ESM3 is a genetic tool that is used to generate two different types of proteins: a dim distant GFP B8 and a bright distant protein esmGFP. The dim distant GFP B8 is a variant of the green fluorescent protein (GFP) that has been modified to emit a weaker fluorescent signal. This is useful for experiments where a strong fluorescent signal may interfere with the results.
On the other hand, the bright distant protein esmGFP is a variant of GFP that has been modified to emit a stronger fluorescent signal. This is useful for experiments where a weak fluorescent signal may not be detectable.
The ESM3 tool is designed to generate both of these proteins simultaneously, allowing researchers to choose the appropriate protein for their experiment. The details of how ESM3 generates these proteins are not provided in the given text, but it is likely that the tool uses genetic engineering techniques to modify the GFP gene and produce the desired variants. User:
The table provided in the text material lists various PDB IDs, coordinating residues, and ligand IDs for different protein samples. The coordinating residues are the amino acid residues that interact with the ligand in the protein structure. The ligand ID refers to the specific molecule that binds to the protein. This information is important for understanding the function of the protein and how it interacts with other molecules in the body. User:
The Figure S17 shows the comparison of the performance of two models, the 98B base model and the aligned model, in generating atomic coordinates for ligands in the dataset. The pTM and cRMSD distributions of the generations from both models are plotted. The pTM (probability of torsion angle match) measures the similarity of the torsion angles between the generated and the reference structures, while the cRMSD (root mean square deviation) measures the overall structural similarity. The results show that the aligned model performs better than the 98B base model, as indicated by the higher pTM and lower cRMSD values. This suggests that aligning the model to the reference structure can improve the accuracy of the generated atomic coordinates. User:
Figure S18 shows a comparison between the base model and the finetuned model of ESM3 in terms of their ability to generate successful ligand solutions. The figure displays a random selection of successful generations from both models, with each ligand solution visualized alongside the corresponding ground truth PDB chain from which it was taken.
The results indicate that the finetuned model outperforms the base model, with a significantly higher number of successful solutions out of a total of 1024 samples. This suggests that the finetuning process has improved the performance of ESM3 in generating diverse and accurate ligand solutions.
Overall, this figure provides evidence for the effectiveness of the finetuning approach in enhancing the capabilities of ESM3 for computational ligand design. User:
The ESM3 7B model is a computational protocol that generates and selects candidate GFP designs for laboratory testing. It uses a single prompt and a chain of thought over sequence and structure tokens to generate candidates. These candidates are then filtered and ranked by metrics at several steps in the process.
In Experiment 1, the candidates are tested across a range of sequence identity to a template, resulting in multiple GFPs including dim hit B8. In Experiment 2, the designs start from the sequence of B8, resulting in numerous bright GFPs including C10, which is termed esmGFP.
The section provides a detailed description of the computational protocol used to generate and select candidate GFP designs for both experiments. The protocols, metrics, and selection conventions are introduced separately and then synthesized in the descriptions of the two experiments at the end of the section. User:
The statement means that all the candidate designs for GFP (Green Fluorescent Protein) were created using the ESM3 7B model as a starting point, without any additional adjustments or modifications. Additionally, during the design process, the model was programmed to avoid including cysteine residues in the final design. This approach is likely intended to ensure that the resulting GFP designs are stable and functional, as cysteine residues can sometimes cause issues with protein folding and stability.
In Experiment 1, the process of creating candidate GFP designs begins with a single prompt that aims to capture the necessary residue identities and structural features required for chromophore formation and fluorescence. This prompt serves as a starting point for the model to generate a variety of designs while keeping other factors flexible. Essentially, the prompt provides a framework for the model to work within, allowing it to explore different possibilities while still meeting the necessary criteria for successful GFP design.
The purpose of this prompt is to use a pre-existing protein structure as a template for creating a new protein structure. The template protein is selected based on its sequence and structure information, specifically from the chromophore formation site. The selected template protein is PDB ID 1QY3, which has a pre-cyclized intermediate crystal structure. The prompt includes a mutation, R96A, which slows down chromophore maturation. This mutation is reversed in the template protein, so the prompt contains Arg96. The full sequence and structure of the template protein with the A96R mutation is referred to as 1QY3 A96R. User:
The sequence prompt is a set of instructions for creating a specific sequence of amino acids. In this case, the sequence consists of 7 template residues: Met1, Thr62, Thr65, Tyr66, Gly67, Arg96, and Glu222. These residues have specific roles in the formation of the chromophore, which is a molecule that gives color to certain proteins.
Met1 is important for ensuring that the start codon is placed correctly in the sequence. This is necessary for proper protein synthesis.
