Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors

Rationalization and Design of the Complementarity Determining Region Sequences in an Antibody-Antigen Recognition Interface

Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

Prevalence and Clinical Features of Occult Hepatitis B Virus Infection in Families with Hepatocellular Carcinoma

研究背景：肝細胞癌（hepatocellular carcinoma；HCC）病例家族是B型肝炎病毒感染的高危險性族群，本研究目的在於調查HCC家族B型肝炎隱性感染的家族聚集程度，及探討B型肝炎隱性感染的臨床表徵。材料與方法：研究個案來自兩個來源：HBsAg陽性之HCC指標病例一等親共601名HBsAg陰性個案，他們來自251個家族；另一個是來自公務人員世代所選取的602名HBsAg陰性個案做為對照組，對照組和HCC指標病例親屬在年齡上配對。我們採用Nested PCR偵測 HBV X 和Core基因區，X基因區可測得HBV DNA者，才進一步分析Core 基因區。B型肝炎病毒隱性感染的定義為X基因區可測得HBV DNA者。對於隱性感染者，進一步使用Real time PCR測量病毒量。結果：HCC家族B型肝炎隱性感染盛行率(26.0%)顯著高於對照組(18.4%)(P=0.0017)，且具有顯著家族聚集(corrected familial recurrence risk ratio:2.59)。和家族內隱性感染正相關的因子包括anti-HCV陽性(P=0.0332)、HCC指標病例為女性(P=0.0433)、及家中HBsAg人數( P trend=0.0414)。對Core和X基因區皆可測得者的HBV DNA濃度顯著高於只有X基因區可測得者（P<0.0001）。ALT/AST異常及肝臟實質病變（由超音波測量)的危險性隨無隱性感染、只有X基因區可測得者、X和Core基因區皆可測得者漸增，對於X和Core基因區皆可測得者和無隱性感染者比較，OR (95% CI) 對於ALT異常、AST異常和肝臟實質病變，分別為5.31 (95%CI=2.66-10.60)、4.15 (95%CI=2.10-8.24)、和2.19 (95%CI=1.40-6.06)。結論：HCC病例家族中有B型肝炎隱性感染的家族聚集。B型肝炎隱性感染可能導致肝臟疾病。Background & Aims: Taiwan is an endemic area of hepatitis B virus (HBV) infection. This study aimed to assess the extent to which occult HBV infection aggregates in families with HCC, and to determine the association between occult HBV infection and hepatic abnormalities. Material and Methods: Study subjects consisted of 601 HBsAg-negative first-degree relatives from 251 families with HCC and 602 HBsAg-negative, age-matched unrelated individuals as controls. Occult HBV infection was defined by the detection of HBV DNA in serum by PCR amplification assay on the X region of HBV. PCR assay on core region was performed in subjects who showed positivity in X region. Results: Occult HBV infection was detected in 26.0% of relatives from families with HCC and in 18.4% of controls (P=0.0017). The familial recurrence risk ratio for occult HBV infection was 2.59. Anti-HCV positivity, female gender of HCC proband, and number of HBsAg-positive relatives in a family were positively associated with an increased likelihood of occult HBV infection. Circulating HBV DNA was higher in relatives who showed positivity in both X and core regions than in those who showed positivity in the X region only (P<0.0001). The presence of occult HBV was associated with elevated ALT (odds ratios were 2.01 [95% CI: 1.06-3.80] and 5.31 [2.66-10.60], respectively, for positivity for X region and both X and core regions), irrespective of age, sex, alcohol drinking, and anti-HCV status. Conclusions: There is familial aggregation of occult HBV infection in families with HCC. Occult HBV infection may increase risk of hepatic abnormalities.致謝…………………………………………………………………i文摘要……………………………………………………………ii文摘要……………………………………………………………iii錄…………………………………………………………………v目錄………………………………………………………………vi究背景……………………………………………………………1料與方法…………………………………………………………4果…………………………………………………………………9論…………………………………………………………………12考文獻……………………………………………………………16錄…………………………………………………………………2

