. 1ktg, 5. 5v1m, and . 2vfk, Simulations de docking Description du jeu de données protéine-nucléotide nonredondant 130 complexes protéine-ribonucléotide-5'-P (nucléotide) non-redondants et de haute résolution ont été sélectionnés (voir matériels et méthodes) pour bénéficier d'un jeu de données spécifiques des interactions entre protéines et acides nucléiques

, L'ensemble des 130 complexes protéine-nucléotide à haute résolution est constitué de différentes catégories fonctionnelles de protéines dont l'essentielle est associée à des activités enzymatiques, le reste se répartissant en protéines de diverses fonctions. La figure 55 montre leur répartition ainsi que le type de ligand nucléotidique complexé à chacune des structures, Notons un enrichissement

S. A. Adam, T. Nakagawa, M. S. Swanson, T. K. Woodruff, and G. Dreyfuss, mRNA polyadenylate-binding protein: gene isolation and sequencing and identification of a ribonucleoprotein consensus sequence, Molecular and Cellular Biology, vol.6, issue.8, pp.2932-2943, 1986.

T. Afroz, Z. Cienikova, A. Cléry, and F. H. Allain, One, two, three, four! How multiple RRMs read the genome sequence, Methods in Enzymology, vol.558, issue.1, pp.235-278, 2015.

F. Agostini, A. Zanzoni, P. Klus, D. Marchese, D. Cirillo et al., catRAPID omics: a web server for large-scale prediction of protein-RNA interactions, Bioinformatics, vol.29, issue.22, pp.2928-2930, 2013.

K. Ainger, D. Avossa, A. S. Diana, C. Barry, E. Barbarese et al., Transport and localization elements in myelin basic protein mRNA, The Journal of Cell Biology, vol.138, issue.5, pp.1077-1087, 1997.

B. Alipanahi, A. Delong, M. T. Weirauch, and B. J. Frey, Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning, Nature Biotechnology, vol.33, issue.8, pp.831-838, 2015.

H. M. Ashtawy and N. R. Mahapatra, A Comparative Assessment of Ranking Accuracies of Conventional and Machine-Learning-Based Scoring Functions for Protein-Ligand Binding Affinity Prediction, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, issue.5, pp.1301-1313, 2012.

S. D. Auweter, F. C. Oberstrass, and F. H. Allain, Sequence-specific binding of singlestranded RNA: Is there a code for recognition?, Nucleic Acids Research, 2006.

N. A. Baker, Biomolecular Applications of Poisson-Boltzmann Methods, 2005.

T. Bakheet, M. Frevel, B. R. Williams, W. Greer, and K. S. Khabar, ARED: human AU-rich element-containing mRNA database reveals an unexpectedly diverse functional repertoire of encoded proteins, Nucleic Acids Research, vol.29, issue.1, pp.246-254, 2001.

P. Ball, Water as an active constituent in cell biology, Chemical Reviews, vol.108, issue.1, pp.74-108, 2008.

R. J. Bandziulis, M. S. Swanson, and G. Dreyfuss, RNA-binding proteins as developmental regulators, Genes & Development, vol.3, issue.4, pp.431-437, 1989.

X. Bao, X. Guo, M. Yin, M. Tariq, Y. Lai et al., Capturing the interactome of newly transcribed RNA, Nature Methods, vol.15, issue.3, pp.213-220, 2018.

S. M. Barabino and W. Keller, Last but Not Least: Regulated Poly(A) Tail Formation, Cell, vol.99, issue.1, pp.9-11, 1999.

J. Barra and E. Leucci, Probing Long Non-coding RNA-Protein Interactions, Frontiers in Molecular Biosciences, 2017.

C. Barreau, L. Paillard, and H. B. Osborne, AU-rich elements and associated factors: are there unifying principles?, Nucleic Acids Research, vol.33, issue.22, pp.7138-7150, 2005.
URL : https://hal.archives-ouvertes.fr/inserm-00292935

B. L. Bass, RNA Editing by Adenosine Deaminases That Act on RNA, Annual Review of Biochemistry, vol.71, issue.1, pp.817-846, 2002.

B. M. Beckmann, R. Horos, B. Fischer, A. Castello, K. Eichelbaum et al., The RNA-binding proteomes from yeast to man harbour conserved enigmRBPs, Nature Communications, vol.6, p.10127, 2015.

P. V. Benos, A. S. Lapedes, and G. D. Stormo, Probabilistic code for DNA recognition by proteins of the EGR family, Journal of Molecular Biology, vol.323, issue.4, pp.701-727, 2002.

H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat et al., The Protein Data Bank, Nucleic Acids Research, vol.28, issue.1, pp.235-242, 2000.

A. L. Beyer, M. E. Christensen, B. W. Walker, and W. M. Lestourgeon, Identification and characterization of the packaging proteins of core 40S hnRNP particles, Cell, vol.11, issue.1, pp.127-138, 1977.

C. Bissantz, G. Folkers, and D. Rognan, Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations, Journal of Medicinal Chemistry, vol.43, issue.25, pp.4759-4767, 2000.

R. Bitetti-putzer, D. Joseph-mccarthy, J. M. Hogle, and M. Karplus, Functional group placement in protein binding sites : a comparison of GRID and MCSS, Biological Chemistry, pp.935-960, 2001.

H. J. Böhm, The computer program LUDI: a new method for the de novo design of enzyme inhibitors, Journal of Computer-Aided Molecular Design, vol.6, issue.1, pp.61-78, 1992.

I. C. Braun, A. Herold, M. Rode, and E. Izaurralde, Nuclear export of mRNA by TAP/NXF1 requires two nucleoporin-binding sites but not p15, Molecular and Cellular Biology, vol.22, issue.15, pp.5405-5418, 2002.

B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S. Swaminathan et al., CHARMM A program for macromolecular energy, minimization, and dynamics calculations, Journal of Computational Chemistry, vol.4, issue.2, pp.187-217, 1983.

B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S. Swaminathan et al., CHARMM: A program for macromolecular energy, minimization, and dynamics calculations, Journal of Computational Chemistry, vol.4, issue.2, pp.187-217, 1983.

A. Caflisch, Computational combinatorial ligand design: application to human alphathrombin, Journal of Computer-Aided Molecular Design, vol.10, issue.5, pp.372-396, 1996.

