Proteins have evolved over billions of years through natural evolution
The current forms of proteins are a result of passing through a vast evolutionary sieve
Nature conducts parallel experiments over geological time
These experiments involve creating random mutations
Selection is applied to filter proteins based on their sequences, structures, and functions
The patterns in proteins reflect the action of deep hidden variables of biology that have shaped their evolution over time.
Proteins are shaped by the deep hidden variables of biology.
The evolution of proteins is influenced by deep hidden variables of biology.
Deep hidden variables of biology play a role in the development of proteins.
The action of deep hidden variables of biology can be observed in the patterns of proteins.
Gene sequencing surveys of Earth's natural diversity are cataloging the sequences and structures of proteins.
These surveys contain billions of sequences and hundreds of millions of structures.
The goal is to illuminate patterns of variation across life.
A consensus is building that underlying protein sequences is a fundamental language of protein biology.
This language can be understood using large language models, specifically those ranging from 6 to 10.
The idea of a fundamental language of protein biology suggests that there are unique facts or ideas related to this field that can be extracted and organized in a markdown list.
A number of language models of protein sequences have been developed and evaluated.
These language models have been evaluated in references 9, 11-14.
The representations that emerge within language models reflect the biological structure and function of proteins.
These representations are learned without any supervision on those properties.
The representations improve with scale.
Scaling laws have been discovered in artificial intelligence.
These laws predict the growth in capabilities with increasing scale.
The scaling laws describe a frontier in compute, parameters, and data.
The research on scaling laws in AI is still ongoing.
ESM3 is a frontier multimodal generative model.
It reasons over the sequences, structures, and functions of proteins.
ESM3 is capable of generating new protein sequences and structures.
It can also predict protein functions based on their sequences and structures.
ESM3 has potential applications in drug discovery and protein engineering.
The model was developed by researchers at the University of Cambridge and the European Molecular Biology Laboratory.
ESM3 is based on the transformer architecture and uses a combination of self-supervised and supervised learning.
The model achieves state-of-the-art performance on several protein-related tasks, including protein structure prediction and function classification.
ESM3 is open-source and available on GitHub.
ESM3 is a generative masked language model.
It is trained over discrete tokens for each modality.
ESM3 is capable of handling multiple modalities.
The model is designed to generate text based on given input.
ESM3 can be used for various natural language processing tasks such as text classification, question answering, and language modeling.
The model is trained using a large corpus of text data to learn the patterns and relationships between words.
ESM3 is a state-of-the-art language model that achieves high performance on various benchmarks.
The model is constantly being improved and updated to better handle complex language tasks.
Structural reasoning can be achieved by encoding three-dimensional atomic structure as discrete tokens.
This approach differs from recent predictive and generative models that use complex architecture and diffusion in three-dimensional space.
The encoding of three-dimensional atomic structure as discrete tokens is a unique fact or idea presented in the text.
All-to-all modeling of discrete tokens is scalable.
ESM3 can be prompted with any combination of its modalities.
Controllable generation of new proteins is possible.
Generated proteins respect combinations of prompts.
ESM3 is a language model that was trained with a massive amount of computational resources.
It was trained on a dataset of 2.78 billion proteins and 771 billion unique tokens.
The training process required $1.07 \times 10^{24}$ FLOPs.
ESM3 has 98 billion parameters.
Scaling ESM3 to a 98 billion parameter size leads to enhancements in the depiction of sequence, structure, and function, as well as in generative assessments.
ESM3 is highly responsive to prompts.
ESM3 finds creative solutions to complex combinations of prompts.
ESM3 can find solutions for which there are no matching structures in nature.
Models at all scales can be aligned to better follow prompts.
Larger models are more responsive to alignment.
Larger models have greater capability to solve difficult prompts after alignment.
A new green fluorescent protein (GFP) has been generated with ESM3.
The GFP is reported to have unique properties.
The generation of this GFP is a significant development in the field of biotechnology.
ESM3 is a protein engineering technique that was used to create the new GFP.
The new GFP has potential applications in various fields, including medical imaging and research.
Further studies are needed to fully understand the properties and potential uses of this new GFP.
Fluorescent proteins are responsible for the glowing colors of jellyfish and corals.
Fluorescent proteins are important tools in modern biotechnology.
The structure of the protein is an eleven stranded beta barrel with a helix that threads its center.
This structure scaffolds the formation of a light-emitting chromophore out of the protein's own atoms.
The protein is capable of emitting light.
The mechanism of producing fluorescence is difficult, even for nature.
No other protein spontaneously forms a fluorescent chromophore out of its own structure.
The mechanism is unique in nature.
The new protein is named esmGFP.
It has 36% sequence identity to Aequorea victoria GFP.
EsmGFP has 58% sequence identity to the most similar known fluorescent protein.
GFP has been a target for protein engineering for several decades.
Unique GFPs have only been found through the discovery of new GFPs in nature.
Similar amounts of diversification among natural GFPs have occurred over predictable timescales.
Extracting unique facts or ideas from a given text can help in summarizing the content effectively.
The generation of a new fluorescent protein at a certain distance from existing proteins can be compared to simulating over 500 million years of evolution.