El algoritmo HyRPNI y una aplicación en bioinformática

Proponemos un algoritmo de inferencia gramatical para lenguajes regulares que permite ahorrar cómputo al usar dos criterios diferentes para elegir los estados a ser procesados, un criterio se usa en la primera fase del proceso de inferencia (al principio) y el otro en el resto del proceso. Realizamos experimentos para observar el desempeño del algoritmo, para aprender sobre el tamaño ideal de su primera fase y para mostrar su aplicación en la solución de un problema específico en bioinformática: la predicción de sitios de corte en poliproteínas codificadas por virus de la familia Potyviridae./ We propose a grammar inference algorithm for regular languages which saves computational cost by using two different criteria to choose states to be processed: one in the first phase of the inference process (the beginning) and another for the rest of the process. We applied experiments to observe performance of the algorithm, to learn about the best size of its first phase and to show results of its application to solve a specific problem in Bioinformatics: the cleavage site prediction problem in polyproteins encoded by viruses of the Potyviridae family

Learning the Language of Biological Sequences

International audienceLearning the language of biological sequences is an appealing challenge for the grammatical inference research field.While some first successes have already been recorded, such as the inference of profile hidden Markov models or stochastic context-free grammars which are now part of the classical bioinformatics toolbox, it is still a source of open and nice inspirational problems for grammatical inference, enabling us to confront our ideas to real fundamental applications. As an introduction to this field, we survey here the main ideas and concepts behind the approaches developed in pattern/motif discovery and grammatical inference to characterize successfully the biological sequences with their specificities

Protein-Ligand Binding Affinity Directed Multi-Objective Drug Design Based on Fragment Representation Methods

Drug discovery is a challenging process with a vast molecular space to be explored and numerous pharmacological properties to be appropriately considered. Among various drug design protocols, fragment-based drug design is an effective way of constraining the search space and better utilizing biologically active compounds. Motivated by fragment-based drug search for a given protein target and the emergence of artificial intelligence (AI) approaches in this field, this work advances the field of in silico drug design by (1) integrating a graph fragmentation-based deep generative model with a deep evolutionary learning process for large-scale multi-objective molecular optimization, and (2) applying protein-ligand binding affinity scores together with other desired physicochemical properties as objectives. Our experiments show that the proposed method can generate novel molecules with improved property values and binding affinities

Quantitative and evolutionary global analysis of enzyme reaction mechanisms

The most widely used classification system describing enzyme-catalysed reactions is the Enzyme Commission (EC) number. Understanding enzyme function is important for both fundamental scientific and pharmaceutical reasons. The EC classification is essentially unrelated to the reaction mechanism. In this work we address two important questions related to enzyme function diversity. First, to investigate the relationship between the reaction mechanisms as described in the MACiE (Mechanism, Annotation, and Classification in Enzymes) database and the main top-level class of the EC classification. Second, how well these enzymes biocatalysis are adapted in nature. In this thesis, we have retrieved 335 enzyme reactions from the MACiE database. We consider two ways of encoding the reaction mechanism in descriptors, and three approaches that encode only the overall chemical reaction. To proceed through my work, we first develop a basic model to cluster the enzymatic reactions. Global study of enzyme reaction mechanism may provide important insights for better understanding of the diversity of chemical reactions of enzymes. Clustering analysis in such research is very common practice. Clustering algorithms suffer from various issues, such as requiring determination of the input parameters and stopping criteria, and very often a need to specify the number of clusters in advance. Using several well known metrics, we tried to optimize the clustering outputs for each of the algorithms, with equivocal results that suggested the existence of between two and over a hundred clusters. This motivated us to design and implement our algorithm, PFClust (Parameter-Free Clustering), where no prior information is required to determine the number of cluster. The analysis highlights the structure of the enzyme overall and mechanistic reaction. This suggests that mechanistic similarity can influence approaches for function prediction and automatic annotation of newly discovered protein and gene sequences. We then develop and evaluate the method for enzyme function prediction using machine learning methods. Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. The machine learning method needs only chemoinformatics descriptors as an input and is applicable for regression analysis. The last phase of this work is to test the evolution of chemical mechanisms mapped onto ancestral enzymes. This domain occurrence and abundance in modern proteins has showed that the / architecture is probably the oldest fold design. These observations have important implications for the origins of biochemistry and for exploring structure-function relationships. Over half of the known mechanisms are introduced before architectural diversification over the evolutionary time. The other halves of the mechanisms are invented gradually over the evolutionary timeline just after organismal diversification. Moreover, many common mechanisms includes fundamental building blocks of enzyme chemistry were found to be associated with the ancestral fold

