Machine Learning Based Algorithms to Investigate Protein Structure

The large scale of biological data becoming available in recent years requires advanced computational methods capable of analyzing these complex, high-dimensional datasets to investigate biological processes and lead to new discoveries. There has been an increase in the utilization of machine learning in biology and proteomics to build predictive models of the underlying biological processes. This dissertation provides machine learning solutions for three problems related to proteins and their structures. The first problem is to investigate how mutations in a protein sequence can affect its structure stability by using machine learning methods to predict the free energy changes comparing the mutated and not mutated (wild type) proteins. In this project, we compare three machine learning models for predicting the mutation effect. The second problem is focused on exploring protein dynamics and conformational changes. We employ a hybrid algorithm that combines Monte-Carlo sampling and a robotics-based method called RRT* to find conformational pathways using rigidity analysis. We also use a topological data analysis algorithm called mapper to find the intermediate conformations by clustering the conformations that are generated most by our algorithm. The last problem is about classifying protein families. In this part, we propose a method comprising two steps of dimensionality reduction and classification. We present a variational autoencoder for the first step and a convolutional neural network classifier for the second step

Integrating Rigidity Analysis into the Exploration of Protein Conformational Pathways Using RRT* and MC

To understand how proteins function on a cellular level, it is of paramount importance to understand their structures and dynamics, including the conformational changes they undergo to carry out their function. For the aforementioned reasons, the study of large conformational changes in proteins has been an interest to researchers for years. However, since some proteins experience rapid and transient conformational changes, it is hard to experimentally capture the intermediate structures. Additionally, computational brute force methods are computationally intractable, which makes it impossible to find these pathways which require a search in a high-dimensional, complex space. In our previous work, we implemented a hybrid algorithm that combines Monte-Carlo (MC) sampling and RRT*, a version of the Rapidly Exploring Random Trees (RRT) robotics-based method, to make the conformational exploration more accurate and efficient, and produce smooth conformational pathways. In this work, we integrated the rigidity analysis of proteins into our algorithm to guide the search to explore flexible regions. We demonstrate that rigidity analysis dramatically reduces the run time and accelerates convergence

Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability

Predicting how a point mutation alters a protein’s stability can guide pharmaceutical drug design initiatives which aim to counter the effects of serious diseases. Conducting mutagenesis studies in physical proteins can give insights about the effects of amino acid substitutions, but such wet-lab work is prohibitive due to the time as well as financial resources needed to assess the effect of even a single amino acid substitution. Computational methods for predicting the effects of a mutation on a protein structure can complement wet-lab work, and varying approaches are available with promising accuracy rates. In this work we compare and assess the utility of several machine learning methods and their ability to predict the effects of single and double mutations. We in silico generate mutant protein structures, and compute several rigidity metrics for each of them. We use these as features for our Support Vector Regression (SVR), Random Forest (RF), and Deep Neural Network (DNN) methods. We validate the predictions of our in silico mutations against experimental Δ Δ G stability data, and attain Pearson Correlation values upwards of 0.71 for single mutations, and 0.81 for double mutations. We perform ablation studies to assess which features contribute most to a model’s success, and also introduce a voting scheme to synthesize a single prediction from the individual predictions of the three models

The effect of Ganoderma lucidum polysaccharide extract on sensitizing prostate cancer cells to flutamide and docetaxel: an in vitro study

Abstract Ganoderma lucidum polysaccharide is the most widely used complementary therapy in cancer. The present study aims to investigate the possible interaction between Ganoderma lucidum polysaccharide and Docetaxel (a chemotherapy drug) and the first-line medication for prostate cancer treatment (Flutamide) and sensitizing the cells to these treatments. The cytotoxic effects of Ganoderma lucidum polysaccharide in combination with Docetaxel and Flutamide on prostate cancer cells were investigated by the MTT test, Hoechst staining, and flow cytometry. In addition, the expression of genes related to apoptosis, angiogenesis, Epithelial-Mesenchymal Transition pathway (EMT), and prostate cancer biomarkers by Real-Time PCR was investigated. The results demonstrated that IC50 values for Ganoderma lucidum polysaccharide (30 μM and 20 μM), Docetaxel (10 μM and 5 μM), and Flutamide (20 μM and 12 μM) with MTT were confirmed by flow cytometry in a dose and time-dependent manner. Regarding the high efficacy of Ganoderma lucidum polysaccharide in combination with Flutamide and Docetaxel, 10 μM and 5 μM Flutamide were used instead of 20 μM and 12 μM and 5 μM and 2 μM Docetaxel was used instead of 10 μM and 5 μM in PC3 and LNCap, respectively. Moreover, for the first time, it was shown that Ganoderma lucidum polysaccharide alone and in combination with Docetaxel and Flutamide significantly augmented apoptosis, reduced cell migration and colonization, and downregulated expression of KLK2 and EMT pathway genes in both PC3 and LNCap cell line (P < 0.01). Ganoderma lucidum polysaccharide synergistically increased the effect of Docetaxel and Flutamide and increased the sensitivity of the prostate cancer cell lines to these drugs. Therefore, it may provide a new therapeutic strategy against prostate cancer