Effect of Cholesterol on C99 Dimerization: Revealed by Molecular Dynamics Simulations.

C99 is the immediate precursor for amyloid beta (Aß) and therefore is a central intermediate in the pathway that is believed to result in Alzheimer's disease (AD). It has been suggested that cholesterol is associated with C99, but the dynamic details of how cholesterol affects C99 assembly and the Aß formation remain unclear. To investigate this question, we employed coarse-grained and all-atom molecular dynamics simulations to study the effect of cholesterol and membrane composition on C99 dimerization. We found that although the existence of cholesterol delays C99 dimerization, there is no direct competition between C99 dimerization and cholesterol association. In contrast, the existence of cholesterol makes the C99 dimer more stable, which presents a cholesterol binding C99 dimer model. Cholesterol and membrane composition change the dimerization rate and conformation distribution of C99, which will subsequently influence the production of Aß. Our results provide insights into the potential influence of the physiological environment on the C99 dimerization, which will help us understand Aß formation and AD's etiology.

DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features.

Drug-target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the current DTI prediction approaches suffer from a problem of low precision and high false-positive rate. In this study, we aim to develop a novel DTI prediction method for improving the prediction performance based on a cascade deep forest (CDF) model, named DTI-CDF, with multiple similarity-based features between drugs and the similarity-based features between target proteins extracted from the heterogeneous graph, which contains known DTIs. In the experiments, we built five replicates of 10-fold cross-validation under three different experimental settings of data sets, namely, corresponding DTI values of certain drugs (SD), targets (ST), or drug-target pairs (SP) in the training sets are missed but existed in the test sets. The experimental results demonstrate that our proposed approach DTI-CDF achieves a significantly higher performance than that of the traditional ensemble learning-based methods such as random forest and XGBoost, deep neural network, and the state-of-the-art methods such as DDR. Furthermore, there are 1352 newly predicted DTIs which are proved to be correct by KEGG and DrugBank databases. The data sets and source code are freely available at https://github.com//a96123155/DTI-CDF.

Irinotecan and vandetanib create synergies for treatment of pancreatic cancer patients with concomitant TP53 and KRAS mutations.

BACKGROUND: The most frequently mutated gene pairs in pancreatic adenocarcinoma (PAAD) are KRAS and TP53, and our goal is to illustrate the multiomics and molecular dynamics landscapes of KRAS/TP53 mutation and also to obtain prospective novel drugs for KRAS- and TP53-mutated PAAD patients. Moreover, we also made an attempt to discover the probable link amid KRAS and TP53 on the basis of the abovementioned multiomics data. METHOD: We utilized TCGA & Cancer Cell Line Encyclopedia data for the analysis of KRAS/TP53 mutation in a multiomics manner. In addition to that, we performed molecular dynamics analysis of KRAS and TP53 to produce mechanistic descriptions of particular mutations and carcinogenesis. RESULT: We discover that there is a significant difference in the genomics, transcriptomics, methylomics, and molecular dynamics pattern of KRAS and TP53 mutation from the matching wild type in PAAD, and the prognosis of pancreatic cancer is directly linked with a particular mutation of KRAS and protein stability. Screened drugs are potentially effective in PAAD patients. CONCLUSIONS: KRAS and TP53 prognosis of PAAD is directly associated with a specific mutation of KRAS. Irinotecan and vandetanib are prospective drugs for PAAD patients with KRASG12Dmutation and TP53 mutation.

T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm.

Type IV secreted effectors (T4SEs) can be translocated into the cytosol of host cells via type IV secretion system (T4SS) and cause diseases. However, experimental approaches to identify T4SEs are time- and resource-consuming, and the existing computational tools based on machine learning techniques have some obvious limitations such as the lack of interpretability in the prediction models. In this study, we proposed a new model, T4SE-XGB, which uses the eXtreme gradient boosting (XGBoost) algorithm for accurate identification of type IV effectors based on optimal features based on protein sequences. After trying 20 different types of features, the best performance was achieved when all features were fed into XGBoost by the 5-fold cross validation in comparison with other machine learning methods. Then, the ReliefF algorithm was adopted to get the optimal feature set on our dataset, which further improved the model performance. T4SE-XGB exhibited highest predictive performance on the independent test set and outperformed other published prediction tools. Furthermore, the SHAP method was used to interpret the contribution of features to model predictions. The identification of key features can contribute to improved understanding of multifactorial contributors to host-pathogen interactions and bacterial pathogenesis. In addition to type IV effector prediction, we believe that the proposed framework can provide instructive guidance for similar studies to construct prediction methods on related biological problems. The data and source code of this study can be freely accessed at https://github.com/CT001002/T4SE-XGB.