Z. T. Campbell, D. Bhimsaria, C. T. Valley, J. A. Rodriguez-martinez, E. Menichelli et al., Cooperativity in RNA-protein interactions: global analysis of RNA binding specificity, Cell Reports, vol.1, issue.5, pp.570-581, 2012.

L. Chaput and L. Mouawad, Efficient conformational sampling and weak scoring in docking programs? Strategy of the wisdom of crowds, Journal of Cheminformatics, vol.9, issue.1, 2017.

P. S. Charifson, J. J. Corkery, M. A. Murcko, and W. P. Walters, Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins, Journal of Medicinal Chemistry, vol.42, issue.25, pp.5100-5109, 1999.

A. Chaudhury, P. Chander, and P. H. Howe, Heterogeneous nuclear ribonucleoproteins (hnRNPs) in cellular processes: Focus on hnRNP E1's multifunctional regulatory roles, RNA, issue.8, pp.1449-1462, 2010.

I. Chauvot-de-beauchene, S. J. De-vries, and M. Zacharias, Binding Site Identification and Flexible Docking of Single Stranded RNA to Proteins Using a Fragment-Based Approach, PLoS Computational Biology, vol.12, issue.1, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01505852

I. Chauvot-de-beauchene, S. J. De-vries, and M. Zacharias, Binding Site Identification and Flexible Docking of Single Stranded RNA to Proteins Using a Fragment-Based Approach, PLoS Computational Biology, vol.12, issue.1, pp.1-21, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01505852

C. Y. Chen, R. Gherzi, S. E. Ong, E. L. Chan, R. Raijmakers et al., AU binding proteins recruit the exosome to degrade ARE-containing mRNAs, Cell, vol.107, issue.4, pp.451-464, 2001.

Z. G. Chen, C. Stauffacher, Y. Li, T. Schmidt, W. Bomu et al., Protein-RNA interactions in an icosahedral virus at 3.0 A resolution, Science, issue.4914, pp.154-159, 1989.

G. Chojnowski, T. Walen, and J. M. Bujnicki, RNA Bricks--a database of RNA 3D motifs and their interactions, Nucleic Acids Research, vol.42, pp.123-154, 2014.

D. Cirillo, F. Agostini, and G. G. Tartaglia, Predictions of protein-RNA interactions, Wiley Interdisciplinary Reviews: Computational Molecular Science, vol.3, issue.2, pp.161-175, 2013.

H. Claußen, C. Buning, M. Rarey, and T. Lengauer, FLEXE: Efficient molecular docking considering protein structure variations, Journal of Molecular Biology, vol.308, issue.2, pp.377-395, 2001.

A. Cléry, F. H. Allain, and .. , FROM STRUCTURE TO FUNCTION OF RNA BINDING DOMAINS, 2013.

A. Cléry, M. Blatter, and F. H. Allain, RNA recognition motifs: boring? Not quite, Current Opinion in Structural Biology, vol.18, issue.3, pp.290-298, 2008.

C. N. Cole and J. J. Scarcelli, Transport of messenger RNA from the nucleus to the cytoplasm, Current Opinion in Cell Biology, vol.18, issue.3, pp.299-306, 2006.

D. R. Colman, G. Kreibich, A. B. Frey, and D. D. Sabatini, Synthesis and incorporation of myelin polypeptides into CNS myelin, The Journal of Cell Biology, vol.95, issue.2, pp.598-608, 1982.

S. R. Comeau, D. W. Gatchell, S. Vajda, and C. J. Camacho, ClusPro: An automated docking and discrimination method for the prediction of protein complexes, Bioinformatics, vol.20, issue.1, pp.45-50, 2004.

K. B. Cook, T. R. Hughes, and Q. D. Morris, High-throughput characterization of protein-RNA interactions, Briefings in Functional Genomics, vol.14, issue.1, pp.74-89, 2015.

K. B. Cook, H. Kazan, K. Zuberi, Q. Morris, and T. R. Hughes, RBPDB: a database of RNA-binding specificities, Nucleic Acids Res, 2010.

D. L. Corcoran, S. Georgiev, N. Mukherjee, E. Gottwein, R. L. Skalsky et al., PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data, Genome Biology, vol.12, issue.8, 2011.

A. Curinha, S. Oliveira-braz, I. Pereira-castro, A. Cruz, and A. Moreira, Implications of polyadenylation in health and disease, Nucleus (Austin, Tex.), vol.5, issue.6, pp.508-519, 2014.

A. M. Davis and S. J. Teague, Hydrogen Bonding, Hydrophobic Interactions, and Failure of the Rigid Receptor Hypothesis, Angewandte Chemie International Edition, vol.38, issue.6, pp.736-749, 1999.

M. E. Davis and J. A. Mccammon, Solving the finite difference linearized Poisson-Boltzmann equation: A comparison of relaxation and conjugate gradient methods, Journal of Computational Chemistry, 1989.

I. C. De-beauchene, S. J. De-vries, and M. Zacharias, Fragment-based modelling of single stranded RNA bound to RNA recognition motif containing proteins, Nucleic Acids Research, vol.44, issue.10, pp.4565-4580, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01505862

R. L. Desjarlais, I. D. Kuntz, R. P. Sheridan, R. Venkataraghavan, and J. S. Dixon, Docking Flexible Ligands to Macromolecular Receptors by Molecular Shape, Journal of Medicinal Chemistry, vol.29, issue.11, pp.2149-2153, 1986.

K. A. Dill, Additivity principles in biochemistry, Journal of Biological Chemistry, vol.272, issue.2, pp.701-704, 1997.

Y. Ding, Y. Tang, C. K. Kwok, Y. Zhang, P. C. Bevilacqua et al., In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features, Nature, issue.7485, pp.696-700, 2014.

D. Dominguez, P. Freese, M. S. Alexis, A. Su, M. Hochman et al., , 2018.

S. Sequence and C. , Preferences of Human RNA Binding Proteins, Molecular Cell, vol.70, issue.5, pp.854-867

G. Dreyfuss, V. N. Kim, and N. Kataoka, Messenger-RNA-binding proteins and the messages they carry, Nature Reviews Molecular Cell Biology, vol.3, issue.3, pp.195-205, 2002.

G. Dreyfuss, M. J. Matunis, S. Pinol-roma, and C. G. Burd, hnRNP Proteins and the Biogenesis of mRNA, Annual Review of Biochemistry, vol.62, issue.1, pp.289-321, 1993.

