Machine-guided dual-objective protein engineering for deimmunization and therapeutic functions DOI

Eric Wolfsberg,

Jean-Sebastien Paul,

Josh Tycko

et al.

Cell Systems, Journal Year: 2025, Volume and Issue: unknown, P. 101299 - 101299

Published: June 1, 2025

Language: Английский

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering DOI Creative Commons
Jason Yang, Francesca-Zhoufan Li, Frances H. Arnold

et al.

ACS Central Science, Journal Year: 2024, Volume and Issue: 10(2), P. 226 - 241

Published: Feb. 5, 2024

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even unlock new activities not found in nature. Because search space possible proteins is vast, enzyme engineering usually involves discovering an starting point that has some desired activity followed by directed evolution improve its "fitness" for a application. Recently, machine learning (ML) emerged powerful tool complement this empirical process. ML models contribute (1) discovery functional annotation known protein or generating novel with functions (2) navigating fitness landscapes optimization mappings between associated values. In Outlook, we explain how complements discuss future potential improved outcomes.

Language: Английский

Citations

78
Sequence modeling and design from molecular to genome scale with Evo DOI

Eric Nguyen,

Michael Poli, Matthew G. Durrant

et al.

Science, Journal Year: 2024, Volume and Issue: 386(6723)

Published: Nov. 14, 2024

The genome is a sequence that encodes the DNA, RNA, and proteins orchestrate an organism’s function. We present Evo, long-context genomic foundation model with frontier architecture trained on millions of prokaryotic phage genomes, report scaling laws DNA to complement observations in language vision. Evo generalizes across proteins, enabling zero-shot function prediction competitive domain-specific models generation functional CRISPR-Cas transposon systems, representing first examples protein-RNA protein-DNA codesign model. also learns how small mutations affect whole-organism fitness generates megabase-scale sequences plausible architecture. These capabilities span molecular scales complexity, advancing our understanding control biology.

Language: Английский

Citations

70
Rapid in silico directed evolution by a protein language model with EVOLVEpro DOI
Kaiyi Jiang, Zhaoqing Yan, Matteo Di Bernardo

et al.

Science, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 21, 2024

Directed protein evolution is central to biomedical applications but faces challenges like experimental complexity, inefficient multi-property optimization, and local maxima traps. While in silico methods using language models (PLMs) can provide modeled fitness landscape guidance, they struggle generalize across diverse families map activity. We present EVOLVEpro, a few-shot active learning framework that combines PLMs regression rapidly improve EVOLVEpro surpasses current methods, yielding up 100-fold improvements desired properties. demonstrate its effectiveness six proteins RNA production, genome editing, antibody binding applications. These results highlight the advantages of with minimal data over zero-shot predictions. opens new possibilities for AI-guided engineering biology medicine.

Language: Английский

Citations

29
Enabling the immune escaped etesevimab fully-armed against SARS-CoV-2 Omicron subvariants including KP.2 DOI Creative Commons
Chao Su,

Juanhua He,

Yufeng Xie

et al.

hLife, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

3
AI in SERS sensing moving from discriminative to generative DOI Creative Commons
Steven M. Quarin, Der Vang, Ruxandra I. Dima

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 2(1)

Published: Feb. 21, 2025

Abstract This perspective discusses the present and future role of artificial intelligence (AI) machine learning (ML) in surface-enhanced Raman scattering (SERS) sensing. Our goal is to guide reader through current applications, mainly focused on discriminative approaches aimed at developing new improved SERS diagnostic capabilities, towards AI sensing, with use generative design materials biomaterials.

Language: Английский

Citations

2
Pretrainable geometric graph neural network for antibody affinity maturation DOI Creative Commons
Huiyu Cai, Zuobai Zhang, Mingkai Wang

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Sept. 6, 2024

Language: Английский

Citations

9
The Nobel Prize in Chemistry: past, present, and future of AI in biology DOI Creative Commons
Luciano A. Abriata

Communications Biology, Journal Year: 2024, Volume and Issue: 7(1)

Published: Oct. 29, 2024

The work by Hassabis and Jumper on protein structure prediction together with Baker's supremacy in de novo design set the stage for a future where AI not only deciphers biology at atomic level but also designs new molecules biotechnology, medicine, beyond. I provide an overview of recent past, present, structural biology, from how it all started Critical Assessment Structure Prediction (CASP) experiments engineering lab, to field could further evolve models that eventually "understand" holistically. A Comment transformative progress artificial intelligence referencing 2024 Nobel Prize Chemistry.

Language: Английский

Citations

7
FASTIA: A rapid and accessible platform for protein variant interaction analysis demonstrated with a single‐domain antibody DOI Creative Commons
Ryo Matsunaga, Kouhei Tsumoto

Protein Science, Journal Year: 2025, Volume and Issue: 34(3)

Published: Feb. 21, 2025

Abstract Antibodies are critical tools in medicine and research, their affinity for target antigens is a key determinant of efficacy. Traditional antibody maturation interaction analyses often hampered by time‐consuming steps such as cloning, expression, purification, assays. To address this, we have developed FASTIA (Fast Affinity Screening Technology Interaction Analysis), novel platform that integrates rapid gene fragment preparation, cell‐free protein synthesis, bio‐layer interferometry with non‐regenerative analysis. Using this approach, can analyze the intermolecular interactions over 20 variants 2 days, requiring only parent expression plasmid basic equipment. We demonstrated ability to discriminate between single‐domain different binding affinities using anti‐HEL VHH D2‐L29, mapped results crystal structure identify sites. provides comparable those obtained traditional methods. Our system bypasses need genetic engineering facilities be easily adopted laboratories, accelerating optimization processes. In addition, applicable other protein–protein interactions, making it versatile tool studying molecular recognition. facilitates efficient maturation, engineering, analysis interactions. This accessible route improving antibodies broader understanding

Language: Английский

Citations

1
Rapid protein evolution by few-shot learning with a protein language model DOI
Kaiyi Jiang, Zhaoqing Yan, Matteo Di Bernardo

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 18, 2024

Directed evolution of proteins is critical for applications in basic biological research, therapeutics, diagnostics, and sustainability. However, directed methods are labor intensive, cannot efficiently optimize over multiple protein properties, often trapped by local maxima.

Language: Английский

Citations

6
Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning DOI Creative Commons

Timothy J O'Donnell,

Chakravarthi Kanduri, Giulio Isacchini

et al.

Cell Systems, Journal Year: 2024, Volume and Issue: 15(12), P. 1168 - 1189

Published: Dec. 1, 2024

Language: Английский

Citations

4