DeepFrag-k: A Fragment-Based Deep Learning Approach for Protein Fold Recognition

Background: One of the most essential problems in structural bioinformatics is protein fold recognition. In this paper, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features at fragment level to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage employs a multi-modal 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 convolutional neural network (CNN) to classify the fragment vector into the corresponding fold. Results: 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. Conclusions: There is a set of fragments that can serve as structural “keywords” distinguishing between major protein folds. The deep learning architecture in DeepFrag-k is able to accurately identify these fragments as structure features to improve protein fold recognition

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

New Methods to Improve Protein Structure Modeling

Proteins are considered the central compound necessary for life, as they play a crucial role in governing several life processes by performing the most essential biological and chemical functions in every living cell. Understanding protein structures and functions will lead to a significant advance in life science and biology. Such knowledge is vital for various fields such as drug development and synthetic biofuels production. Most proteins have definite shapes that they fold into, which are the most stable state they can adopt. Due to the fact that the protein structure information provides important insight into its functions, many research efforts have been conducted to determine the protein 3-dimensional structure from its sequence. The experimental methods for protein 3-dimensional structure determination are often time-consuming, costly, and even not feasible for some proteins. Accordingly, recent research efforts focus more and more on computational approaches to predict protein 3-dimensional structures. Template-based modeling is considered one of the most accurate protein structure prediction methods. The success of template-based modeling relies on correctly identifying one or a few experimentally determined protein structures as structural templates that are likely to resemble the structure of the target sequence as well as accurately producing a sequence alignment that maps the residues in the target sequence to those in the template. In this work, we aim at improving the template-based protein structure modeling by enhancing the correctness of identifying the most appropriate templates and precisely aligning the target and template sequences. Firstly, we investigate employing inter-residue contact score to measure the favorability of a target sequence fitting in the folding topology of a certain template. Secondly, we design a multi-objective alignment algorithm extending the famous Needleman-Wunsch algorithm to obtain a complete set of alignments yielding Pareto optimality. Then, we use protein sequence and structural information as objectives and generate the complete Pareto optimal front of alignments between target sequence and template. The alignments obtained enable one to analyze the trade-offs between the potentially conflicting objectives. These approaches lead to accuracy enhancement in template-based protein structure modeling