Computational prediction of compound-protein interactions (CPIs) is of great importance for drug design as the first step in in-silico screening. We previously proposed chemical genomics-based virtual screening (CGBVS), which predicts CPIs by using a support vector machine (SVM). However, the CGBVS has problems when training using more than a million datasets of CPIs since SVMs require an exponential increase in the calculation time and computer memory. To solve this problem, we propose the CGBVS-DNN, in which we use deep neural networks, a kind of deep learning technique, instead of the SVM. Deep learning does not require learning all input data at once because the network can be trained with small mini-batches. Experimental results show that the CGBVS-DNN outperformed the original CGBVS with a quarter million CPIs. Results of cross-validation show that the accuracy of the CGBVS-DNN reaches up to 98.2 % (σ<0.01) with 4 million CPIs.
Dynamic allostery on proteins, triggered by regulator binding or chemical modifications, transmits information from the binding site to distant regions, dramatically altering protein function. It is accompanied by subtle changes in side-chain conformations of the protein, indicating that the changes in dynamics, and not rigid or large conformational changes, are essential to understand regulation of protein function. Although a lot of experimental and theoretical studies have been dedicated to investigate this issue, the regulation mechanism of protein function is still being debated. Here, we propose an autoencoder-based method that can detect dynamic allostery. The method is based on the comparison of time fluctuations of protein structures, in the form of distance matrices, obtained from molecular dynamics simulations in ligand-bound and -unbound forms. Our method detected that the changes in dynamics by ligand binding in the PDZ2 domain led to the reorganization of correlative fluctuation motions among residue pairs, which revealed a different view of the correlated motions from the PCA and DCCM. In addition, other correlative motions were also found as a result of the dynamic perturbation from the ligand binding, which may lead to dynamic allostery. This autoencoder-based method would be usefully applied to the signal transduction and mutagenesis systems involved in protein functions and severe diseases.
BackgroundWe aimed to develop an adaptable prognosis prediction model that could be applied at any time point during the treatment course for patients with cancer receiving chemotherapy, by applying time-series real-world big data.MethodsBetween April 2004 and September 2014, 4,997 patients with cancer who had received systemic chemotherapy were registered in a prospective cohort database at the Kyoto University Hospital. Of these, 2,693 patients with a death record were eligible for inclusion and divided into training (n = 1,341) and test (n = 1,352) cohorts. In total, 3,471,521 laboratory data at 115,738 time points, representing 40 laboratory items [e.g., white blood cell counts and albumin (Alb) levels] that were monitored for 1 year before the death event were applied for constructing prognosis prediction models. All possible prediction models comprising three different items from 40 laboratory items (40C3 = 9,880) were generated in the training cohort, and the model selection was performed in the test cohort. The fitness of the selected models was externally validated in the validation cohort from three independent settings.ResultsA prognosis prediction model utilizing Alb, lactate dehydrogenase, and neutrophils was selected based on a strong ability to predict death events within 1–6 months and a set of six prediction models corresponding to 1,2, 3, 4, 5, and 6 months was developed. The area under the curve (AUC) ranged from 0.852 for the 1 month model to 0.713 for the 6 month model. External validation supported the performance of these models.ConclusionBy applying time-series real-world big data, we successfully developed a set of six adaptable prognosis prediction models for patients with cancer receiving chemotherapy.
BackgroundThe purposes of this study were to determine whether adjuvant chemotherapy (AC) improved the prognosis of patients with high-risk upper urinary tract urothelial carcinoma (UTUC)and to identify the patients who benefited from AC.MethodsAmong a multi-center database of 1014 patients who underwent RNU for UTUC, 344 patients with ≥ pT3 or the presence of lymphovascular invasion (LVI) were included. Cancer-specific survival (CSS) estimates were calculated by the Kaplan-Meier method, and groups were compared by the log-rank test. Each patient’s probability of receiving AC depending on the covariates in each group was estimated by logistic regression models. Propensity score matching was used to adjust the confounding factors for selecting patients for AC, and log-rank tests were applied to these propensity score-matched cohorts. Cox proportional hazards regression modeling was used to identify the variables with significant interaction with AC. Variables included age, pT category, LVI, tumor grade, ECOG performance status and low sodium or hemoglobin score, which we reported to be a prognostic factor of UTUC.ResultsOf the 344 patients, 241 (70%) had received RNU only and 103 (30%) had received RNU+AC. The median follow-up period was 32 (range 1–184) months. Overall, AC did not improve CSS (P = 0.12). After propensity score matching, the 5-year CSS was 69.0% in patients with RNU+AC versus 58.9% in patients with RNU alone (P = 0.030). Subgroup analyses of survival were performed to identify the patients who benefitted from AC. Subgroups of patients with low preoperative serum sodium (≤ 140 mEq/ml) or hemoglobin levels below the normal limit benefitted from AC (HR 0.34, 95% CI 0.15–0.61, P = 0.001). In the subgroup of patients with normal sodium and normal hemoglobin levels, 5-year CSS was 77.7% in patients with RNU+AC versus 80.2% in patients with RNU alone (P = 0.84). In contrast, in the subgroup of patients with low sodium or low hemoglobin levels, 5-year CSS was 71.0% in patients with RNU+AC versus 38.5% in patients with RNU alone (P < 0.001).ConclusionsHigh-risk UTUC patients, especially subgroups of patients with lower sodium and hemoglobin levels, could benefit from AC after RNU.
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