R. M. Drosphila, D. Lu, M. A. Searles, and A. Klug, Crystal structure of a zinc-finger-.pdf, vol.426, pp.471-475, 2003.

J. B. Dunbar, R. D. Smith, C. Yang, P. M. Ung, .. Lexa et al., CSAR Benchmark Exercise of 2010: Selection of the Protein-Ligand Complexes, Journal of Chemical Information and Modeling, vol.51, issue.9, pp.2036-2046, 2011.

R. L. Dunbrack and M. Karplus, Backbone-dependent rotamer library for proteins: Application to side-chain prediction, Journal of Molecular Biology, vol.230, pp.543-574, 1993.

J. D. Durrant and J. A. Mccammon, BINANA: a novel algorithm for ligand-binding characterization, Journal of Molecular Graphics & Modelling, vol.29, issue.6, pp.888-893, 2011.

H. L. Eaton and D. F. Wyss, Effective Progression of Nuclear Magnetic Resonance-Detected Fragment Hits, Methods in Enzymology, 2011.

M. B. Eisen, D. C. Wiley, M. Karplus, and R. E. Hubbard, HOOK: A program for finding novel molecular architectures that satisfy the chemical and steric requirements of a macromolecule binding site, Proteins: Structure, Function, and Bioinformatics, vol.19, issue.3, pp.199-221, 1994.

M. D. Eldridge, C. W. Murray, T. R. Auton, G. V. Paolini, and R. P. Mee, Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes, Journal of Computer-Aided Molecular Design, vol.11, issue.5, pp.425-445, 1997.

A. D. Ellington and J. W. Szostak, In vitro selection of RNA molecules that bind specific ligands, Nature, vol.346, issue.6287, pp.818-822, 1990.

S. S. Ericksen, H. Wu, H. Zhang, L. A. Michael, M. A. Newton et al., Machine Learning Consensus Scoring Improves Performance Across Targets in Structure-Based Virtual Screening, Journal of Chemical Information and Modeling, vol.57, issue.7, pp.1579-1590, 2017.

D. A. Erlanson, Fragment-based lead discovery: a chemical update, Current Opinion in Biotechnology, 2006.

E. Evensen, D. Joseph-mccarthy, G. A. Weiss, S. L. Schreiber, and M. Karplus, Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-DR4, Journal of Computer-Aided Molecular Design, vol.21, issue.7, pp.395-418, 2007.

J. Fernández-recio, M. Totrov, and R. Abagyan, Identification of Protein-Protein Interaction Sites from Docking Energy Landscapes, Journal of Molecular Biology, vol.335, issue.3, pp.843-865, 2004.

S. J. Fleishman, J. E. Corn, E. M. Strauch, T. A. Whitehead, I. Andre et al., Rosetta in CAPRI rounds 13-19. Proteins: Structure, Function, and Bioinformatics, vol.78, pp.3212-3218, 2010.

O. Fornes, J. Garcia-garcia, J. Bonet, and B. Oliva, On the Use of Knowledge-Based Potentials for the Evaluation of Models of Protein-Protein, Protein-DNA, and Protein-RNA Interactions, Advances in Protein Chemistry and Structural Biology, vol.94, pp.77-120, 2014.

M. B. Friedersdorf and J. D. Keene, Advancing the functional utility of PAR-CLIP by quantifying background binding to mRNAs and lncRNAs, Genome Biology, vol.15, issue.1, 2014.

R. A. Friesner, J. L. Banks, R. B. Murphy, T. A. Halgren, J. J. Klicic et al., Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy, Journal of Medicinal Chemistry, vol.47, issue.7, pp.1739-1749, 2004.

R. A. Friesner, R. B. Murphy, M. P. Repasky, L. L. Frye, J. R. Greenwood et al., Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein?Ligand Complexes, Journal of Medicinal Chemistry, vol.49, issue.21, pp.6177-6196, 2006.

Y. Furukawa, Y. Suzuki, M. Fukuoka, K. Nagasawa, K. Nakagome et al., A molecular mechanism realizing sequence-specific recognition of nucleic acids by TDP-43, Scientific Reports, vol.6, issue.1, p.20576, 2016.

J. Gabel, J. Desaphy, and D. Rognan, Beware of machine learning-based scoring functionson the danger of developing black boxes, Journal of Chemical Information and Modeling, vol.54, issue.10, pp.2807-2815, 2014.

Q. Q. Gao, W. E. Putzbach, A. E. Murmann, S. Chen, A. A. Sarshad et al., 6mer seed toxicity in tumor suppressive microRNAs, Nature Communications, vol.9, issue.1, p.4504, 2018.

N. L. Garneau, J. Wilusz, and C. J. Wilusz, The highways and byways of mRNA decay, Nature Reviews Molecular Cell Biology, vol.8, issue.2, pp.113-126, 2007.

S. Gerstberger, M. Hafner, M. Ascano, and T. Tuschl, Evolutionary conservation and expression of human RNA-binding proteins and their role in human genetic disease, Advances in Experimental Medicine and Biology, vol.825, pp.1-55, 2014.

S. Gerstberger, M. Hafner, and T. Tuschl, A census of human RNA-binding proteins, 2014.

T. Geuens, D. Bouhy, and V. Timmerman, The hnRNP family: insights into their role in health and disease, Human Genetics, vol.135, issue.8, pp.851-867, 2016.

A. Ghosh, C. S. Rapp, and R. A. Friesner, Generalized Born Model Based on a Surface Integral Formulation, The Journal of Physical Chemistry B, 2002.

G. Giudice, F. Sánchez-cabo, C. Torroja, and E. Lara-pezzi, ATtRACT-a database of RNA-binding proteins and associated motifs, Database, 2016.

P. J. Goodford, A computational procedure for determining energetically favorable binding sites on biologically important macromolecules, Journal of Medicinal Chemistry, vol.28, issue.7, pp.849-857, 1985.

D. S. Goodsell and A. J. Olson, Automated docking of substrates to proteins by simulated annealing, Proteins: Structure, Function, and Bioinformatics, vol.8, issue.3, pp.195-202, 1990.

C. Grauffel, R. H. Stote, and A. Dejaegere, Force field parameters for the simulation of modified histone tails, Journal of Computational Chemistry, vol.31, issue.13, pp.2434-2451, 2010.

N. V. Grishin, KH domain: one motif, two folds, Nucleic Acids Research, vol.29, issue.3, pp.638-643, 2001.

M. K. Haider, H. O. Bertrand, and R. E. Hubbard, Predicting fragment binding poses using a combined MCSS MM-GBSA approach, Journal of Chemical Information and Modeling, vol.51, issue.5, pp.1092-1105, 2011.

