dataesr/scientific-paragraphs-categorization huggingface dataset:
Large-scale stability analysis to characterize unique designs, identify stabilizing mutations, and evaluate design methods The unique scale of cDNA display proteolysis creates new opportunities for improving protein design. Here, we examine three applications of our method and massive dataset: (1) characterizing the stability determinants of rare, highly polar proteins, (2) identifying stabilizing mutations, and (3) benchmarking the protein design tool PROSS  (80). The hydrophobic effect is considered the dominant force in protein folding  (1) , and measuring stability for thousands of our previously-designed domains (29) by cDNA display proteolysis revealed a general trend of increasing stability with increasing hydrophobicity (Fig. 7A ). However, increased hydrophobicity can promote protein aggregation, non-specific interactions, and low expression yield.
To study the properties of high stability, low hydrophobicity proteins, we examined hundreds of designed proteins by deep mutational scanning across a wide range of hydrophobicity and stability. Although the mutational scanning patterns for low hydrophobicity proteins were not obviously different from other designs, we identified several designs that possessed exceptionally strong polar interactions (large dots in Fig. 7A ). In Fig. 7B , we highlight stabilizing polar networks and a cation-π interaction in these unusual designs (see Fig. S17  for full mutational scanning results). The average ∆∆G for substitutions at these polar sites ranges from -0. 20 to -1. 33 kcal/mol, corresponding to the top 63 to 1. 5%ile for all 3,694 polar sites in 145 designs. Our unusually massive dataset made it possible to identify these rare highly stabilizing interactions.
Notably, the second hydrogen bond network in EHEE_rd2_0152 is also found in two other more hydrophobic designs. However, the network is less sensitive to substitution in those designs, highlighting how the overall protein environment mediates the effects of substitutions even on the protein surface (Fig. S18 ). We next examined how our approach could be used to identify stabilizing mutations. Predicting and designing stabilizing mutations is a major goal of protein modeling, but prediction accuracy remains low  (22). In part, this is because stabilizing mutants are rare in current databases  (14, 15)  (outside of reverting a destabilizing mutant), limiting the data available for improving modeling. In contrast, our large-scale approach revealed 2,600 stabilizing mutations, defined as mutations that increase folding stability by at least 1 kcal/mol.
The overall fraction of stabilizing mutations was 0. 06% to 0. 6% for different protein types (Fig. 7C ). Stabilizing mutations were enriched at functional sites (23% of the stabilizing mutations from 7. 5% of sites classified as functional), but these were still a small fraction of the total. Notably, our set includes 112 examples of stabilizing insertions and deletions which are nearly absent from current databases. In Fig. 7D , we show three examples of different classes of stabilizing mutations found in our dataset with effects ranging from +1. 2 to +3. 1 kcal/mol (Fig. S19 ). Finally, we applied our method to evaluate PROSS (80), an automated method for enhancing folding stability within sequence constraints inferred from a multiple sequence alignment. We tested 1,156 PROSS designs for 266 protein domains (a 10-100x increase over previous benchmarking study  (81) ).
By comprehensively measuring single mutants across nearly all small structures in the Protein Data Bank, we quantified several global trends: trends in amino acid fitness at different sites, trends in the effects of single and double mutants, and trends in how stability influences sequence evolution. Along with these global trends, our large-scale analysis also uncovered hundreds of exceptional cases that would be challenging to identify by smaller-scale methods. These include mutations with extreme effects, sites with unusual stability landscapes, and pair interactions with unusually strong thermodynamic couplings. The strong thermodynamic couplings we identified in the J domain of human HSJ1a (Fig. 4G ) -missing in the deposited NMR structure -highlight how large-scale stability assays can complement other methods for revealing structural details in solution.
Due to the scale and efficiency of cDNA display proteolysis, the main limit to measuring stability for millions of small domains is the cost of DNA synthesis  (86) (87) (88)  and sequencing (89, 90) -both of which are rapidly decreasing  (91) (92) (93) (94). With the flexibility of DNA oligo synthesis, cDNA display proteolysis can assay massive mutational libraries (as shown here) as well as massive libraries of unrelated sequences and structures, which will add essential diversity in training datasets. The size and diversity of protein sequence space creates enormous challenges for biology and protein design. The cDNA display proteolysis method offers a powerful approach to map folding stability across this space on an unprecedented scale.
