# In-silico prediction of protein-peptide interactions.

> **NIH NIH R01** · SCRIPPS RESEARCH INSTITUTE, THE · 2020 · $399,375

## Abstract

IN-SILICO PREDICTION OF PROTEIN-PEPTIDE INTERACTIONS
Automated docking methods are used extensively for gaining a mechanistic understanding of the molecular
interactions underpinning cellular processes. While these tools work well for small molecules they perform
poorly for peptides and cannot handle Intrinsically Disordered Proteins (IDPs) which play very important roles
in these processes. The goal of this project is the development of an efficient and practical peptide docking
software, useful for designing therapeutic peptides and gaining insight into IDPs binding ordered proteins.
The proposed software supports biomedical applications ranging from investigating chemical pathways to
designing and optimizing therapeutic molecules for diseases such as cancer and metabolic disorders. Under the
previous award we developed and released a new method for docking fully-flexible peptides with up to 20
standard amino acids: AutoDock CrankPep (ADCP). We showed that it outperforms current state-of-the-art
docking methods. For the next award, we propose to: 1) further develop ADCP to support docking IPDs with up
to 70 amino acids and improve support for therapeutic peptides containing modified amino acids and complex
macrocycles; 2) develop peptide-specific scoring functions to increase docking success rates and methods for
predicting the free energy of binding of peptides. This will be done by exploiting the latest advances in
statistical potentials for docking, as well as applying machine-learning techniques; 3) test and validate the
software on our datasets, community benchmarks, and through our collaborations with outstanding biologists
working on biomedical applications spanning from designing drugs for thrombosis and influenza, to modeling
IDPs interacting with globular proteins; and 4) document the software and release it under an open source
license on a regular basis along with datasets we compile and update on regularly.
The proposed research will occur in the context of collaborations with experimental biologists working on
highly relevant biomedical projects and providing experimental feedback and validation. In addition, this
project will benefit from various collaborations with experts in the fields of computational biology, applied
mathematics and artificial intelligence. This docking software tool will be developed by applying best practices
in software engineering and be implemented as a modular, extensible, component-based software framework
for peptide docking. This docking engine will be part of the widely used AutoDock software suite. The ability
to model complexes formed by proteins and fully-flexible peptides or IDPs is in high demand and will greatly
extend the range of peptide-based therapeutic approaches for which automated docking can be successfully
applied. It will also support gaining insights into interactions of IDPs with proteins. As such, it will impact the
research of many medicinal chemists and biologist and extend the ...

## Key facts

- **NIH application ID:** 10116950
- **Project number:** 2R01GM096888-10A1
- **Recipient organization:** SCRIPPS RESEARCH INSTITUTE, THE
- **Principal Investigator:** MICHEL F. SANNER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $399,375
- **Award type:** 2
- **Project period:** 2011-04-15 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10116950

## Citation

> US National Institutes of Health, RePORTER application 10116950, In-silico prediction of protein-peptide interactions. (2R01GM096888-10A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10116950. Licensed CC0.

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