P. J. Hajduk and J. Greer, A decade of fragment-based drug design: Strategic advances and lessons learned, Nature Reviews Drug Discovery, 2007.

P. J. Hajduk and D. R. Sauer, Statistical Analysis of the Effects of Common Chemical Substituents on Ligand Potency, Journal of Medicinal Chemistry, vol.51, issue.3, pp.553-564, 2008.

D. Hall, S. Li, K. Yamashita, R. Azuma, J. A. Carver et al., A novel protein distance matrix based on the minimum arc-length between two amino-acid residues on the surface of a globular protein, Biophysical Chemistry, pp.50-55, 2014.

D. Hall, S. Li, K. Yamashita, R. Azuma, J. A. Carver et al., RNA-LIM: a novel procedure for analyzing protein/single-stranded RNA propensity data with concomitant estimation of interface structure, Analytical Biochemistry, vol.472, pp.52-61, 2015.

M. J. Hartshorn, M. L. Verdonk, G. Chessari, S. C. Brewerton, W. T. Mooij et al., Diverse, high-quality test set for the validation of protein-ligand docking performance, Journal of Medicinal Chemistry, vol.50, issue.4, pp.726-741, 2007.

S. Helder, A. J. Blythe, C. S. Bond, and J. P. Mackay, Determinants of affinity and specificity in RNA-binding proteins, Current Opinion in Structural Biology, vol.38, pp.83-91, 2016.

J. Hennig and M. Sattler, Deciphering the protein-RNA recognition code: Combining largescale quantitative methods with structural biology, 2015.

M. W. Hentze, A. Castello, T. Schwarzl, and T. Preiss, A brave new world of RNA-binding proteins, Nature Reviews Molecular Cell Biology, vol.19, issue.5, pp.327-341, 2018.

D. Hollingworth, A. M. Candel, G. Nicastro, S. R. Martin, P. Briata et al., KH domains with impaired nucleic acid binding as a tool for functional analysis, Nucleic Acids Research, vol.40, issue.14, pp.6873-6886, 2012.

M. Hoque, Z. Ji, D. Zheng, W. Luo, W. Li et al., Analysis of alternative cleavage and polyadenylation by 3? region extraction and deep sequencing, Nature Methods, vol.10, issue.2, pp.133-139, 2013.

K. N. Houk, A. G. Leach, S. P. Kim, and X. Zhang, Binding affinities of host-guest, proteinligand, and protein-transition-state complexes, International Ed. in English), vol.42, issue.40, pp.4872-4897, 2003.

B. Hu, Y. T. Yang, Y. Huang, Y. Zhu, and Z. J. Lu, POSTAR: a platform for exploring post-transcriptional regulation coordinated by RNA-binding proteins, Nucleic Acids Research, vol.45, issue.D1, pp.104-114, 2017.

R. Huang, M. Han, L. Meng, and X. Chen, Transcriptome-wide discovery of coding and noncoding RNA-binding proteins, Proceedings of the National Academy of Sciences of the United States of America, vol.115, 2018.

S. Huang and X. Zou, A knowledge-based scoring function for protein-RNA interactions derived from a statistical mechanics-based iterative method, Nucleic Acids Research, vol.42, issue.7, 2014.

S. Huang and X. Zou, A knowledge-based scoring function for protein-RNA interactions derived from a statistical mechanics-based iterative method, Nucleic Acids Research, vol.42, issue.7, 2014.

S. Y. Huang and X. Zou, Ensemble docking of multiple protein structures: Considering protein structural variations in molecular docking, Proteins: Structure, Function and Genetics, vol.66, issue.2, pp.399-421, 2007.

S. Y. Huang and X. Zou, An iterative knowledge-based scoring function for protein-protein recognition, Proteins: Structure, Function and Genetics, vol.72, issue.2, pp.557-579, 2008.

S. Y. Huang and X. Zou, A nonredundant structure dataset for benchmarking protein-RNA computational docking, Journal of Computational Chemistry, vol.34, issue.4, pp.311-318, 2013.

N. Ji, V. Ostroverkhov, C. S. Tian, and Y. R. Shen, Characterization of Vibrational Resonances of Water-Vapor Interfaces by Phase-Sensitive Sum-Frequency Spectroscopy, Physical Review Letters, vol.100, issue.9, p.96102, 2008.

F. Jiang and S. H. Kim, Soft docking": Matching of molecular surface cubes, Journal of Molecular Biology, vol.219, issue.1, pp.79-102, 1991.

S. Jo, T. Kim, V. G. Iyer, and W. Im, CHARMM-GUI: A web-based graphical user interface for CHARMM, Journal of Computational Chemistry, vol.29, issue.11, pp.1859-1865, 2008.

G. Jones, P. Willett, R. C. Glen, A. R. Leach, and R. Taylor, Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen, Journal of Molecular Biology, vol.267, issue.3, pp.727-748, 1997.

J. E. Jones and S. J. , Evaluating conformational changes in protein structures binding RNA, pp.498-508, 2008.

S. Jones, Protein-RNA interactions: structural biology and computational modeling techniques, Biophysical Reviews, vol.8, issue.4, pp.359-367, 2016.

M. A. Jonikas, R. J. Radmer, A. Laederach, R. Das, S. Pearlman et al., Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters, RNA, vol.15, issue.2, pp.189-199, 2009.

D. Joseph-mccarthy, Computational approaches to structure-based ligand design, Pharmacology & Therapeutics, vol.84, issue.2, pp.179-191, 1999.

A. Kahvejian, G. Roy, and N. Sonenberg, The mRNA closed-loop model: The function of PABP and PABP-interacting proteins in mRNA translation, Cold Spring Harbor Symposia on Quantitative Biology, vol.66, pp.293-300, 2001.

P. Källblad and P. M. Dean, Efficient conformational sampling of local side-chain flexibility, Journal of Molecular Biology, vol.326, issue.5, pp.1651-1665, 2003.

K. Kappel and R. Das, Sampling Native-like Structures of RNA-Protein Complexes through Rosetta Folding and Docking, Structure, vol.27, issue.1, pp.140-151, 2019.

T. Kaserer, V. Temml, Z. Kutil, T. Vanek, P. Landa et al., Prospective performance evaluation of selected common virtual screening tools. Case study: Cyclooxygenase (COX) 1 and 2, European Journal of Medicinal Chemistry, vol.96, pp.445-457, 2015.

