Accurate proteome-wide missense variant effect prediction with AlphaMissense
The vast majority of missense variants observed in the human genome are of unknown
clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on …
clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on …
Mega-scale experimental analysis of protein folding stability in biology and design
Advances in DNA sequencing and machine learning are providing insights into protein
sequences and structures on an enormous scale. However, the energetics driving folding …
sequences and structures on an enormous scale. However, the energetics driving folding …
Lynch syndrome, molecular mechanisms and variant classification
Patients with the heritable cancer disease, Lynch syndrome, carry germline variants in the
MLH1, MSH2, MSH6 and PMS2 genes, encoding the central components of the DNA …
MLH1, MSH2, MSH6 and PMS2 genes, encoding the central components of the DNA …
Language models enable zero-shot prediction of the effects of mutations on protein function
Modeling the effect of sequence variation on function is a fundamental problem for
understanding and designing proteins. Since evolution encodes information about function …
understanding and designing proteins. Since evolution encodes information about function …
Proteingym: Large-scale benchmarks for protein fitness prediction and design
Predicting the effects of mutations in proteins is critical to many applications, from
understanding genetic disease to designing novel proteins to address our most pressing …
understanding genetic disease to designing novel proteins to address our most pressing …
Protst: Multi-modality learning of protein sequences and biomedical texts
Current protein language models (PLMs) learn protein representations mainly based on
their sequences, thereby well capturing co-evolutionary information, but they are unable to …
their sequences, thereby well capturing co-evolutionary information, but they are unable to …
Is novelty predictable?
C Fannjiang, J Listgarten - Cold Spring Harbor …, 2024 - cshperspectives.cshlp.org
Machine learning–based design has gained traction in the sciences, most notably in the
design of small molecules, materials, and proteins, with societal applications ranging from …
design of small molecules, materials, and proteins, with societal applications ranging from …
Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning
Accurately modeling the protein fitness landscapes holds great importance for protein
engineering. Pre-trained protein language models have achieved state-of-the-art …
engineering. Pre-trained protein language models have achieved state-of-the-art …
Proteinnpt: Improving protein property prediction and design with non-parametric transformers
Protein design holds immense potential for optimizing naturally occurring proteins, with
broad applications in drug discovery, material design, and sustainability. However …
broad applications in drug discovery, material design, and sustainability. However …
Poet: A generative model of protein families as sequences-of-sequences
T Truong Jr, T Bepler - Advances in Neural Information …, 2023 - proceedings.neurips.cc
Generative protein language models are a natural way to design new proteins with desired
functions. However, current models are either difficult to direct to produce a protein from a …
functions. However, current models are either difficult to direct to produce a protein from a …