Globally ncRNAs Expression Profiling of TNBC and Screening of Functional lncRNA.

One of the most well-known cancer subtypes worldwide is triple-negative breast cancer (TNBC) which has reduced prediction due to its antagonistic biotic actions and target's deficiency for the treatment. The current work aims to discover the countenance outlines and possible roles of lncRNAs in the TNBC via computational approaches. Long non-coding RNAs (lncRNAs) exert profound biological functions and are widely applied as prognostic features in cancer. We aim to identify a prognostic lncRNA signature for the TNBC. First, samples were filtered out with inadequate tumor purity and retrieved the lncRNA expression data stored in the TANRIC catalog. TNBC sufferers were divided into two prognostic classes which were dependent on their survival time (shorter or longer than 3 years). Random forest was utilized to select lncRNA features based on the lncRNAs differential expression between shorter and longer groups. The Stochastic gradient boosting method was used to construct the predictive model. As a whole, 353 lncRNAs were differentially transcribed amongst the shorter and longer groups. Using the recursive feature elimination, two lncRNAs were further selected. Trained by stochastic gradient boosting, we reached the highest accuracy of 69.69% and area under the curve of 0.6475. Our findings showed that the two-lncRNA signs can be proved as potential biomarkers for the prognostic grouping of TNBC's sufferers. Many lncRNAs remained dysregulated in TNBC, while most of them are likely play a role in cancer biology. Some of these lncRNAs were linked to TNBC's prediction, which makes them likely to be promising biomarkers.

New strategy for identifying potential natural HIV-1 non-nucleoside reverse transcriptase inhibitors against drug-resistance: an in silico study.

Non-nucleosides reverse transcriptase inhibitors (NNRTIs), specifically targeting the HIV-1 reverse transcriptase (RT), play a unique role in anti-AIDS agents due to their high antiviral potency, structural diversity, and low toxicity in antiretroviral combination therapies used to treat HIV. However, due to the emergence of new drug-resistant strains, the development of novel NNRTIs with adequate potency, improved resistance profiles and less toxicity is highly required. In this work, a novel virtual screening strategy combined with structure-based drug design was proposed to discover the potential inhibitors against drug-resistant HIV strains. Seven structure-variant RTs, ranging from the wild type to a hypothetical multi-mutant were regarded as target proteins to perform structure-based virtual screening. Totally 23 small molecules with good binding affinity were identified from the Traditional Chinese Medicine database (TCM) as potential NNRTIs candidates. Among these hits, (+)-Hinokinin has confirmed anti-HIV activity, and some hits are structurally identical with anti-HIV compounds. Almost all these hits are consistent with external experimental results. Molecular simulations analysis revealed that top 2 hits (Pallidisetin A and Pallidisetin B) bind stably and in high affinity to HIV-RT, which are ready to be experimental confirmed. These results suggested that the strategy we proposed is feasible, trustworthy and effective. Our finding might be helpful in the identification of novel NNRTIs against drug-resistant, and also provide a new clue for the discovery of HIV drugs in natural products.Communicated by Ramaswamy H. Sarma.

Identification of chlorprothixene as a potential drug that induces apoptosis and autophagic cell death in acute myeloid leukemia cells.

Although acute myeloid leukemia (AML) is a highly heterogeneous malignance, the common molecular mechanisms shared by different AML subtypes play critical roles in AML development. It is possible to identify new drugs that are effective for various AML subtypes based on the common molecular mechanisms. Therefore, we developed a hypothesis-driven bioinformatic drug screening framework by integrating multiple omics data. In this study, we identified that chlorprothixene, a dopamine receptor antagonist, could effectively inhibit growth of AML cells from different subtypes. RNA-seq analysis suggested that chlorprothixene perturbed a series of crucial biological processes such as cell cycle, apoptosis, and autophagy in AML cells. Further investigations indicated that chlorprothixene could induce both apoptosis and autophagy in AML cells, and apoptosis and autophagy could act as partners to induce cell death cooperatively. Remarkably, chlorprothixene was found to inhibit tumor growth and induce in situ leukemic cell apoptosis in the murine xenograft model. Furthermore, chlorprothixene treatment could reduce the level of oncofusion proteins PML-RARα and AML1-ETO, thus elevate the expression of apoptosis-related genes, and lead to AML cell death. Our results provided new insights for drug repositioning of AML therapy and confirmed that chlorprothixene might be a potential candidate for treatment of different subtypes of AML by reducing expression of oncofusion proteins. DATABASE: RNA-seq data are available in GEO database under the accession number GSE124316.

