scholarly journals Estimating local protein model quality: prospects for molecular replacement

2020 ◽  
Vol 76 (3) ◽  
pp. 285-290
Author(s):  
Björn Wallner
Keyword(s):  
Error Estimates ◽  
Added Value ◽  
Local Error ◽  
Molecular Replacement ◽  
Model Quality ◽  
Data Set ◽  
Protein Model ◽  
Protein Models ◽  
Local Protein

Model quality assessment programs estimate the quality of protein models and can be used to estimate local error in protein models. ProQ3D is the most recent and most accurate version of our software. Here, it is demonstrated that it is possible to use local error estimates to substantially increase the quality of the models for molecular replacement (MR). Adjusting the B factors using ProQ3D improved the log-likelihood gain (LLG) score by over 50% on average, resulting in significantly more successful models in MR compared with not using error estimates. On a data set of 431 homology models to address difficult MR targets, models with error estimates from ProQ3D received an LLG of >50 for almost half of the models 209/431 (48.5%), compared with 175/431 (40.6%) for the previous version, ProQ2, and only 74/431 (17.2%) for models with no error estimates, clearly demonstrating the added value of using error estimates to enable MR for more targets. ProQ3D is available from http://proq3.bioinfo.se/ both as a server and as a standalone download.

scholarly journals A Framework to Assess the Quality of Non-traditional Articles in the Field of Disaster Response and Management

2018 ◽  
Vol 13 (02) ◽  
pp. 147-151
Author(s):  
Mary Hall ◽  
Chris Cartwright ◽  
Andrew C. K. Lee
Keyword(s):  
Scoping Review ◽  
Disaster Response ◽  
Meta Analysis ◽  
Theoretical Framework ◽  
Added Value ◽  
Quality Of Reporting ◽  
Data Set ◽  
Critical Appraisal Skills ◽  
Meta Analyses

AbstractObjectiveWhile carrying out a scoping review of earthquake response, we found that there is no universal standardized approach for assessing the quality of disaster evidence, much of which is variable or not peer reviewed. With the lack of a framework to ascertain the value and validity of this literature, there is a danger that valuable insights may be lost. We propose a theoretical framework that may, with further validation, address this gap.MethodsExisting frameworks – quality of reporting of meta-analyses (QUORUM), meta-analysis of observational studies in epidemiology (MOOSE), the Cochrane assessment of bias, Critical Appraisal Skills Programme (CASP) checklists, strengthening the reporting of observation studies in epidemiology (STROBE), and consensus guidelines on reports of field interventions in disasters and emergencies (CONFIDE)–were analyzed to identify key domains of quality. Supporting statements, based on these existing frameworks were developed for each domain to form an overall theoretical framework of quality. This was piloted on a data set of publications from a separate scoping review.ResultsFour domains of quality were identified: robustness, generalizability, added value, and ethics with 11 scored, supporting statements. Although 73 out of 111 papers (66%) scored below 70%, a sizeable portion (34%) scored higher.ConclusionOur theoretical framework presents, for debate and further validation, a method of assessing the quality of non-traditional studies and thus supporting the best available evidence approach to disaster response. (Disaster Med Public Health Preparedness. 2019;13:147–151)

Phase perturbation as a means of electron density map quality evaluation

2008 ◽  
Vol 41 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Olga Kirillova
Keyword(s):  
Electron Density ◽  
Data Sets ◽  
Molecular Replacement ◽  
Data Set ◽  
Density Maps ◽  
Phase Perturbation ◽  
Replacement Solution ◽  
Electron Density Map ◽  
Trial Phase

This paper describes a new means for evaluating the quality of crystallographic electron density maps. It has been found that a better data set possesses greater robustness against perturbations applied to the phases. Thus it allows recognition of a more precise phase set and provides a way to select the best or reject the worst from several noisy data sets derived from the same crystal structure. The results indicate that calculation of the correlations by the procedure described here can be useful in ranking electron density maps in this aspect of quality. The method suggested has potential use for selecting a better molecular replacement solution, as well as for evaluating trial phase sets inab initiophasing procedures.

scholarly journals PSICA: a fast and accurate web service for protein model quality analysis

2019 ◽  
Vol 47 (W1) ◽  
pp. W443-W450 ◽  
Author(s):  
Wenbo Wang ◽  
Zhaoyu Li ◽  
Junlin Wang ◽  
Dong Xu ◽  
Yi Shang
Keyword(s):  
Web Service ◽  
Structural Information ◽  
Protein Structures ◽  
Quality Analysis ◽  
Consensus Method ◽  
Model Quality ◽  
Protein Model ◽  
Local Quality ◽  
The Difference

