Prospection of Peptide Inhibitors of Thrombin from Diverse Origins Using a Machine Learning Pipeline
Publication Date
11-1-2023
Document Type
Article
Publication Title
Bioengineering
Volume
10
Issue
11
DOI
10.3390/bioengineering10111300
Abstract
Thrombin is a key enzyme involved in the development and progression of many cardiovascular diseases. Direct thrombin inhibitors (DTIs), with their minimum off-target effects and immediacy of action, have greatly improved the treatment of these diseases. However, the risk of bleeding, pharmacokinetic issues, and thrombotic complications remain major concerns. In an effort to increase the effectiveness of the DTI discovery pipeline, we developed a two-stage machine learning pipeline to identify and rank peptide sequences based on their effective thrombin inhibitory potential. The positive dataset for our model consisted of thrombin inhibitor peptides and their binding affinities (KI) curated from published literature, and the negative dataset consisted of peptides with no known thrombin inhibitory or related activity. The first stage of the model identified thrombin inhibitory sequences with Matthew’s Correlation Coefficient (MCC) of 83.6%. The second stage of the model, which covers an eight-order of magnitude range in KI values, predicted the binding affinity of new sequences with a log room mean square error (RMSE) of 1.114. These models also revealed physicochemical and structural characteristics that are hidden but unique to thrombin inhibitor peptides. Using the model, we classified more than 10 million peptides from diverse sources and identified unique short peptide sequences (<15 aa) of interest, based on their predicted KI. Based on the binding energies of the interaction of the peptide with thrombin, we identified a promising set of putative DTI candidates. The prediction pipeline is available on a web server.
Keywords
anticoagulant, antithrombotic, classification, peptide design, regression
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Chemical and Materials Engineering
Recommended Citation
Nivedha Balakrishnan, Rahul Katkar, Peter V. Pham, Taylor Downey, Prarthna Kashyap, David C. Anastasiu, and Anand K. Ramasubramanian. "Prospection of Peptide Inhibitors of Thrombin from Diverse Origins Using a Machine Learning Pipeline" Bioengineering (2023). https://doi.org/10.3390/bioengineering10111300
