Pub Date : 2024-01-17DOI: 10.33696/proteomics.4.014
Muhammad Adib Uz Zaman
This study aims to reveal some important insights into the different diagnoses that are listed in Medical Information Mart for Intensive Care (MIMIC) dataset. This dataset includes patients from diverse backgrounds, ethnicity, demographics, etc. The diagnosis records are stored electronically using ICD-09 and ICD-10 codes. It is found that most of the patients were diagnosed at least once for essential hypertension and other related diseases.
{"title":"Assessing Different Diagnoses in MIMIC-IV v2.2 and MIMIC-IV-ED Datasets","authors":"Muhammad Adib Uz Zaman","doi":"10.33696/proteomics.4.014","DOIUrl":"https://doi.org/10.33696/proteomics.4.014","url":null,"abstract":"This study aims to reveal some important insights into the different diagnoses that are listed in Medical Information Mart for Intensive Care (MIMIC) dataset. This dataset includes patients from diverse backgrounds, ethnicity, demographics, etc. The diagnosis records are stored electronically using ICD-09 and ICD-10 codes. It is found that most of the patients were diagnosed at least once for essential hypertension and other related diseases.","PeriodicalId":87222,"journal":{"name":"Archives of proteomics and bioinformatics","volume":"10 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-23DOI: 10.33696/proteomics.3.013
Tatiana Hillman
Bacterial metabolism affects the effectiveness of antibiotics. Bacterial metabolism is linked to the ability of an antibiotic to be bactericidal or bacteriostatic because a bacterium can metabolize carbohydrates that affect its pH and its ability to use the proton motive force (PMF). When the pH is low, there is more availability of protons that can help to power the proton motive force needed for the efflux of antibiotics. Antibiotics increase the internal pH of a bacterial cell, but when the external pH is low or acidic, the lethality of the antibiotics dwindles. Adding an efflux inhibitor (EI) can block the efflux of antibiotics; however, the pH also affects the effectiveness of the efflux inhibitor. At a low pH the efflux inhibitor cannot block the efflux of antibiotics. This is important for the effectiveness of EIs to block efflux in acidic bacterial environments such as in the stomach or in the small intestines where the pH is highly acidic and low. However, in the colon the pH is highly alkaline and higher leading to a lesser availability of protons, in which the bacterial cells must rely on carbohydrate metabolism to expel any noxious agent such as an antibiotic via the ATP activation of the ABC transporter. As a consequence, for an efflux inhibitor to be effective the pH and the metabolism of carbohydrates to power the ABC transporter must be considered in the design of potential efflux inhibitors. This commentary will offer support for the arguments made in the article, Reducing bacterial antibiotic resistance by targeting bacterial metabolic pathways and disrupting RND efflux pump activity, by presenting the results of experiments that prove the gene inhibition of the AcrAB-TolC subunits of AcrB and TolC as a potent and effective EI design.
{"title":"Antibiotics, Efflux, and pH","authors":"Tatiana Hillman","doi":"10.33696/proteomics.3.013","DOIUrl":"https://doi.org/10.33696/proteomics.3.013","url":null,"abstract":"Bacterial metabolism affects the effectiveness of antibiotics. Bacterial metabolism is linked to the ability of an antibiotic to be bactericidal or bacteriostatic because a bacterium can metabolize carbohydrates that affect its pH and its ability to use the proton motive force (PMF). When the pH is low, there is more availability of protons that can help to power the proton motive force needed for the efflux of antibiotics. Antibiotics increase the internal pH of a bacterial cell, but when the external pH is low or acidic, the lethality of the antibiotics dwindles. Adding an efflux inhibitor (EI) can block the efflux of antibiotics; however, the pH also affects the effectiveness of the efflux inhibitor. At a low pH the efflux inhibitor cannot block the efflux of antibiotics. This is important for the effectiveness of EIs to block efflux in acidic bacterial environments such as in the stomach or in the small intestines where the pH is highly acidic and low. However, in the colon the pH is highly alkaline and higher leading to a lesser availability of protons, in which the bacterial cells must rely on carbohydrate metabolism to expel any noxious