Determination Of Protein Secondary Structure Research Articles

DSSP in GROMACS: Tool for Defining Secondary Structures of Proteins in Trajectories.

This work describes a fast implementation of a software algorithm associated with determination of protein secondary structure based on the Define Secondary Structure of Proteins (DSSP) algorithm. This implementation is fully compatible with the DSSP v.4 and DSSP v.2 algorithms and implemented as a native GROMACS trajectory analysis module, which allows us to analyze molecular dynamics trajectories without any restrictions of the original DSSP implementation. This implementation works much faster than the original DSSP v.4 and DSSP v.2 algorithms.

Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors

Protein secondary structure discrimination is crucial for understanding their biological function. It is not generally possible to invert spectroscopic data to yield the structure. We present a machine learning protocol which uses two-dimensional UV (2DUV) spectra as pattern recognition descriptors, aiming at automated protein secondary structure determination from spectroscopic features. Accurate secondary structure recognition is obtained for homologous (97%) and nonhomologous (91%) protein segments, randomly selected from simulated model datasets. The advantage of 2DUV descriptors over one-dimensional linear absorption and circular dichroism spectra lies in the cross-peak information that reflects interactions between local regions of the protein. Thanks to their ultrafast (∼200 fs) nature, 2DUV measurements can be used in the future to probe conformational variations in the course of protein dynamics.

QCL-IR Spectroscopy for In-Line Monitoring of Proteins from Preparative Ion-Exchange Chromatography.

In this study, an external cavity-quantum cascade laser-based mid-infrared (IR) spectrometer was applied for in-line monitoring of proteins from preparative ion-exchange chromatography. The large optical path length of 25 μm allowed for robust spectra acquisition in the broad tuning range between 1350 and 1750 cm–1, covering the most important spectral region for protein secondary structure determination. A significant challenge was caused by the overlapping mid-IR bands of proteins and changes in the background absorption of water due to the NaCl gradient. Implementation of advanced background compensation strategies resulted in high-quality protein spectra in three different model case studies. In Case I, a reference blank run was directly subtracted from a sample run with the same NaCl gradient. Case II and III included sample runs with different gradient profiles than the one from the reference run. Here, a novel compensation approach based on a reference spectra matrix was introduced, where the signal from the conductivity detector was employed for correlating suitable reference spectra for correction of the sample run spectra. With this method, a single blank run was sufficient to correct various gradient profiles. The obtained IR spectra of hemoglobin and β-lactoglobulin were compared to off-line reference measurements, showing excellent agreement for all case studies. Moreover, the concentration values obtained from the mid-IR spectrometer agreed well with conventional UV detectors and high-performance liquid chromatography off-line measurements. LC–QCL–IR coupling thus holds high potential for replacing laborious and time-consuming off-line methods for protein monitoring in complex downstream processes.

Minimising sample volume and run times for circular dichroism spectroscopy.

Circular dichroism (CD) spectroscopy is widely used to characterise chiral structures in solution, including to determine protein secondary structure content. However, protein CD experiments typically require 10-20 μg of protein. An additional challenge is that some proteins change their structure when their concentration is changed (they unfold or aggregate) so, particularly for protein biopharmaceuticals, it is attractive to have a range of different path lengths to hand. This paper reports the validation of demountable DMV Bio-cells of path lengths 0.5 mm, 0.2 mm and 0.125 mm, which have approximately 1 mm diameter windows, for CD spectroscopy. When complemented with a 10 cm focal length focusing lens positioned 9.5 cm in front of the sample, the signal : noise ratio of the final spectrum is equivalent to data collected on a standard cell wide enough to allow the full light beam to pass through. The DMV Bio-cells require 2-3% of the sample needed for a standard full-width cuvette and can be assembled without bubbles with the nominal path length. In addition, they are easy to clean and less likely to be chipped or broken than standard cuvettes which, together with the sample saved, compensates for their much higher price. For smaller path lengths the Jasco MSD-462 microsampling disk enabled fairly reproducible assembly at 4-7 μm depending on the sample viscosity and similarly high quality data.

