Intrahost Selection Pressures Drive Rapid Dengue Virus Microevolution in Acute Human Infections.

Dengue, caused by four dengue virus serotypes (DENV-1 to DENV-4), is a highly prevalent mosquito-borne viral disease in humans. Yet, selection pressures driving DENV microevolution within human hosts (intrahost) remain unknown. We employed a whole-genome segmented amplification approach coupled with deep sequencing to profile DENV-3 intrahost diversity in peripheral blood mononuclear cell (PBMC) and plasma samples from 77 dengue patients. DENV-3 intrahost diversity appears to be driven by immune pressures as well as replicative success in PBMCs and potentially other replication sites. Hotspots for intrahost variation were detected in 59%-78% of patients in the viral Envelope and pre-Membrane/Membrane proteins, which together form the virion surface. Dominant variants at the hotspots arose via convergent microevolution, appear to be immune-escape variants, and were evolutionarily constrained at the macro level due to viral replication defects. Dengue is thus an example of an acute infection in which selection pressures within infected individuals drive rapid intrahost virus microevolution

Exploiting structural and topological information to improve prediction of RNA-protein binding sites

The breast and ovarian cancer susceptibility gene BRCA1 encodes a multifunctional tumor suppressor protein BRCA1, which is involved in regulating cellular processes such as cell cycle, transcription, DNA repair, DNA damage response and chromatin remodeling. BRCA1 protein, located primarily in cell nuclei, interacts with multiple proteins and various DNA targets. It has been demonstrated that BRCA1 protein binds to damaged DNA and plays a role in the transcriptional regulation of downstream target genes. As a key protein in the repair of DNA double-strand breaks, the BRCA1-DNA binding properties, however, have not been reported in detail

The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases

One of the most intriguing groups of enzymes, the feruloyl esterases (FAEs), is ubiquitous in both simple and complex organisms. FAEs have gained importance in biofuel, medicine and food industries due to their capability of acting on a large range of substrates for cleaving ester bonds and synthesizing high-added value molecules through esterification and transesterification reactions. During the past two decades extensive studies have been carried out on the production and partial characterization of FAEs from fungi, while much less is known about FAEs of bacterial or plant origin. Initial classification studies on FAEs were restricted on sequence similarity and substrate specificity on just four model substrates and considered only a handful of FAEs belonging to the fungal kingdom. This study centers on the descriptor-based classification and structural analysis of experimentally verified and putative FAEs; nevertheless, the framework presented here is applicable to every poorly characterized enzyme family. 365 FAE-related sequences of fungal, bacterial and plantae origin were collected and they were clustered using Self Organizing Maps followed by k-means clustering into distinct groups based on amino acid composition and physico-chemical composition descriptors derived from the respective amino acid sequence. A Support Vector Machine model was subsequently constructed for the classification of new FAEs into the pre-assigned clusters. The model successfully recognized 98.2% of the training sequences and all the sequences of the blind test. The underlying functionality of the 12 proposed FAE families was validated against a combination of prediction tools and published experimental data. Another important aspect of the present work involves the development of pharmacophore models for the new FAE families, for which sufficient information on known substrates existed. Knowing the pharmacophoric features of a small molecule that are essential for binding to the members of a certain family opens a window of opportunities for tailored applications of FAEs