Hallucination design method We used a TrRosetta hallucination protocol described previously in  (42, 67)  and available at https://github. com/gjoni/trDesign/tree/master/02-GD to unconditionally generate protein backbones and sequences with lengths ranging from 46 to 69 amino acids by maximizing the Kullback-Leibler divergence between the predicted and background distance/angle distributions. Predicted distograms and anglegrams were used to obtain 3D structures of these models as described in the TrRosetta paper  (98). We selected the best designs according to the predicted distogram and 3D structure match. DNA and mRNA preparation for cDNA display proteolysis method Oligo libraries were amplified by PCR using KOD PCR Master Mix (TOYOBO) to add T7 promoter, PA tag to an N-terminal, and His tag to an C-terminal of the proteins.
Processing of next-generation sequencing data Each library in a sequencing run was identified via a unique 6 or 8 bp barcode. Following sequencing, reads were paired using the PEAR program (100) then the adapter sequences were moved by Cutadapt  (101). Reads were considered counts for a sequence if the read perfectly matched the ordered sequences at the nucleotide level. Overall strategy for inferring K 50 and ΔG from sequencing data We used Bayesian inference to infer K 50 and ΔG values for all sequences in our library. This analysis uses two main models. The first model is called the "K 50 model" and infers each sequence's K 50 values based on the sequencing count data. The second model is called the "unfolded state model" and predicts each sequence's unfolded state K 50 value (K 50,U ) based on its sequence.
Both models are implemented in Python 3. 9 using the Numpyro package (102) version 0. 80. Here, we first describe the structure of each model, and then we describe the practical process of fitting the parameters of each model. Our scripts to reproduce the complete fitting process are provided in the Supplementary Materials. Structure of the K 50 model to infer K 50 values from next-generation sequencing data We modeled our selection results using the single turnover kinetics model described in Fig. 1B. We chose this model because we expect that the total concentration of protein-cDNA complex is low compared to the amount of added enzyme and because the model captures the saturation behavior observed by qPCR at high enzyme concentration (Fig. S1 ). Instead of attempting to capture the microscopic complexity of our system (millions of different substrates and potential inhibitors), the purpose of the model is to treat each substrate in a consistent, simplified manner and infer reasonable parameters.
1  eqs. 2 and 3, after re-normalizing the total surviving fraction of all sequences to 1. Finally, we assume that our deep sequencing counts result from n sel independent selections from the cDNA E distribution, where n sel is the number of sequencing reads that exactly matched our specified DNA sequences. We apply the K 50 model in two different ways based on whether K 50 values for trypsin and chymotrypsin are Independent or Coupled. The "Independent" procedure is used in Steps 1, 2 and 5 in the section "Procedure for fitting all data". In the independent procedure, the inputs to the model are the sequencing counts data from experiments with one protease, the enzyme concentrations, the reaction time, and the k max constant. We fit the model by sampling two parameters per sequence from normal prior distributions: (1) K 50 , and (2) the initial fraction of each sequence in the cDNA 0 distribution.
Full results from both the independent and coupled fitting procedure are provided in K50_dG_Dataset1_Dataset2. csv and K50_Dataset3. csv. For our stability parameters (protease-specific K 50 in the independent procedure and ΔG in the coupled procedure) we report the median of the posterior distribution as well as the upper and lower limits of the 95% confidence interval (the 2. 5%ile and 97. 5%ile values of the posterior distribution). We also used the protease-specific K 50 values from the independent procedure to compute protease-specific ΔG values. We do this using the same K 50,F and K 50,U values used in the coupled procedure according to Fig. 1  Eqs. 5 and 7. These protease-specific ΔG estimates are also reported in K50_dG_Dataset1_Dataset2. csv and are only used to examine the consistency between different proteases (e.
