Stephen Nayfach

Jan 6, 2025 | biorxiv.org | Stephen Nayfach |Aadyot Bhatnagar |Gabriella O. Estevam |Andrey Novichkov

AbstractCRISPR-Cas enzymes must recognize a protospacer-adjacent motif (PAM) to edit a genomic site, significantly limiting the range of targetable sequences in a genome.

Aug 3, 2024 | biorxiv.org | Jeffrey A. Ruffolo |Aadyot Bhatnagar |Stephen Nayfach |Joel Beazer

AbstractGenerative models for protein design trained on experimentally determined structures have proven useful for a variety of design tasks. However, such methods are limited by the quantity and diversity of structures used for training, which represent a small, biased fraction of protein space. Here, we describe proseLM, a method for protein sequence design based on adaptation of protein language models to incorporate structural and functional context.

Apr 22, 2024 | biorxiv.org | Jeffrey A. Ruffolo |Stephen Nayfach |Joseph Gallagher |Aadyot Bhatnagar

AbstractGene editing has the potential to solve fundamental challenges in agriculture, biotechnology, and human health. CRISPR-based gene editors derived from microbes, while powerful, often show significant functional tradeoffs when ported into non-native environments, such as human cells. Artificial intelligence (AI) enabled design provides a powerful alternative with potential to bypass evolutionary constraints and generate editors with optimal properties.

Oct 11, 2023 | nature.com | Fotis A. Baltoumas |Antonio Pedro Camargo |Stephen Nayfach |Natalia N. Ivanova |David Paez-Espino |Konstantinos T Konstantinidis | +7 more

AbstractMetagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes.

Sep 21, 2023 | nature.com | Antonio Pedro Camargo |Frederik Schulz |Yan Xu |Stephen Nayfach

AbstractIdentifying and characterizing mobile genetic elements in sequencing data is essential for understanding their diversity, ecology, biotechnological applications and impact on public health. Here we introduce geNomad, a classification and annotation framework that combines information from gene content and a deep neural network to identify sequences of plasmids and viruses.