Artificial intelligence analysis of FTIR and CD spectroscopic data for predicting and quantifying the length and content of protein secondary structures

Besides NMR and X-ray crystallography, FTIR and CD spectroscopy are widely considered to be useful for determining protein secondary structure. These techniques can be used to obtain data in few minutes, using small quantities of proteins, which make them amenable for proteomics research. Here we explore the possibility of using artificial intelligence techniques to simultaneously analyse both FTIR and CD spectroscopic data for an identical set of proteins. Neural network analysis was carried out on normalised regions of FTIR (1700-1600 cm−1) and CD (180-259 nm) spectral data both with and without boxcar averaging in order to quantify the average length and percentages of secondary structures. A hybrid genetic algorithm/neural network approach, that automatically selects structure-sensitive wavelength/frequency, was used for the quantification of the protein secondary structure. Using this algorithm we also successfully identified the region of the CD spectrum that contains the most structure-sensitive information. This was located between 214-251 nm, suggesting that this region alone may be sufficient to rapidly determine the secondary structure content from CD spectral data. Overall, CD spectroscopic analysis produced better results compared to FTIR spectroscopy when selected wavelengths were used, although FTIR was better when the entire region between 1700-1600 cm−1 (FTIR), and 180-259 nm (CD), was subjected to neural network analysis. Application of Adaptive Neuro-Fuzzy Inference System (ANFIS) with fuzzy subtractive clustering for the analysis of the spectral data led to a slightly better prediction of the average helix/sheet length for FTIR spectroscopy compared to CD. Our findings reveal the potential of using artificial intelligence techniques for not only extracting structural information but also for better understanding of the relationship between complex spectral data and biologically important information.

Accurate Machine Learning Prediction of Protein Circular Dichroism Spectra with Embedded Density Descriptors.

A data-driven approach to simulate circular dichroism (CD) spectra is appealing for fast protein secondary structure determination, yet the challenge of predicting electric and magnetic transition dipole moments poses a substantial barrier for the goal. To address this problem, we designed a new machine learning (ML) protocol in which ordinary pure geometry-based descriptors are replaced with alternative embedded density descriptors and electric and magnetic transition dipole moments are successfully predicted with an accuracy comparable to first-principle calculation. The ML model is able to not only simulate protein CD spectra nearly 4 orders of magnitude faster than conventional first-principle simulation but also obtain CD spectra in good agreement with experiments. Finally, we predicted a series of CD spectra of the Trp-cage protein associated with continuous changes of protein configuration along its folding path, showing the potential of our ML model for supporting real-time CD spectroscopy study of protein dynamics.

Protein secondary structure prediction (PSSP) using different machine algorithms

BackgroundThe computational biology approach has advanced exponentially in protein secondary structure prediction (PSSP), which is vital for the pharmaceutical industry. Extracting protein structure from the laboratory has insufficient information for PSSP that is used in bioinformatics studies. In this paper, the support vector machine (SVM) model and decision tree are presented on the RS126 dataset to address the problem of PSSP. A decision tree is applied for the SVM outcome to obtain the relevant guidelines possible for PSSP. Furthermore, the number of produced rules was fairly small, and they show a greater degree of comprehensibility compared to other rules. Several of the proposed principles have compelling and relevant biological clarification.ResultsThe results confirmed that the existence of a particular amino acid in a protein sequence increases the stability for the forecast of protein secondary structure. The suggested algorithm achieved 85% accuracy for the E|~E classifier.ConclusionsThe proposed rules can be very important in managing wet laboratory experiments intended at determining protein secondary structure. Lastly, future work will focus mainly on large protein datasets without overfitting and expand the amount of extracted regulations for PSSP.