Transcript identification from deep sequencing data

Ribonucleic acid (RNA) sequences are polymeric molecules ubiquitous in every living cell. RNA molecules mediate the flow of information from the DNA sequence to most functional elements in the cell. Therefore, it is of great interest in biological and biomedical research to associate RNA molecules to a biological function and to understand mechanisms of their regulation. The goal of this study is the characterization of the RNA sequence composi- tion of biological samples (transcriptome) to facilitate the understanding of RNA function and regulation. Traditionally, a similar task has been addressed by algorithms called gene finding systems, predicting RNA sequences (transcripts) from features of the genomic DNA sequence. Lacking sufficient experimental evidence for most of the genes, these systems learn sequence patterns on a few genes with direct evidence to identify many additional genes in the genome. High-throughput sequencing of RNA (RNA-Seq) has recently become a powerful tech- nology in studying the transcriptome. This technology identifies millions of short RNA fragments (reads of ≈100 letters length), holding direct evidence for a large fraction of the genes. However, the analysis of RNA-Seq data faces profound challenges. Firstly, the distribution of RNA-Seq reads is highly uneven among genes, resulting in a considerable fraction of genes with very few reads and the stochastic nature of the technology leads to gaps even for well covered genes. To accurately predict transcripts in cases with incomplete evidence, we need to combine RNA-Seq evidence with features derived from the genomic DNA sequence. We therefore developed a method to learn the integration of both information sources and implemented this strategy as an extension of the gene finder mGene. The system, now called mGene.ngs, determines close approximations of potentially non-linear transformations for all features on the training set, such that the prediction performance is maximized. With this ability, which is to our knowledge unique among gene finding systems, mGene.ngs can not only learn complex relationships between the two mentioned information sources, but gains the flexibility to take many additional information sources into account. mGene.ngs has been independently evaluated within the context of an international competition (RGASP) for RNA-Seq-based reannotation and has shown very favourable performance for two out of three model organisms. Moreover, we generated and analyzed RNA-Seq-based annotations for 20 Arabidopsis thaliana strains, to facilitate a deeper understanding of phenotypic variation in this natural plant population. A second major challenge in transcriptome reconstruction lies in the complexity of the transcriptome itself. A process called alternative splicing generates multiple mature RNA sequences from a single primary RNA sequence by cutting out so-called introns, typically in a tightly regulated manner. Inference algorithms of almost all gene finding systems are limited to predict transcripts not overlapping in their genomic region of origin. To overcome this limitation, purely RNA-Seq-based approaches have been developed. However, biologically implausible assumptions or the neglect of available information often led to unsatisfactory results. A major contribution of this study is the integer optimization-based transcriptome reconstruction approach MiTie. MiTie utilizes a biologically motivated loss function, can take advantage of a priori known genome annotations and gains predictive power by considering multiple RNA-Seq samples simultaneously. Based on simulated data for the human genome as well as on an extensive RNA-Seq data set for the model organism Drosophila melanogaster we show that MiTie predicts transcripts significantly more accurate than state-of-the-art methods like Cufflinks and Trinity

Motif discovery in sequential data

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (v. 2, leaves [435]-467).In this thesis, I discuss the application and development of methods for the automated discovery of motifs in sequential data. These data include DNA sequences, protein sequences, and real-valued sequential data such as protein structures and timeseries of arbitrary dimension. As more genomes are sequenced and annotated, the need for automated, computational methods for analyzing biological data is increasing rapidly. In broad terms, the goal of this thesis is to treat sequential data sets as unknown languages and to develop tools for interpreting an understanding these languages. The first chapter of this thesis is an introduction to the fundamentals of motif discovery, which establishes a common mode of thought and vocabulary for the subsequent chapters. One of the central themes of this work is the use of grammatical models, which are more commonly associated with the field of computational linguistics. In the second chapter, I use grammatical models to design novel antimicrobial peptides (AmPs). AmPs are small proteins used by the innate immune system to combat bacterial infection in multicellular eukaryotes. There is mounting evidence that these peptides are less susceptible to bacterial resistance than traditional antibiotics and may form the basis for a novel class of therapeutics.(cont.) In this thesis, I described the rational design of novel AmPs that show limited homology to naturally-occurring proteins but have strong bacteriostatic activity against several species of bacteria, including Staphylococcus aureus and Bacillus anthracis. These peptides were designed using a linguistic model of natural AmPs by treating the amino acid sequences of natural AmPs as a formal language and building a set of regular grammars to describe this language. is set of grammars was used to create novel, unnatural AmP sequences that conform to the formal syntax of natural antimicrobial peptides but populate a previously unexplored region of protein sequence space. The third chapter describes a novel, GEneric MOtif DIscovery Algorithm (Gemoda) for sequential data. Gemoda can be applied to any dataset with a sequential character, including both categorical and real-valued data. As I show, Gemoda deterministically discovers motifs that are maximal in composition and length. As well, the algorithm allows any choice of similarity metric for finding motifs. These motifs are representation-agnostic: they can be represented using regular expressions, position weight matrices, or any other model for sequential data.(cont.) I demonstrate a number of applications of the algorithm, including the discovery of motifs in amino acids and DNA sequences, and the discovery of conserved protein sub-structures. The final chapter is devoted to a series of smaller projects, employing tool methods indirectly related to motif discovery in sequential data. I describe the construction of a software tool, Biogrep that is designed to match large pattern sets against large biosequence databases in a parallel fashion. is makes biogrep well-suited to annotating sets of sequences using biologically significant patterns. In addition, I show that the BLOSUM series of amino acid substitution matrices, which are commonly used in motif discovery and sequence alignment problems, have changed drastically over time.The fidelity of amino acid sequence alignment and motif discovery tools depends strongly on the target frequencies implied by these underlying matrices. us, these results suggest that further optimization of these matrices is possible. The final chapter also contains two projects wherein I apply statistical motif discovery tools instead of grammatical tools.(cont.) In the first of these two, I develop three different physiochemical representations for a set of roughly 700 HIV-I protease substrates and use these representations for sequence classification and annotation. In the second of these two projects, I develop a simple statistical method for parsing out the phenotypic contribution of a single mutation from libraries of functional diversity that contain a multitude of mutations and varied phenotypes. I show that this new method successfully elucidates the effects of single nucleotide polymorphisms on the strength of a promoter placed upstream of a reporter gene. The central theme, present throughout this work, is the development and application of novel approaches to finding motifs in sequential data. The work on the design of AmPs is very applied and relies heavily on existing literature. In contrast, the work on Gemoda is the greatest contribution of this thesis and contains many new ideas.by Kyle L. Jensen.Ph.D