dataesr/scientific-paragraphs-categorization huggingface dataset:
A comparison between computational molecular movies obtained from simulations with molecular movies recorded in hs-AFM experiments is therefore available. The BioAFMviewer software takes that into account by providing simulated AFM graphics of molecular movies provided in the PDB format. In this case simulated AFM images are generated frame by frame from the molecular representation of the loaded movie with the same previous described versatile functionality. For the user-selected parameter set, both the movie in the molecular representation and the corresponding simulated AFM movie are synchronously displayed. In the Results section we provide an example.
Comparison of simulated AFM graphics to high-speed AFM experiments We demonstrate how the BioAFMviewer software can be employed in the interpretation of experimental hs-AFM images. Examples for three different protein machines from previously published high-impact publications  [8, 20, 26]  are provided. Our workflow is as follows. For the investigated proteins, we select PDB structures which are related to the experimental situation, e. g. , possibly taking into account structures complexed with other molecules (such as ligands). When the selected PDB structure is loaded into the BioAFMviewer, the VdW representation and the corresponding simulated AFM image is shown. Then, the scanning parameters are adjusted and the orientation of the protein is changed to obtain a simulated AFM image which resembles the experimental target AFM snapshot.
Our goal was to demonstrate that the BioAFMviewer software can readily produce such simulated images with ease, not employing additional dependencies on other commercial software, and therefore providing a public platform available to the broad community of AFM experimentalists. Within the current workflow, the comparison of simulated AFM graphics to experimental AFM images is rather qualitative. While for the given examples convincing visual agreement is demonstrated, we also provide a quantitative score to evaluate the similarity between simulated and experimental AFM results in terms of their image correlations (see S1 Text). A similar method has been employed in a recent application of flexible fitting protein structures to AFM templates  [21]. The results of our analysis are shown in S2  examples from Fig 3 correlations are larger than 0.
Discussion The BioAFMviewer is the first publicly available software which provides simulated AFM graphics of biomolecular structures and movies of their conformational motions. Our emphasis is to provide a platform which combines an user-friendly interface with the functionality required to make comparison to experiments available. Applications to interpret images of high-speed AFM experiments of proteins demonstrate the importance of the BioAFMviewer to complement experimental observations. Moreover, simulated AFM experiments become in principle possible, which will facilitate the interpretation of resolution-limited experimental observations.
Availability and future directions We report here the BioAFMviewer in its present version. The software is already used by many AFM groups worldwide, and we are getting valuable response which helps to further advance the platform. Based on the feedback received so far, we are expecting the BioAFMviewer software to become a standard platform used by the broad community of Bio-AFM experimentalists. Beyond that, it also provides the interface for researchers from the fields of computational biology and bioinformatics to foster their interdisciplinary collaborations. The BioAFMviewer software is available for download on the www. bioafmviewer. com website, where we also provide a discussion channel. All software updates will be made available there. The upcoming improvements of the software will focus on implementing tools which allow to extend comparison of simulated and experimental AFM images to a quantitative level.
The CHRNA5-A3-B4 gene cluster and NA vulnerability The identification of specific genes and loci contributing to addiction vulnerability has been challenging, likely because of the genetic complexity of psychiatric diseases such as addictive disorders. Such complexity includes multiple risk alleles, gene x gene interactions and epigenetic effects  (Gelernter, 2015). In this context, genome-wide association studies (GWAS) have the advantages of covering the entire genome, implementing a 'hypothesis-free' approach, and having the power for detecting relatively common alleles. The potential of GWAS in the addiction field will most likely increase in the future with more extensive data sharing, larger samples, and the optimization of phenotyping and refinement of statistical analytic methods. So far, the strongest and widely reproduced GWAS finding for addiction has been the identification of the CHRNA5-A3-B4 gene cluster on chromosome 15q25, in association with a propensity to NA  (Berrettini et al.
