Stereochemical quality of protein structure coordinates

1992 ◽  
Vol 12 (4) ◽  
pp. 345-364 ◽  
Author(s):  
Anne Louise Morris ◽  
Malcolm W. MacArthur ◽  
E. Gail Hutchinson ◽  
Janet M. Thornton
Keyword(s):  
Protein Structure ◽  
Stereochemical Quality

scholarly journals Physicochemical Characterization Cascade of Nanoadjuvant–Antigen Systems for Improving Vaccines

Vaccines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 544
Author(s):  
Giuditta Guerrini ◽  
Antonio Vivi ◽  
Sabrina Gioria ◽  
Jessica Ponti ◽  
Davide Magrì ◽  
...  
Keyword(s):  
Immune Response ◽  
Protein Structure ◽  
Physicochemical Properties ◽  
Physicochemical Characterization ◽  
Protein Antigen ◽  
Efficacy And Safety

Adjuvants have been used for decades to enhance the immune response to vaccines, in particular for the subunit-based adjuvants. Physicochemical properties of the adjuvant-protein antigen complexes, such as size, morphology, protein structure and binding, influence the overall efficacy and safety of the vaccine. Here we show how to perform an accurate physicochemical characterization of the nanoaluminum–ovalbumin complex. Using a combination of existing techniques, we developed a multi-staged characterization strategy based on measurements of increased complexity. This characterization cascade has the advantage of being very flexible and easily adaptable to any adjuvant-protein antigen combinations. It will contribute to control the quality of antigen–adjuvant complexes and immunological outcomes, ultimately leading to improved vaccines.

scholarly journals Effects of ultrahigh pressure treatment on eating quality of steamed oat and oat protein structure

2021 ◽  
Vol 19 (1) ◽  
pp. 56-62
Author(s):  
Sarina Ma ◽  
Meili Zhang ◽  
Yanbin Shi ◽  
Hongli Wang ◽  
Huan Chu
Keyword(s):  
Protein Structure ◽  
Ultrahigh Pressure ◽  
Eating Quality ◽  
Pressure Treatment

scholarly journals Reducing Ensembles of Protein Tertiary Structures Generated De Novo via Clustering

Molecules ◽  
2020 ◽  
Vol 25 (9) ◽  
pp. 2228
Author(s):  
Ahmed Bin Zaman ◽  
Parastoo Kamranfar ◽  
Carlotta Domeniconi ◽  
Amarda Shehu
Keyword(s):  
Protein Structure ◽  
Structure Prediction ◽  
De Novo ◽  
Source Code ◽  
Protein Molecule ◽  
Biologically Active ◽  
Major Drawback ◽  
Tertiary Structures ◽  
Active Structure

Controlling the quality of tertiary structures computed for a protein molecule remains a central challenge in de-novo protein structure prediction. The rule of thumb is to generate as many structures as can be afforded, effectively acknowledging that having more structures increases the likelihood that some will reside near the sought biologically-active structure. A major drawback with this approach is that computing a large number of structures imposes time and space costs. In this paper, we propose a novel clustering-based approach which we demonstrate to significantly reduce an ensemble of generated structures without sacrificing quality. Evaluations are related on both benchmark and CASP target proteins. Structure ensembles subjected to the proposed approach and the source code of the proposed approach are publicly-available at the links provided in Section 1.

scholarly journals MAD data collection – current trends

1999 ◽  
Vol 55 (10) ◽  
pp. 1726-1732 ◽  
Author(s):  
Martin A. Walsh ◽  
Gwyndaf Evans ◽  
Ruslan Sanishvili ◽  
Irene Dementieva ◽  
Andrzej Joachimiak
Keyword(s):  
Protein Structure ◽  
Data Collection ◽  
Structure Determination ◽  
Protein Structure Determination ◽  
Quality Of Data ◽  
Charge Coupled Device ◽  
Current Trends ◽  
Recent Developments ◽  
Widespread Availability

The multiwavelength anomalous dispersion (MAD) method of protein structure determination is becoming a routine technique in protein crystallography. The increased number of wavelength-tuneable synchrotron beamlines capable of performing challenging MAD experiments, coupled with the widespread availability of charge-coupled device (CCD) based X-ray detectors with fast read-out times have brought MAD structure determination to a new exciting level. Ultrafast MAD data collection is now possible and, with the widespread use of selenium in the form of selenomethionine for phase determination, the method is growing in popularity. Recent developments in crystallographic software are complementing the above advances, paving the way for rapid protein structure determination. An overview of a typical MAD experiment is described, with emphasis on the rates and quality of data acquisition now achievable at third-generation synchrotron sources.

