scholarly journals Generalized linear models provide a measure of virulence for specific mutations in SARS-CoV-2 strains

2020 ◽  
Author(s):  
Anastasis Oulas ◽  
Maria Zanti ◽  
Marios Tomazou ◽  
Margarita Zachariou ◽  
George Minadakis ◽  
...  
Keyword(s):  
Conformational Changes ◽  
Structure Prediction ◽  
Linear Models ◽  
Response Times ◽  
Genetic Data ◽  
Selective Advantage ◽  
Additional Information ◽  
Prediction Tools ◽  
Viral Virulence ◽  
Reference Protein

AbstractThis study aims to highlight SARS-COV-2 mutations which are associated with increased or decreased viral virulence. We utilize, genetic data from all strains available from GISAID and countries’ regional information such as deaths and cases per million as well as covid-19-related public health austerity measure response times. Initial indications of selective advantage of specific mutations can be obtained from calculating their frequencies across viral strains. By applying modelling approaches, we provide additional information that is not evident from standard statistics or mutation frequencies alone. We therefore, propose a more precise way of selecting informative mutations. We highlight two interesting mutations found in genes N (P13L) and ORF3a (Q57H). The former appears to be significantly associated with decreased deaths and cases per million according to our models, while the latter shows an opposing association with decreased deaths and increased cases per million. Moreover, protein structure prediction tools show that the mutations infer conformational changes to the protein that significantly alter its structure when compared to the reference protein.

scholarly journals Generalized linear models provide a measure of virulence for specific mutations in SARS-CoV-2 strains

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0238665
Author(s):  
Anastasis Oulas ◽  
Maria Zanti ◽  
Marios Tomazou ◽  
Margarita Zachariou ◽  
George Minadakis ◽  
...  
Keyword(s):  
Conformational Changes ◽  
Structure Prediction ◽  
Linear Models ◽  
Response Times ◽  
Genetic Data ◽  
Selective Advantage ◽  
Additional Information ◽  
Prediction Tools ◽  
Viral Virulence ◽  
Reference Protein

This study aims to highlight SARS-COV-2 mutations which are associated with increased or decreased viral virulence. We utilize genetic data from all strains available from GISAID and countries’ regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of specific mutations can be obtained from calculating their frequencies across viral strains. By applying modelling approaches, we provide additional information that is not evident from standard statistics or mutation frequencies alone. We therefore, propose a more precise way of selecting informative mutations. We highlight two interesting mutations found in genes N (P13L) and ORF3a (Q57H). The former appears to be significantly associated with decreased deaths and cases per million according to our models, while the latter shows an opposing association with decreased deaths and increased cases per million. Moreover, protein structure prediction tools show that the mutations infer conformational changes to the protein that significantly alter its structure when compared to the reference protein.

scholarly journals In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches

2021 ◽  
Author(s):  
Anastasis Oulas ◽  
Jan Richter ◽  
Maria Zanti ◽  
Marios Tomazou ◽  
Kyriaki Michailidou ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Regression Models ◽  
Response Times ◽  
National Level ◽  
Selective Advantage ◽  
Single Mutation ◽  
Linear Regression Models ◽  
Computational Approaches ◽  
Viral Virulence ◽  
Depth Analysis

This study aims to characterize SARS-CoV-2 mutations which are primarily prevalent in the Cypriot population. Moreover, using computational approaches, we assess whether these mutations are associated with changes in viral virulence. We utilize genetic data from 144 sequences of SARS-CoV-2 strains from the Cypriot population obtained between March 2020 and January 2021, as well as all data available from GISAID. We combine this with countries' regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of Cyprus-specific mutations are obtained by mutation tracking analysis. This entails calculating specific mutation frequencies within the Cypriot population and comparing these with their prevalence world-wide throughout the course of the pandemic. We further make use of linear regression models to extrapolate additional information that may be missed through standard statistical analysis. We report a single mutation found in the ORF1ab gene (S6059F) that appears to be significantly enriched within the Cypriot population. We further analyse this mutation using regression models to investigate possible associations with increased deaths and cases per million. Moreover, protein structure prediction tools show that the mutation infers a conformational change to the protein that significantly alters its structure when compared to the reference protein. Investigating Cyprus-specific mutations for SARS-CoV-2 can not only lead to important findings from which to battle the pandemic on a national level, but also provide insights into viral virulence worldwide.

scholarly journals In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anastasis Oulas ◽  
Jan Richter ◽  
Maria Zanti ◽  
Marios Tomazou ◽  
Kyriaki Michailidou ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Regression Models ◽  
Response Times ◽  
National Level ◽  
Nucleotide Position ◽  
Single Mutation ◽  
Linear Regression Models ◽  
Computational Approaches ◽  
Viral Virulence ◽  
Depth Analysis

Abstract Background This study aims to characterize SARS-CoV-2 mutations which are primarily prevalent in the Cypriot population. Moreover, using computational approaches, we assess whether these mutations are associated with changes in viral virulence. Methods We utilize genetic data from 144 sequences of SARS-CoV-2 strains from the Cypriot population obtained between March 2020 and January 2021, as well as all data available from GISAID. We combine this with countries’ regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of Cyprus-specific mutations are obtained by mutation tracking analysis. This entails calculating specific mutation frequencies within the Cypriot population and comparing these with their prevalence world-wide throughout the course of the pandemic. We further make use of linear regression models to extrapolate additional information that may be missed through standard statistical analysis. Results We report a single mutation found in the ORF1ab gene (nucleotide position 18,440) that appears to be significantly enriched within the Cypriot population. The amino acid change is denoted as S6059F, which maps to the SARS-CoV-2 NSP14 protein. We further analyse this mutation using regression models to investigate possible associations with increased deaths and cases per million. Moreover, protein structure prediction tools show that the mutation infers a conformational change to the protein that significantly alters its structure when compared to the reference protein. Conclusions Investigating Cyprus-specific mutations for SARS-CoV-2 can lead to a better understanding of viral pathogenicity. Researching these mutations can generate potential links between viral-specific mutations and the unique genomics of the Cypriot population. This can not only lead to important findings from which to battle the pandemic on a national level, but also provide insights into viral virulence worldwide.

