Delineation of protein structure classes from multivariate analysis of protein Raman optical activity data

Vibrational Raman optical activity (ROA), measured as a small difference in the intensity of Raman scattering from chiral molecules in right and left-circularly polarized incident light, or as the intensity of a small circularly polarized component in the scattered light, is a powerful probe of the aqueous solution structure of proteins. On account of the large number of structure-sensitive bands in protein ROA spectra, multivariate analysis techniques such as non-linear mapping (NLM) are especially favourable for determining structural relationships between different proteins. Here NLM is used to map a dataset of 80 polypeptide, protein and virus ROA spectra, considered as points in a multidimensional space with axes representing the digitized wavenumbers, into readily visualizable two and three-dimensional spaces in which points close to or distant from each other, respectively, represent similar or dissimilar structures. Discrete clusters are observed which correspond to the seven structure classes all alpha, mainly alpha, alphabeta, mainly beta, all beta, mainly disordered/irregular and all disordered/irregular. The average standardised ROA spectra of the proteins falling within each structure class have distinct features characteristic of each class. A distinct cluster containing the wheat protein A-gliadin and the plant viruses potato virus X, narcissus mosaic virus, papaya mosaic virus and tobacco rattle virus, all of which appear in the mainly alpha cluster in the two-dimensional representation, becomes clearly separated in the direction of increasing disorder in the three-dimensional representation. This suggests that the corresponding five proteins, none of which to date has yielded high-resolution X-ray structures, consist mainly of alpha-helix and disordered structure with little or no beta-sheet. This combination of structural elements may have functional significance, such as facilitating disorder-to-order transitions (and vice versa) and suppressing aggregation, in these proteins and also in sequences within other proteins. The use of ROA to identify proteins containing significant amounts of disordered structure will, inter alia, be valuable in structural genomics/proteomics since disordered regions often inhibit crystallization.     

DFT study of structure and vibrational spectra of ceramide 3: comparison to experimental data

The Fourier transform Raman and infrared (IR) spectra of the Ceramide 3 (CER3) have been recorded in the regions 200–3500 cm^? 1 and 680–4000 cm^? 1, respectively. We have calculated the equilibrium geometry, harmonic vibrational wavenumbers, electrostatic potential surfaces, absolute Raman scattering activities and IR absorption intensities by the density functional theory with B3LYP functionals having extended basis set 6-311G. This work is undertaken to study the vibrational spectra of CER3 completely and to identify the various normal modes with better wavenumber accuracy. Good consistency is found between the calculated results and experimental data for the IR and Raman spectra.     

Segregation of ovarian cancer stage exploiting spectral biomarkers derived from blood plasma or serum analysis: ATR‐FTIR spectroscopy coupled with variable selection methods

Ovarian cancer is a solid tumor and a leading cause of mortality. Diagnostic tools for the detection of early stage (stage I) ovarian cancer are urgently needed. For this purpose, attenuated total reflection Fourier‐transform infrared spectroscopy (ATR‐FTIR) coupled with variable selection methods, successive projection algorithm or genetic algorithm (GA) combined with linear discriminant analysis (LDA), were employed to identify spectral biomarkers in blood plasma or serum samples for accurate diagnosis of different stages of ovarian cancer, histological type and segregation based on age. Three spectral datasets (stage I vs. stage II–IV; serous vs. non‐serous carcinoma; and, ≤60 years vs. >60 years) were processed: sensitivity and specificity required for real‐world diagnosis of ovarian cancer was achieved. Toward segregating stage I vs. stage II–IV, sensitivity and specificity (plasma blood) of 100% was achieved using a GA‐LDA model with 33 wavenumbers. For serous vs. non‐serous category (plasma blood), the sensitivity and specificity levels, using 29 wavenumbers by GA‐LDA, were remarkable (up to 94%). For ≤60 years and >60 years categories (plasma blood), the sensitivity and specificity, using 42 wavenumbers by GA‐LDA, gave complete accuracy (100%). For serum samples, sensitivity and specificity results gave relatively high accuracy (up to 91.6% stage I vs. stage II–IV; up to 93.0% serous vs. non‐serous; and, up to 96.0% ≤60 years vs. >60 years) using several wavenumbers. These findings justify a prospective population‐based assessment of biomarkers signatures using ATR‐FTIR spectroscopy as a screening tool for stage of ovarian cancer. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:832–839, 2015     

