주형 기반 도킹과 Ab Initio 도킹을 이용한 단백질 복합체 구조 예측

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 화학부, 2021.8. 석차옥.Protein-protein interactions play crucial roles in diverse biological processes, including various disease progressions. Atomistic structural details of protein-protein interactions that can be obtained from protein complex structures may provide vital information for the design of therapeutic agents. However, a large portion of protein complex structures is hard to be experimentally captured due to their weak and transient protein-protein interactions. Indeed, a limited fraction of protein-protein interactions happening in the human body has been experimentally determined. Computational protein complex structure prediction methods have been spotlighted for their roles in providing insights into protein-protein interactions in the absence of complete structural information by experiment. In this dissertation, three protein complex structure prediction methods are explained: GalaxyTongDock, GalaxyHeteromer, and GalaxyHomomer2. GalaxyTongDock performs ab initio docking for structure prediction of hetero- and homo-oligomers. GalaxyHeteromer and GalaxyHomomer2 predict heterodimer and homo-oligomer structures, respectively, by template-based docking and ab initio docking depending on the template's availability. Lastly, examples of how these methods were utilized to predict protein complex structures in CASP and CAPRI, community-wide prediction experiments, are presented.단백질 사이의 상호작용은 세포분열, 항상성 유지, 면역반응, 질병의 발생 등 많은 생물학적 과정에서 핵심적인 역할을 한다. 단백질 복합체 구조로부터 얻을 수 있는 단백질 상호작용에 대한 구조적 이해는 효과적인 항체 신약, 단백질 상호작용 저해제 등의 약물 설계를 위해 필수적인 요소이다. 그러나 단백질 복합체는 대체로 약한 상호작용에 의해 일시적으로 형성되어 실험을 통해 결정하기가 어렵다. 실제로 우리 몸에서 일어나는 수많은 단백질 상호작용 중 극히 일부에 대해서만 복합체 구조가 알려져 있다. 컴퓨터를 이용한 단백질 복합체 구조 예측 방법은 실험에 의해 결정된 단백질 복합체 구조가 없는 경우에 단백질 상호작용에 대한 정보를 제공하는 중요한 역할을 해왔다. 이 논문에서는 단백질 복합체 구조 예측 방법인 GalaxyTongDock과 GalaxyHomomer2, GalaxyHeteromer에 대해서 소개한다. GalaxyTongDock은 ab initio 도킹을 통해 동종 올리고머 단백질과 이종 올리고머 단백질의 구조를 예측한다. GalaxyHomomer2와 GalaxyHeteromer는 각각 동종 올리고머 단백질과 이종 올리고머 단백질의 구조를 주형 기반 도킹과 ab initio 도킹을 모두 이용하여 예측한다. 마지막으로, 이 방법들이 국제 단백질 구조 및 복합체 구조 예측 대회인 CASP과 CAPRI에서 단백질 복합체 구조를 예측하기 위해 어떻게 활용되었는지 몇 가지 예시를 통해 소개한다.1. Introduction 1 2. GalaxyTongDock 4 2.1. Methods 4 2.2. Performance of GalaxyTongDock 21 3. GalaxyHeteromer 27 3.1. Methods 27 3.2. Performance of GalaxyHeteromer 34 4. GalaxyHomomer2 40 4.1. Methods 41 4.2. Performance of GalaxyHomomer2 47 5. CASP and CAPRI 54 5.1. CASP13 54 5.2. CASP14 57 5.3. CAPRI 64 6. Conclusion 65 7. References 67 국문초록 71 감사의 글 73박

거친 노면에서의 실화 판정을 위한 퍼지 로직의 적용

학위논문(석사)--서울대학교 대학원 :기계공학과,1997.Maste

A Study on the Short-Term Demand Forecasting Model of Jeju Airport Passenger Using Internet Search Traffic