agent such as an antibiotic via the ATP activation of the ABC transporter. As a consequence, for an efflux inhibitor to be effective the pH and the metabolism of carbohydrates to power the ABC transporter must be considered in the design of potential efflux inhibitors. This commentary will offer support for the arguments made in the article, Reducing bacterial antibiotic resistance by targeting bacterial metabolic pathways and disrupting RND efflux pump activity, by presenting the results of experiments that prove the gene inhibition of the AcrAB-TolC subunits of AcrB and TolC as a potent and effective EI design.","PeriodicalId":87222,"journal":{"name":"Archives of proteomics and bioinformatics","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78142898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-02DOI: 10.33696/proteomics.3.011
Jingling Wang
Jing-Lin Wang* Director for Department of Bacteriology, Institute of Microbiology and Epidemiology, State Key Laboratory of Pathogens and Biosecurity, AMMS, Beijing 100071, China *Correspondence should be addressed to Jing-Lin Wang, wjlwjl0801@sina.com Received date: January 09, 2022, Accepted date: January 19, 2022 Citation: Wang JL. Commentary on “Integrative Transcriptomics, Proteomics, and Metabolomics Data Analysis Exploring the Injury Mechanism of Ricin on Human Lung Epithelial Cells”. Arch Proteom and Bioinform. 2022;3(1):1-2.
{"title":"Commentary on “Integrative Transcriptomics, Proteomics, and Metabolomics Data Analysis Exploring the Injury Mechanism of Ricin on Human Lung Epithelial Cells”","authors":"Jingling Wang","doi":"10.33696/proteomics.3.011","DOIUrl":"https://doi.org/10.33696/proteomics.3.011","url":null,"abstract":"Jing-Lin Wang* Director for Department of Bacteriology, Institute of Microbiology and Epidemiology, State Key Laboratory of Pathogens and Biosecurity, AMMS, Beijing 100071, China *Correspondence should be addressed to Jing-Lin Wang, wjlwjl0801@sina.com Received date: January 09, 2022, Accepted date: January 19, 2022 Citation: Wang JL. Commentary on “Integrative Transcriptomics, Proteomics, and Metabolomics Data Analysis Exploring the Injury Mechanism of Ricin on Human Lung Epithelial Cells”. Arch Proteom and Bioinform. 2022;3(1):1-2.","PeriodicalId":87222,"journal":{"name":"Archives of proteomics and bioinformatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75431391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-11DOI: 10.21203/rs.3.rs-1242644/v1
Arpana Parihar, Tabassum Zafar, R. Khandia, Dipesh Singh Parihar, R. Dhote, Y. Mishra
Background: Amidst the second wave of COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) led the world devastated, and resulted in the death of millions of people with its deadly virulence potential. In comparison to similar virus outbreaks, such as severe acute respiratory syndrome coronavirus (SARS CoV) and middle east respiratory syndrome coronavirus (MERS CoV), COVID-19 led to severe morbidity and mortality. Various therapeutic interventions to combat the SARS-CoV-2 infection are actively investigated, but still, there is no specific drug with high anti-viral efficacy against the SARS-CoV-2 virus has been reported yet. The present work is an effort to represent the promising therapeutic efficacy of 52 broad-spectrum anti-viral drugs as a potential lead molecule to suppress SARS-CoV-2 infection. These are the drugs that have shown potential efficacy against several viral infections earlier. The present article discusses the comparative analysis of the therapeutic efficacy of available broad-spectrum anti-viral drugs via assessment of receptor-ligand interaction using the molecular docking approach. Results: Based on the molecular docking indications, we predict the potential importance of various broad-spectrum antiviral drugs that can be repurposed for the treatment of SARS-CoV-2. Molecular docking revealed that Remedesivir, Imatinib, Herbacetin, Zanamivir, Ribavirin, Dasabuvir, Rhoifolin, Sofosbuvir, Cirsimaritin, and 2H-Cyclohepta[b]thiophene-3-carboxamide having strong interactions with respective targets. Conclusion: The present piece of work strongly recommends the anti-viral potential of Zanamivir for RdRp enzyme inhibition, Herbacetin against receptor binding domain of spike protein, and main protease target, Adefovir for ACE2, and Ribavirin for endoribonuclease active site. The current predictions will enhance the clinical development of potential therapeutic drugs to combat the pandemic significantly.
