dataesr/scientific-paragraphs-categorization huggingface dataset:
On the other hand, programs based on physicochemical  (7, 8)  and evolutionary  (9)  features are not competitive in terms of prediction accuracy  (10). Furthermore, the approach of NA binding has not been unified in prediction. RNA-and DNA-binding residue predictions are always treated as different problems or trained with different datasets within the same framework  (5, 11, 12). Finally, the current predictions formulate the problem as a binary classification problem (binding or not binding) that tends to overemphasize comparisons between residues in different proteins (Supplementary Note 1). Here, on the basis of the coordinates of protein structures, we show that the protein electrostatics potential, accessible surface area (ASA) and sequence conservation entropy (CE) can be used in predicting RNA-binding residues achieving both high and stable accuracies on different datasets.
Datasets RBP structures were obtained from NPIDB database (13) (Jan 2014) with resolution better than 3. 5 Å and R factor <0. 3 as criteria. PISCES  (14)  and TMalign  (15)  were used to check sequence and structural similarity. Sequence identity <25% and TMscore <0. 7 were used as thresholds to remove redundancy. In the results, 130 protein chains (named as R130) were collected as a training set, while other 117 protein chains (R117) were taken as an independent test set. The 130 proteins in the R130 training set are annotated by protein names, and several typical RNA-binding domains are found and annotated. RNA-binding domains have also been checked for redundancy. Some previous works, especially sequence-based predictors that only consider sequence identity to remove redundancy of the datasets, cannot guarantee the absence of homology between the training and test datasets.
In such cases, the resulting datasets would lead to a situation of training and test with similar data included, and the models do not have predictive ability. Besides, a test set of 381 DBP (D381) was also prepared in the same way but with sequence identity <25%, resolution better than 3. 0 Å and R factor <0. 3. Since D381 is only used for test, structural similarity was not considered. A further 14 RBP and 11 DBP datasets were collected from previous programs to assess prediction accuracy, including BindN R107 (107RBP) and BindN D62 (62DBP) from BindN+ (5), PPRInt R86 (86 RBP) from PPRInt  (16) , RNABindR R144  (17) , RNABindR R147  (18) , RNABindR R44 and RNABindR R111(6) from RNABindR/RNABindRPlus, meta2 R44 from (4), aaRNA R67, aaRNA R141 and aaRNA R205 from aaRNA  (19) , Sungwook R267 and Sungwook R727 from  (20) , Shandar D140 from  (21) , Susan D56 from  (22) , DBindR D374 from DBindR  (23) , DISPLAR D428 from DISPLAR  (24) , DNABINDPROT D54 from DNABINDPROT  (25) , PreDNA D224 from PreDNA  (26) , metaDBSite D232 and metaDBSite D316 from metaDBSite  (27) , SDCPred D159 from SDCPred  (28).
Some unreasonable cases were excluded from the assessment datasets: (i) the presence of a DBP in an RBP set (PDB ID 1a1v); (ii) superseded PDB structures; (iii) peptides shorter than 20 residues; (iv) weak and uncertain NA binding proteins including those with less than three binding residues; (cascade complex as an example in Supplementary Note 2) (v) PDB chains containing only C␣ atoms; (vi) proteins constituted by two separate short peptides.
We suggest the weighted arithmetic mean of AUC (wAUC) and mean of AUC (mAUC) as two criteria of accuracy for a set of proteins: wAUC = AUC(i ) × len(i ) len(i ) mAUC = AUC(i ) N For a protein i, AUC(i) is its AUC value and len(i) is length of the protein, while N is the number of proteins in a dataset. We call the AUC that compare all the residues in a dataset together as total AUC (tAUC) and still use it as a reference for comparison. However, even a high tAUC does not necessary imply a high accuracy on each of the proteins when wAUC and mAUC are low, since tAUC overestimates unnecessary comparisons between residues of different proteins.
Support vector machine based approach As a reference, a support vector machine (SVM) based approach similar to other machine-learning methods was adopted  (5, 16, 40). The position specific scoring matrix (PSSM) is generated by PSI-BLAST  (41)  program against Swiss-Prot (42) sequence database. An 11-mers (five sequential neighbors on each side of target residue) slide window is used to represent local information of a residue. The input information that is directly adopted from PSSM includes 220 (11×20) integer values. The SVMlight  (43)  was used to construct SVM classifier. RNA binding sites are defined as residue within 3. 5 Å of any RNA atom. A 5-fold crossvalidation was used to train on the 130 RBP training set.
