Semantic search using protein large language models detects class II microcins in bacterial genomes.

Unlabelled: Class II microcins are antimicrobial peptides that have shown some potential as novel antibiotics. However, to date, only 10 class II microcins have been described, and the discovery of novel microcins has been hampered by their short length and high sequence divergence. Here, we ask if we can use numerical embeddings generated by protein large language models to detect microcins in bacterial genome assemblies and whether this method can outperform sequence-based methods such as BLAST.

PlanoUp!: A pilot program for the identification and treatment of depression for youth in low-income secondary schools.

Rates of depression in youth are continuing to increase at a steady rate, yet these youth often do not receive mental health services (Bertha & Balázs, 2013; Thomas et al., 2011). Schools are an ideal setting to connect youth to mental health services; however, many barriers exist with respect to schools having adequate resources and access to the appropriate levels of services (Duong et al.

RNase H-dependent DNA thresholder modulated by cancer marker concentration.

Threshold antisense oligonucleotide constructs were designed to cleave mRNA within different biomarker concentrations. The mRNA cleavage is activated by 2.6, 7.

Semantic search using protein large language models detects class II microcins in bacterial genomes.

Class II microcins are antimicrobial peptides that have shown some potential as novel antibiotics. However, to date only ten class II microcins have been described, and discovery of novel microcins has been hampered by their short length and high sequence divergence. Here, we ask if we can use numerical embeddings generated by protein large language models to detect microcins in bacterial genome assemblies and whether this method can outperform sequence-based methods such as BLAST.

Two sequence- and two structure-based ML models have learned different aspects of protein biochemistry.

Deep learning models are seeing increased use as methods to predict mutational effects or allowed mutations in proteins. The models commonly used for these purposes include large language models (LLMs) and 3D Convolutional Neural Networks (CNNs). These two model types have very different architectures and are commonly trained on different representations of proteins.