De novo gene emergence provides a route for new proteins to be formed from previously
non-coding DNA. Proteins born in this way are considered random sequences and typically …

We present three deep learning sequence-based prediction models for peptide properties
including hemolysis, solubility, and resistance to nonspecific interactions that achieve …

The importance of protein engineering in the research and development of
biopharmaceuticals and biomaterials has increased. Machine learning in computer-aided …

Accurate variant effect prediction has broad impacts on protein engineering. Recent
machine learning approaches toward this end are based on representation learning, by …

Pre-trained models have been transformative in natural language, computer vision, and now
protein sequences by enabling accuracy with few training examples. We show how to use …

Clinical diagnoses rely on a wide variety of laboratory tests and imaging studies, interpreted
alongside physical examination findings and the patient's history and symptoms. Currently …

Over the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics
has been steadily increasing as evident with FDA's recent landmark approval of the 100th …

Engineered proteins have emerged as novel diagnostics, therapeutics, and catalysts. Often,
poor protein developability─ quantified by expression, solubility, and stability─ hinders …

Genome engineering projects often utilize bacterial artificial chromosomes (BACs) to carry
multi-kilobase DNA segments at low copy number. However, all stages of whole-genome …

Abstract Adeno-associated viral (AAV)-based therapeutics have the potential to transform
the lives of patients by delivering one-time treatments for a variety of diseases. However, a …