Homology modeling test Alignments used to build homologous models were generated by TMalign  (15)  between the bound and unbound structures. Structure models used in prediction were built by MODELLER  (44)  based on the unbound state structures as templates and the alignments. Datasets for the homology modeling test are adopted from  (45) , DR bind1  (46) , DRNA  (47) , OPRA  (48) , Protein-RNA docking benchmark 1. 0 (49), Protein-RNA docking benchmark I (50) and II  (51) , DBD-Hunter (52), Protein-DNA docking benchmark  (53) , DISPLAR  (24)  and DNABINDPROT  (25).
Accuracy comparison with SVM Current prediction methods for NA-binding residues normally compare all residues in all proteins together to measure the area under the ROC curve (AUC) for assessment. However, the comparison between residues of different proteins is not necessary. To avoid a biased assessment, we first measure the AUC of each protein and assess the prediction accuracy of a dataset with the weighted mean of AUC (wAUC) or mean of AUC (mAUC) (see Supplementary Note 1 and Materials and Methods for descriptions). An SVM-based approach similar to previous studies  (5, 6, 16)  was built alongside RBscore, as a reference, sharing the same training (R130) and test (R117) datasets (see Materials and Methods). A 5-fold cross-validation was carried out on the training set and the best model was used for tests.
The results of the cross-validation can be found in Supplementary Table  S2. Both the approaches were tested on 14 RBP datasets and 11 DBP datasets. As shown in Figure  1A  and Supplementary Table  S3 , although the SVM approach achieves a much higher accuracy, 0. 947 wAUC, on the training set than RBscore, 0. 886, it drops significantly to 0. 719 in contrast to the stable accuracy of 0. 867 for RBscore in test set. Consistently, tAUC and mAUC also demonstrate a stable high accuracy for RBscore on all other datasets no matter what distance cutoff is used to define the NA binding sites. Besides, Figure  1B  also illustrates that RBscore is less likely to be subject to accuracy fluctuation with distance cutoff compared with the SVM approach. Its accuracy variation is less than that of the SVM approach on the majority of the datasets.
This implies that RBscore is stable in its predictive capability regardless of dataset and distance cutoff used in binding site definition. Furthermore, RBscore exhibits high prediction accuracies consistently on all 11 DBP test sets with ∼0. 90 wAUC, compared with <0. 80 wAUC for the SVM approach (see Table  1  and Figure  1 ). According to the results, the conclusion also holds true as the distance cutoff used to define binding sites change or assessed with other accuracy criteria. Unexpectedly, RBscore achieves even higher accuracy on DBP than on RBP. This demonstrates that although RBscore is trained with cross-validation on RBP, it can capture the key features of all NA-binding residues including DBP. Also, we have an indication that proteins bind to both DNA and RNA following the same rules of recognition or employ the same driving force, such as electrostatics potential and residue accessibility.
Contributions of the three features and neighboring network As the three features (Electrostatics, CE and Solvation energy, see Materials and Methods for detail) used in RBscore stand for different aspects of NA-binding residues, Figure  2  and Supplementary Table  S3  illustrate that there is no overlap amongst them and that each of these features improves the prediction accuracy. RBscore is consistently better than predictions with single feature alone. And such improvements hold true when tested on every dataset. The neighboring networks in RBscore include two parts: (i) the spatial neighborhood (based on surface continuity, as described in Materials and Methods) for residues forming a binding interface as a continuous patch on the protein surface, and (ii) the sequential neighborhood that reflects the covalent and local non-bonded contacts.
Comparison with other programs For comparisons, nine currently available web servers (BindN  (12) , BindN+ (5), PPRInt  (16) , KYG  (54) , RN-ABindRPlus (6), DISPLAR  (24) , DBS-Pred (  21 )), DBS-PSSM (  55 ) and  (19)  for predicting RNA/DNA binding were tested together with RBscore and the SVM approach on 25 different datasets (see Supplementary Table  S3  for detailed results). The top three programs BindN+, RN-ABindRPlus and aaRNA are compared in Figure  3. A priori, it is not surprising that DBP and RBP may adopt similar driving features in binding and the prediction programs can achieve the two simultaneously. However, such a prediction achievement is not reached by most predictors, except for RBscore and aaRNA (see Figure  3 ). Machine-learning methods can demonstrate strong advantages in interpolation but not in extrapolation, the prediction power on DBP may be limited.