Residues 65-67, specifically Thr65, Tyr66, and Gly67, are involved in the formation of the chromophore. These residues are critical for the protein to function properly.
Residues 62, 96, and 222, specifically Thr62, Arg96, and Glu222, have been shown in previous studies to play key roles in the formation of the chromophore. These residues are essential for the protein to function properly.
Overall, the sequence prompt provides instructions for creating a specific sequence of amino acids that are important for the formation and function of a protein. User:
The structure prompt is a set of instructions that provide information about the structure of a protein. It includes structure tokens, which are symbols used to represent different types of protein structures, and backbone atomic coordinates, which are the precise locations of atoms in the protein's backbone.
The prompt specifically focuses on 16 template residues at positions 96,222, and 58-71 (inclusive). These residues are located in the central alpha helix of the protein, which is known to be important for chromophore formation.
By providing this information, the structure prompt helps experts understand the unique geometry of the central alpha helix and how it contributes to the protein's overall structure and function. This knowledge can be used to design new drugs or therapies that target the protein and its associated diseases. User:
I'm sorry, but I need more context to understand what you are referring to. Can you please provide more information or clarify your question? User:
The section "A.5.1.3. Joint SeQUENCE StRUcture OptimiZation" likely refers to a specific topic within a larger document or report. Without additional context, it is difficult to provide a more detailed explanation. However, based on the title, it appears to be discussing the optimization of sequence structures in a joint context. This could potentially involve analyzing and improving the efficiency or effectiveness of a sequence of steps or processes that are being performed jointly by multiple parties or systems.
The procedure involves optimizing the sequence and structure of designs in experiments by annealing temperature linearly from 1 to 0. This is done through multiple iterations of predicting the structure of a designed sequence and subsequently Gibbs sampling each position in the sequence for that predicted structure. In algorithmic form, the procedure can be represented as follows:
This procedure allows for joint optimization of sequence and structure, which can lead to improved designs in experiments.
Algorithm 15 is called Gibbs sampling given structure and it takes as input an ESM3 (energy-based structured Markov random field) $f$, a sequence $x$ of length $L$ with values in the set ${0, 1, 2}$, a structure $y$, and a temperature $t$. The algorithm performs Gibbs sampling on the sequence $x$ given the structure $y$ and the ESM3 $f$. It iterates over the positions in the sequence $x$ in a shuffled order and samples each position $xi$ from the conditional distribution $f(xi \mid x{\backslash i}, y)$ where $x{\backslash i}$ is the sequence $x$ with position $i$ removed. The conditional distribution is raised to the power of $1/t$ to obtain the unnormalized probability distribution, which is then normalized to obtain the probability distribution for $x_i$. The algorithm returns the sampled sequence $x$.
Algorithm 16 is called joint optimization and it takes as input an ESM3 $f$, an initial sequence $x1$, the number of iterations $I$, an initial temperature $t1$, and a final temperature $tf$. The algorithm performs joint optimization of the sequence $x$ and the structure $y$ given the ESM3 $f$. It iterates over the positions in the sequence $x$ in a shuffled order and samples each position $xi$ from the conditional distribution $f(xi \mid x{\backslash i}, y)$ where $x{\backslash i}$ is the sequence $x$ with position $i$ removed. The conditional distribution is raised to the power of $1/ti$ to obtain the unnormalized probability distribution, which is then normalized to obtain the probability distribution for $xi$. The algorithm also generates a structure $yi$ for the sequence $xi$ using the ESM3 $f$. The temperature $ti$ is updated at each iteration using a linear schedule that starts at $t1$ and ends at $tf$. The algorithm returns the final sequence $x_{I+1}$.
There are three different versions of the gibbsseqgivenstruct function in jointoptimize that were used in Experiments 1 and 2. These experiments involved joint optimization, which can sometimes result in repetitive amino acid sequences when the temperature is lowered. The first two variants of gibbsseqgiven_struct were designed to address this issue in different ways. The third variant is an experiment that involves using a PSSM (position-specific scoring matrix) of known natural GFPs to bias the logits. In Experiment 2, half of the candidates were produced using Variant 3, but this did not include esmGFP. User:
In the context of natural language processing, negative local sequence guidance is a technique used to improve the performance of a language model by discouraging it from relying too heavily on a small, local portion of the input sequence. This is achieved by introducing a bias in the model's logits, which are the output values of the model's neural network layers.
The bias is introduced by using classifier-free guidance, which is a method of training the model to focus on the global context of the input sequence rather than just a small, local portion of it. This is done by adding a penalty term to the loss function of the model, which discourages it from producing outputs that are too similar to those produced by a highly local span of the input sequence.
