These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
2. Evaluation of residue-residue contact predictions in CASP9. Monastyrskyy B; Fidelis K; Tramontano A; Kryshtafovych A Proteins; 2011; 79 Suppl 10(Suppl 10):119-25. PubMed ID: 21928322 [TBL] [Abstract][Full Text] [Related]
3. A large-scale comparative assessment of methods for residue-residue contact prediction. Wuyun Q; Zheng W; Peng Z; Yang J Brief Bioinform; 2018 Mar; 19(2):219-230. PubMed ID: 27802931 [TBL] [Abstract][Full Text] [Related]
4. Assessment of protein disorder region predictions in CASP10. Monastyrskyy B; Kryshtafovych A; Moult J; Tramontano A; Fidelis K Proteins; 2014 Feb; 82 Suppl 2(0 2):127-37. PubMed ID: 23946100 [TBL] [Abstract][Full Text] [Related]
5. Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning. Adhikari B; Hou J; Cheng J Proteins; 2018 Mar; 86 Suppl 1(Suppl 1):84-96. PubMed ID: 29047157 [TBL] [Abstract][Full Text] [Related]
6. Improving accuracy of protein contact prediction using balanced network deconvolution. Sun HP; Huang Y; Wang XF; Zhang Y; Shen HB Proteins; 2015 Mar; 83(3):485-96. PubMed ID: 25524593 [TBL] [Abstract][Full Text] [Related]
7. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model. Wang S; Sun S; Li Z; Zhang R; Xu J PLoS Comput Biol; 2017 Jan; 13(1):e1005324. PubMed ID: 28056090 [TBL] [Abstract][Full Text] [Related]
8. New encouraging developments in contact prediction: Assessment of the CASP11 results. Monastyrskyy B; D'Andrea D; Fidelis K; Tramontano A; Kryshtafovych A Proteins; 2016 Sep; 84 Suppl 1(Suppl 1):131-44. PubMed ID: 26474083 [TBL] [Abstract][Full Text] [Related]
9. Assessing the accuracy of contact and distance predictions in CASP14. Ruiz-Serra V; Pontes C; Milanetti E; Kryshtafovych A; Lepore R; Valencia A Proteins; 2021 Dec; 89(12):1888-1900. PubMed ID: 34595772 [TBL] [Abstract][Full Text] [Related]
10. A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks. Eickholt J; Cheng J BMC Bioinformatics; 2013; 14 Suppl 14(Suppl 14):S12. PubMed ID: 24267585 [TBL] [Abstract][Full Text] [Related]
11. KScons: a Bayesian approach for protein residue contact prediction using the knob-socket model of protein tertiary structure. Li Q; Dahl DB; Vannucci M; Joo H; Tsai JW Bioinformatics; 2016 Dec; 32(24):3774-3781. PubMed ID: 27559156 [TBL] [Abstract][Full Text] [Related]
13. Assessment of protein assembly prediction in CASP12. Lafita A; Bliven S; Kryshtafovych A; Bertoni M; Monastyrskyy B; Duarte JM; Schwede T; Capitani G Proteins; 2018 Mar; 86 Suppl 1(Suppl 1):247-256. PubMed ID: 29071742 [TBL] [Abstract][Full Text] [Related]
14. Assessment of CASP10 contact-assisted predictions. Taylor TJ; Bai H; Tai CH; Lee B Proteins; 2014 Feb; 82 Suppl 2(Suppl 2):84-97. PubMed ID: 23873510 [TBL] [Abstract][Full Text] [Related]
15. Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. Hou J; Wu T; Cao R; Cheng J Proteins; 2019 Dec; 87(12):1165-1178. PubMed ID: 30985027 [TBL] [Abstract][Full Text] [Related]
16. A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments. Abriata LA; Tamò GE; Dal Peraro M Proteins; 2019 Dec; 87(12):1100-1112. PubMed ID: 31344267 [TBL] [Abstract][Full Text] [Related]
17. Assessment of ligand binding site predictions in CASP10. Gallo Cassarino T; Bordoli L; Schwede T Proteins; 2014 Feb; 82 Suppl 2(0 2):154-63. PubMed ID: 24339001 [TBL] [Abstract][Full Text] [Related]
18. Analysis of deep learning methods for blind protein contact prediction in CASP12. Wang S; Sun S; Xu J Proteins; 2018 Mar; 86 Suppl 1(Suppl 1):67-77. PubMed ID: 28845538 [TBL] [Abstract][Full Text] [Related]
19. Assessment of domain boundary predictions and the prediction of intramolecular contacts in CASP8. Ezkurdia I; Graña O; Izarzugaza JM; Tress ML Proteins; 2009; 77 Suppl 9():196-209. PubMed ID: 19714769 [TBL] [Abstract][Full Text] [Related]
20. Predicting protein residue-residue contacts using random forests and deep networks. Luttrell J; Liu T; Zhang C; Wang Z BMC Bioinformatics; 2019 Mar; 20(Suppl 2):100. PubMed ID: 30871477 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]