Probability-based protein secondary structure identification using combined NMR chemical-shift data.

Yunjun Wang; Oleg Jardetzky

doi:10.1110/ps.3180102

Home/Archive/PubMed

PubMed · 11910028

Probability-based protein secondary structure identification using combined NMR chemical-shift data.

Yunjun Wang·Oleg Jardetzky

View original source ↗Export metadata ↓

Abstract

For a long time, NMR chemical shifts have been used to identify protein secondary structures. Currently, this is accomplished through comparing the observed (1)H(alpha), (13)C(alpha), (13)C(beta), or (13)C' chemical shifts with the random coil values. Here, we present a new protocol, which is based on the joint probability of each of the three secondary structural types (beta-strand, alpha-helix, and random coil) derived from chemical-shift data, to identify the secondary structure. In combination with empirical smooth filters/functions, this protocol shows significant improvements in the accuracy and the confidence of identification. Updated chemical-shift statistics are reported, on the basis of which the reliability of using chemical shift to identify protein secondary structure is evaluated for each nucleus. The reliability varies greatly among the 20 amino acids, but, on average, is in the order of: (13)C(alpha)>(13)C'>(1)H(alpha)>(13)C(beta)>(15)N>(1)H(N) to distinguish an alpha-helix from a random coil; and (1)H(alpha)>(13)C(beta) >(1)H(N) approximately (13)C(alpha) approximately (13)C' approximately (15)N for a beta-strand from a random coil. Amide (15)N and (1)H(N) chemical shifts, which are generally excluded from the application, in fact, were found to be helpful in distinguishing a beta-strand from a random coil. In addition, the chemical-shift statistical data are compared with those reported previously, and the results are discussed. A JAVA User Interface program has been developed to make the entire procedure fully automated and is available via http://ccsr3150-p3.stanford.edu.

Explore related subjects

Amino Acids Chemical Phenomena Chemistry, Physical Hydrogen Bonding Hydrogen-Ion Concentration Magnetic Resonance Spectroscopy Mathematical Computing Molecular Structure Protein Conformation Protein Structure, Secondary Structure-Activity Relationship Journal Article Research Support, U.S. Gov't, P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Yunjun Wang, Oleg Jardetzky. 2002. Probability-based protein secondary structure identification using combined NMR chemical-shift data.. https://doi.org/10.1110/ps.3180102

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Chimeric structural isomer fragments as cost-efficient internal standards for amino acid quantification by mass spectrometry.

Amino acid (AA) profiles from body fluids such as blood and urine are clinical indicators for diagnosing metabolic and hepatic diseases. Current quantitative methods, such as liquid chromatography-mass spectrometry (LC-MS) with isotopically labelled internal standards (ISs), are costly and technically demanding. This study proposes a cost-efficient alternative using structural isomers as ISs in a direct liquid infusion (DLI) tandem mass spectrometry (MS/MS) approach. The method leverages chimeric spectra and fragment intensity ratios to quantify AAs, demonstrating high linearity and precision even with a 3D ion trap mass analyser. This approach offers a viable strategy for AA quantification in preventive medicine, particularly for screening metabolic diseases such as phenylketonuria, diabetes, and liver dysfunction.

Amino Acids↗

PubMed2007

MODEL-molecular descriptor lab: a web-based server for computing structural and physicochemical features of compounds.

Molecular descriptors represent structural and physicochemical features of compounds. They have been extensively used for developing statistical models, such as quantitative structure activity relationship (QSAR) and artificial neural networks (NN), for computer prediction of the pharmacodynamic, pharmacokinetic, or toxicological properties of compounds from their structure. While computer programs have been developed for computing molecular descriptors, there is a lack of a freely accessible one. We have developed a web-based server, MODEL (Molecular Descriptor Lab), for computing a comprehensive set of 3,778 molecular descriptors, which is significantly more than the approximately 1,600 molecular descriptors computed by other software. Our computational algorithms have been extensively tested and the computed molecular descriptors have been used in a number of published works of statistical models for predicting variety of pharmacodynamic, pharmacokinetic, and toxicological properties of compounds. Several testing studies on the computed molecular descriptors are discussed. MODEL is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/model/model.cgi free of charge for academic use.

Amino Acids↗

PubMed2007

The penicillin G acylase production by B. megaterium is amino acid consumption dependent.

Aiming at to enhance the production of penicillin G acylase (PGA) by Bacillus megaterium, we have performed flasks experiments using different medium composition. Using 51 g/L of casein hydrolyzed with Alcalase and 2.7 g/L of phenylacetic acid (PhAc), the following carbon substrates were tested, individually and combined: glucose, glycerol, and lactose (present in cheese whey). Glycerol and glucose showed to be effective nutrients for the microorganism growth but delayed the PGA production. Cheese whey always increased enzyme production and cell mass. However, lactose (present in cheese whey) was not a significant carbon source for B. megaterium. PhAc, amino acids, and small peptides present in the hydrolyzed casein were the actual carbon sources for enzyme production. Replacement of hydrolyzed casein by free amino acids, 10.0 g/L, led to a significant increase in enzyme production (app. 150%), with a preferential consumption of alanine, aspartic acid, glycine, serine, arginine, threonine, lysine, and glutamic acid. A decrease of the enzyme production was observed when 20.0 g/L of amino acids were used. Using the single omission technique, it was shown that none of the 18 tested amino acids was essential for enzyme production. The use of a medium containing eight of the preferentially consumed amino acids lead to similar enzyme production level obtained when using 18 amino acids. PhAc, up to 2.7 g/L, did not inhibit enzyme production, even if added at the beginning of the cultivation.

Amino Acids↗