Use of cumulative distribution functions to characterize mass spectra of intact proteins

Quantification of Antiviral Drug Tenofovir (TFV) by Surface-Enhanced Raman Spectroscopy (SERS) Using Cumulative Distribution Functions (CDFs)

Surface-enhanced Raman spectroscopy (SERS) is an ultrasensitive spectroscopic technique that generates signal-enhanced fingerprint vibrational spectra of small molecules. However, without rigorous control of SERS substrate active sites, geometry, surface area, or surface functionality, SERS is notoriously irreproducible, complicating the consistent quantitative analysis of small molecules. While evaporatively prepared samples yield significant SERS enhancement resulting in lower detection limits, the distribution of these enhancements along the SERS surface is inherently stochastic. Acquiring spatially resolved SERS spectra of these dried surfaces, we have shown that this enhancement is governed by a power law as a function of analyte concentration. Consequently, by definition, there is no true mean of SERS enhancement, requiring an alternative approach to achieve reproducible quantitative results. In this study, we introduce a new method of analysis of SERS data using a cumulative distribution function (CDF). The antiviral drug tenofovir (TFV) in an aqueous matrix was quantified down to a clinically relevant concentration of 25 ng/mL using hydroxylamine-reduced silver colloids evaporated to dryness. The data presented in this study provide a rationale for the benefits of combining a novel statistical approach using CDFs with simple and inexpensive experimental techniques to increase the precision, accuracy, and analytical sensitivity of aqueous TFV quantification by SERS. TFV calibration curves generated using CDF analysis showed higher analytical sensitivity (in the form of a normalized calibration curve average slope increase of 0.25) compared to traditional SERS intensity calculations. A second aliquot of nanoparticles and analyte dried on the SERS surface followed by CDF analysis showed further analytical sensitivity with a normalized calibration curve slope increase of 0.23 and decreased variation among replicates represented by an average standard deviation decrease of 0.02 with a second aliquot. The quantitative analysis of SERS data using CDFs presented here shows promise to be a reproducible method for quantitative analysis of SERS data, a significant step toward implementing SERS as an analytical method in clinical and industrial settings.

False precision of mass domain in HPLC-HRMS data representation

The term precision of a value describes the total number of significant digits that are used to express that value. False precision is presented in data, where the sampling and chosen data file format coding return values with extra non-valid digits. The proper precision in value thus depends on so-called magnitude in digital data coding. The presence of the false precision is file format dependent. The amount of dataset points could be decreased by removing false precision (where it is present) to almost 10% of the raw measurement without loss of information.

Molecular Isotopic Distribution Analysis (MIDAs) with adjustable mass accuracy

In this paper, we present Molecular Isotopic Distribution Analysis (MIDAs), a new software tool designed to compute molecular isotopic distributions with adjustable accuracies. MIDAs offers two algorithms, one polynomial-based and one Fourier-transform-based, both of which compute molecular isotopic distributions accurately and efficiently. The polynomial-based algorithm contains few novel aspects, whereas the Fourier-transform-based algorithm consists mainly of improvements to other existing Fourier-transform-based algorithms. We have benchmarked the performance of the two algorithms implemented in MIDAs with that of eight software packages (BRAIN, Emass, Mercury, Mercury5, NeutronCluster, Qmass, JFC, IC) using a consensus set of benchmark molecules. Under the proposed evaluation criteria, MIDAs's algorithms, JFC, and Emass compute with comparable accuracy the coarse-grained (low-resolution) isotopic distributions and are more accurate than the other software packages. For fine-grained isotopic distributions, we compared IC, MIDAs's polynomial algorithm, and MIDAs's Fourier transform algorithm. Among the three, IC and MIDAs's polynomial algorithm compute isotopic distributions that better resemble their corresponding exact fine-grained (high-resolution) isotopic distributions. MIDAs can be accessed freely through a user-friendly web-interface at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/midas/index.html.

Calculation of the isotope cluster for polypeptides by probability grouping

This paper presents a novel theoretical basis for accurately calculating the isotope cluster of polypeptides. In contrast to previous approaches to this problem, which consider exhaustive or near exhaustive combinations of isotopic species, the program, Neutron Cluster, groups probabilities to yield highly accurate information without elucidating any fine structure within a nominal mass unit. This is a fundamental difference from any previously described algorithm for calculating the isotope cluster. As a result of this difference, the accurate isotope clusters for high molecular weight polypeptides can be calculated rapidly without any pruning. When applied to isotope enriched polypeptides, the algorithm introduces "grouping error", which is described, quantified, and avoided by using probability partitioning.

Intraresidue Distribution of Energy in Proteins

Boltzmann-like distributions appear in many properties and energy-related quantities of proteins. A few examples are hydrophobicity, various types of side-chain/side-chain interactions, proline isomerization, hydrogen bonds, internal cavities, interactions at the level of specific atom types, and the propensity of the phi/'phi' ratio. Here, we conjecture that the Boltzmann hypothesis also holds for the intra-residue energy distribution. We confirm the conjecture by calculating the energies of 41,672 residues of the structures of highly resolved proteins, where at least 12 out of 20 naturally occurring amino acids follow Boltzmann's law. We further examine the entire set of all residue energies and find that the convolution of the individual distributions gives a Poisson function, which is followed by approximately 50% of individual proteins' structures.