Computational protein structure refinement: Almost there, yet still so far to go
By Michael Feig. 2017
83 citations recorded by the source corpus.
Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template‐based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near‐experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high‐resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high‐resolution refinement methods that improve local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful. WIREs Comput Mol Sci 2017, 7:e1307. doi: 10.1002/wcms.1307
Subfields: Materials Chemistry, Molecular Biology, Spectroscopy
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Materials Chemistry
- David Baker
- Sergey Ovchinnikov
- Hahnbeom Park
- Lim Heo
- David E. Kim
- Michael Feig
- Frank DiMaio
- Yang Zhang
In Molecular Biology
- David Baker
- Sergey Ovchinnikov
- Hahnbeom Park
- Lim Heo
- David E. Kim
- Frank DiMaio
- Michael Feig
- Yang Zhang
In Spectroscopy
- Lim Heo
- Hahnbeom Park
- David Baker
- Sergey Ovchinnikov
- Collin F Arbour
- David E. Kim
- Frank DiMaio
- Michael Feig
Citations in the corpus
- Improved protein structure refinement guided by deep learning based accuracy estimation (2020, background): “The major challenge in refinement is sampling; the space of possible structures that must be searched through even in the vicinity of a starting model is extremely large (5, 7) .”
- Driven to Near-Experimental Accuracy by Refinement via Molecular Dynamics Simulations (2019, methodology): “increased efforts on improving predicted models via refinement as a final touch.(12) In the early 2010s, a few successful protein model refinement protocols began to emerge.”
- Protein homology model refinement by large-scale energy optimization (2018, background): “However, when starting models contain significant errors, the conformational phase space exceeds by orders of magnitude what can be explored using such methods, and little or no accuracy increase is observed (2).”
- Structure Refinement of Membrane Proteins via Molecular Dynamics Simulations (2018, methodology): “Computational structure predictions is an alternative to overcome the experimental limitations.(7) Initially, ab initio methods were developed to perform structure prediction purely by computational sampling from extended chains just based on the information of the amino acid sequence of a given pro”