Jung-Eun Shin

TL;DR: In this article, a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments is proposed, which performs state-of-the-art prediction of missense and indel effects and successfully design and test a diverse 105-nanobody library.

TL;DR: This work borrows from recent advances in natural language processing and speech synthesis to develop a generative deep neural network-powered autoregressive model for biological sequences that captures functional constraints without relying on an explicit alignment structure.

TL;DR: In this paper, an autonomous hypermutation yeast surface display (AHEAD) was proposed to generate potent nanobodies against the SARS-CoV-2 S glycoprotein, a G-protein-coupled receptor and other targets.

TL;DR: In this article, a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments is proposed, which performs state-of-the-art prediction of missense and indel effects.

TL;DR: This work describes Autonomous Hypermutation yEast surfAce Display (AHEAD), a synthetic recombinant antibody generation technology that imitates somatic hypermutation inside engineered yeast and provides a template for streamlined antibody generation at large with salient utility in rapid response to current and future viral outbreaks.