Identification Of Secondary Structure Research Articles

Rapid Identification of Secondary Structure and Binding Site Residues in an Intrinsically Disordered Protein Segment.

Mycobacterium tuberculosis harbours nine toxin-antitoxin (TA) systems of the MazEF family. MazEF TA modules are of immense importance due to the perceived role of the MazF toxin in M. tuberculosis persistence and disease. The MazE antitoxin has a disordered C-terminal domain that binds the toxin, MazF and neutralizes its endoribonuclease activity. However, the structure of most MazEF TA complexes remains unsolved till date, obscuring structural and functional information about the antitoxins. We present a facile method to identify toxin binding residues on the disordered antitoxin. Charged residue scanning mutagenesis was used to screen a yeast surface displayed MazE6 antitoxin library against its purified cognate partner, the MazF6 toxin. Binding residues were deciphered by probing the relative reduction in binding to the ligand by flow cytometry. We have used this to identify putative antitoxin interface residues and local structure attained by the antitoxin upon interaction in the MazEF6 TA system and the same methodology is readily applicable to other intrinsically disordered protein regions.

PSIQUE: Protein Secondary Structure Identification on the Basis of Quaternions and Electronic Structure Calculations

The secondary structure is important in protein structure analysis, classification, and modeling. We have developed a novel method for secondary structure assignment, termed PSIQUE, based on the potential energy surface (PES) of polyalanine obtained using an infinitely long chain model and density functional theory calculations. First, uniform protein segments are determined in terms of a difference of quaternions between neighboring amino acids along the protein backbone. Then, the identification of the secondary structure motifs is carried out based on the minima found in the PES. PSIQUE shows good agreement with other secondary structure assignment methods. However, it provides better discrimination of subtle secondary structures (e.g., helix types) and termini and produces more uniform segments while also accounting for local distortions. Overall, PSIQUE provides a precise and reliable assignment of secondary structures, so it should be helpful for the detailed characterization of the protein structure.

Identification of Secondary Structure of Extracellular Signal Regulated Kinase (ERK) Interacting Proteins and Their Domain: An in Silico Study

ERK is involved in multiple cell signaling pathways through its interacting proteins. By in silico analysis, earlier we have identified 22 putative ERK interacting proteins namely; ephrin type-B receptor 2 isoform 2 precursor (EPHB2), mitogen-activated protein kinase 1 (MAPK1), interleukin-17 receptor D precursor (IL17RD), WD repeat domain containing 83 (WDR83), tescalcin (Tesc), mitogen-activated protein kinase kinase kinase 4 (MAPP3K4), kinase suppressor of Ras2 (KSR2), mitogen-activated protein kinase kinase 6 (MAP3K6), UL16 binding protein 2 (ULBP2), UL16 binding protein 1 (ULBP1), dual specificity phosphatase 14 (DUSP14), dual specificity phosphatase 6 (DUSP6), hyaluronan-mediated motility receptor (RHAMM), kinase D interacting substrate of 220 kDa (KININS220), membrane-associated guanylate kinase (MAGI3), phosphoprotein enriched in astrocytes 15 (PEA15), typtophenyl-tRNA synthetase, cytoplasmic (WARS), dual specificity phosphatase 9 (DUSP9), mitogen-activated protein kinase kinase kinase 1 (MAP3K1), UL16 binding protein 3 (ULBP3), SLAM family member 7 isoform a precursor (SLAMMF7) and mitogen activated protein kinase kinase kinase 11 (MAP3K11) (Table 1). However, prediction of secondary structure and domain/motif present in aforementioned ERK interacting proteins is not studied. In this paper, in silico prediction of secondary structure of ERK interacting proteins was done by SOPMA and motif/domain identification using motif search. Briefly, SOPMA predicted higher random coil and alpha helix percentage in these proteins (Table 2) and motif scan predicted serine/threonine kinases active site signature and protein kinase ATP binding region in majority of ERK interacting proteins. Moreover, few have commonly dual specificity protein phosphatase family and tyrosine specific protein phosphatase domains (Table 3). Such study may be helpful to design engineered molecules for regulating ERK dependent pathways in disease condition.

Accurate prediction of genome-wide RNA secondary structure profile based on extreme gradient boosting.

