Comparison of geological clusters between influenza and COVID-19 in Thailand with unsupervised clustering analysis.

The coronavirus disease (COVID-19) pandemic has considerably impacted public health, including the transmission patterns of other respiratory pathogens, such as the 2009 pandemic influenza (H1N1). COVID-19 and influenza are both respiratory infections that started with a lack of vaccination-based immunity in the population. However, vaccinations have been administered over time, resulting in a transition of the status of both diseases from a pandemic to an endemic. In this study, unsupervised clustering techniques were used to identify clusters of disease trends in Thailand. The analysis incorporated three distinct surveillance datasets: the pandemic influenza outbreak, influenza in the endemic stage, and the early stages of COVID-19. The analysis demonstrated a significant difference in the distribution of provinces between Cluster -1, representing those with unique transmission patterns, and the other clusters, indicating provinces with similar transmission patterns among their members. Specifically, for Pandemic Influenza, the ratio was 61:16, while for Pandemic COVID-19, it was 65:12. In contrast, Endemic Influenza exhibited a ratio of 46:31, with a notable emergence of more clustered provinces in the southern, western, and central regions. Furthermore, a pair of provinces with highly similar spreading patterns were identified during the pandemic stages of both influenza and COVID-19. Although the similarity decreased slightly for endemic influenza, they still belonged to the same cluster. Our objective was to identify the transmission patterns of influenza and COVID-19, with the aim of providing quantitative and spatial information to aid public health management in preparing for future pandemics or transitioning into an endemic phase.

Analysis of COVID-19 vaccine adverse event using language model and unsupervised machine learning.

BACKGROUND: After the COVID-19 pandemic, the world has made efforts to recover from the chaotic situation. Vaccination is a way to help control infectious diseases, and many people have been vaccinated against COVID-19 by this point. However, an extremely small number of those who received the vaccine have experienced diverse side effects. METHODS AND FINDINGS: In this study, we examined people who experienced adverse events with the COVID-19 vaccine by gender, age, vaccine manufacturer, and dose of vaccinations by using the Vaccine Adverse Event Reporting System datasets. Then we used a language model to vectorize symptom words and reduced their dimensionality. We also clustered symptoms by using unsupervised machine learning and analyzed the characteristics of each symptom cluster. Lastly, to discover any association rules among adverse events, we used a data mining approach. The frequency of adverse events was higher for women than men, for Moderna than for Pfizer or Janssen, and for the first dose than for the second dose. However, we found that characteristics of vaccine adverse events, including gender, vaccine manufacturer, age, and underlying diseases were different for each symptom cluster, and that fatal cases were significantly related to a particular cluster (associated with hypoxia). Also, as a result of the association analysis, the {chills ↔ pyrexia} and {vaccination site pruritus ↔ vaccination site erythema} rules had the highest support value of 0.087 and 0.046, respectively. CONCLUSIONS: We aim to contribute accurate information on the adverse events of the COVID-19 vaccine to relieve public anxiety due to unconfirmed statements about vaccines.

NGS data vectorization, clustering, and finding key codons in SARS-CoV-2 variations.

The rapid global spread and dissemination of SARS-CoV-2 has provided the virus with numerous opportunities to develop several variants. Thus, it is critical to determine the degree of the variations and in which part of the virus those variations occurred. Therefore, in this study, methods that could be used to vectorize the sequence data, perform clustering analysis, and visualize the results were proposed using machine learning methods. To conduct this study, a total of 224,073 cases of SARS-CoV-2 sequence data were collected through NCBI and GISAID, and the data were visualized using dimensionality reduction and clustering analysis models such as T-SNE and DBSCAN. The SARS-CoV-2 virus, which was first detected, was distinguished from different variations, including Omicron and Delta, in the cluster results. Furthermore, it was possible to examine which codon changes in the spike protein caused the variants to be distinguished using feature importance extraction models such as Random Forest or Shapely Value. The proposed method has the advantage of being able to analyse and visualize a large amount of data at once compared to the existing tree-based sequence data analysis. The proposed method was able to identify and visualize significant changes between the SARS-CoV-2 virus, which was first detected in Wuhan, China, in December 2019, and the newly formed mutant virus group. As a result of clustering analysis using sequence data, it was possible to confirm the formation of clusters among various variants in a two-dimensional graph, and by extracting the importance of variables, it was possible to confirm which codon changes played a major role in distinguishing variants. Furthermore, since the proposed method can handle a variety of data sequences, it can be used for all kinds of diseases, including influenza and SARS-CoV-2. Therefore, the proposed method has the potential to become widely used for the effective analysis of disease variations.