학위논문(석사)--서울대학교 대학원 :행정대학원 공기업정책학과,2019. 8. 이수영.어느 산업분야를 막론하고 정확한 수요예측은 해당 산업의 적정 공급량을 결정하기 위하여 매우 중요하다. 항공분야의 수요예측도 마찬가지로 적정 시설규모의 결정과 시설 운영계획을 수립하기 위하여 정확한 예측이 필수적이다. 하지만 국내의 수요예측은 중장기 수요예측만 시행하고 있으며 그 정확성이 높지 않아 중장기 수요예측의 실패는 18년 현재 김해공항, 제주공항, 대구공항 등의 공항시설 용량포화로 이어지고 있다. 이에 체계적이고 정확한 단기 수요예측은 여객수요 급증의 추세를 보다 빠르게 예보하여 중장기 수요예측을 서둘러 보완이 가능케 하고, 포화된 공항의 운영측면의 단기적 계획 조정을 용이하게 하여 그 필요성이 높다. 단기 수요예측을 위해서는 항공수요의 특성이 일정한 계절성과 추세성을 가지고 있으므로 시계열적인 수요예측 모형을 기본으로 하였으며, 여객수요의 급증 및 급감의 단기적 변동성을 반영하기 위하여 인터넷 검색트래픽에 주목하였다. 인터넷 활용이 높은 현대 사회에서 여행 전 사전에 여행정보를 인터넷으로 검색하는 행태에 착안하여 인터넷 검색트래픽이 실제 항공수요 발생에 선행할 것으로 보아 이 검색트래픽의 추이를 단기 수요예측 모형에 적용하였다. 본 연구의 단기 수요예측 모형은 제주공항 국내선 출·도착 여객 합계를 대상으로 하였다. 그간의 여객처리 실적을 기반으로 시계열 모형인 계절적 ARIMA 모형을 기본 모형으로 형성하였으며, 단기적 변동성으로 볼 수 있는 계절적 ARIMA 모형의 잔차를 보정하기 위한 방법으로 잔차를 종속변수, 인터넷 검색트래픽을 독립변수로 하는 회귀모형을 형성하여 이를 다시 계절적 ARIMA 모형과 결합하여 최종 혼합모형을 형성하였다. 잔차를 인터넷 검색트래픽으로 적합시키는 회귀모형을 형성을 위하여 제주여행과 관련된 검색어 후보군을 선정하였으며, 이 검색트래픽이 실제 수요변화에 선행한다고 보아 시간차의 개념을 적용하였다. 잔차를 보정하지 않은 계절적 ARIMA 모형과 최종 혼합모형의 예측 정확성을 비교 결과 회귀모형 형성 구간인 트레이닝 세트에서는 MAPE가 ARIMA 모형의 경우 3.84%, 혼합모형의 경우 3.21%로 0.63%p 향상되었다. 그리고 실제 예측으로 활용하는 구간인 테스트 세트에서는 3개월 간 예측의 경우 MAPE가 ARIMA 모형의 경우 3.21%, 혼합모형의 경우 2.54%로 0.67%p 향상되었다. 하지만 7개월 간 예측의 경우는 ARIMA 모형 2.95%, 혼합모형 4.27%로 오차율이 1.32%p 증가하였다. 이는 회귀모형을 형성하는 트레이닝 세트의 일부 기간에서 기존 추세와 다르게 감소하여 깨끗하지 못한 시계열 추세로 정확도가 떨어지는 결과로 이어졌을 가능성이 있지만 그럼에도 불구하고 3개월까지의 초단기 항공수요 예측에는 혼합모형이 보다 정확도가 향상되었음을 확인하였다. 본 연구에서 활용한 인터넷 검색트래픽을 활용한 미래 수요예측 방법의 핵심은 적절한 검색트래픽 데이터를 찾아내는 것이라 볼 수 있다. 본 연구방법은 항공분야 뿐 아니라 분야를 확장하여 타 산업군의 수요예측에서도 실정에 맞게 활용한다면 보다 예측력이 높은 결과를 얻을 수 있을 것으로 기대한다.Accurate demand forecasts for any industry are important for determining the appropriate amount of supply for that industry. Likewise, demand forecasts for aviation are essential to determine the appropriate size of the facility and to establish a facility operating plan. However, in domestic airport policy, only mid- to long-term demand forecasts are implemented and the accuracy of the mid- to long-term demand forecast is not high. So, the failure of the mid- to long-term demand forecast has led to the capacity saturation of airport facilities in Gimhae, Jeju and Daegu airports as of 2018. Thus, systematic and accurate short-term demand forecasts are needed to predict the trend of a surge in passenger demand more quickly. The recognition of the expected surge in passenger traffic makes it possible to quickly supplement mid- to long-term demand forecasts and facilitate short-term planning adjustments on the operational side of the saturated airport. For short-term demand forecasts, the characteristics of air demand have a constant seasonality and trend, so a time-series demand prediction model was based on the basis of a time-series demand prediction model, and the