RNA secondary structure plays a vital role in fundamental cellular processes, and identification of RNA secondary structure is a key step to understand RNA functions. Recently, a few experimental methods were developed to profile genome-wide RNA secondary structure, i.e. the pairing probability of each nucleotide, through high-throughput sequencing techniques. However, these high-throughput methods have low precision and cannot cover all nucleotides due to limited sequencing coverage. Here, we have developed a new method for the prediction of genome-wide RNA secondary structure profile from RNA sequence based on the extreme gradient boosting technique. The method achieves predictions with areas under the receiver operating characteristic curve (AUC) >0.9 on three different datasets, and AUC of 0.888 by another independent test on the recently released Zika virus data. These AUCs are consistently >5% greater than those by the CROSS method recently developed based on a shallow neural network. Further analysis on the 1000 Genome Project data showed that our predicted unpaired probabilities are highly correlated (>0.8) with the minor allele frequencies at synonymous, non-synonymous mutations, and mutations in untranslated regions, which were higher than those generated by RNAplfold. Moreover, the prediction over all human mRNA indicated a consistent result with previous observation that there is a periodic distribution of unpaired probability on codons. The accurate predictions by our method indicate that such model trained on genome-wide experimental data might be an alternative for analytical methods. The GRASP is available for academic use at https://github.com/sysu-yanglab/GRASP. Supplementary data are available online.

Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo-Electron Microscopy Maps.

In recent years, three‐dimensional density maps reconstructed from single particle images obtained by electron cryo‐microscopy (cryo‐EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de‐novo model building or are very mobile. Herein, we demonstrate the potential of convolutional neural networks for the annotation of cryo‐EM maps: our network Haruspex has been trained on a carefully curated set of 293 experimentally derived reconstruction maps to automatically annotate RNA/DNA as well as protein secondary structure elements. It can be straightforwardly applied to newly reconstructed maps in order to support domain placement or as a starting point for main‐chain placement. Due to its high recall and precision rates of 95.1 % and 80.3 %, respectively, on an independent test set of 122 maps, it can also be used for validation during model building. The trained network will be available as part of the CCP‐EM suite.

Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo‐Electron Microscopy Maps

AbstractIn recent years, three‐dimensional density maps reconstructed from single particle images obtained by electron cryo‐microscopy (cryo‐EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de‐novo model building or are very mobile. Herein, we demonstrate the potential of convolutional neural networks for the annotation of cryo‐EM maps: our network Haruspex has been trained on a carefully curated set of 293 experimentally derived reconstruction maps to automatically annotate RNA/DNA as well as protein secondary structure elements. It can be straightforwardly applied to newly reconstructed maps in order to support domain placement or as a starting point for main‐chain placement. Due to its high recall and precision rates of 95.1 % and 80.3 %, respectively, on an independent test set of 122 maps, it can also be used for validation during model building. The trained network will be available as part of the CCP‐EM suite.

The influenza NS1 protein modulates RIG-I activation via a strain-specific direct interaction with the second CARD of RIG-I

The influenza NS1 protein modulates RIG-I activation via a strain-specific direct interaction with the second CARD of RIG-I

Surfactin-Laden Aqueous–Liquid Crystal Interface Enabled Identification of Secondary Structure of Proteins

The development of stimuli-responsive biomimetic systems to understand interactions between proteins and surfaces is of an increasingly important scientific interest due to its potential applicatio...

RNAdemocracy: an ensemble method for RNA secondary structure prediction using consensus scoring

The identification of RNA secondary structure has been an important tool for the characterization of nucleic acids. Computational structure prediction has been an effective approach toward this end, but improvement of established methods is often slow and reliant on redundant methodology. Here we present a novel consensus scoring approach, created to incorporate inputs from an array of established methods with the goal of producing outputs that contain mutual structures from these programs. This method is implemented in RNAdemocracy, a python program capable of competing with existing methods. This ensemble approach was limited by commonalities in established methods like parameter sourcing, which may lead to agreement error, an unavoidable outcome due to the limit of available RNA structure datasets. The modular construction of RNAdemocracy allows for its easy upgrading and customization to suit user’s needs. RNAdemocracy, while capable of accurate predictions, is best suited to guide users to regions of the sequence space that exhibit agreement instead of a totally reliant predictor of structure. It is also capable of grading predictions for potential accuracy by providing a percentage of consensus between contributing methods in the final structure.