Germline breast cancer susceptibility gene mutations and breast cancer outcomes

Abstract Background It is unclear whether germline breast cancer susceptibility gene mutations affect breast cancer related outcomes. We wanted to evaluate mutation patterns in 20 breast cancer susceptibility genes and correlate the mutations with clinical characteristics to determine the effects of these germline mutations on breast cancer prognosis. Methods The study cohort included 480 ethnic Chinese individuals in Taiwan with at least one of the six clinical risk factors for hereditary breast cancer: family history of breast or ovarian cancer, young age of onset for breast cancer, bilateral breast cancer, triple negative breast cancer, both breast and ovarian cancer, and male breast cancer. PCR-enriched amplicon-sequencing on a next generation sequencing platform was used to determine the germline DNA sequences of all exons and exon-flanking regions of the 20 genes. Protein-truncating variants were identified as pathogenic. Results We detected a 13.5% carrier rate of pathogenic germline mutations, with BRCA2 being the most prevalent and the non-BRCA genes accounting for 38.5% of the mutation carriers. BRCA mutation carriers were more likely to be diagnosed of breast cancer with lymph node involvement (66.7% vs 42.6%; P = 0.011), and had significantly worse breast cancer specific outcomes. The 5-year disease-free survival was 73.3% for BRCA mutation carriers and 91.1% for non-carriers (hazard ratio for recurrence or death 2.42, 95% CI 1.29–4.53; P = 0.013). After adjusting for clinical prognostic factors, BRCA mutation remained an independent poor prognostic factor for cancer recurrence or death (adjusted hazard ratio 3.04, 95% CI 1.40–6.58; P = 0.005). Non-BRCA gene mutation carriers did not exhibit any significant difference in cancer characteristics or outcomes compared to those without detected mutations. Among the risk factors for hereditary breast cancer, the odds of detecting a germline mutation increased significantly with having bilateral breast cancer (adjusted odds ratio 3.27, 95% CI 1.64–6.51; P = 0.0008) or having more than one risk factor (odds ratio 2.07, 95% CI 1.22–3.51; P = 0.007). Conclusions Without prior knowledge of the mutation status, BRCA mutation carriers had more advanced breast cancer on initial diagnosis and worse cancer-related outcomes. Optimal approach to breast cancer treatment for BRCA mutation carriers warrants further investigation

動床條件下定量流及變量流橋墩沖刷深度變化分析與模擬

This paper aims to analyze temporal evolutions of pier scour in live-bed condition. Five steady flow experiments are conductedwith different flow intensitiesratio of mean velocity to critical velocity varying from 1.18 to 2.21. To find out the effects of unsteady flow on scour depth, different steady flow intensities were combined as four unsteady flow experiments, including: (1) advanced hydrograph, (2) delayed hydrograph, (3) symmetric hydrograph and (4) symmetric hydrograph with low peak. A semi-empirical model proposed by Hong et al. (2014) was used topredict thetemporal variation of pier-scour depth. Moreover, alinear-combination hypothesis, i.e. scour depth hydrograph of unsteady flow can be simulated by a linear-combination ofscour depth hydrographs of steady flows,was proposed to extend Hong et al.'s model inunsteady flow conditions. Comparison of the experimental and model resultsshowed that the present modeldid not perform perfectly on the variation of dune migration velocity with changing flow intensity is changing. Simulation and experiment resultsrevealed a phase difference because of the effect of dune migration velocity.However,the error betweenthe simulatedand experimentalscour depths ranged from 8% to10% , including that the present modelcan estimate the temporal variationof scour depth for cyliderical piersunder unsteady flow conditionsreasonably well.本研究利用明渠水槽試驗探討定量流況與不同流量歷線鋒型之變量流況，在動床條件下橋墩前方沖刷深度隨時間變化。試驗完成五組定量流流量，其水流強度 (平均流速與臨界流速之比值) 分別為1.18、1.38、1.6、1.91、2.21，以及由前述五組定量流水流強度組合而成之四組不同鋒型變量流之橋墩沖刷，包括：前峰型、後峰型、對稱型 (高洪峰)、對稱型 (低洪峰) 之變量流流量歷線。試驗設計定量流試驗歷時為三小時，變量流試驗歷時則為五小時，變量流每間隔一個小時改變一次水流強度，試驗過程以橋墩內架設之攝影機記錄沖刷歷程。結果分析比較不同水流強度定量流與不同峰型變量流橋墩沖刷歷程外，也將試驗結果引用Honget al.(2014) 發展之半經驗模式進行定量流沖刷歷程預測，並透過線性組合假設，延伸模式應用對變量流沖刷歷程進行推估。研究結果顯示，半經驗模式配合線性組合雖無法完全反應受水流強度變動對砂丘推移速度之影 響，致使模擬與試驗之沖刷歷程在部分時刻有相位差發生的情況，然變量流沖刷深度之變化整體發展趨勢仍為模式所掌握，不同峰型變量流之模擬與試驗沖刷深度平均誤差僅介於8%~10%之間，可推論Hong et al.(2014)半經驗模式配合線性組合假設，能合理推估變量流動床條件下橋墩前方沖刷深度隨時間之變化

Deep Learning Applied to Defect Detection in Powder Spreading Process of Magnetic Material Additive Manufacturing

Due to its advantages of high customization and rapid production, metal laser melting manufacturing (MAM) has been widely applied in the medical industry, manufacturing, aerospace and boutique industries in recent years. However, defects during the selective laser melting (SLM) manufacturing process can result from thermal stress or hardware failure during the selective laser melting (SLM) manufacturing process. To improve the product’s quality, the use of defect detection during manufacturing is necessary. This study uses the process images recorded by powder bed fusion equipment to develop a detection method, which is based on the convolutional neural network. This uses three powder-spreading defect types: powder uneven, powder uncovered and recoater scratches. This study uses a two-stage convolutional neural network (CNN) model to finish the detection and segmentation of defects. The first stage uses the EfficientNet B7 to classify the images with/without defects, and then to locate the defects by evaluating three different instance segmentation networks in second stage. Experimental results show that the accuracy and Dice measurement of Mask-R-CNN network with ResNet 152 backbone can reach 0.9272 and 0.9438. The computational time of an image only takes approximately 0.2197 sec. The used CNN model meets the requirements of the early detected defects, regarding the SLM manufacturing process