Internet search traffic was noted to reflect the short-term volatility of the sharp increase and decline in passenger demand. In today's society with high Internet utilization, people search for travel information on the Internet before traveling. Thus, the trend of search traffic was applied to the short-term demand forecast model, as Internet search traffic is expected to precede actual air demand. Based on past passenger demand, a time series model, the seasonal ARIMA model, was formed as a basic model. To compensate for the residuals of the seasonal ARIMA model, which can be referred to as short-term variability, a regression model with residuals as dependent variables and Internet search traffic as independent variables was formed. It was then combined with the seasonal ARIMA model to form the final mixed model. To form a regression model that fits Internet search traffic into residuals, a group of search words related to Jeju trip was selected and the concept of time difference was applied as it was assumed that this search traffic precedes actual demand changes. Comparing the predicted accuracy of the final mixed model with the seasonal ARIMA model that did not calibrate the residuals, the MAAPE improved 0.63%p to 3.84% for the ARIMA model and 3.21% for the mixed model in the training set, which is the regression model formation segment. In addition, in a test set that is used as an actual forecast, MAPE improved 0.67%p to 3.21% for ARIMA models and 2.54% for mixed models over a three-month period. However, for the seven-month forecast, ARIMA model 2.95% and mixed model 4.27% increased the error rate by 1.32%. This result may be the result of a series trend that is not clean, decreasing differently from normal trends in some periods of the training set forming the regression model. Nevertheless, short-term air demand forecasts up to three months confirmed that the mixed model was more accurate. The key to the future demand prediction method using Internet search traffic used in this study can be to find appropriate search traffic data. This research method is expected to achieve more predictive results if it expands the aviation sector as well as other industrial groups' demand forecasts.제 1 장 서 론 1 제 1 절 연구의 배경 및 목적 1 제 2 절 연구의 범위 및 방법 5 2.1 연구의 범위 5 2.2 연구의 방법 5 제 2 장 이론적 배경 및 선행연구 검토 10 제 1 절 항공시장과 수요 10 1.1 항공수요와 공항시설 용량 10 1.2 항공수요 예측 방법 11 1.3 항공수요 예측 선행연구 15 제 2 절 빅데이터 분석 예측 23 2.1 빅데이터 개요 23 2.2 인터넷 검색 트래픽을 활용한 예측 선행연구 25 제 3 장 단기 항공수요 예측모형 개발 28 제 1 절 단기 항공수요 예측모형 개발 절차 28 제 2 절 ARIMA 모형 개발 33 제 3 절 회귀 및 혼합모형 개발 49 3.1 독립변수 50 3.2 시차개념을 적용한 상관관계 검토 51 3.3 회귀모형 도출 53 제 4 절 혼합모형 시뮬레이션 및 정확성 검증 56 제 4 장 결 론 60 제 1 절 연구방법 요약 60 제 2 절 연구결과 요약 62 제 3 절 연구의 의의 62 제 4 절 연구의 한계점 63 참고문헌 65 Abstract 69Maste

