dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Mickael Dupont ,  Mickael Delanoue ,  Ludovic Metivier , and  Christophe Mouaze  of the  Experimental Unit UEPAO  for animal management and involvement in the experiment. N. Mellouk  is a PhD student supported by the  Ministère de la Recherche (Ministry of Research). We thank also ProofReading  Service Company  for the English editing of this manuscript.
However, elucidation by experimental methods of the structure of protein-peptide complexes still remains a challenge, due to the high flexibility of linear peptides  [17, 18]  and their transient interactions. In addition, peptides may have a variable size, from a few residues to dozens of amino acids  [19]. Consequently, the use of bioinformatics and in silico tools is a complementary approach that is increasingly applied to predict the structure of peptide-protein complexes  [20]. Several molecular modeling methods and bioinformatics tools dedicated to the prediction of the structure of peptide-protein complexes have been developed in the past decade  [18]. Methods such as PepATTRACT  [21] , PEP-FOLD3  [22] , Rosetta FlexPepDock  [23] , HADDOCK  [24] , SP-peptide module of Glide  [25] , and others  [26, 27]  have proven their capacity to predict peptide-protein complexes.
Fragmentation of peptides is one of the methods to address the problem of peptide flexibility. Many in silico tools have already been developed to design ligands from fragments, such as LUDI  [29] , Dynamic Ligand Design (DLD)  [30] , or AutoGrow  [31]. For the latter, the program is now able to predict drug-like properties and to optimize ligands with click chemistry rules  [32]. However, those tools, which are used in several docking programs, such as Hammerhead  [33]  or FlexX  [34] , are not specifically developed for peptides, for which few tools currently exist. In the 1990's, GROW was developed for the prediction of peptide-protein interactions, using single amino acids to construct peptides of interest within cavities  [35]. Even if the approach brought, at that time, very useful insights into the design of peptidomimetics for the inhibition of HIV protease  [36] [37] [38] , it suffers from several drawbacks, such as the conformational sampling of the molecules in order to propose different solutions, or the prediction of the position of the starting seed.
In this current work, starting from the initial idea to consider peptide fragments instead of the whole peptides, we have designed a novel approach to predict peptide-protein interactions with the use of a covalent docking approach. The covalent docking protocol was initially developed to predict the covalent bond between an inhibitor and its biological target. We applied this concept to the prediction of protein-peptide interactions, but without creating a covalent bond between the peptide and the protein. Covalent docking is used here to iteratively build the peptide from several segments inside the binding site of a given receptor, which consists of a novel methodology employing this method. Our new approach has been compared to two docking modules of Glide  [39] , from the Schrödinger Suite, Standard Precision (SP), and SP-Peptide.
Furthermore, we compare the results obtained with this model to those obtained by two classical docking protocols present in the same Glide software: standard precision (SP) protocol and SP-peptide protocol, developed specifically for protein-peptide interactions. All docking poses were rescored with the Extra Precision (XP) scoring function. After rescoring with the XP scoring function, we selected all docking poses from the highest scoring pose to the pose with the lowest RMSD to the X-ray structure. We also took into account the position of reasonable docking poses, meaning poses with a deviation lower than 2 Å and 3 Å, considering the backbone or the whole peptide, respectively. For analysis, RMSD calculation was performed without hydrogens. In general, the crystal structure of the peptides with a RMSD lower than 2 Å, considering the whole peptide compared to the docking pose, was found in at least one protocol for all case studies, but did not match with the top scoring position.
Highest Scoring Pose The top scoring solution of each methodology was obtained by ranking all poses with Glide XP scoring function. In addition, we also analyzed the rank of the first pose having a root-mean square deviation (RMSD) below 2 Å and 3 Å for the backbone and the whole peptide, respectively. Figure  1  represents the RMSD of the top scoring pose, after rescoring all poses with Glide XP scoring function, compared to the X-ray structure of each peptide using the three protocols. Details of all calculated RMSD values of the top scoring position are given in Table  S3. Taking into account only the top scoring solution, the results show good RMSD values for some peptide-protein complexes but are unreasonable for some other examples, regardless of the applied protocols. SP protocol fails to retrieve a correct solution for four complexes (PDB ID 1OSZ, 3CH8, 3MMG, and 4GRV) with a RMSD below 2 or 3 Å when considering the backbone or the whole peptide, respectively, but it can predict a solution very close to the X-ray structure, such as for PDB ID 4NNM.