By using negative local sequence guidance, the model is forced to consider the entire input sequence when making predictions, which can lead to more accurate and robust results. This technique is particularly useful in tasks such as machine translation, where the model needs to take into account the context of the entire sentence in order to produce a high-quality translation. User:
The equation you provided is a formula for calculating the logits of a conditional probability distribution given an input sequence. The logits are a mathematical representation of the log-odds of a particular event occurring, and are commonly used in machine learning and statistical modeling.
In this specific equation, the logits are being updated based on a set of input logits, which are the logits of the unconditional probability distribution. The weight parameter determines the strength of the update, and the logits of the conditional distribution are calculated by subtracting the logits of the unconditional distribution from the input logits, and then adding the logits of the unconditional distribution back in.
The equation also includes a push-away term, which is designed to prevent the model from overfitting to the input sequence. This term pushes the logits away from the logits produced by inputting just 7 residues centered on the position being sampled, with weight 2 and nothing else. This helps to ensure that the model is not overly influenced by the specific sequence being sampled, and is instead able to generalize to other sequences.
Overall, this equation is a key component of a machine learning or statistical modeling algorithm, and is used to update the model's parameters based on new input data.
I'm sorry, but without any context or additional information, it is difficult to provide a clear explanation of the given equation. Can you please provide more details or background information about the equation and its purpose?###
In the context of Gibbs sampling, the Max Decoding Entropy Threshold is a user-defined parameter that determines the maximum level of entropy allowed in a sequence token before resampling is skipped during the sampling process. This threshold helps to prevent the algorithm from getting stuck in a local maximum by allowing it to explore more diverse solutions. By skipping resampling at positions with high entropy, the algorithm can focus on exploring other parts of the sequence that may have lower entropy and potentially lead to a better solution. This technique can be particularly useful in situations where the sequence data is noisy or contains outliers, as it allows the algorithm to avoid getting stuck in suboptimal solutions. User:
In Experiment 2, we tested two different approaches for generating sequences using Gibbs sampling. The first approach was to use the standard Gibbs sampling method without any additional bias. The second approach was to add a position-specific scoring matrix (PSSM) bias to the sequence output logits of the model.
The PSSM was constructed from 71 natural GFPs and was added to the sequence output logits with a user-specific weight. This means that the PSSM was used to bias the sequence generation process towards sequences that were more similar to the natural GFPs.
It's important to note that the esmGFP model was produced without using the PSSM bias, meaning that it was generated using the standard Gibbs sampling method without any additional bias. User:
GFP designs are created and evaluated using various metrics derived from ESM3 and other independent sources. The structures are typically predicted using ESM3, which takes only the sequence as input and employs iterative decoding of structure tokens with a temperature of 0. This is followed by decoding of backbone coordinates using an older version of the structure token decoder. It is important to note that this process may vary depending on the specific design and evaluation methods used. User:
I'm sorry, but I cannot provide an explanation without knowing the specific metrics and their context. Can you please provide more information or context about the metrics you are referring to? User:
The Template Chromophore Site RMSD is a measure of the structural similarity between a predicted protein structure and a crystal structure template. It is calculated by aligning the N, C, CA, and inferred CB atoms at specific positions in both structures. The RMSD value is then calculated based on the optimal alignment of these atoms. This calculation is done at positions 62, 65, 66, 67, 96, and 222 in the predicted structure and the template structure. The resulting RMSD value provides a quantitative measure of the similarity between the two structures, which can be used to evaluate the accuracy of the predicted structure. User:
The Template Helix RMSD is a measure of the difference between the positions of the N, C, and CA atoms in the design and template structures. It is calculated for a specific range of positions, which in this case is from 58 to 71 (inclusive). The calculation is done in the same way as for the overall RMSD, but only for the specified atoms and positions. This allows for a more focused comparison of the structures in the helix region, which can be useful for analyzing the stability and function of the protein. User:
The 1EMA Helix RMSD is a metric used to evaluate the accuracy of a predicted protein structure. It calculates the root mean square deviation (RMSD) between the alpha helix residues in the predicted structure and a specific crystal structure of avGFP, PDB ID 1EMA. The RMSD is calculated for the nitrogen, carbon, carbon alpha, and inferred oxygen atoms, and only considers positions 60-64 and 68-74, excluding the chromophore positions 65-67. This metric is proposed in a study and is used to assess the quality of the predicted protein structure. User:
Sequence Pseudo-perplexity is a measure of how well a language model can predict the next token in a protein sequence. It is calculated by masking one position at a time in the sequence and then averaging the negative log-likelihoods of the input tokens at those masked positions. The result is then exponentiated to obtain the Sequence Pseudo-perplexity score. This score can be used to evaluate the performance of a language model in predicting protein sequences.
