dataesr/scientific-paragraphs-categorization huggingface dataset:
Neural network potentials (NNPs) have great potential to bridge the gap between the accuracy of quantum mechanics and the efficiency of molecular mechanics in molecular simulation. However, most of the NNPs remain restricted by the locality assumption that ensures the model's transferability and scalability but misses out the long-range interactions. Here we present an integrated non-reactive hybrid model, AMOEBA+NN, which employs the AMOEBA potential for the short-and long-range non-bonded interactions and an NNP to capture the remaining local (covalent) contributions. A first AMOEBA+NN model was trained on the conformational energy of ANI-1x dataset and tested on several external datasets ranging from small molecules to tetrapeptides. It was encouraging to see that the hybrid model becomes significantly advantageous over the baseline models in term of accuracy as the molecules get larger, offering perspectives for the development of a generalized and improved approach.
g. , ANI-1x. Our NN term only has 1 neural network for each element, instead of an ensemble of 8 networks in ANI-1x, in order to keep the computational cost low and as comparable to AMOEBA as possible, The hybrid AMOEBA+NN model was trained on the ANI-1x dataset 46 consisting of ∼5 million conformations of ∼51 thousand molecules of four elements, H, C, N, and O (Fig. 1 ). A diverse chemical space is covered, including small molecules of <=10 heavy atoms, dimers of small organic molecules, amino acids and dipeptides. The dipeptides are peptides composed of two residues with neutral side chains and neutral termini. The reference QM energy of each conformation is computed at the level of ωB97x 47 /6-31G*. [48] [49] [50]  The dimers were removed since our NN term only intends to learn the local covalent interactions.
6). This strategy helps circumvent the aforementioned issues by offering precisely the right amount of supervision-neither excessive nor inadequate. U (i) N N -U (j) N N → (U (i) QM -U (j) QM ) -(U non-bonded, (i) AM OEBA -U non-bonded, (j) AM OEBA ) (6) In order to gain an unbiased insight on how the model will generalize to new molecules, we evaluated the model with 5-fold cross validation (Fig. 2 ). The data was split at the molecule level to ensure that no conformation of the same molecule were in both the training and the validation sets. The ANI-1x model  18   The best AMOEBA+NN model from the cross validation was subsequently tested on an external torsion scan dataset using CCSD(T)/CBS. 55  After we removed the fragments with elements other than the four, this dataset consists of 1620 conformations of 45 drug-like fragments.
Given that the fragments are small, long-range interactions contributed little in this evaluation. We expected that AMOEBA+NN's performance would be comparable yet no better than ANI-1x's (Fig. 2 ). However, it is worth mentioning that AMOEBA+NN was only composed of 1 MLP for each element and still competed with ANI-1X that has an ensemble of 8 MLPs. In addition, the significant out-performance of AMOEBA+NN over AMOEBA suggested that a better description of torsional energy had been learned to reproduce the QM energies. We subsequently moved onto multiple external datasets closely reflecting the future application scenario of the model. First, alanine dipeptide in the gas phase was used to test the model. The dataset  53  was generated by a 2D rigid scan of the backbone dihedral angles in the increment of 15 degrees, totaling 576 energy points at level MP2 56 /CBS.
42  That might be related to the issue of unstable gradients observed recently in NNPs. Next, a subset of ∼16 thousand conformations was extracted from the dipeptides in the SPICE dataset 60 to test the model. The dataset contained all the dipeptides of element H, C, N and O and relative QM energy <50 kcal/mol at level ωB97M-D3BJ 61,62 /def2-TZVPPD  63, 64  in the gas phase. However, both AMOEBA+NN and ANI-1x did not transfer well and perform as satisfactorily as they did on alanine dipeptide (Fig. 4 ). Intrinsic differences were indicated between the two datasets. One of them could be the different levels of theory and/or basis sets used to generate the two datasets. Another factor to consider was that the dipeptides in SPICE are peptides of one residue with ACE and NME caps, which are quite different from the ANI-1x dipeptides.
Overall, the performance of all three models exhibited minimal differences, with the ranking being ANI-1x > AMOEBA+NN > AMOEBA. Finally, a tetrapeptide dataset was selected for testing the model on larger molecules. The dataset covers a diverse conformational space for tetrapeptides of sequence ACE-ALA-XXX-ALA-NME as well as peptides of the half-cut sequences, ACE-ALA-XXX and XXX-ALA-NME, with zwitterionic termini at the cutting points. The QM reference energy was computed at level MP2/aug-cc-pvtz. 65, 66  Surprisingly, AMOEBA+NN outperformed both AMOEBA and ANI-1x to a great extent (Fig. 5 ). We also conducted restrained minimization with AMOEBA to relax the bond and angle strain, which improved the root mean square error (RMSE) of AMOEBA by ∼4 kcal/mol. The relaxed results are slightly better than those of AN-1X but still far away from AMOEBA+NN values.