This classical docking protocol developed for protein-ligand complexes has shown high success in many previous protein-ligand studies  [40] [41] [42]. For half of the systems, IRDL can retrieve the top scoring solution with a RMSD below 2 or 3 Å when considering the backbone or the whole peptide, respectively. Results associated with the second half of the system are sometimes slightly above the limits of 2 and 3 Å, except for PDB ID 4RXH and 1OSZ systems, where the RMSD is high. Similar results are obtained with the SP-Peptide protocol. Considering not only the top scoring docking solutions but also the rank of the first structure fulfilling the criteria for a correct docking solution, i. e. , with a RMSD below 2 Å or 3 Å when considering the peptide backbone or the whole peptide, respectively, the results are slightly different (Table  1 ).
Furthermore, those poses conserve most of the hydrogen bond interactions present in the crystal structure. As often observed in docking experiments, the missed hydrogen bonds are compensated by other hydrogen bonds present with close residues  [41]. These observations assess the good efficiency of the IRDL protocol to retrieve the correct docking poses of the peptides and the conserved interactions observed in crystal structures. While SP protocol is able to correctly retrieve the binding mode of the opioid peptide Tynorphin (PDB ID 3T6B) at the first position, SP-peptide is, surprisingly, not able to retrieve any correct docking solution for this complex. For SP and SP-peptide, several complexes lead to docking issues. Four peptides are not docked correctly using the SP protocol (PDB ID 1OSZ, 3MMG, 4GRV, and 3CH8), and likewise for two peptides using the SP-peptide protocol (PDB ID 3T6B and 4RXH).
For these six complexes, SP and SP-peptide protocols are not able to retrieve any docking solution with a RMSD below 2 Å or 3 Å, considering the peptide backbone or the whole peptide, respectively. However, one has to keep in mind that the SP protocol was not developed specifically for protein-peptide complexes. Furthermore, considering the results presented in Table  1 , the IRDL method outperforms the two other protocols regarding the ranking of a correct docking solution. While the SP-Peptide can provide a correct solution for nine systems, the IRDL protocol always suggests a better rank of a correct solution regarding our RMSD criteria, considering either the backbone or the whole peptide, except for PDB ID 4Q6H, where IRDL retrieves the correct solution for the second docking solutions.
PDB Code IRDL SP SP-Peptide BB WP H-Bond Number Ratio a BB WP BB WP 1OSZ 2 2 1 - - 12 2 2D5W 1 1 1 1 1 1 1 3CH8 1 1 1 - - 1 1 3DRF 6 1 0. 9 11 1 32 4 3MMG 1 1 1 - - 3 2 3T6B 67 1 0. 75 1 1 - - 4GRV 1 1 0. 5 - - 1 Despite the fact that we succeeded in demonstrating the efficiency and the originality of the IRDL method, in its current state, some limitations still exist. First, the position of the first fragment is a crucial step for building the peptide. Currently, the C-terminus is initially docked in the binding site, allowing building from the C-terminus to N-terminus only. Second, the first fragment must interact strongly with the receptor, since its position in the active site is critical for the success of peptide building. One improvement would be to dock all fragments and then grow them from the C-to N-terminus and N-to C-terminus sides.
Poses with the Lowest RMSD to the Crystal Structure In a second analysis, we checked if the IRDL protocol was able to retrieve the crystal structures of the eleven considered peptides, regardless of their ranking. Results, presented in Figure  2 , show that in each case, IRDL was able to retrieve the crystal structures of the peptides bound to proteins with excellent precision. The docking pose with the lowest RMSD to the crystal structure was below 2 Å or 3 Å for each system, considering the backbone only or all atoms of the peptide, respectively. Remarkably, for some systems (PDB ID 1OSZ, 2D5W, 3CH8, 4GYO, and 4NNM), IRDL protocol was able to retrieve the top scoring pose with sub-angstrom deviation compared to the crystal structure. In comparison, considering the peptide backbone only, SP method was not able to retrieve a correct solution for four complexes, namely PDB ID 1OSZ, 3CH8, 3MMG, and 4GRV.