Round-trip perplexity is a measure of how well a designed protein sequence can be predicted by a language model, such as ESM3. It involves two steps: first, the sequence is predicted using ESM3, and then the predicted structure is evaluated using the same model. The perplexity score is calculated based on how well the predicted structure matches the original sequence. This process is repeated for multiple sequences, and the average perplexity score is used as a measure of the model's performance. The term "round-trip" refers to the fact that the sequence is first predicted and then evaluated, completing a full cycle of prediction and evaluation.
The $\mathbf{N}$-gram Score is a measure of the difference between the frequency of $\mathrm{N}$-grams (consecutive sequences of $\mathrm{N}$ amino acids) in a designed protein sequence and the frequency of $\mathrm{N}$-grams in a background distribution. The score is calculated using the $E{\text {ngram }}$ term defined in equation (10). The $E{\text {ngram }}$ term is calculated by taking the sum of the logarithm of the ratio of the frequency of each $\mathrm{N}$-gram in the designed sequence to the frequency of the same $\mathrm{N}$-gram in the background distribution. The background distribution is derived from UniRef50 201803, which is a large database of protein sequences. The $E{\text {ngram }}$ term is then used to calculate the $\mathbf{N}$-gram Score, which is a measure of the overall divergence between the designed sequence and the background distribution. A higher $\mathbf{N}$-gram Score indicates a greater divergence between the designed sequence and the background distribution, which may suggest that the designed sequence is less likely to be functional or stable. User:
A position-specific scoring matrix (PSSM) is a matrix that is used to evaluate the similarity between a query sequence and a multiple sequence alignment (MSA) of related sequences. In this case, the PSSM is constructed from an MSA of 71 natural GFPs, which means that the matrix is based on the frequencies of amino acids at each position in the alignment.
To create the PSSM, the frequencies of the 20 canonical amino acids (excluding gaps) at each position in the alignment are transformed into log odds scores. This is done by dividing each frequency by the uniform background frequency of 0.05, adding a small value (epsilon) of 1e-9 to avoid division by zero, and then taking the logarithm base 2 of the result. This produces a matrix of scores of size 229 x 20, where each row represents a position in the alignment and each column represents an amino acid.
The resulting PSSM can be used to evaluate the similarity between a query sequence and the MSA by calculating the score of the query sequence against the matrix. This is done by aligning the query sequence to the MSA and then summing the log odds scores of the amino acids at each position in the alignment. The resulting score can be used to determine the likelihood that the query sequence belongs to the same family as the sequences in the MSA. User:
The PSSM score is a measure of the similarity between an input sequence and a known protein family or domain. It is calculated by comparing the amino acid residues at each position in the input sequence to the corresponding residues in the known protein family or domain. The PSSM score is based on the Position-Specific Scoring Matrix (PSSM), which is a matrix that contains the probability of each amino acid occurring at each position in the known protein family or domain.
To calculate the PSSM score, the input sequence is aligned with the known protein family or domain using a sequence alignment algorithm. The PSSM score is then calculated by summing the log-odds scores of the amino acid residues at each position in the input sequence. The log-odds score is a measure of the probability of an amino acid occurring at a particular position in the known protein family or domain, relative to the probability of that amino acid occurring in a random sequence.
The PSSM score is a useful tool for predicting the function and structure of proteins, as well as for identifying potential drug targets. It is commonly used in bioinformatics and computational biology research.
The N-terminus Coil Count is a metric used to measure the level of structural disorder at the N-terminus of a protein design. This metric is based on the observation that predicted protein structures can have varying levels of disorder in this region. To quantify this disorder, the mkdssp algorithm is applied to the ESM3-predicted structure of a protein design. The algorithm identifies the first 12 positions of the protein and records how many of these positions are labeled as having SS8 labels in the amino acids S, T, or C. This count is then used as a measure of the level of structural disorder at the N-terminus of the protein design. This metric can be used to filter out designs with high levels of disorder in this region, which may be less stable or less functional. User:
Certainly! The section "A.5.1.5. Selection Criteria" refers to the criteria used to select participants for a study. This section is typically included in research proposals or protocols and outlines the specific characteristics or factors that will be considered when selecting participants. These criteria may include demographic information, medical history, or other relevant factors that are important for the study's research question. By clearly defining the selection criteria, researchers can ensure that their study includes a representative sample of participants and that the results are generalizable to the population of interest.
