Novel machine learning approaches revolutionize protein knowledge
Breakthrough methods in machine learning (ML), protein structure prediction, and novel
ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models …
ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models …
Using machine learning to predict the effects and consequences of mutations in proteins
Abstract Machine and deep learning approaches can leverage the increasingly available
massive datasets of protein sequences, structures, and mutational effects to predict variants …
massive datasets of protein sequences, structures, and mutational effects to predict variants …
Nucleotide Transformer: building and evaluating robust foundation models for human genomics
The prediction of molecular phenotypes from DNA sequences remains a longstanding
challenge in genomics, often driven by limited annotated data and the inability to transfer …
challenge in genomics, often driven by limited annotated data and the inability to transfer …
<? sty\usepackage {wasysym}?> Bilingual language model for protein sequence and structure
Adapting language models to protein sequences spawned the development of powerful
protein language models (pLMs). Concurrently, AlphaFold2 broke through in protein …
protein language models (pLMs). Concurrently, AlphaFold2 broke through in protein …
Prottrans: Toward understanding the language of life through self-supervised learning
Computational biology and bioinformatics provide vast data gold-mines from protein
sequences, ideal for Language Models (LMs) taken from Natural Language Processing …
sequences, ideal for Language Models (LMs) taken from Natural Language Processing …
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 …
Fine-tuning protein language models boosts predictions across diverse tasks
Prediction methods inputting embeddings from protein language models have reached or
even surpassed state-of-the-art performance on many protein prediction tasks. In natural …
even surpassed state-of-the-art performance on many protein prediction tasks. In natural …
Updated benchmarking of variant effect predictors using deep mutational scanning
The assessment of variant effect predictor (VEP) performance is fraught with biases
introduced by benchmarking against clinical observations. In this study, building on our …
introduced by benchmarking against clinical observations. In this study, building on our …
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 …