The results for BindN and KYG show stable but low prediction accuracies, 0. 68-0. 72 and 0. 73-0. 77. BindN+ and PPRInt present high accuracy in terms of total AUC, but are very unstable in different datasets. wAUC ranges are 0. 76-0. 89 and 0. 70-0. 85, while mAUC also shows the same trends. As BindN+ employ two SVM models to predict RNA and DNA binding sites, each model performs better on its respecting type of proteins. RNABindRPlus, that integrates machine learning and homologous search strategies in RNA binding site prediction, shows good accuracies    of tAUC on some of the RBP datasets, but is less accurate in terms of wAUC and in DBP datasets. In brief, relative to all these comparisons, RBscore achieves wAUC >0. 85 (3. 5 Å distance cutoff) on all types of datasets with stable accuracies.
As different programs may use different distance cutoffs to define RNA binding sites, assessments with different cutoffs from 3. 5 Å to 6 Å were also carried out and similar conclusions could be drawn. We found that the programs normally favor the implemented distance cutoffs, but the accuracy variations are less than the differences between different datasets. Generally, wAUC accuracies for RBscore are still in the range >0. 83. The machine-learning-based methods  (5, 16)  have similar philosophy and accuracy distribution as our SVM approach. This may result from (i) the bias introduced by cross-validation in pattern recognition  (56)  (see Supplementary Note 3 for discussion); (ii) some sequence-based predictors include datasets without removing structural homology, leading to the overestimation in accuracy (see Supplementary Note 4 for discussion).
Thus, RBscore represents adequately the binding probability of NA and the energy funnel on protein surface. When we compare RBscore with the minimum distance from a residue to NA (Figure  4E ), we find that the residues closer to NA normally have better RBscore and the distribution is similar to an energy funnel that has been described in molecular docking. Although mixing all the proteins together for comparison is unreasonable, Figure  4F  still shows the trends of an energy funnel when 44 RBPs were mixed together. Additionally, we measured the correlation coefficient between RBscore and the minimum distance from a residue to RNA/DNA for residues around the binding region. The average values of these correlation coefficients of all the test sets are listed in Supplementary Table  S4. As all the average correlation coefficients of the datasets are around 0.
Interestingly, three of them (R, G, K) belong to the disorder-promoting amino acid types and the three other belong to the ambivalent class (H, T, D)  (58). Even if RNA-binding residues are defined by different distance cutoffs or tested in different datasets, the Pearson correlation efficiency between the ratios of the six residues and ratios of RNA-binding residues is always around 0. 8. Thus, the number of NA binding sites can be roughly estimated according to the proportion of these six residue types, a detailed example can be found in Supplementary Figure  S4.
Prediction based on homologous structures In a real-world case, since the starting structure influences the prediction accuracy, prediction should be based on the unbound state structure rather than on the bound state. Therefore, we tested RBscore together with other programs on 11 unbound protein datasets, including seven RBP datasets and four DBP datasets. Each protein in the datasets had a corresponding bound state structure. Structures used in prediction were modeled by homology modeling according to the unbound state structure as the template and predicted binding sites are compared with the observed binding sites in the bound state. The results are plotted in Figure  5 , while the relationships between the predicted structure model quality and binding site prediction accuracy are plotted in Supplementary Figure  S5.
Similarly to the test shown in Figure  3 , RBscore and aaRNA demonstrate stable high accuracy on all the datasets, while RNABindR-Plus exert highest accuracy in all RBP datasets but accuracy drops on three DBP datasets. Such high accuracy on RBP datasets may be attributed to the homologous search approach (HomPRIP) integrated in RNABindRPlus. Although tested with limited number of proteins in unbound test, RBscore still achieves one of the best predictions. And this shows that RBscore predictions are tolerant to structural variation or noise.
Web server description The prediction method is available as a web server at http: //ahsoka. u-strasbg. fr/rbscore/. Both RBscore and SVM approaches are carried out when a protein structure is available as input in the PDB format. Scores predicted by RBscore with different features are plotted on the protein structure and illustrated with JSmol  (59). Besides, the electrostatics potential is also mapped onto the protein surface as well as the Shannon CE, similarly to PatchFinderPlus  (7, 39)  and Consurf  (9, 60). If only the protein sequence is available, the prediction is based only on the SVM approach and only the prediction score and binary prediction of RNA binding sites are given as results. The results of the prediction are returned by email and by web page updates. All the datasets used in this work are also available on the website.