General considerations for human genetic analysis The sub-phenotypes of tobacco addiction implicate different biological and genetic correlates  (Lassi et al. , 2016). In the present review, we will focus on associations between NA and genetic variants in the genes coding for the α5 (CHRNA5), α3 (CHRNA3) and β4 (CHRNB4) nAChR subunits, considering all kinds of genetic variants: rare and frequent single nucleotide variants (SNVs), insertions and deletions (InDels) and copy number variations (CNVs). We will report data from both candidate SNV/gene association studies and GWAS. The functional impact of these variants will be discussed based on direct evidence from preclinical models or from in silico databases describing the impact of both exonic (synonymous or not) and intronic/intergenic variants at multiple levels, from protein structure changes to close-and long-range promoter interactions (Table  1 ).
Such discrepancy likely reflects methodological issues, notably as regards brain function studies which tended to rely on few (if not single) numbers of SNPs, possibly overlooking the functional and/or linkage interplay between many SNPs in the 15q25 region. Furthermore, several of these studies were performed after nicotine deprivation (minutes to weeks), limiting our ability to disentangle between vulnerability issues and the effects of alleviating withdrawal symptoms. Finally, to date, standardized methods that take into account LD to moderate the associations between genotypes and phenotypes require large GWAS datasets to run properly  (Kranzler et al. , 2019) , leaving researchers with only assumptions or empirical/intuition-based methods to account for LD when characterizing the effects of a selected set of genetic variants in the CHRNA5-A3-B4 cluster.
State of the art of the associations identified between CHRNB4 variants, NA and brain function in humans Unlike the 5 subunit, the nAChR 4 subunit participates in forming ACh binding sites in nAChR pentamers  (Changeux, 2010). CHRNB4, the gene encoding the 4 subunit  (HGNC 1964, ENSG00000117971) , is also located at 15q25. It harbors ~23400 SNVs, including ~2600 InDels, and is encompassed by 54 CNVs ranging from short tandem repeats to large mosaic rearrangements (see Table  1  for database references). Those larger CNVs have been associated with severe neurodevelopmental delay, although the CHRNB4 gene itself is tolerant to mutations (pLI =0. 019). Several CHRNB4 SNPs have been associated with NA. Recently, it was made clear that some of these variants act as enhancers toward other genes, notably through increased methylation of cis CpG (cytosine nucleotide followed by a guanine nucleotide) sites, rather than directly affecting 4*nAChR function.
Taken altogether, association studies and GWAS evidence followed by assessment of gene expression and methylation uncovered a key functional role of several CHRNB4 SNPs in the relationship between the CHRNA5-A3-B4 cluster and NA. Along with CHRNA3 (see above) and intergenic variants, the cluster has been legitimately described as a regulome containing multigenic haplotypes, including rs16969968 or not, showing complex associations with NA  (Barrie et al. , 2017). Two sets of CHRNB4 SNPs showing opposite regulatory features can be identified from gene expression (Fig. 2  for rs8023462) and methylation databases, some data being redundant due to high LD (Fig. 3 ). All CHRNB4 SNPs reviewed here form a haplotype block (Fig. 3 , bottom-right) and, thus, carry the same statistical information although they show distinct regulatory characteristics.
SNP id, SNP name according to the dbSNP database v. 153 ; eQTL, expression quantitative trait loci (i. e. associations with altered gene expression) ; mQTL, methylation quantitative trait loci (i. e. associations with altered gene methylation) in samples from middle-aged individuals. All mQTL sites were cis, meaning that increased methylation is associated with decreased DNA transcription. RegulomeDB scores read as listed. N/A, not available. a cluster refers to CHRNA5-A3-B4 genes and RP11-650L12. 2 and brain to brain regions as defined by GTEx portal (https://gtexportal. org/home/), except for the spinal chord. b CpG islands located 50 kbp up-or downstream CHRNA5, CHRNA3 or CHRNB4 were considered. Polymorphisms associated with Nicotine Addiction in the cluster. LD is expressed as minor alleles correlation (r 2 ).
