Enhanced Chemical Shift Analysis for Secondary Structure prediction of protein
نویسندگان
چکیده
منابع مشابه
Protein secondary structure prediction.
The past year has seen a consolidation of protein secondary structure prediction methods. The advantages of prediction from an aligned family of proteins have been highlighted by several accurate predictions made 'blind', before any X-ray or NMR structure was known for the family. New techniques that apply machine learning and discriminant analysis show promise as alternatives to neural networks.
متن کاملPrediction of Protein Secondary Structure
In the wake of large-scale DNA sequencing projects, accurate tools are needed to predict protein structures. The problem of predicting protein structure from DNA sequence remains fundamentally unsolved even after more than three decades of intensive research. In this paper, fundamental theory of the protein structure will be presented as a general guide to protein secondary structure prediction...
متن کاملSHIFTX2: significantly improved protein chemical shift prediction
A new computer program, called SHIFTX2, is described which is capable of rapidly and accurately calculating diamagnetic (1)H, (13)C and (15)N chemical shifts from protein coordinate data. Compared to its predecessor (SHIFTX) and to other existing protein chemical shift prediction programs, SHIFTX2 is substantially more accurate (up to 26% better by correlation coefficient with an RMS error that...
متن کاملSolid-State NMR Determination of 13CR Chemical Shift Anisotropies for the Identification of Protein Secondary Structure
A solid-state nuclear magnetic resonance (NMR) method for the site-resolved identification of the secondary structure of solid peptides and proteins is presented. This technique exploits the correlation between the backbone conformation and the CR chemical shift anisotropies (CSA) of proteins. The 13CR CSAs are measured under fast magic-angle-spinning using a new sequence of sixteen 180° pulses...
متن کاملBidirectional Dynamics for Protein Secondary Structure Prediction
For certain categories of sequences, information from both the past and the future can be used for analysis and predictions at time t. This is the case for biological sequences where the nature and function of a region in a sequence may strongly depend on events located both upstream and downstream. We develop a new family of adaptive graphical model architectures for learning non-causal sequen...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Korean Magnetic Resonance Society
سال: 2014
ISSN: 1226-6531
DOI: 10.6564/jkmrs.2014.18.1.036