doi.bio/esm3/esm3.intro.full11
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- Proteins have evolved over billions of years through natural evolution
In parallel experiments conducted over geological time, nature creates random mutations and applies selection, filtering proteins by their myriad sequences, structures, and functions.
- 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
As a result, the patterns in the proteins we observe reflect the action of the deep hidden variables of the biology that have shaped their evolution across time.
- 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 $(1-3)$ and structures $(4,5)$ of proteins, containing billions of sequences and hundreds of millions of structures that illuminate patterns of variation across life.
- 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 these sequences is a fundamental language of protein biology that can be understood using large language models (6-10).
- 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.
A number of language models of protein sequences have now been developed and evaluated ( $9,11-14$ ).
- A number of language models of protein sequences have been developed and evaluated.
- These language models have been evaluated in references 9, 11-14.
It has been found that the representations that emerge within language models reflect the biological structure and function of proteins $(6,15,16)$, and are learned without any supervision on those properties, improving with scale $(5,17,18)$.
- 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.
In artificial intelligence, scaling laws have been found that predict the growth in capabilities with increasing scale, describing a frontier in compute, parameters and data (19-21).
- 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.
We present ESM3, a frontier multimodal generative model, that reasons over the sequences, structures, and functions of proteins.
- 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 trained as a generative masked language model over discrete tokens for each modality.
- 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 is achieved by encoding three-dimensional atomic structure as discrete tokens rather than with the complex architecture and diffusion in three-dimensional space employed in recent predictive (22) and generative models $(14,23-25)$ of proteins.
- 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, and allows ESM3 to be prompted with any combination of its modalities, enabling controllable generation of new proteins that respect combinations of prompts.
- 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 at its largest scale was trained with $1.07 \times 10^{24}$ FLOPs on 2.78 billion proteins and 771 billion unique tokens, and has 98 billion parameters.
- 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 this 98 billion parameter size results in improvements in the representation of sequence, structure, and function, as well as on generative evaluations.
- 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.
We find that ESM3 is highly responsive to prompts, and finds creative solutions to complex combinations of prompts, including solutions for which we can find no matching structure in nature.
- 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.
We find that models at all scales can be aligned to better follow prompts.
- Models at all scales can be aligned to better follow prompts.
Larger models are far more responsive to alignment, and
show greater capability to solve the hardest prompts after alignment.
- Larger models are more responsive to alignment.
- Larger models have greater capability to solve difficult prompts after alignment.
We report the generation of a new green fluorescent protein (GFP) with ESM3.
- 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.
- 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.
Our new protein, which we have named esmGFP, has $36 \%$ sequence identity to Aequorea victoria GFP, and $58 \%$ sequence identity to the most similar known fluorescent protein.
- 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.
Despite GFP's intense focus as a target for protein engineering over several decades, as far as we are aware, proteins this distant have only been found through the discovery of new GFPs in nature.
- 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.
- Similar amounts of diversification among natural GFPs have occurred over predictable timescales.
Understood in these terms, the generation of a new fluorescent protein at this distance from existing proteins appears to be equivalent to simulating over 500 million years of evolution.
- 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.
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