| scholarly article | Q13442814 |
| P819 | ADS bibcode | 2014PLoSO...992197K |
| P356 | DOI | 10.1371/JOURNAL.PONE.0092197 |
| P932 | PMC publication ID | 3956894 |
| P698 | PubMed publication ID | 24637808 |
| P5875 | ResearchGate publication ID | 260875130 |
| P2093 | author name string | David T Jones | |
| Tomasz Kosciolek | |||
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| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P433 | issue | 3 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | protein structure prediction | Q899656 |
| P304 | page(s) | e92197 | |
| P577 | publication date | 2014-03-17 | |
| P1433 | published in | PLOS One | Q564954 |
| P1476 | title | De novo structure prediction of globular proteins aided by sequence variation-derived contacts | |
| P478 | volume | 9 |
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| Q30374675 | CONFOLD: Residue-residue contact-guided ab initio protein folding. |
| Q36225597 | COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator. |
| Q58117076 | Co-Evolution of Intrinsically Disordered Proteins with Folded Partners Witnessed by Evolutionary Couplings |
| Q30382978 | Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction. |
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| Q38974044 | Comparing co-evolution methods and their application to template-free protein structure prediction. |
| Q30396077 | ConEVA: a toolbox for comprehensive assessment of protein contacts |
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| Q93106190 | Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints |
| Q35716090 | Did α-Synuclein and Glucocerebrosidase Coevolve? Implications for Parkinson's Disease |
| Q36407915 | EPSILON-CP: using deep learning to combine information from multiple sources for protein contact prediction |
| Q36349867 | EigenTHREADER: Analogous protein fold recognition by efficient contact map threading |
| Q90921835 | EncoderMap(II): Visualizing Important Molecular Motions with Improved Generation of Protein Conformations |
| Q94481764 | Enhancing fragment-based protein structure prediction by customising fragment cardinality according to local secondary structure |
| Q47567885 | Finding the needle in the haystack: towards solving the protein-folding problem computationally. |
| Q64124974 | High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features |
| Q58747425 | Improved fragment-based protein structure prediction by redesign of search heuristics |
| Q30399508 | Improving prediction of helix-helix packing in membrane proteins using predicted contact numbers as restraints |
| Q92826872 | Inferring protein 3D structure from deep mutation scans |
| Q30368992 | MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins. |
| Q39018179 | Mutation effects predicted from sequence co-variation |
| Q30372362 | NMR data-driven structure determination using NMR-I-TASSER in the CASD-NMR experiment. |
| Q40256442 | New encouraging developments in contact prediction: Assessment of the CASP11 results |
| Q94564584 | Not Enough Natural Data? Sequence and Ye Shall Find |
| Q52597588 | Ordering Protein Contact Matrices. |
| Q38647905 | Predictions of Backbone Dynamics in Intrinsically Disordered Proteins Using De Novo Fragment-Based Protein Structure Predictions |
| Q26749162 | Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics |
| Q30387362 | Protein Residue Contacts and Prediction Methods |
| Q30373991 | RBO Aleph: leveraging novel information sources for protein structure prediction. |
| Q70500337 | Recent Developments in Deep Learning Applied to Protein Structure Prediction |
| Q30390756 | Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds. |
| Q47307343 | Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction. |
| Q55294549 | Subtractive proteomics to identify novel drug targets and reverse vaccinology for the development of chimeric vaccine against Acinetobacter baumannii. |
| Q38287400 | Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently |
| Q58611951 | Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction |
| Q45945125 | Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure. |
| Q47589415 | Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12. |
| Q38261764 | The simultaneous occurrence of both hypercoagulability and hypofibrinolysis in blood and serum during systemic inflammation, and the roles of iron and fibrin(ogen). |
| Q30388897 | UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling |
| Q38404174 | XSuLT: a web server for structural annotation and representation of sequence-structure alignments |
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