity of our proposal. Furthermore, we also propose a multi-objective evolutionary approach for contact map prediction based on physico-chemical properties of amino acids. The evolutionary algorithm produces a set of decision rules that identifies contacts between amino acids. The rules obtained by the algorithm impose a set of conditions based on amino acid properties in order to predict contacts. Results obtained by our approach on four different protein data sets are also presented. Finally, a statistical study was performed to extract valid conclusions from the set of prediction rules generated by our algorithm.Universidad Pablo de Olavide. Centro de Estudios de Postgrad

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

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

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

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Secondary structure-based template selection for fragment-assembly protein structure prediction

Proteins play critical biochemical roles in all living organisms; in human beings, they are the targets of 50% of all drugs. Although the first protein structure was determined 60 years ago, experimental techniques are still time and cost consuming. Consequently, in silico protein structure prediction, which is considered a main challenge in computational biology, is fundamental to decipher conformations of protein targets. This thesis contributes to the state of the art of fragment-assembly protein structure prediction. This category has been widely and thoroughly studied due to its application to any type of targets. While the majority of research focuses on enhancing the functions that are used to score fragments by incorporating new terms and optimising their weights, another important issue is how to pick appropriate fragments from a large pool of candidate structures. Since prediction of the main structural classes, i.e. mainly-alpha, mainly-beta and alpha-beta, has recently reached quite a high level of accuracy, we have introduced a novel approach by decreasing the size of the pool of candidate structures to comprise only proteins that share the same structural class a target is likely to adopt. Picking fragments from this customised set of known structures not only has contributed in generating decoys with higher level of accuracy but also has eliminated irrelevant parts of the search space which makes the selection of first models a less complicated process, addressing the inaccuracies of energy functions. In addition to the challenge of adopting a unique template structure for all targets, another one arises whenever relying on the same amount of corrections and fine tunings; such a phase may be damaging to “easy’ targets, i.e. those that comprise a relatively significant percentage of alpha helices. Owing to the sequence-structure correlation based on which fragment-based protein structure prediction was born, we have also proposed a customised phase of correction based on the structural class prediction of the target in question. After using secondary structure prediction as a “global feature” of a target, i.e. structural classes, we have also investigated its usage as a “local feature” to customise the number of candidate fragments, which is currently the same at all positions. Relying on the known facts regarding diversity of short fragments of helices, sheets and loops, the fragment insertion process has been adjusted to make “changes” relative to the expected complexity of each region. We have proved in this thesis the extent to which secondary structure features can be used implicitly or explicitly to enhance fragment assembly protein structure prediction

Automated Reasoning

This volume, LNAI 13385, constitutes the refereed proceedings of the 11th International Joint Conference on Automated Reasoning, IJCAR 2022, held in Haifa, Israel, in August 2022. The 32 full research papers and 9 short papers presented together with two invited talks were carefully reviewed and selected from 85 submissions. The papers focus on the following topics: Satisfiability, SMT Solving,Arithmetic; Calculi and Orderings; Knowledge Representation and Jutsification; Choices, Invariance, Substitutions and Formalization; Modal Logics; Proofs System and Proofs Search; Evolution, Termination and Decision Prolems. This is an open access book

Computational Analysis of T Cell Receptor Repertoire and Structure

The human adaptive immune system has evolved to provide a sophisticated response to a vast body of pathogenic microbes and toxic substances. The primary mediators of this response are T and B lymphocytes. Antigenic peptides presented at the surface of infected cells by major histocompatibility complex (MHC) molecules are recognised by T cell receptors (TCRs) with exceptional specificity. This specificity arises from the enormous diversity in TCR sequence and structure generated through an imprecise process of somatic gene recombination that takes place during T cell development. Quantification of the TCR repertoire through the analysis of data produced by high-throughput RNA sequencing allows for a characterisation of the immune response to disease over time and between patients, and the development of methods for diagnosis and therapeutic design. The latest version of the software package Decombinator extracts and quantifies the TCR repertoire with improved accuracy and compatibility with complementary experimental protocols and external computational tools. The software has been extended for analysis of fragmented short-read data from single cells, comparing favourably with two alternative tools. The development of cell-based therapeutics and vaccines is incomplete without an understanding of molecular level interactions. The breadth of TCR diversity and cross-reactivity presents a barrier for comprehensive structural resolution of the repertoire by traditional means. Computational modelling of TCR structures and TCR-pMHC complexes provides an efficient alternative. Four generalpurpose protein-protein docking platforms were compared in their ability to accurately model TCR-pMHC complexes. Each platform was evaluated against an expanded benchmark of docking test cases and in the context of varying additional information about the binding interface. Continual innovation in structural modelling techniques sets the stage for novel automated tools for TCR design. A prototype platform has been developed, integrating structural modelling and an optimisation routine, to engineer desirable features into TCR and TCR-pMHC complex models