DISCUSSION RBscore is first built on three main physicochemical and evolutionary features that are subsequently integrated into a neighboring network as a linear combination. The score, thus, directly transforms the key features by weighing factors into NA binding probability without the complicated process of machine learning or database search. As NAbinding residues on a protein normally occur as patches, the neighboring network that considers both structural and sequential neighborhoods not only helps to describe the relations between residue neighbors of NA binding patches but also makes RBscore continuous on the protein surface. As a probability score, RBscore avoids unnecessary comparisons between residues of different proteins and uses wAUC and mAUC as criteria to achieve a better accuracy for each predicted protein.
Therefore, one can imagine the NAprotein binding process as following dynamic process with NA first binding to sub-optimal locations and then sliding alongside down the energy funnel. This dynamic process, constrained by each residue environment, can be partly described by the proposed residue neighboring network incorporated in RBscore. Compared to numerous programs on various datasets with different criteria, we found that RBscore has consistently wAUC >0. 83 on all datasets regardless of the protein types (DBP/RBP) or distance cutoff used to define binding sites, a result that cannot be achieved by other currently available programs. Still, one can find certain machinelearning methods performing better on some datasets. The general features used in RBscore cannot capture all the detailed binding properties of all proteins unless overtrained.
Compared to many machine-learning approaches that employ the PSSM facility, the number of parameters in RBscore is not large. Indeed, the number of parameters in the machine-learning prediction models would always be larger than the number of input vector, which may include n×20, where n is the window length  (61). For instance, the reference SVM approach of 11-mer window length has a 220-column input vector. Normally, parameters in machine-learning models are much larger than this: aaRNA has >668 parameters and DISPLAR includes 195 840 parameters. Compared with these numbers of parameters, RBscore of 104 weighing factors is relatively small. And the stable accuracy on all types of datasets regardless of distance cutoff difference implies that it is less likely to be overtrained than others.
ACKNOWLEDGEMENTS We thank  Pascal Auffinger ,  Drena Dobbs  and  Rasna R. Walia  for inspiring discussion and  Yanli Wang  for information of the cascade complex. We thank  Shandar Ahmad  for beneficial discussions on assessment criteria and tests of DBS-PSSM, DBS-Pred, SRCPred and SDCPred, aaRNA developers for scripts in running aaRNA in batch mode,  Piero Fariselli  for helpful discussion of machine-learning techniques and problem in dataset redundancy,  Sanbo Qin  for program package of DISPLAR.
796 0. 805 0. 810 metaDBSite D232 0. 898 0. 889 0. 872 0. 753 0. 744 0. 761 0. 858 0. 856 0. 858 0. 769 0. 769 0. 763 0. 798 0. 799 0. 812 0. 804 0. 818 0. 821 metaDBSite D316 0. 898 0. 885 0. 878 0. 755 0. 756 0. 772 0. 853 0. 851 0. 859 0. 770 0. 770 0. 769 0. 799 0. 802 0. 818 0. 809 0. 829 0. 834 SDCPred D159 0. 902 0. 896 0. 880 0. 760 0. 769 0. 773 0. 854 0. 857 0. 856 0. 772 0. 772 0. 753 0. 812 0. 814 0. 823 0. 827 0. 848 0. 843 Datasets after BindN D62 are DBP datasets. RBscore R130 is the training set of RBscore and the SVM approach. See Materials and Methods for descriptions of the dataset.
Methods Patients with 1-3 prior therapies for mRCC received nivolumab 0. 3, 2, or 10 mg/kg IV every 3 weeks (Q3W); treatment-naïve patients received 10 mg/kg IV Q3W. Biopsies and peripheral blood mononuclear cells were obtained at baseline and cycle 2 day 8. Tumor burden reduction was defined as a ≥20% decrease. Gene expression data were obtained on Affymetrix U219. OS parameters were estimated by the Kaplan-Meier method or by Cox proportional hazards regression. PD-1 ligand 1 (PD-L1) expression was measured by tumor membrane immunohistochemical staining (28-8 antibody; Dako) in baseline biopsies. Serumsoluble factors were quantified using a Luminex multiplex panel (Myriad Rules-Based Medicine). T cell receptor sequencing was conducted with the immunoSEQ assay (Adaptive Biotechnologies).