L. P. Keegan, A. Gallo, and M. A. Connell, The many roles of an RNA editor, Nature Reviews Genetics, vol.2, issue.11, pp.869-878, 2001.

R. M. Knegtel, I. D. Kuntz, and C. M. Oshiro, Molecular docking to ensembles of protein structures, Journal of Molecular Biology, vol.266, issue.2, pp.424-440, 1997.

S. N. Kobren and M. Singh, Systematic domain-based aggregation of protein structures highlights DNA-, RNA-and other ligand-binding positions, Nucleic Acids Research, issue.1, 2018.

D. R. Koes, M. P. Baumgartner, and C. J. Camacho, Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise, Journal of Chemical Information and Modeling, vol.53, issue.8, pp.1893-1904, 2013.

D. Kozakov, K. H. Clodfelter, S. Vajda, and C. J. Camacho, Optimal Clustering for Detecting Near-Native Conformations in Protein Docking, Biophysical Journal, vol.89, issue.2, pp.867-875, 2005.

K. Kramer, T. Sachsenberg, B. M. Beckmann, S. Qamar, K. Boon et al., Photo-cross-linking and high-resolution mass spectrometry for assignment of RNAbinding sites in RNA-binding proteins, Nature Methods, vol.11, issue.10, pp.1064-1070, 2014.

I. D. Kuntz, J. M. Blaney, S. J. Oatley, R. Langridge, and T. E. Ferrin, A geometric approach to macromolecule-ligand interactions, Journal of Molecular Biology, vol.161, issue.2, pp.269-288, 1982.

A. Lal, K. Mazan-mamczarz, T. Kawai, X. Yang, J. L. Martindale et al., Concurrent versus individual binding of HuR and AUF1 to common labile target mRNAs, The EMBO Journal, vol.23, issue.15, pp.3092-3102, 2004.

N. Lambert, A. Robertson, M. Jangi, S. Mcgeary, P. A. Sharp et al., RNA Bind-n-Seq: Quantitative Assessment of the Sequence and Structural Binding Specificity of RNA Binding Proteins, Molecular Cell, vol.54, issue.5, pp.887-900, 2014.

B. Lang, A. Armaos, and G. G. Tartaglia, RNAct: Protein-RNA interaction predictions for model organisms with supporting experimental data, Nucleic Acids Research, vol.47, issue.D1, pp.601-606, 2019.

A. R. Leach, Ligand docking to proteins with discrete side-chain flexibility, Journal of Molecular Biology, vol.235, issue.1, pp.345-356, 1994.

A. R. Leach and I. D. Kuntz, Conformational analysis of flexible ligands in macromolecular receptor sites, Journal of Computational Chemistry, vol.13, issue.6, pp.730-748, 1992.

F. Leclerc and M. Karplus, MCSS-based predictions of RNA binding sites, Theoretical Chemistry Accounts: Theory, Computation, and Modeling, vol.101, pp.131-137, 1999.

M. F. Lensink, S. Velankar, M. Baek, L. Heo, C. Seok et al., The challenge of modeling protein assemblies: the CASP12-CAPRI experiment, Proteins: Structure, Function, and Bioinformatics, vol.86, pp.257-273, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02389365

M. F. Lensink and S. J. Wodak, Docking and scoring protein interactions: CAPRI, Proteins, vol.78, issue.15, pp.3073-3084, 2009.

C. S. Leung, S. S. Leung, J. Tirado-rives, and W. L. Jorgensen, Methyl effects on protein-ligand binding, Journal of Medicinal Chemistry, vol.55, issue.9, pp.4489-4500, 2012.

C. H. Li, L. Cao, . Bin, J. G. Su, Y. X. Yang et al., A new residue-nucleotide propensity potential with structural information considered for discriminating protein-RNA docking decoys, Proteins: Structure, Function, and Bioinformatics, vol.80, issue.1, pp.14-24, 2012.

H. Li, K. S. Leung, M. H. Wong, and P. J. Ballester, Improving autodock vina using random forest: The growing accuracy of binding affinity prediction by the effective exploitation of larger data sets, Molecular Informatics, vol.34, issue.2-3, pp.115-126, 2015.

S. Li, K. Yamashita, K. M. Amada, and D. M. Standley, Quantifying sequence and structural features of protein-RNA interactions, Nucleic Acids Research, vol.42, issue.15, p.10086, 2014.

X. Li, H. Kazan, H. D. Lipshitz, and Q. D. Morris, Finding the target sites of RNA-binding proteins, Wiley Interdisciplinary Reviews: RNA, vol.5, issue.1, pp.111-130, 2014.

Y. Li, L. Han, Z. Liu, and R. Wang, Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods and General Results, Journal of Chemical Information and Modeling, vol.54, issue.6, pp.1717-1736, 2014.

D. D. Licatalosi and R. B. Darnell, Splicing Regulation in Neurologic Disease, Neuron, vol.52, issue.1, pp.93-101, 2006.

Z. Liu, M. Su, L. Han, J. Liu, Q. Yang et al., Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions, Accounts of Chemical Research, vol.50, issue.2, pp.302-309, 2017.

X. Lu and W. K. Olson, 3DNA: a versatile, integrated software system for the analysis, rebuilding and visualization of three-dimensional nucleic-acid structures, Nature Protocols, vol.3, issue.7, pp.1213-1227, 2008.

B. M. Lunde, M. Hörner, and A. Meinhart, Structural insights into cis element recognition of non-polyadenylated RNAs by the Nab3-RRM, Nucleic Acids Research, vol.39, issue.1, pp.337-346, 2011.

B. M. Lunde, C. Moore, and G. Varani, RNA-binding proteins: modular design for efficient function, Nat Rev Mol Cell Biol, vol.8, issue.6, pp.479-490, 2007.

J. Lykke-andersen and E. Wagner, Recruitment and activation of mRNA decay enzymes by two ARE-mediated decay activation domains in the proteins TTP and BRF-1, Genes & Development, vol.19, issue.3, pp.351-361, 2005.

T. Maniatis and R. Reed, An extensive network of coupling among gene expression machines, Nature, issue.6880, pp.499-506, 2002.

D. Marchese, N. S. De-groot, N. Lorenzo-gotor, C. M. Livi, and G. G. Tartaglia, Advances in the characterization of RNA-binding proteins, Wiley Interdisciplinary Reviews. RNA, vol.7, issue.6, pp.793-810, 2016.