English

Microbial extracellular chitinases are used in agriculture as effective biocontrol agents and in waste degradation, pharmaceutical and food industry. Actinomycetes are widely tapped group for production of extracellular chitinases. In the present study, approximately 260 actinomycetes were isolated from various ecological habitats was subjected to primary analyses and screened for production of chitinase by plate assay method. Diameter of zones of hydrolysis ranged from 8 to 16 mm. Based on the results, isolates 130, 194, 184, NRRLB 24916 (Streptomyces mexicanus) and NRRLB 16746 (Streptomyces albidoflavus, positive control) were selected for secondary screening and purification. Enzyme activity was estimated in crude cell free extract and partially purified samples. Activity ranged from 7.16 to 14.12 IU/ml (in crude extracts) and 12.1 to 23.10 IU/ml (in partially purified samples). In case of highest chitinase producing isolate 130, effect of various fermentation conditions (pH, temperature and substrate concentration) was studied in crude extract. Furthermore, complete purification of isolate 130 was done by column chromatography and the activity in purified fraction was found to be 32.12 IU/ml. The Km and Vmax values of the purified fractions for isolate 130 were 2.11 µg/ml/min and 53.11mg/ml respectively. This shows that the enzyme has high affinity for the substrate. SDS gel electrophoresis of the purified fraction showed presence of single band of approximately 65 to 70 kDa. Analyses of purified chitinase were done using MS/MS technique. N-terminal sequence corresponded to chitinase, the gene encodes a protein of 453 amino acid residues. Comparison of deduced amino acid sequence to other chitinases in the database indicated that enzyme showed 70% similarity with chitinase from Streptomyces plicatus and belongs to glycoside hydrolase family 18. Homology modeling showed that the enzyme was folded into a domain of (α/β)8 barrel structure. Identification of secondary structure was done by CD spectroscopy. Isolate 130 was capable of degrading biodegradable wastes such as crustacean shells. Key words: Actinomycetes, extracellular chitinase, primary screening, secondary screening, purification, MS/MS analyses, homology modelling, protein structure, biodegradation.

Nanoplasmonic mid-infrared biosensor for in vitro protein secondary structure detection

Plasmonic nanoantennas offer new applications in mid-infrared (mid-IR) absorption spectroscopy with ultrasensitive detection of structural signatures of biomolecules, such as proteins, due to their strong resonant near-fields. The amide I fingerprint of a protein contains conformational information that is greatly important for understanding its function in health and disease. Here, we introduce a non-invasive, label-free mid-IR nanoantenna-array sensor for secondary structure identification of nanometer-thin protein layers in aqueous solution by resolving the content of plasmonically enhanced amide I signatures. We successfully detect random coil to cross β-sheet conformational changes associated with α-synuclein protein aggregation, a detrimental process in many neurodegenerative disorders. Notably, our experimental results demonstrate high conformational sensitivity by differentiating subtle secondary-structural variations in a native β-sheet protein monolayer from those of cross β-sheets, which are characteristic of pathological aggregates. Our nanoplasmonic biosensor is a highly promising and versatile tool for in vitro structural analysis of thin protein layers.

De Novo modeling in cryo-EM density maps with Pathwalking.

As electron cryo-microscopy (cryo-EM) can now frequently achieve near atomic resolution, accurate interpretation of these density maps in terms of atomistic detail has become paramount in deciphering macromolecular structure and function. However, there are few software tools for modeling protein structure from cryo-EM density maps in this resolution range. Here, we present an extension of our original Pathwalking protocol, which can automatically trace a protein backbone directly from a near-atomic resolution (3-6Å) density map. The original Pathwalking approach utilized a Traveling Salesman Problem solver for backbone tracing, but manual adjustment was still required during modeling. In the new version, human intervention is minimized and we provide a more robust approach for backbone modeling. This includes iterative secondary structure identification, termini detection and the ability to model multiple subunits without prior segmentation. Overall, the new Pathwalking procedure provides a more complete and robust tool for annotating protein structure function in near-atomic resolution density maps.

Extracting Infrared Spectra of Protein Secondary Structures Using a Library of Protein Spectra and the Ramachandran Plot.

