pepsickle rapidly and accurately predicts proteasomal cleavage sites for improved neoantigen identification

Author:

Weeder Benjamin R12ORCID,Wood Mary A3ORCID,Li Ellysia4,Nellore Abhinav125,Thompson Reid F12678ORCID

Affiliation:

1. Computational Biology Program, Oregon Health & Science University, Portland, OR 97239, USA

2. Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA

3. Phase Genomics Inc., Seattle, WA 98109, USA

4. Pacific University, Forest Grove, OR 97116, USA

5. Department of Surgery, Oregon Health & Science University, Portland, OR 97239, USA

6. Department of Radiation Medicine, Oregon Health & Science University, Portland, OR 97239, USA

7. Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, USA

8. Division of Hospital and Specialty Medicine, VA Portland Healthcare System, Portland, OR 97239, USA

Abstract

Abstract Motivation Proteasomal cleavage is a key component in protein turnover, as well as antigen processing and presentation. Although tools for proteasomal cleavage prediction are available, they vary widely in their performance, options and availability. Results Herein, we present pepsickle, an open-source tool for proteasomal cleavage prediction with better in vivo prediction performance (area under the curve) and computational speed than current models available in the field and with the ability to predict sites based on both constitutive and immunoproteasome profiles. Post hoc filtering of predicted patient neoepitopes using pepsickle significantly enriches for immune-responsive epitopes and may improve current epitope prediction and vaccine development pipelines. Availability and implementation pepsickle is open source and available at https://github.com/pdxgx/pepsickle. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

VA Career Development Award

Sunlin & Priscilla Chou Foundation

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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