Determination and evaluation of secondary structure content derived from calcium-induced conformational changes in wild-type and mutant mnemiopsin 2 by synchrotron-based Fourier-transform infrared spectroscopy

Mnemiopsin 2 from a luminous ctenophore with two functional EF-hand motifs is a calcium-regulated photoprotein that is responsible for emitting a bright blue bioluminescence upon reacting with coelenterazine and calcium ions in Mnemiopsis leidyi. Synchrotron radiation-based Fourier-transform infrared (SR-FTIR) spectroscopy was applied to analyze the distribution of secondary structures, the conformational changes resulting from calcium binding and the structural stabilities in wild-type mnemiopsin 2, as well as its mutant type that possesses three EF-hand motifs. The distribution of secondary structures of these proteins indicates that mutant apo-mnemiopsin 2 has a more stable secondary structure than the wild-type. Analyses of the SR-FTIR spectra revealed that the conformational changes at the secondary structures of both mnemiopsin 2 depend on the calcium concentrations, such that the most noticeable changes in structures of wild-type and mutant mnemiopsin 2 occur at optimum concentrations 6 and 2 mM of calcium chloride, respectively. The addition of calcium to both proteins increases the proportions of their secondary structures in the amide I and II regions. The major amide I bands in the IR spectra of both mnemiopsin‑calcium complexes shift towards smaller wavenumbers, whereas their main amide II bands are identified at larger wavenumbers.     

Infrared spectroscopy with multivariate analysis potentially facilitates the segregation of different types of prostate cell

The prostate gland is conventionally divided into zones or regions. This morphology is of clinical significance as prostate cancer (CaP) occurs mainly in the peripheral zone (PZ). We obtained tissue sets consisting of paraffin-embedded blocks of cancer-free transition zone (TZ) and PZ and adjacent CaP from patients (n = 6) who had undergone radical retropubic prostatectomy; a seventh tissue set of snap-frozen PZ and TZ was obtained from a CaP-free gland removed after radical cystoprostatectomy. Paraffin-embedded tissue slices were sectioned (10-mum thick) and mounted on suitable windows to facilitate infrared (IR) spectra acquisition before being dewaxed and air dried; cryosections were dessicated on BaF(2) windows. Spectra were collected employing synchrotron Fourier-transform infrared (FTIR) microspectroscopy in transmission mode or attenuated total reflection-FTIR (ATR) spectroscopy. Epithelial cell and stromal IR spectra were subjected to principal component analysis to determine whether wavenumber-absorbance relationships expressed as single points in "hyperspace" might on the basis of multivariate distance reveal biophysical differences between cells in situ in different tissue regions. After spectroscopic analysis, plotted clusters and their loadings curves highlighted marked variation in the spectral region containing DNA/RNA bands ( approximately 1490-1000 cm(-1)). By interrogating the intrinsic dimensionality of IR spectra in this small cohort sample, we found that TZ epithelial cells appeared to align more closely with those of CaP while exhibiting marked structural differences compared to PZ epithelium. IR spectra of PZ stroma also suggested that these cells are structurally more different to CaP than those located in the TZ. Because the PZ exhibits a higher occurrence of CaP, other factors (e.g., hormone exposure) may modulate the growth kinetics of initiated epithelial cells in this region. The results of this pilot study surprisingly indicate that TZ epithelial cells are more likely to exhibit what may be a susceptibility-to-adenocarcinoma spectral signature. Thus, IR spectroscopy on its own may not be sufficient to identify premalignant prostate epithelial cells most likely to progress to CaP.     

Normal coordinate analysis and vibrational spectra of 9-β-D-arabinofuranosyladenine hydrochloride (ara-A.HCl)

The vibrational spectra of a synthetic purine nucleoside with known antiviral activity, 9--D-arabino-furanosyladenine hydrochloride (ara-A.HCl) are reported. The Fourier transform infrared (FT-IR) and Fourier transform Raman (FT-Raman) spectra were recorded in the 4000-30 cm^–1 spectral region. The harmonic frequencies and potential energy distributions (PED) of the vibrational modes of ara-A.HCI were calculated by two different methods: a classical molecular mechanics method and a semiempirical molecular orbital (MO) method, PM3. The results of both computational methods, based on the Wilson GF method, are compared with observed spectra, and an assignment of the vibrational modes of ara-A.HCl is proposed on the basis of the potential energy distributions (PED). It is found that the wavenumbers can be calculated with remarkable accuracy (1% deviation in most cases), with the classical mechanics method, by transferring a sufficiently large set of available harmonic force constants, thus permitting a reliable assignment. The semiempirical MO method, PM3, is found to be useful for the assignment of experimental frequencies although it is less accurate (10% deviation). IR intensities calculated by this method did not coincide with the experimental values. Certain out-of-plane vibrations in the base, not reported in previous studies, have been observed. The performance of both methods was related to the crystallographic and ab initio data available. Previous normal coordinate calculations for the adenine base and the nucleoside 5-dGMP are compared with our results and discussed, in relation to the crystal structure of Ara-A.HCl. Correspondence to: A. Hernanz     