To conclude, we have reported the initial framework to build the hybrid model, AMOEBA+NN, by replacing the "bonded" terms in AMOEBA with the new NN term. The NN term is fully integrated instead of being merged by distance, leaving no physical boundary between NN and AMOEBA. It is also worth noting that this framework can be seamlessly extended to other types of PFFs such as AMOEBA+,  67  which further incorporates charge penetration and charge transfer effects at close contact distances. AMOEBA+NN has been preliminarily trained on the ANI-1x dataset and tested on multiple external datasets. Its advantage becomes more significant over both AMOEBA and ANI-1x as the test molecules get larger. We expect future improvements are needed as both NNPs and force fields are being progressed, especially the quantity and quality of QM data being constantly improved.
Computational Methods Poltype 2 automatically assigns small molecule parameters from a database or derived via fitting to ab initio data generated on the fly based on the input chemical structure. The input configuration of the levels of theory and basis sets used in the ab initio calculations invoked by Poltype 2 are as the follows: • optbasisset=6-31G* • optmethod=MP2 • dmabasisset=6-311G** 69 • espbasisset=aug-cc-pvtz The SPICE dataset has some molecules deposited with incorrect energy values for some reason. 70  We removed those entries by filtering out any conformation that has any force component > 1 atomic unit. All the invocation of the ANI-1x model, including energy evaluations and structural minimization, was performed with torchANI 71 in Python. All the AMOEBA energy evaluations and structural minimization were performed with Tinker  68, 72  and Tinker-HP.
Acknowledgement This work is supported by  National Institutes of Health  ( R01GM106137  and  R01GM114237 ). This work was also made possible thanks to funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  810367 ), project  EMC2  (JPP). TJI is grateful to the  US-France   Fulbright program  for funding a research stay at  UT Austin. The authors thank  Dr. Yong Duan  at the  University of California at Davis  for providing the tetrapeptide dataset.
Acknowledgements This work was supported in part by  CNRS  through the  PICS CNRS program  " Microfluidics for Soft Matter ", and by  INSIS-CNRS. This work was partly supported by  LAAS-CNRS  micro and nanotechnologies platform member of the  French RENATECH  network. Funding by " ADI ,  Université de Toulouse ,  Région Occitanie ", and by  Federation Fermat, Université de Toulouse , are acknowledged. We thank  Benjamin Reig  for SEM imaging,  Sandrine Souleille  for microfluidics experiments,  Charline Blatché  for cell culture,  Julien Roul  for help in microscopy experiments. We thank  Karine Guevorkian  and  Gregory Beaune  for gently providing the S180 cell line.
Data Processing Protocol Relative intensity-based label-free quantification (LFQ) was processed using the MaxLFQ algorithm from the freely available MaxQuant computational proteomics platform, version 1. 6. 3. 4  46, 47. The acquired raw LC Orbitrap MS data were first processed using the integrated Andromeda search engine  48. Spectra were searched against the Mus Musculus proteome extracted from UniProt on December 2020 and containing 55508 entries (UP000000589). The false discovery rate (FDR) at the peptide and protein levels were set to 1% and determined by searching a reverse database. For protein grouping, all proteins that cannot be distinguished based on their identified peptides were assembled into a single entry according to the MaxQuant rules. The statistical analysis was done with Perseus program (version 1.
ACKNOWLEDGMENTS We thank  Marc Bartoli  for recommendations in murine muscle atrophy exploration,  Jérôme Robin  and  Arnaud Mourier  for advices in isolation of active mitochondria,  Sofia Vikstrom  and  May Sanderhoff  ( Seahorse/Agilent Support Scientists ) for help in Seahorse experiments on isolated mitochondria,  Karim Sari  and  Régis Vitestelle  for assistance with the use of the PSEA animal housing facility, and  Nuno Luis ,  Jérôme Avellaneda , and  Frank Schnorrer  for their suggestions and comments on the results. We are grateful to members of the  PiCSL-FBI core facility (IBDM, AMU-Marseille, France)  belonging to the  France-BioImaging national research infrastructure  for the electron microscopy experiments. This work benefited from equipment and services from the  iGenSeq core facility  (Genotyping and sequencing), at  Institut du Cerveau et de la Moelle épinière (ICM, Paris, France).