{"title":"In silico analysis for the repurposing of broad-spectrum antiviral drugs against multiple targets from SARS-CoV-2: A molecular docking and ADMET approach","authors":"Arpana Parihar, Tabassum Zafar, R. Khandia, Dipesh Singh Parihar, R. Dhote, Y. Mishra","doi":"10.21203/rs.3.rs-1242644/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-1242644/v1","url":null,"abstract":"\u0000 Background: Amidst the second wave of COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) led the world devastated, and resulted in the death of millions of people with its deadly virulence potential. In comparison to similar virus outbreaks, such as severe acute respiratory syndrome coronavirus (SARS CoV) and middle east respiratory syndrome coronavirus (MERS CoV), COVID-19 led to severe morbidity and mortality. Various therapeutic interventions to combat the SARS-CoV-2 infection are actively investigated, but still, there is no specific drug with high anti-viral efficacy against the SARS-CoV-2 virus has been reported yet. The present work is an effort to represent the promising therapeutic efficacy of 52 broad-spectrum anti-viral drugs as a potential lead molecule to suppress SARS-CoV-2 infection. These are the drugs that have shown potential efficacy against several viral infections earlier. The present article discusses the comparative analysis of the therapeutic efficacy of available broad-spectrum anti-viral drugs via assessment of receptor-ligand interaction using the molecular docking approach. Results: Based on the molecular docking indications, we predict the potential importance of various broad-spectrum antiviral drugs that can be repurposed for the treatment of SARS-CoV-2. Molecular docking revealed that Remedesivir, Imatinib, Herbacetin, Zanamivir, Ribavirin, Dasabuvir, Rhoifolin, Sofosbuvir, Cirsimaritin, and 2H-Cyclohepta[b]thiophene-3-carboxamide having strong interactions with respective targets. Conclusion: The present piece of work strongly recommends the anti-viral potential of Zanamivir for RdRp enzyme inhibition, Herbacetin against receptor binding domain of spike protein, and main protease target, Adefovir for ACE2, and Ribavirin for endoribonuclease active site. The current predictions will enhance the clinical development of potential therapeutic drugs to combat the pandemic significantly.","PeriodicalId":87222,"journal":{"name":"Archives of proteomics and bioinformatics","volume":"151 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86162635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-31DOI: 10.33696/proteomics.2.009
Liara Villalobos-Piña, A. Rojas, H. Acosta
Liara Villalobos-Piña1,2*, Ascanio Rojas2, Héctor Acosta3 1Laboratorio de Fisiología. Departamento de Biología, Facultad de Ciencias, Universidad de Los Andes, Mérida 5101, Venezuela 2Centro de Cálculo Científico de la Universidad de Los Andes (CeCalCULA), Mérida 5101, Venezuela 3Laboratorio de Enzimología de Parásitos, Departamento de Biología, Facultad de Ciencias, Universidad de Los Andes, Mérida 5101, Venezuela
{"title":"First In silico Structural Model of Glucokinase-1 from Phytophthora infestans Reveals a Possible Pyrophosphate Binding Site","authors":"Liara Villalobos-Piña, A. Rojas, H. Acosta","doi":"10.33696/proteomics.2.009","DOIUrl":"https://doi.org/10.33696/proteomics.2.009","url":null,"abstract":"Liara Villalobos-Piña1,2*, Ascanio Rojas2, Héctor Acosta3 1Laboratorio de Fisiología. Departamento de Biología, Facultad de Ciencias, Universidad de Los Andes, Mérida 5101, Venezuela 2Centro de Cálculo Científico de la Universidad de Los Andes (CeCalCULA), Mérida 5101, Venezuela 3Laboratorio de Enzimología de Parásitos, Departamento de Biología, Facultad de Ciencias, Universidad de Los Andes, Mérida 5101, Venezuela","PeriodicalId":87222,"journal":{"name":"Archives of proteomics and bioinformatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79688834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-31DOI: 10.33696/proteomics.2.006
S. Morozov, D. Ryazantsev, T. Erokhina
Sergey Y. Morozov1,2*, Dmitriy Y. Ryazantsev3, Tatiana N. Erokhina3 1Department of Virology, Biological Faculty, Lomonosov Moscow State University, Moscow 119234, Russia 2Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow 119992, Russia 3Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science, Moscow, Russia *Correspondence should be addressed to Dr. Sergey Morozov; morozov@genebee.msu.ru
