Abstract A015: Design and deciphering of precision peptide inhibitors for cancer stemness using generative deep learning and molecular dynamics simulations
Bibliographic record
Abstract
Abstract The use of artificial intelligence (AI) and machine learning (ML) for exploring vast amino acid sequence spaces has recently gained traction in the discovery of antibiotics and the design of biomaterials with a wide array of algorithms. However, despite some advancements, designing peptide inhibitors to specifically modulate protein-protein interactions remains a significant challenge. In this contribution, we explore the use of a Long Short-Term Memory (LSTM) network - a type of recurrent neural network - to model peptide sequences, given its ability to process sequential data and capture long-term dependencies, critical for peptide design. Our research focuses on developing precision peptides that target the interaction between the Notch intracellular domain (NICD) and CBF1/RBPJ transcription factors, key regulators of the Notch signaling pathway implicated in breast and pancreatic cancer stemness. We inferred hydrophobic, hydrophilic, van der Waals, and salt bridge interactions from experimentally determined three-dimensional protein complex structures, which were used for feature engineering via one-hot encoding. Peptides, each 20 amino acids in length, were generated using temperature scaling in the LSTM model. These peptides were then structurally optimized and subjected to molecular dynamics (MD) simulations, followed by molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) analysis to assess their interactions and binding affinities with the Notch receptor. The MD simulations provide valuable molecular-level insights into the peptide-Notch interactions, helping to evaluate their binding strength. Further biological testing is underway to validate the efficacy of these lead peptide inhibitors and elucidate their molecular mechanisms in targeting cancer stem cells associated with breast cancer. Citation Format: Gurudeeban Selvaraj, Satyavani Kaliamurthi, Gilles H. Peslherbe. Design and deciphering of precision peptide inhibitors for cancer stemness using generative deep learning and molecular dynamics simulations. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr A015
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".