Proteomic analyses were performed at the mass spectrometry facility of Marseille Proteomics (marseille-proteomique. univ-amu. fr) supported by  IBISA (Infrastructures Biologie   Santé et Agronomie) ,  Plateforme Technologique Aix-Marseille , the  Cancéropôle PACA , the  Provence-Alpes-Côte d'Azur Région , the  Institut Paoli-Calmettes  and the  Centre de Recherche en Cancérologie de Marseille ,  Fonds Européen de Developpement Régional , and  Plan Cancer. This work was supported by  Institut National de la Santé et de la Recherche Médicale ,  Centre National de la Recherche Scientifique ,  French Agence Nationale de la Recherche  ( ANR-18-CE45-0016-03 « MITODYNAMICS » ). This work also benefited from grants from  Cancéropôle Provence Alpes Côte d'Azur (PACA)  and  Fédération GEFLUC  for the acquisition of the  Seahorse XFe24  device.
Annotating variants with SIFT The effect of variants on individual's phenotype was predicted using SIFT 4G  (Vaser et al. 2016). SIFT (Sorting Intolerant From Tolerant) is an algorithm that classifies a variant as synonymous or non-synonymous (non-synonymous, stop-loss, stop-gain, stop-loss), and predicts whether the amino acid substitution is deleterious to protein function. No database was available for the reference genome Mmul_10 so we had to created it using the genome and gene annotations from UCSC Genome Browser, and the UniRef90 protein data base (Uniprot). We used the multi-transcript option, meaning that for one substitution, the effect on all transcripts were predicted. We filtered low-quality predictions, and when two or more transcripts of the same gene were given opposite predictions, we kept the worst prediction.
tped -tfam input. tfam -tgls input. tgls -gl-type GQ -resample 40 -centromere cen-tromere_file. txt -out output_file. txt). For each individual, we obtained the list of ROH with their position on the genome (chromosome, beginning and end), the size class they were classified into, and their length. score in the top 1%. Chromosomal regions under selection were then annotated with the Ensembl Genes database  (Martin et al. 2023 ) using the UCSC Table Browser  (Nassar et al. 2023; Karolchik et al. 2004 ).
Identifying high frequency deleterious variants in regions under selection Deleterious variants present in the regions under selection were identified using BEDTools  (Quinlan et al. 2010). We then computed the site-frequency-spectrium (SFS) on variants inside and outside these regions. We identified the sites at high frequency by keeping the alternate alleles present at a frequency equal or higher than 0. 7 in each sub-species. These sites were annotated with the Ensembl database, and the genes ontologies were extracted with PANTHER  (Thomas et al. 2022). To be able to compare the SFS of the different sub-species (see Figure  S3 ), we re-sampled the SFS with the smallest sample size, 10 haploid individuals, using the following equation: S ′ j = n-1 ∑ i= j S i i j n-i h-j n h where S is the original SFS of size n and S i the number of snps carried by i (haploid) individuals, S ′ is the re-sampled SFS of size h and S ′ j the number of variants carried by j individuals, and with h ≤ n.
pH-dependent activity and solubility of porcine pepsin in H 2 O (dashed line) and D 2 O (solid line). (a) Pepsin activity for pH reading range from 1 to 5. 0. (b) Pepsin solubility for pH reading range of 1-9. (c) 3-D ribbon model of the pepsin structure. PyMOL 2. 3. 4 (Schrödinger) was used to visualize protein structure (PDB code: 4PEP). Hydrophobic surface patches are in yellow and hydrophilic surface patches are in blue.
Acknowledgement This research was undertaken on the SAXS/WAXS beamline at the  Australian Synchrotron , part of  ANSTO  and the authors would like to acknowledge the  CSIRO AIM Future Science Platform  for supporting this work. The authors also acknowledge the help given by  Shveta Pandiancherri  and  Maggie Zhai  ( RMIT University ) for CD experiments and thank  Sampa Sarkar ,  Priscila Cardoso , and  Celine Valery  ( RMIT University ) for their help at the  Australian Synchrotron.
