Machine Learning and Graph Theory Approaches for Classification and Prediction of Protein Structure

Recently, many methods have been proposed for the classification and prediction problems in bioinformatics. One of these problems is the protein structure prediction. Machine learning approaches and new algorithms have been proposed to solve this problem. Among the machine learning approaches, Support Vector Machines (SVM) have attracted a lot of attention due to their high prediction accuracy. Since protein data consists of sequence and structural information, another most widely used approach for modeling this structured data is to use graphs. In computer science, graph theory has been widely studied; however it has only been recently applied to bioinformatics. In this work, we introduced new algorithms based on statistical methods, graph theory concepts and machine learning for the protein structure prediction problem. A new statistical method based on z-scores has been introduced for seed selection in proteins. A new method based on finding common cliques in protein data for feature selection is also introduced, which reduces noise in the data. We also introduced new binary classifiers for the prediction of structural transitions in proteins. These new binary classifiers achieve much higher accuracy results than the current traditional binary classifiers

A NEW METHODOLOGY FOR IDENTIFYING INTERFACE RESIDUES INVOLVED IN BINDING PROTEIN COMPLEXES

Genome-sequencing projects with advanced technologies have rapidly increased the amount of protein sequences, and demands for identifying protein interaction sites are significantly increased due to its impact on understanding cellular process, biochemical events and drug design studies. However, the capacity of current wet laboratory techniques is not enough to handle the exponentially growing protein sequence data; therefore, sequence based predictive methods identifying protein interaction sites have drawn increasing interest. In this article, a new predictive model which can be valuable as a first approach for guiding experimental methods investigating protein-protein interactions and localizing the specific interface residues is proposed. The proposed method extracts a wide range of features from protein sequences. Random forests framework is newly redesigned to effectively utilize these features and the problems of imbalanced data classification commonly encountered in binding site predictions. The method is evaluated with 2,829 interface residues and 24,616 non-interface residues extracted from 99 polypeptide chains in the Protein Data Bank. The experimental results show that the proposed method performs significantly better than two other conventional predictive methods and can reliably predict residues involved in protein interaction sites. As blind tests, the proposed method predicts interaction sites and constructs three protein complexes: the DnaK molecular chaperone system, 1YUW and 1DKG, which provide new insight into the sequence-function relationship. Finally, the robustness of the proposed method is assessed by evaluating the performances obtained from four different ensemble methods

Highly Accurate Fragment Library for Protein Fold Recognition

Proteins play a crucial role in living organisms as they perform many vital tasks in every living cell. Knowledge of protein folding has a deep impact on understanding the heterogeneity and molecular functions of proteins. Such information leads to crucial advances in drug design and disease understanding. Fold recognition is a key step in the protein structure discovery process, especially when traditional computational methods fail to yield convincing structural homologies. In this work, we present a new protein fold recognition approach using machine learning and data mining methodologies. First, we identify a protein structural fragment library (Frag-K) composed of a set of backbone fragments ranging from 4 to 20 residues as the structural “keywords” that can effectively distinguish between major protein folds. We firstly apply randomized spectral clustering and random forest algorithms to construct representative and sensitive protein fragment libraries from a large-scale of high-quality, non-homologous protein structures available in PDB. We analyze the impacts of clustering cut-offs on the performance of the fragment libraries. Then, the Frag-K fragments are employed as structural features to classify protein structures in major protein folds defined by SCOP (Structural Classification of Proteins). Our results show that a structural dictionary with ~400 4- to 20-residue Frag-K fragments is capable of classifying major SCOP folds with high accuracy. Then, based on Frag-k, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage employs a multimodal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolution neural network (CNN) to classify the fragment vectors into the corresponding folds. Our results show that DeepFrag-k yields 92.98% accuracy in predicting the top-100 most popular fragments, which can be used to generate discriminative fragment feature vectors to improve protein fold recognition

Biology-guided algorithms:Improved cardiovascular risk prediction and biomarker discovery

Medical research has seen a stark increase in the amount of available data. The sheer volume and complexity of measured variables challenge the use of traditional statistical methods and are beyond the ability of any human to comprehend. Solving this problem demands powerful models capable of capturing the variable interactions and how those are non-linearly related to the condition under study. In this thesis, we first use Machine Learning (ML) methods to achieve better cardiovascular risk prediction/disease biomarker identification and then describe novel bio-inspired algorithms to solve some of the challenges. On the clinical side, we demonstrate how combining targeted plasma proteomics with ML models outperforms traditional clinical risk factors in predicting first-time acute myocardial infarction as well as recurrent atherosclerotic cardiovascular disease. We then shed some light on the pathophysiological pathways involved in heart failure development using a multi-domain ML model. To improve prediction, we develop a novel graph kernel that incorporates protein-protein interaction information, and suggest a manifold mixing algorithm to increase inter-domain information flow in multi-domain models. Finally, we address global model interpretability to uncover the most important variables governing the prediction. Permutation importance is an intuitive and scalable method commonly used in practice, but it is biased in the presence of covariates. We propose a novel framework to disentangle the shared information between covariates, making permutation importance competitive against methodologies where all marginal contributions of a feature are considered, such as SHAP

Big data analytics in computational biology and bioinformatics

Big data analytics in computational biology and bioinformatics refers to an array of operations including biological pattern discovery, classification, prediction, inference, clustering as well as data mining in the cloud, among others. This dissertation addresses big data analytics by investigating two important operations, namely pattern discovery and network inference. The dissertation starts by focusing on biological pattern discovery at a genomic scale. Research reveals that the secondary structure in non-coding RNA (ncRNA) is more conserved during evolution than its primary nucleotide sequence. Using a covariance model approach, the stems and loops of an ncRNA secondary structure are represented as a statistical image against which an entire genome can be efficiently scanned for matching patterns. The covariance model approach is then further extended, in combination with a structural clustering algorithm and a random forests classifier, to perform genome-wide search for similarities in ncRNA tertiary structures. The dissertation then presents methods for gene network inference. Vast bodies of genomic data containing gene and protein expression patterns are now available for analysis. One challenge is to apply efficient methodologies to uncover more knowledge about the cellular functions. Very little is known concerning how genes regulate cellular activities. A gene regulatory network (GRN) can be represented by a directed graph in which each node is a gene and each edge or link is a regulatory effect that one gene has on another gene. By evaluating gene expression patterns, researchers perform in silico data analyses in systems biology, in particular GRN inference, where the “reverse engineering” is involved in predicting how a system works by looking at the system output alone. Many algorithmic and statistical approaches have been developed to computationally reverse engineer biological systems. However, there are no known bioin-formatics tools capable of performing perfect GRN inference. Here, extensive experiments are conducted to evaluate and compare recent bioinformatics tools for inferring GRNs from time-series gene expression data. Standard performance metrics for these tools based on both simulated and real data sets are generally low, suggesting that further efforts are needed to develop more reliable GRN inference tools. It is also observed that using multiple tools together can help identify true regulatory interactions between genes, a finding consistent with those reported in the literature. Finally, the dissertation discusses and presents a framework for parallelizing GRN inference methods using Apache Hadoop in a cloud environment