Literature DB >> 31344278

Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions.

Amanda L Loshbaugh1,2, Tanja Kortemme1,2,3,4.   

Abstract

Computational design of binding sites in proteins remains difficult, in part due to limitations in our current ability to sample backbone conformations that enable precise and accurate geometric positioning of side chains during sequence design. Here we present a benchmark framework for comparison between flexible-backbone design methods applied to binding interactions. We quantify the ability of different flexible backbone design methods in the widely used protein design software Rosetta to recapitulate observed protein sequence profiles assumed to represent functional protein/protein and protein/small molecule binding interactions. The CoupledMoves method, which combines backbone flexibility and sequence exploration into a single acceptance step during the sampling trajectory, better recapitulates observed sequence profiles than the BackrubEnsemble and FastDesign methods, which separate backbone flexibility and sequence design into separate acceptance steps during the sampling trajectory. Flexible-backbone design with the CoupledMoves method is a powerful strategy for reducing sequence space to generate targeted libraries for experimental screening and selection.
© 2019 Wiley Periodicals, Inc.

Entities:  

Keywords:  algorithms; amino acid sequence; binding sites; computational biology/*methods; molecular models

Mesh:

Substances:

Year:  2019        PMID: 31344278      PMCID: PMC6901717          DOI: 10.1002/prot.25790

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  60 in total

1.  Side-chain and backbone flexibility in protein core design.

Authors:  J R Desjarlais; T M Handel
Journal:  J Mol Biol       Date:  1999-07-02       Impact factor: 5.469

2.  Protein topology and stability define the space of allowed sequences.

Authors:  Patrice Koehl; Michael Levitt
Journal:  Proc Natl Acad Sci U S A       Date:  2002-01-22       Impact factor: 11.205

3.  Control of protein signaling using a computationally designed GTPase/GEF orthogonal pair.

Authors:  Gregory T Kapp; Sen Liu; Amelie Stein; Derek T Wong; Attila Reményi; Brian J Yeh; James S Fraser; Jack Taunton; Wendell A Lim; Tanja Kortemme
Journal:  Proc Natl Acad Sci U S A       Date:  2012-03-07       Impact factor: 11.205

4.  Structure-based prediction of the peptide sequence space recognized by natural and synthetic PDZ domains.

Authors:  Colin A Smith; Tanja Kortemme
Journal:  J Mol Biol       Date:  2010-07-21       Impact factor: 5.469

5.  Kemp elimination catalysts by computational enzyme design.

Authors:  Daniela Röthlisberger; Olga Khersonsky; Andrew M Wollacott; Lin Jiang; Jason DeChancie; Jamie Betker; Jasmine L Gallaher; Eric A Althoff; Alexandre Zanghellini; Orly Dym; Shira Albeck; Kendall N Houk; Dan S Tawfik; David Baker
Journal:  Nature       Date:  2008-03-19       Impact factor: 49.962

6.  Improving the accuracy of protein stability predictions with multistate design using a variety of backbone ensembles.

Authors:  James A Davey; Roberto A Chica
Journal:  Proteins       Date:  2013-11-22

7.  Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions.

Authors:  K T Simons; C Kooperberg; E Huang; D Baker
Journal:  J Mol Biol       Date:  1997-04-25       Impact factor: 5.469

8.  Multistate Computational Protein Design with Backbone Ensembles.

Authors:  James A Davey; Roberto A Chica
Journal:  Methods Mol Biol       Date:  2017

9.  Assessment of flexible backbone protein design methods for sequence library prediction in the therapeutic antibody Herceptin-HER2 interface.

Authors:  Mariana Babor; Daniel J Mandell; Tanja Kortemme
Journal:  Protein Sci       Date:  2011-05-03       Impact factor: 6.725

10.  Predicting the tolerated sequences for proteins and protein interfaces using RosettaBackrub flexible backbone design.

