State-of-the-art web services for de novo protein structure prediction

[1]  D. Baker,et al.  Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information , 2014, eLife.

[2]  Thomas A. Hopf,et al.  Protein 3D Structure Computed from Evolutionary Sequence Variation , 2011, PloS one.

[3]  M. dal Peraro,et al.  An experiment-informed signal transduction model for the role of the Staphylococcus aureus MecR1 protein in β-lactam resistance , 2019, Scientific Reports.

[4]  M. Bronstein,et al.  Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning , 2019, Nature Methods.

[5]  Jinbo Xu Distance-based protein folding powered by deep learning , 2019, Proceedings of the National Academy of Sciences.

[6]  David T Jones,et al.  Prediction of interresidue contacts with DeepMetaPSICOV in CASP13 , 2019, Proteins.

[7]  J. Skolnick,et al.  TM-align: a protein structure alignment algorithm based on the TM-score , 2005, Nucleic acids research.

[8]  David E. Kim,et al.  Large-scale determination of previously unsolved protein structures using evolutionary information , 2015, eLife.

[9]  Yang Zhang,et al.  The I-TASSER Suite: protein structure and function prediction , 2014, Nature Methods.

[10]  Ben M. Webb,et al.  Protein Structure Modeling with MODELLER. , 2017, Methods in molecular biology.

[11]  Maher M. Kassem,et al.  Structure of the Bacterial Cytoskeleton Protein Bactofilin by NMR Chemical Shifts and Sequence Variation. , 2016, Biophysical journal.

[12]  Kuldip K. Paliwal,et al.  Sixty-five years of the long march in protein secondary structure prediction: the final stretch? , 2016, Briefings Bioinform..

[13]  Luciano A. Abriata,et al.  Homology- and coevolution-consistent structural models of bacterial copper-tolerance protein CopM support a ‘metal sponge’ function and suggest regions for metal-dependent protein-protein interactions , 2015, bioRxiv.

[14]  Dongsheng Li,et al.  Cryo-EM structure of the protein-conducting ERAD channel Hrd1 in complex with Hrd3 , 2017, Nature.

[15]  Massimiliano Pontil,et al.  PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments , 2012, Bioinform..

[16]  D. Baker,et al.  Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM , 2020, bioRxiv.

[17]  Torsten Schwede,et al.  Critical assessment of methods of protein structure prediction (CASP)—Round XIII , 2019, Proteins.

[18]  E. Aurell,et al.  Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  David Baker,et al.  Origins of coevolution between residues distant in protein 3D structures , 2017, Proceedings of the National Academy of Sciences.

[20]  A. Tramontano,et al.  New encouraging developments in contact prediction: Assessment of the CASP11 results , 2016, Proteins.

[21]  Matteo Dal Peraro,et al.  A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments , 2019, Proteins.

[22]  Qing Wu,et al.  ComplexContact: a web server for inter-protein contact prediction using deep learning , 2018, Nucleic Acids Res..

[23]  F. Dimaio,et al.  Structure of the type VI secretion system TssK–TssF–TssG baseplate subcomplex revealed by cryo-electron microscopy , 2018, Nature Communications.

[24]  Roland L. Dunbrack,et al.  ProtCID: a data resource for structural information on protein interactions , 2020, Nature Communications.

[25]  Zhen Li,et al.  Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model , 2016, bioRxiv.

[26]  Georgios A. Pavlopoulos,et al.  Protein structure determination using metagenome sequence data , 2017, Science.

[27]  Jose M. Duarte,et al.  Assessment of protein assembly prediction in CASP13 , 2019, Proteins.

[28]  Radka Svobodová Vareková,et al.  PDBe: improved findability of macromolecular structure data in the PDB , 2019, Nucleic Acids Res..

[29]  Luciano A. Abriata,et al.  GtrA Protein Rv3789 Is Required for Arabinosylation of Arabinogalactan in Mycobacterium tuberculosis , 2015, Journal of bacteriology.

[30]  Demis Hassabis,et al.  Improved protein structure prediction using potentials from deep learning , 2020, Nature.

[31]  Sivaraman Balakrishnan,et al.  Learning generative models for protein fold families , 2011, Proteins.

[32]  David T. Jones,et al.  Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints , 2018, Nature Communications.

[33]  D. Baker,et al.  Structurally Mapping Endogenous Heme in the CcmCDE Membrane Complex for Cytochrome c Biogenesis. , 2018, Journal of molecular biology.

[34]  Oliver F. Lange,et al.  Structure prediction for CASP8 with all‐atom refinement using Rosetta , 2009, Proteins.

[35]  M. Maher,et al.  Structural and functional characterization of the mitochondrial complex IV assembly factor Coa6 , 2019, Life Science Alliance.

[36]  Luciano A. Abriata,et al.  Structural models and considerations on the COA6, COX18 and COX20 factors that assist assembly of human cytochrome c oxidase subunit II , 2017, bioRxiv.

[37]  Minkyung Baek,et al.  Assessment of protein model structure accuracy estimation in CASP13: Challenges in the era of deep learning , 2019, Proteins.

[38]  Torsten Schwede,et al.  SWISS-MODEL: homology modelling of protein structures and complexes , 2018, Nucleic Acids Res..