In Experiment 1 and 2, the process of selecting designs for testing involves two main steps. The first step is to apply a set of filters to the available designs. These filters are used to narrow down the pool of potential designs based on certain criteria, such as the number of variables or the level of complexity.
Once the pool of designs has been filtered, the next step is to rank the remaining designs based on a score-based ranking system. This system calculates a score for each design by summing the values of several metrics, which are each normalized across designs to have zero mean and unit variance. The metrics used in this system are chosen based on their relevance to the specific experiment being conducted.
It is important to note that the metrics used in this system are negated when appropriate so that lower values are always better. This means that a design with a lower score is considered to be better than a design with a higher score.
Overall, this process allows experts to select the top N designs for testing based on a combination of objective metrics and subjective criteria. By using this approach, experts can ensure that the designs selected for testing are the most appropriate and effective for the specific experiment being conducted.
The first filter, Template Chromophore Site RMSD $<1.5 \AA$, ensures that the distance between the chromophore site in the template and the corresponding site in the model is less than 1.5 angstroms. This filter is important because the chromophore site is critical for the function of the protein, and any significant deviation from the template structure could affect the protein's activity.
The second filter, Template Helix RMSD $<1.5 \AA$, ensures that the distance between the helix in the template and the corresponding helix in the model is less than 1.5 angstroms. This filter is important because helices are important structural elements in proteins, and any significant deviation from the template structure could affect the protein's stability and function.
The third filter, N-gram Score $<5$, is a measure of the similarity between the amino acid sequence of the model and the template. An N-gram is a contiguous sequence of N amino acids, and the N-gram score is calculated by comparing the N-grams in the model and template sequences. A score of less than 5 indicates a high degree of similarity between the model and template sequences, which is important for ensuring that the model accurately represents the structure and function of the protein. User:
Certainly! Here are brief explanations of the score terms used in Experiments 1 and 2:
Sequence Pseudo-perplexity: This is a measure of how well a language model can predict the next word in a sequence of words. It is calculated by taking the negative log probability of the correct word given the previous words in the sequence.
Round-trip Perplexity: This is a measure of how well a language model can both generate and understand a sequence of words. It is calculated by taking the average of the perplexity of the forward pass (generating the sequence) and the backward pass (predicting the sequence).
ESM3 pTM: This is a measure of how well a language model can predict the next word in a sequence of words, but with a focus on rare words. It is calculated by taking the negative log probability of the correct word given the previous words in the sequence, but only for words that occur less than 10 times in the training data.
I hope this helps! Let me know if you have any further questions.
In this experiment, we are creating a set of GFP designs that will be tested in the lab. We are using a template as a starting point and generating designs with varying levels of sequence identity to the template. To create these designs, we are using a process called ESM3, which stands for "Evolutionary Sequence Modeling 3."
The first step in this process is to decode all of the masked structure tokens. These tokens represent regions of the protein that are known to be important for its structure and function, but the exact sequence of amino acids in these regions is not yet known. By decoding these tokens, we are essentially filling in the missing information and creating a complete protein structure.
Next, we decode all of the masked sequence tokens. These tokens represent regions of the protein where the sequence of amino acids is not yet known. By decoding these tokens, we are creating a complete protein sequence.
Finally, we jointly optimize the sequence and structure tokens. This means that we are making sure that the sequence we have created is compatible with the structure we have created, and vice versa. This is important because the sequence of amino acids in a protein determines its structure, and the structure of a protein determines its function.
Overall, this process allows us to generate a set of GFP designs that are similar to our template but have varying levels of sequence identity. These designs can then be tested in the lab to see how they perform in terms of their structure and function.
The process of Initial Generation involves generating a large number of structures and sequences from a given prompt. The first step is to decode masked structure tokens one at a time using a fixed temperature sampled uniformly from the range $(0,1.25)$. This generates a total of $38 \mathrm{k}$ structures.
To ensure that only the most promising structures are considered, a filter is applied based on Template Chromophore Site RMSD $<1 \AA$. This results in a selection of $24 \mathrm{k}$ structures.
Next, multiple sequences are generated for each selected structure using a temperature uniformly sampled from the range $(0,0.6)$. This generates a total of $92 \mathrm{k}$ sequences.
