Abstract
Knowledge on protein folding has a profound impact on understanding the heterogeneity and molecular function of proteins, further facilitating drug design. Predicting the 3D structure (fold) of a protein is a key problem in molecular biology. Determination of the fold of a protein mainly relies on molecular experimental methods. With the development of next-generation sequencing techniques, the discovery of new protein sequences has been rapidly increasing. With such a great number of proteins, the use of experimental techniques to determine protein folding is extremely difficult because these techniques are time consuming and expensive. Thus, developing computational prediction methods that can automatically, rapidly, and accurately classify unknown protein sequences into specific fold categories is urgently needed. Computational recognition of protein folds has been a recent research hotspot in bioinformatics and computational biology. Many computational efforts have been made, generating a variety of computational prediction methods. In this review, we conduct a comprehensive survey of recent computational methods, especially machine learning-based methods, for protein fold recognition. This review is anticipated to assist researchers in their pursuit to systematically understand the computational recognition of protein folds.
Highlights
Understanding how proteins adopt their 3D structure remains one of the greatest challenges in science
Determination of protein structure relies on traditional experimental methods, such as X-ray crystallography and nuclear magnetic resonance spectroscopy
We have systematically reviewed the recent progress in machine learning-based protein fold recognition methods
Summary
Understanding how proteins adopt their 3D structure remains one of the greatest challenges in science. To determine the optimal alignments, scoring functions are usually used as measures to evaluate the similarity between the profiles derived from a query protein and those of template proteins with known structures. Jaroszewski et al [1] developed a protein recognition method called Fold and Function Assignment System (FFAS) by using a profile-profile alignment strategy without using any structural information. Template-free methods seek to build models and accurately predict protein structures solely based on amino acid sequences rather than on known structural proteins as templates. Machine learning aims to build a prediction model by learning the differences between different protein fold categories and use the learned model to automatically assign a query protein to a specific protein fold class This approach is more efficient for large-scale predictions and can examine a large number of promising candidates for further experimental validation. We evaluate and compare the recognition performance of existing methods used in the last 10 years on a benchmark dataset
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