Abstract This paper presents a new fast and accurate web service for protein model quality analysis, called PSICA (Protein Structural Information Conformity Analysis). It is designed to evaluate how much a tertiary model of a given protein primary sequence conforms to the known protein structures of similar protein sequences, and to evaluate the quality of predicted protein models. PSICA implements the MUfoldQA_S method, an efficient state-of-the-art protein model quality assessment (QA) method. In CASP12, MUfoldQA_S ranked No. 1 in the protein model QA select-20 category in terms of the difference between the predicted and true GDT-TS value of each model. For a given predicted 3D model, PSICA generates (i) predicted global GDT-TS value; (ii) interactive comparison between the model and other known protein structures; (iii) visualization of the predicted local quality of the model; and (iv) JSmol rendering of the model. Additionally, PSICA implements MUfoldQA_C, a new consensus method based on MUfoldQA_S. In CASP12, MUfoldQA_C ranked No. 1 in top 1 model GDT-TS loss on the select-20 QA category and No. 2 in the average difference between the predicted and true GDT-TS value of each model for both select-20 and best-150 QA categories. The PSICA server is freely available at http://qas.wangwb.com/∼wwr34/mufoldqa/index.html.

scholarly journals The use of local structural similarity of distant homologues for crystallographic model building from a molecular-replacement solution

2020 ◽  
Vol 76 (3) ◽  
pp. 248-260 ◽  
Author(s):  
Grzegorz Chojnowski ◽  
Koushik Choudhury ◽  
Philipp Heuser ◽  
Egor Sobolev ◽  
Joana Pereira ◽  
...  
Keyword(s):  
Model Building ◽  
Structural Information ◽  
Structural Similarity ◽  
Molecular Replacement ◽  
Sequence Coverage ◽  
Protein Model ◽  
Model Extension ◽  
Replacement Solution ◽  
Almost All ◽  
Protein Models

The performance of automated protein model building usually decreases with resolution, mainly owing to the lower information content of the experimental data. This calls for a more elaborate use of the available structural information about macromolecules. Here, a new method is presented that uses structural homologues to improve the quality of protein models automatically constructed using ARP/wARP. The method uses local structural similarity between deposited models and the model being built, and results in longer main-chain fragments that in turn can be more reliably docked to the protein sequence. The application of the homology-based model extension method to the example of a CFA synthase at 2.7 Å resolution resulted in a more complete model with almost all of the residues correctly built and docked to the sequence. The method was also evaluated on 1493 molecular-replacement solutions at a resolution of 4.0 Å and better that were submitted to the ARP/wARP web service for model building. A significant improvement in the completeness and sequence coverage of the built models has been observed.

scholarly journals Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiao Chen ◽  
Jian Liu ◽  
Zhiye Guo ◽  
Tianqi Wu ◽  
Jie Hou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structure Prediction ◽  
Structural Models ◽  
Single Model ◽  
Model Accuracy ◽  
Model Quality ◽  
Distance Information ◽  
Evaluation Of Performance ◽  
Protein Model

AbstractThe inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). To further leverage the improved inter-residue distance predictions to enhance EMA, during the 2020 CASP14 experiment, we integrated several new inter-residue distance features with the existing model quality assessment features in several deep learning methods to predict the quality of protein structural models. According to the evaluation of performance in selecting the best model from the models of CASP14 targets, our three multi-model predictors of estimating model accuracy (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) achieve the averaged loss of 0.073, 0.079, and 0.081, respectively, in terms of the global distance test score (GDT-TS). The three methods are ranked first, second, and third out of all 68 CASP14 predictors. MULTICOM-DEEP, the single-model predictor of estimating model accuracy (EMA), is ranked within top 10 among all the single-model EMA methods according to GDT-TS score loss. The results demonstrate that inter-residue distance features are valuable inputs for deep learning to predict the quality of protein structural models. However, larger training datasets and better ways of leveraging inter-residue distance information are needed to fully explore its potentials.

Practical strategies for classification of unexploded ordnance

Geophysics ◽  
2013 ◽  
Vol 78 (1) ◽  
pp. E41-E46 ◽  
Author(s):  
Laurens Beran ◽  
Barry Zelt ◽  
Leonard Pasion ◽  
Stephen Billings ◽  
Kevin Kingdon ◽  
...  
Keyword(s):  
Classification Performance ◽  
Training Data ◽  
Sensor Data ◽  
Data Sets ◽  
Unexploded Ordnance ◽  
Model Quality ◽  
Data Set ◽  
Time Domain Electromagnetic

We have developed practical strategies for discriminating between buried unexploded ordnance (UXO) and metallic clutter. These methods are applicable to time-domain electromagnetic data acquired with multistatic, multicomponent sensors designed for UXO classification. Each detected target is characterized by dipole polarizabilities estimated via inversion of the observed sensor data. The polarizabilities are intrinsic target features and so are used to distinguish between UXO and clutter. We tested this processing with four data sets from recent field demonstrations, with each data set characterized by metrics of data and model quality. We then developed techniques for building a representative training data set and determined how the variable quality of estimated features affects overall classification performance. Finally, we devised a technique to optimize classification performance by adapting features during target prioritization.

