Multivariate curve resolution: a powerful tool for the analysis of conformational transitions in nucleic acids

Abstract

A successful application is reported of the multivariate curve resolution alternating least-squares method (MCR-ALS) for the analysis of nucleic acid melting and salt-induced transitions. Under conditions where several structures co-exist in a conformational equilibrium, MCR-ALS analysis of the UV and circular dichroism (CD) spectra at different temperatures, ionic strength and oligonucleotide concentration allows for the resolution of concentration profiles and pure spectra of the different species. The methodology is illustrated by the case of the cyclic oligonucleotide d<pTGCTCGCT>. The melting transition of this molecule at different oligonucleotide concentrations was studied at 0, 2 and 10 mM MgCl2 by UV and CD spectroscopy. In addition, salt titration experiments were carried out at 21.0 and 54.0 degrees C. The MCR-ALS analysis indicates that three different conformations of this molecule co-exist in solution. In agreement with previous NMR studies, these conformations were assigned to a monomeric dumbbell-like structure, a dimeric four-stranded conformation and a disordered (random coil) structure. The MCR-ALS methodology allows for a detailed analysis of how this equilibrium is affected by temperature, salt and oligonucleotide concentration.

The RNA Stem–Loop to G-Quadruplex Equilibrium Controls Mature MicroRNA Production inside the Cell

“…The structural equilibria and associated conformational changes between different nucleic acid structures can be resolved using multivariate curve resolution analysis method based on alternating least squares (MCR-ALS). 42 MCR-ALS assesses and resolves different possible conformations and estimates their distribution profile and pure spectra based on the data collected for multiple wavelengths under varying temperature and salt concentrations. 42 CD melting experiments were performed for Quad-pre-miR-27a, Stem-loop pre-miR-27a, pre-let-7e and mutant pre-let-7e in different ionic 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 .…”

“…42 MCR-ALS assesses and resolves different possible conformations and estimates their distribution profile and pure spectra based on the data collected for multiple wavelengths under varying temperature and salt concentrations. 42 CD melting experiments were performed for Quad-pre-miR-27a, Stem-loop pre-miR-27a, pre-let-7e and mutant pre-let-7e in different ionic 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 . CD spectra for duplex and G-quadruplex RNA display a maxima at 263 nm and a minima at 240 nm making it difficult to determine the identity and contribution of both structures towards the spectrum.…”

Comparison of two methods for extraction of dill essential oil by gas chromatography-mass spectrometry coupled with chemometric resolution techniques

“…The multivariate curve resolution (MCR) has been shown to be a powerful tool for the investigation of complex chemical systems, particularly for the investigation of chemical systems about which there was little or no previous knowledge. [33,34] The MCR methods perform the decomposition of an experimental data matrix in the production of two simpler matrices, one related to the rows of the original data matrix (usually related to the changes in chemical composition) and another related to the columns of the original matrix (usually related to the measured instrumental or spectroscopic changes). The MCR methods can be divided into iterative and noniterative types.…”

Multivariate curve resolution for infrared spectrometric monitoring of the microwave assisted synthesis of bio-based polyols

“…In the present study, rows of matrix D are the spectra recorded during the experimental step, the column profiles of matrix C and the row profiles of S T are usually associated respectively with the concentration and pure spectral profiles of the resolved components which are obtained by ALS. The obtained results must optimally fit the experimental data matrix (D) 13 . This optimization is carried out using initial estimates of either C or S T by some routes derived from SIMPLISMA or evolving factor analysis, applying constraints to model the shapes of the profiles 14 .…”