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.
197 related articles for article (PubMed ID: 34396417)
1. APPTEST is a novel protocol for the automatic prediction of peptide tertiary structures. Timmons PB; Hewage CM Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34396417 [TBL] [Abstract][Full Text] [Related]
2. ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides. Timmons PB; Hewage CM Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34297817 [TBL] [Abstract][Full Text] [Related]
3. Structure prediction of linear and cyclic peptides using CABS-flex. Badaczewska-Dawid A; Wróblewski K; Kurcinski M; Kmiecik S Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38305457 [TBL] [Abstract][Full Text] [Related]
4. HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks. Timmons PB; Hewage CM Sci Rep; 2020 Jul; 10(1):10869. PubMed ID: 32616760 [TBL] [Abstract][Full Text] [Related]
6. In silico predictions of 3D structures of linear and cyclic peptides with natural and non-proteinogenic residues. Beaufays J; Lins L; Thomas A; Brasseur R J Pept Sci; 2012 Jan; 18(1):17-24. PubMed ID: 22033979 [TBL] [Abstract][Full Text] [Related]
7. Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core breakthrough in peptide structure and function prediction? Jia W; Peng J; Zhang Y; Zhu J; Qiang X; Zhang R; Shi L Food Res Int; 2023 Dec; 174(Pt 1):113640. PubMed ID: 37986483 [TBL] [Abstract][Full Text] [Related]
8. A Generalized Attraction-Repulsion Potential and Revisited Fragment Library Improves PEP-FOLD Peptide Structure Prediction. Binette V; Mousseau N; Tuffery P J Chem Theory Comput; 2022 Apr; 18(4):2720-2736. PubMed ID: 35298162 [TBL] [Abstract][Full Text] [Related]
9. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. Lamiable A; Thévenet P; Rey J; Vavrusa M; Derreumaux P; Tufféry P Nucleic Acids Res; 2016 Jul; 44(W1):W449-54. PubMed ID: 27131374 [TBL] [Abstract][Full Text] [Related]
10. PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides. Thévenet P; Shen Y; Maupetit J; Guyon F; Derreumaux P; Tufféry P Nucleic Acids Res; 2012 Jul; 40(Web Server issue):W288-93. PubMed ID: 22581768 [TBL] [Abstract][Full Text] [Related]
11. ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides. Timmons PB; Hewage CM Biomed Pharmacother; 2021 Jan; 133():111051. PubMed ID: 33254015 [TBL] [Abstract][Full Text] [Related]
12. SeqPredNN: a neural network that generates protein sequences that fold into specified tertiary structures. Lategan FA; Schreiber C; Patterton HG BMC Bioinformatics; 2023 Oct; 24(1):373. PubMed ID: 37789284 [TBL] [Abstract][Full Text] [Related]
13. UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity. Du Z; Ding X; Xu Y; Li Y Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37020337 [TBL] [Abstract][Full Text] [Related]
14. Monte Carlo simulations of peptide-membrane interactions with the MCPep web server. Gofman Y; Haliloglu T; Ben-Tal N Nucleic Acids Res; 2012 Jul; 40(Web Server issue):W358-63. PubMed ID: 22695797 [TBL] [Abstract][Full Text] [Related]
15. PEP-FOLD: an online resource for de novo peptide structure prediction. Maupetit J; Derreumaux P; Tuffery P Nucleic Acids Res; 2009 Jul; 37(Web Server issue):W498-503. PubMed ID: 19433514 [TBL] [Abstract][Full Text] [Related]
16. Machine learning study of classifiers trained with biophysiochemical properties of amino acids to predict fibril forming Peptide motifs. Kumaran Nair SS; Subba Reddy NV; Hareesha KS Protein Pept Lett; 2012 Sep; 19(9):917-23. PubMed ID: 22486618 [TBL] [Abstract][Full Text] [Related]
17. Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network. Xu YC; ShangGuan TJ; Ding XM; Cheung NJ Sci Rep; 2021 Oct; 11(1):21033. PubMed ID: 34702851 [TBL] [Abstract][Full Text] [Related]
18. KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides. Pandey P; Patel V; George NV; Mallajosyula SS J Proteome Res; 2018 Sep; 17(9):3214-3222. PubMed ID: 30032609 [TBL] [Abstract][Full Text] [Related]
19. The world of beta- and gamma-peptides comprised of homologated proteinogenic amino acids and other components. Seebach D; Beck AK; Bierbaum DJ Chem Biodivers; 2004 Aug; 1(8):1111-239. PubMed ID: 17191902 [TBL] [Abstract][Full Text] [Related]
20. Exploratory studies of ab initio protein structure prediction: multiple copy simulated annealing, AMBER energy functions, and a generalized born/solvent accessibility solvation model. Liu Y; Beveridge DL Proteins; 2002 Jan; 46(1):128-46. PubMed ID: 11746709 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]