An Integrated Pan-Cancer Analysis and Structure-Based Virtual Screening of GPR15.

G protein-coupled receptor 15 (GPR15, also known as BOB) is an extensively studied orphan G protein-coupled receptors (GPCRs) involving human immunodeficiency virus (HIV) infection, colonic inflammation, and smoking-related diseases. Recently, GPR15 was deorphanized and its corresponding natural ligand demonstrated an ability to inhibit cancer cell growth. However, no study reported the potential role of GPR15 in a pan-cancer manner. Using large-scale publicly available data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, we found that GPR15 expression is significantly lower in colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) than in normal tissues. Among 33 cancer types, GPR15 expression was significantly positively correlated with the prognoses of COAD, neck squamous carcinoma (HNSC), and lung adenocarcinoma (LUAD) and significantly negatively correlated with stomach adenocarcinoma (STAD). This study also revealed that commonly upregulated gene sets in the high GPR15 expression group (stratified via median) of COAD, HNSC, LUAD, and STAD are enriched in immune systems, indicating that GPR15 might be considered as a potential target for cancer immunotherapy. Furthermore, we modelled the 3D structure of GPR15 and conducted structure-based virtual screening. The top eight hit compounds were screened and then subjected to molecular dynamics (MD) simulation for stability analysis. Our study provides novel insights into the role of GPR15 in a pan-cancer manner and discovered a potential hit compound for GPR15 antagonists.

STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity.

Membrane transport proteins play crucial roles in the pharmacokinetics of substrate drugs, the drug resistance in cancer and are vital to the process of drug discovery, development and anti-cancer therapeutics. However, experimental methods to profile a substrate drug against a panel of transporters to determine its specificity are labor intensive and time consuming. In this article, we aim to develop an in silico multi-label classification approach to predict whether a substrate can specifically recognize one of the 13 categories of drug transporters ranging from ATP-binding cassette to solute carrier families using both structural fingerprints and chemical ontologies information of substrates. The data-driven network-based label space partition (NLSP) method was utilized to construct the model based on a hybrid of similarity-based feature by the integration of 2D fingerprint and semantic similarity. This method builds predictors for each label cluster (possibly intersecting) detected by community detection algorithms and takes union of label sets for a compound as final prediction. NLSP lies into the ensembles of multi-label classifier category in multi-label learning field. We utilized Cramér's V statistics to quantify the label correlations and depicted them via a heatmap. The jackknife tests and iterative stratification based cross-validation method were adopted on a benchmark dataset to evaluate the prediction performance of the proposed models both in multi-label and label-wise manner. Compared with other powerful multi-label methods, ML-kNN, MTSVM, and RAkELd, our multi-label classification model of NLPS-RF (random forest-based NLSP) has proven to be a feasible and effective model, and performed satisfactorily in the predictive task of transporter-substrate specificity. The idea behind NLSP method is intriguing and the power of NLSP remains to be explored for the multi-label learning problems in bioinformatics. The benchmark dataset, intermediate results and python code which can fully reproduce our experiments and results are available at https://github.com/dqwei-lab/STS.

Prediction of CYP450 Enzyme-Substrate Selectivity Based on the Network-Based Label Space Division Method.

A drug may be metabolized by multiple cytochrome P450 (CYP450) isoforms. Predicting the metabolic fate of drugs is very important to prevent drug-drug interactions in the development of novel pharmaceuticals. Prediction of CYP450 enzyme-substrate selectivity is formulized as a multilabel learning task in this study. First, we compared the performance of feature combinations based on four different categories of features, which are physiochemical property descriptors, mol2vec descriptors, extended connectivity fingerprints, and molecular access system key fingerprints on modeling. After identifying the best combination of features, we applied seven different multilabel models, which are multilabel k-nearest neighbor (ML-kNN), multilabel twin support vector machine, and five network-based label space division (NLSD)-based methods (NLSD-MLP, NLSD-XGB, NLSD-EXT, NLSD-RF, and NLSD-SVM). All of the six models (ML-kNN, NLSD-MLP, NLSD-XGB, NLSD-EXT, NLSD-RF, and NLSD-SVM) in this paper exhibit better performances than the previous work. Besides, NLSD-XGB achieves the best performance with the average top-1 prediction success of 91.1%, the average top-2 prediction success of 96.2%, and the average top-3 prediction success of 98.2%. When compared with the previous work, NLSD-XGB shows a significant improvement over 11% on top-1 in the 10 times repeated 5-fold cross-validation test and over 14% on top-1 in the 10 times repeated hold-out method. To the best of our knowledge, the network-based label space division model is first introduced in drug metabolism and performs well in this task.

