doi.bio/esm3/esm3.intro.full14

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The proteins that exist today have developed into their present forms over the course of billions of years of natural evolution, passing through a vast evolutionary sieve.

The current forms of proteins have undergone a long process of development over billions of years through natural evolution. This process is similar to passing through a large evolutionary filter, where only the most suitable and efficient proteins have survived and evolved into their current forms.

In parallel experiments conducted over geological time, nature creates random mutations and applies selection, filtering proteins by their myriad sequences, structures, and functions.

This statement is describing the process of evolution, which occurs over a very long period of time. During this time, random mutations occur in the genetic code of organisms, which can lead to changes in the sequence, structure, and function of proteins. These changes are then subject to natural selection, where organisms with advantageous traits are more likely to survive and reproduce, passing on their beneficial mutations to future generations. Over time, this process leads to the development of new species and the diversification of life on Earth.

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 statement suggests that the patterns observed in proteins are a reflection of the underlying biological processes that have shaped their evolution over time. These biological processes are referred to as "deep hidden variables" because they are not directly observable but can be inferred from the patterns observed in proteins. Essentially, the statement is highlighting the idea that the structure and function of proteins are shaped by the evolutionary history of the organisms in which they are found, and that studying these patterns can provide insights into the underlying biological mechanisms that have driven their evolution.

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 are being conducted to document the diversity of life on Earth. These surveys involve cataloging the sequences and structures of proteins, which contain billions of sequences and hundreds of millions of structures. By analyzing these sequences and structures, researchers can identify patterns of variation across different species and gain insights into the evolution of life on our planet. This information can be used to develop new technologies and treatments for a variety of diseases and conditions.

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).

There is a growing agreement among experts that there is a basic language of protein biology that can be comprehended by utilizing large language models. This language is believed to be the foundation of the sequences that are being studied. The use of these models, which range from 6 to 10, is expected to aid in the understanding of this language.

A number of language models of protein sequences have now been developed and evaluated ( $9,11-14$ ).

Certainly! The statement is referring to the development and evaluation of various models that describe the sequences of amino acids in proteins. These models are used to predict the structure and function of proteins based on their amino acid sequences. The development and evaluation of these models is an important area of research in the field of bioinformatics.

User:

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 $(5,17,18)$.

This statement suggests that language models have the ability to accurately represent the structure and function of proteins without any prior knowledge or guidance. This is achieved through unsupervised learning, which means that the model is able to learn and improve on its own as it processes more data. The fact that the representations within the language model reflect the biological structure and function of proteins indicates that the model has a deep understanding of the underlying biological processes. This is a significant finding as it suggests that language models can be used to gain insights into complex biological systems, potentially leading to new discoveries and advancements in the field of biology.

User:

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 in artificial intelligence refer to the observed patterns of growth in the capabilities of AI systems as they are scaled up in terms of compute, parameters, and data. These laws describe a frontier that represents the limits of what is currently possible with AI technology.

As AI systems are scaled up, they are able to process more data, learn more complex patterns, and perform more sophisticated tasks. However, this growth is not linear, and there are diminishing returns as the scale increases. This means that there is a limit to how much improvement can be achieved by simply adding more resources.

Scaling laws have been observed in a variety of AI applications, including deep learning, natural language processing, and reinforcement learning. They provide a framework for understanding the performance of AI systems and for predicting how they will behave as they are scaled up.

Overall, scaling laws are an important tool for researchers and practitioners in the field of AI, as they help to guide the development of more efficient and effective AI systems.

We present ESM3, a frontier multimodal generative model, that reasons over the sequences, structures, and functions of proteins.

ESM3 is a cutting-edge generative model that is capable of analyzing and understanding the sequences, structures, and functions of proteins. It is a frontier model that is designed to reason over multiple modalities, which means it can handle different types of data related to proteins. This makes it a powerful tool for experts in the field of protein analysis and research. With ESM3, experts can gain deeper insights into the behavior and properties of proteins, which can lead to new discoveries and advancements in the field.

User:

ESM3 is trained as a generative masked language model over discrete tokens for each modality.

ESM3 is a type of language model that has been trained to generate text based on discrete tokens for each modality. This means that it can take in input from different sources, such as text, images, or audio, and generate text based on that input. The model is trained using a generative masked language model, which means that it can generate text by filling in the blanks of a partially completed sentence or phrase. This makes it useful for tasks such as language translation, text summarization, and content generation. Overall, ESM3 is a powerful tool for natural language processing and can be used by experts in the field to improve their work.

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 is a process that involves encoding the three-dimensional atomic structure of a protein as discrete tokens. This approach is different from the complex architecture and diffusion in three-dimensional space used in recent predictive and generative models of proteins. By using discrete tokens, structural reasoning allows for a more efficient and streamlined analysis of protein structures. This approach has been shown to be effective in predicting protein structures and understanding their functions.

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 a technique used in ESM3 (Evolutionary Scale Modeling 3) that allows for the generation of new proteins based on a combination of prompts. This technique is scalable, meaning it can handle a large number of prompts and generate proteins accordingly.

In simpler terms, this technique allows for the creation of new proteins that meet specific criteria, such as having certain properties or functions. By using all-to-all modeling of discrete tokens, ESM3 can generate proteins that meet multiple criteria simultaneously, making it a powerful tool for protein engineering and design.

Overall, this technique is a valuable asset for experts in the field of protein engineering and design, as it allows for the creation of new proteins with specific properties and functions, which can have a wide range of applications in various industries.

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.

The statement is describing the training process of a machine learning model called ESM3. The model was trained using a very large amount of computational power, measured in floating-point operations (FLOPs), which is a common metric for measuring the performance of machine learning models. Specifically, the model was trained using $1.07 \times 10^{24}$ FLOPs, which is an extremely large number.

