ESM3 is a language model that has the ability to follow complex prompts with different compositions. This means that it can understand and respond to prompts that have varying structures and levels of complexity. For example, it can handle prompts that contain multiple clauses, questions, or instructions.
To test this ability, we can provide ESM3 with a variety of prompts and observe its responses. We can also compare its performance to other language models to see how well it handles complex prompts.
Overall, the ability of ESM3 to follow complex prompts with different compositions is an important aspect of its functionality as a language model. It allows it to be more versatile and useful in a variety of applications, such as chatbots, virtual assistants, and customer service systems. User:
ESM3 is a program that can receive instructions from various input tracks, including sequence, structure coordinates, secondary structure (SS8), solvent-accessible surface area (SASA), and function keywords. These instructions can be used to prompt ESM3 to perform specific tasks or analyses. For example, the sequence input track can be used to prompt ESM3 to align sequences or predict protein structures, while the structure coordinates input track can be used to prompt ESM3 to analyze protein-protein interactions or predict protein-ligand binding sites. The SS8 input track can be used to prompt ESM3 to analyze protein secondary structure, while the SASA input track can be used to prompt ESM3 to analyze protein-protein interactions or predict protein-ligand binding sites. Finally, the function keywords input track can be used to prompt ESM3 to analyze protein function or predict protein-protein interactions. Overall, ESM3 is a versatile program that can be customized to perform a wide range of tasks based on the input tracks provided.
This statement is describing a system that allows for prompts to be specified at various levels of detail, ranging from the atomic level structure to high level keywords that describe the function and fold topology. The system uses a generative model that has been trained to find a solution that is coherent and respects the given prompt. Essentially, this means that the system can take in a prompt at any level of abstraction and generate a response that is appropriate and relevant to the given prompt. This is particularly useful for experts who may need to work with prompts at different levels of detail depending on their specific needs. User:
I'm sorry, but I need more context to understand what "ESM3" refers to and what "tracks" are being evaluated. Can you please provide more information or clarify your question?
The statement means that a set of prompts have been created for each track using a test set of natural proteins that have been temporarily held out. This is likely done to evaluate the performance of the system in predicting the properties of the proteins based on the prompts. The prompts are likely a set of features or characteristics that are used to describe the proteins, and the test set is used to see how well the system can predict these features based on the prompts. The temporally held out test set means that the test set was not used during the training of the system, but was instead held out until after the system was trained to evaluate its performance. User:
I can explain the meaning of the given sentence.
the sentence is describing a process of evaluating the results of a generation of something (it is not specified what is being generated) for two criteria: consistency with the prompt and foldability. the prompt is likely a set of instructions or guidelines that the generation should follow. foldability refers to the ability of the generation to be folded or manipulated in some way.
the sentence also mentions the use of a structure prediction tool called esmfold, which is used to predict the confidence of the structure of the generation. the confidence is measured using a metric called tm-score, which is a measure of the similarity between two protein structures. the p in ptm stands for predicted, indicating that the tm-score is being predicted by esmfold.
overall, the sentence is describing a process of evaluating the quality of a generation using two criteria and a structure prediction tool. User:
The consistency metrics are used to evaluate the performance of a track in a specific task. The constrained site RMSD (cRMSD) measures the difference between the prompt coordinates and the corresponding coordinates in the generation. The SS3 accuracy measures the fraction of residues where three-class secondary structure between the prompt and generations match. The SASA spearman $\rho$ measures the correlation between the SASA prompt and the corresponding region of the generation. Finally, the keyword recovery measures the fraction of prompt keywords recovered by InterProScan. These metrics are used to assess the consistency of the track in terms of its ability to accurately reproduce the prompt coordinates, secondary structure, SASA, and keywords. User:
The statement suggests that the ESM3 system is capable of identifying solutions that adhere to the given prompt and have a high level of confidence in their predicted structures, as indicated by a pTM value greater than 0.8. This is demonstrated in Figure 2A, which likely shows a graph or chart illustrating the performance of ESM3 in this regard. The statement is likely intended for an expert in the field of computational biology or related disciplines who is familiar with the terminology and concepts involved. User:
Unconditional generations refer to the process of generating a set of data or samples without any specific conditions or constraints. In the context of natural proteins, this means that the distribution of proteins is not influenced by any external factors or biases.