Amino acid composition and hydrophobicity patterns of protein domains correlate with their structures

We examine the correlation between the sequence and tertiary structure for 212 domains from globular proteins and polypeptides. The sequence of each domain is described as a set of 25 features: the mole percent of 20 amino acids, the number of residues in the domain, and the abundance of four simple patterns in the hydrophobicity profile of the sequence. Each domain, then, is described as a location in 25-dimensional sequence-feature space. We use pattern-recognition methods to find the two axes through the 25-dimensional sequence-feature space that best discriminate, respectively, predominantly α-helix domains from predominantly β-strand domains (the “secondary structure vector,” SV) and parallel α/β domains from other domains (the “parallel vector,” PV). When we divide the domains into two categories based on whether the cysteine content is above (CYS -RICH ) or below (NORMAL ) 4.5%, we find the secondary structure vector for the subset of CYS -RICH domains points in a significantly different direction than the equivalent vector for the NORMAL domains. Thus, CYS -RICH and NORMAL , domains are best treated separately. The secondary structure vector and the parallel vector for NORMAL domains describes statistically meaningful information, but the secondary structure vector for CYS -RICH domains may not be as reliable. We show how the secondary structure content of a NORMAL domain can be predicted by projecting the domain in the feature space onto the secondary structure vector. We subdivide the domains into five structural classes based on whether there is a parallel or mixed β-sheet in the domain and whether there are more helix or strand residues: NORMAL ALPHA , NORMAL BETA , NORMAL PARALLEL , CYS -RICH ALPHA , and CYS -RICH BETA . When we project the NORMAL domains onto the plane containing the origin of the feature space and SV and PV, we see that ALPHA , BETA , and PARALLEL , domains cluster in the plane, with the BETA cluster partially overlapping the PARALLEL cluster. The separations between the clusters are such that, by looking at the location of any given NORMAL domain in the plane, we can correctly predict its structural class with 83% accuracy. CYS -RICH ALPHA and BETA domains cluster when projected onto the CYS -RICH SV vector, and the classes can be preducted with 83% accuracy, but this accuracy for CYS -RICH domains may not be statistically meaningful.     

Imaging of colorectal adenocarcinoma using FT-IR microspectroscopy and cluster analysis

In this paper, three different clustering algorithms were applied to assemble infrared (IR) spectral maps from IR microspectra of tissues. Using spectra from a colorectal adenocarcinoma section, we show how IR images can be assembled by agglomerative hierarchical (AH) clustering (Ward's technique), fuzzy C-means (FCM) clustering, and k-means (KM) clustering. We discuss practical problems of IR imaging on tissues such as the influence of spectral quality and data pretreatment on image quality. Furthermore, the applicability of cluster algorithms to the spatially resolved microspectroscopic data and the degree of correlation between distinct cluster images and histopathology are compared. The use of any of the clustering algorithms dramatically increased the information content of the IR images, as compared to univariate methods of IR imaging (functional group mapping). Among the cluster imaging methods, AH clustering (Ward's algorithm) proved to be the best method in terms of tissue structure differentiation.     

Peculiarities of secondary structure of serum albumin of some representatives of the animal kingdom

Methods of infrared (IR) spectroscopy and circular dichroism (CD) are suitable techniques for detection of proteins structural changes. These methods were used for determinating peculiarities of the secondary structure of serum albumins in some representatives of two classes of reptiles: Horsfield's tortoise (Testudo horsfieldi), water snake (Natrix tessellata) and grass snake (Natrix natrix) and birds: domestic goose (Anser anser), domestic chicken (Gallus domesticus), domestic duck (Anas platyrhyncha) and dove colored (Columba livia). An analysis of IR spectra and spectra obtained by the method of CD of serum albumins of both classes representatives revealed that beta-folding structure and alpha-helical sections that form the alpha-conformation play an important role in conformational structure formation of polypeptide chain and also disordered sites of molecules of these proteins. It was observed that certain redistribution depending on animals species exists, in the formation of secondary structure of serum albumins of the investigated representatives of reptiles and birds classes between the content of beta-folding structure, alpha-helical sections and disordered sites in molecules of these proteins.     