A Study on a RCS Prediction Code for Battleships

Maste

Combinatorial optimization of catalysts in growth of carbon nanotubes

Maste

Reduction of Visceral Adiposity as a Predictor for Resolution of Nonalcoholic Fatty Liver in Potential Living Liver Donors

This study aimed to determine the factors associated with resolution of nonalcoholic fatty liver (NAFL) after lifestyle intervention in potential living liver donors as assessed by the gold standards in a longitudinal setting. This retrospective study included 115 potential living liver donors (mean age, 30.5 +/- 7.5 years; 101 men) with NAFL who underwent paired liver biopsies and abdominal computed tomography (CT) examinations before and after lifestyle intervention between January 2011 and December 2018. Anthropometry, laboratory parameters, body composition, and hepatic steatosis (HS) were evaluated before and after lifestyle intervention. Anthropometry, laboratory parameters, body composition, and HS were significantly decreased after lifestyle intervention (all, P < 0.001). Relative changes in HS were weakly correlated with relative changes in the visceral fat area (VFA; r = 0.278; P = 0.003) and subcutaneous fat area (r = 0.382; P < 0.001), but not with body weight, body mass index, or skeletal muscle area. Patients with resolved NAFL after lifestyle intervention had significantly lower VFA at follow-up than those with persistent NAFL (mean +/- standard deviation, 69.8 +/- 39.1 versus 91.5 +/- 41.4 cm(2); P = 0.01). Multivariable logistic regression analysis demonstrated that the relative reduction of VFA (odds ratio per percent, 1.031; 95% confidence interval, 1.010-1.053; P = 0.004) was a significant independent factor associated with resolved NAFL after lifestyle intervention. In potential living liver donors with NAFL, the reduction of VFA is a significant factor associated with the resolution of NAFL after lifestyle intervention

Change in hepatic volume profile in potential live liver donors after lifestyle modification for reduction of hepatic steatosis

Purpose We aimed to evaluate changes in hepatic volume and hemiliver volume percentage in potential liver donors after hepatic steatosis (HS) reduction through lifestyle modification. Methods Fifty liver donor candidates with HS (macrovesicular fat [MaF] >= 20%) underwent abdominal computed tomography (CT) and liver biopsy before (baseline) and after (follow-up) lifestyle modification. According to the change in MaF, subjects were classified as group A (MaF reduction >= 20%, n = 25), and group B (MaF reduction < 20%, n = 25). The hepatic volume and hemiliver volume percentage were measured using CT volumetric analysis. Results Volume percentage of the left hemiliver + S1 (over the whole liver) significantly increased at follow-up in group A (P < 0.001) but not in group B (P = 0.598). The absolute volume change of the right hemiliver and its percentage change from the baseline were significantly greater than those of the left hemiliver + S1 in group A (P < 0.007). There were no significant differences in these values in group B (P = 0.064 and 0.507, respectively). The percentage of subjects that earned the benefit of becoming suitable donors from the change in hepatic volume distribution caused by HS improvement was 52.0% (13/25) and 40.0% (10/25) in group A and group B, respectively. Regarding posthepatectomy liver failure, none was identified in group A after donation, whereas 12% (3/25) was identified in group B. Conclusion Hepatic volume profile may change considerably in potential liver donors with HS (MaF >= 20%) after HS reduction through lifestyle modification. Reevaluation of the hepatic volume is required before liver procurement after lifestyle modification in these subjects

Reliability of Skeletal Muscle Area Measurement on CT with Different Parameters: A Phantom Study

Objective: To evaluate the reliability of CT measurements of muscle quantity and quality using variable CT parameters. Materials and Methods: A phantom, simulating the L2-4 vertebral levels, was used for this study. CT images were repeatedly acquired with modulation of tube voltage, tube current, slice thickness, and the image reconstruction algorithm. Reference standard muscle compartments were obtained from the reference maps of the phantom. Cross-sectional area based on the Hounsfield unit (HU) thresholds of muscle and its components, and the mean density of the reference standard muscle compartment, were used to measure the muscle quantity and quality using different CT protocols. Signal-to-noise ratios (SNRs) were calculated in the images acquired with different settings. Results: The skeletal muscle area (threshold,-29 to 150 HU) was constant, regardless of the protocol, occupying at least 91.7% of the reference standard muscle compartment. Conversely, normal attenuation muscle area (30-150 HU) was not constant in the different protocols, varying between 59.7% and 81.7% of the reference standard muscle compartment. The mean density was lower than the target density stated by the manufacturer (45 HU) in all cases (range, 39.0-44.9 HU). The SNR decreased with low tube voltage, low tube current, and in sections with thin slices, whereas it increased when the iterative reconstruction algorithm was used. Conclusion: Measurement of muscle quantity using HU threshold was reliable, regardless of the CT protocol used. Conversely, the measurement of muscle quality using the mean density and narrow HU thresholds were inconsistent and inaccurate across different CT protocols. Therefore, further studies are warranted in future to determine the optimal CT protocols for reliable measurements of muscle quality