[39]  Thomas A. Hopf,et al.  Three-Dimensional Structures of Membrane Proteins from Genomic Sequencing , 2012, Cell.

[40]  P. Lindahl,et al.  COA6 Is Structurally Tuned to Function as a Thiol-Disulfide Oxidoreductase in Copper Delivery to Mitochondrial Cytochrome c Oxidase , 2019, Cell reports.

[41]  Ronan M Keegan,et al.  Molecular replacement using structure predictions from databases , 2019, Acta crystallographica. Section D, Structural biology.

[42]  Andriy Kryshtafovych,et al.  Assessment of contact predictions in CASP12: Co‐evolution and deep learning coming of age , 2017, Proteins.

[43]  Silvio C. E. Tosatto,et al.  The Pfam protein families database in 2019 , 2018, Nucleic Acids Res..

[44]  N. Strynadka,et al.  Crystal structure of an intramembranal phosphatase central to bacterial cell-wall peptidoglycan biosynthesis and lipid recycling , 2018, Nature Communications.

[45]  Luciano A. Abriata,et al.  Structural database resources for biological macromolecules , 2016, Briefings Bioinform..

[46]  Jia Wang,et al.  Structural basis for copper/silver binding by the Synechocystis metallochaperone CopM. , 2016, Acta crystallographica. Section D, Structural biology.

[47]  David Baker,et al.  High-resolution comparative modeling with RosettaCM. , 2013, Structure.

[48]  Chi Zhang,et al.  Crystal structures of a ZIP zinc transporter reveal a binuclear metal center in the transport pathway , 2017, Science Advances.

[49]  David Baker,et al.  Computation and Functional Studies Provide a Model for the Structure of the Zinc Transporter hZIP4* , 2015, The Journal of Biological Chemistry.

[50]  Jiye Shi,et al.  Comparing co-evolution methods and their application to template-free protein structure prediction , 2016, Bioinform..

[51]  A. Kruse,et al.  FtsW is a peptidoglycan polymerase that is functional only in complex with its cognate penicillin-binding protein , 2018, Nature Microbiology.

[52]  Jia Geng,et al.  Crystal structure of the bacterial acetate transporter SatP reveals that it forms a hexameric channel , 2018, The Journal of Biological Chemistry.

[53]  Rojan Shrestha,et al.  Assessing the accuracy of contact predictions in CASP13 , 2019, Proteins.

[54]  Marcin J. Skwark,et al.  Predicting accurate contacts in thousands of Pfam domain families using PconsC3 , 2017, Bioinform..

[55]  C. Sander,et al.  Direct-coupling analysis of residue coevolution captures native contacts across many protein families , 2011, Proceedings of the National Academy of Sciences.

[56]  Jianyi Yang,et al.  Improved protein structure prediction using predicted interresidue orientations , 2020, Proceedings of the National Academy of Sciences.

[57]  Jilong Li,et al.  Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11 , 2016, Proteins.

[58]  Daniel J Rigden,et al.  Applications of contact predictions to structural biology , 2017, IUCrJ.

[59]  Andriy Kryshtafovych,et al.  Assessment of hard target modeling in CASP12 reveals an emerging role of alignment‐based contact prediction methods , 2018, Proteins.

[60]  T. Blundell,et al.  Comparative protein modelling by satisfaction of spatial restraints. , 1993, Journal of molecular biology.

[61]  Thomas A. Hopf,et al.  Sequence co-evolution gives 3D contacts and structures of protein complexes , 2014, eLife.

[62]  Molly C. Sutherland,et al.  Structure-Function Analysis of the Bifunctional CcsBA Heme Exporter and Cytochrome c Synthetase , 2018, mBio.

[63]  Liisa Holm,et al.  Benchmarking fold detection by DaliLite v.5 , 2019, Bioinform..

[64]  D. Baker,et al.  Functional expression and characterization of the envelope glycoprotein E1E2 heterodimer of hepatitis C virus , 2019, PLoS pathogens.

[65]  Qifang Xu,et al.  Assignment of protein sequences to existing domain and family classification systems: Pfam and the PDB , 2012, Bioinform..

[66]  Yong Wang,et al.  Structure of a functional amyloid protein subunit computed using sequence variation. , 2015, Journal of the American Chemical Society.

[67]  Yan Wang,et al.  Fueling ab initio folding with marine metagenomics enables structure and function predictions of new protein families , 2019, Genome Biology.

[68]  Luciano A. Abriata,et al.  About the need to make computational models of biological macromolecules available and discoverable , 2020, Bioinform..

[69]  Matteo Dal Peraro,et al.  Modelling structures in cryo-EM maps. , 2019, Current opinion in structural biology.

[70]  Yang Zhang,et al.  Deep‐learning contact‐map guided protein structure prediction in CASP13 , 2019, Proteins.

[71]  Jinbo Xu,et al.  Analysis of distance‐based protein structure prediction by deep learning in CASP13 , 2019, Proteins.

[72]  Tsutomu Suzuki,et al.  Biogenesis and functions of aminocarboxypropyluridine in tRNA , 2019, Nature Communications.

[73]  A. Lesk,et al.  How different amino acid sequences determine similar protein structures: the structure and evolutionary dynamics of the globins. , 1980, Journal of molecular biology.