Moreover, except for PDB ID 4NNM complex, SP protocol was not able to retrieve any docking solutions with sub-angstrom precision compared to the crystal structure, considering the entire peptide. Details of the best RMSD values are given in Table  S2. Interestingly, SP-peptide protocol, which was developed specifically for the docking of peptide-protein complexes, did not outperform the two other methods in the prediction of protein-peptide interactions. For example, SP-Peptide was not able to find a satisfying docking pose for PDB ID 4RXH, the best pose with the lowest RMSD value, which presents a deviation equal to 4. 7 Å and 7. 5 Å compared to the crystal structure, considering the backbone and the whole peptide, respectively. In comparison, IRDL approach and SP protocol are able to retrieve excellent binding modes for the peptide for this complex with a similar RMSD of 1.
5 and 2. 0 Å for the backbone and whole peptide, respectively. Considering the PDB ID 1OSZ, 3MMG, and 3T6B, SP-peptide predicts the best poses of the peptides with a RMSD above 3 Å deviation from the crystal structure, considering the whole peptide (Figure  2 ). Regarding the IRDL approach, it appears that the results are always better than SP-peptide for those three cases (RMSD below 3 Å). However, results are better for SP-peptide when considering only the backbone (RMSD between 2. 0 and 3. 5 Å), but the values are still too high for accurate protein-peptide studies in drug design projects. Indeed, such deviations can bring about large errors in the orientation of the side chains, as shown in Figure  3  for PDB ID 3MMG. The RMSD of 2. 0 Å obtained for PDB ID 3MMG is mainly due to the side chain of the last residues of the peptide.
In the case of IRDL, the RMSD is only 0. 8 Å and the last residues are better superimposed on the crystal structure. Surprisingly, for both complexes of PDB ID 4RXH and 3T6B, SP protocol gives better results for the pose with the lowest RMSD of the X-ray structure than SP-peptide, suggesting that these protocols are also protein-peptide complex dependent. For the other systems, we observe that IRDL protocol provides equivalent or better results (PDB ID 1OSZ) than those obtained with SP and SP-peptide protocols. The same observation could be made considering the whole peptide. Figure  3  represents the superimposition of the crystallographic structures, with the docking poses having the lowest RMSD to the X-ray structure obtained for PDB ID 3T6B, 1OSZ, and 3MMG for the three different docking protocols.
For PDB ID 3T6B complex, IRDL is able to predict the crystallographic binding mode of the opioid peptide Tynorphin with excellent precision (RMSD 1. 1 Å) when considering the backbone. Regarding the whole peptide, it appears that the side-chain of the tyrosine residue is misplaced compared to the crystallographic position, where it interacts with F443 of human DPPIII. In this case, the phenol interacts with H450 in a buried part of the cavity, and makes a hydrogen bond with I390 (see Figure  S1 ). The positions of the others residues are well superimposed with the residues of the crystal structure. SP method also suggests a correct solution (RMSD 1. 4 Å), but it provides the wrong placement for the last tryptophan residue. The position of its side chain and the C-terminus are significantly shifted, pointing toward I672 (see Figure  S1 ).
Thus, it seems that in this particular case, splitting the whole peptide into segments induces a better exploration of the conformational space, and allows the docking algorithm to find the correct binding mode of the fragment more easily. In the case of PDB ID 1OSZ, RMSD values for the pose with the lowest RMSD to the X-ray structure show that our method suggests an excellent solution that is very close to the X-ray structure of the 8-residue mutated vesicular stomatitis virus (VSV8 V4L) peptide. The deviation shows sub-angstrom accuracy regarding the backbone (RMSD of 0. 6 Å) or the whole peptide (RMSD of 0. 9 Å). For comparison, SP-peptide provides a reasonable docking solution of 1. 9 and 3. 1 Å for the backbone and the whole peptide, respectively, while SP method is not able to retrieve a solution below 6 Å.
