Prediction of protein structure by emphasizing local side-chain/backbone interactions in ensembles of turn fragments

2003 ◽  
Vol 53 (S6) ◽  
pp. 486-490 ◽  
Author(s):  
Qiaojun Fang ◽  
David Shortle
2009 ◽  
Vol 25 (19) ◽  
pp. 2552-2558 ◽  
Author(s):  
Pablo Carbonell ◽  
Antonio del Sol

2012 ◽  
Vol 116 (16) ◽  
pp. 4754-4759 ◽  
Author(s):  
Aleksandr B. Sahakyan ◽  
Andrea Cavalli ◽  
Wim F. Vranken ◽  
Michele Vendruscolo

2016 ◽  
Vol 113 (33) ◽  
pp. 9187-9192 ◽  
Author(s):  
Loren B. Andreas ◽  
Kristaps Jaudzems ◽  
Jan Stanek ◽  
Daniela Lalli ◽  
Andrea Bertarello ◽  
...  

Protein structure determination by proton-detected magic-angle spinning (MAS) NMR has focused on highly deuterated samples, in which only a small number of protons are introduced and observation of signals from side chains is extremely limited. Here, we show in two fully protonated proteins that, at 100-kHz MAS and above, spectral resolution is high enough to detect resolved correlations from amide and side-chain protons of all residue types, and to reliably measure a dense network of 1H-1H proximities that define a protein structure. The high data quality allowed the correct identification of internuclear distance restraints encoded in 3D spectra with automated data analysis, resulting in accurate, unbiased, and fast structure determination. Additionally, we find that narrower proton resonance lines, longer coherence lifetimes, and improved magnetization transfer offset the reduced sample size at 100-kHz spinning and above. Less than 2 weeks of experiment time and a single 0.5-mg sample was sufficient for the acquisition of all data necessary for backbone and side-chain resonance assignment and unsupervised structure determination. We expect the technique to pave the way for atomic-resolution structure analysis applicable to a wide range of proteins.


2013 ◽  
Vol 135 (4) ◽  
pp. 1177-1180 ◽  
Author(s):  
Stephan Warnke ◽  
Gert von Helden ◽  
Kevin Pagel

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Juan Salamanca Viloria ◽  
Maria Francesca Allega ◽  
Matteo Lambrughi ◽  
Elena Papaleo

2020 ◽  
Vol 36 (12) ◽  
pp. 3758-3765 ◽  
Author(s):  
Xiaoqiang Huang ◽  
Robin Pearce ◽  
Yang Zhang

Abstract Motivation Protein structure and function are essentially determined by how the side-chain atoms interact with each other. Thus, accurate protein side-chain packing (PSCP) is a critical step toward protein structure prediction and protein design. Despite the importance of the problem, however, the accuracy and speed of current PSCP programs are still not satisfactory. Results We present FASPR for fast and accurate PSCP by using an optimized scoring function in combination with a deterministic searching algorithm. The performance of FASPR was compared with four state-of-the-art PSCP methods (CISRR, RASP, SCATD and SCWRL4) on both native and non-native protein backbones. For the assessment on native backbones, FASPR achieved a good performance by correctly predicting 69.1% of all the side-chain dihedral angles using a stringent tolerance criterion of 20°, compared favorably with SCWRL4, CISRR, RASP and SCATD which successfully predicted 68.8%, 68.6%, 67.8% and 61.7%, respectively. Additionally, FASPR achieved the highest speed for packing the 379 test protein structures in only 34.3 s, which was significantly faster than the control methods. For the assessment on non-native backbones, FASPR showed an equivalent or better performance on I-TASSER predicted backbones and the backbones perturbed from experimental structures. Detailed analyses showed that the major advantage of FASPR lies in the optimal combination of the dead-end elimination and tree decomposition with a well optimized scoring function, which makes FASPR of practical use for both protein structure modeling and protein design studies. Availability and implementation The web server, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/FASPR and https://github.com/tommyhuangthu/FASPR. Supplementary information Supplementary data are available at Bioinformatics online.


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