We used the Independent K 50 model for this step. The input data were the sequencing counts data from two replicates (i. e. 12 protease concentrations x 2 replicates = 24 data points per sequence), the reaction time (t), the k max constant, and the effective protease concentrations obtained in Step 1. We sampled the initial sequence distribution cDNA 0 (a separate cDNA 0 for each replicate) and K 50 for all sequences included in Library 2. Normal priors (with lower/upper boundaries for some parameters) covering the range of experimentally relevant values were used for the model parameters. Sampling was performed using the No U-Turn Sampler (NUTS) in Numpyro with 100 steps of equilibration and 50 steps of production. Step 3: Construction of unfolded state model: We trained the unfolded state model for predicting K 50,U using K 50 values obtained in Step 2.
These scrambled sequences required no additional padding. After measuring K 50 for all scrambles, we only used sequences with a 95% confidence interval smaller than 0. 5 log 10 molar units for model training for model fitting  (64, 238  sequences in total, see Fig. S3 ). In addition to the exact experimental sequences, we also augmented the training dataset with dummy sequences where GS linkers were replaced by the blank 'X' amino acid. The inputs for the model are amino acid sequences created as described above, and their observed K 50 for trypsin and chymotrypsin obtained in Step 2. The parameters of the model are fit by assuming that all observed K 50 values should agree (with small, normally distributed errors) with the theoretical K 50 values. In this model, we sampled the 21 x 9 =189 elements of the PSSM, the site bias, the maxK 50,U , the scale, and the 10 threshold values.
These parameters determine K 50,U for each sequence by Eqs. 9 and 10. In addition to these parameters, we also sample the ΔG values for each scrambled sequence during fitting. Normal priors (with lower/upper boundaries for some parameters) covering the range of experimentally relevant values were used for the model parameters. Using NUTS model, we sampled the parameters described above, then reported the median of the 100 posteriors after removing the initial 400 steps. In Step 4, we used these final model parameters to calculate K 50,U for all sequences in our experiments without considering any uncertainty from the model posterior distribution. Step 4: Prediction of unfolded K 50 values (K 50,U ) across the full dataset: Using the final model parameters obtained in Step 3, we predicted K 50,U values for each amino acid sequence in the libraries without considering any uncertainty.
S5 ), and the K 50,U values predicted by the unfolded model in Step 4. Using the inputs, we sampled ΔG shared between trypsin and chymotrypsin, and initial sequence distribution cDNA 0 for each protease for each replicate (although our experiments utilized the same batch of the cDNA-protein complex for two replicates). Normal priors (with lower/upper boundaries for some parameters) covering the range of experimentally relevant values were used for the model parameters. Using NUTS in Numpyro module, we sampled the posteriors of shared ΔG along with other parameters, then obtained the median of the 50 posterior samples after removing the initial 100 steps. Full results from both the independent and coupled fitting procedure are provided in K50_dG_Dataset1_Dataset2. csv and K50_Dataset3. csv.
Normal priors (with lower/upper boundaries for some parameters) covering the range of experimentally relevant values were used for the model parameters. Using NUTS in Numpyro module, we sampled the posteriors of K 50 for trypsin and K 50 for chymotrypsin along with other parameters, then obtained the median of the 50 posterior samples after removing the initial 100 steps. Then, we computed protease-specific ΔG values using the protease-specific K 50 values from the Independent model. We do this using the same K 50,F and K 50,U values used in the coupled procedure according to Fig. 1  Eqs. 5 and 7. These protease-specific ΔG estimates are also reported in K50_dG_Dataset1_Dataset2. csv and K50_Dataset3. csv, and are only used to examine the consistency between different proteases (e. g. Fig.
1E  and F  We show all data from Library 3 within the range -2 < ΔG < 5 kcal/mol & log10_K50_trypsin < 1. 75 & log10_K50_chymotrypsin < 2. 25. We then overlaid the wild-type and four mutants of Protein G measured in Library 2. Replicate analysis of K 50 (Fig. 1E ) Instead of sampling K 50 values using 24 samples per protease at one time as described in Step 5 above, we sampled K 50 values using one experiment set (i. e. 12 samples) and obtained K 50 for trypsin replicate 1 and 2, and chymotrypsin replicate 1 and 2. Note that we still used the calibrated protease concentrations to improve consistency between replicates. The replicates were conducted on different days using the same preparation of the protein-cDNA complex. Classification of Datasets #1, #2, and #3 based on the quality of the data (For Fig.