Infectious disease outbreak prediction using media articles with machine learning models.

When a newly emerging infectious disease breaks out in a country, it brings critical damage to both human health conditions and the national economy. For this reason, apprehending which disease will newly emerge, and preparing countermeasures for that disease, are required. Many different types of infectious diseases are emerging and threatening global human health conditions. For this reason, the detection of emerging infectious disease pattern is critical. However, as the epidemic spread of infectious disease occurs sporadically and rapidly, it is not easy to predict whether an infectious disease will emerge or not. Furthermore, accumulating data related to a specific infectious disease is not easy. For these reasons, finding useful data and building a prediction model with these data is required. The Internet press releases numerous articles every day that rapidly reflect currently pending issues. Thus, in this research, we accumulated Internet articles from Medisys that were related to infectious disease, to see if news data could be used to predict infectious disease outbreak. Articles related to infectious disease from January to December 2019 were collected. In this study, we evaluated if newly emerging infectious diseases could be detected using the news article data. Support Vector Machine (SVM), Semi-supervised Learning (SSL), and Deep Neural Network (DNN) were used for prediction to examine the use of information embedded in the web articles: and to detect the pattern of emerging infectious disease.

Forecasting imported COVID-19 cases in South Korea using mobile roaming data.

As the number of global coronavirus disease (COVID-19) cases increases, the number of imported cases is gradually rising. Furthermore, there is no reduction in domestic outbreaks. To assess the risks from imported COVID-19 cases in South Korea, we suggest using the daily risk score. Confirmed COVID-19 cases reported by John Hopkins University Center, roaming data collected from Korea Telecom, and the Oxford COVID-19 Government Response Tracker index were included in calculating the risk score. The risk score was highly correlated with imported COVID-19 cases after 12 days. To forecast daily imported COVID-19 cases after 12 days in South Korea, we developed prediction models using simple linear regression and autoregressive integrated moving average, including exogenous variables (ARIMAX). In the validation set, the root mean squared error of the linear regression model using the risk score was 6.2, which was lower than that of the autoregressive integrated moving average (ARIMA; 22.3) without the risk score as a reference. Correlation coefficient of ARIMAX using the risk score (0.925) was higher than that of ARIMA (0.899). A possible reason for this time lag of 12 days between imported cases and the risk score could be the delay that occurs before the effect of government policies such as closure of airports or lockdown of cities. Roaming data could help warn roaming users regarding their COVID-19 risk status and inform the national health agency of possible high-risk areas for domestic outbreaks.

Forecasting seasonal influenza-like illness in South Korea after 2 and 30 weeks using Google Trends and influenza data from Argentina.

We aimed to identify variables for forecasting seasonal and short-term targets for influenza-like illness (ILI) in South Korea, and other input variables through weekly time-series of the variables. We also aimed to suggest prediction models for ILI activity using a seasonal autoregressive integrated moving average, including exogenous variables (SARIMAX) models. We collected ILI, FluNet surveillance data, Google Trends (GT), weather, and air-pollution data from 2010 to 2019, applying cross-correlation analysis to identify the time lag between the two respective time-series. The relationship between ILI in South Korea and the input variables were evaluated with Linear regression models. To validate selected input variables, the autoregressive moving average, including exogenous variables (ARMAX) models were used to forecast seasonal ILI after 2 and 30 weeks with a three-year window for the training set used in the fixed rolling window analysis. Moreover, a final SARIMAX model was constructed. Influenza A virus activity peaks in South Korea were roughly divided between the 51st and the 7th week, while those of influenza B were divided between the 3rd and 14th week. GT showed the highest correlation coefficient with forecasts from a week ahead, and seasonal influenza outbreak patterns in Argentina showed a high correlation with those 30 weeks ahead in South Korea. The prediction models after 2 and 30 weeks using ARMAX models had R2 values of 0.789 and 0.621, respectively, indicating that reference models using only the previous seasonal ILI could be improved. The currently eligible input variables selected by the cross-correlation analysis helped propose short-term and long-term predictions for ILI in Korea. Our findings indicate that influenza surveillance in Argentina can help predict seasonal ILI patterns after 30 weeks in South Korea, and these can help the Korea Centers for Disease Control and Prevention determine vaccine strategies for the next ILI season.

