ArticleThe Journal of chemical physics2013
Funneling and frustration in the energy landscapes of some designed and simplified proteins.
Article in The Journal of chemical physics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Maximum Entropy Optimized Force Field for Intrinsically Disordered Proteins.Journal of chemical theory and computation · 2020Article
- Competition of individual domain folding with inter-domain interaction in WW domain engineered repeat proteins.Physical chemistry chemical physics : PCCP · 2019Article
- Localizing Frustration in Proteins Using All-Atom Energy Functions.The journal of physical chemistry. B · 2019Article
- Nonnative Energetic Frustrations in Protein Folding at Residual Level: A Simulation Study of Homologous Immunoglobulin-likeInternational journal of molecular sciences · 2018Article
- Effects of thymic selection on T cell recognition of foreign and tumor antigenic peptides.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Protein Folding and Structure Prediction from the Ground Up II: AAWSEM for α/β Proteins.The journal of physical chemistry. B · 2017Article
- Protein Frustratometer 2: a tool to localize energetic frustration in protein molecules, now with electrostatics.Nucleic acids research · 2016Article
- Human Frataxin Folds Via an Intermediate State. Role of the C-Terminal Region.Scientific reports · 2016Article
- Review
- Protein misfolding occurs by slow diffusion across multiple barriers in a rough energy landscape.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Native contact density and nonnative hydrophobic effects in the folding of bacterial immunity proteins.PLoS computational biology · 2015Article
- cgDNA: a software package for the prediction of sequence-dependent coarse-grain free energies of B-form DNA.Nucleic acids research · 2014Article
- Frustration in biomolecules.Quarterly reviews of biophysics · 2014Review
- Learning To Fold Proteins Using Energy Landscape Theory.Israel journal of chemistry · 2014Article
- Predictive energy landscapes for folding α-helical transmembrane proteins.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- Sequence analysis on the information of folding initiation segments in ferredoxin-like fold proteins.BMC structural biology · 2014Article
- Perspective: Reaches of chemical physics in biology.The Journal of chemical physics · 2013Article
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Authors and funding
4 authors.
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Abstract
We explore the similarities and differences between the energy landscapes of proteins that have been selected by nature and those of some proteins designed by humans. Natural proteins have evolved to function as well as fold, and this is a source of energetic frustration. The sequence of Top7, on the other hand, was designed with architecture alone in mind using only native state stability as the optimization criterion. Its topology had not previously been observed in nature. Experimental studies show that the folding kinetics of Top7 is more complex than the kinetics of folding of otherwise comparable naturally occurring proteins. In this paper, we use structure prediction tools, frustration analysis, and free energy profiles to illustrate the folding landscapes of Top7 and two other proteins designed by Takada. We use both perfectly funneled (structure-based) and predictive (transferable) models to gain insight into the role of topological versus energetic frustration in these systems and show how they differ from those found for natural proteins. We also study how robust the folding of these designs would be to the simplification of the sequences using fewer amino acid types. Simplification using a five amino acid type code results in comparable quality of structure prediction to the full sequence in some cases, while the two-letter simplification scheme dramatically reduces the quality of structure prediction.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.