PEPstrMOD2: Next-generation tertiary structure prediction of chemically modified and non-natural peptides

Abstract While most existing methods are limited to predicting the tertiary structures of proteins containing only canonical residues, the PEPstrMOD server (developed in 2015) pioneered structure prediction for chemically modified and non-natural peptides. Despite its widespread use, the original framework was restricted to peptides of 7 to 25 residues and relied on older backbone-prediction algorithms. To address these limitations, we present PEPstrMOD2, which introduces three major advancements over its predecessor. First, it replaces the original in-house coordinate generation with state-of-the-art deep learning (DL) algorithms, leveraging AlphaFold2 and ESMFold for highly accurate initial structure prediction. Secondly, it greatly expands the accessible chemical space through incorporation of new, AMBER force-field compatible library of 257 post-translational modifications (PTMs), 428 non-canonical amino acids (NCAAs), and 243 terminal modifications. Lastly, through the application of native scalability of AlphaFold2 (AF2) and ESMFold (EF), PEPstrMOD2 eliminates the original restrictions of the length, enabling the structural modeling of longer, complex therapeutic peptides and small proteins. We evaluated the performance of PEPstrMOD2 against state-of-the-art methods across three distinct peptide datasets. For the AfCyc dataset consisting of 80 cyclic peptides, PEPstrMOD2 obtained a competitive average atom-level Root Mean Square Deviation (RMSD) of 2.05 Å, compared to 1.13 Å by AlphaFold3 (AF3) and 1.82 Å by AfCycDesign. Remarkably, for the modified peptide ModPep433 dataset, PEPstrMOD2 outperformed AF3, achieving the lower average RMSD score of 4.49 Å against 4.67 Å of AF3. Furthermore, in the case of the ModPep16 benchmark, PEPstrMOD2 achieved 2.50 Å average RMSD value, which is two times more accurate than that of the original PEPstrMOD (5.84 Å). In summary, PEPstrMOD2 provides a powerful, high-throughput, and highly accurate platform to facilitate peptide-based drug development and structural biology research. While the original PEPstrMOD was restricted to a web server interface, PEPstrMOD2 is available as both an intuitive webserver and a standalone command-line tool via GitHub, featuring Docker support for easy deployment and reproducible, large-scale modeling pipelines ( https://webs.iiitd.edu.in/raghava/pepstrmod/ ). Highlights PEPstrMOD2 predicts structures of chemically modified peptides using deep learning and molecular dynamics refinement. Supports 928 chemical modifications through custom AMBER based force-field libraries including PTMs, NCAAs, terminal, D- as well as cyclic modifications. Removes peptide length restrictions through AlphaFold2 and ESMFold integration. utperforms PEPstrMOD and is competitive with AF3 on different benchmark datasets. Available as both a web server and a standalone platform via GitHub and Docker. Author’s Biography Saloni Jain is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India Naman Kumar Mehta is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India Sahil Raina is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India Pankaj Kumar is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India Varun is currently an integrated BS-MS student at Indian Institute of Science Education and Research (IISER) Pune, India. He is currently working as an intern on a project position at Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT), New Delhi, India. Gajendra P. S. Raghava is currently working as a Professor in the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.