Evaluation of protein secondary structure from FTIR spectra improved after partial deuteration

FTIR spectroscopy has become a major tool to determine protein secondary structure. One of the identified obstacle for reaching better predictions is the strong overlap of bands assigned to different secondary structures. Yet, while for instance disordered structures and α-helical structures absorb almost at the same wavenumber, the absorbance bands are differentially shifted upon deuteration, in part because exchange is much faster for disordered structures. We recorded the FTIR spectra of 85 proteins at different stages of hydrogen/deuterium exchange process using protein microarrays and infrared imaging for high throughput measurements. Several methods were used to relate spectral shape to secondary structure content. While in absolute terms, β-sheet is always better predicted than α-helix content, results consistently indicate an improvement of secondary structure predictions essentially for the α-helix and the category called “Others” (grouping random, turns, bends, etc.) after 15 min of exchange. On the contrary, the β-sheet fraction is better predicted in non-deuterated conditions. Using partial least square regression, the error of prediction for the α-helix content is reduced after 15-min deuteration. Further deuteration degrades the prediction. Error on the prediction for the “Others” structures also decreases after 15-min deuteration. Cross-validation or a single 25-protein test set result in the same overall conclusions.

A Machine Learning Protocol for Predicting Protein Infrared Spectra.

Infrared (IR) absorption provides important chemical fingerprints of biomolecules. Protein secondary structure determination from IR spectra is tedious since its theoretical interpretation requires repeated expensive quantum-mechanical calculations in a fluctuating environment. Herein we present a novel machine learning protocol that uses a few key structural descriptors to rapidly predict amide I IR spectra of various proteins and agrees well with experiment. Its transferability enabled us to distinguish protein secondary structures, probe atomic structure variations with temperature, and monitor protein folding. This approach offers a cost-effective tool to model the relationship between protein spectra and their biological/chemical properties.

Automated, High-Throughput Infrared Spectroscopy for Secondary Structure Analysis of Protein Biopharmaceuticals

Protein higher order structure (HOS) is an important product quality attribute that governs the structure-function characteristics, safety, and efficacy of therapeutic proteins. Infrared (IR) spectroscopy has long been recognized as a powerful biophysical tool in determining protein secondary structure and monitoring the dynamic structural changes. Such biophysics analyses help establish process and product knowledge, understand the impact of upstream (cell culture) and downstream (purification) process conditions, create stable formulations, monitor product stability, and assess product comparability when process improvements are implemented (or establish biosimilarity to originator products). This paper provides an overview of a novel automated mid-IR spectroscopic technique called microfluidic modulation spectroscopy (MMS) for the characterization of protein secondary structure. The study demonstrates that MMS secondary structure analysis of therapeutic monoclonal antibodies (mAb) is comparable with a conventional Fourier transform infrared (FTIR) method. More importantly the study shows MMS exhibits higher sensitivity and repeatability for low concentration samples over FTIR, as well as provides automated operation and superior robustness with simplified data analysis, increasing the utility of the instrument in determination of mAb secondary structure. Therefore, we propose that the MMS method can be widely applied in characterization and comparability/biosimilarity studies for biopharmaceutical process and product development.

ИНФРАКРАСНАЯ СПЕКТРОСКОПИЯ СЫВОРОТКИ КРОВИ БОЛЬНЫХ ОНКОГЕМАТОЛОГИЧЕСКИМИ ЗАБОЛЕВАНИЯМИ

A comparative analysis was performed of the secondary structure of serum proteins in patients with multiple myeloma, chronic lymphocytic leukemia patients and healthy donors. Determination of serum protein secondary structure was accomplished by the application of IR spectroscopy. It has been shown that the proposed approach allows to detect a decrease in the length of α-helical regions and an increase in the content of β-sheets in the serum of patients with multiple myeloma compared to healthy donors and chronic lymphocytic leukemia patients.

Optimization of Infrared Microscopy to Assess Secondary Structure of Insulin Molecules Within Individual Subvisible Particles in Aqueous Formulations

The analysis of subvisible particles is currently challenging but pivotal to the understanding and control of the quality of protein therapeutics. While a range of characterization methods is available for subvisible particles, information on the protein conformation in a particle—considered a possible parameter in eliciting unwanted immunogenicity of protein therapeutics—is especially challenging in the lower micrometer range using existing analytical technologies. Using 6 different protein particle populations, we show that transmission Fourier transform infrared (FTIR) microscopy can determine protein secondary structure in single particles down to 10 μm. The analytical setup presented here is able to immobilize protein particles and obtain transmission FTIR spectra on individual protein particles in their intact aqueous environment. Spectra of dried particles, on the other hand, were found to occasionally differ from spectra of particles in aqueous environment. In summary, using the analytical setup described in this study, transmission FTIR microscopy uniquely provides information on single protein particles in particle populations in their aqueous environment without interference from the background protein solution.