Sergey Y. morozo1,2*， Dmitriy Y. ryazantse3, Tatiana N. erokhin3 1莫斯科国立罗蒙诺索夫大学生物学院病毒学系，莫斯科119234 2莫斯科国立罗蒙诺索夫大学别洛泽斯基物理化学生物研究所，莫斯科119992 3莫斯科俄罗斯科学院舍米亚金-奥夫钦尼科夫生物有机化学研究所，莫斯科*通信地址:Sergey Y. Morozov博士;morozov@genebee.msu.ru
{"title":"Possible Functions of the Conserved Peptides Encoded by the RNAprecursors of miRNAs in Plants","authors":"S. Morozov, D. Ryazantsev, T. Erokhina","doi":"10.33696/proteomics.2.006","DOIUrl":"https://doi.org/10.33696/proteomics.2.006","url":null,"abstract":"Sergey Y. Morozov1,2*, Dmitriy Y. Ryazantsev3, Tatiana N. Erokhina3 1Department of Virology, Biological Faculty, Lomonosov Moscow State University, Moscow 119234, Russia 2Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow 119992, Russia 3Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science, Moscow, Russia *Correspondence should be addressed to Dr. Sergey Morozov; morozov@genebee.msu.ru","PeriodicalId":87222,"journal":{"name":"Archives of proteomics and bioinformatics","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87964417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-31DOI: 10.33696/proteomics.2.010
Vuong
Vuong, L.M.1,2, Donovan, P.J.1,2,3* 1Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, 92617, USA 2Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92617, USA 3Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, 92617, USA *Correspondence should be addressed to Peter J Donovan; pdonovan@uci.edu
{"title":"LINE-1 Retrotransposon-derived Proteins: The ORFull Truth?","authors":"Vuong","doi":"10.33696/proteomics.2.010","DOIUrl":"https://doi.org/10.33696/proteomics.2.010","url":null,"abstract":"Vuong, L.M.1,2, Donovan, P.J.1,2,3* 1Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, 92617, USA 2Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92617, USA 3Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, 92617, USA *Correspondence should be addressed to Peter J Donovan; pdonovan@uci.edu","PeriodicalId":87222,"journal":{"name":"Archives of proteomics and bioinformatics","volume":"363 9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82749646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-31DOI: 10.33696/proteomics.2.008
{"title":"Do Support Vector Machines Play a Role in Stratifying Patient Population Based on Cancer Biomarkers","authors":"","doi":"10.33696/proteomics.2.008","DOIUrl":"https://doi.org/10.33696/proteomics.2.008","url":null,"abstract":"","PeriodicalId":87222,"journal":{"name":"Archives of proteomics and bioinformatics","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77533821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Biomarkers are known to be the key driver behind targeted cancer therapies by either stratifying the patients into risk categories or identifying patient subgroups most likely to benefit. However, the ability of a biomarker to stratify patients relies heavily on the type of clinical endpoint data being collected. Of particular interest is the scenario when the biomarker involved is a continuous one where the challenge is often to identify cut-offs or thresholds that would stratify the population according to the level of clinical outcome or treatment benefit. On the other hand, there are well-established Machine Learning (ML) methods such as the Support Vector Machines (SVM) that classify data, both linear as well as non-linear, into subgroups in an optimal way. SVMs have proven to be immensely useful in data-centric engineering and recently researchers have also sought its applications in healthcare. Despite their wide applicability, SVMs are not yet in the mainstream of toolkits to be utilised in observational clinical studies or in clinical trials. This research investigates the very role of SVMs in stratifying the patient population based on a continuous biomarker across a variety of datasets. Based on the mathematical framework underlying SVMs, we formulate and fit algorithms in the context of biomarker stratified cancer datasets to evaluate their merits. The analysis reveals their superior performance for certain data-types when compared to other ML methods suggesting that SVMs may have the potential to provide a robust yet simplistic solution to stratify real cancer patients based on continuous biomarkers, and hence accelerate the identification of subgroups for improved clinical outcomes or guide targeted cancer therapies.
