ArticleJournal of molecular biology2012
Improved modeling of side-chain--base interactions and plasticity in protein--DNA interface design.
Article in Journal of molecular biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues.Nature communications · 2024Article
- New computational protein design methods for de novo small molecule binding sites.PLoS computational biology · 2020Article
- Macromolecular modeling and design in Rosetta: recent methods and frameworks.Nature methods · 2020Review
- The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design.Journal of chemical theory and computation · 2017Article
- A workflow for in silico design of hIL-10 and ebvIL-10 inhibitors using well-known miniprotein scaffolds.Journal of molecular modeling · 2017Article
- A human transcription factor in search mode.Nucleic acids research · 2016Article
- Structure-based modeling of protein: DNA specificity.Briefings in functional genomics · 2015Review
- Massively parallel determination and modeling of endonuclease substrate specificity.Nucleic acids research · 2014Article
- Reprogramming homing endonuclease specificity through computational design and directed evolution.Nucleic acids research · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Combinatorial sequence optimization for protein design requires libraries of discrete side-chain conformations. The discreteness of these libraries is problematic, particularly for long, polar side chains, since favorable interactions can be missed. Previously, an approach to loop remodeling where protein backbone movement is directed by side-chain rotamers predicted to form interactions previously observed in native complexes (termed "motifs") was described. Here, we show how such motif libraries can be incorporated into combinatorial sequence optimization protocols and improve native complex recapitulation. Guided by the motif rotamer searches, we made improvements to the underlying energy function, increasing recapitulation of native interactions. To further test the methods, we carried out a comprehensive experimental scan of amino acid preferences in the I-AniI protein-DNA interface and found that many positions tolerated multiple amino acids. This sequence plasticity is not observed in the computational results because of the fixed-backbone approximation of the model. We improved modeling of this diversity by introducing DNA flexibility and reducing the convergence of the simulated annealing algorithm that drives the design process. In addition to serving as a benchmark, this extensive experimental data set provides insight into the types of interactions essential to maintain the function of this potential gene therapy reagent.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.