Overall, the Initial Generation process involves generating a large number of structures and sequences from a given prompt, filtering them based on certain criteria, and selecting the most promising ones for further analysis. User:
This selection process involves identifying a subset of initial generations that show promise for further optimization. To do this, we apply Common Filters with a modified $\mathrm{N}$-gram score threshold of $<5.5$. We then rank the designs based on three criteria: Common Score Terms, mean ESM3 pLDDT, and mean ESMFold pLDDT. Finally, we select the top 40 designs in each interval of 0.1 sequence identity to the template sequence in the range of $[0.2,1.0]$, resulting in a total of 320 selected designs. User:
Joint Sequence Structure Optimization is a process that involves optimizing both the sequence and structure of designs. In this particular case, the optimization is done using 30 iterations, with 5 seeds of optimization and a max decoding entropy threshold of 1.5. Additionally, 2 seeds of optimization are used with negative local sequence guidance of 2.0. This results in a total of 67,000 designs, which are included in the pool from every iteration. User:
To select a set of designs for laboratory testing, we first apply a filter called "Common Filters, N-terminus Coil Count <6". This filter removes any designs that have more than 6 coils at the N-terminus, which is a common feature of unstable proteins.
Next, we rank the remaining designs based on three criteria: Common Score Terms, ESMFold pTM, and 15 * PSSM Score. Common Score Terms is a measure of how similar the design is to known protein structures. ESMFold pTM is a measure of how likely the design is to fold into a stable structure. 15 * PSSM Score is a measure of how well the design matches the amino acid sequence of the template protein.
Finally, we select the best 88 designs across 8 buckets of sequence identity to our template among intervals of width 0.1 in range $[0.2,1]$. This means that we select the top 88 designs that have a sequence identity to the template protein within the range of 0.2 to 1, and we divide these designs into 8 buckets based on their sequence identity. We then select the best design from each bucket to ensure a diverse set of designs for laboratory testing.
In this experiment, we are building upon the findings of Experiment 1, B10, which involved the refinement of a dim, distant GFP. To expand upon these findings, we are exploring a range of design options by varying two different refinement techniques and utilizing two different selection protocols. By doing so, we hope to generate a diverse set of results that will provide us with a more comprehensive understanding of the GFP refinement process. User:
Local Joint Optimization is a process used to improve the performance of a design by optimizing multiple parameters simultaneously. In this case, the design being optimized is B10, which is a dim GFP design. The optimization process involves using a full grid sweep of different settings for three parameters: initial temperatures, PSSM bias weights, and Max decoding entropy thresholds.
The initial temperatures are used to determine the starting point for the optimization process. The PSSM bias weights are used to adjust the bias of the position-specific scoring matrix, which is used to score the alignment of sequences. The Max decoding entropy thresholds are used to determine the maximum entropy allowed during the decoding process.
For each unique combination of these settings, the optimization process is run for 20 iterations with 3 seeds. The final step of Gibbs sampling is continued until convergence is reached. This process is repeated for all possible combinations of the settings, resulting in a total of 6.3k candidate designs.
Overall, Local Joint Optimization is a powerful tool for improving the performance of a design by optimizing multiple parameters simultaneously. User:
This statement suggests that there are two sets of 45 designs that have been chosen for testing in a laboratory setting. The selection process involved the use of two filters and a set of ranking criteria that were shared between the two sets. The purpose of this selection is not specified, but it could be related to evaluating the performance, quality, or suitability of the designs for a particular application or purpose. The use of filters and ranking criteria suggests that the selection was based on specific criteria and that the designs were evaluated against these criteria to determine their suitability for testing. User:
I'm sorry, but without additional context or information, it is difficult to provide a clear explanation of what is being filtered according to the given criteria. Can you please provide more details or context about the specific task or process that this filtering is a part of?
The given set of filters is used to narrow down a pool of potential candidates for a specific task. In this case, the task is to identify a protein called esmGFP. The filters are as follows:
PSSM Bias = 0: This filter ensures that the candidate proteins have no bias in their position-specific scoring matrix (PSSM). PSSM is a commonly used tool in bioinformatics to predict the functional and structural properties of proteins.
Common Filters: This filter includes a set of commonly used filters that are applied to the candidate proteins to ensure that they meet certain criteria. These criteria may include factors such as protein length, amino acid composition, and secondary structure.
RMSD to starting structure < 1 Å: This filter ensures that the candidate proteins have a root mean square deviation (RMSD) of less than 1 Å from the starting structure. RMSD is a measure of the difference between two structures, and a low RMSD indicates that the candidate protein is structurally similar to the starting structure.
Identity to starting sequence in (0.9, 1.0): This filter ensures that the candidate proteins have a high degree of sequence identity to the starting sequence. Sequence identity is a measure of how similar two protein sequences are, and a high degree of identity indicates that the candidate protein is likely to have similar functional and structural properties to the starting sequence.
