Software
EvoPro: A design pipeline that uses DL models for structure prediction and sequence optimization to design new proteins and protein binders. Code is available through GitHub.
Goudy OJ, Nallathambi A, Kinjo T, Randolph N, Kuhlman B. In silico evolution of protein binders with deep learning models for structure prediction and sequence design. bioRxiv [Preprint]. 2023 May 3:2023.05.03.539278. doi: 10.1101/2023.05.03.539278. PMID: 37205527; PMCID: PMC10187191.
ThermoMPNN: A graph neural network trained to predict how amino acid mutations will influence protein stability. Code is available through GitHub, and the protocol can be run through Google Colab.
Dieckhaus H, Brocidiacono M, Randolph N, Kuhlman B. Transfer learning to leverage larger datasets for improved prediction of protein stability changes. bioRxiv [Preprint]. 2023 Jul 30:2023.07.27.550881. doi: 10.1101/2023.07.27.550881. PMID: 37547004; PMCID: PMC10402116.
PIPPack: A graph neural network for the rapid sampling of protein side chain conformations. Code is available through GitHub, and the protocol can be run through Google Colab.
Randolph N and Kuhlman B. Invariant point message passing for protein side chain packing and design. bioRxiv [Preprint]. 2023 Aug 3:2023.08.03.551328. doi: 10.1101/2023.08.03.551328.
Stabilizing proteins with Rosetta: An online server for predicting how amino acid mutations will influence protein stability.
Thieker DF, Maguire JB, Kudlacek ST, Leaver-Fay A, Lyskov S, Kuhlman B. Stabilizing proteins, simplified: A Rosetta-based webtool for predicting favorable mutations. Protein Sci. 2022 Oct;31(10):e4428. doi: 10.1002/pro.4428. PMID: 36173174; PMCID: PMC9490798.
RosettaDesign: An online server for performing protein design simulations with the standard fixed backbone (fixbb) protocol in Rosetta.
Liu Y, Kuhlman B. RosettaDesign server for protein design. Nucleic Acids Res. 2006 Jul 1;34(Web Server issue):W235-8. doi: 10.1093/nar/gkl163. PMID: 16845000; PMCID: PMC1538902.
SwiftLib: An online tool for designing combinatorial libraries from degenerate codons
Jacobs TM, Yumerefendi H, Kuhlman B, Leaver-Fay A. SwiftLib: rapid degenerate-codon-library optimization through dynamic programming. Nucleic Acids Res. 2015 Mar 11;43(5):e34. doi: 10.1093/nar/gku1323. Epub 2014 Dec 24. PMID: 25539925; PMCID: PMC4357694.
Rosetta: Macromolecular structure prediction and design (developed by a consortium of over 50 universities in the Rosetta Commons). Specific contributions from the Kuhlman lab include protocols for fixed backbone design, protein design coupled with backbone relaxation, multistate protein design, joint hydrogen bond/electrostatic models, and protocols for designing hbond networks.
Kuhlman B, Baker D. Native protein sequences are close to optimal for their structures. Proc Natl Acad Sci U S A. 2000 Sep 12;97(19):10383-8. doi: 10.1073/pnas.97.19.10383. Erratum in: Proc Natl Acad Sci U S A. 2000 Nov 21;97(24):13460. PMID: 10984534; PMCID: PMC27033.
Leaver-Fay A, Jacak R, Stranges PB, Kuhlman B. A generic program for multistate protein design. PLoS One. 2011;6(7):e20937. doi: 10.1371/journal.pone.0020937. Epub 2011 Jul 6. PMID: 21754981; PMCID: PMC3130737.
O’Meara MJ, Leaver-Fay A, Tyka MD, Stein A, Houlihan K, DiMaio F, Bradley P, Kortemme T, Baker D, Snoeyink J, Kuhlman B. 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. PMID: 25866491; PMCID: PMC4390092.
Maguire JB, Boyken SE, Baker D, Kuhlman B. Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design. J Chem Theory Comput. 2018 May 8;14(5):2751-2760. doi: 10.1021/acs.jctc.8b00033. Epub 2018 Apr 20. Erratum in: J Chem Theory Comput. 2018 Oct 9;14(10):5434. PMID: 29652499; PMCID: PMC5940506.
Maguire JB, Haddox HK, Strickland D, Halabiya SF, Coventry B, Griffin JR, Pulavarti SVSRK, Cummins M, Thieker DF, Klavins E, Szyperski T, DiMaio F, Baker D, Kuhlman B. Perturbing the energy landscape for improved packing during computational protein design. Proteins. 2021 Apr;89(4):436-449. doi: 10.1002/prot.26030. Epub 2020 Dec 11. PMID: 33249652; PMCID: PMC8299543.