Infrared (IR) spectra from 1200 to 1800 cm(-1) of the pure α-helix and β-sheet secondary structures have been extracted using a covariant least-squares procedure which relates a library of 40 infrared (IR) solution protein spectra from the work of Dong, Carpenter, and Caughey and amino acid fractions of the proteins based on assignments by STRIDE (secondary structure identification) of Eisenhaber and Argos. The excitonic splitting of the β-sheet structures is determined for this library of solution proteins. The method is extended to find a set of spectral basis functions that analyze IR spectra of protein samples for α-helix and β-sheet content. A rigorous error analysis including covariance, the correlations between the input library spectra, was used to justify the results and avoid less meaningful results. The utility of the results on α-helix and β-sheet regions is demonstrated by detecting protein changes due to cancer in imaging Fourier transform IR (FTIR) spectra of liver tissue slices. This work ends with a method to extract IR spectra of less prominent torsional angle distributions.

CSI 2.0: a significantly improved version of the Chemical Shift Index.

Protein chemical shifts have long been used by NMR spectroscopists to assist with secondary structure assignment and to provide useful distance and torsion angle constraint data for structure determination. One of the most widely used methods for secondary structure identification is called the Chemical Shift Index (CSI). The CSI method uses a simple digital chemical shift filter to locate secondary structures along the protein chain using backbone (13)C and (1)H chemical shifts. While the CSI method is simple to use and easy to implement, it is only about 75-80% accurate. Here we describe a significantly improved version of the CSI (2.0) that uses machine-learning techniques to combine all six backbone chemical shifts ((13)Cα, (13)Cβ, (13)C, (15)N, (1)HN, (1)Hα) with sequence-derived features to perform far more accurate secondary structure identification. Our tests indicate that CSI 2.0 achieved an average identification accuracy (Q3) of 90.56% for a training set of 181 proteins in a repeated tenfold cross-validation and 89.35% for a test set of 59 proteins. This represents a significant improvement over other state-of-the-art chemical shift-based methods. In particular, the level of performance of CSI 2.0 is equal to that of standard methods, such as DSSP and STRIDE, used to identify secondary structures via 3D coordinate data. This suggests that CSI 2.0 could be used both in providing accurate NMR constraint data in the early stages of protein structure determination as well as in defining secondary structure locations in the final protein model(s). A CSI 2.0 web server (http://csi.wishartlab.com) is available for submitting the input queries for secondary structure identification.

In Silico Prediction and In Vivo Validation of Daphnia pulex Micrornas

Daphnia pulex, the crustacean with the first sequenced genome, is an important organism that has been widely used in ecological and toxicological research. MicroRNAs (miRNAs) are 21–25 nucleotide small non-coding RNAs that are involved in a myriad of physiological processes. In this research, we predicted 75 D. pulex miRNAs by sequence homology and secondary structure identification from the full genome sequence. Fourteen predicted miRNAs were selected for quantitative real time polymerase chain reaction (RT-PCR) validation. Out of these, eight (mir-8, mir-9, mir-12, mir-92, mir-100, mir-133, mir-153 and mir-283) were successfully amplified and validated. Next, expression levels were quantified at three different life stages (days 4, 8 and 12 of age) using U6 spliceosomal RNA as a reference gene. The expression of mir-8, mir-9, mir-12, mir-92 and mir-100 significantly differed across time suggesting these microRNAs might play a critical role during D. pulex development. This is the first study to identify and validate miRNAs in D. pulex, which is an important first step in further studies that evaluate their roles in development and response to environmental and ecological stimuli.

Mutation of cysteine residues alters the heme-binding pocket of indoleamine 2,3-dioxygenase-1

The hemoprotein indoleamine 2,3-dioxygenase-1 (IDO1) is the first and rate-limiting enzyme in mammalian tryptophan metabolism. Interest in IDO1 continues to grow, due to the ever expanding influence IDO1 plays in the immune response. This study examined the contribution of all individual cysteine residues towards the overall catalytic properties and stability of recombinant human IDO1 via mutagenesis studies using a range of biochemical and spectroscopic techniques, including in vitro kinetic assessment, secondary structure identification via circular dichroism spectroscopy and thermal stability assessment. Upon mutation of cysteine residues we observed changes in secondary structure (principally, shifting from α-helix/β-sheet features to random coil structures) that produced out of plane heme torsion and puckering, changes to thermal stability (including gains in stability for one mutant protein) and differences in enzymatic activity (such as, increased ability to convert non-natural substrates, e.g.d-tryptophan) from wild type IDO1 enzyme.