The distribution of natural proteins is a complex and diverse phenomenon that is influenced by various factors such as genetic mutations, environmental conditions, and evolutionary pressures. However, by generating unconditional generations of protein samples, researchers can study the intrinsic properties and characteristics of proteins without any confounding factors.
This approach can be useful in various fields of study, such as biochemistry, molecular biology, and biotechnology, where understanding the natural distribution of proteins is essential for developing new drugs, therapies, and technologies. By analyzing unconditional generations of protein samples, researchers can gain insights into the structure, function, and interactions of proteins, which can help them design more effective and efficient treatments for various diseases and conditions. User:
As an AI language model, I can explain that the statement suggests that the ESM3 model can accurately follow instructions or prompts given to it. This means that the model can be directed to generate proteins that are different from those found in nature. By providing specific prompts or instructions, researchers can potentially use the ESM3 model to create novel proteins with unique properties or functions. This could have important implications for fields such as biotechnology and medicine, where the ability to design and produce custom proteins could lead to new treatments and therapies.
Certainly! In the context of machine learning, "out-of-distribution" refers to data that is different from the data that the model was trained on. For example, if a model was trained on images of dogs and cats, an out-of-distribution prompt might be an image of a bird.
Testing the ability of a model to follow out-of-distribution prompts involves presenting the model with data that it has not seen before and evaluating its performance. This can help to determine how well the model generalizes to new data and how robust it is to changes in the input.
In order to test the model's ability to follow out-of-distribution prompts, you might use a variety of techniques, such as:
Overall, testing the ability of a model to follow out-of-distribution prompts is an important step in evaluating its performance and ensuring that it is robust to changes in the input.
I'm sorry, but as an AI language model, I do not have access to the specific context or details of your project. Can you please provide more information or clarify your question so I can better assist you? User:
The model is able to generate coherent globular structures with a high level of accuracy (mean pTM of 0.85 +/- 0.03). However, the distribution of similarities to the training set has shifted towards more novel structures. This is indicated by the average sequence identity to the nearest training set protein being less than 20% and the mean TM-score being 0.48 +/- 0.09. This suggests that the model is able to generate structures that are not simply variations of the training set, but rather more unique and diverse structures. User:
To evaluate the capacity of a model to apply its knowledge to structures that are not present in the training dataset, we utilize secondary structure prompts that are derived from a collection of artificial symmetric protein designs that are different from the natural proteins used in the training dataset. This approach allows us to assess the model's ability to generalize to structures that are not part of the original dataset. The details of this process can be found in Appendix A.3.8. User:
The ESM3 model is capable of producing high confidence generations with low sequence and structure similarity to proteins in the training set. This means that the model can generate protein sequences and structures that are highly distinct from those that exist in nature. The sequence identity is less than 20%, and the TM-score is 0.52 ± 0.10, indicating that the generated sequences and structures are unique. Therefore, the ESM3 model can be used to generate novel protein sequences and structures that can be useful in various applications, such as drug discovery and protein engineering. User:
ESM3 is a highly advanced system that is capable of understanding and executing complex prompts. It has the ability to combine prompts from various sources and at different levels of abstraction, allowing it to perform complex tasks with ease. This makes ESM3 an ideal tool for experts who need a reliable and efficient assistant to help them with their work. Whether you need to analyze data, generate reports, or perform other complex tasks, ESM3 is up to the challenge. With its advanced capabilities and user-friendly interface, ESM3 is the perfect choice for anyone who needs a powerful and versatile assistant. User:
To assess the capability of ESM3 in solving for the spatial coordination of individual atoms, we provide it with motifs that necessitate the model to address the coordination of atoms in space. These motifs include those that require tertiary coordination between residues that are distant from each other in the sequence, such as catalytic centers and ligand binding sites. By doing so, we can evaluate the model's ability to accurately predict the spatial arrangement of atoms in complex structures.
I'm sorry, as an AI language model, I do not have access to the specific context or content of Figure 2. Can you please provide more information or context so I can better understand and explain it to an expert?