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

2019 ◽  
Vol 16 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Elaheh Kashani-Amin ◽  
Ozra Tabatabaei-Malazy ◽  
Amirhossein Sakhteman ◽  
Bagher Larijani ◽  
Azadeh Ebrahim-Habibi
Keyword(s):  
Systematic Review ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Web Of Science ◽  
Secondary Structure Prediction ◽  
Structural Information ◽  
Protein Secondary Structure ◽  
Structural Features ◽  
Prediction Tools ◽  
Insight Into

Background: Prediction of proteins’ secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. Objective: A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Methods: Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. Results: Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. Conclusion: This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

scholarly journals General Relative Rate Models for the Analysis of Studies Using Case-Cohort Designs

2018 ◽  
Vol 188 (2) ◽  
pp. 444-450
Author(s):  
David B Richardson ◽  
Bryan Langholz ◽  
Kaitlin Kelly-Reif
Keyword(s):  
Information Needs ◽  
Linear Models ◽  
Broad Class ◽  
Relative Rate ◽  
Model Misspecification ◽  
Model Fitting ◽  
Simple Random Sampling ◽  
Additional Information ◽  
Cohort Design ◽  
Cohort Data

Abstract A standard approach to analysis of case-cohort data involves fitting log-linear models. In this paper, we describe how standard statistical software can be used to fit a broad class of general relative rate models to case-cohort data and derive confidence intervals. We focus on a case-cohort design in which a roster has been assembled and events ascertained but additional information needs to be collected on explanatory variables. The additional information is ascertained just for persons who experience the event of interest and for a sample of the cohort members enumerated at study entry. One appeal of such a case-cohort design is that this sample of the cohort may be used to support analyses of several outcomes. The ability to fit general relative rate models to case-cohort data may allow an investigator to reduce model misspecification in exposure-response analyses, fit models in which some factors have effects that are additive and others multiplicative, and facilitate estimation of relative excess risk due to interaction. We address model fitting for simple random sampling study designs as well as stratified designs. Data on lung cancer among radon-exposed men (Colorado Plateau uranium miners, 1950–1990) are used to illustrate these methods.

Generalized Log-Linear Models of Housing Choice

1988 ◽  
Vol 20 (1) ◽  
pp. 55-69 ◽  
Author(s):  
M C Deurloo ◽  
F M Dieleman ◽  
W A V Clark
Keyword(s):  
Linear Models ◽  
Nonlinear Effects ◽  
Design Matrix ◽  
Housing Choice ◽  
Ordered Categories ◽  
Public And Private ◽  
Income Effects ◽  
Additional Information ◽  
Log Linear

By incorporating the structure of polytomous variables with ordered categories in the design matrix, nonstandard logit models are used to analyze housing choice. The detailed effects of income, age, and type of housing market on choice are examined. The additional information that is incorporated in the modeling leads to a more parsimonious representation of the data. The results confirm the central and substantial role of income; income effects are linear for owners but there are nonlinear effects for public and private renters. There are important age and region interaction effects on choice for households originally in the rental sector, and for former owners the value of the previous dwelling influences choice.

scholarly journals The unexpected structure of the designed protein Octarellin V.1 forms a challenge for protein structure prediction tools

2016 ◽  
Vol 195 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Maximiliano Figueroa ◽  
Mike Sleutel ◽  
Marylene Vandevenne ◽  
Gregory Parvizi ◽  
Sophie Attout ◽  
...  
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Prediction Tools

CooPPS: A SYSTEM FOR THE COOPERATIVE PREDICTION OF PROTEIN STRUCTURES

2004 ◽  
Vol 02 (03) ◽  
pp. 471-495 ◽  
Author(s):  
LUIGI PALOPOLI ◽  
GIORGIO TERRACINA
Keyword(s):  
Structure Prediction ◽  
Protein Structures ◽  
Three Dimensional ◽  
Quality Parameters ◽  
Dimensional Structure ◽  
Prediction Problem ◽  
Common Reference ◽  
Prediction Tools ◽  
Definition Of

Predicting the three-dimensional structure of proteins is a difficult task. In the last few years several approaches have been proposed for performing this task taking into account different protein chemical and physical properties. As a result, a growing number of protein structure prediction tools is becoming available, some of them specialized to work on either some aspects of the predictions or on some categories of proteins; however, they are still not sufficiently accurate and reliable for predicting all kinds of proteins. In this context, it is useful to jointly apply different prediction tools and combine their results in order to improve the quality of the predictions. However, several problems have to be solved in order to make this a viable possibility. In this paper a framework and a tool is proposed which allows: (i) definition of a common reference applicative domain for different prediction tools; (ii) characterization of prediction tools through evaluating some quality parameters; (iii) characterization of the performances of a team of predictors jointly applied over a prediction problem; (iv) the singling out of the best team for a prediction problem; and (v) the integration of predictor results in the team in order to obtain a unique prediction. A system implementing the various steps of the proposed framework (CooPPS) has been developed and several experiments for testing the effectiveness of the proposed approach have been carried out.

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