Synthetic Biology

Generative design of novel bacteriophages with genome language models

1. Samuel H. King
2. Claudia L. Driscoll
3. David B. Li
4. Daniel Guo
5. Aditi T. Merchant
6. Garyk Brixi
7. Max E. Wilkinson
8. Brian L. Hie

Reviewed by PREreview

Sustainable production of plastic-degrading enzymes in Chlamydomonas pacifica

1. Crisandra Jade Diaz
2. João Vitor Dutra Molino
3. Barbara Saucedo
4. Kalisa Kang
5. Évellin do Espirito Santo
6. Marissa Tessman
7. Abhishek Gupta
8. Michael D. Burkart
9. Ryan Simkovsky
10. Stephen Mayfield

Reviewed by Arcadia Science

CF2H: a Cell-Free Two-Hybrid platform for rapid protein binder screening

1. Julien Capin
2. Pauline Mayonove
3. Angelique DeVisch
4. Amon Becher
5. Giang Ngo
6. Alexis P Courbet
7. Robert J Ragotte
8. Martin Cohen-Gonsaud
9. Julien Espeut
10. Jerome Bonnet

Reviewed by Arcadia Science

APEX: Automated Protein EXpression in Escherichia coli

1. Martyna Kasprzyk
2. Michael A. Herrera
3. Giovanni Stracquadanio

Reviewed by Arcadia Science

OT-Mation: an open-source code for parsing CSV files into python scripts for control of OT-2 liquid handling robotics

1. Alex Laverick
2. Katherine Convey
3. Catherine Harrison
4. Jenny Tomlinson
5. Jem Stach
6. Thomas P. Howard

Reviewed by Arcadia Science

Life as a Function: Why Transformer Architectures Struggle to Gain Genome-Level Foundational Capabilities

1. Hassan Hassan
2. Kyle Puhger
3. Ali Saadat
4. Johannes Mayer
5. Maximilian Sprang

Reviewed by Arcadia Science

Machine learning-assisted enzyme engineering through ultra-high throughput sorting and large-scale sequence-function data generation

1. Jingyun Zhang
2. Sangeetha Shanmugam
3. Jing Wui Yeoh
4. Dan Zheng
5. Jan Ron Goh
6. Zhangyuan Lin
7. Chueh Loo Poh

Reviewed by Arcadia Science

Systematic comparison of Generative AI-Protein Models reveals fundamental differences between structural and sequence-based approaches.

1. Alexander J Barnett
2. Rajendra KC
3. Pratikshya Pandey
4. Pamodha Somasiri
5. Kirsten A Fairfax
6. Alex W Hewitt

Reviewed by Arcadia Science

High-resolution deep mutational scanning of the melanocortin-4 receptor enables target characterization for drug discovery

1. Conor J Howard
2. Nathan S Abell
3. Beatriz A Osuna
4. Eric M Jones
5. Leon Y Chan
6. Henry Chan
7. Dean R Artis
8. Jonathan B Asfaha
9. Joshua S Bloom
10. Aaron R Cooper
11. Andrew Liao
12. Eden Mahdavi
13. Nabil Mohammed
14. Alan L Su
15. Giselle A Uribe
16. Sriram Kosuri
17. Diane E Dickel
18. Nathan B Lubock

• Curated by eLife

  eLife Assessment

  The authors use deep mutational scanning to assess the effect of ~6,600 protein-coding variants in MC4R, a G protein coupled receptor associated with obesity. They develop new, more precise approaches to deep mutational scanning, enabling them to probe molecular phenotypes directly relevant to the development of drugs that target this receptor. In this important work, the authors provide compelling evidence that variants impact signaling through MC4R in different ways, that some defective variants are amenable to a corrector drug and that deep mutational scanning data could guide compound optimization.

Reviewed by eLife

Cell-free protein synthesis as a method to rapidly screen machine learning-directed protease variants

1. Ella Lucille Thornton
2. Jeremy T. Boyle
3. Nadanai Laohakunakorn
4. Lynne Regan

Reviewed by Arcadia Science