A novel fusion based on the evolutionary features for protein fold recognition using support vector machines

Abstract

protein fold recognition plays a crucial role in discovering three-dimensional structure of proteins and protein functions. Several approaches have been employed for the prediction of protein folds. Some of these approaches are based on extracting features from protein sequences and using a strong classifier. Feature extraction techniques generally utilize syntactical-based information, evolutionarybased information and physicochemical-based information to extract features. In recent years, finding an efficient technique for integrating discriminate features have been received advancing attention. in this study, we integrate Auto-cross-covariance and Separated dimer evolutionary feature extraction methods. The results' features are scored by Information gain to define and select several discriminated features. According to three benchmark datasets, DD, RDD ,and eDD, the results of the support vector machine show more than 6 % improvement in accuracy on these benchmark datasets. Proteins are Jack of all trades biological macromolecules. They are involved in almost every biological reaction; Protein plays a critical role in many different areas such as building muscle, hormone production, enzyme, immune function, and energy. Typically more than 20,000 proteins exist in human cells 1 , to acquire knowledge about the protein function and interactions, the prediction of protein structural classes is extremely useful 2. Fold recognition is one of the fundamental methods in protein structure and function prediction. Each type of protein has a particular three-dimensional structure, which is determined by the order of the amino acids in its polypeptide chain. A protein' s structure begins with its amino acid sequence, which is thus considered its primary structure. The next level of the organization includes the α helix and β sheets that forms with certain segments of the polypeptide chain; these folds are elements of secondary structure. The full, threedimensional conformation formed by an entire polypeptide chain is referred to as tertiary structure 3. One of the main steps which can be assumed as a vital stage for predicting protein fold(secondary structure) is feature extraction. Computational feature extraction methods are divided into syntactical, physicochemical and evolutionary methods. Syntactical methods pay attention only to the protein sequence, like composition and occurrence 4-6. physicochemical methods consider some physical and chemical properties of protein sequences. Evolutionary methods extract features from Basic Local Alignment Search Tool(BLAST). When attempting to solve many biological problems, it is obvious that a single data source might not be informative, and combining several complementary biological data sources will lead to a more accurate result. When we studied methods of protein fold recognition, we found that less attention has been paid to the fusion of features to get more comprehensive features. In recent studies, researchers attempted to find new feature extraction methods 7-12 or t...

Scorpio : Enhancing Embeddings to Improve Downstream Analysis of DNA sequences

“…In this study, we introduced the Scorpio framework, which leverages triplet networks with contrastive learning to enhance the analysis of sequenced DNA data. The adoption of nucleotide-based models, unlike traditional protein-focused models 41,42 , could revolutionize our understanding of gene expression and protein synthesis by capturing nuances in nucleotide sequences and revealing hidden relationships. By using both pre-trained language models and k-mer frequency embeddings, we aimed to demonstrate the robustness of our framework across multiple types of encoders, all of which showed promising results.…”