| Critical assessment of methods of protein structure prediction (CASP)Round XII |
55 |
| NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning |
48 |
| Critical assessment of methods of protein structure prediction (CASP)-Round XIII |
38 |
| Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13) |
30 |
| Assessment of contact predictions in CASP12: Co-evolution and deep learning coming of age |
29 |
| Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12 |
25 |
| Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13 |
25 |
| Deep-learning contact-map guided protein structure prediction in CASP13 |
22 |
| The challenge of modeling protein assemblies: the CASP12-CAPRI experiment |
21 |
| Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment |
20 |
| Prediction of interresidue contacts with DeepMetaPSICOV in CASP13 |
19 |
| MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction |
18 |
| Comparative analysis of nanobody sequence and structure data |
16 |
| Protein structure prediction using Rosetta in CASP12 |
16 |
| Analysis of deep learning methods for blind protein contact prediction in CASP12 |
15 |
| Analysis of distance-based protein structure prediction by deep learning in CASP13 |
15 |
| Recent developments in deep learning applied to protein structure prediction |
14 |
| Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12 |
14 |
| Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13 |
13 |
| Assessing the accuracy of contact predictions in CASP13 |
12 |
| A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments |
12 |
| Evaluation of template-based modeling in CASP13 |
11 |
| Comprehensive mapping of cystic fibrosis mutations to CFTR protein identifies mutation clusters and molecular docking predicts corrector binding site |
10 |
| Assessment of model accuracy estimations in CASP12 |
10 |
| iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations |
10 |
| Mycobacterium tuberculosis UvrB forms dimers in solution and interacts with UvrA in the absence of ligands |
9 |
| Assessment of hard target modeling in CASP12 reveals an emerging role of alignment-based contact prediction methods |
9 |
| Evaluation of the template-based modeling in CASP12 |
9 |
| AggScore: Prediction of aggregation-prone regions in proteins based on the distribution of surface patches |
8 |
| Improved performance in CAPRI round 37 using LZerD docking and template-based modeling with combined scoring functions |
7 |
| What makes it difficult to refine protein models further via molecular dynamics simulations? |
7 |
| Molecular dynamics simulation, binding free energy calculation and unbinding pathway analysis on selectivity difference between FKBP51 and FKBP52: Insight into the molecular mechanism of isoform selectivity |
7 |
| Assessment of chemical-crosslink-assisted protein structure modeling in CASP13 |
7 |
| SPIN2: Predicting sequence profiles from protein structures using deep neural networks |
7 |
| The Short-chain Dehydrogenase/Reductase Engineering Database (SDRED): A classification and analysis system for a highly diverse enzyme family |
7 |
| Assessment of protein model structure accuracy estimation in CASP13: Challenges in the era of deep learning |
6 |
| Prediction of cross-clade HIV-1 T-cell epitopes using immunoinformatics analysis |
6 |
| Improved protein contact predictions with the MetaPSICOV2 server in CASP12 |
6 |
| Template-based modeling by ClusPro in CASP13 and the potential for using co-evolutionary information in docking |
5 |
| Automatic structure prediction of oligomeric assemblies using Robetta in CASP12 |
5 |
| Modeling CAPRI targets 110-120 by template-based and free docking using contact potential and combined scoring function |
5 |
| Accurate template-based modeling in CASP12 using the IntFOLD4-TS, ModFOLD and ReFOLD methods |
5 |
| Disorder guides domain rearrangement in elongation factor Tu |
5 |
| High-throughput prediction of disordered moonlighting regions in protein sequences |
5 |
| Evaluation of model refinement in CASP13 |
5 |
| Estimation of model accuracy in CASP13 |
5 |
| The structure of the N-terminal module of the cell wall hydrolase RipA and its role in regulating catalytic activity |
5 |
| DelPhiPKa: Including salt in the calculations and enabling polar residues to titrate |
5 |
| Effects of force fields on the conformational and dynamic properties of amyloid beta(1-40) dimer explored by replica exchange molecular dynamics simulations |
5 |
| Assessment of protein assembly prediction in CASP13 |
5 |
