155 related articles for article (PubMed ID: 36564857)
1. Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs.
Li AJ; Lu M; Desta I; Sundar V; Grigoryan G; Keating AE
Protein Sci; 2023 Feb; 32(2):e4554. PubMed ID: 36564857
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. A backbone-centred energy function of neural networks for protein design.
Huang B; Xu Y; Hu X; Liu Y; Liao S; Zhang J; Huang C; Hong J; Chen Q; Liu H
Nature; 2022 Feb; 602(7897):523-528. PubMed ID: 35140398
[TBL] [Abstract][Full Text] [Related]
4. Accurate prediction for atomic-level protein design and its application in diversifying the near-optimal sequence space.
Fromer M; Yanover C
Proteins; 2009 May; 75(3):682-705. PubMed ID: 19003998
[TBL] [Abstract][Full Text] [Related]
5. Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles.
Li Z; Yang Y; Faraggi E; Zhan J; Zhou Y
Proteins; 2014 Oct; 82(10):2565-73. PubMed ID: 24898915
[TBL] [Abstract][Full Text] [Related]
6. De novo design of cavity-containing proteins with a backbone-centered neural network energy function.
Xu Y; Hu X; Wang C; Liu Y; Chen Q; Liu H
Structure; 2024 Apr; 32(4):424-432.e4. PubMed ID: 38325370
[TBL] [Abstract][Full Text] [Related]
7. Using neural networks and evolutionary information in decoy discrimination for protein tertiary structure prediction.
Tan CW; Jones DT
BMC Bioinformatics; 2008 Feb; 9():94. PubMed ID: 18267018
[TBL] [Abstract][Full Text] [Related]
8. DenseCPD: Improving the Accuracy of Neural-Network-Based Computational Protein Sequence Design with DenseNet.
Qi Y; Zhang JZH
J Chem Inf Model; 2020 Mar; 60(3):1245-1252. PubMed ID: 32126171
[TBL] [Abstract][Full Text] [Related]
9. Protein secondary structure assignment using residual networks.
Antony JV; Koya R; Pournami PN; Nair GG; Balakrishnan JP
J Mol Model; 2022 Aug; 28(9):269. PubMed ID: 35997827
[TBL] [Abstract][Full Text] [Related]
10. Benchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models.
Jacquin H; Gilson A; Shakhnovich E; Cocco S; Monasson R
PLoS Comput Biol; 2016 May; 12(5):e1004889. PubMed ID: 27177270
[TBL] [Abstract][Full Text] [Related]
11. GraphGPSM: a global scoring model for protein structure using graph neural networks.
He G; Liu J; Liu D; Zhang G
Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37317619
[TBL] [Abstract][Full Text] [Related]
12. Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks.
Pan X; Rijnbeek P; Yan J; Shen HB
BMC Genomics; 2018 Jul; 19(1):511. PubMed ID: 29970003
[TBL] [Abstract][Full Text] [Related]
13. Foldamer Tertiary Structure through Sequence-Guided Protein Backbone Alteration.
George KL; Horne WS
Acc Chem Res; 2018 May; 51(5):1220-1228. PubMed ID: 29672021
[TBL] [Abstract][Full Text] [Related]
14. Machine learning in biological physics: From biomolecular prediction to design.
Martin J; Lequerica Mateos M; Onuchic JN; Coluzza I; Morcos F
Proc Natl Acad Sci U S A; 2024 Jul; 121(27):e2311807121. PubMed ID: 38913893
[TBL] [Abstract][Full Text] [Related]
15. SPIN2: Predicting sequence profiles from protein structures using deep neural networks.
O'Connell J; Li Z; Hanson J; Heffernan R; Lyons J; Paliwal K; Dehzangi A; Yang Y; Zhou Y
Proteins; 2018 Jun; 86(6):629-633. PubMed ID: 29508448
[TBL] [Abstract][Full Text] [Related]
16. An artificial neural network approach to improving the correlation between protein energetics and the backbone structure.
Fawcett TM; Irausquin SJ; Simin M; Valafar H
Proteomics; 2013 Jan; 13(2):230-8. PubMed ID: 23184572
[TBL] [Abstract][Full Text] [Related]
17. Tertiary alphabet for the observable protein structural universe.
Mackenzie CO; Zhou J; Grigoryan G
Proc Natl Acad Sci U S A; 2016 Nov; 113(47):E7438-E7447. PubMed ID: 27810958
[TBL] [Abstract][Full Text] [Related]
18. Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks.
Helles G; Fonseca R
BMC Bioinformatics; 2009 Oct; 10():338. PubMed ID: 19835576
[TBL] [Abstract][Full Text] [Related]
19. Selection of sequence motifs and generative Hopfield-Potts models for protein families.
Shimagaki K; Weigt M
Phys Rev E; 2019 Sep; 100(3-1):032128. PubMed ID: 31639992
[TBL] [Abstract][Full Text] [Related]
20. SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network.
Zhang X; Yin H; Ling F; Zhan J; Zhou Y
PLoS Comput Biol; 2023 Dec; 19(12):e1011330. PubMed ID: 38060617
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]