scholarly journals Protein structure prediction using AI and quantum computers

2021 ◽  
Author(s):  
Ben Geoffrey A S
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Machine Learning Algorithms ◽  
Quantum Computers ◽  
Data Set ◽  
Backbone Atoms ◽  
Protein Contact Maps

This work seeks to combine the combined advantage of leveraging these emerging areas of Artificial Intelligence and quantum computing in applying it to solve the specific biological problem of protein structure prediction using Quantum Machine Learning algorithms. The CASP dataset from ProteinNet was downloaded which is a standardized data set for machine learning of protein structure. Its large and standardized dataset of PDB entries contains the coordinates of the backbone atoms, corresponding to the sequential chain of N, C_alpha, and C' atoms. This dataset was used to train a quantum-classical hybrid Keras deep neural network model to predict the structure of the proteins. To visually qualify the quality of the predicted versus the actual protein structure, protein contact maps were generated with the experimental and predicted protein structure data and qualified. Therefore this model is recommended for the use of protein structure prediction using AI leveraging the power of quantum computers. The code is provided in the following Github repository https://github.com/bengeof/Protein-structure-prediction-using-AI-and-quantum-computers.

scholarly journals Improving the quality of protein structure models by selecting from alignment alternatives

2006 ◽  
Vol 7 (1) ◽  
Author(s):  
Ingolf Sommer ◽  
Stefano Toppo ◽  
Oliver Sander ◽  
Thomas Lengauer ◽  
Silvio CE Tosatto
Keyword(s):  
Protein Structure

scholarly journals Equipping Decoy Generation Algorithms for Template-free Protein Structure Prediction with Maps of the Protein Conformation Space

10.29007/j5p9 ◽  
2019 ◽  
Author(s):  
Ahmed Bin Zaman ◽  
Amarda Shehu
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structures ◽  
Free Protein ◽  
Structure Space ◽  
Template Free ◽  
Low Dimensional ◽  
Protein Structure Space

A central challenge in template-free protein structure prediction is controlling the quality of computed tertiary structures also known as decoys. Given the size, dimensionality, and inherent characteristics of the protein structure space, this is non-trivial. The current mechanism employed by decoy generation algorithms relies on generating as many decoys as can be afforded. This is impractical and uninformed by any metrics of interest on a decoy dataset. In this paper, we propose to equip a decoy generation algorithm with an evolving map of the protein structure space. The map utilizes low-dimensional representations of protein structure and serves as a memory whose granularity can be controlled. Evaluations on diverse target sequences show that drastic reductions in storage do not sacrifice decoy quality, indicating the promise of the proposed mechanism for decoy generation algorithms in template-free protein structure prediction.

scholarly journals A single-model quality assessment method for poor quality protein structure

2020 ◽  
Author(s):  
Jianquan Ouyang ◽  
Ningqiao Huang ◽  
Yunqi Jiang
Keyword(s):  
Protein Structure ◽  
Quality Assessment ◽  
Structure Prediction ◽  
Assessment Method ◽  
Poor Quality ◽  
Single Model ◽  
Model Quality ◽  
Model Quality Assessment ◽  
Quality Assessment Method

Abstract Quality assessment of protein tertiary structure prediction models, in which structures of the best quality are selected from decoys, is a major challenge in protein structure prediction, and is crucial to determine a model’s utility and potential applications. Estimating the quality of a single model predicts the model’s quality based on the single model itself. In general, the Pearson correlation value of the quality assessment method increases in tandem with an increase in the quality of the model pool. However, there is no consensus regarding the best method to select a few good models from the poor quality model pool. In this work, we introduce a novel single-model quality assessment method for poor quality models that uses simple linear combinations of six features. We perform weighted search and linear regression on a large dataset of models from the 12th Critical Assessment of Protein Structure Prediction (CASP12) and benchmark the results on CASP13 models. We demonstrate that our method achieves outstanding performance on poor quality models.

scholarly journals Basic protein structure prediction for the biologist: A review

2010 ◽  
Vol 62 (4) ◽  
pp. 857-871 ◽  
Author(s):  
M. Mihăşan
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Basic Protein ◽  
Point Of View ◽  
Exponential Rate ◽  
Computational Structure ◽  
Number Of Citations ◽  
Selection Of

As the field of protein structure prediction continues to expand at an exponential rate, the bench-biologist might feel overwhelmed by the sheer range of available applications. This review presents the three main approaches in computational structure prediction from a non-bioinformatician?s point of view and makes a selection of tools and servers freely available. These tools are evaluated from several aspects, such as number of citations, ease of usage and quality of the results. Finally, the applications of models generated by computational structure prediction are discussed.

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