Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

The secondary structure of protein G', a robust molecule.

ABSTRACT: The secondary structure of recombinant streptococcal Protein G' was predicted and compared with spectropolarimetric data. The predicted secondary structure consisted of 37 +/- 4% alpha-helix and 30 +/- 5% beta-sheet, whereas the values obtained from c.d. data were 29 +/- 2% alpha-helix and 41 +/- 3% beta-sheet. An alpha-helix-beta-sheet/turn-alpha-helix motif is conjectured to comprise the Fc-binding unit. The c.d. spectra in the near u.v. and far u.v. show that the Protein G' molecule is stable to heating at 100 degrees C and to extremes of pH (pH 1.5 to 11.0). The protein retained biological activity at these extremes. The molecule uncoils above pH 11.5 in a time-dependent fashion. Unfolding of the molecule in guanidinium chloride was monitored by c.d. and fluorescence emission; 3 M-guanidinium chloride was required to unfold the protein by 50%. The protein was completely unfolded in 5.5 M-guanidinium chloride and fully refolded with restoration of activity after removal of guanidinium chloride.

SUBMITTER: Goward CR

PROVIDER: S-EPMC1150167 | biostudies-other | 1991 Mar

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

Protein secondary structure appears to be robust under in silico evolution while protein disorder appears not to be.

Project description:MotivationThe mutation of amino acids often impacts protein function and structure. Mutations without negative effect sustain evolutionary pressure. We study a particular aspect of structural robustness with respect to mutations: regular protein secondary structure and natively unstructured (intrinsically disordered) regions. Is the formation of regular secondary structure an intrinsic feature of amino acid sequences, or is it a feature that is lost upon mutation and is maintained by evolution against the odds? Similarly, is disorder an intrinsic sequence feature or is it difficult to maintain? To tackle these questions, we in silico mutated native protein sequences into random sequence-like ensembles and monitored the change in predicted secondary structure and disorder.ResultsWe established that by our coarse-grained measures for change, predictions and observations were similar, suggesting that our results were not biased by prediction mistakes. Changes in secondary structure and disorder predictions were linearly proportional to the change in sequence. Surprisingly, neither the content nor the length distribution for the predicted secondary structure changed substantially. Regions with long disorder behaved differently in that significantly fewer such regions were predicted after a few mutation steps. Our findings suggest that the formation of regular secondary structure is an intrinsic feature of random amino acid sequences, while the formation of long-disordered regions is not an intrinsic feature of proteins with disordered regions. Put differently, helices and strands appear to be maintained easily by evolution, whereas maintaining disordered regions appears difficult. Neutral mutations with respect to disorder are therefore very unlikely.

| S-EPMC2828120 | biostudies-literature

A water molecule participates in the secondary structure of hyaluronan.

Project description:The structure of hyaluronan was investigated in water/dimethyl sulphoxide mixtures by using high-field n.m.r. and space-filling molecular models. The secondary structure previously established in detail in 'dry' dimethyl sulphoxide [Heatley, Scott & Hull (1984) Biochem. J. 220, 197-205] undergoes changes on addition of water, compatible with the incorporation of a water bridge between the uronate carboxylate and acetamido NH groups. Molecular models show that such a configuration is highly probable, and saturation-transfer experiments yield rates of NH proton exchange that support this proposed structure. The existence of two distinct stable configurations for hyaluronan, in water-rich and water-poor conditions respectively, may have biological implications, e.g. during its biosynthesis in cell membranes. There are extensive hydrophobic regions in both forms, which may be important for interactions with e.g., membranes, proteins and itself.

| S-EPMC1135104 | biostudies-other

Single molecule secondary structure determination of proteins through infrared absorption nanospectroscopy.

Project description:The chemical and structural properties of biomolecules determine their interactions, and thus their functions, in a wide variety of biochemical processes. Innovative imaging methods have been developed to characterise biomolecular structures down to the angstrom level. However, acquiring vibrational absorption spectra at the single molecule level, a benchmark for bulk sample characterization, has remained elusive. Here, we introduce off-resonance, low power and short pulse infrared nanospectroscopy (ORS-nanoIR) to allow the acquisition of infrared absorption spectra and chemical maps at the single molecule level, at high throughput on a second timescale and with a high signal-to-noise ratio (~10-20). This high sensitivity enables the accurate determination of the secondary structure of single protein molecules with over a million-fold lower mass than conventional bulk vibrational spectroscopy. These results pave the way to probe directly the chemical and structural properties of individual biomolecules, as well as their interactions, in a broad range of chemical and biological systems.

| S-EPMC7287102 | biostudies-literature

Robust translation of the nucleoid protein Fis requires a remote upstream AU element and is enhanced by RNA secondary structure.

