|
Basic Characteristics of Mutations
|
|
Mutation Site
|
D25N |
|
Mutation Site Sentence
|
The initial configuration of our simulations is the X-ray crystal structure of the active-site mutant D25N bound to the inhibitor Darunavir (PDB code 3BVB). |
|
Mutation Level
|
Amino acid level |
|
Mutation Type
|
nonsynonymous substitution |
|
Gene/Protein/Region
|
PR |
|
Standardized Encoding Gene
|
gag-pol
|
|
Genotype/Subtype
|
HIV-1 |
|
Viral Reference
|
-
|
|
Functional Impact and Mechanisms
|
|
Disease
|
HIV Infections
|
|
Immune
|
- |
|
Target Gene
|
-
|
|
Clinical and Epidemiological Correlations
|
|
Clinical Information
|
- |
|
Treatment
|
inhibitor Darunavir |
|
Location
|
- |
|
Literature Information
|
|
PMID
|
33481784
|
|
Title
|
DEER-PREdict: Software for efficient calculation of spin-labeling EPR and NMR data from conformational ensembles
|
|
Author
|
Tesei G,Martins JM,Kunze MBA,Wang Y,Crehuet R,Lindorff-Larsen K
|
|
Journal
|
PLoS computational biology
|
|
Journal Info
|
2021 Jan 22;17(1):e1008551
|
|
Abstract
|
Owing to their plasticity, intrinsically disordered and multidomain proteins require descriptions based on multiple conformations, thus calling for techniques and analysis tools that are capable of dealing with conformational ensembles rather than a single protein structure. Here, we introduce DEER-PREdict, a software program to predict Double Electron-Electron Resonance distance distributions as well as Paramagnetic Relaxation Enhancement rates from ensembles of protein conformations. DEER-PREdict uses an established rotamer library approach to describe the paramagnetic probes which are bound covalently to the protein.DEER-PREdict has been designed to operate efficiently on large conformational ensembles, such as those generated by molecular dynamics simulation, to facilitate the validation or refinement of molecular models as well as the interpretation of experimental data. The performance and accuracy of the software is demonstrated with experimentally characterized protein systems: HIV-1 protease, T4 Lysozyme and Acyl-CoA-binding protein. DEER-PREdict is open source (GPLv3) and available at github.com/KULL-Centre/DEERpredict and as a Python PyPI package pypi.org/project/DEERPREdict.
|
|
Sequence Data
|
-
|