Effects of temperature, humidity, and diurnal temperature range on influenza incidence in a temperate region.

BACKGROUND: The effect of temperature and humidity on the incidence of influenza may differ by climate region. In addition, the effect of diurnal temperature range on influenza incidence is unclear, according to previous study findings. OBJECTIVES: The aim of this study was to analyze the effects of temperature, humidity, and diurnal temperature range on the incidence of influenza in Seoul, Republic of Korea, which is located in a temperate region. METHODS: We used Korean National Health insurance data to assess the weekly influenza incidence between 2010 and 2016, and used meteorological data from Seoul. To investigate the effect of temperature, relative humidity, and diurnal temperature range levels on influenza incidence, we used a distributed lag non-linear model. RESULTS: The risk of influenza incidence was significantly increased with low daily temperatures of 0-5°C and low (30%-40%) or high (70%) relative humidity. We found a positive significant association between diurnal temperature range and influenza incidence in this study. CONCLUSIONS: Influenza incidence increased with low temperature and low/high humidity in a temperate region. Influenza incidence also increased with high diurnal temperature range, after considering temperature and humidity.

Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries.

To identify countries that have seasonal patterns similar to the time series of influenza surveillance data in the United States and other countries, and to forecast the 2018-2019 seasonal influenza outbreak in the U.S., we collected the surveillance data of 164 countries using the FluNet database, search queries from Google Trends, and temperature from 2010 to 2018. Data for influenza-like illness (ILI) in the U.S. were collected from the Fluview database. We identified the time lag between two time-series which were weekly surveillances for ILI, total influenza (Total INF), influenza A (INF A), and influenza B (INF B) viruses between two countries using cross-correlation analysis. In order to forecast ILI, Total INF, INF A, and INF B of next season (after 26 weeks) in the U.S., we developed prediction models using linear regression, auto regressive integrated moving average, and an artificial neural network (ANN). As a result of cross-correlation analysis between the countries located in northern and southern hemisphere, the seasonal influenza patterns in Australia and Chile showed a high correlation with those of the U.S. 22 weeks and 28 weeks earlier, respectively. The R2 score of ANN models for ILI for validation set in 2015-2019 was 0.758 despite how hard it is to forecast 26 weeks ahead. Our prediction models forecast that the ILI for the U.S. in 2018-2019 may be later and less severe than those in 2017-2018, judging from the influenza activity for Australia and Chile in 2018. It allows to estimate peak timing, peak intensity, and type-specific influenza activities for next season at 40th week. The correlation between seasonal influenza patterns in the U.S., Australia, and Chile could be used to forecast the next seasonal influenza pattern, which can help to determine influenza vaccine strategy approximately six months ahead in the U.S.

Weekly ILI patient ratio change prediction using news articles with support vector machine.

BACKGROUND: Influenza continues to pose a serious threat to human health worldwide. For this reason, detecting influenza infection patterns is critical. However, as the epidemic spread of influenza occurs sporadically and rapidly, it is not easy to estimate the future variance of influenza virus infection. Furthermore, accumulating influenza related data is not easy, because the type of data that is associated with influenza is very limited. For these reasons, identifying useful data and building a prediction model with these data are necessary steps toward predicting if the number of patients will increase or decrease. On the Internet, numerous press releases are published every day that reflect currently pending issues. RESULTS: In this research, we collected Internet articles related to infectious diseases from the Centre for Health Protection (CHP), which is maintained the by Hong Kong Department of Health, to see if news text data could be used to predict the spread of influenza. In total, 7769 articles related to infectious diseases published from 2004 January to 2018 January were collected. We evaluated the predictive ability of article text data from the period of 2013-2018 for each of the weekly time horizons. The support vector machine (SVM) model was used for prediction in order to examine the use of information embedded in the web articles and detect the pattern of influenza spread variance. The prediction result using news text data with SVM exhibited a mean accuracy of 86.7 % on predicting whether weekly ILI patient ratio would increase or decrease, and a root mean square error of 0.611 on estimating the weekly ILI patient ratio. CONCLUSIONS: In order to remedy the problems of conventional data, using news articles can be a suitable choice, because they can help estimate if ILI patient ratio will increase or decrease as well as how many patients will be affected, as shown in the result of research. Thus, advancements in research on using news articles for influenza prediction should continue to be pursed, as the result showed acceptable performance as compared to existing influenza prediction researches.

