Toward High-Resolution de Novo Structure Prediction for Small Proteins

By Philip Bradley, Kira M. S. Misura, David Baker. 2005

874 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, background): “libraries [22,23] or other strategies to search conformational space [24,25], followed by sophisticated energy optimization or”
Protein structure prediction from sequence variation (2012, methodology): “Such approaches have worked favorably in cases for smaller proteins that have fewer than ~90 amino acids 7 and need to be combined with experimental data for larger proteins 8,9 .”
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “While these approaches work reasonably well for smaller proteins, they have generally required further difficult-to-obtain experimental data for larger proteins (Bradley et al., 2005; Moult et al., 2014, 2016).”
One contact for every twelve residues allows robust and accurate topology-level protein structure modeling (2013, methodology): “Comformational sampling proceeds by Monte Carlo symmetric fragment replacement, supplemented with two types of symmetric rigid body subunit perturbations for every 10 fragment moves, (1) random rotation and translation, and (2) translation along the symmetry axis into atomic contact.”
Alternate states of proteins revealed by detailed energy landscape mapping (2010, positive): “Each trajectory consists of an initial lowresolution search followed by detailed all-atom refinement.(1) To enhance sampling near the native structure, which is generally sampled quite rarely, in a subset of the trajectories bias toward the native structure was included in the move set used in the i”
Ab Initio Protein Structure Assembly Using Continuous Structure Fragments and Optimized Knowledge-based Force Field (2012, background): “(3) and the helix-packing energy term in Eq.”
In silico methods and tools for drug discovery (2021, methodology): “Template-free modeling techniques use energy functions to find the most stable (energetically lowest) conformations, and these approaches can predict short proteins ( 100 amino acids) with a root mean square deviation (RMSD) of 2 – 5 Å [141 – 143].”
Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. (2011, background): “A further full-atomic refinement simulation is often necessary to obtain high-resolution models (4).”
The protein folding problem. (2008, background): “Web servers and software packages often predict the native structure of small, singledomain proteins to within about 2–6 Å of their experimental structures (8, 17, 242).”
Structure prediction for CASP8 with all-atom refinement using Rosetta (2009, methodology): “2,3 Rosetta all-atom refinement employs a Monte Carlo Minimization protocol in which each attempted move consists of (1) perturbations to randomly selected backbone torsion angles, (2) discrete combinatorial optimization of sidechain rotamer conformations, and (3) quasi Newton minimization with resp”
Improved beta‐protein structure prediction by multilevel optimization of nonlocal strand pairings and local backbone conformation (2006, positive): “It has been demonstrated previously 9,10 that simulations with sequence homologues can be a powerful tool for enhancing sampling in de novo structure prediction.”