Integrated Machine Learning and Bioinformatics Approaches for Prediction of Cancer-Driving Gene Mutations

Cancer arises from the accumulation of somatic mutations and genetic alterations in cell division checkpoints and apoptosis, this often leads to abnormal tumor proliferation. Proper classification of cancer-linked driver mutations will considerably help our understanding of the molecular dynamics of cancer. In this study, we compared several cancer-specific predictive models for prediction of driver mutations in cancer-linked genes that were validated on canonical data sets of functionally validated mutations and applied to a raw cancer genomics data. By analyzing pathogenicity prediction and conservation scores, we have shown that evolutionary conservation scores play a pivotal role in the classification of cancer drivers and were the most informative features in the driver mutation classification. Through extensive comparative analysis with structure-functional experiments and multicenter mutational calling data from PanCancer Atlas studies, we have demonstrated the robustness of our models and addressed the validity of computational predictions. We evaluated the performance of our models using the standard diagnostic metrics such as sensitivity, specificity, area under the curve and F-measure. To address the interpretability of cancer-specific classification models and obtain novel insights about molecular signatures of driver mutations, we have complemented machine learning predictions with structure-functional analysis of cancer driver mutations in several key tumor suppressor genes and oncogenes. Through the experiments carried out in this study, we found that evolutionary-based features have the strongest signal in the machine learning classification VII of driver mutations and provide orthogonal information to the ensembled-based scores that are prominent in the ranking of feature importance

Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to preprocess the DNA information. These classifiers were benchmarked against their tree-based alternatives in order to evaluate the performance on a relative scale. We then integrated DNA-based scores generated by CNN with various categories of conservational, evolutionary and functional features into a generalized random forest classifier. The results of this study have demonstrated that CNN can learn high level features from genomic information that are complementary to the ensemble-based predictors often employed for classification of cancer mutations. By combining deep learning-generated score with only two main ensemble-based functional features, we can achieve a superior performance of various machine learning classifiers. Our findings have also suggested that synergy of nucleotide-based deep learning scores and integrated metrics derived from protein sequence conservation scores can allow for robust classification of cancer driver mutations with a limited number of highly informative features. Machine learning predictions are leveraged in molecular simulations, protein stability, and network-based analysis of cancer mutations in the protein kinase genes to obtain insights about molecular signatures of driver mutations and enhance the interpretability of cancer-specific classification models

Evaluation of Antimicrobial Activities of Albizia zygia DC Leaf Extracts against Some Clinically Important Pathogens

In vitro antimicrobial effects of aqueous, ethanolic and methanolic extracts of Albizia zygia dc leaf against some clinically important bacterial and fungal pathogens were reported. Following extraction of air dried A. zygia dc leaf by different solvents (water, ethanol and methanol), the filtrates were concentrated in vacuo using rotary evaporator. The antibacterial and antifungal activities were assayed by agar diffusion method on Muller-Hinton Agar (Himedia Laboratories Pvt. Ltd, Vadhani) and Potato dextrose agar (Oxoid, Ltd, Bashingstoke, Hampshire, England) plates, respectively. Standard methods were used to determine the time-kill assay of methanolic extract, the amount of protein and potassium ion leaked in the test bacteria. All the extracts (aqueous, ethanolic and methanolic) did not possess any antifungal property. The aqueous and ethanolic extracts were not active against the test bacteria.  Methanolic extract showed significant antibacterial effect on greater percentage of the test bacteria with diameter of zones of inhibition ranging from 3.0 to 21.12 mm at 30 mg/ml and 5.2 to 25.4 mm at 50 mg/ml of the extract. The minimum inhibitory concentration (MIC) of the methanolic extract ranged between 3.75 and 15.3 mg/ml. The methanolic extract of A. zygia leaf showed a significant bactericidal and bacteriostatic activity against Bacillus subtilis and Klebsiella pneumoniae over the time range (15-120 min) at different MIC concentrations. The time-kill assay of methanolic extract of A. zygia against K. pneumoniae was dose dependent. The amount of protein leaked was higher in B. subtilis than K. pneumoniae at 30 µg/ml (P = 0.05). There was no significant difference in the level of  K+ leaked at 15 µg/ml (1 X MIC) and 30 µg/ml (2 X MIC) of the extract. The methanolic leaf extract of A. zygia showed a considerable inhibitory effect on greater percentage of the test bacterial pathogens, but did not possess antifungal property. The antibacterial potential could be harnessed in the folklore management of infections caused by the susceptible test bacteria.

Integrated computational approaches and tools for allosteric drug discovery:

Understanding molecular mechanisms underlying the complexity of allosteric regulation in proteins has attracted considerable attention in drug discovery due to the benefits and versatility of allosteric modulators in providing desirable selectivity against protein targets while minimizing toxicity and other side effects. The proliferation of novel computational approaches for predicting ligand–protein interactions and binding using dynamic and network-centric perspectives has led to new insights into allosteric mechanisms and facilitated computer-based discovery of allosteric drugs. Although no absolute method of experimental and in silico allosteric drug/site discovery exists, current methods are still being improved. As such, the critical analysis and integration of established approaches into robust, reproducible, and customizable computational pipelines with experimental feedback could make allosteric drug discovery more efficient and reliable. In this article, we review computational approaches for allosteric drug discovery and discuss how these tools can be utilized to develop consensus workflows for in silico identification of allosteric sites and modulators with some applications to pathogen resistance and precision medicine. The emerging realization that allosteric modulators can exploit distinct regulatory mechanisms and can provide access to targeted modulation of protein activities could open opportunities for probing biological processes and in silico design of drug combinations with improved therapeutic indices and a broad range of activities