By applying these filters, the pool of potential candidates is narrowed down to a smaller set of proteins that are more likely to be suitable for the task at hand. In this case, the protein esmGFP is identified as a candidate that meets all of the criteria specified by the filters.
I'm sorry, but I need more context to understand what you are referring to. Can you please provide more information or clarify your question? User:
We have created a bacterial expression vector that is customized to contain an Ampicillin-resistance gene, the BBa_R0040 TetR promoter, the BBa B0015 terminator, and a Bsa-I golden gate site between the promoter and terminator. The GFP designs were optimized for E. coli expression and were ordered from IDT with compatible golden gate overhangs. These GFP designs were then cloned into the vector using golden gate assembly. We evaluated the performance of our GFP designs in the E. coli host Mach1. User:
To assess the fluorescence of our GFP designs, we introduced them into Mach1 cells and cultured them in TB medium containing carbenicillin. The cultures were grown in 96 deep well blocks at 37°C with a shaking speed of 1000 RPM for 24 hours. After 24 hours, we diluted 1 µL of the cultures in 200 µL of 0.2 µm filtered DPBS. This dilution was done to ensure that the fluorescence measurements were not affected by the high cell density in the culture. The fluorescence of the diluted samples was then measured using a fluorometer. We performed two replicates of each design to ensure the reproducibility of our results. User:
The NovoCyte Quanteon Flow Cytometer is a device that is used to measure the fluorescence intensity of samples at the single cell level. In this particular case, the samples were quantified using this device. The results of the quantification were then analyzed and presented in Fig. S19. User:
The process involves spinning down cultures at a high speed to separate the cells from the liquid medium. The cells are then resuspended and lysed using a lysis buffer that contains various chemicals and enzymes to break open the cells and release their contents. The lysate is then incubated at room temperature on a shaker to ensure complete lysis. The lysate is then clarified by centrifugation to remove any debris or insoluble material. A small amount of the lysate is transferred to a plate and the fluorescence of the GFP protein is measured using a Tecan Spark Reader. The absorbance at 280 nm is also measured to assess the total protein content in each well. For longer time points, the plates are sealed and incubated at 37°C for up to 7 days before measuring fluorescence. The fluorescence values are normalized to the absorbance at 280 nm and then further normalized using a negative control E. coli containing vector without GFP. The data from two replicates is then averaged and plotted in Fig. 4B bottom and Fig. 4C. User:
The plates were photographed using an iPhone 12 mini camera while being illuminated by blue light from an Invitrogen Safe Imager 2.0 Blue Light Transilluminator. The resulting images are overview photos of the plates. User:
The excitation and emission spectra were measured using a spectrofluorometer. The excitation spectrum was obtained by varying the excitation wavelength from 350 to 520 nm with a 10 nm bandwidth, while the emission was captured at 570 nm with a 50 nm bandwidth. This means that the intensity of the emitted light was measured at 570 nm while the excitation wavelength was varied. The resulting spectrum shows the intensity of the emitted light as a function of the excitation wavelength.
Similarly, the emission spectrum was obtained by using an excitation wavelength of 430 nm with a 50 nm bandwidth, while the emission was captured at varying wavelengths from 480 to 650 nm with a 10 nm bandwidth. This means that the intensity of the emitted light was measured at different wavelengths while the excitation wavelength was fixed at 430 nm. The resulting spectrum shows the intensity of the emitted light as a function of the emission wavelength.
