1.6k citations · 3.3k across the 12 of their papers we have counts for
12 papers
Biological Sequence Kernels with Guaranteed Flexibility
Alan Nawzad Amin, Eli Nathan Weinstein, Debora Susan Marks
Applying machine learning to biological sequences - DNA, RNA and protein - has enormous potential to advance human health, environmental sustainability, and fundamental biological…
Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval
Pascal Notin, Mafalda Dias, Jonathan Frazer +4
The ability to accurately model the fitness landscape of protein sequences is critical to a wide range of applications, from quantifying the effects of human variants on disease li…
RITA: a Study on Scaling Up Generative Protein Sequence Models
Daniel Hesslow, Niccoló Zanichelli, Pascal Notin +2
In this work we introduce RITA: a suite of autoregressive generative models for protein sequences, with up to 1.2 billion parameters, trained on over 280 million protein sequences…
Deep generative models of genetic variation capture mutation effects
Adam J. Riesselman, John B. Ingraham, Debora S. Marks
The functions of proteins and RNAs are determined by a myriad of interactions between their constituent residues, but most quantitative models of how molecular phenotype depends on…
Population-specific design of de-immunized protein biotherapeutics
Benjamin Schubert, Charlotta Schärfe, Pierre Dönnes +3
Immunogenicity is a major problem during the development of biotherapeutics since it can lead to rapid clearance of the drug and adverse reactions. The challenge for biotherapeutic…
Genetic variation in human drug-related genes
Charlotta P. I. Schärfe, Roman Tremmel, Matthias Schwab +2
Variability in drug efficacy and adverse effects are observed in clinical practice. While the extent of genetic variability in classical pharmacokinetic genes is rather well unders…
Variational Inference for Sparse and Undirected Models
John Ingraham, Debora Marks
Undirected graphical models are applied in genomics, protein structure prediction, and neuroscience to identify sparse interactions that underlie discrete data. Although Bayesian m…
Quantification of the effect of mutations using a global probability model of natural sequence variation
Thomas A. Hopf, John B. Ingraham, Frank J. Poelwijk +3
Modern biomedicine is challenged to predict the effects of genetic variation. Systematic functional assays of point mutants of proteins have provided valuable empirical information…
3D RNA and functional interactions from evolutionary couplings
Caleb Weinreb, Adam J. Riesselman, John B. Ingraham +3
Non-coding RNAs are ubiquitous, but the discovery of new RNA gene sequences far outpaces research on their structure and functional interactions. We mine the evolutionary sequence…
Sequence co-evolution gives 3D contacts and structures of protein complexes
Thomas A. Hopf, Charlotta P. I. Schärfe, João P. G. L. M. Rodrigues +4
Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions and structural biology has provide…
Direct-coupling analysis of residue co-evolution captures native contacts across many protein families
Faruck Morcos, Andrea Pagnani, Bryan Lunt +7
The similarity in the three-dimensional structures of homologous proteins imposes strong constraints on their sequence variability. It has long been suggested that the resulting co…
3D Protein Structure Predicted from Sequence
Debora S. Marks, Lucy J. Colwell, Robert Sheridan +4
The evolutionary trajectory of a protein through sequence space is constrained by function and three-dimensional (3D) structure. Residues in spatial proximity tend to co-evolve, ye…