| AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models M Varadi, S Anyango, M Deshpande, S Nair, C Natassia, G Yordanova, ... Nucleic acids research 50 (D1), D439-D444, 2022 | 4284 | 2022 |
| pE-DB: a database of structural ensembles of intrinsically disordered and of unfolded proteins M Varadi, S Kosol, P Lebrun, E Valentini, M Blackledge, AK Dunker, ... Nucleic acids research 42 (D1), D326-D335, 2014 | 229 | 2014 |
| PDBe: improved findability of macromolecular structure data in the PDB DR Armstrong, JM Berrisford, MJ Conroy, A Gutmanas, S Anyango, ... Nucleic acids research 48 (D1), D335-D343, 2020 | 152 | 2020 |
| Functional advantages of conserved intrinsic disorder in RNA-binding proteins M Varadi, F Zsolyomi, M Guharoy, P Tompa PloS one 10 (10), e0139731, 2015 | 122 | 2015 |
| PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins T Lazar, E Martínez-Pérez, F Quaglia, A Hatos, LB Chemes, JA Iserte, ... Nucleic acids research 49 (D1), D404-D411, 2021 | 107 | 2021 |
| PDBe: towards reusable data delivery infrastructure at protein data bank in Europe S Mir, Y Alhroub, S Anyango, DR Armstrong, JM Berrisford, AR Clark, ... Nucleic acids research 46 (D1), D486-D492, 2018 | 94 | 2018 |
| PDBe-KB: a community-driven resource for structural and functional annotations M Varadi, J Berrisford, M Deshpande, SS Nair, A Gutmanas, D Armstrong, ... Nucleic Acids Research 48 (D1), D344-D353, 2020 | 75 | 2020 |
| Clustering predicted structures at the scale of the known protein universe I Barrio-Hernandez, J Yeo, J Jänes, M Mirdita, CLM Gilchrist, T Wein, ... Nature 622 (7983), 637-645, 2023 | 69 | 2023 |
| AmyPro: a database of proteins with validated amyloidogenic regions M Varadi, G De Baets, WF Vranken, P Tompa, R Pancsa Nucleic acids research 46 (D1), D387-D392, 2018 | 69 | 2018 |
| Linking functions: an additional role for an intrinsically disordered linker domain in the transcriptional coactivator CBP S Contreras-Martos, A Piai, S Kosol, M Varadi, A Bekesi, P Lebrun, ... Scientific reports 7 (1), 4676, 2017 | 51 | 2017 |
| Computational approaches for inferring the functions of intrinsically disordered proteins M Varadi, W Vranken, M Guharoy, P Tompa Frontiers in molecular biosciences 2, 45, 2015 | 49 | 2015 |
| The impact of AlphaFold Protein Structure Database on the fields of life sciences M Varadi, S Velankar Proteomics 23 (17), 2200128, 2023 | 44 | 2023 |
| The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies VP Waman, N Sen, M Varadi, A Daina, SJ Wodak, V Zoete, S Velankar, ... Briefings in Bioinformatics 22 (2), 742-768, 2021 | 36 | 2021 |
| Start2Fold: a database of hydrogen/deuterium exchange data on protein folding and stability R Pancsa, M Varadi, P Tompa, WF Vranken Nucleic acids research 44 (D1), D429-D434, 2016 | 35 | 2016 |
| Just a flexible linker? The structural and dynamic properties of CBP-ID4 revealed by NMR spectroscopy A Piai, EO Calçada, T Tarenzi, A Del Grande, M Varadi, P Tompa, IC Felli, ... Biophysical journal 110 (2), 372-381, 2016 | 34 | 2016 |
| PDBe-KB: collaboratively defining the biological context of structural data Nucleic acids research 50 (D1), D534-D542, 2022 | 32 | 2022 |
| The protein ensemble database M Varadi, P Tompa Intrinsically disordered proteins studied by NMR spectroscopy, 335-349, 2015 | 29 | 2015 |
| DisCons: a novel tool to quantify and classify evolutionary conservation of intrinsic protein disorder M Varadi, M Guharoy, F Zsolyomi, P Tompa BMC bioinformatics 16, 1-9, 2015 | 24 | 2015 |
| Predicting the predictive power of IDP ensembles P Tompa, M Varadi Structure 22 (2), 177-178, 2014 | 24 | 2014 |
| The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors M Varadi, N Bordin, C Orengo, S Velankar Current Opinion in Structural Biology 79, 102543, 2023 | 15 | 2023 |
