Albany 2019: 20th Conversation - Abstracts

category image Albany 2019
Conversation 20
June 11-15 2019
Adenine Press (2019)

Ultra-fast modeling of peptide-MHC interactions with 3D Convolutional Neural Net

Association of a peptide with Major Histocompatibility Complex (MHC) is crucial for adaptive immune system in vertebrates. Upon binding to an MHC molecule, the peptide is presented to T-cells, which triggers an immune response, if the peptide is recognized as foreign. In recent years personalized treatment approaches such as cancer immunotherapy based on the knowledge of MHC selectivity of a particular patient started to emerge. Detection of peptides bound to MHC on the tumor cell surface, containing cancer driver mutations (neoantigens) is essential for efficiency of the treatment, and therefore, it is important to understand mechanisms, which drive MHC-peptide complex formation. Over the years many complexes have been crystallized and several approaches were developed for bound peptide structure prediction. However, existing docking methods are not suitable for a large scale structural analysis of multiple peptides (~10^4) due to large execution times, thus faster and more efficient modeling techniques are required. Machine Learning approaches have demonstrated promising results in protein structure and ligand binding prediction. Here we present an ultra-fast peptide-MHC docking method based on 3D Convolutional Neural Network (CNN) scoring and constrained inverse kinematics peptide sampling. Our algorithm is suitable for docking of multiple peptide-MHC complexes and can provide insights for selectivity and preferential binding of different MHC alleles and facilitate structure based binding analysis.


Mikhail Ignatov
Evangelos Coutsias
Dima Kozakov

Laufer Center for Physical and Quantitative Biology
Stony Brook University
Stony Brook, NY

Email: midas@laufercenter.org