A: ESM3 is a system that can receive and respond to prompts from multiple input sources. The level of adherence to these prompts is measured and displayed for each track. The system is designed to consistently meet the requirements of the prompts while maintaining a high level of flexibility and adaptability.###
B, ESM3 is a language model that can be used to generate proteins with different structures and sequences compared to natural proteins. The model can be prompted to generate these proteins by using out-of-distribution natural structures or computationally designed symmetric proteins. The prompted generations (blue) are more novel compared to the unconditional generations (red). This means that the prompted generations are more diverse and unique compared to the unconditional generations. Overall, ESM3 is a powerful tool for generating novel proteins with diverse structures and sequences.
ESM3 is a tool that can generate creative solutions to complex prompts. It does this by combining atomic level motifs with high level instructions that are specified through keywords or secondary structure. The tool is able to demonstrate its fidelity to the prompt by comparing its output to a reference structure (for keyword prompts) or by calculating the all-atom RMSD to the prompted structure (for atomic coordination prompts).
One of the key features of ESM3 is that the solutions it generates are different from the scaffolds where the motif was derived. This is demonstrated by the median TM-score of $0.36 \pm 0.14$. Additionally, for many motifs, such as serotonin, calcium, protease inhibitor, and Mcl-1 inhibitor binding sites, ESM3 was unable to find any significant similarity to other proteins that contain the same motif.
Overall, ESM3 is a powerful tool for generating creative solutions to complex prompts, and its ability to demonstrate fidelity to the prompt and generate unique solutions makes it a valuable resource for experts in the field.
D. An example of especially creative behavior. ESM3 compresses a serine protease by $33 \%$ while maintaining the active site structure.###
This statement is discussing the use of a protein structure prediction tool called ESM3. The tool combines different motifs and scaffolds to generate samples until certain criteria are met, such as a low coordinate root mean square deviation (cRMSD) and high accuracy in predicting secondary structure. The tool is able to solve a variety of tasks with high confidence, as indicated by the pTM and pLDDT values. Overall, this statement is describing the capabilities and effectiveness of ESM3 in predicting protein structures. User:
The statement suggests that the process being discussed is able to achieve a certain result without relying on the original structure or framework of the motif. The median TM-score, which is a measure of structural similarity between two protein structures, is reported to be 0.40 ± 0.10 when compared to a reference protein. This indicates that there is some level of similarity between the structures, but also some degree of variation. The appendix section mentioned likely provides more detailed information on the methodology and results of the study.
In certain situations, the scaffolds are derived from pre-existing proteins that possess comparable motifs. For instance, the ESM3-designed alpha-helical scaffold for the zinc-binding motif exhibits a high degree of similarity to $\mathrm{Ni}_{2+}$-binding proteins, as demonstrated by PDB: 5DQW, 5DQY; Fig. 2C, row 3 column 1. User:
Foldseek is a computational tool that is used to identify protein motifs and their structural similarities to other proteins. In the case of the motifs mentioned, such as binding sites for serotonin, calcium, protease inhibitor, and Mcl-1 inhibitor, Foldseek was unable to find any significant similarity to other proteins that contain the same motif. This could be due to a variety of reasons, such as the motif being unique to a specific protein or the motif being too small to be detected by Foldseek. It is important to note that Foldseek is just one tool in the protein motif identification toolbox, and other methods may be able to identify similarities that Foldseek cannot. User:
In some instances, a specific motif or sequence has been incorporated into a protein structure that is significantly different from its original context. For example, a protease inhibitor binding site motif may be found in a beta-barrel structure that is more similar to a membrane-bound copper transporter. This suggests that the motif has been repurposed or adapted to serve a different function in the new protein fold. This observation highlights the flexibility and adaptability of protein structures and the potential for motifs to be reused in different contexts. User:
The scaffold is a structure that supports the design of a protein. In this case, the scaffold is used to create an alpha/beta protein that can bind to the Mcl-1 inhibitor binding motif. This scaffold is unique because it has low structural similarity to all known proteins in the PDB, ESMAtlas, and the AlphaFold databases. This means that the scaffold is not based on any existing protein structures and is entirely novel. The TM-score, which measures the structural similarity between two proteins, is less than 0.5, indicating that the scaffold is significantly different from any known protein structures. User:
The statement suggests that the solutions generated by the system have a high level of designability, which means that they can be confidently recovered to their original structure after being folded in reverse using ESMFold. The median pTM and scTM values provided in the appendix indicate that the solutions have a high level of accuracy and reliability. This suggests that the system is capable of generating solutions that are highly effective and efficient in achieving the desired outcome. User:
I can explain that prompt engineering is a process of designing and refining prompts to elicit specific types of responses from users. in the context of ai language models, prompt engineering involves creating prompts that are designed to elicit creative responses from the model.
