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1,428 claims from 888 papers are on the record. 46 have been checked so far; the other 1,382 have no check with a result yet.

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Status: Unchecked ×Keyword: protein sequence generation ×Clear all

3 claims from 3 papers

1. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Large language models generate functional protein sequences across diverse families

   Madani, Krause, Greene et al. · Nature Biotechnology · 2023

   The authors describe ProGen, a language model trained on protein sequences that can generate new proteins with predictable function across large families, including lysozymes, chorismate mutase and malate dehydrogenase.

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   1. UncheckedArtificial lysozymes from a fine-tuned language model showed catalytic efficiencies similar to natural ones, with sequence identity as low as 31.4%.“Artificial proteins fine-tuned to five distinct lysozyme families showed similar catalytic efficiencies as natural lysozymes, with sequence identity to natural proteins as low as 31.4%.”

2. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   ProGen2: Exploring the Boundaries of Protein Language Models

   Nijkamp, Ruffolo, Weinstein, Naik and Ali · arXiv (Cornell University) · 2022

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   1. Unchecked“ProGen2 models show state-of-the-art performance in capturing the distribution of observed evolutionary sequences, generating novel viable sequences, and predicting protein fitness without additional finetuning.”

3. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   ProGen: Language Modeling for Protein Generation

   Madani, Bryan, Naik et al. · arXiv (Cornell University) · 2020

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   1. Unchecked“This provides ProGen with an unprecedented range of evolutionary sequence diversity and allows it to generate with fine-grained control as demonstrated by metrics based on primary sequence similarity, secondary structure accuracy, and conformational energy.”