Exploring the Protein Sequence Space with Global Generative Models

Large language models generate functional protein sequences across diverse families

(2023) Ali Madani et al.   NATURE BIOTECHNOLOGY

A primer to directed evolution: current methodologies and future directions

(2023) Lara Sellés Vidal et al.   RSC Chemical Biology

Efficient evolution of human antibodies from general protein language models

(2023) Brian L. Hie et al.   NATURE BIOTECHNOLOGY

Evolutionary-scale prediction of atomic-level protein structure with a language model

(2023) Zeming Lin et al.   SCIENCE

ProteinBERT: a universal deep-learning model of protein sequence and function

(2022) Nadav Brandes et al.   BIOINFORMATICS

Learning protein fitness models from evolutionary and assay-labeled data

(2022) Chloe Hsu et al.   NATURE BIOTECHNOLOGY

Conditional generative modeling for de novo protein design with hierarchical functions

(2022) Tim Kucera et al.   BIOINFORMATICS

Machine learning-aided engineering of hydrolases for PET depolymerization

(2022) Hongyuan Lu et al.   NATURE

Scaffolding protein functional sites using deep learning

(2022) Jue Wang et al.   SCIENCE

ProtGPT2 is a deep unsupervised language model for protein design

(2022) Noelia Ferruz et al.   Nature Communications

DistilProtBert: a distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts

(2022) Yaron Geffen et al.   BIOINFORMATICS

Directed evolution of phosphite dehydrogenase to cycle noncanonical redox cofactors via universal growth selection platform

(2022) Linyue Zhang et al.   Nature Communications

From sequence to function through structure: Deep learning for protein design

(2022) Noelia Ferruz et al.   Computational and Structural Biotechnology Journal

Deep diversification of an AAV capsid protein by machine learning

(2021) Drew H. Bryant et al.   NATURE BIOTECHNOLOGY

Generating functional protein variants with variational autoencoders

(2021) Alex Hawkins-Hooker et al.   PLoS Computational Biology

Low-N protein engineering with data-efficient deep learning

(2021) Surojit Biswas et al.   NATURE METHODS

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

(2021) Alexander Rives et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Protein design and variant prediction using autoregressive generative models

(2021) Jung-Eun Shin et al.   Nature Communications

Advances in machine learning for directed evolution

(2021) Bruce J Wittmann et al.   CURRENT OPINION IN STRUCTURAL BIOLOGY

Evolution, folding, and design of TIM barrels and related proteins

(2021) Sergio Romero-Romero et al.   CURRENT OPINION IN STRUCTURAL BIOLOGY

Informed training set design enables efficient machine learning-assisted directed protein evolution

(2021) Bruce J. Wittmann et al.   Cell Systems

adabmDCA: adaptive Boltzmann machine learning for biological sequences

(2021) Anna Paola Muntoni et al.   BMC BIOINFORMATICS

The generative capacity of probabilistic protein sequence models

(2021) Francisco McGee et al.   Nature Communications

PepVAE: Variational Autoencoder Framework for Antimicrobial Peptide Generation and Activity Prediction

(2021) Scott N. Dean et al.   Frontiers in Microbiology

A Reflection on 50 Years of John Maynard Smith’s “Protein Space”

(2020) C. Brandon Ogbunugafor   GENETICS

Signal Peptides Generated by Attention-Based Neural Networks

(2020) Zachary Wu et al.   ACS Synthetic Biology

Variational Autoencoder for Generation of Antimicrobial Peptides

(2020) Scott N. Dean et al.   ACS Omega

Machine learning-assisted directed protein evolution with combinatorial libraries

(2019) Zachary Wu et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Machine-learning-guided directed evolution for protein engineering

(2019) Kevin K. Yang et al.   NATURE METHODS

Unified rational protein engineering with sequence-based deep representation learning

(2019) Ethan C. Alley et al.   NATURE METHODS

Deep generative models of genetic variation capture the effects of mutations

(2018) Adam J. Riesselman et al.   NATURE METHODS

Genetically programmed chiral organoborane synthesis

(2017) S. B. Jennifer Kan et al.   NATURE

Mutation effects predicted from sequence co-variation

(2017) Thomas A Hopf et al.   NATURE BIOTECHNOLOGY

Anti-Markovnikov alkene oxidation by metal-oxo–mediated enzyme catalysis

(2017) Stephan C. Hammer et al.   SCIENCE

Algorithms for protein design

(2016) Pablo Gainza et al.   CURRENT OPINION IN STRUCTURAL BIOLOGY

The thermostability and specificity of ancient proteins

(2016) Lucas C Wheeler et al.   CURRENT OPINION IN STRUCTURAL BIOLOGY

The coming of age of de novo protein design

(2016) Po-Ssu Huang et al.   NATURE

Epistasis in protein evolution

(2016) Tyler N. Starr et al.   PROTEIN SCIENCE

De novo protein design: how do we expand into the universe of possible protein structures?

(2015) Derek N Woolfson et al.   CURRENT OPINION IN STRUCTURAL BIOLOGY

Topological features of rugged fitness landscapes in sequence space

(2015) Dmitry A. Kondrashov et al.   TRENDS IN GENETICS

Evolution of a designed retro-aldolase leads to complete active site remodeling

(2013) Lars Giger et al.   Nature Chemical Biology

Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models

(2012) Hashem A. Shihab et al.   HUMAN MUTATION

Exploring protein fitness landscapes by directed evolution

(2009) Philip A. Romero et al.   NATURE REVIEWS MOLECULAR CELL BIOLOGY

Neutralism and selectionism: a network-based reconciliation

(2008) Andreas Wagner   NATURE REVIEWS GENETICS