Data deposition: The Mollivirus genome sequence reported in this paper has been deposited in the GenBank database (accession no. KR921745). The transcriptomic data have been deposited in the Sequence Read Archive, www. ncbi. nlm. nih. gov/Traces/sra/ [accession no. SRX1078581 (SRR2084123 for the early class of expression, SRR2103267 for the intermediate one, and SRR2103268 for the late class of expression)]. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium, proteomecentral. proteomexchange. org, via the Proteomics Identifications partner repository [dataset identifiers <PXD002375> (Particule and Surfome) and <PXD002374> (Time Course)].
The sequences of the Mollivirus predicted proteins were analyzed using BLAST against the nonredundant protein sequence database (National Center for Biotechnology Information)  (19)  and a combination of motif search and protein-fold recognition methods [as previously described (  16 )]. As it is customary upon discovery of the first member of a previously unknown virus group, the proportion of Mollivirus protein without a recognizable homolog was high (337/523 = 64. 4%). Among the Mollivirus proteins with homolog in the databases, 93 (17. 8%) were most similar to a virus protein, 50 (9. 6%) to an A. castellanii protein, 22 (4. 2%) to proteins of other eukaryotes, and 18 (3. 4%) to prokaryotic proteins (Fig. 4 ). In contrast to Pithovirus (also the unique representative of its kind), a highly dominant proportion (83/93 = 89.
ACKNOWLEDGMENTS. We thank  Dr. J. -P. Chauvin ,  Dr. A. Kosta ,  F. Richard , and  A. Aouane  and  Serge Nitsche  for their expert assistance on the imaging platforms,  Dr. Dorothée Murat  for providing some transmission electron microscopy (TEM) images, and  Miguel Ortiz Lombardia  for his thorough reading of the manuscript. This work was partially supported by  France Génomique Grant   ANR-10-INSB-01-01 ,  French National Research Agency  Grant  ANR-14-CE14-0023-01 , the  Provence-Alpes-Côte-d'Azur Région  ( 2010 12125 ),  ProFi  Grant  ANR-10-INBS-08-01 , and  Russian Scientific Fund   14-14-01115.
Related literature For the synthesis of phthalazine derivatives, see  : Hasaninejed et al. , (2012) ;  Keshipour et al. , (2012). For applications of this class of compounds, see:  Soliman et al. (1981) ;  Nomoto et al. (1990) ; Abd  El-Wahab et al. (2013) ;  Cashman & Ghirmai (2009) ;  Hall et al. (1992 Hall et al. ( , 2001)). For the synthesis of the title compound, see:  Khurana & Magoo (2009). For similar condensation reactions as used here, see:  Atar et al. (2015). For the Cambridge Structural Database, see:  Groom & Allen (2014).
Experimental 2. 1. Crystal data  Hydrogen-bond geometry (A ˚, ). Cg3 is the centroid of ring C2-C7. D-HÁ Á ÁA D -H HÁ Á ÁA D Á Á ÁA D -HÁ Á ÁA C22-H22BÁ Á ÁO1 i 0. 96 2. 43 3. 379 (2) 168 C20-H20Á Á ÁCg3 ii 0. 93 2. 90 3. 726 (2) 149 Symmetry codes: (i) Àx þ 2; Ày; Àz þ 1; (ii) Àx þ 2; y þ 1 2 ; Àz þ 3 2. Data collection: APEX2  (Bruker, 2011) ; cell refinement: SAINT  (Bruker, 2011) ; data reduction: SAINT; program(s) used to solve structure: SIR2002  (Burla et al. , 2005) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg & Berndt, 2001) ; software used to prepare material for publication: WinGX  (Farrugia, 2012).
27 N m -1 ). The images were processed with the PicoImage software. The surface roughness was calculated according to ISO 25178 for an area of 7 × 7 µm 2. The Young's modulus of PAAm hydrogels was probed by indentation using the MFP-3D-Bio (Asylum Research) atomic force microscope by Prof A. Engler's laboratory at UCSD and was kindly provided to us. Silicone nitride (SiN) cantilevers attached to pyramid shape tips (PNP-TR; NanoWorld) with nominal spring constants of 50 pN nm -1 were indented into the hydrogels at a velocity of 2 µm s -1 until a trigger of 2 nN was detected. [ 42 ] Roughness and image acquisition was made with PAAm hydrogels coated with collagen I. Determination of the Young's modulus was performed with the surfaces uncoated in order to avoid interference from the collagen coating and extract only the mechanical information from the PAAm hydrogels.