Atomic Detail of Protein Folding Revealed by an Ab Initio Reappraisal of Circular Dichroism.

Circular dichroism (CD) is known to be an excellent tool for the determination of protein secondary structure due to fingerprint signatures of α and β domains. However, CD spectra are also sensitive to the 3D arrangement of the chain as a result of the excitonic nature of additional signals due to the aromatic residues. This double sensitivity, when extended to time-resolved experiments, should allow protein folding to be monitored with high spatial resolution. To date, the exploitation of this very appealing idea has been limited, due to the difficulty in relating the observed spectral evolution to specific configurations of the chain. Here, we demonstrate that the combination of atomistic molecular dynamics simulations of the folding pathways with a quantum chemical evaluation of the excitonic spectra provides the missing key. This is exemplified for the folding of canine milk lysozyme protein.

Solid-film sampling method for the determination of protein secondary structure by Fourier transform infrared spectroscopy.

Fourier transform infrared (FTIR) spectroscopy is one of the widely used vibrational spectroscopic methods in protein structural analysis. The protein solution sample loaded in demountable CaF2 liquid cell presents a challenge and is limited to high concentrations. Some researchers attempted the simpler solid-film sampling method for the collection of protein FTIR spectra. In this study, the solid-film sampling FTIR method was studied in detail. The secondary structure components of some globular proteins were determined by this sampling method, and the results were consistent with those data determined by the traditional solution sampling FTIR method and X-ray crystallography, indicating that this sampling method is feasible and efficient for the structural characterization of proteins. Furthermore, much lower protein concentrations (~0.5mg/mL) were needed to obtain high-quality FTIR spectra, which expands the application of FTIR spectroscopy to almost the same concentration range used for circular dichroism and fluorescence spectroscopy, making comparisons among three commonly used techniques possible in protein studies. Graphical Abstract ᅟ.

Analysis of Prion Protein Conformation Using Circular Dichroism Spectroscopy.

According to the protein-only hypothesis of prion propagation, the pathogenesis of prion disease is due to the misfolding of cellular PrP (PrPC) which gives rise to disease-associated PrPSc. This misfolding results in the predominantly α-helix secondary structure of PrP becoming increasingly β-sheet. Prion protein researchers often employ circular dichroism (CD) spectroscopy to rapidly analyze and identify the degree of α-helix and β-sheet content in their recombinant protein and peptide samples. CD is a nondestructive method of determining protein secondary structure and can be used to monitor the protein structural changes in various environments, e.g., pH and temperature. CD can also be used to investigate kinetic and thermodynamic characteristics of proteins and peptides.

Determination of Protein Secondary Structure from Infrared Spectra Using Partial Least-Squares Regression.

Infrared (IR) spectra contain substantial information about protein structure. This has previously most often been exploited by using known band assignments. Here, we convert spectral intensities in bins within Amide I and II regions to vectors and apply machine learning methods to determine protein secondary structure. Partial least squares was performed on spectra of 90 proteins in H2O. After preprocessing and removal of outliers, 84 proteins were used for this work. Standard normal variate and second-derivative preprocessing methods on the combined Amide I and II data generally gave the best performance, with root-mean-square values for prediction of ∼12% for α-helix, ∼7% for β-sheet, 7% for antiparallel β-sheet, and ∼8% for other conformations. Analysis of Fourier transform infrared (FTIR) spectra of 16 proteins in D2O showed that secondary structure determination was slightly poorer than in H2O. Interval partial least squares was used to identify the critical regions within spectra for secondary structure prediction and showed that the sides of bands were most valuable, rather than their peak maxima. In conclusion, we have shown that multivariate analysis of protein FTIR spectra can give α-helix, β-sheet, other, and antiparallel β-sheet contents with good accuracy, comparable to that of circular dichroism, which is widely used for this purpose.

