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1
Toward the Accuracy and Speed of Protein Side-Chain Packing: A Systematic Study on Rotamer Libraries.
J Chem Inf Model. 2020 Jan 27;60(1):410-420. doi: 10.1021/acs.jcim.9b00812. Epub 2019 Dec 31.
2
A protein-dependent side-chain rotamer library.
BMC Bioinformatics. 2011 Dec 14;12 Suppl 14(Suppl 14):S10. doi: 10.1186/1471-2105-12-S14-S10.
3
Rotamer libraries and probabilities of transition between rotamers for the side chains in protein-protein binding.
Proteins. 2012 Aug;80(8):2089-98. doi: 10.1002/prot.24103. Epub 2012 Jun 12.
5
Incorporating knowledge-based biases into an energy-based side-chain modeling method: application to comparative modeling of protein structure.
Biopolymers. 2001 Aug;59(2):72-86. doi: 10.1002/1097-0282(200108)59:2<72::AID-BIP1007>3.0.CO;2-S.
6
Rotamers: to be or not to be? An analysis of amino acid side-chain conformations in globular proteins.
J Mol Biol. 1993 Mar 20;230(2):592-612. doi: 10.1006/jmbi.1993.1172.
7
Design of a rotamer library for coarse-grained models in protein-folding simulations.
J Chem Inf Model. 2014 Jan 27;54(1):302-13. doi: 10.1021/ci4005833. Epub 2013 Dec 31.
8
Advantages of fine-grained side chain conformer libraries.
Protein Eng. 2003 Dec;16(12):963-9. doi: 10.1093/protein/gzg143.
10
Exploiting Sequence-Dependent Rotamer Information in Global Optimization of Proteins.
J Phys Chem B. 2022 Oct 27;126(42):8381-8390. doi: 10.1021/acs.jpcb.2c04647. Epub 2022 Oct 18.

引用本文的文献

1
To pack or not to pack: revisiting protein side-chain packing in the post-AlphaFold era.
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf297.
2
To pack or not to pack: revisiting protein side-chain packing in the post-AlphaFold era.
bioRxiv. 2025 Feb 27:2025.02.22.639681. doi: 10.1101/2025.02.22.639681.
3
Amino-Acid Characteristics in Protein Native State Structures.
Biomolecules. 2024 Jul 7;14(7):805. doi: 10.3390/biom14070805.
4
Invariant point message passing for protein side chain packing.
Proteins. 2024 Oct;92(10):1220-1233. doi: 10.1002/prot.26705. Epub 2024 May 24.
5
Invariant point message passing for protein side chain packing.
bioRxiv. 2023 Dec 21:2023.08.03.551328. doi: 10.1101/2023.08.03.551328.
6
A feature engineering-based machine learning technique to detect and classify lung and colon cancer from histopathological images.
Med Biol Eng Comput. 2024 Mar;62(3):913-924. doi: 10.1007/s11517-023-02984-y. Epub 2023 Dec 13.
7
Solvent Accessibility Promotes Rotamer Errors during Protein Modeling with Major Side-Chain Prediction Programs.
J Chem Inf Model. 2023 Jul 24;63(14):4405-4422. doi: 10.1021/acs.jcim.3c00134. Epub 2023 Jul 6.
8
Decoding CRISPR-Cas PAM recognition with UniDesign.
Brief Bioinform. 2023 May 19;24(3). doi: 10.1093/bib/bbad133.
9
GeoPacker: A novel deep learning framework for protein side-chain modeling.
Protein Sci. 2022 Dec;31(12):e4484. doi: 10.1002/pro.4484.
10
Exploiting Sequence-Dependent Rotamer Information in Global Optimization of Proteins.
J Phys Chem B. 2022 Oct 27;126(42):8381-8390. doi: 10.1021/acs.jpcb.2c04647. Epub 2022 Oct 18.

本文引用的文献

1
EvoEF2: accurate and fast energy function for computational protein design.
Bioinformatics. 2020 Feb 15;36(4):1135-1142. doi: 10.1093/bioinformatics/btz740.
3
The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design.
J Chem Theory Comput. 2017 Jun 13;13(6):3031-3048. doi: 10.1021/acs.jctc.7b00125. Epub 2017 May 12.
4
Quantifying side-chain conformational variations in protein structure.
Sci Rep. 2016 Nov 15;6:37024. doi: 10.1038/srep37024.
5
Simultaneous Optimization of Biomolecular Energy Functions on Features from Small Molecules and Macromolecules.
J Chem Theory Comput. 2016 Dec 13;12(12):6201-6212. doi: 10.1021/acs.jctc.6b00819. Epub 2016 Nov 7.
6
Protein side-chain packing problem: is there still room for improvement?
Brief Bioinform. 2017 Nov 1;18(6):1033-1043. doi: 10.1093/bib/bbw079.
7
Use of an Improved Matching Algorithm to Select Scaffolds for Enzyme Design Based on a Complex Active Site Model.
PLoS One. 2016 May 31;11(5):e0156559. doi: 10.1371/journal.pone.0156559. eCollection 2016.
9
Combined covalent-electrostatic model of hydrogen bonding improves structure prediction with Rosetta.
J Chem Theory Comput. 2015 Feb 10;11(2):609-22. doi: 10.1021/ct500864r.
10
The I-TASSER Suite: protein structure and function prediction.
Nat Methods. 2015 Jan;12(1):7-8. doi: 10.1038/nmeth.3213.

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