Single Nucleotide Polymorphisms (SNPs) reliably associated with nicotine addiction and the width of their functional impact on gene expression, known and modulation of DNA transcription. SNP id, SNP name according to the dbSNP database v. 153 ; eQTL, expression quantitative trait loci (i. e. associations with altered gene expression) ; mQTL, methylation quantitative trait loci (i. e. associations with altered gene methylation) in samples from middleaged individuals. All mQTL sites were cis, meaning that increased methylation is associated with decreased DNA transcription. RegulomeDB scores read as listed. N/A, not available. a cluster refers to CHRNA5-A3-B4 genes and RP11-650L12. 2 and brain to brain regions as defined by GTEx portal (https://gtexportal. org/home/), except for the spinal chord.
, 2019) http://dna. cs. miami. edu/TADKB/ Exploring topologically associating domains (TADs) DatabasE of genomiC varIation and Gene tolerance to mutations Phenotype in Ensembl Resources (DECIPHER) v. Humans using https://decipher. sanger. ac. uk [score of tolerance to loss-of-function mutations (pLI), level of 9. 30 (Lek et al. , 2016) haploinsufficiency].
Results Combinatorial space of cell-free buffer compositions. To study cell-free systems productivity, we developed an automatable strategy coupling an acoustic liquid handling robot (Echo 550, Labcyte, USA) and a plate reader (Infinite MF500, Tecan, USA) to measure ~4000 cell-free reactions (including controls and triplicates) and provide data to train machine learning models. The lysate was obtained by sonication and supplemented with compounds described in Fig. 1a. The reference concentrations are based on the protocol developed by the Noireaux laboratory  24  (see Methods section, we fixed the maximal Mg-glutamate and Kglutamate concentration based on an initial buffer composition optimization as described in Noireaux laboratory protocol, Supplementary Fig. 1 ). We fixed four concentration levels for each of the 11 compounds leading to a combinatorial space of 4,194,304 possible compositions (Fig.
3d ). A one-step method for lysate-specific optimization. Next, we investigated whether protein production in cell-free using lysates made in other conditions (different experimentalists, using a different strain or supplemented with antibiotics) could be quickly predicted with a one-step method (Fig. 4a ). We selected 102 cell-free buffer compositions representative of the 1017 already tested with the original lysate (see Methods section, Supplementary Fig. 2a ). Amongst the 102 compositions, 20 were used to train the model and 82 to test its predictive accuracy (Fig. 4a ). The challenge lies in the model's ability to accurately predict a large diversity of yields based on a small training dataset. The 20 compositions (magenta dots in Fig. 2  and Supplementary Table  2 ) were chosen to be highly informative (see Methods section).
We calculated the concentrations of our chemical compounds stocks so the final volumes sent to the destination well were multiples of 2. 5 nL (the droplet size managed by the Echo machine). The scripts generating the CSV file are presented below in "concentrations to instructions workflow". We chose our stock volumes so that the minimal volume to transfer was 12. 5 (=5 droplets). General script descriptions. All scripts mentioned below were written in Python (version 3. 6. 5), executed in Jupyter notebooks (version 1. 0. 0). Scripts are available online at github (https://github. com/brsynth/active_learning_cell_free). The libraries numpy and csv were used to handle files between different scripts. We used scitkit-learn  29  (version 0. 19. 1) for all model training. Concentrations to instructions workflow.
It also provides a file containing the name of the wells with their corresponding transfer volumes. This file is used to match the well compositions with the fluorescence measurements obtained later with the plate reader. The amino acids and water were pipetted manually (for volumes >1 μL). Named volumes to concentrations: maps the volumes and the associated well name to a concentration file with the associated well name, for integration with the fluorescent plate reader at the next step. The script matched the named concentration with the yield value as described in "Data analysis" of those methods. The chemical compounds were dispensed using BP2 fluid class except for Kglutamate and 3PGA (CP fluid class). Data analysis. We provide a script to map the fluorescence quantification (see "fluorescence quantification" above) to the tested concentrations with well names (last step of "concentrations to instructions workflow" above).