Regarding the structural details, IRDL is able to retrieve all major protein-peptide interactions present in the crystal structure (Figure  3 ). SP-Peptide protocol retrieves the atomic positions of the backbone with a good accuracy, but the orientation of the side chain of the docked tyrosine at the fifth position (Y5) is poorly predicted, since this residue interacts with E152 of class I MHC H-2 Kb instead of with hydrophobic residues V9 and V97. Consequently, Q6 residue of the peptide, which interacts with E152 in the crystal structure, is also mispredicted and points toward the bulk solvent. Regarding the docked solution obtained by SP, the peptide position is strongly deviated compared to the crystal structure. The same observation can be made for the case of the PDB ID 3MMG complex, where the mutated Tobacco vein mottling virus (TVMV) is bound to canonical peptide substrates of 8 residues.
Datasets Peptide-protein systems were downloaded from the PDB database (www. rcsb. org) [43]. Several criteria have been considered for system selection: only complexes containing peptides interacting with proteins are kept, resolved by X-ray crystallography method, and with a resolution below 3 Å. Structures have only natural residues in their sequence. Complexes in which the peptides are highly solvent-exposed and make numerous interactions with crystallographic water molecules were discarded in order to avoid additional complexity in evaluating IRDL. Visual inspection was performed to check the mode of interaction of the peptides. Finally, 11 structures were selected for this study (PDB ID: 1OSZ, 2D5W, 3CH8, 3DRF, 3MMG, 3T6B, 4GRV, 4GY0, 4NNM, 4Q6H, 4RXH). Details of these structures are presented in Table  2.
As mentioned in Table  2 , the 11 peptide sequences contain between 5 and 8 amino acids, and between 15 (PDB ID 3T6B) and 43 (PDB ID 4RXH) torsion angles. Peptide sequences are detailed in the Supplementary Materials (Table  S1 ). One can observe that different types of peptides were used, covering a diversity in flexibility and peptide types (charged, uncharged, hydrophobic).
SP and SP-Peptide Docking All peptides were prepared with the same protocol: a conformational search was performed using MacroModel  [46]  in order to propose different starting points for docking calculation. Default parameters for the conformational search (Mixed Torsional or Low-mode sampling algorithm) were considered. All conformations generated by MacroModel (up to 200 conformations) were used as starting points for docking. This first step was used to avoid the bias during docking introduced by using the crystallographic coordinates as the starting point. SP and SP-Peptide modules from Glide 6. 5  [39]  were used to perform the docking of the 11 ligands with default parameters. During docking, the receptor is considered fixed, while the peptide is kept fully flexible.
To reconstruct the peptide, segments were linked together by intramolecular covalent docking. In the covalent docking module of Glide  [47] , the calculation must be associated with a chemical reaction. A set of reactions is already included in the module, but the user can add new synthesis reactions in the database. Consequently, there is currently no limit on the reaction needed to perform the fragment growth. Here, we decided to choose a simple nucleophile addition/electrophile reaction, as shown in Figure  4 , involving an acid chloride moiety on one of the fragments. This fragment (fragment n + 1) will react with the N-terminal amine of fragment n in order to create the peptide bond. This means that the reconstruction process of the peptide within the binding site goes from C-terminus to N-terminus.
Regarding the fragmentation of the peptides into short segments, we set up several rules to optimize the procedure: (a) Segments must have 10 or less rotatable bonds. This criterion allows simplification of each calculation by reducing flexibility, therefore improving the conformational search during docking. (b) To avoid unfeasible covalent docking solutions, we considered that the N-terminal residue should not contain any nucleophilic sidechains. Thus, residues such as Tyr, Cys, Ser, Thr, Asn, and Gln were excluded as N-terminal residues in a fragment, since the covalent docking module of Glide considers those amino acids as nucleophilic moieties. Otherwise, when the molecular topology of the fragment is not conserved after intramolecular covalent docking, the solutions are removed during the post-process analysis (see below).
