339 related articles for article (PubMed ID: 33893299)
1. Protein design and variant prediction using autoregressive generative models.
Shin JE; Riesselman AJ; Kollasch AW; McMahon C; Simon E; Sander C; Manglik A; Kruse AC; Marks DS
Nat Commun; 2021 Apr; 12(1):2403. PubMed ID: 33893299
[TBL] [Abstract][Full Text] [Related]
2. Can computationally designed protein sequences improve secondary structure prediction?
Bondugula R; Wallqvist A; Lee MS
Protein Eng Des Sel; 2011 May; 24(5):455-61. PubMed ID: 21282334
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Simplified synthetic antibody libraries.
Rajan S; Sidhu SS
Methods Enzymol; 2012; 502():3-23. PubMed ID: 22208979
[TBL] [Abstract][Full Text] [Related]
5. The Framework of Computational Protein Design.
Samish I
Methods Mol Biol; 2017; 1529():3-19. PubMed ID: 27914044
[TBL] [Abstract][Full Text] [Related]
6. Analysis of covariation in an SH3 domain sequence alignment: applications in tertiary contact prediction and the design of compensating hydrophobic core substitutions.
Larson SM; Di Nardo AA; Davidson AR
J Mol Biol; 2000 Oct; 303(3):433-46. PubMed ID: 11031119
[TBL] [Abstract][Full Text] [Related]
7. Enhancing missense variant pathogenicity prediction with protein language models using VariPred.
Lin W; Wells J; Wang Z; Orengo C; Martin ACR
Sci Rep; 2024 Apr; 14(1):8136. PubMed ID: 38584172
[TBL] [Abstract][Full Text] [Related]
8. RosettaAntibodyDesign (RAbD): A general framework for computational antibody design.
Adolf-Bryfogle J; Kalyuzhniy O; Kubitz M; Weitzner BD; Hu X; Adachi Y; Schief WR; Dunbrack RL
PLoS Comput Biol; 2018 Apr; 14(4):e1006112. PubMed ID: 29702641
[TBL] [Abstract][Full Text] [Related]
9. Bayesian coestimation of phylogeny and sequence alignment.
Lunter G; Miklós I; Drummond A; Jensen JL; Hein J
BMC Bioinformatics; 2005 Apr; 6():83. PubMed ID: 15804354
[TBL] [Abstract][Full Text] [Related]
10. Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface.
Lowegard AU; Frenkel MS; Holt GT; Jou JD; Ojewole AA; Donald BR
PLoS Comput Biol; 2020 Jun; 16(6):e1007447. PubMed ID: 32511232
[TBL] [Abstract][Full Text] [Related]
11. Generative models for protein sequence modeling: recent advances and future directions.
Mardikoraem M; Wang Z; Pascual N; Woldring D
Brief Bioinform; 2023 Sep; 24(6):. PubMed ID: 37864295
[TBL] [Abstract][Full Text] [Related]
12. Computational Tools for Aiding Rational Antibody Design.
Krawczyk K; Dunbar J; Deane CM
Methods Mol Biol; 2017; 1529():399-416. PubMed ID: 27914064
[TBL] [Abstract][Full Text] [Related]
13. ProteInfer, deep neural networks for protein functional inference.
Sanderson T; Bileschi ML; Belanger D; Colwell LJ
Elife; 2023 Feb; 12():. PubMed ID: 36847334
[TBL] [Abstract][Full Text] [Related]
14. OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design.
Ojewole A; Lowegard A; Gainza P; Reeve SM; Georgiev I; Anderson AC; Donald BR
Methods Mol Biol; 2017; 1529():291-306. PubMed ID: 27914058
[TBL] [Abstract][Full Text] [Related]
15. Aligning, analyzing, and visualizing sequences for antibody engineering: Automated recognition of immunoglobulin variable region features.
Jarasch A; Skerra A
Proteins; 2017 Jan; 85(1):65-71. PubMed ID: 27770557
[TBL] [Abstract][Full Text] [Related]
16. Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs.
Várnai C; Burkoff NS; Wild DL
PLoS One; 2017; 12(2):e0169356. PubMed ID: 28166227
[TBL] [Abstract][Full Text] [Related]
17. cnnAlpha: Protein disordered regions prediction by reduced amino acid alphabets and convolutional neural networks.
Oberti M; Vaisman II
Proteins; 2020 Nov; 88(11):1472-1481. PubMed ID: 32535960
[TBL] [Abstract][Full Text] [Related]
18. Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences.
Oldfield CJ; Chen K; Kurgan L
Methods Mol Biol; 2019; 1958():73-100. PubMed ID: 30945214
[TBL] [Abstract][Full Text] [Related]
19. Fast and Flexible Protein Design Using Deep Graph Neural Networks.
Strokach A; Becerra D; Corbi-Verge C; Perez-Riba A; Kim PM
Cell Syst; 2020 Oct; 11(4):402-411.e4. PubMed ID: 32971019
[TBL] [Abstract][Full Text] [Related]
20. A germline knowledge based computational approach for determining antibody complementarity determining regions.
Zhao S; Lu J
Mol Immunol; 2010 Jan; 47(4):694-700. PubMed ID: 19939452
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]