A Visualization Tool for Calculating the Genetic Substitution Patterns Between Two Different Groups.

We developed simulation tool for influenza virus variation (SimFluVar), an analytics software for calculating genomic variation among members of the influenza virus group. This study is related to computational evolutionary biology and evolutionary bioinformatics. SimFluVar is an analytical tool that can be used to calculate codon substitution patterns of viral genes. Designed to compare a large number of nucleotide sequences, SimFluVar provides precise patterns of codon variations between two viral groups, especially for the influenza virus. SimFluVar also provides useful functions, such as editing and visualization of the result matrix. This new tool can be used to analyze codon variation patterns over time as well as to analyze the genomic differences between viruses obtained from different geographical locations. SimFluVar is developed in C++, and Java RCP is used as a distribution package. SimFluVar, including the associated documentation, manuals, and examples, is publicly available at http://lcbb.snu.ac.kr/simfluvar.

Correlations Between the Incidence of National Notifiable Infectious Diseases and Public Open Data, Including Meteorological Factors and Medical Facility Resources.

OBJECTIVES: This study was performed to investigate the relationship between the incidence of national notifiable infectious diseases (NNIDs) and meteorological factors, air pollution levels, and hospital resources in Korea. METHODS: We collected and stored 660,000 pieces of publicly available data associated with infectious diseases from public data portals and the Diseases Web Statistics System of Korea. We analyzed correlations between the monthly incidence of these diseases and monthly average temperatures and monthly average relative humidity, as well as vaccination rates, number of hospitals, and number of hospital beds by district in Seoul. RESULTS: Of the 34 NNIDs, malaria showed the most significant correlation with temperature (r=0.949, p<0.01) and concentration of nitrogen dioxide (r=-0.884, p<0.01). We also found a strong correlation between the incidence of NNIDs and the number of hospital beds in 25 districts in Seoul (r=0.606, p<0.01). In particular, Geumcheon-gu was found to have the lowest incidence rate of NNIDs and the highest number of hospital beds per patient. CONCLUSIONS: In this study, we conducted a correlational analysis of public data from Korean government portals that can be used as parameters to forecast the spread of outbreaks.

SimFlu: a simulation tool for predicting the variation pattern of influenza A virus.

Since the first pandemic outbreak of avian influenza A virus (H5N1 subtype) in 1997, the National Center for Biotechnology Information (NCBI) has provided a large number of influenza virus sequences with well-organized annotations. Using the time-series sequences of influenza A viruses, we developed a simulation tool for influenza virus, named SimFlu, to predict possible future variants of influenza viruses. SimFlu can create variants from a seed nucleotide sequence of influenza A virus using the codon variation parameters included in the SimFlu package. The SimFlu library provides pre-calculated codon variation parameters for the H1N1, H3N2, and H5N1 subtypes of influenza A virus isolated from 2000 to 2011, allowing the users to simulate their own nucleotide sequences by selecting their preferred parameter options. SimFlu supports three operating systems - Windows, Linux, and Mac OS X. SimFlu is publicly available at http://lcbb.snu.ac.kr/simflu.

Discriminant analysis of prion sequences for prediction of susceptibility.

Prion diseases, including ovine scrapie, bovine spongiform encephalopathy (BSE), human kuru and Creutzfeldt-Jakob disease (CJD), originate from a conformational change of the normal cellular prion protein (PrP(C)) into abnormal protease-resistant prion protein (PrP(Sc)). There is concern regarding these prion diseases because of the possibility of their zoonotic infections across species. Mutations and polymorphisms of prion sequences may influence prion-disease susceptibility through the modified expression and conformation of proteins. Rapid determination of susceptibility based on prion-sequence polymorphism information without complex structural and molecular biological analyses may be possible. Information regarding the effects of mutations and polymorphisms on prion-disease susceptibility was collected based on previous studies to classify the susceptibilities of sequences, whereas the BLOSUM62 scoring matrix and the position-specific scoring matrix were utilised to determine the distance of target sequences. The k-nearest neighbour analysis was validated with cross-validation methods. The results indicated that the number of polymorphisms did not influence prion-disease susceptibility, and three and four k-objects showed the best accuracy in identifying the susceptible group. Although sequences with negative polymorphisms showed relatively high accuracy for determination, polymorphisms may still not be an appropriate factor for estimating variation in susceptibility. Discriminant analysis of prion sequences with scoring matrices was attempted as a possible means of determining susceptibility to prion diseases. Further research is required to improve the utility of this method.