We performed outlier removal based on the following criteria: if the coefficient of variation, amongst three replicates, was higher than 30%, we removed the value farthest from the other 2. This concerned 27 values of our 1017 values tested during the active learning. Those are identified in the online data on Github with the third value of fluorescence is set to -1. This script also outputs csv files allowing for visualization of where the outliers are, in order to spot potential border effects. It also separately outputs the outliers for further analysis. Data normalization. We normalized using the following equation: Yield composition ¼ Fluorescence composition À Autofluorescence Fluorescence reference À Autofluorescence Where autofluorescence is the fluorescence measured in the cell-free reaction supplemented with water and using the reference composition.
1, 0. 3, or 0. 5 as described in the legend of Fig. 1. While unnormalized inputs could be used, we strongly encourage normalization due to scale differences between the inputs. We trained an ensemble of n models, where n = 25. For each model, training was performed ten times (models_number) using the whole dataset so far acquired (e. g. , 3 × 102 values at the 3rd iteration). Training the model multiple times allows for optimizing for random weight initialization of the model. We kept the best model of the ten models we trained (with the highest regression from scitkit-learn R 2 score) and repeated the procedure 25 times to obtain an ensemble of 25 best models. Multilayer perceptrons gave the best results (random forests and linear regressions were also investigated early on). They were trained with the default parameters from scitkit-learn except the following parameters: maximum iteration of 20,000, adaptive learning rate, adam solver, early stopping and the following layers: (10, 100,100, 20)  We obtained mean and standard error from our predictions by taking the mean and standard error from the n results generated by our ensemble of n models.
Our scripts output the best 500 compositions based on the mean and standard deviation predictions of the yields. A high standard deviation value stands for an uncertain yield value. We output compositions for full exploitation, full exploration and maximization of the above formula but use the third option for the rest of the workflow. We are therefore querying points with both high yield and uncertainty. Model statistics. For model statistics presented in Fig. 3b , we used the same models as described in the active learning section above, but using 5-fold cross validation instead of the whole training set. The full dataset is separated into five subsets then the 25 models are trained on four subsets, and used to predict the 5th, where scores are obtained. This is done five times, once on each subset.
Acknowledgements O. B. is supported by  Genopole  " Allocation Recherche 2017 " and  CRI Paris "Short-term. Fellows". M. K. is supported by  DGA  ( French Ministry of Defense ) and  Ecole Polytechnique. PS is supported by the  ANR SynBioDiag  grant number  ANR-18-CE33-0015. A. C. B. acknowledge funding provided by the  ANR SINAPUV  grant number  ANR-17-CE07-0046. A. P. is supported by  INRAE  ( National Institute for Agricultural, Alimentation, and Environmental Research ) and an  idEx interdisciplinary scholarship  from the  University of Paris-Saclay. J-L. F. acknowledges support from  BBSRC/EPSRC  (grant number  BB/M017702/1 ) and the  Life Science Department of the University of Paris.
Here, we explore the combined mutational landscape of 30 genes using 33 deep mutational scans from 28 studies. We show that multiple datasets can be meaningfully combined and use them to explore the diverse functions of each amino acid. First, suitable studies were selected and their scores normalised to a common scale. Second, we derived and analysed their combined mutational landscape, relating it to a range of biophysical properties. Finally, we explored the diversity of amino acid roles using the mutational landscape, clustering each amino acid into typical subtypes and demonstrating these subtypes represent meaningful biological groups.
A unified deep mutational scanning landscape The first step to utilising a dataset drawn from many deep mutational scanning studies is combining them in a meaningful way, allowing scores to be compared. We selected 33 deep mutational scans from 28 studies covering 6,291 positions in 30 proteins across nine species  (Fig 1A, Dataset EV1). These proteins have a broad range of structures and functions, meaning the analysis applies to general protein properties rather than those of a single protein or protein family. We required that studies applied selection pressures directly related to natural function and that their fitness scores could be transformed to a common scale. To prepare each study  (Fig 1B) , we first averaged replicates. When multiple selection pressures were measured, we either averaged comparable conditions or preferred those selecting for broader aspects of protein function.