Once the peptides were fragmented, IRDL methodology was applied as follows: (1) After a conformational search with MacroModel of the first fragment, as performed for SP and SP-Peptide docking, all conformers were docked in the binding site using the SP-Peptide module of Glide with default parameters. Then, we kept all docking poses up to the pose with the lowest RMSD to crystal structure. In a more realistic project, one would keep all docking poses, which will increase computational time but could improve the results. (2) The second segment is docked using the covalent docking module of Glide (version 1. 3) on each of the selected scoring poses of the first fragment. To allow the chemical reaction to occur, an acid chloride moiety is used on the C-terminal residue of the second fragment. During docking, this acid chloride moiety is automatically placed nearby the primary amine backbone of the first docked fragment.
iORF annotation and conservation level Orthologous intergenic regions in contemporary strains and their ancestral sequences were aligned using MUSCLE  (Edgar 2004 ) with default parameters. Intergenic regions with a global alignment of <50% identity among strains (including gaps) were removed. We defined iORFs as any sequence between canonical start and stop codons, in the same reading frame, and with a minimum size of three codons, using a custom Python script (Supplemental Code). We extracted a presence/absence matrix based on the exact conservation of the start and the stop codon in the same reading frame (Fig. 1 ; Supplemental Fig. S1 ). iORF alignment coordinates were converted to genomic coordinates on the respective genomes and were removed if there was any overlap with a known feature annotation (rRNA, tRNA, ncRNA, snoRNA, nonconserved genes, pseudogenes) on the reference S288C.
S1 ). For iORFs with a minimum size of 60 nt, we also performed a sequence similarity search against the proteome of NCBI RefSeq database  (O'Leary et al. 2016)  for 417 species in the reference RefSeq category and in the representative fungi RefSeq category (containing 237 fungi species). iORFs with a significant hit (e-value <10 -3 ) were removed to exclude any risk of having an ancient pseudogene. The genomic coordinates of annotated genes and iORFs are available in Supplemental Data.
Libraries were sequenced on an Illumina HiSeq 2500 platform at the Génome Québec Innovation Centre  (Montréal, Canada). Raw reads are available from the NCBI SRA database (https://www. ncbi. nlm. nih. gov/sra) under accession numbers SRR5996796 to SRR5996803 for ribosome profiling and SRR6022778 to SRR6022785 for total mRNA experiments.
Detection of translated iORFs All sequencing libraries were processed using the same procedure. Raw sequences were trimmed of 3 ′ adapters using Cutadapt  (Martin 2011). For RPF data, reads with lengths of 27-33 nt were retained as this size is the most likely to represent footprinted fragments. For mRNA, reads of 27-40 nt were retained. Trimmed reads were aligned to the respective genome of each sample using Bowtie version 1. 1. 2 (Langmead et al. 2009 ) with parameters -best -chunkmbs 500. The identification of translation signatures was performed on annotated genes conserved by synteny and on iORFs ≥60 nt. We used ribosome profiling read density to detect the translated iORF among overlapping ones. Metagene analysis was performed to detect the P-site for each read length between 28 and 33 nt using the metagene, psite, and get_count_vectors scripts from the Plastid package  (Dunn and Weissman 2016)  on the two pooled replicates of each strain.
We extracted the read densities from the 5 ′ end of the mapped reads, and the distance between the largest peak upstream of the start codon and the start codon itself is taken to be the P-site offset per read length. The 5 ′ end of each mapped read was then subtracted by the P-offset estimate to determine the P-site of each RPF read. Figure  2  and Supplemental Figure  S3  were made using an R script (Supplemental Code; R Core Team 2013). We identified translation initiation signals from ribosome profiling per base read densities by detecting peaks at the start codon using a custom R script (Supplemental Code). We defined three precision levels of peak initiation: "p3" if the highest peak is located at the first nucleotide of the start codon; "p2" if there is a peak, which is not the highest, at the first position of the start codon; and "p1" if there is a peak at the first position of the start codon ±1 nucleotide.
A minimum of five reads was required for peak detection. Read phasing was estimated by the number of aligned reads at the first, second, or the third position for all codons, excluding the first codon, in the first 51 nt of the considered iORF or gene. We then tested for a significant deviation from the expected ratio with no periodicity, that is, 1/3 of each, with a binomial test. We applied an FDR correction for multiple testing. A minimum of 15 reads was required for phasing detection. iORF families or genes with an initiation peak and a significant periodicity, that is, an FDR corrected P-value <0. 05, in at least one strain were considered as translated and named tORFs. For translation signature detection with the RiboTaper software  (Calviello et al. 2016) , we used read lengths, for which we obtained the best in-frame phasing with annotated genes according to quality check plots provided by RiboTaper, and which are 30-31 nt for SpA, 30-32 for SpB, and 31-32 for SpC, as well as a P-offset of 13.