Protein function prediction using functional inter-relationship. (December 2021)

• Record Type: Journal Article
• Authors: Dhanuka, Richa; Singh, Jyoti Prakash
• Is Part Of: Computational biology and chemistry. Volume 95(2021)
• Publication Date: 2021-12
• Subjects: Protein function -- Multi layer Neural Network -- Correlation -- Functional redundancy; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2021.107593
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: With the growth of high throughput sequencing techniques, the generation of protein sequences has become fast and cheap, leading to a huge increase in the number of known proteins. However, it is challenging to identify the functions being performed by these newly discovered proteins. Machine learning techniques have improved traditional methods' efficiency by suggesting relevant functions but fails to perform well when the number of functions to be predicted becomes large. In this work, we propose a machine learning-based approach to predict huge set of protein functions that use the inter-relationships between functions to improve the model's predictability. These inter-relationships of functions is used to reduce the redundancy caused by highly correlated functions. The proposed model is trained on the reduced set of non-redundant functions hindering the ambiguity caused due to inter-related functions. Here, we use two statistical approaches 1) Pearson's correlation coefficient 2) Jaccard similarity coefficient, as a measure of correlation to remove redundant functions. To have a fair evaluation of the proposed model, we recreate our original function set by inverse transforming the reduced set using the two proposed approaches: Direct mapping and Ensemble approach. The model is tested using different feature sets and function sets of biological processes and molecular functions to get promising results on DeepGO and CAFA3 dataset. The proposed model is able to predict specific functions for the test data which were unpredictable by other compared methods. The experimental models, code and other relevant data are available at https://github.com/richadhanuka/PFP-using-Functional-interrelationship. Graphical Abstract: ga1 Highlights: A machine learning based approach of protein function prediction for huge set of functions. Creation of non-redundant reduced functions set using similarity measures signifying functional inter relationships. Inclusion of functional inter relationships improves the predictability of protein functions. The proposed approach is able to predict specific functions which were not predictable by other compared tools.