Project description:Synthesis of the Fis nucleoid protein rapidly increases in response to nutrient upshifts, and Fis is one of the most abundant DNA binding proteins in Escherichia coli under nutrient-rich growth conditions. Previous work has shown that control of Fis synthesis occurs at transcription initiation of the dusB-fis operon. We show here that while translation of the dihydrouridine synthase gene dusB is low, unusual mechanisms operate to enable robust translation of fis. At least two RNA sequence elements located within the dusB coding region are responsible for high fis translation. The most important is an AU element centered 35 nucleotides (nt) upstream of the fis AUG, which may function as a binding site for ribosomal protein S1. In addition, a 44-nt segment located upstream of the AU element and predicted to form a stem-loop secondary structure plays a prominent role in enhancing fis translation. On the other hand, mutations close to the AUG, including over a potential Shine-Dalgarno sequence, have little effect on Fis protein levels. The AU element and stem-loop regions are phylogenetically conserved within dusB-fis operons of representative enteric bacteria.

| S-EPMC3347164 | biostudies-literature

Secondary structure and secondary structure dynamics of DNA hairpins complexed with HIV-1 NC protein.

Project description:Reverse transcription of the HIV-1 RNA genome involves several complex nucleic acid rearrangement steps that are catalyzed by the HIV-1 nucleocapsid protein (NC), including for example, the annealing of the transactivation response (TAR) region of the viral RNA to the complementary region (TAR DNA) in minus-strand strong-stop DNA. We report herein single-molecule fluorescence resonance energy transfer measurements on single immobilized TAR DNA hairpins and hairpin mutants complexed with NC (i.e., TAR DNA/NC). Using this approach we have explored the conformational distribution and dynamics of the hairpins in the presence and absence of NC protein. The data demonstrate that NC shifts the equilibrium secondary structure of TAR DNA hairpins from a fully "closed" conformation to essentially one specific "partially open" conformation. In this specific conformation, the two terminal stems are "open" or unwound and the other stems are closed. This partially open conformation is arguably a key TAR DNA intermediate in the NC-induced annealing mechanism of TAR DNA.

| S-EPMC1304694 | biostudies-literature

A physical basis for protein secondary structure.

Project description:A physical theory of protein secondary structure is proposed and tested by performing exceedingly simple Monte Carlo simulations. In essence, secondary structure propensities are predominantly a consequence of two competing local effects, one favoring hydrogen bond formation in helices and turns, the other opposing the attendant reduction in sidechain conformational entropy on helix and turn formation. These sequence specific biases are densely dispersed throughout the unfolded polypeptide chain, where they serve to preorganize the folding process and largely, but imperfectly, anticipate the native secondary structure.

| S-EPMC24424 | biostudies-literature

Synthetic mimetics of protein secondary structure domains.

Project description:Proteins modulate the majority of all biological functions and are primarily composed of highly organized secondary structural elements such as helices, turns and sheets. Many of these functions are affected by a small number of key protein-protein contacts, often involving one or more of these well-defined structural elements. Given the ubiquitous nature of these protein recognition domains, their mimicry by peptidic and non-peptidic scaffolds has become a major focus of contemporary research. This review examines several key advances in secondary structure mimicry over the past several years, particularly focusing upon scaffolds that show not only promising projection of functional groups, but also a proven effect in biological systems.

| S-EPMC3263801 | biostudies-literature

DeepSS2GO: protein function prediction from secondary structure.

Project description:Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features while streamlining the redundant data in primary sequences and bypassing the time-consuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-of-the-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO.

| S-EPMC11066904 | biostudies-literature

Coupled prediction of protein secondary and tertiary structure.

Project description:The strong coupling between secondary and tertiary structure formation in protein folding is neglected in most structure prediction methods. In this work we investigate the extent to which nonlocal interactions in predicted tertiary structures can be used to improve secondary structure prediction. The architecture of a neural network for secondary structure prediction that utilizes multiple sequence alignments was extended to accept low-resolution nonlocal tertiary structure information as an additional input. By using this modified network, together with tertiary structure information from native structures, the Q3-prediction accuracy is increased by 7-10% on average and by up to 35% in individual cases for independent test data. By using tertiary structure information from models generated with the ROSETTA de novo tertiary structure prediction method, the Q3-prediction accuracy is improved by 4-5% on average for small and medium-sized single-domain proteins. Analysis of proteins with particularly large improvements in secondary structure prediction using tertiary structure information provides insight into the feedback from tertiary to secondary structure.

| S-EPMC218720 | biostudies-literature

A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction.

Project description:Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM.

| S-EPMC8318245 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data