D. Marchese, N. S. De-groot, N. Lorenzo-gotor, C. M. Livi, and G. G. Tartaglia, Advances in the characterization of RNA-binding proteins, Wiley Interdisciplinary Reviews: RNA, 2016.

C. Maris, C. Dominguez, F. H. Allain, and .. , The RNA recognition motif, a plastic RNAbinding platform to regulate post-transcriptional gene expression, FEBS Journal, vol.272, issue.9, pp.2118-2131, 2005.

E. L. Matunis, M. J. Matunis, and G. Dreyfuss, Characterization of the major hnRNP proteins from Drosophila melanogaster, Journal of Cell Biology, vol.116, issue.2, pp.257-269, 1992.

C. A. Mchugh, P. Russell, and M. Guttman, Methods for comprehensive experimental identification of RNA-protein interactions, Genome Biology, vol.15, issue.1, p.203, 2014.

Z. Miao and E. Westhof, Prediction of nucleic acid binding probability in proteins: a neighboring residue network based score, Nucleic Acids Research, vol.43, issue.11, pp.5340-5351, 2015.

A. Miranker and M. Karplus, Functionality maps of binding sites: A multiple copy simultaneous search method, Proteins: Structure, Function, and Bioinformatics, vol.11, issue.1, pp.29-34, 1991.

Y. Miyamura, T. Suzuki, M. Kono, K. Inagaki, S. Ito et al., Mutations of the RNA-specific adenosine deaminase gene (DSRAD) are involved in dyschromatosis symmetrica hereditaria, American Journal of Human Genetics, vol.73, issue.3, pp.693-699, 2003.

M. J. Moore, From birth to death: The complex lives of eukaryotic mRNAs, Science, vol.309, issue.5740, pp.1514-1518, 2005.

G. M. Morris, D. S. Goodsell, R. S. Halliday, R. Huey, W. E. Hart et al., , pp.1639-1662, 1998.

G. M. Morris, R. Huey, W. Lindstrom, M. F. Sanner, R. K. Belew et al., AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility, Journal of Computational Chemistry, vol.30, issue.16, p.2785, 2009.

I. Muegge and Y. C. Martin, A General and Fast Scoring Function for Protein?Ligand Interactions: A Simplified Potential Approach, Journal of Medicinal Chemistry, vol.42, issue.5, pp.791-804, 1999.

T. P. Munro, R. J. Magee, G. J. Kidd, J. H. Carson, E. Barbarese et al., Mutational analysis of a heterogeneous nuclear ribonucleoprotein A2 response element for RNA trafficking, The Journal of Biological Chemistry, vol.274, issue.48, pp.34389-34395, 1999.

J. Murn, M. Teplova, K. Zarnack, Y. Shi, and D. J. Patel, Recognition of distinct RNA motifs by the clustered CCCH zinc fingers of neuronal protein Unkempt, Nature Structural & Molecular Biology, vol.23, issue.1, p.16, 2016.

C. W. Murray and D. C. Rees, The rise of fragment-based drug discovery, Nature Chemistry, 2009.

T. W. Nilsen and B. R. Graveley, Expansion of the eukaryotic proteome by alternative splicing, Nature, vol.463, issue.7280, pp.457-463, 2010.

N. T. Southall, ?. , K. A. Dill, ?. Haymet, *. et al., A View of the Hydrophobic Effect, 2001.

F. C. Oberstrass, S. D. Auwetor, M. Erat, Y. Hargous, A. Henning et al., Structural biology -Structure of PTB bound to RNA: Specific binding and implications for splicing regulation, Science, vol.309, issue.5743, pp.2054-2057, 2005.

Q. Pan, O. Shai, L. J. Lee, B. J. Frey, and B. J. Blencowe, Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing, Nature Genetics, vol.40, issue.12, pp.1413-1415, 2008.

X. Pan, P. Rijnbeek, J. Yan, and H. Shen, Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks, BMC Genomics, vol.19, issue.1, p.511, 2018.

L. Pedro and R. J. Quinn, Native mass spectrometry in fragment-based drug discovery, 2016.

H. R. Pelham, , pp.4170-4174, 1980.

L. F. Pemberton and B. M. Paschal, Mechanisms of receptor-mediated nuclear import and nuclear export, Traffic, vol.6, issue.3, pp.187-198, 2005.

L. Pérez-cano and J. Fernández-recio, Optimal protein-RNA area, OPRA: A propensitybased method to identify RNA-binding sites on proteins, Proteins: Structure, Function, and Bioinformatics, vol.78, issue.1, pp.25-35, 2010.

C. Pons, S. Grosdidier, A. Solernou, L. Pérez-cano, and J. Fernández-recio, Present and future challenges and limitations in protein-protein docking, Proteins: Structure, Function, and Bioinformatics, vol.78, issue.1, pp.95-108, 2010.

S. Qamar, K. Kramer, and H. Urlaub, Studying RNA-Protein Interactions of Pre-mRNA Complexes by Mass Spectrometry, In Methods in enzymology, vol.558, pp.417-463, 2015.

,

R. Quiroga and M. A. Villarreal, Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening, PloS One, vol.11, issue.5, 2016.

M. Rabani, M. Kertesz, and E. Segal, Computational prediction of RNA structural motifs involved in posttranscriptional regulatory processes, Proceedings of the National Academy of Sciences of the United States of America, vol.105, pp.14885-14890, 2008.

M. Ramanathan, D. F. Porter, and P. A. Khavari, Methods to study RNA-protein interactions, Nature Methods, vol.16, issue.3, pp.225-234, 2019.

A. Ramos, S. Gru, J. Adams, D. R. Micklem, M. R. Proctor et al., RNA recognition by a Staufen double-stranded RNA-binding domain |, The EMBO Journal, vol.19, issue.5, pp.997-1009, 2000.

G. Rastelli, G. Degliesposti, A. Del-rio, and M. Sgobba, Binding Estimation after Refinement, a New Automated Procedure for the Refinement and Rescoring of Docked Ligands in Virtual Screening, Chemical Biology & Drug Design, vol.73, issue.3, pp.283-286, 2009.

D. Ray, H. Kazan, E. T. Chan, L. P. Castillo, S. Chaudhry et al., Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins, Nature Biotechnology, vol.27, issue.7, pp.667-670, 2009.