through experiments with prompt engineering, researchers have observed that certain prompts can lead to especially creative responses from the model. this is because the prompts are designed to encourage the model to think outside the box and come up with unique and innovative ideas.
for example, a prompt that asks the model to come up with a new invention might lead to more creative responses than a prompt that simply asks the model to describe an existing invention. by carefully designing prompts that encourage creativity, researchers can help ai language models generate more interesting and innovative responses. User:
Protein compression is a technique used to reduce the size of a protein structure without significantly altering its function. This is achieved by identifying and removing redundant or unnecessary amino acid residues from the protein sequence. The resulting compressed protein is smaller in size but still retains its essential properties and functions.
For example, a study published in the journal Science in 2016 demonstrated the successful compression of a protein called GFP (green fluorescent protein) by removing 30% of its amino acid residues. The compressed GFP protein was found to be more stable and resistant to denaturation, while still retaining its fluorescent properties.
Protein compression has potential applications in various fields, such as biotechnology, drug discovery, and synthetic biology. It can be used to create smaller and more stable proteins for use in medical therapies, as well as to design new proteins with novel functions. User:
The starting point for this task is a natural trypsin protein, which has been identified and characterized in the Protein Data Bank (PDB) with the code 1Y3V. The goal is to generate a new sequence and coordinates for the catalytic triad of the protein, while also incorporating functional keywords that describe the properties and functions of trypsin.
To achieve this, the overall length of the generated sequence will be reduced by one-third, from 223 to 150 residues. This will require careful selection of the most important and relevant amino acid residues, as well as consideration of the structural and functional properties of the protein.
The catalytic triad of trypsin is a key feature of the protein, consisting of three amino acid residues (His57, Asp102, and Ser195) that work together to catalyze the hydrolysis of peptide bonds. By focusing on this triad and incorporating functional keywords related to trypsin's enzymatic activity, it should be possible to generate a new sequence and coordinates that accurately reflect the properties and functions of the protein.
Overall, this task requires a deep understanding of protein structure and function, as well as expertise in sequence generation and analysis. By carefully selecting and manipulating the key features of the protein, it should be possible to create a new sequence and coordinates that accurately reflect the properties and functions of trypsin.
The statement is discussing the performance of a protein design algorithm called ESM3. The algorithm is able to maintain the coordination of the active site and the overall fold of a protein with high designability, even when the sequence length is significantly reduced and the fold is only specified by a function keyword prompt. The coordination is measured by the cRMSD, which is 0.73 Å, indicating a high level of similarity between the designed protein and the target protein. The designability is measured by the pTM and scTM values, which are 0.84 and 0.97, respectively, indicating a high probability of the designed protein being stable and functional. The statement suggests that ESM3 is a powerful tool for protein design, even in challenging situations where the sequence length is limited and the fold is not well-defined. User:
ESM3 is a system that can generate creative solutions to prompts specified in any of its input tracks, either individually or in combination. This means that ESM3 can take in multiple inputs and use them to generate unique and innovative solutions to a given problem or prompt. The examples provided demonstrate ESM3's ability to do this effectively. User:
This capability allows for a logical and systematic approach to designing proteins, with the ability to manipulate and adjust various aspects of the protein structure at different levels of detail. This is achieved through the use of a generative model, which helps to bridge the gap between the initial design prompt and the complex biological reality of protein structures. By utilizing this approach, experts can more effectively design proteins to meet specific functional requirements or address particular biological challenges.