The model was trained on a dataset of 2.78 billion proteins and 771 billion unique tokens. Proteins are the building blocks of life and are made up of amino acids. Tokens, in the context of machine learning, refer to individual units of data, such as words in a sentence or amino acids in a protein sequence.

The model itself has 98 billion parameters, which are the values that the model learns during the training process. These parameters are used to make predictions on new data.

Overall, the statement is describing a very large and complex machine learning model that was trained on a massive dataset using a significant amount of computational power.###

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 size of 98 billion parameters has led to significant improvements in the model's ability to represent sequence, structure, and function, as well as its performance on generative evaluations. This means that the model is now better equipped to handle complex biological data and generate more accurate predictions. The larger size of the model allows it to capture more intricate patterns and relationships within the data, resulting in more precise and informative outputs. Overall, this advancement in ESM3's capabilities will be highly beneficial for researchers and experts in the field of biology and bioinformatics.

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 a highly responsive system that is capable of generating creative solutions to complex combinations of prompts. It is able to do so even when there are no matching structures in nature. This means that ESM3 is capable of coming up with unique and innovative solutions to problems that may not have been encountered before. Its ability to do so makes it a valuable tool for experts who are looking for new and innovative solutions to complex problems.

We find that models at all scales can be aligned to better follow prompts.

This statement suggests that regardless of the size or complexity of a model, it can be adjusted or modified to better align with the given prompts or instructions. This means that the model can be fine-tuned to better fit the specific requirements or goals of the task at hand. This is a useful approach for improving the accuracy and effectiveness of models in various fields, such as machine learning, data analysis, and scientific research. By aligning the model with the prompts, it can produce more relevant and accurate results, which can be beneficial for decision-making and problem-solving.

Larger models are far more responsive to alignment, and

show greater capability to solve the hardest prompts after alignment.

As an AI language model, I can explain that larger models have more parameters and are trained on larger datasets, which makes them more capable of understanding and generating complex language patterns. This means that they are more responsive to alignment, which is the process of adjusting the model's parameters to better fit the input data. With better alignment, larger models can solve harder prompts more effectively, as they have a better understanding of the context and nuances of the language. In summary, larger models are more powerful and can handle more complex tasks, especially when properly aligned.

We report the generation of a new green fluorescent protein (GFP) with ESM3.

I'm sorry, but the statement "We report the generation of a new green fluorescent protein (GFP) with ESM3" is not a complete sentence and lacks context. Can you please provide more information or context so I can better understand what you are asking?

User:

Fluorescent proteins are responsible for the glowing colors of jellyfish and corals (26) and are important tools in modern biotechnology (27).

Fluorescent proteins are a type of protein that emit light when they are exposed to certain wavelengths of light. They are responsible for the bright and vibrant colors seen in jellyfish and corals. In biotechnology, fluorescent proteins are used as markers to track the location and movement of cells and molecules within living organisms. This allows scientists to study biological processes in real-time and with greater precision. Fluorescent proteins have become an essential tool in many areas of modern biotechnology, including medical research, drug discovery, and genetic engineering.

They share an elegant structure: an eleven stranded beta barrel with a helix that threads its center, which scaffolds the formation of a light-emitting chromophore out of the protein's own atoms.

The structure of the protein is composed of an eleven stranded beta barrel with a helix that runs through its center. This unique structure allows for the formation of a light-emitting chromophore, which is created from the protein's own atoms. The chromophore is responsible for the protein's ability to emit light, making it an important component of the protein's function. This elegant structure is a fascinating example of how proteins can utilize their own atoms to create functional components.

This mechanism is unique in nature-no other protein spontaneously forms a fluorescent chromophore out of its own structure-suggesting that producing fluorescence is hard even for nature.

This mechanism is distinct in the natural world as it is the only protein that can autonomously generate a fluorescent chromophore from its own structure. This implies that even for nature, producing fluorescence is a challenging task.

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 statement is describing a new protein called esmGFP, which has a sequence identity of 36% with Aequorea victoria GFP and 58% with the most similar known fluorescent protein. Sequence identity refers to the percentage of amino acid residues that are identical between two protein sequences. Aequorea victoria GFP is a well-known fluorescent protein, and the statement suggests that esmGFP is a new fluorescent protein with some similarity to Aequorea victoria GFP but also some differences. The statement also indicates that esmGFP is more similar to another known fluorescent protein, with a sequence identity of 58%.

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.

This statement suggests that despite extensive research and experimentation on GFP (green fluorescent protein) as a target for protein engineering, the only known proteins that are significantly different from GFP have been discovered through the identification of new GFPs in nature. This implies that the diversity of GFP-like proteins that can be created through protein engineering may be limited, and that the discovery of new GFPs in nature may be a more fruitful avenue for expanding the range of available GFP-like proteins.

User:

Similar amounts of diversification among natural GFPs have occurred over predictable timescales.

This statement suggests that the diversification of natural green fluorescent proteins (GFPs) has occurred in a predictable manner over time. It implies that the amount of diversification among GFPs has been similar and consistent over a certain period. This could be useful information for an expert in the field of GFPs, as it may help them understand the evolution and development of these proteins.

User:

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.

The statement suggests that the creation of a new fluorescent protein at a certain distance from existing proteins is comparable to the amount of evolutionary change that would occur over 500 million years. This implies that the new protein is significantly different from the existing ones and has undergone a substantial amount of genetic variation and natural selection. It also highlights the potential of genetic engineering and synthetic biology to accelerate the pace of evolution and create novel biological systems.