D. Ray, H. Kazan, K. B. Cook, M. T. Weirauch, H. S. Najafabadi et al., A compendium of RNA-binding motifs for decoding gene regulation, Nature, vol.499, issue.7457, pp.172-177, 2013.

R. Rentzsch and B. Y. Renard, Docking small peptides remains a great challenge: an assessment using AutoDock Vina, Briefings in Bioinformatics, vol.16, issue.6, pp.1045-1056, 2015.

P. Richard and J. L. Manley, Transcription termination by nuclear RNA polymerases, Genes & Development, vol.23, issue.11, pp.1247-1269, 2009.

G. Richmond and . Urfaces, Annual Review of Physical Chemistry, vol.52, issue.1, pp.357-389, 2001.

N. Ripin, J. Boudet, M. M. Duszczyk, A. Hinniger, M. Faller et al., Molecular basis for AU-rich element recognition and dimerization by the HuR Cterminal RRM, Proceedings of the National Academy of Sciences, vol.116, pp.2935-2944, 2019.

M. S. Rodriguez, C. Dargemont, and F. Stutz, Nuclear export of RNA, Biology of the Cell, vol.96, issue.8, pp.639-655, 2004.

S. H. Rotstein and M. A. Murcko, GroupBuild: a fragment-based method for de novo drug design, Journal of Medicinal Chemistry, vol.36, issue.12, pp.1700-1710, 1993.

S. Rouskin, M. Zubradt, S. Washietl, M. Kellis, and J. S. Weissman, Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo, Nature, issue.7485, pp.701-705, 2014.

M. F. Schmidt and J. Rademann, Dynamic template-assisted strategies in fragment-based drug discovery, Trends in Biotechnology, 2009.

G. Schneider and U. Fechner, Computer-based de novo design of drug-like molecules, Nature Reviews Drug Discovery, 2005.

Y. Shi, D. C. Di-giammartino, D. Taylor, A. Sarkeshik, W. J. Rice et al., Molecular Architecture of the Human Pre-mRNA 3? Processing Complex, Molecular Cell, vol.33, issue.3, pp.365-376, 2009.

S. B. Shuker, P. J. Hajduk, R. P. Meadows, and S. W. Fesik, Discovering high-affinity ligands for proteins: SAR by NMR, Science, vol.274, issue.5292, pp.1531-1534, 1996.

J. Si, J. Cui, J. Cheng, and R. Wu, Computational Prediction of RNA-Binding Proteins and Binding Sites, International Journal of Molecular Sciences, vol.16, issue.11, pp.26303-26317, 2015.

J. Si, J. Cui, J. Cheng, and R. Wu, Computational Prediction of RNA-Binding Proteins and Binding Sites, International Journal of Molecular Sciences, vol.16, issue.11, pp.26303-26317, 2015.

R. Singh and J. Valcárcel, Building specificity with nonspecific RNA-binding proteins, Nature Structural & Molecular Biology, vol.12, issue.8, pp.645-653, 2005.

L. Skrisovska, C. F. Bourgeois, R. Stefl, S. N. Grellscheid, L. Kister et al., The testis-specific human protein RBMY recognizes RNA through a novel mode of interaction, EMBO Reports, vol.8, issue.4, pp.372-379, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00166254

C. Sotriffer and H. Matter, The Challenge of Affinity Prediction: Scoring Functions for Structure-Based Virtual Screening, Virtual Screening: Principles, Challenges, and Practical Guidelines, 2011.

R. Stefl, F. C. Oberstrass, J. L. Hood, M. Jourdan, M. Zimmermann et al., The Solution Structure of the ADAR2 dsRBM-RNA Complex Reveals a Sequence-Specific Readout of the Minor Groove, Cell, vol.143, issue.2, pp.225-237, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01662748

C. M. Stultz and M. Karplus, Dynamic Ligand Design and Combinatorial Optimization : Designing Inhibitors to Endothiapepsin, vol.289, pp.258-289, 1999.

M. Su, Q. Yang, Y. Du, G. Feng, Z. Liu et al., Comparative Assessment of Scoring Functions: The CASF-2016 Update, Journal of Chemical Information and Modeling, vol.59, issue.2, pp.895-913, 2019.

Y. Sugimoto, J. König, S. Hussain, B. Zupan, T. Curk et al., Analysis of CLIP and iCLIP methods for nucleotide-resolution studies of protein-RNA interactions, 2012.

, Genome Biology, vol.13, issue.8

J. M. Sutherland, N. A. Siddall, G. R. Hime, and E. A. Mclaughlin, RNA binding proteins in spermatogenesis: an in depth focus on the Musashi family, Asian Journal of Andrology, vol.17, issue.4, pp.529-536, 2015.

M. Teplova, M. Hafner, D. Teplov, K. Essig, T. Tuschl et al., Structure-function studies of STAR family quaking proteins bound to their in vivo RNA target sites, Genes and Development, vol.27, issue.8, pp.928-940, 2013.

G. E. Terp, B. N. Johansen, I. T. Christensen, and F. S. Jørgensen, A new concept for multidimensional selection of ligand conformations (MultiSelect) and multidimensional scoring (MultiScore) of protein-ligand binding affinities, Journal of Medicinal Chemistry, vol.44, issue.14, pp.2333-2343, 2001.

P. D. Thomas and K. A. Dill, Structures : How Accurate Are They ? g g, Journal of Molecular Biology, vol.257, pp.457-469, 1996.

T. Treiber, N. Treiber, U. Plessmann, S. Harlander, J. Daiß et al., A Compendium of RNA-Binding Proteins that Regulate MicroRNA Biogenesis, Molecular Cell, vol.66, issue.2, pp.270-284, 2017.

O. Trott and A. J. Olson, AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, Journal of Computational Chemistry, vol.31, issue.2, pp.455-461, 2010.

K. Tsuda, K. Kuwasako, M. Takahashi, T. Someya, M. Inoue et al., Structural basis for the sequence-specific RNA-recognition mechanism of human CUG-BP1 RRM3, Nucleic Acids Research, vol.37, issue.15, pp.5151-5166, 2009.

P. Tuffery, C. Etchebest, S. Hazout, and R. Lavery, A new approach to the rapid determination of protein side chain conformations, Journal of Biomolecular Structure and Dynamics, vol.8, issue.6, pp.1267-1289, 1991.
URL : https://hal.archives-ouvertes.fr/hal-00313445

I. Tuszynska, M. Magnus, K. Jonak, W. Dawson, J. M. Bujnicki et al., NPDock: a web server for protein-nucleic acid docking, Nucleic Acids Research, vol.43, issue.W1, 2015.

