Affiliation:
1. Department of Chemical and Materials Engineering, San José State University, San Jose, CA 95192, USA
2. Department of Computer Science and Engineering, Santa Clara University, Santa Clara, CA 95053, USA
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.
Funder
College of Engineering, San José State University
Reference103 articles.
1. Defending the priority of “remarkable researches”: The discovery of fibrin ferment;Marcum;Hist. Philos. Life Sci.,1998
2. Mechanisms coupling thrombin to metastasis and tumorigenesis;Remiker;Thromb. Res.,2018
3. Thrombin Inhibition by Argatroban: Potential Therapeutic Benefits in COVID-19;Aliter;Cardiovasc. Drugs Ther.,2021
4. Directing thrombin;Lane;Blood,2005
5. Thrombin formation;Mann;Chest,2003