Hematopoietic differentiation from iPSCs Hematopoietic differentiation of iPSCs has been performed using a STEMDiff hematopoietic kit (05310; STEMCELL Technologies, France) according to the manufacturer's recommendations. Briefly, iPSCs have been dissociated in aggregates of 50À100 mm by using EDTA (0. 5 mM). Fifty aggregates have been seeded per well in a 12-well cell culture plate (Corning, France) coated with Geltrex (A1413202; Gibco, France). The medium was changed according to the manufacturer's instruction and floating cells were harvested on day +13 of hematopoietic differentiation. For the GDNF/GFRa1 experiment, 100 ng/mL of GDNF & GFRa1 mixed 1:1; (212-GD-010, 714-GR-100; R&D Systems, France) were added to the media. Cells were thereafter washed and resuspended in PBS. Stained cells were analyzed with a BD LSRFortessaTM (BD Biosciences, USA) flow cytometer and FlowJo analysis software.
Western blots Cells were lysed in ice with RIPA buffer. Separation of proteins was done by electrophoretic migration on a NuPAGE TM 4À12% Bis-Tris gel (NP0323BOX; ThermoFisher, France) under denaturing conditions. The proteins were transferred onto a PVDF membrane pre-activated with methanol. After saturation with TBS-Tween 5% BSA for 1h and hybridization of the membranes with primary antibody overnight (1:500, Ret #132507, R&D System, USA; 1:1000, Phospho-p44/ 42 MAPK (Erk1/2) (Thr202/Tyr204) (E10) #9106, CellSignaling, USA; 1:200, p-Ret (Tyr 1062)-R: sc-20252-R, SantaCruz, USA; p63 [EPR5701] #ab124762, Abcam, USA; 1:60000, B-Actin-Peroxidase #A3854, Sigma, France) and secondary antibodies coupled to HRP. Membranes were revealed by chemiluminescence with SuperSignal West Dura or Femto reagents and data were acquired using G:BOX iChemi Chemiluminescence Image Capture system.
Bioinformatics analysis Bioinformatics analyses were performed in R software environment version 4. 1. 0. Supervised differential expression analysis between RET-mutated cells and their CRISPR-corrected counterparts was performed LIMMA R bioconductor package version 3. 48. 3  [28]. Expression heatmap upregulated genes by RET mutation was drawn with pheatmap R-package version 1. 0. 12. Heatmap classification was performed with Euclidean distances and Ward. D2 method. Functional enrichment was performed with Toppgene website on Gene Ontology Biological Function and DisGeNET diseases databases  [29]. Functional molecular networks were drawn with Cytoscape standalone software version 3. 6. 0  [30].
RET C634Y mutation activates a hematopoietic molecular network that overlaps with MAPK cascade With the 109 genes found upregulated by RET C634Y mutation during iRET C634Y hematopoietic differentiation, a functional enrichment was performed with the Gene Ontology Biological Process database (GO-BP) (Figure  5A ). This analysis highlighted best-ranking enrichment with several gene sets implicated in the MAPK cascade, followed by enrichment with gene set implicated in the immune system and hemopoiesis (Figure  5A ). Overlap between these enrichments shows that 4 genes belonging to the 3 enriched functions including SRFP1, FCGR2B, HLA-DRB1, JUN (Figure  5B ). Similarly, 6 genes were both implicated in the immune system and hemopoietic regulations including NR4A3, DLK1, TMEM17B, JAG1, TCTA and TMEM176A (Figure  5B ).
DLK1 is known to be a negative regulator of emerging hematopoietic stem and progenitor cells  [37]  and NR4A3 is known to restrict HSC proliferation via reciprocal regulation of C/ EBPa and inflammatory signaling  [38]. All together, these results suggest that MAPK cascade could potentially be activated downstream to RET C634Y mutation and promote the regulation of HSCs potential during iPSC-derived hematopoietic differentiation. The action of RET C634Y mutation could occur at a primitive stage of hematopoiesis because both hemopoiesis and lymphopoiesis are affected by these regulations (Figure  5C ). This idea was confirmed by the fact that after functional enrichment on disease, with the DisGeNET database, a larger functional enrichment network could be built with molecules implicated both in lymphoid (Chronic lymphoid leukemia, multiple myeloma) and myeloid (acute promyelocytic leukemia, myelodysplastic syndrome) disorders (Supplemental Figure  2AÀB ).
Mediterranean Basin Sampling of 27 assumed wild olive tree sites was conducted along a north-south gradient from southern France to southern Morocco in 2021 and early 2022. Sites were selected via the Conservatoire Botanique National Simethis database (http://simethis. eu) for southern France and northern Spain whereas for Corsica, central and southern Spain and Morocco, the delineation of wild populations was based on plastid polymorphism as reported by  Besnard, Bakkali, et al. (2013). In addition to information from the Simethis database, we used environmental criteria to limit sampling of admixed olive populations and disregard olive orchards, agricultural and urban environments  (Copernicus Land Monitoring Service, 2020). This resulted in the selection of 27 sites (Table  1 ; Figure  1A ).
