esm3.discussion.full3

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We have found that language models can reach a design space of proteins that is distant from the space explored by natural evolution, and generate functional proteins that would take evolution hundreds of millions of years to discover

language models design space natural evolution functional proteins

Protein language models do not explicitly work within the physical constraints of evolution, but instead can implicitly construct a model of the multitude of potential paths evolution could have followed

Protein language models do not explicitly work within the physical constraints of evolution, but instead can implicitly construct a model of the multitude of potential paths evolution could have followed.

Proteins can be seen as existing within an organized space where each protein is neighbored by every other that is one mutational event away (54)

In this context, "mutational event" refers to a change in the DNA sequence that results in a different amino acid being incorporated into the protein. This can occur due to a variety of factors, including errors during DNA replication or exposure to mutagens such as radiation or certain chemicals.

Mutational events can have a range of effects on the resulting protein, from minor changes in its structure or function to complete loss of function or even gain of new functions. Understanding the relationship between mutational events and protein structure and function is a key area of research in fields such as genetics, biochemistry, and molecular biology.

Overall, the concept of mutational events highlights the dynamic nature of proteins and the complex interplay between genetic information and the physical properties of these essential molecules.

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The structure of evolution appears as a network within this space, connecting all proteins by the paths that evolution can take between them

The structure of evolution appears as a network within this space, connecting all proteins by the paths that evolution can take between them.

In this context, "evolution" refers to the process by which species change over time through natural selection and genetic variation. It is a fundamental concept in biology and is often used to explain the diversity of life on Earth.

Proteins are large molecules that play a crucial role in the functioning of cells and organisms. They are made up of amino acids and can perform a wide variety of tasks, such as catalyzing chemical reactions, transporting molecules, and providing structural support.

The "network" mentioned in the text refers to the complex web of relationships between different proteins and the ways in which they can evolve and change over time. By studying these networks, scientists can gain insights into the mechanisms of evolution and the ways in which proteins interact with each other.

Overall, the text is discussing the use of network analysis to study the evolution of proteins and the complex relationships between different biological molecules.

The paths that evolution can follow are the ones by which each protein transforms into the next without the collective loss of function of the system it is a part of

evolution protein system

It is in this space that a language model sees proteins

It is in this space that a language model sees proteins.

It sees the data of proteins as filling this space, densely in some regions, and sparsely in others, revealing the parts that are accessible to evolution

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Since the next token is generated by evolution, it follows that to solve the training task of predicting the next token, a language model must predict how evolution moves through the space of possible proteins

evolution protein space predict model task next token solve training language moves possible

To do so it will need to learn what determines whether a path is feasible for evolution

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Simulations are computational representations of reality

Simulations are computational representations of reality. They are used to model complex systems and phenomena that are difficult or impossible to observe directly. Simulations can be used in a variety of fields, including physics, engineering, biology, and economics.

In physics, simulations are used to study the behavior of particles and the interactions between them. For example, simulations can be used to study the behavior of atoms and molecules in a material, or the behavior of stars and galaxies in the universe.

In engineering, simulations are used to design and test new products and systems. For example, simulations can be used to test the strength and durability of a new material, or to optimize the design of a new airplane.

In biology, simulations are used to study the behavior of cells and organisms. For example, simulations can be used to study the growth and development of a plant, or the behavior of a group of animals in their natural habitat.

In economics, simulations are used to study the behavior of markets and the impact of different policies. For example, simulations can be used to study the effects of a new tax policy on the economy, or the behavior of stock prices in response to different market conditions.

Overall, simulations are a powerful tool for understanding complex systems and phenomena, and they play an important role in many different fields of study.

In that sense a language model which can predict possible outcomes of evolution can be said to be a simulator of it

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- simulator

ESM3 is an emergent simulator that has been learned from solving a token prediction task on data generated by evolution

ESM3

It has been theorized that neural networks discover the underlying structure of the data they are trained to predict $(55,56)$

In this way, solving the token prediction task would require the model to learn the deep structure that determines which steps evolution can take, i

.e. the fitness landscape. fitness landscape

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the fundamental biology of proteins

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- fundamental

In ESM3's generation of a new fluorescent protein, it is the first chain of thought to B8 that is the most intriguing

In ESM3's generation of a new fluorescent protein, it is the first chain of thought to B8 that is the most intriguing.

ESM3 stands for Evolutionary Structural Modeling 3, which is a computational method used to predict protein structures. B8 is a specific amino acid residue in the protein structure that is being studied.

The first chain of thought refers to the initial hypothesis or idea that led to the development of the new fluorescent protein. This is an important concept in scientific research, as it sets the foundation for the entire project.

Fluorescent proteins are proteins that emit light when they are exposed to certain wavelengths of light. They are commonly used in biological research as markers or indicators of specific cellular processes.

The development of a new fluorescent protein is significant because it can provide researchers with a new tool to study biological processes that were previously difficult to observe. The fact that the first chain of thought in this project led to the development of a new fluorescent protein is what makes it so intriguing.

Overall, the use of ESM3 and the focus on B8 in the development of a new fluorescent protein highlights the importance of computational methods and specific amino acid residues in protein structure and function.

At 96 mutations to B8's closest neighbor there are $\binom{229}{96} \times 19^{96}$ possible proteins, an astronomical number out of which only a vanishingly small fraction can have function, since fluorescence falls off sharply even after just a few random mutations

B8 closest neighbor mutations proteins fluorescence

The existence of $\mathrm{C} 10$ and other bright designs in the neighborhood of B8 confirms that in the first chain of thought to B8, ESM3 found a new part of the space of proteins that, although unexplored by nature, is dense with fluorescent proteins

$\mathrm{C} 10$ B8 ESM3 fluorescent proteins

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