scholarly journals DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network

2021 ◽  
Author(s):  
Xiao Chen ◽  
Jianling Cheng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Structural Models ◽  
Model Quality ◽  
Distance Map ◽  
Protein Model ◽  
Single Protein ◽  
Ranking Loss

AbstractBackgroundEstimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality of protein models. Inter-residue distances are key information for predicting protein’s tertiary structures and therefore have good potentials to predict the quality of protein structural models. However, few methods have been developed to fully take advantage of predicted inter-residue distance maps to estimate the accuracy of a single protein structural model.ResultWe developed an attentive 2D convolutional neural network (CNN) with channel-wise attention to take only a raw difference map between the inter-residue distance map calculated from a single protein model and the distance map predicted from the protein sequence as input to predict the quality of the model. The network comprises multiple convolutional layers, batch normalization layers, dense layers, and Squeeze-and-Excitation blocks with attention to automatically extract features relevant to protein model quality from the raw input without using any expert-curated features. We evaluated DISTEMA’s capability of selecting the best models for CASP13 targets in terms of ranking loss of GDT-TS score. The ranking loss of DISTEMA is 0.079, lower than several state-of-the-art single-model quality assessment methods. The work demonstrates that using raw inter-residue distance information alone with deep learning can predict the quality of protein structural models reasonably well.

scholarly journals Improved protein model quality assessments by changing the target function

2018 ◽  
Author(s):  
Karolis Uziela ◽  
David Menéndez Hurtado ◽  
Nanjiang Shu ◽  
Björn Wallner ◽  
Arne Elofsson
Keyword(s):  
Structure Prediction ◽  
Target Function ◽  
Local Environment ◽  
Global Model ◽  
Model Quality ◽  
Quality Estimation ◽  
Protein Model ◽  
Local Quality ◽  
Quality Assessments

AbstractProtein modeling quality is an important part of protein structure prediction. We have for more than a decade developed a set of methods for this problem. We have used various types of description of the protein and different machine learning methodologies. However, common to all these methods has been the target function used for training. The target function in ProQ describes the local quality of a residue in a protein model. In all versions of ProQ the target function has been the S-score. However, other quality estimation functions also exist, which can be divided into superposition- and contact-based methods. The superposition-based methods, such as S-score, are based on a rigid body superposition of a protein model and the native structure, while the contact-based methods compare the local environment of each residue. Here, we examine the effects of retraining our latest predictor, ProQ3D, using identical inputs but different target functions. We find that the c ntact-based methods are easier to predict and that predictors trained on these measures provide some advantages when it comes to identifying the best model. One possible reason for this is that contact based methods are better at estimating the quality of multi-domain targets. However, training on the S-score gives the best correlation with the GDT_TS score, which is commonly used in CASP to score the global model quality. To take the advantage of both of these features we provide an updated version of ProQ3D that predicts local and global model quality estimates based on different quality estimates.

MODEL MANAJEMEN MUTU MADRASAH KAJIAN DI MIN & MI MA’RIF PAGERAJI PURWOKERTO

2018 ◽  
Vol 19 (1) ◽  
pp. 74-105
Author(s):  
Rohmat Rohmat
Keyword(s):  
Quality Management ◽  
Quality Management System ◽  
Management Model ◽  
Teacher Competence ◽  
Model Quality ◽  
Community Environment ◽  
The Family ◽  
Quality Management Model ◽  
Community Cooperation

Abstract: The quality of madrasah education needs to get serious attention both from process aspect and its result. The quality of madrasah education is also influenced by the family and community environment. This brings with it the need for a review and mapping of the quality management model of education in Madrasah Ibtidaiyah. The Madrasah Ibtidaiyah of Purwokerto and MI Ma'arif Pageraji Purwokerto were chosen to be the subject of this study with the consideration that the school has a good quality management system. This study aims to find the typology of quality management education in MI that is effective so that it can be replicated. Based on the findings of the data, it can be concluded as follows: (1) Quality planning conducted in MIN and MI Ma’arif pageraji Purwokerto through (a) improvement of teacher competence, through OJT activity (on the job trainning), (b) teacher. (c) Development of natural competence matrix. (d) Parent and community cooperation in establishing some madrasah programs. (2) Quality control system conducted in MIN and MI Ma’arif pageraji Purwokerto done through supervision activities. (3) Quality assurance conducted in MIN and MI Ma’arif pageraji Purwokerto done internally by madrasah and audited through accreditation activities run by the accreditation bodies of madrasah or other institutions externally. Keywords: Management model, quality of madrasah.

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