Protein backbone torsion angle-based structure comparison and secondary structure database web server.

Structural information has been a major concern for biological and pharmaceutical studies for its intimate relationship to the function of a protein. Three-dimensional representation of the positions of protein atoms is utilized among many structural information repositories that have been published. The reliability of the torsional system, which represents the native processes of structural change in the structural analysis, was partially proven with previous structural alignment studies. Here, a web server providing structural information and analysis based on the backbone torsional representation of a protein structure is newly introduced. The web server offers functions of secondary structure database search, secondary structure calculation, and pair-wise protein structure comparison, based on a backbone torsion angle representation system. Application of the implementation in pair-wise structural alignment showed highly accurate results. The information derived from this web server might be further utilized in the field of ab initio protein structure modeling or protein homology-related analyses.

Computational model for analyzing the evolutionary patterns of the neuraminidase gene of influenza A/H1N1.

In this study, we performed computer simulations to evaluate the changes of selection potentials of codons in influenza A/H1N1 from 1999 to 2009. We artificially generated the sequences by using the transition matrices of positively selected codons over time, and their similarities against the database of influenzavirus A genus were determined by BLAST search. This is the first approach to predict the evolutionary direction of influenza A virus (H1N1) by simulating the codon substitutions over time. We observed that the BLAST results showed the high similarities with pandemic influenza A/H1N1 in 2009, suggesting that the classical human-origin influenza A/H1N1 isolated before 2009 might contain some selection potentials of swine-origin viruses. Computer simulations using the time series codon substitution patterns resulted dramatic changes of BLAST results in influenza A/H1N1, providing a possibility of developing a method for predicting the viral evolution in silico.

Evolutionary analysis of human-origin influenza A virus (H3N2) genes associated with the codon usage patterns since 1993.

This study investigated genetic variations in eight major genes (hemagglutinin, HA; neuraminidase, NA; matrix protein, MP; non-structural protein, NS; nucleoprotein, NP; polymerase, PA; PA basic protein 1, PB1; and PA basic protein 2, PB2) of the influenza A virus subtype H3N2 (A/H3N2) to determine the evolutionary pattern in codon bias. A total of 6,881 sequences isolated between 1993 and 2010 were used. The relative synonymous codon usage (RSCU) and G+C% content at the three codon positions were analyzed by calculating the codon substitution patterns were analyzed by calculating the percentage of synonymously substituted codons (SSCs) and that of codons substituted to the same codon within each synonymous codon group (EMC) between 1993 and subsequent years. In the multivariate analysis of RSCU, we observed directional changes in HA, NA, PB1, and PB2, and these changes were significantly correlated with the variation in the G+C contents at the first (GC(1st)) and second (GC(2nd)) codon positions over time. These directional changes in HA and NA appear to affect their antigenic characteristics by altering their SSCs gradually, and NP, PA, PB1, and PB2 genes also continuously changed their substitution patterns by accumulating the decrements of EMC values over a long term. Our findings suggest that, in human populations, A/H3N2 viruses have gradually changed their SSCs in two external genes, HA and NA, and that these accumulated alteration patterns may result in the antigenic changes over time. Moreover, A/H3N2 viruses also appear to change synonymous codon usage patterns in NP, PA, PB1, and PB2 genes by accumulating decrements in EMCs within synonymous codon groups over time.

Comparative study of codon substitution patterns in foot-and-mouth disease virus (serotype O).

We compared genetic variations in the VP1 gene of foot-and-mouth disease viruses (FMDVs) isolated since 2000 from various region of the world. We analyzed relative synonymous codon usage (RSCU) and phylogenetic relationship between geographical regions, and calculated the genetic substitution patterns between Korean isolate and those from other countries. We calculated the ratios of synonymously substituted codons (SSC) to all observed substitutions and developed a new analytical parameter, EMC (the ratio of exact matching codons within each synonymous substitution group) to investigate more detailed substitution patterns within each synonymous codon group. We observed that FMDVs showed distinct RSCU patterns according to phylogenetic relationships in the same serotype (serotype O). Moreover, while the SSC and EMC values of FMDVs decreased according to phylogenetic distance, G + C composition at the third codon position was strictly conserved. Although there was little variation among the SSC values of 18 amino acids, more dynamic differences were observed in EMC values. The EMC values of 4- and 6-fold degenerate amino acids showed significantly lower values while most 2-fold degenerate amino acids showed no significant difference. Our findings suggest that different EMC patterns among the 18 amino acids might be an important factor in determining the direction of evolution in FMDV.