For example, when processing data on BRCA1 from  Starita et al (2015) , we used measurements of overall E3 ubiquitin ligase activity rather than BARD1 binding because it may reflect protein function more generally. Conditions were considered comparable when they test similar aspects of protein function and have correlated results, for example different drug types in  Melnikov et al, (2014). Some studies generated sequences with multiple variants, so when individual substitutions were not explicitly measured, we averaged across variant sequences containing each substitution, after validating that this produces approximately correct scores for explicitly measured variants  (Appendix Fig S1). Next, substitution fitness scores were transformed into a common enrichment ratio (ER) score, which measures the enrichment of a variant during selection relative to the change in the wild type.
Thus, positive ER scores mean the variant is advantageous, zero is neutral and negative scores deleterious. We normalised scores from each study against the median of the lowest 10% of scores, reasoning that the worst substitutions (e. g. nonsense mutations) result in complete loss of function and are comparable between studies. We excluded 626 positions (9% of the starting dataset) with scores for < 15 of the possible 20 nonsynonymous substitutions (including nonsense) to focus on positions with sufficient data and then imputed the remaining missing data (see  Methods). This resulted in complete sets of normalised ER scores for substitutions to all 20 amino acids at 6291 unique positions across 30 genes, with 98. 16% of nonsynonymous ER scores experimentally measured and the remaining 1. 84% imputed.
The two TEM1 studies were so similar to each other that only the most recent was used. On the other hand, the HSP90 and UBI studies were sufficiently different that they could all be retained and used to check consistency of later results. The fact that scans under different selection criteria and experimental conditions correlate suggests many protein properties are somewhat independent of conditions, for instance relating instead to the basic structure of the protein. Deep mutational scanning results should relate to evolutionary conservation, as natural selection also samples variants and enriches them based on fitness. We validated our approach to combine DMS datasets by showing this relationship was maintained in bulk statistics and for individual variants in our data. Substitution matrices, A Proteins in the combined dataset, with the structure used, number of positions with mutational profiles and the percentage of these in the structure model.
B Normalisation pipeline. C Correlation between ER scores from two deep mutational scans on ubiquitin, both from the Bolon Lab (r 2 ¼ 0:4676, Pearson's correlation coefficient t-test: P <2:2 Â 10 À16 ). A simple linear regression best fit is shown, with a 95% confidence interval in grey shading. D Relationship between mean normalised ER score for an amino acid substitution and corresponding BLOSUM62 score. The scores for missense variants strongly correlate (r 2 ¼ 0:4194, Pearson's correlation coefficient t-test: P <2:2 Â 10 À16   BLOSUM62 also provides substitution scores for synonymous substitutions, which measure the average conservation of positions of each amino acid. While synonymous substitutions tend to have close to 0 ER in deep mutational scans (92% have |ER| < 0. 05 in this dataset), it is possible to estimate the impact of mutating away from each amino acid by calculating the mean ER score for all substitutions away from that amino acid.
▸ The combined dataset gives us a large number of mutational profiles, together constituting a subset of the overall proteome mutational landscape. While it covers a relatively small subset of the vast proteome space, the dataset does cover a reasonably varied and representative sample, with proteins covering a range of species, functions and environments. Thus, we generated an approximation for the mutational landscape of proteins that can be used to analyse the range and frequency of amino acid roles in an unbiased manner.
Instead, all studies are distributed across UMAP space, suggesting there is not a strong study bias in the normalised mutational profiles  (Appendix Fig S3). In contrast, the same protein positions assayed in different studies are significantly more similar and closer together in UMAP space, on average, than random pairs of positions (Appendix Fig S4, mean 2. 105 units closer, one-tailed Mann-Whitney U-test: P ¼ 1:141 Â 10 À13 ), which suggests the method is replicable and validates our approach to building a combined dataset. The relationship between the mutational landscape and the biophysical properties of protein positions is most clearly illustrated by highlighting protein domains in UMAP space  (Fig 2A)  and observing separation by function. In this case, we highlight positions in three transmembrane proteins (ADRB2, CCR5 and CXCR4) and show they divide primarily by the solvent environment they are exposed to either lipid membrane or hydrophilic intra/extracellular solvent.