Synthetic Biology

  1. 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

    1 evaluationAppears in 1 listLatest version Latest activity

  2. 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

    2 evaluationsAppears in 1 listLatest version Latest activity

  3. APEX: Automated Protein EXpression in Escherichia coli

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

    Reviewed by Arcadia Science

    1 evaluationAppears in 1 listLatest version Latest activity

  4. 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

    1 evaluationAppears in 1 listLatest version Latest activity

  5. 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

    6 evaluationsAppears in 1 listLatest version Latest activity

  6. 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

    4 evaluationsAppears in 1 listLatest version Latest activity

  7. 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

    5 evaluationsAppears in 1 listLatest version Latest activity

  8. 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

    8 evaluationsAppears in 1 listLatest version Latest activity

  9. 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

    6 evaluationsAppears in 1 listLatest version Latest activity

  10. A practical DNA data storage using an expanded alphabet introducing 5-methylcytosine

    1. Deruilin Liu
    2. Demin Xu
    3. Liuxin Shi
    4. Jiayuan Zhang
    5. Kewei Bi
    6. Bei Luo
    7. Chen Liu
    8. Yuxiang Li
    9. Guangyi Fan
    10. Wen Wang
    11. Zhi Ping

    • Curated by GigaByte

      Editors Assessment:

      DNA has huge potential as a data storage medium because of its incredibly high storage density and stability. This work addresses the potential of modified bases, specifically 5-methylcytosine (5mC), in enhancing DNA data storage systems. This paper introduces a transcoding scheme named R+, which incorporates this modified 5mC base to increase information density beyond the standard limits. By encoding various file types into DNA sequences of between 1.3 to 1.6 kb in size, this method achieves an average recovery rate of 98.97% (with reference), validating the effectiveness of the method. On top of a wet-lab protocol (hosted in protocols.io) for the experimental validation of the transcoding scheme, it also includes open source code for in-silico simulation tests. Peer review scruitinising the protocols and validation are reusable and provide convincing results. As nanopore sequencing has enabled reading of these modified bases, it is timely making them applicable as extra letters in the molecular alphabet for DNA data storage

      This evaluation refers to version 1 of the preprint

    Reviewed by GigaByte

    2 evaluationsAppears in 2 listsLatest version Latest activity
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