Ensemble multivariate analysis to improve identification of articular cartilage disease in noisy Raman spectra

The development of new methods for the early diagnosis of cartilage disease could offer significant improvement in patient care. Raman spectroscopy is an emerging biomedical technology with unique potential to recognize disease tissues, though difficulty in obtaining the samples needed to train a diagnostic and excessive signal noise could slow its development into a clinical tool. In the current report we detail the use of principal component analysis – linear discriminant analysis (PCA‐LDA) on spectra from pairs of materials modeling cartilage disease to create multiple spectral scoring metrics, which could limit the reliance on primary training data for identifying disease in low signal‐to‐noise‐ratio (SNR) Raman spectra. Our proof‐of‐concept experiments show that combinations of these model‐metrics has the potential to improve the classification of low‐SNR Raman spectra from human normal and osteoarthritic (OA) cartilage over a single metric trained with spectra from the same healthy and OA tissues.

Scatter plot showing the PCA‐LDA derived human‐disease‐metric scores versus rat‐model‐metric scores for 7656 low signal‐to‐noise spectra from healthy (blue) and osteoarthritic (red) cartilage. Light vertical and horizontal lines represent the optimized single metric classification boundary. Dark diagonal line represents the classification of boundary resulting from the optimized combination of the two metrics. Abbreviations: er (error rate), PCA‐LDA (principal component analysis – linear discriminant analysis), HOA (human osteoarthritis), HAC (human articular cartilage), RIF (rat injury fibrocartilage), RAC (rat articular cartilage).     

Chemometric tools for classification and elucidation of protein secondary structure from infrared and circular dichroism spectroscopic measurements

Protein classification and characterization often rely on the information contained in the protein secondary structure. Protein class assignment is usually based on X-ray diffraction measurements, which need the protein in a crystallized form, or on NMR spectra, to obtain the structure of a protein in solution. Simple spectroscopic techniques, such as circular dichroism (CD) and infrared (IR) spectroscopies, are also known to be related to protein secondary structure, but they have seldom been used for protein classification. To see the potential of CD, IR, and combined CD/IR measurements for protein classification, unsupervised pattern recognition methods, Principal Component Analysis (PCA) and cluster analysis, are proposed first to check for natural grouping tendencies of proteins according to their measured spectra. Partial Least Squares Discriminant Analysis (PLS-DA), a supervised pattern recognition method, is used afterwards to test the possibility to model explicitly each protein class and to test these models in class assignment of unknown proteins. Determination of the protein secondary structure, understood as the prediction of the abundance of the different secondary structure motifs in the biomolecule, was carried out with the local regression method interval Partial Least Squares (iPLS). CD, IR, and CD/IR measurements were correlated to the fraction of the motif to be predicted, determined from X-ray measurements. iPLS builds models extracting the spectral information most correlated to a specific secondary motif and avoids the use of irrelevant spectral regions. Spectral intervals chosen by iPLS models provide structural information which can be used to confirm previous biochemical assignments or identify new motif-related spectral features. The predictive ability of the models built with the selected spectral regions has a quality similar to previous classical approaches.     

An ab initio and DFT study of structure and vibrational spectra of γ form of Oleic acid: Comparison to experimental data

Oleic acid (cis-9-octadecenoic acid) is the most abundant cis-unsaturated fatty acid in nature; it is distributed in almost all organisms. In this work, we present a detailed vibrational spectroscopy investigation of Oleic acid by using infrared and Raman spectroscopies. These data are supported by quantum mechanical calculations, which allow us to characterize completely the vibrational spectra of this compound. The equilibrium geometry, harmonic vibrational frequencies, infrared intensities and activities of Raman scattering were calculated by ab initio Hartree-Fock (HF) and density functional theory (DFT) employing B3LYP with complete relaxation in the potential energy surface using 6-311G(d, p) basis set. After a proper scaling the calculated wavenumbers show a very good agreement with the observed values. A complete vibrational assignment is provided for the observed Raman and infrared spectra of Oleic acid. In this work, we also investigate the deviation of vibrational wavenumbers computed with two quantum chemical methods (HF and B3LYP).     

