ATPdock: a template-based method for ATP-specific protein-ligand docking.

MOTIVATION: Accurately identifying protein-ATP binding poses is significantly valuable for both basic structure biology and drug discovery. Although many docking methods have been designed, most of them require a user-defined binding site and are difficult to achieve a high-quality protein-ATP docking result. It is critical to develop a protein-ATP-specific blind docking method without user-defined binding sites. RESULTS: Here, we present ATPdock, a template-based method for docking ATP into protein. For each query protein, if no pocket site is given, ATPdock first identifies its most potential pocket using ATPbind, an ATP-binding site predictor; then, the template pocket, which is most similar to the given or identified pocket, is searched from the database of pocket-ligand structures using APoc, a pocket structural alignment tool; thirdly, the rough docking pose of ATP (rdATP) is generated using LS-align, a ligand structural alignment tool, to align the initial ATP pose to the template ligand corresponding to template pocket; finally, the Metropolis Monte Carlo simulation is used to fine-tune the rdATP under the guidance of AutoDock Vina energy function. Benchmark tests show that ATPdock significantly outperforms other state-of-the-art methods in docking accuracy. AVAILABILITY AND IMPLEMENTATION: https://jun-csbio.github.io/atpdock/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Advances in aptamer-based sensing assays for C-reactive protein.

C-reactive protein (CRP), a non-specific acute-phase indicator of inflammation, has been widely recognized for its value in clinical diagnostic applications. With the advancement of testing technologies, there have been many reports on fast, simple, and reliable methods for CRP testing. Among these, the aptamer-based biosensors are the focus and hotspot of research for achieving high-sensitivity analysis of CRP. This review summarizes the progress of in vitro aptamer screening for CRP and the recent advances in aptamer-based CRP sensor applications, thus developing insight for the new CRP aptasensor design strategy.

TargetDBP+: Enhancing the Performance of Identifying DNA-Binding Proteins via Weighted Convolutional Features.

Protein-DNA interactions exist ubiquitously and play important roles in the life cycles of living cells. The accurate identification of DNA-binding proteins (DBPs) is one of the key steps to understand the mechanisms of protein-DNA interactions. Although many DBP identification methods have been proposed, the current performance is still unsatisfactory. In this study, a new method, called TargetDBP+, is developed to further enhance the performance of identifying DBPs. In TargetDBP+, five convolutional features are first extracted from five feature sources, i.e., amino acid one-hot matrix (AAOHM), position-specific scoring matrix (PSSM), predicted secondary structure probability matrix (PSSPM), predicted solvent accessibility probability matrix (PSAPM), and predicted probabilities of DNA-binding sites (PPDBSs); second, the five features are weightedly and serially combined using the weights of all of the elements learned by the differential evolution algorithm; and finally, the DBP identification model of TargetDBP+ is trained using the support vector machine (SVM) algorithm. To evaluate the developed TargetDBP+ and compare it with other existing methods, a new gold-standard benchmark data set, called UniSwiss, is constructed, which consists of 4881 DBPs and 4881 non-DBPs extracted from the UniprotKB/Swiss-Prot database. Experimental results demonstrate that TargetDBP+ can obtain an accuracy of 85.83% and precision of 88.45% covering 82.41% of all DBP data on the independent validation subset of UniSwiss, with the MCC value (0.718) being significantly higher than those of other state-of-the-art control methods. The web server of TargetDBP+ is accessible at http://csbio.njust.edu.cn/bioinf/targetdbpplus/; the UniSwiss data set and stand-alone program of TargetDBP+ are accessible at https://github.com/jun-csbio/TargetDBPplus.

Identification of ligand-binding residues using protein sequence profile alignment and query-specific support vector machine model.

Information embedded in ligand-binding residues (LBRs) of proteins is important for understanding protein functions. How to accurately identify the potential ligand-binding residues is still a challenging problem, especially only protein sequence is given. In this paper, we establish a new query-specific computational method, named I-LBR, for the identification of LBRs without directly using the information of protein 3D structure. I-LBR includes two modes, named as I-LBRGP and I-LBRLS, for the general-purpose and ligand-specific LBR identification. For both modes, I-LBR first construct the specific training subset based on the query sequence information; then use support vector machine (SVM) algorithm to learn the LBR identification model; finally, predict the probability of each residue in query protein belongs to the class of LBR. Experimental results on four testing dataset demonstrate that I-LBRLS is the better choice against I-LBRGP, when the ligand type/types of the query protein binds is/are known. Comparing to other state-of-the-art LBR identification methods, I-LBR can achieve a better or comparable performance. The web-server of I-LBR and dataset used in this study are freely available for academic use at https://jun-csbio.github.io/I-LBR.

[Comparison of (18)F-FDG PET/CT and enhancement CT in diagnosing regional lymph node metastasis of colorectal cancer].