R. Valverde, L. Edwards, L. ;. Regan, and E. J. Wagner, Structure and function of KH domains, Trends in Biochemical Sciences, vol.275, issue.11, pp.585-592, 2008.

K. Vanommeslaeghe and A. D. Mackerell, Automation of the CHARMM general force field (CGenFF) I: Bond perception and atom typing, Journal of Chemical Information and Modeling, 2012.

H. F. Velec, H. Gohlke, and G. Klebe, DrugScore CSD Knowledge-Based Scoring Function Derived from Small Molecule Crystal Data with Superior Recognition Rate of Near-Native Ligand Poses and Better Affinity Prediction, Journal of Medicinal Chemistry, vol.48, issue.20, pp.6296-6303, 2005.

M. L. Verdonk, J. C. Cole, M. J. Hartshorn, C. W. Murray, and R. D. Taylor, Improved protein-ligand docking using GOLD, Proteins: Structure, Function, and Bioinformatics, vol.52, issue.4, pp.609-623, 2003.

M. Vieth, J. D. Hirst, A. Kolinski, and C. L. Brooks, Assessing energy functions for flexible docking, Journal of Computational Chemistry, vol.19, issue.14, pp.1096-987, 1998.

R. R. Walia, C. Caragea, B. A. Lewis, F. Towfic, M. Terribilini et al., Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art, BMC Bioinformatics, vol.13, issue.1, p.89, 2012.

R. R. Walia, Y. El-manzalawy, V. G. Honavar, and D. Dobbs, Sequence-Based Prediction of RNA-Binding Residues in Proteins, 2017.

C. Wang and Y. Zhang, Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest, Journal of Computational Chemistry, vol.38, issue.3, pp.169-177, 2017.

E. T. Wang, R. Sandberg, S. Luo, I. Khrebtukova, L. Zhang et al., Alternative isoform regulation in human tissue transcriptomes, Nature, vol.456, issue.7221, pp.470-476, 2008.

H. Wang, F. Zeng, Q. Liu, H. Liu, Z. Liu et al., The structure of the AREbinding domains of Hu antigen R (HuR) undergoes conformational changes during RNA binding, Acta Crystallographica Section D Biological Crystallography, vol.69, issue.3, pp.373-380, 2013.

R. Wang, X. Fang, Y. Lu, and S. Wang, The PDBbind Database: Collection of Binding Affinities for Protein?Ligand Complexes with Known Three-Dimensional Structures, Journal of Medicinal Chemistry, vol.47, issue.12, pp.2977-2980, 2004.

R. Wang, L. Lai, and S. Wang, Further development and validation of empirical scoring functions for structure-based binding affinity prediction, Journal of Computer-Aided Molecular Design, vol.16, issue.1, pp.11-26, 2002.

S. J. Weiner, P. A. Kollman, D. T. Nguyen, and D. A. Case, An all atom force field for simulations of proteins and nucleic acids, Journal of Computational Chemistry, 1986.

W. Welch, J. Ruppert, and A. N. Jain, Hammerhead: Fast, fully automated docking of flexible ligands to protein binding sites, Chemistry and Biology, vol.3, issue.6, pp.90093-90102, 1996.

E. C. Wheeler, E. L. Van-nostrand, and G. W. Yeo, Advances and challenges in the detection of transcriptome-wide protein-RNA interactions, Wiley Interdisciplinary Reviews: RNA, 2018.

M. Wójcikowski, P. J. Ballester, and P. Siedlecki, Performance of machine-learning scoring functions in structure-based virtual screening, Scientific Reports, vol.7, issue.1, p.46710, 2017.

Y. Xu, K. Vanommeslaeghe, A. Aleksandrov, A. D. Mackerell, L. Nilsson et al., , 2016.

, Additive CHARMM force field for naturally occurring modified ribonucleotides, Journal of Computational Chemistry, vol.37, issue.10, pp.896-912

L. Yang, C. Wang, F. Li, J. Zhang, A. Nayab et al., The human RNAbinding protein and E3 ligase MEX-3C binds the MEX-3-recognition element (MRE) motif with high affinity, The Journal of Biological Chemistry, vol.292, issue.39, pp.16221-16234, 2017.

J. Yu, A. Ciancetta, S. Dudas, S. Duca, J. Lottermoser et al., Structure-Guided Modification of Heterocyclic Antagonists of the P2Y 14 Receptor, Journal of Medicinal Chemistry, vol.61, issue.11, pp.4860-4882, 2018.

Z. Deng, C. Chuaqui, *. Singh, and J. , Structural Interaction Fingerprint (SIFt): A Novel Method for Analyzing Three-Dimensional Protein?Ligand Binding Interactions, 2003.

Y. Zhang and J. Skolnick, TM-align: a protein structure alignment algorithm based on the TM-score, Nucleic Acids Research, vol.33, issue.7, pp.2302-2309, 2005.

H. Zhao, Y. Yang, S. C. Janga, C. C. Kao, and Y. Zhou, Prediction and validation of the unexplored RNA-binding protein atlas of the human proteome, Proteins, vol.82, issue.4, 2014.

C. Zheng, Y. Zhou, J. Zhu, H. Ji, J. Chen et al., Construction of a three-dimensional pharmacophore for Bcl-2 inhibitors by flexible docking and the multiple copy simultaneous search method, Bioorganic & Medicinal Chemistry, vol.15, pp.6407-6417, 2007.

Y. Zhu, G. Xu, Y. T. Yang, Z. Xu, X. Chen et al., POSTAR2: deciphering the post-transcriptional regulatory logics, Nucleic Acids Research, vol.47, issue.D1, pp.203-211, 2019.

D. Zilian and C. A. Sotriffer, SFCscore RF : A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein-Ligand Complexes, Journal of Chemical Information and Modeling, vol.53, issue.8, pp.1923-1933, 2013.

M. A. Zipeto, Q. Jiang, E. Melese, and C. H. Jamieson, RNA rewriting, recoding, and rewiring in human disease, Trends in Molecular Medicine, vol.21, issue.9, pp.549-559, 2015.

V. Zoete, A. Grosdidier, and O. Michielin, Docking, virtual high throughput screening and in silico fragment-based drug design, Journal of Cellular and Molecular Medicine, vol.13, issue.2, pp.238-248, 2009.