The Evolutionary and Functional Roles of Synonymous Codon Usage in Eukaryotes

Most amino acids are encoded by multiple synonymous codons. Although alternative usage of synonymous codons does not affect the amino acid sequences of proteins, researchers have been reporting evidence for functional synonymous codon usage at the species- and gene-specific levels for over four decades. It has been shown that variations in synonymous codon usage can affect phenotypes through diverse mechanisms such as shaping translation efficiency and mRNA stability. On the other hand, the common view that cellular and organismal phenotypes are primarily determined by proteins whose functions are primarily determined by amino acid sequences, often drives the assumption that synonymous mutations are evolutionarily neutral. Consequently, this assumption has been used extensively in evolutionary biology, population genetics, and structural biology. One explanation of the apparent contradiction between the empirical findings, which indicate that synonymous mutations can affect related phenotypes, and the theoretical models, which stipulate that synonymous mutations are neutral, is that neutral synonymous mutations represent the general rule while non-neutral synonymous mutations represent the rare exceptions. In my thesis, I examined this explanation by applying computational and experimental approaches, which indicated that: 1) Non-neutral synonymous mutations significantly affect a considerable proportion of protein-coding genes; 2) Gene-specific codon usage patterns, such as the preference for a specific combination of rare codons, are possibly associated with specific gene functions, such as enhancing tissue-specific gene expression; 3) Some protein-coding genes include codon clusters whose codon usage patterns cannot be explained by selection-independent processes, and thus such codon clusters seem to serve as domains affecting protein functions. Together, these data suggest that synonymous mutations should not be a priori considered neutral. Furthermore, my studies suggest that the biochemical functions of at least some proteins are not only shaped by the constituent amino acid residues but also by codon usage biases at the gene-specific and sub-genic levels. In conclusion, my thesis work suggests that many of the commonly used approaches for analyzing the selection on protein-coding DNA sequences, which rely on the assumption that synonymous mutations are generally neutral, may generate biased results. Furthermore, my studies indicate that selection on gene-specific codon usage bias has evolved to serve diverse biological functions, which are still mostly uncharacterized

Studies of protein designability using reduced models

One the most important problems in computational structural biology is protein designability, that is, why protein sequences are not random strings of amino acids but instead show regular patterns that encode protein structures. Many previous studies that have attempted to solve the problem have relied upon reduced models of proteins. In particular, the 2D square and the 3D cubic lattices together with reduced amino acid alphabets have been examined extensively and have lead to interesting results that shed some light on evolutionary relationship among proteins. Here, additionally to the 2D square lattice, we study the 2D triangular and 3D face centered cubic (fcc) lattices, we perform designability studies using different shapes embedded in the 2D square lattice, and we use machine learning algorithms to classify binary sequences folding to highly- or poorly-designable conformations.;In the first part of the thesis we extend the transfer matrix method to the 2D triangular lattice. The transfer matrix method is a highly efficient method of enumerating all conformations within a compact lattice area that has earlier been developed for the 2D square and 3D cubic lattices. In addition we also enumerated all compact conformations within simple geometries on the 2D triangular and 3D face centered cubic (fcc) lattices using a standard backtracking algorithm.;In the second part of the thesis we described protein designability studies on various shapes in the 2D square lattice using a reduced hydrophobic-polar (HP) amino acid alphabet. We used a simple energy function that counted the number of H-H, H-P and P-P interactions within a restricted set of protein shapes that have the same number of residues and non-bonded contacts. We found a difference in the designabilities of different protein shapes.;Finally, in the third part of the thesis we used standard machine learning algorithms to classify two classes of protein sequences. We first performed a designability study for two shapes, using a binary HP alphabet, on the 2D triangular lattice and separated highly- and poorly-designable conformations. Highly-designable conformations had many sequences folding to them with the lowest energy and poorly-designable conformations had few or no sequences folding to them. Sequences were classified as highly- or poorly-designable depending on whether they folded to highly- or poorly-designable structures. Using several machine learning algorithms such as Decision Tree, Naive Bayes, and Support Vector Machine, we were able to classify highly- and poorly-designable sequences with high accuracy

Sequential Pattern Mining Aide to Bio-Informatics

Practical Bio-Informatic is the study of all vicinities of development, testing and novel appliances for statistical and computational techniques for prototype and study of all types of scientific data, in addition to further areas of Information Technology and Sciences. Bio-Informatics is a novel approach to conceptualize the natural science in provisions of molecules and apply Informatic methods is derived from computer science and applied mathematics regulation, for instance, info to be grateful for and systematize them in order to relate with these molecules, on a large scale for use in future research studies. Bio-Informatics is the study of upcoming appliances in natural science, chemistry, pharmaceuticals, medicine, and agriculture and various additional fields of research and development. Many pharmaceutical manufacturing companies are attracted in mining sequential patterns from the databases. Sequential Pattern Mining is doing good technique of data mining, which recognizes the temporal relationship between different drugs and it can help in estimating the treatment course for patients. These studies give an improvement in the sympathetic of the loom of Sequential Pattern Mining and Bio-Informatics play a part to a vital role in a biomedical study in the storage of patient’s case reports which is useful in providing treatment to other patients

