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A guide to machine learning for biologists
The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …
use of machine learning in biology to build informative and predictive models of the …
From variant to function in human disease genetics
T Lappalainen, DG MacArthur - Science, 2021 - science.org
Over the next decade, the primary challenge in human genetics will be to understand the
biological mechanisms by which genetic variants influence phenotypes, including disease …
biological mechanisms by which genetic variants influence phenotypes, including disease …
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 …
Efficient evolution of human antibodies from general protein language models
Natural evolution must explore a vast landscape of possible sequences for desirable yet
rare mutations, suggesting that learning from natural evolutionary strategies could guide …
rare mutations, suggesting that learning from natural evolutionary strategies could guide …
Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure
Most known pathogenic mutations occur in protein-coding regions of DNA and change the
way proteins are made. Taking protein structure into account has therefore provided great …
way proteins are made. Taking protein structure into account has therefore provided great …
Unsupervised evolution of protein and antibody complexes with a structure-informed language model
Large language models trained on sequence information alone can learn high-level
principles of protein design. However, beyond sequence, the three-dimensional structures of …
principles of protein design. However, beyond sequence, the three-dimensional structures of …
An Atlas of Variant Effects to understand the genome at nucleotide resolution
Sequencing has revealed hundreds of millions of human genetic variants, and continued
efforts will only add to this variant avalanche. Insufficient information exists to interpret the …
efforts will only add to this variant avalanche. Insufficient information exists to interpret the …
Protein design and variant prediction using autoregressive generative models
The ability to design functional sequences and predict effects of variation is central to protein
engineering and biotherapeutics. State-of-art computational methods rely on models that …
engineering and biotherapeutics. State-of-art computational methods rely on models that …
Embeddings from protein language models predict conservation and variant effects
The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret
the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational …
the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational …
Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models
Protein engineering is an emerging field in biotechnology that has the potential to
revolutionize various areas, such as antibody design, drug discovery, food security, ecology …
revolutionize various areas, such as antibody design, drug discovery, food security, ecology …