{"title":"Do Support Vector Machines Play a Role in Stratifying Patient Population Based on Cancer Biomarkers?","authors":"Ben Lanza, Deepak Parashar","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Biomarkers are known to be the key driver behind targeted cancer therapies by either stratifying the patients into risk categories or identifying patient subgroups most likely to benefit. However, the ability of a biomarker to stratify patients relies heavily on the type of clinical endpoint data being collected. Of particular interest is the scenario when the biomarker involved is a continuous one where the challenge is often to identify cut-offs or thresholds that would stratify the population according to the level of clinical outcome or treatment benefit. On the other hand, there are well-established Machine Learning (ML) methods such as the Support Vector Machines (SVM) that classify data, both linear as well as non-linear, into subgroups in an optimal way. SVMs have proven to be immensely useful in data-centric engineering and recently researchers have also sought its applications in healthcare. Despite their wide applicability, SVMs are not yet in the mainstream of toolkits to be utilised in observational clinical studies or in clinical trials. This research investigates the very role of SVMs in stratifying the patient population based on a continuous biomarker across a variety of datasets. Based on the mathematical framework underlying SVMs, we formulate and fit algorithms in the context of biomarker stratified cancer datasets to evaluate their merits. The analysis reveals their superior performance for certain data-types when compared to other ML methods suggesting that SVMs may have the potential to provide a robust yet simplistic solution to stratify real cancer patients based on continuous biomarkers, and hence accelerate the identification of subgroups for improved clinical outcomes or guide targeted cancer therapies.</p>","PeriodicalId":87222,"journal":{"name":"Archives of proteomics and bioinformatics","volume":"2 1","pages":"20-38"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7611982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39733091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-31DOI: 10.33696/PROTEOMICS.1.001
A. Nandy, S. Basak, S. Manna
The currently ongoing coronavirus pandemic, the SARSCOV-2, interchangeably referred to as the COVID-19 infection, has in a short span of time altered the ways and means of almost all of mankind. So strong has been its effect that all human activity ceased in one way or another for a considerable time, led to significant loss of life and economic drain of untold proportions such that there are open debates on whether the world will forever now change from the way we were used to [1]. Just like in the case of the several epidemics that have plagued human society in this 21st century, such as severe acute respiratory syndrome coronavirus (SARS-CoV) from 2002 to 2003 [2], the Middle East respiratory syndrome coronavirus (MERS-CoV) of 2012 [3], and H1N1 influenza in 2009 [4], there are no drugs or vaccines available at this time for the SARS-CoV-2, but considering its impact efforts are under way in more than 100 labs worldwide to develop a vaccine with great urgency.
{"title":"A Bioinformatics Protocol for Rational Design of Peptide Vaccines and the COVID-19 Rampage","authors":"A. Nandy, S. Basak, S. Manna","doi":"10.33696/PROTEOMICS.1.001","DOIUrl":"https://doi.org/10.33696/PROTEOMICS.1.001","url":null,"abstract":"The currently ongoing coronavirus pandemic, the SARSCOV-2, interchangeably referred to as the COVID-19 infection, has in a short span of time altered the ways and means of almost all of mankind. So strong has been its effect that all human activity ceased in one way or another for a considerable time, led to significant loss of life and economic drain of untold proportions such that there are open debates on whether the world will forever now change from the way we were used to [1]. Just like in the case of the several epidemics that have plagued human society in this 21st century, such as severe acute respiratory syndrome coronavirus (SARS-CoV) from 2002 to 2003 [2], the Middle East respiratory syndrome coronavirus (MERS-CoV) of 2012 [3], and H1N1 influenza in 2009 [4], there are no drugs or vaccines available at this time for the SARS-CoV-2, but considering its impact efforts are under way in more than 100 labs worldwide to develop a vaccine with great urgency.","PeriodicalId":87222,"journal":{"name":"Archives of proteomics and bioinformatics","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87337387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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