Ais-Psmaca: Towards Proposing an Artificial Immune System for Strengthening Psmaca: An Automated Protein Structure Prediction using Multiple Attractor Cellular Automata

Predicting the structure of proteins from their amino acid sequences has gained a remarkable attention in recent years. Even though there are some prediction techniques addressing this problem, the approximate accuracy in predicting the protein structure is closely 75%. An automated procedure was evolved with MACA (Multiple Attractor Cellular Automata) for predicting the structure of the protein. Artificial Immune System (AIS-PSMACA) a novel computational intelligence technique is used for strengthening the system (PSMACA) with more adaptability and incorporating more parallelism to the system. Most of the existing approaches are sequential which will classify the input into four major classes and these are designed for similar sequences. AIS-PSMACA is designed to identify ten classes from the sequences that share twilight zone similarity and identity with the training sequences with mixed and hybrid variations. This method also predicts three states (helix, strand, and coil) for the secondary structure. Our comprehensive design considers 10 feature selection methods and 4 classifiers to develop MACA (Multiple Attractor Cellular Automata) based classifiers that are build for each of the ten classes. We have tested the proposed classifier with twilight-zone and 1-high-similarity benchmark datasets with over three dozens of modern competing predictors shows that AIS-PSMACA provides the best overall accuracy that ranges between 80% and 89.8% depending on the dataset

Evaluation of machine learning approaches for prediction of protein coding genes in prokaryotic DNA sequences

According to the National Human Genome Research Institute the amount of genomic data generated on a yearly basis is constantly increasing. This rapid growth in genomic data has led to a subsequent surge in the demand for efficient analysis and handling of said data. Gene prediction involves identifying the areas of a DNA sequence that code for proteins, also called protein coding genes. This task falls within the scope of bioinformatics, and there has been surprisingly little development in this field of study, over the past years. Despite there being sufficient state-of-the-art gene prediction tools, there is still room for improvement in terms of efficiency and accuracy. Advances made within the field of gene prediction can, among other things, aid the medical and pharmaceutical industry, as well as impact environmental and anthropological research. Machine learning techniques such as the Random Forest classifiers and Artificial Neural Networks (ANN) have proved successful at the task of gene prediction. In this thesis one deep learning model and two other machine learning models were tested. The first model implemented was the established Random Forest classifier. When it comes to the use of ensemble methods, such as the Random Forest classifier, feature engineering is critical for the success of such models. The exploration of different feature selection and extraction techniques underpinned its relevance. It also showed that feature importance varies greatly among genomes, and revealed possibilities that can be further explored in future work. The second model tested was the ensemble method Extreme Gradient Boosting (XGBoost), which served as a good competitor to the Random Forest classifier. Finally, a Recurrent Neural Network (RNN) was implemented. RNNs are known to be good with handling sequential data, therefore it seemed like a good candidate for gene prediction. The 15 prokaryotic genomes used to train the models were extracted from the NCBI genome database. Each model was tasked with classifying sub-sequences of the genomes, called open reading frames (ORFs), as either protein coding ORFs, or non-coding ORFs. One challenge when preparing these datasets was that the number of protein coding ORFs was very small compared to the number of non-coding ORFs. Another problem encountered in the dataset was that protein coding ORFs in general are longer than non-coding ORFs, which can bias the models to simply classify long ORFs as protein coding, and short ORFs as non-coding. For these reasons, two datasets for each genome were created, taking each imbalance into account. The models were trained, tuned and tested on both datasets for all genomes, and a combination of genomes. The models were evaluated with regard to accuracy, precision and recall. The results show that all three methods have potential and attained somewhat similar performance scores. Despite the fact that both time and data were limited during model development, they still yielded promising results. Considering there are several parameters that have not yet been tuned in all models, many possibilities for further research remain. The fact that a relatively simple RNN architecture performed so well, and has no requirement for feature engineering, shows great promise for further applications in gene prediction, and possibly other fields in bioinformatics.M-D