北京大学定量生物学中心
学术报告
题 目: Probing long range effects in an enzyme with large-scale mutational experiments and statistical evolutionary analysis
报告人: Clement Nizak, Ph.D.
CNRS research director at Sorbonne University, Paris.
时 间: 4月20日（周一）13:00-14:00
地 点: 吕志和楼B101
主持人: 齐志 教授
摘要:
Most of our understanding of the activity and specificity of enzymes focuses on their active site, which is a small part of their structure in direct interaction with the substrate. Yet, as illustrated by many examples of protein engineering success and failure, enzymatic function often depends crucially on residues away from the active site.
Using the well-known example of serine proteases, which display such poorly understood yet pervasive long-range effects, we explore the sequence-function relationship systematically through large-scale experiments and statistical evolutionary analysis. I will present an experimental approach based on droplet microfluidics to analyze the effect of nearly all single mutations of the protease trypsin on its activity towards peptide substrates. The substrate specificity profile of this enzyme is extremely robust to single mutations, as we did not detect any activity towards new substrates in the single mutant library. Substrate specificity can only be fine-tuned towards the initial substrates by single mutations at a handful of residues in and out of the active site. These rare specificity fine-tuning residues are consistent with the analysis of pairwise residue coevolution in the protease family. Further, statistical modeling of pairwise residue coevolution allows engineering trypsin specificity towards new substrates with combinations of mutations.
I will briefly mention our parallel work on antibodies, in which we also highlight the impact of the sequence outside of the antigen binding pocket on antibody binding affinity and evolvability.
Reference：
1. Fernandez-de-Cossio-Diaz et al. Inference and design of antibody specificity: From experiments to models and back. PLOS Comput. Biol. 20, e1012522 (2024).
2. Schulz et al. Parameters and determinants of responses to selection in antibody libraries. PLOS Comput. Biol. 17, e1008751 (2021).
3. Boyer et al. Hierarchy and extremes in selections from pools of randomized proteins. PNAS 113, 3482–3487 (2016).
4. Eyer, K. et al. Single-cell deep phenotyping of IgG-secreting cells for high-resolution immune monitoring. Nature biotech 35, 977–982 (2017).
5. Gérard, A. et al. High-throughput single-cell activity-based screening and sequencing of antibodies using droplet microfluidics. Nature biotech 38, 715–721 (2020).
6. Genettais et al., Continuous emergence of phototaxis in Dictyostelium discoideum. PLOS One 20, e0321614 (2025).
报告人简介：
Clement Nizak is a CNRS research director at Sorbonne University, Paris. After completing in 1999 his undergraduate studies in physics and chemistry at ESPCI, Paris, he received his PhD in biophysics in 2003 from Paris VII University. In 2003-2007, he was a postdoctoral associate at Rockefeller University, New York. His current research focuses on large-scale experiments to probe the sequence-function relationship in proteins for downstream statistical modeling, using antibodies and proteases as model systems. His past work includes the development of single-cell phenotyping and sequencing of immune repertoires based on droplet microfluidics, and an experimental physicist perspective on the unicellular/multicellular transition in social microbe populations.
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