Structure-activity relationships of peptides incorporating a bioactive reverse-turn heterocycle at the melanocortin receptors: identification of a 5800-fold mouse melanocortin-3 receptor (mMC3R) selective antagonist/partial agonist versus the mouse melanocortin-4 receptor (mMC4R).

The melanocortin-3 (MC3) and melanocortin-4 (MC4) receptors regulate energy homeostasis, food intake, and associated physiological conditions. The melanocortin-4 receptor (MC4R) has been studied extensively. Less is known about specific physiological roles of the melanocortin-3 receptor (MC3R). A major obstacle to this lack of knowledge is attributed to a limited number of identified MC3R selective ligands. We previously reported a spatial scanning approach of a 10-membered thioether-heterocycle ring incorporated into a chimeric peptide template that identified a lead nM MC4R ligand. Upon the basis of those results, 17 compounds were designed and synthesized that focused upon modification in the pharmacophore domain. Notable results include the identification of a 0.13 nM potent 5800-fold mMC3R selective antagonist/slight partial agonist versus a 760 nM mMC4R full agonist (ligand 11). Biophysical experiments (two-dimensional (1)H NMR and computer-assisted molecular modeling) of this ligand resulted in the identification of an inverse γ-turn secondary structure in the ligand pharmacophore domain.

Chemical shift prediction for denatured proteins.

While chemical shift prediction has played an important role in aspects of protein NMR that include identification of secondary structure, generation of torsion angle constraints for structure determination, and assignment of resonances in spectra of intrinsically disordered proteins, interest has arisen more recently in using it in alternate assignment strategies for crosspeaks in (1)H-(15)N HSQC spectra of sparsely labeled proteins. One such approach involves correlation of crosspeaks in the spectrum of the native protein with those observed in the spectrum of the denatured protein, followed by assignment of the peaks in the latter spectrum. As in the case of disordered proteins, predicted chemical shifts can aid in these assignments. Some previously developed empirical formulas for chemical shift prediction have depended on basis data sets of 20 pentapeptides. In each case the central residue was varied among the 20 amino common acids, with the flanking residues held constant throughout the given series. However, previous choices of solvent conditions and flanking residues make the parameters in these formulas less than ideal for general application to denatured proteins. Here, we report (1)H and (15)N shifts for a set of alanine based pentapeptides under the low pH urea denaturing conditions that are more appropriate for sparse label assignments. New parameters have been derived and a Perl script was created to facilitate comparison with other parameter sets. A small, but significant, improvement in shift predictions for denatured ubiquitin is demonstrated.

Gorgon and pathwalking: macromolecular modeling tools for subnanometer resolution density maps.

The complex interplay of proteins and other molecules, often in the form of large transitory assemblies, are critical to cellular function. Today, X-ray crystallography and electron cryo-microscopy (cryo-EM) are routinely used to image these macromolecular complexes, though often at limited resolutions. Despite the rapidly growing number of macromolecular structures, few tools exist for modeling and annotating structures in the range of 3-10 Å resolution. To address this need, we have developed a number of utilities specifically targeting subnanometer resolution density maps. As part of the 2010 Cryo-EM Modeling Challenge, we demonstrated two of our latest de novo modeling tools, Pathwalking and Gorgon, as well as a tool for secondary structure identification (SSEHunter) and a new rigid-body/flexible fitting tool in Gorgon. In total, we submitted 30 structural models from ten different subnanometer resolution data sets in four of the six challenge categories. Each of our utlities produced accurate structural models and annotations across the various density maps. In the end, the utilities that we present here offer users a robust toolkit for analyzing and modeling protein structure in macromolecular assemblies at non-atomic resolutions.

1H, 13C and 15N resonance assignments of the pyrin domain from human PYNOD

PYNOD is a novel protein belonging to a large family of proteins containing the nucleotide-binding and oligomerization domain (NOD) involved in inflammation and apoptosis. Human PYNOD inhibits inflammatory response mediated by caspase-1 and apoptosis-associated speck-like protein containing a caspase-recruitment domain (ASC). Here we report the (1)H, (13)C and (15)N resonance assignments and secondary structure identification of the pyrin domain (PYD) of human PYNOD as the first step towards elucidating the structural basis of the anti-inflammatory activity of PYNOD.