ATC-NLSP: Prediction of the Classes of Anatomical Therapeutic Chemicals Using a Network-Based Label Space Partition Method.

Anatomical Therapeutic Chemical (ATC) classification system proposed by the World Health Organization is a widely accepted drug classification scheme in both academic and industrial realm. It is a multilabeling system which categorizes drugs into multiple classes according to their therapeutic, pharmacological, and chemical attributes. In this study, we adopted a data-driven network-based label space partition (NLSP) method for prediction of ATC classes of a given compound within the multilabel learning framework. The proposed method ATC-NLSP is trained on the similarity-based features such as chemical-chemical interaction and structural and fingerprint similarities of a compound to other compounds belonging to the different ATC categories. The NLSP method trains predictors for each label cluster (possibly intersecting) detected by community detection algorithms and takes the ensemble labels for a compound as final prediction. Experimental evaluation based on the jackknife test on the benchmark dataset demonstrated that our method has boosted the absolute true rate, which is the most stringent evaluation metrics in this study, from 0.6330 to 0.7497, in comparison to the state-of-the-art approaches. Moreover, the community structures of the label relation graph were detected through the label propagation method. The advantage of multilabel learning over the single-label models was shown by label-wise analysis. Our study indicated that the proposed method ATC-NLSP, which adopts ideas from network research community and captures the correlation of labels in a data driven manner, is the top-performing model in the ATC prediction task. We believed that the power of NLSP remains to be unleashed for the multilabel learning tasks in drug discovery. The source codes are freely available at https://github.com/dqwei-lab/ATC.

Evaluation of anti-EGFR-iRGD recombinant protein with GOLD nanoparticles: synergistic effect on antitumor efficiency using optimized deep neural networks.

The epidermal growth factor receptor, also known as EGFR, is a tyrosine kinase receptor commonly found in epithelial tumors. As part of the first target for cancer treatment, EGFR has been the subject of intense research for more than 20 years; as a result, there are a number of anti-EGFR agents currently available. More recently, with our basic understanding of mechanisms related to receptor activation and function, both the secondary and primary forms of EGFR somatic mutations have led to the discovery of new anti-EGFR agents aimed at providing new insights into the clinical targeting of this receptor and possibly acting as an ideal model for developing strategies to target other types of receptors. In this study, we use genomic pattern to prove that EGFR is most frequently altered in GBM, glioma and astrocytoma; and analysed the prognostic potentiality of EGFR in glioma, which is a major type of brain tumor. Further we proposed a new screening technique for EGFR inhibitors by employing an in silico optimized deep neural network approach. This method was applied to screen a nanoparticle (NP) library, and it was concluded that gold NPs (AuNPs) induced significant inhibition of EGFR compared with other selected NPs. These findings were further analyzed by molecular docking, systems biology, time course simulations and synthetic biology (biological circuits), revealing that anti-EGFR-iRGD and AuNP showed potential inhibition against tumors caused by EGFR.

SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction.

Drug discovery is an academical and commercial process of global importance. Accurate identification of drug-target interactions (DTIs) can significantly facilitate the drug discovery process. Compared to the costly, labor-intensive and time-consuming experimental methods, machine learning (ML) plays an ever-increasingly important role in effective, efficient and high-throughput identification of DTIs. However, upstream feature extraction methods require tremendous human resources and expert insights, which limits the application of ML approaches. Inspired by the unsupervised representation learning methods like Word2vec, we here proposed SPVec, a novel way to automatically represent raw data such as SMILES strings and protein sequences into continuous, information-rich and lower-dimensional vectors, so as to avoid the sparseness and bit collisions from the cumbersomely manually extracted features. Visualization of SPVec nicely illustrated that the similar compounds or proteins occupy similar vector space, which indicated that SPVec not only encodes compound substructures or protein sequences efficiently, but also implicitly reveals some important biophysical and biochemical patterns. Compared with manually-designed features like MACCS fingerprints and amino acid composition (AAC), SPVec showed better performance with several state-of-art machine learning classifiers such as Gradient Boosting Decision Tree, Random Forest and Deep Neural Network on BindingDB. The performance and robustness of SPVec were also confirmed on independent test sets obtained from DrugBank database. Also, based on the whole DrugBank dataset, we predicted the possibilities of all unlabeled DTIs, where two of the top five predicted novel DTIs were supported by external evidences. These results indicated that SPVec can provide an effective and efficient way to discover reliable DTIs, which would be beneficial for drug reprofiling.