FT-IR Spectroscopy and Artificial Neural Network Identification of Fusarium Species

A rapid spectroscopic approach for whole‐organism fingerprinting of Fourier transform infrared (FT‐IR) spectroscopy was used to analyse 16 isolates from five closely related species of Fusarium: F. graminearum, F. moniliforme, F. nivale, F. semitectum and F. oxysporum. Principal components analysis and hierarchical cluster analysis were used to study the clusters in the data. On visual inspection of the clusters from both methods, the spectra were not differentiated into five separate clusters corresponding to species and these unsupervised methods failed to identify these fungal strains. When the data were trained by back propagation algorithm of artificial neural networks (ANNs) with principal components scores of spectra used as input modes, the strains were accurately predicted and recognized. The results in this study show that FT‐IR spectroscopy in combination with principal component artificial neural networks (PC‐ANNs) is well suited for identifying Fusarium spp. It would be advantageous to establish a comprehensive database of taxonomically well‐defined Fusarium species to aid the identification of unknown strains.     

Histology Verification Demonstrates That Biospectroscopy Analysis of Cervical Cytology Identifies Underlying Disease More Accurately than Conventional Screening: Removing the Confounder of Discordance

Background

Subjective visual assessment of cervical cytology is flawed, and this can manifest itself by inter- and intra-observer variability resulting ultimately in the degree of discordance in the grading categorisation of samples in screening vs. representative histology. Biospectroscopy methods have been suggested as sensor-based tools that can deliver objective assessments of cytology. However, studies to date have been apparently flawed by a corresponding lack of diagnostic efficiency when samples have previously been classed using cytology screening. This raises the question as to whether categorisation of cervical cytology based on imperfect conventional screening reduces the diagnostic accuracy of biospectroscopy approaches; are these latter methods more accurate and diagnose underlying disease? The purpose of this study was to compare the objective accuracy of infrared (IR) spectroscopy of cervical cytology samples using conventional cytology vs. histology-based categorisation.

Methods

Within a typical clinical setting, a total of n = 322 liquid-based cytology samples were collected immediately before biopsy. Of these, it was possible to acquire subsequent histology for n = 154. Cytology samples were categorised according to conventional screening methods and subsequently interrogated employing attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy. IR spectra were pre-processed and analysed using linear discriminant analysis. Dunn’s test was applied to identify the differences in spectra. Within the diagnostic categories, histology allowed us to determine the comparative efficiency of conventional screening vs. biospectroscopy to correctly identify either true atypia or underlying disease.

Results

Conventional cytology-based screening results in poor sensitivity and specificity. IR spectra derived from cervical cytology do not appear to discriminate in a diagnostic fashion when categories were based on conventional screening. Scores plots of IR spectra exhibit marked crossover of spectral points between different cytological categories. Although, significant differences between spectral bands in different categories are noted, crossover samples point to the potential for poor specificity and hampers the development of biospectroscopy as a diagnostic tool. However, when histology-based categories are used to conduct analyses, the scores plot of IR spectra exhibit markedly better segregation.

Conclusions

Histology demonstrates that ATR-FTIR spectroscopy of liquid-based cytology identifies the presence of underlying atypia or disease missed in conventional cytology screening. This study points to an urgent need for a future biospectroscopy study where categories are based on such histology. It will allow for the validation of this approach as a screening tool.     

Latent Cluster Analysis of ALS Phenotypes Identifies Prognostically Differing Groups

Background

Amyotrophic lateral sclerosis (ALS) is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes.

Methods

Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method.

Results

The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001). Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb) and time from symptom onset to diagnosis (p<0.00001).

Conclusion

The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research.     