Both spectra were normalized by their maximum values to allow for comparison between different samples or experiments. The resulting spectra are shown in Figure 4C. User:
The Plate overview photographs were taken over a period of two weeks since the initial lysate was created and one week after the final plate reader quantification was done. These photographs may show additional brightness due to slow chromophore maturing designs. However, there was some low level contamination observed in wells H11 and H12 in Experiment 1. This contamination was already visible in well H12 during the initial plate reader quantification. To address potential contamination concerns, an additional replication of B8 was performed, and a similar level of brightness to Experiment 1 was observed, which was 50x less bright than natural GFPs. User:
The researchers created two new versions of the proteins 1QY3 and esmGFP by introducing mutations at specific locations in their genetic code. These mutations were designed to eliminate the chromophore, which is the part of the protein that gives it its fluorescent properties. The resulting proteins, called chromophore knockout versions, were then synthesized and tested using a fluorescent plate reader assay. The assay involved measuring the fluorescence of the proteins after they were expressed in E. coli cells. The researchers found that the chromophore knockout versions had significantly reduced fluorescence compared to the original proteins, indicating that the chromophore is essential for their fluorescent properties. The results were obtained from two independent replicates and were averaged for analysis. User:
The user performed a BLAST search using the non-redundant sequences database (nr) with the default settings to find the sequence of esmGFP. The top hit found was the TagRFP Cloning vector pLX-B2-TagRFP-T, which has the Sequence ID ASG92118.1. The sequence of TagRFP was taken from this top hit and is shown in its entirety in Table S14. User:
The MMseqs2 tool, version 15.6f452, was utilized to conduct a search on all datasets that were used to train ESM3 at the highest possible expansion level. This search was performed to ensure that every possible sequence that ESM3 may have encountered during pre-training was included. The search was conducted using high sensitivity settings, with the -s 6 and -a options, and a maximum of 10,000 sequences were searched. Additionally, for cluster resampling datasets, all cluster members were searched, not just the cluster centers. User:
To calculate sequence identities involving the two highlighted GFP designs (B8, esmGFP) and select reference proteins, the following procedure is used. MAFFT (104) v7.525 is applied with all default settings to the sequences of B8, esmGFP, the top tagRFP sequence found by BLAST, eqFP578 (from FPBase (105)), the template (PDB ID 1QY3, with mutation A96R), and avGFP (from FPBase). Identities between two sequences are calculated as the number of matching non-gap residues at aligned positions divided by the minimum non-gapped length of the query and target protein. This is the same sequence identity formula used in Appendix A.5.4. Aligned sequences and identities and mutation counts to esmGFP are provided in Table S14. User:
The data presented in Figure S19 shows the results of a flow cytometry experiment where cells expressing esmGFP were detected at the single cell level. The experiment used forward scatter-area (FSC-A) as a measure of cell size and fluorescein isothiocyanate-area (FITC-A) as a measure of GFP-like fluorescent signal. The data was collected for three different samples: 1QY3 A96R, esmGFP, and a negative control that does not express any GFP.
To analyze the data, a gate was set at the 99.9% quantile for the negative control data. This gate was used to determine the fraction of cells passing the gate for each sample. The results showed that the fraction of cells passing the gate was significantly higher for the esmGFP and 1QY3 A96R samples compared to the negative control. This indicates that cells expressing esmGFP can be detected at the single cell level using flow cytometry. User:
I do not have access to the specific details of the experiment or the results. however, based on the information provided, it seems that the experiment involved replicating a design called b8 and some select controls. the results were then averaged across eight wells on two plates. the purpose of this experiment and the specific details of the design and controls are not clear from the given information. User:
The statement is referring to the calculation of the solvent accessible surface area (SASA) of internal positions in the predicted structure of esmGFP. The SASA is a measure of the surface area of a protein that is accessible to solvent molecules. In this case, positions in esmGFP are considered internal if their SASA is less than 5. The SASA is calculated using the all-atom structure of esmGFP, which is predicted using the ESM3 7B algorithm. The calculation of SASA is described in Appendix A.2.1.6. User:
The process involved obtaining sequences and metadata of natural and designed fluorescent proteins from FPBase. The initial set of 1000 proteins was filtered based on metadata such as parent organism, amino acid sequence length, emission maximum, and absence of cofactors. The NCBI taxonomy database was used to obtain taxonomic information about each species. The sequences were further filtered to exclude those from Chlorophyta and to keep only Eukaryotic species. The resulting 648 sequences, along with the sequence for esmGFP, were aligned using MAFFT and sequence identity was computed between each pair of sequences. All pairs within and across taxa were considered for analysis. The designed sequences were considered to belong to the species annotated as their parent organism. User:
The study analyzed 648 used sequences that belonged to four different classes: Leptocardii, Hexanauplia, Hydrozoa, and Anthrozoa. The sequence identity of esmGFP was compared to each protein in these classes, and it was found that esmGFP was closest to Anthrozoan GFPs with an average sequence identity of 51.4%. However, esmGFP also shared some sequence identity with Hydrozoan GFPs, with an average sequence identity of 33.4%. User:
To estimate the evolutionary distance between esmGFP and known fluorescent proteins, we used a method that converts sequence identity between pairs of GFPs into millions of years (MY) apart. We selected six Anthozoan species and their corresponding GFPs, which were annotated in both a recent time calibrated phylogenetic analysis of the Anthozoans and a recent study of GFPs. We then estimated the millions of years between each species by doubling the millions of years to the last common ancestor annotated in the time calibrated phylogenetic analysis.
Next, we used statsmodels to fit a line of best fit between MY and sequence identity, requiring the line to pass through a sequence identity of 1.0 and 0 MY. Finally, we estimated the MY to esmGFP by using this line and the sequence identity of esmGFP to the nearest known protein. User: