dataesr/scientific-paragraphs-categorization huggingface dataset:
Compared with the previous yeast display proteolysis method  18  , cDNA display proteolysis resolves a wider dynamic range of stability and is more reproducible even at a 50-fold larger experimental scale. Large-scale proteolysis data have already had a key role in the development of machine learning methods for protein design and protein biophysics  19, 20. The cDNA display proteolysis method massively expands this capability and has the potential to expand our knowledge of stability to the scale of all known small domains. The dataset of 776,298 absolute folding stabilities is unique in size and character. Current thermodynamic databases contain a skewed assortment of mutations measured under many varied conditions  11. By contrast, this new dataset comprehensively measures all single mutants for 331 natural domains and 148 designed proteins-including single deletions and two insertions at each position-all under identical conditions.
High-throughput data are accurate Our cDNA display proteolysis measurements are highly consistent with published studies using purified protein samples for 1,188 variants of 10 proteins (Fig. 1g  and Supplementary Fig. 1  for more details on GB1  29  ). All Pearson correlations are above 0. 75. Our measurements for these sequences were all performed in libraries of 244,000-900,000 total sequences. Several sets of mutants show systematic offsets (y-intercept values) between literature values and our measurements. We attribute these offsets to temperature differences between our conditions and the published experiments, and the offsets are correlated with temperature (except for NTL9 (Protein Data Bank (PDB) ID: 2HBB); Extended Data Fig. 3 ; see Supplementary Table  1  for all experimental conditions and references).
Note that the structures of these designs have not been validated experimentally. Our 983 wild-type sequences also include 121 'destabilized wild-type backgrounds' designed to resolve the effects of mutants on highly stable domains (Extended Data Fig. 4 ). We collected these data using four giant synthetic DNA oligonucleotide libraries and obtained K 50 values for 2,520,337 sequences; 1,841,285 of these measurements are included here. K 50 values were reproducible across libraries (Extended Data Fig. 5 ). Deep mutational scanning of hundreds of domains revealed several overall patterns. The largest fraction of these domains showed clear, biophysically reasonable sequence-stability relationships that were consistent between independent experiments with trypsin and chymotrypsin. However, other domains were completely unfolded, too stable to resolve or produced inconsistent results between the proteases (Fig.
2b ). To construct a reliable dataset of ΔG and ΔΔG measurements 4 k obs = k max [E] K 50 + [E] (3) = exp(-k obs × t) Survival = cDNA cDNA initial E + S E S E + P (1) ΔG = -RT ln ( ) [U] [F] ( 5 ) K 50 ≈ K d = ([U] + [F]) [E] [UE] + [FE] [S] [E] [ES] [U] + [F] [U]/K 50,U + [F]/K 50,F ≈ = (6) ≈ [U] [F] 1/K 50 -1/K 50,F 1/K 50,U -1/K 50 ( 7 ) (4) F + E F E E + P U + E U E E + P (2) Fig. 1  | cDNA display enables massively parallel measurement of protein folding stability. a, A DNA oligonucleotide (oligo) library is expressed using cell-free cDNA display, producing proteins with an N-terminal 14-amino-acid PA tag and C-terminal covalent linkage to cDNA. After protease challenge, magnetic beads with anti-PA antibodies pull down proteins by their N termini. Sequencing the cDNA that is pulled down with the intact proteins enables quantification of the distribution of intact proteins.
Article from folded domains, we filtered the mutational scans on the basis of the consistency of the trypsin and chymotrypsin ΔG estimates and other criteria designed to remove domains showing cleavage from folded or partially folded states (Supplementary Fig. 2  and Methods ). Many domains that showed evidence of cleavage from folded states had cleavable sites near the termini or in flexible loop regions. Indeed, four of our deep mutational scans use modified wild-type backgrounds in which folded state cleavage sites (identified in earlier mutational scans) had been removed (Extended Data Fig. 6a-c ). However, other domains with long loops appeared fully cooperative, without evidence of cleavage of the folded state (Extended Data Fig. 6d ). Along with susceptibility to cleavage of folded or partially-folded states, other domains taken from the PDB failed in our assay for a variety of reasons, including poor cell-free expression (category G2), structures that were probably stabilized by crystal contacts (PDB ID: 2CUW and 2FGG), missing N-C cyclization incompatible with cDNA display (PDB ID: 1E68 and 2MP8), poor stability at room temperature (PDB ID: 2C0S and 2LGN), and large numbers of cysteine residues (category G9).
Our final quality-filtered datasets are shown in Extended Data Table  1  and Supplementary Fig. 3. Mutational scanning results are shown for seven domains in Fig. 2d, e. Like all mutational scans that passed filtering, these examples show a strong consistency between independent ΔG measurements with trypsin and chymotrypsin (Pearson correlation 0. 94 ± 0. 04 (median ± s. d. ) for 478 domains in dataset 2). The most critical sites (those where mutations are highly destabilizing) are located in the hydrophobic core. However, our data also reveal many other critical interactions, such as a side chain hydrogen bond between S23 and D42 in the U-box domain of human E4B ubiquitin ligase and a cation-π interaction between R10 and W32 in the chromodomain of human chromobox protein homologue 7 (residues have been renumbered based on the exact sequence included in our experiments).
Global trends in amino acid fitness We first sought to define the major sources of variation between protein sites that influence the relative stabilities of all 20 amino acids at that site. To this end, we performed principal component analysis using 325,132 ΔG measurements at 17,093 sites in 365 domains (dataset 3). Each principal component expresses specific properties of a site that determine which amino acids are stabilizing or destabilizing. Based on the loadings of the amino acids onto each principal component (Fig. 3a ), we interpreted the first four components to reflect whether a site is stabilized by hydrophobic amino acids (principal component 1 (PC1); 31% of the total variance explained by this principal component), helix-favouring amino acids (PC2; 15%), aliphatic versus aromatic amino acids (PC3; 12%), and positive versus negative charges (PC4; 7%).
5 kcal mol -1 ; Fig. 4g , middle and right and Supplementary Fig. 4d ). These results reveal a strong third-order coupling: the interaction between two amino acids is mediated by a third amino acid. This strong three-way coupling is noteworthy because the interactions do not appear in the deposited NMR ensemble (PDB ID: 2LGW; Extended Data Fig. 8a, b ). The NMR ensemble for 2LGW positions Y5 (Y3 in our numbering) away from the helix containing R62 and D66, making the interaction network impossible. However, the AlphaFoldpredicted structure shown in Fig. 4g  (the highest confidence model out of five predictions) does include these interactions, which are also seen in other J-domain crystal structures from Caenorhabditis elegans (PDB ID: 2OCH) and Plasmodium falciparum (PDB ID: 6RZY). The strong couplings that we identify support the AlphaFold model and suggest the deposited ensemble is missing conserved interactions that form in HSJ1a, perhaps owing to the specific experimental conditions used.
We defined all sites in the upper left region (where the site is sensitive to mutations yet unimportant for stability, 9% in total) to be 'functional' sites. This classification correctly identifies key binding residues in the chromodomain of HP1 and the SH3 domain of BBC1 (Extended Data Fig. 9b, c , full data in Supplementary Fig. 6 ). Across all 104 domains, Gly, Asp and the bulky amino acids (Trp, Arg and Tyr) were frequently classified as functional (Extended Data Fig. 9d ), and the fraction of functional sites ranged from 0 to approximately 25% (Extended Data Fig. 9e ). The domains with the highest fraction of functional sites were the nucleic acid binding domains Sso7d (PDB ID: 1JIC) and ribosomal protein S19 (PDB ID: 1QKH; Extended Data Fig. 9f ). Our original criteria classify nearly all buried sites as non-functional; the mutational sensitivity at these sites is attributed to the destabilizing effects of hydrophilic substitutions.
10a ). In Extended Data Fig. 10b , we highlight stabilizing polar networks and a cation-π interaction in these unusual designs (see Supplementary Fig. 7  for full mutational scanning results). The average ΔΔG for substitutions at these polar sites ranged from -0. 20 to -1. 33 kcal mol -1 , corresponding to the top 63 to 1. 5 percentile for all 3,694 polar sites in 145 designs. Our 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 (Extended Data Fig. 10c-e ).
We next examined how our approach could identify stabilizing mutations. Predicting and designing stabilizing mutations is a major goal of protein modelling, but prediction accuracy remains low  14. In part, this is because stabilizing mutants are rare in current databases  11  (outside of reverting a destabilizing mutant), limiting the data available for improving modelling. Our large-scale approach revealed 2,600 mutations that increase folding stability by at least 1 kcal mol -1. The fraction of stabilizing mutations was approximately 0. 2% to 0. 6% for different protein types (Fig. 6a ). Stabilizing mutations were enriched at functional sites (23% of the stabilizing mutations from 8% of sites classified as functional). Notably, our set includes 112 examples of stabilizing insertions and deletions.
Advances in DNA synthesis  47, 48  will also make it possible to expand cDNA display proteolysis beyond the largest domains studied here (72 amino acids). Finally, multiplexed measurements and automated data processing have the potential to introduce inaccuracies, although we worked to exclude unreliable data. For notable individual results, examining the raw data can be helpful, and we included all data and code to regenerate all fits. Despite these limitations, the unique scale of cDNA display proteolysis opens completely new possibilities for studying protein stability. 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.
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Hallucination design method We used a TrRosetta hallucination protocol described previously in the previous reports  31, 32  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  53. We selected the best designs according to the predicted distogram and 3D structure match.
qPCR analysis of cDNA display proteolysis results on individual proteins The cDNA amount for each specific sequence in the eluents was quantified by qPCR using SsoAdvanced Universal SYBR Green Supermix and specific primers for each sequence. The qPCR was performed using CFX96 Touch Real-Time PCR Detection System (Bio-Rad), and the qPCR cycles were determined by the CFX Maestro Software (Bio-Rad). Extended Data Fig. 1.
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  58  version 0. 80. In Supplementary Notes, we describe the structure of each model and the procedure for fitting each model. Our scripts to reproduce the complete fitting process are provided in the Source Data.
Data quality filtering and classification of datasets 1-3 Our data (Fig. 2 ) were filtered for quality in three stages. First, our Bayesian procedure produces confidence intervals for K 50 and ΔG estimates, producing a quality estimate for each individual measurement. Second, we evaluated the quality of each full mutational scan, classified these into categories, and removed the low quality categories from our main analysis (below). Third, we filtered our mutational scanning data to remove mutants that showed evidence of causing cleavage from the folded state or intermolecular disulfide cross-linking.
Because these conditions lower the reliability of our ΔG estimates, we removed these mutational scans from analysis. The code to perform this filtering is provided (Data_quality_ filtering_script. ipynb). Removal of individual mutants that may disrupt the assay. In the previous stage, we filtered out entire domains; here, we filtered out data from individual mutants in domains that otherwise passed filtering (that is, were in group 0 or group 1). We focused on two specific types of mutations that could disrupt our assay. First, we filtered out data where introducing new cleavage sites into poorly structured regions of a protein resulted in apparent destabilization. Because these mutants are located in poorly structured sites, the apparent destabilization may result from cleavage from folded or partially folded states.
These mutants were identified based on (1) apparent destabilization from introducing the new cleavage site, and (2) a low variance in stability between the other amino acids, which indicates a poorly structured region of the protein where cleavage might occur in the folded state. Second, we filtered out data where introducing Cys mutants into poorly structured regions of a protein resulted in apparent stabilization. Again, because these mutants are located in poorly structured sites, the apparent stabilization may result from the formation of inter-or intramolecular disulfide linkages that prevent the dissociation of the C-terminal cDNA following protease cleavage. The code to perform this filtering is provided (Data_quality_filtering_script. ipynb). All sequences in dataset 2 and dataset 3 are included in Tsuboy-ama2023_Dataset2_Dataset3_20230416.
csv. All sequences in this file have an inferred ΔG estimate, but only sequences in dataset 3 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 2 and 3 include a very small number of sequences with low-quality data (wide confidence intervals) because these sequences come from mutational scans that are high quality overall. Although these tables include all K 50 , ΔG and ΔΔG data (for dataset 3), low-quality data (including mutant data filtered in Stage 3) have been filtered out and replaced by a -symbol in the columns labelled '_ML' (for machine learning).
Principal component analysis We performed principal component analysis to determine the factors influencing stability of different amino acids (Fig. 3 ). To this end, we used 17,093 sites in the 365 domains that are classified as G0 in the above. All folding stability data were clipped between from -1 and 5 kcal mol -1 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 principal components analysis using the scikit-learn library implemented in Python 3.
Side chain contacts and burial analysis Burial values and contact counts (Fig. 3b  and Extended Data Fig. 9h ) were computed based on AlphaFold models 1 of all sequences using the included script Burial_side_chain_contact_Fig3_Fig6. ipynb based on Bio. PDB  72  and BioPython  73. The calculation is based on the Rosetta 'side-chain_neighbors' LayerDesign method previously reported  18. In brief, 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. 3d ) counts all Cβ atoms; 'aromatic side chain contact count' counts all CE2 atoms of Phe, Tyr, and Trp; 'acidic side chain contact count' counts all Glu OE1 and Asp OD1 atoms; and 'Basic side chain contact count' counts all Lys NZ and Arg NE atoms.
Secondary structure determination Using the DSSP algorithm  74, 75  , we obtained secondary structure information based on AlphaFold models (Fig. 3b ). Selection method of site pairs for double mutational analysis Double mutants (Fig. 4 ) were selected for analysis in two ways. First, we manually selected polar interactions where either amino acid appeared important for stability in single mutational analysis. These pairs were mainly included in library 3. Second, we used the program confind  76, 77  to identify interacting residues. All confind pairs with notable interactions such as polar interactions and cation-π interactions were selected, along with a randomly chosen subset of more common   (d, e ) As above, for chymotrypsin. As in our previous report  18  , the coefficients resemble established features of chymotrypsin specificity, including the preference for F/Y/W followed by M/L at P1, the inhibitory effect of P at P3, P1', and P2', and the general unfavorability of D and E  [83] [84] [85] [86].
Acknowledgements The authors thank  Epsilon Molecular Engineering (EME)  for providing cnvK linker for cDNA display,  Rush University  and  Genome Research Core at University of Illinois Chicago  for performing next-generation sequencing, and  D. Minh ,  T. Whitehead ,  K. Lindorff-Larsen ,  D. M. McCandlish ,  J. Maguire ,  J. Chodera ,  P. Hosseinzadeh , and the members of the  Rocklin laboratory  for discussions and comments on the manuscript. This work was supported by  Northwestern University Startup Funding  (G. J. R. ),  JSPS   KAKENHI 19J30003  (K. T. ),  Human Frontier Science Program   Long-Term Fellowship  (K. T. ) and  JST PRESTO  Grant  JPMJPR21E9  (K. T. ). This research was supported in part through the computational resources and staff contributions provided for the Quest high performance computing facility at  Northwestern University  which is jointly supported by the  Office of the Provost , the  Office for Research , and  Northwestern University Information Technology.
Methods The present study used data from the first 3 years (period in which PA data were available) of the Multidomain Alzheimer Preventive Trial (MAPT) (clinicaltrials. gov; NCT00672685). MAPT was a 3-year interventional placebo-controlled study followed by a 2-year observational follow-up that demonstrated no effect of omega-3 supplementation, multidomain intervention (composed of cognitive training as well as nutritional and PA counselling) or their combination on cognitive function in adults aged 70 and above. S4  The MAPT study was conducted in agreement with the Declaration of Helsinki's principles and its protocol was approved by the French Health Authority and was authorized by the advisory Committee for the Protection of Persons involved in Biomedical Research of the Toulouse University Hospital (CPP Sud-Ouest et Outre-Mer).
Study population In accordance with the MAPT study inclusion criteria, the present investigation involved community-dwelling older adults aged 70 and over who met one or more of the following criteria: spontaneous memory complaints, slow gait speed (as defined by the inability to walk 4 m within 5 s or less) and limitation in one instrumental activity of daily living. Subjects were not included if they had omega-3 supplementation, struggles with daily living basic activities, diagnosed dementia or a score in the Mini Mental State Examination (MMSE) lower than 24. The study sample was restricted to the subjects who underwent GDF-15 measurement at 1 year (n = 1096) and also had full data for PA from baseline to 1 year and bodyweight information at 1 year, which led to a final sample of 1083 subjects.
Plasma growth differentiation factor 15 measurement Blood testing was performed in 1096 subjects at the 1-year time point of MAPT. The fully automated immunoassay platform, Ella (ProteinSimple/Bio-techne, San Jose, CA, USA), was used to quantify GDF-15 plasma levels (pg/mL). Single disposable microfluidic SimplePlex ™ cartridges that can hold up to 30 plasma samples per assay were used to quantify GDF-15. Plasma samples were thawed on ice and diluted 1:4 in a sample diluent (SD 13) before being loaded into cartridges with relevant high and low control concentrates. Each plasma sample was run in triplicates using three analyte-specific glass nanoreactors (GNRs). Each cartridge included a built-in lot-specific standard curve for GDF-15. All steps in the procedure and the obtained data were automatically run and calculated by the instrument with no user intervention.
This analysis was performed because we hypothesized that PA would be associated with weight maintenance (not necessarily with weight changes). To do such, bodyweight stability was defined using the cut-off values of 2. 5% for 6-month changes or 5% for 1-year changes presented previously. For the mediation analyses, bodyweight was considered stable if it never went out of these cut-off values throughout each considered time point from 1 to 3 years. All statistical analyses were undertaken using IBM SPSS Statistics Version 23 (Chicago, IL, USA). Mediation analyses were performed using the PROCESS macro Version 3. 5. 3 for SPSS developed by Andrew F. Hayes (http://www. afhayes. com). The level of significance was defined as P values < 0. 05, or 95% bootstrap confidence intervals not containing zero for indirect effects estimation in the mediation analyses, as recommended.