OBJECTIVE: The aim of this study was to compare the diagnostic value of (18)F-FDG PET/CT with contrast CT for regional lymph node metastasis of colorectal cancer. METHODS: Imaging results of 40 patients with colorectal cancer were retrospectively reviewed, and all the patients underwent both abdomen contrast CT and (18)F-FDG PET/CT imaging. Final diagnosis was made by histopathology. RESULTS: 20 colorectal cancer patients were verified with lymph node metastasis while the other 20 patients were absent. Finally, (18)F-FDG PET/CT produced 4 false-positive patients and 3 false-negative patients. (18)F-FDG PET/CT displayed sensitivity 85.0% (17/20), specificity 80.0% (16/20), positive predictive value 81.0% (17/21), negative predictive value 84.2% (16/19) and accuracy 82.5% (33/40) in the diagnosis of lymph node metastasis. Contrast CT produced 5 false-positive patients and 8 false-negative patients. Contrast CT had the sensitivity 60.0% (12/20), specificity 75.0% (15/20) , positive predictive value 70.6% (12/17), negative predictive value 65.2% (15/23) and accuracy 67.5% (27/40). Sensitivity and specificity of PET/CT was better than of contrast CT, but no statistically significant difference between PET/CT and contrast CT (P = 0.478, P = 0.893; P = 0.344. Fisher's exact test and McNemar test). CONCLUSION: Sensitivity and specificity of PET/CT was better than of contrast CT, but no statistically significant difference. Clinical works also need to select the appropriate inspection methods based on the patient's situation.

[Impacts of Land Use and Climate Change on Ecosystem Services in Agro-pastoral Ecotone].

Land use and climate change are the most important factors driving the change in ecosystem services (ESs). It is critical to understand the mechanisms behind such changes for improving ESs. However, there is still a lack of accurate understanding of change and dominant influencing factors of ESs in the agro-pastoral ecotone. This study took Naiman Banner, a typical farming pastoral ecotone in China, as the case study area. Based on the InVEST model, the revised wind erosion equation (RWEQ) and the revised universal soil loss equation (RUSLE) were used to calculate water yield, soil retention, and windbreak and sand-fixing in Naiman Banner in 2005 and 2015. Finally, the impacts of land use and climate change on these three ecosystem services were analyzed by using contribution rate formula, Pearson correlation coefficient, and geodetector methods. The results indicate that:① from 2005 to 2015, water yield and soil retention in Naiman Banner showed an overall upward trend, increasing by 22.41% and 6.74%, respectively, and windbreak and sand-fixing decreased by 66.24%. ② The change in water yield and windbreak and sand-fixing was mainly affected by climate change, and the change in soil retention was mainly affected by land use change. ③ Actual evapotranspiration change and land use change were the main factors affecting the spatial differentiation of water yield, with the explanatory powers of 94.50% and 50.05%, respectively. The main factors influencing the spatial differentiation of windbreak and sand-fixing were actual evapotranspiration change and land desertification degree, with the explanatory power of 19.84% and 16.15%, respectively. ④ The correlation of ESs in Naiman Banner was weak, and only windbreak and sand-fixing and water yield showed a weak significant synergy. Based on the results, we recommend that managers increase the proportion of grassland in sandy areas, implement closed management in pastoral areas, and introduce drip irrigation and other water-saving technologies in farmland, and ecological protection should continue to be given priority in city.

[Diagnostic value of (18)F-labeled NaF positron emission tomography-computed tomography imaging in the detection of bone metastases in patients with lung cancer before treatment].

OBJECTIVE: To evaluate the diagnostic values of (18)F-sodium fluoride ((18)F-NaF) positron emission tomography/computed tomography (PET/CT) imaging in the detection of bone metastases of lung cancer. METHODS: A total of 107 lung cancer patients were diagnosed by histopathology and undergone (18)F-NaF PET-CT imaging. RESULTS: Among them, the histopathologically diagnosed tumors included adenocarcinoma (n = 47), squamous cell carcinoma (n = 19), small cell lung cancer (n = 4) and other malignant types (n = 34). Bone metastases occurred in adenocarcinoma (n = 19) and squamous cell carcinoma (n = 4).(18)F-NaF PET/CT imaging had a high sensitivity of 100% in the diagnosis of bone metastases with malignant lung tumors. And its specificity was 98.7%, accuracy 99% and positive predictive value 97.0%. CONCLUSION: (18)F-NaF PET-CT imaging can detect the bone metastases of lung cancer preoperatively with a high level of sensitivity and accuracy.

[Comparison of (18)F-FDG PET-CT and abdomen enhancement CT in diagnosing peritoneal metastases].

OBJECTIVE: To compare the diagnostic value of (18)F-FDG PET-CT with abdomen contrast CT in the diagnosis of peritoneal metastases. METHODS: Between January 2008 and May 2011, imaging results of 97 patients with suspicious peritoneal metastases were retrospectively reviewed, and all the patients underwent both abdomen contrast CT and (18)F-FDG PET-CT imaging. Final diagnosis was made by histopathology or follow up. RESULTS: Seventy-seven patients were verified as peritoneal metastases after pathological examination(n=88) or follow up(n=9), while the other 20 patients were absent. The sensitivity of (18)F-FDG PET-CT was 90.9%(70/77), the specificity 85.0%(17/20), and the accuracy 89.7%(87/97). There were 3 false positive and 7 false negative. The sensitivity of contrast CT was 66.2%(51/77), the specificity 80.0%(16/20), and the accuracy 69.1%(67/97). There were 4 false positive and 26 false negative. The difference in diagnostic accuracy was statistically significantly between these two methods(P<0.05). CONCLUSION: The diagnostic value of (18)F-FDG PET-CT is significantly higher than that of abdominal enhanced CT for peritoneal metastases.