SOURCE-LINKED INTELLIGENCE

Learning the interaction rules of antibody-antigen binding

CORDIS · observation · Publication date unknown

muno-biotechnological challenge: understanding the interaction rules that predict Ab-Ag binding. Solving this challenge demands the convergence of biotechnology, computational structural biology, and machine learning (ML). My lab is one of the few worldwide to have this transdisciplinary expertise. Research problem: Currently, the predictive performance of Ab-Ag binding is poor, and an understanding of the underlying rules of Ab-Ag binding is mostly absent. We previously showed that both unprecedentedly large datasets (>10^5 Ab-Ag sequence pairs) and extensive structural information on the Ab-Ag binding interface (paratope, epitope) are needed to increase prediction accuracy and recover binding rules. Targeted breakthrough: To address the lack of large-scale Ab-Ag sequence and structural data, we will develop a method for high-throughput screening of >10^3 Ab paratope-mutated variants binding to >10^3 of Ag epitope-mutated variants, generating sequence data of Ab-Ag binding pairs at an unprecedented scale (>10^6 sequence Ab-Ag pairs). Structural information of the entirety of the

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

2000000

unit

EUR