Comparative study of the nucleotide bias between the novel H1N1 and H5N1 subtypes of influenza A viruses using bioinformatics techniques.

Novel influenza A (H1N1) is a newly emerged flu virus which was first detected in April, 2009. Unlike the avian influenza (H5N1), this virus has been known to be able to spread from human to human directly. Although it is uncertain that how severe this novel H1N1 virus will be in terms of human illness, illness may be more widespread because most people will not have immunity to it. In this study, we compared the codon usage bias between the novel H1N1 influenza A viruses and other viruses such as H1N1 and H5N1 subtypes to investigate the genomic patterns of novel influenza A (H1N1). Totally 1,675 nucleotide sequences of the hemagglutinin (HA) and neuraminidase (NA) genes of influenza A virus including H1N1 and H5N1 subtypes occurred from 2004 to 2009 were used. As a result, we found that the novel H1N1 influenza A viruses showed the most close correlations with the swine-origin H1N1 subtypes than other H1N1 viruses in the result from not only the analysis of nucleotide compositions, but also the phylogenetic analysis. Although the genetic sequences of novel H1N1 subtypes were not exactly same as the other H1N1 subtypes, the HA and NA genes of novel H1N1s showed very similar codon usage patterns with other H1N1 subtypes, especially with the swine-origin H1N1 influenza A viruses. Our findings strongly suggested that those novel H1N1 viruses seemed to be originated from the swine-host H1N1 viruses in terms of the codon usage patterns.

Comparative study of synonymous codon usage variations between the nucleocapsid and spike genes of coronavirus, and C-type lectin domain genes of human and mouse.

Coronaviruses (CoVs) are single-stranded RNA viruses which contain the largest RNA genomes, and severe acute respiratory syndrome coronavirus (SARS-CoV), a newly found group 2 CoV, emerged as infectious disease with high mortality rate. In this study, we compared the synonymous codon usage patterns between the nucleocapsid and spike genes of CoVs, and C-type lectin domain (CTLD) genes of human and mouse on the codon basis. Findings indicate that the nucleocapsid genes of CoVs were affected from the synonymous codon usage bias than spike genes, and the CTLDs of human and mouse partially overlapped with the nucleocapsid genes of CoVs. In addition, we observed that CTLDs which showed the similar relative synonymous codon usage (RSCU) patterns with CoVs were commonly derived from the human chromosome 12, and mouse chromosome 6 and 12, suggesting that there might be a specific genomic region or chromosomes which show a more similar synonymous codon usage pattern with viral genes. Our findings contribute to developing the codon-optimization method in DNA vaccines, and further study is needed to determine a specific correlation between the codon usage patterns and the chromosomal locations in higher organisms.

Comparative bioinformatics analysis of prion proteins isolated from reptile, rodent, ruminant, and human species.

Prion proteins (PrPs) are infectious pathogens that cause a group of invariably fatal, neurodegenerative diseases, including Creutzfeldt-Jakob disease, by means of an entirely novel mechanism. They are produced by various species, including reptile, rodent, ruminant and mammals, during normal metabolic processes, but they can be slowly changed into pathogenic isoforms upon contact with other infectious PrP isoforms. This transmission can occur across species barriers. In the present study, phylogram for each PrP sequence was generated by PAUP* 4.0 program using Neighbor-Joining method with 1,000 times bootstrapping process for the phylogenetic analysis. The molecular dynamics (MD) simulations were performed by the SANDER module in the AMBER 7 package using Amber 99 force field. All the simulation process was conducted in the IBM p690 Supercomputing System in Korea Institute of Science and Technology Information. To reduce the calculation time, we used the Generalized Born (GB) model. We compared the sequences and structural characteristics of normal and pathogenic (E200K) human PrPs with those of other reptile, rodent, ruminant and mammalian PrPs. Phylogenetic analysis revealed that, although the turtle PrP sequence is the most distinct of the PrPs analyzed, it nonetheless retains five conserved secondary structural elements that are similar to those found in the mammalian PrPs, suggesting that these elements have important functions in vivo. The RMS deviation between the normal and E200K human PrPs was larger than that between the normal human and bovine PrPs, and all of the beta-sheet structures in human E200K PrP were very stable during MD simulations.