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Articles by Pascal Notin
Large-scale discovery, analysis and design of protein energy landscapes
Abstract All folded proteins continuously fluctuate between their low-energy native structures and higher-energy conformations that can be partially or fully unfolded. These rare states influence protein function1,2, interactions3, aggregation4,5,6,7 and immunogenicity8,9, yet they remain far less understood than protein native states.
AI expands the repertoire of CRISPR-associated proteins for genome editing
Despite emerging clinical successes, current genome editors suffer from off-target effects and can trigger unwanted responses from the immune system, limiting their broader therapeutic applications. Writing in Nature, Ruffolo et al.1 present OpenCRISPR-1, the first AI-generated CRISPR–Cas protein to edit human DNA successfully. The work demonstrates how machine learning can be used to engineer functional biological systems that extend beyond those found in nature. Enjoying our latest content?
Evolutionary-scale enzymology enables exploration of a rugged catalytic landscape
Editor’s summary Structured Abstract Abstract Supplementary Materials References and Notes Information & Authors Metrics & Citations Check Access References Figures Tables Media Share Editor’s summary The catalytic activity of a particular enzyme can vary widely between orthologs in different species, depending on their environment and specific metabolic needs. But how do such differences evolve and relate to specific structural features? Using a high-throughput microfluidic system, Muir et al.
Computationally designed proteins mimic antibody immune evasion in viral evolution
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Keywords protein design machine learning viral evolution antibody escape generative protein models vaccine evaluation immune landscape Introduction The emergence of viral variants that evade protective immunity induced by prior infections, vaccines, and therapeutics is a challenge for the control of viral spread. This is exemplified by recurrent breakthrough infections observed with SARS-CoV-2 and influenza.
By Noor Youssef, Sarah Gurev, Fadi Ghantous, Kelly Brock, Javier A. Jaimes, Nicole N. Thadani, Ann Dauphin, Amy Sherman, Leonid A. Yurkovetskiy, Daria Soto, Ralph Estanboulieh, Ben Kotzen, Pascal Notin, Aaron W. Kollasch, Alexander Cohen, Sandra E. Dross, Jesse Erasmus, Deborah Fuller, Pamela J. Bjorkman, Jacob Lemieux, Jeremy Luban, Michael S. Seaman, Debora S. Marks
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Cell Press
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Evolutionary-Scale Enzymology Enables Biochemical Constant Prediction Across a Multi-Peaked Catalytic Landscape
Abstract Quantitatively mapping enzyme sequence-catalysis landscapes remains a critical challenge in understanding enzyme function, evolution, and design. Here, we expand an emerging microfluidic platform to measure catalytic constants - kcat and KM - for hundreds of diverse naturally occurring sequences and mutants of the model enzyme Adenylate Kinase (ADK).
ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction
Abstract Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare.
ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers
Abstract Protein design holds immense potential for optimizing naturally occurring proteins, with broad applications in drug discovery, material design, and sustainability. However, computational methods for protein engineering are confronted with significant challenges, such as an expansive design space, sparse functional regions, and a scarcity of available labels.
Learning from pre-pandemic data to forecast viral escape
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