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

Davey, James A.; Chica, Roberto A.

doi:10.1002/prot.24457

Cited by 47 publications

(60 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The improved agreement in these energies shows the value of NSRs and confirms our previous in silico observations in a different system (Leaver-Fay et al, 2011). Although NSRs do not avoid all false collisions, they improve over previous MSD approaches where negative states were modeled only onto the backbones of existing structures (Havranek and Harbury, 2003), or where the extra backbone conformations were generated using only the wild-type sequence (Allen et al, 2010; Davey and Chica, 2014). …”

Section: Resultsmentioning

confidence: 99%

“…Aggressive sampling, however, would dramatically increase running time, which is already long. Perhaps incorporating additional low-energy backbone conformations that are held fixed throughout simulation could sufficiently mimic internal backbone flexibility (Davey and Chica, 2014). …”

Section: Discussionmentioning

confidence: 99%

“…A potential solution to this problem is to use an ensemble of backbones to represent how the complexes might adjust to accommodate mutations. Davey and Chica found that considering many near-native conformations improved ΔΔG predictions (Davey and Chica, 2014). Here, we build on this result by using negative-state repertoires (NSRs) during the sequence optimization process, which involves sampling a large sequence space (~19 40 , i.e.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Computationally Designed Bispecific Antibodies using Negative State Repertoires

et al. 2016

View full text Add to dashboard Cite

A challenge in the structure-based design of specificity is modeling the negative states, i.e. the complexes that you do not want to form. This is a difficult problem because mutations predicted to destabilize the negative state might be accommodated by small conformational rearrangements. To overcome this challenge, we employ an iterative strategy that cycles between sequence design and protein docking in order to build up an ensemble of alternative negative state conformations for use in specificity prediction. We have applied our technique to the design of heterodimeric CH3 interfaces in the Fc region of antibodies. Combining computationally- and rationally-designed mutations produced unique designs with heterodimer purities greater than 90%. Asymmetric Fc crystallization was able to resolve the interface mutations; the heterodimer structures confirmed that the interfaces formed as designed. With these CH3 mutations, and those made at the heavy-/light-chain interface, we demonstrate one-step synthesis of four fully IgG bispecific antibodies.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computationally Designed Bispecific Antibodies using Negative State Repertoires

et al. 2016

View full text Add to dashboard Cite

show abstract

“…20 Various strategies have been proposed to model backbone flexibility in design, such as small random perturbations in torsional or Cartesian space, 7,21-23 normal mode analysis, 24 or "backrub" motions shown to underlie commonly observed protein structural heterogeneity in high resolution crystal structures 6 that have proven useful to model structural changes in response to mutations. 8,25,26 Several strategies have used protein backbone ensembles for design, which are typically generated computationally employing various sampling methods 21,22,27 including backrub motions [28][29][30][31] but can also be taken from crystal structures. 23,28,31 Within the structure modeling and design program Rosetta, 32 backbone flexibility has been treated in a number of ways.…”

Section: Introductionmentioning

confidence: 99%

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

Loshbaugh

Kortemme

2019

Proteins

View full text Add to dashboard Cite

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.

show abstract

“…This is why multi-state design (MSD) methodology is an emerging field in CPD [23] that extends the application spectrum and promises high success rates. Even the design of stable proteins profits from using backbone ensembles [24]. …”

Section: Introductionmentioning

confidence: 99%

Rosetta:MSF: a modular framework for multi-state computational protein design

et al. 2017

View full text Add to dashboard Cite

Computational protein design (CPD) is a powerful technique to engineer existing proteins or to design novel ones that display desired properties. Rosetta is a software suite including algorithms for computational modeling and analysis of protein structures and offers many elaborate protocols created to solve highly specific tasks of protein engineering. Most of Rosetta’s protocols optimize sequences based on a single conformation (i. e. design state). However, challenging CPD objectives like multi-specificity design or the concurrent consideration of positive and negative design goals demand the simultaneous assessment of multiple states. This is why we have developed the multi-state framework MSF that facilitates the implementation of Rosetta’s single-state protocols in a multi-state environment and made available two frequently used protocols. Utilizing MSF, we demonstrated for one of these protocols that multi-state design yields a 15% higher performance than single-state design on a ligand-binding benchmark consisting of structural conformations. With this protocol, we designed de novo nine retro-aldolases on a conformational ensemble deduced from a (βα)8-barrel protein. All variants displayed measurable catalytic activity, testifying to a high success rate for this concept of multi-state enzyme design.

show abstract

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

Cited by 47 publications

References 43 publications

Computationally Designed Bispecific Antibodies using Negative State Repertoires

Computationally Designed Bispecific Antibodies using Negative State Repertoires

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

Rosetta:MSF: a modular framework for multi-state computational protein design

Contact Info

Product

Resources

About