Authors:  Colin A Smith; Tanja Kortemme
Journal:  PLoS One       Date:  2011-07-18       Impact factor: 3.240

View more
  12 in total

1.  Design of peptides with high affinity binding to a monoclonal antibody as a basis for immunotherapy.

Authors:  Surendra S Negi; Randall M Goldblum; Werner Braun; Terumi Midoro-Horiuti
Journal:  Peptides       Date:  2021-08-16       Impact factor: 3.750

Review 2.  The stability and dynamics of computationally designed proteins.

Authors:  Natali A Gonzalez; Brigitte A Li; Michelle E McCully
Journal:  Protein Eng Des Sel       Date:  2022-02-17       Impact factor: 1.952

3.  Perturbing the energy landscape for improved packing during computational protein design.

Authors:  Jack B Maguire; Hugh K Haddox; Devin Strickland; Samer F Halabiya; Brian Coventry; Jermel R Griffin; Surya V S R K Pulavarti; Matthew Cummins; David F Thieker; Eric Klavins; Thomas Szyperski; Frank DiMaio; David Baker; Brian Kuhlman
Journal:  Proteins       Date:  2020-12-11

4.  An automated protocol for modelling peptide substrates to proteases.

Authors:  Rodrigo Ochoa; Mikhail Magnitov; Roman A Laskowski; Pilar Cossio; Janet M Thornton
Journal:  BMC Bioinformatics       Date:  2020-12-29       Impact factor: 3.169

5.  Rosetta:MSF:NN: Boosting performance of multi-state computational protein design with a neural network.

Authors:  Julian Nazet; Elmar Lang; Rainer Merkl
Journal:  PLoS One       Date:  2021-08-26       Impact factor: 3.240

6.  Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks.

Authors:  Julia Koehler Leman; Sergey Lyskov; Steven M Lewis; Jared Adolf-Bryfogle; Rebecca F Alford; Kyle Barlow; Ziv Ben-Aharon; Daniel Farrell; Jason Fell; William A Hansen; Ameya Harmalkar; Jeliazko Jeliazkov; Georg Kuenze; Justyna D Krys; Ajasja Ljubetič; Amanda L Loshbaugh; Jack Maguire; Rocco Moretti; Vikram Khipple Mulligan; Morgan L Nance; Phuong T Nguyen; Shane Ó Conchúir; Shourya S Roy Burman; Rituparna Samanta; Shannon T Smith; Frank Teets; Johanna K S Tiemann; Andrew Watkins; Hope Woods; Brahm J Yachnin; Christopher D Bahl; Chris Bailey-Kellogg; David Baker; Rhiju Das; Frank DiMaio; Sagar D Khare; Tanja Kortemme; Jason W Labonte; Kresten Lindorff-Larsen; Jens Meiler; William Schief; Ora Schueler-Furman; Justin B Siegel; Amelie Stein; Vladimir Yarov-Yarovoy; Brian Kuhlman; Andrew Leaver-Fay; Dominik Gront; Jeffrey J Gray; Richard Bonneau
Journal:  Nat Commun       Date:  2021-11-29       Impact factor: 17.694

7.  Molecular basis of the new COVID-19 target neuropilin-1 in complex with SARS-CoV-2 S1 C-end rule peptide and small-molecule antagonists.

Authors:  Methus Klaewkla; Thanapon Charoenwongpaiboon; Panupong Mahalapbutr
Journal:  J Mol Liq       Date:  2021-05-20       Impact factor: 6.165

Review 8.  Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering.

Authors:  Bartłomiej Surpeta; Carlos Eduardo Sequeiros-Borja; Jan Brezovsky
Journal:  Int J Mol Sci       Date:  2020-04-14       Impact factor: 5.923

9.  Computational Design of 25-mer Peptide Binders of SARS-CoV-2.

Authors:  Thassanai Sitthiyotha; Surasak Chunsrivirot
Journal:  J Phys Chem B       Date:  2020-11-17       Impact factor: 2.991

10.  Computational design of SARS-CoV-2 peptide binders with better predicted binding affinities than human ACE2 receptor.

Authors:  Thassanai Sitthiyotha; Surasak Chunsrivirot
Journal:  Sci Rep       Date:  2021-08-02       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.