2 ) All mutational scanning data was classified into nine groups (0 through 8) according to the protocol in Fig. S8. We determined that a mutational scan was high quality (suitable for Dataset #2) if there was minimal missing data, minimal low confidence data, an appropriate slope, intercept, and correlation between the trypsin and chymotrypsin samples, sufficient wild-type stability, and the mutational scan did not include an unusual fraction of stabilizing mutations suggesting poor folding. For inclusion in the smaller Dataset #1, we additionally required that the wild-type stability was lower than 4. 5 kcal/mol so that stabilizing mutations could still fall within the assay's dynamic range. These sequences are considered "Group 0"; the remaining sequences in Dataset 2 are considered "Group 1".
Double mutant sequences were included in Datasets 1 and 2 based on whether the original wild-type mutational scan was included in that dataset. All sequences in Dataset 1 and Dataset 2 are included in K50_dG_Dataset1_Dataset2. csv. All sequences in this file have an inferred ΔG estimate value, but only sequences in Dataset 1 have a tabulated ΔΔG estimate. Of course, one can calculate ΔΔG for the remaining sequences in Dataset 2, but these ΔΔG values will be biased toward destabilizing mutations because stabilizing mutations would typically be indistinguishable from the wild-type stability. Note that Datasets 1 and 2 include a small number of sequences with low quality data because these sequences come from mutational scans that are high quality overall. Although these tables include all K 50 , Δ G, and ΔΔG data (for Dataset 1), low quality data have been filtered out and replaced by asymbol in the columns labeled "_ML" (for machine learning).
The remaining groups were defined this way: Group 2: The wild-type protein is too unstable to see sequence-stability relationships. Group 3: Poor expression (low counts in next-generation sequencing) for the assay. Group 4: Very few destabilizing mutations, suggesting aggregation and/or molten globule formation Group 5: The wild-type is too stable to see consistency between trypsin and chymotrypsin Group 6 and 7: Low agreement between trypsin and chymotrypsin due to the absence of aromatic amino acids (i. e. chymotrypsin cleavage sites) or the presence of protease recognition sequences in the linker region. Group 8: Did not fit into groups 2-7, but did not pass the quality metrics for groups 0 and 1. Dataset #3 includes all data combined (Groups 0-8), even the data from Groups 2-8 that were excluded from Datasets 1 and 2.
Although many of the K 50 values from Groups 2-8 likely reflect factors other than folding stability (e. g. aggregation, low expression, etc. ), these data can still be used to train models that directly predict K 50. Again, a small fraction (~4%) of the K 50 values in Dataset #3 are low confidence and have been replaced by asymbol in the "_ML" columns. Principal component analysis (related to Fig. 3 ) We performed principal component analysis to determine the factors influencing stability of different amino acids. To this end, we utilized 15,440 sites in the 337 domains that are classified as G0 in the above. All folding stability data were clipped between from -1 to 5 (kcal/mol) because the folding stability outside the dynamic range is not reliable, and then the average of the stability for 20 amino acids for each site was subtracted from the data.
Using the data, we performed PC analysis using the scikit-learn library implemented in Python 3. Side chain contacts and burial analysis (Fig. 3D  and 6H ) Burial values and contact counts were computed based on AlphaFold models (18) of all sequences using the included script Burial_side_chain_contact_Fig3_Fig6. ipynb based on Bio. PDB  (103)  and BioPython  (104) ). The calculation is based on the Rosetta "sidechain_neighbors" LayerDesign method previously reported  (29). Briefly, to calculate the burial or contacts of residue X, we added up the number of residues in a cone projecting out 9 Å away from the Cβ atom on residue X in the direction of the residue X Cα-Cβ vector. "Burial" (Fig. 6H ) indicates the number of Cα atoms in the cone. Contact counts (Fig. 3D ) each count different atoms inside the cone: "Side chain contact count" (Fig.