“Parallel factor analysis of multi-excitation ultraviolet resonance Raman spectra for protein secondary structure determination”

Protein secondary structural analysis is important for understanding the relationship between protein structure and function, or more importantly how changes in structure relate to loss of function. The structurally sensitive protein vibrational modes (amide I, II, III and S) in deep-ultraviolet resonance Raman (DUVRR) spectra resulting from the backbone C–O and N–H vibrations make DUVRR a potentially powerful tool for studying secondary structure changes. Experimental studies reveal that the position and intensity of the four amide modes in DUVRR spectra of proteins are largely correlated with the varying fractions of α-helix, β-sheet and disordered structural content of proteins. Employing multivariate calibration methods and DUVRR spectra of globular proteins with varying structural compositions, the secondary structure of a protein with unknown structure can be predicted. A disadvantage of multivariate calibration methods is the requirement of known concentration or spectral profiles. Second-order curve resolution methods, such as parallel factor analysis (PARAFAC), do not have such a requirement due to the “second-order advantage.” An exceptional feature of DUVRR spectroscopy is that DUVRR spectra are linearly dependent on both excitation wavelength and secondary structure composition. Thus, higher order data can be created by combining protein DUVRR spectra of several proteins collected at multiple excitation wavelengths to give multi-excitation ultraviolet resonance Raman data (ME-UVRR). PARAFAC has been used to analyze ME-UVRR data of nine proteins to resolve the pure spectral, excitation and compositional profiles. A three factor model with non-negativity constraints produced three unique factors that were correlated with the relative abundance of helical, β-sheet and poly-proline II dihedral angles. This is the first empirical evidence that the typically resolved “disordered” spectrum represents the better defined poly-proline II type structure.

Obtaining information about protein secondary structures in aqueous solution using Fourier transform IR spectroscopy.

Fourier transform IR (FTIR) spectroscopy is a nondestructive technique for structural characterization of proteins and polypeptides. The IR spectral data of polymers are usually interpreted in terms of the vibrations of a structural repeat. The repeat units in proteins give rise to nine characteristic IR absorption bands (amides A, B and I-VII). Amide I bands (1,700-1,600 cm(-1)) are the most prominent and sensitive vibrational bands of the protein backbone, and they relate to protein secondary structural components. In this protocol, we have detailed the principles that underlie the determination of protein secondary structure by FTIR spectroscopy, as well as the basic steps involved in protein sample preparation, instrument operation, FTIR spectra collection and spectra analysis in order to estimate protein secondary-structural components in aqueous (both H2O and deuterium oxide (D2O)) solution using algorithms, such as second-derivative, deconvolution and curve fitting. Small amounts of high-purity (>95%) proteins at high concentrations (>3 mg ml(-1)) are needed in this protocol; typically, the procedure can be completed in 1-2 d.

In Situ Protein Secondary Structure Determination in Ice: Raman Spectroscopy-Based Process Analytical Tool for Frozen Storage of Biopharmaceuticals

A Raman spectroscopy-based method for in situ monitoring of secondary structural composition of proteins during frozen and thawed storage was developed. A set of reference proteins with different α-helix and β-sheet compositions was used for calibration and validation in a chemometric approach. Reference secondary structures were quantified with circular dichroism spectroscopy in the liquid state. Partial least squares regression models were established that enable estimation of secondary structure content from Raman spectra. Quantitative secondary structure determination in ice was accomplished for the first time and correlation with existing (qualitative) protein structural data from the frozen state was achieved. The method can be used in the presence of common stabilizing agents and is applicable in an industrial freezer setup. Raman spectroscopy represents a powerful, noninvasive, and flexibly applicable tool for protein stability monitoring during frozen storage.

Retraction Note: Overestimated accuracy of circular dichroism in determining protein secondary structure

Circular dichroism (CD) is a spectroscopic technique widely used for estimating protein secondary structures in aqueous solution, but its accuracy has been doubted in recent work. In the present paper, the contents of nine globular proteins with known secondary structures were determined by CD spectroscopy and Fourier transform infrared spectroscopy (FTIR) in aqueous solution. A large deviation was found between the CD spectra and X-ray data, even when the experimental conditions were optimized. The content determined by FTIR was in good agreement with the X-ray crystallography data. Therefore, CD spectra are not recommended for directly calculating the content of a protein’s secondary structure.