Physio-climatic classification of South Africa's woodland biome

In an effort to develop more holistic ecosystem approaches to resource assessment and management, landscapes need to be stratified into homogeneous geographic regions. These regions can then be used in a monitoring framework to develop reliable estimates of ecosystem productivity. A regional characterization of the woodland biome has been developed for South Africa, delineated by satellite imagery and using environmental data and a rigorous statistical methodology. Distribution maps of key environmental variables are analyzed by factor analysis, an iterative clustering technique and maximum likelihood classification to quantify and identify homogeneous physio-climatic units.A spatial clustering technique was used to identify regions, which are statistically different with regard to five physiographic, climatic and edaphic variables deemed important within southern African savanna woodlands. The woodland biome of South Africa at 1km resolution was successively divided. Thirty year mean monthly temperature, total plant-available water balance of soil, elevation, landscape topographic position, and landscape soil fertility were used as input classification variables.The map data were submitted to a factor analysis and varimax axis rotation. The factor analysis removes correlations from the input variables, reduces the dimensionality, and normalizes the axis measurements. A cluster analysis was performed on the three principal factor scores using a modified iterative optimization clustering procedure to determine the finest level of classes statistically permitable. Twenty-seven identified unimodal cluster signatures were then submitted to a maximum likelihood classification where the statistical probability of the GIS cell assignment is carried out to determine class membership. The final map of custom physio-climatic regions is described, and these custom regions are compared with a vegetation potential map of the woodland types identified in the South African summer rainfall zone.     

SamCluster: an integrated scheme for automatic discovery of sample classes using gene expression profile

MOTIVATION: Feature (gene) selection can dramatically improve the accuracy of gene expression profile based sample class prediction. Many statistical methods for feature (gene) selection such as stepwise optimization and Monte Carlo simulation have been developed for tissue sample classification. In contrast to class prediction, few statistical and computational methods for feature selection have been applied to clustering algorithms for pattern discovery. RESULTS: An integrated scheme and corresponding program SamCluster for automatic discovery of sample classes based on gene expression profile is presented in this report. The scheme incorporates the feature selection algorithms based on the calculation of CV (coefficient of variation) and t-test into hierarchical clustering and proceeds as follows. At first, the genes with their CV greater than the pre-specified threshold are selected for cluster analysis, which results in two putative sample classes. Then, significantly differentially expressed genes in the two putative sample classes with p-values < or = 0.01, 0.05, or 0.1 from t-test are selected for further cluster analysis. The above processes were iterated until the two stable sample classes were found. Finally, the consensus sample classes are constructed from the putative classes that are derived from the different CV thresholds, and the best putative sample classes that have the minimum distance between the consensus classes and the putative classes are identified. To evaluate the performance of the feature selection for cluster analysis, the proposed scheme was applied to four expression datasets COLON, LEUKEMIA72, LEUKEMIA38, and OVARIAN. The results show that there are only 5, 1, 0, and 0 samples that have been misclassified, respectively. We conclude that the proposed scheme, SamCluster, is an efficient method for discovery of sample classes using gene expression profile. AVAILABILITY: The related program SamCluster is available upon request or from the web page http://www.sph.uth.tmc.edu:8052/hgc/Downloads.asp.     

Accurate prediction of protein structural class

Because of the increasing gap between the data from sequencing and structural genomics, the accurate prediction of the structural class of a protein domain solely from the primary sequence has remained a challenging problem in structural biology. Traditional sequence-based predictors generally select several sequence features and then feed them directly into a classification program to identify the structural class. The current best sequence-based predictor achieved an overall accuracy of 74.1% when tested on a widely used, non-homologous benchmark dataset 25PDB. In the present work, we built a multiple linear regression (MLR) model to convert the 440-dimensional (440D) sequence feature vector extracted from the Position Specific Scoring Matrix (PSSM) of a protein domain to a 4-dimensinal (4D) structural feature vector, which could then be used to predict the four major structural classes. We performed 10-fold cross-validation and jackknife tests of the method on a large non-homologous dataset containing 8,244 domains distributed among the four major classes. The performance of our approach outperformed all of the existing sequence-based methods and had an overall accuracy of 83.1%, which is even higher than the results of those predicted secondary structure-based methods.     

Theoretical study of the infrared spectrum of 5-phenyl-1,3,4-oxadiazole-2-thiol by using DFT calculations

We have carried out a structural and vibrational study for 5-phenyl-1,3,4-oxadiazole-2-thiol by using the infrared (IR) spectrum and theoretical calculations. For a complete assignment of the compound IR spectrum, density functional theory calculations were combined with Pulay's scaled quantum mechanical force field methodology in order to fit the theoretical wavenumber values to the experimental ones. An agreement between theoretical and available experimental results was found. The theoretical vibrational calculations allowed us to obtain a set of scaled force constants fitting the observed wavenumbers. The results were then used to predict the Raman spectra, for which there are no experimental data. The nature of the benzyl and oxadiazole rings was studied by means of natural bond order and atoms in molecules theory calculations. In addition, the frontier molecular (highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO)) orbitals were analysed and compared with those calculated for the oxadiazole molecule.