We define this standardized score for each site as the "Normalized GEMME score". To build the input multiple sequence alignments, we performed five iterations of the profile HMM homology search tool Jackhmmer  (109, 110)  against the UniRef100 database of non-redundant proteins (111) using the EVcouplings framework  (112). We used the default bitscore threshold of 0. 5 bit per residue. 𝑑[𝑃] 𝑑𝑡 = 𝑘 2 [𝐸 𝑡𝑜𝑡𝑎𝑙 ] 𝐾 50 +[𝐸 𝑡𝑜𝑡𝑎𝑙 ] [𝑆 𝑡𝑜𝑡𝑎𝑙 ] Thus, the observed kinetic rate is (This eq. 3) 𝑘 𝑜𝑏𝑠 = 𝑘 2 [𝐸 𝑡𝑜𝑡𝑎𝑙 ] 𝐾 50 + [𝐸 𝑡𝑜𝑡𝑎𝑙 ] Derivation of eq. 6 and eq. 7 in Fig1B We modeled the cleavage events, where Protease enzymes (E) and folded substrates (F) or unfolded substrates (U) form a FE or UE complex to produce cleaved protein products (P F or P U ). The goal is to get a product formation equation in terms of the total product, initial enzyme and substrate concentrations and kinetic constants.
Acknowledgments: We thank  Epsilon Molecular Engineering (EME) Corp  for providing us with cnvK linker for cDNA display,  Rush University and Genome Research Core at University of Illinois Chicago  for performing next-generation sequencing, and  David Minh ,  Timothy Whitehead ,  Kresten Lindorff-Larsen ,  David M. McCandlish ,  Jack Maguire ,  John Chodera ,  Parisa Hosseinzadeh , and the members of the  Rocklin lab  for discussions and comments on the manuscript.
This can be done either directly (for example, robotic weeding without using chemicals) or indirectly (for example, supporting optimal application of bio-pesticides and bio-fertilizers based on precise but fast measurements). The DSS have the potential to facilitate: (a) farmers' decision-making about adopting agroecology, (b) impact assessment of the applied methods, (c) standardization and regulation of efficient approaches, (d) communication between farmers and other actors, and (e) track market trends. Data-based decision-support mechanisms that support agroecology are largely unavailable at present, although some European initiatives are trying to bridge this gap. For instance, using a database of over 30,000 plant images and botanist Gérard Ducerf 's analysis method  (55) , a mobile artificial intelligence (IA) app has been developed called AGRODIAG (see soildiag on Google store) to provide a soil diagnostics from the analysis of the bio-indicator plants found in a particular spot (Figure  2 ).
Adaptations can concern different features such as visual style, modality, content presentation, service availability, and interaction modes  (Krings et al. , 2020). Creating context-aware AR applications has lot of challenges such as modeling the context of use, defining when and how each content element is displayed according to this context, and dealing with a constantly evolving context of use. Multiple solutions exist for the creation of such applications and to deal with these challenges such as AARCon  (Krings et al. , 2020) , ACARS  (Zhu et al. , 2013)  and the work of Lindlbauer et al. (Lindlbauer et al. , 2019). However, they require the knowledge of a particular framework, the creation of adaptation rules in a dedicated programming language, or the parametrization of a complex scoring system.
A digital twin is a virtual clone that can fully describe a real or potential product  (Grieves and Vickers, 2017). In our case, a digital twin of a smart environment is composed of its geometry and its information about its sensors and actuators. Regarding previous work, our approach has two main advantages. First, our VR authoring tool aims to allow creators to prototype AR content and precisely define in which context of use it will be shown without using any programming language. When editing a set of content elements (3D meshes and widgets, texts, audio etc. ), that we call here an augmentation layer, the user chooses the context it will correspond to by selecting a set of context variables values in a diegetic user interface. Target adaptations can then concern most of the categories cited in  (Krings et al.
, 2020). In Figure  1 , the blue nodes of the tree were pre-defined. This first approach provides developers with a lot of freedom for creating context variables and for defining how they will be set at runtime. It could allow them to develop simple mechanisms, such as connecting some variables values to an existing database, as well as more complex ones such as using artificial intelligence algorithms to deduce other variables values. As an example, for the "Device" variable shown in Figure  1 , we used this API to implement a program that checks at runtime the device physical model that runs the application and then determines accordingly its value between "Smartphone", "Tablet" and "HMD". Secondly, we propose a new dynamic approach where the author can add new boolean context variables in our VR authoring tool and define their range of action in the 3D space without recompiling any piece of code.