Acknowledgements We thank  Guillaume Perez ,  Magalie Delalande ,  Karine Loridon ,  Juliette Arzul  and  Sylvia Lochon-Menseau  for helping us in wild olive sampling from in situ areas;  Anaïs Fossot ,  Hélène Vignes  and  Ronan Rivallan  for the laboratory help. We acknowledge the helpful comments from  Joëlle Ronfort  and the English corrections from  David Manley. A part of a field work has been supported by the  Conservatoire Botanique National Méditerranéen de Porquerolles, l'Institut Agro Montpellier and INRA Morocco. This work has been realized with the support of  MESO@LR-Platform  at the  University of Montpellier , the financial support of  CIRAD , and the technical support of the bioinformatics group of the  UMR AGAP Institute , member of the  French Institute of Bioinformatics (IFB)  -South  Green Bioinformatics Platform   Lison Zunino  received a PhD scholarship from the  French government.
Sequence analysis from public databased to determine M-protein Fab N-glycosylation site The construction of M-protein Fab region sequences has been described extensively elsewhere  3, 6. In short, RNA-seq datasets from myeloma plasma cells were processed by MiXCR to construct the CDR3 sequences  22. In case of incomplete sequences, Targeted Assembly of Short Sequence Reads was used  23. Nucleotide sequences were then translated into (poly)peptide sequences and germline amino acid alterations were determined by HIGH-VQUEST on the IMGT platform. The Multiple Myeloma Research Foundation (https://themmrf. org/finding-a-cure) provided access to the data from the CoMMpass study on the NIH dbGAP platform (Accession: phs000748. v6. p4). At the time of analysis, 107 full length Fab region sequences were available for analysis.
The cAb-Rep is a database of curated immunoglobulin repertoires from both healthy and diseased donors  24. The cAb-Rep database was accessed through the web browser interface, where FASTA files containing the nucleotide sequences of the Fab region of immune repertoires of all healthy donors were retrieved. Sequences from either IgG heavy chain (HC) or Kappa or Lambda LCs were used for sequence analysis. The Fab region sequences were analyzed by an in-house made R script (Version 3. 5. 3) that determines whether an N-glycosylation motif is present in the sequence. An N-glycosylation motif was defined as a asparagine residue followed by any amino acid besides proline followed by a serine, threonine or cysteine (non-canonical) residue (Asn-X-Ser/Thr/Cys)  25.
Samples from patients with Multiple Myeloma Genomic data and serum samples were collected from the IFM 2009 clinical study (ClinicalTrials. gov identifier NCT01191060 trial) after written informed consent, and clinical and genomic data were de-identified in accordance with the Declaration of Helsinki and approval for this study was provided by our Institutional Review Board (2018-4140)  26. The Fab M-protein sequences were determined by analysis of a 100 000 read sample extracted from RNA sequencing data. All serum samples were stored at -80 0 C upon arrival.
Data Analysis The Data Analysis of glycoproteomic LC-MS/MS datasets and their integration with genomic data is extensively described in the supplementary information (Supplementary Materials & Methods). In short, LC-MS/MS datasets were analyzed to identify glycopeptide fragmentation spectra, of which the glycan moiety and peptide were identified based on database searches. For the peptide database search, a custom database was created containing the M-protein Fab region sequences. The Mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  27  partner repository with the dataset identifier PXD040718.
Increased de novo M-protein N-glycosylation sites in patients with Multiple Myeloma The presence of Fab N-glycosylation on the M-protein LC has proven prognostic value for plasma cell dyscrasia, mainly in AL amyloidosis  16, 17. Here, we investigated whether Fab N-glycosylation sites are more commonly observed in the M-proteins of patients with Multiple Myeloma compared to immunoglobulins in healthy individuals. To determine the occurrence of an N-glycosylation sites in the Mproteins of patients with MM, we reconstructed the M-protein Fab sequences based on RNA-seq data present in the CoMMpass database. We could reconstruct the full-length sequence of 104 HCs and 93 LCs. An N-glycosylation site was observed in 11. 5% of the HCs and in 11. 8% of the LCs (Figure  1A ). This implies that Fab-glycosylation sites were observed in 22% of the M-proteins.