ivis Dimensionality Reduction Framework for Biomacromolecular Simulations

Molecular dynamics (MD) simulations have been widely applied to study macromolecules including proteins. However, high-dimensionality of the datasets produced by simulations makes it difficult for thorough analysis, and further hinders a deeper understanding of biomacromolecules. To gain more insights into the protein structure-function relations, appropriate dimensionality reduction methods are needed to project simulations onto low-dimensional spaces. Linear dimensionality reduction methods, such as principal component analysis (PCA) and time-structure based independent component analysis (t-ICA), could not preserve sufficient structural information. Though better than linear methods, nonlinear methods, such as t-distributed stochastic neighbor embedding (t-SNE), still suffer from the limitations in avoiding system noise and keeping inter-cluster relations. ivis is a novel deep learning-based dimensionality reduction method originally developed for single-cell datasets. Here we applied this framework for the study of light, oxygen and voltage (LOV) domain of diatom Phaeodactylum tricornutum aureochrome 1a (PtAu1a). Compared with other methods, ivis is shown to be superior in constructing Markov state model (MSM), preserving information of both local and global distances and maintaining similarity between high dimension and low dimension with the least information loss. Moreover, ivis framework is capable of providing new prospective for deciphering residue-level protein allostery through the feature weights in the neural network. Overall, ivis is a promising member in the analysis toolbox for proteins

Bridging Between Protein Dynamics and Evolution Through Simulations and Machine Learning Approaches

Antibiotics resistance posed a serious threat to the public health and caused huge economic cost. β-Lactamases, which are enzymes produced by bacteria to hydrolyze β-lactam based antibiotics, are one of the driving forces behind antibiotic resistance. To explore the antibiotic resistance effect, understanding the mechanistic and dynamical features of β-lactamases through their interactions with antibiotics is critical. In my doctoral research, I applied both molecular dynamic (MD) simulations and machine learning approaches to explore these crucial interactions. Vancomycin is a typical glycopeptide antibiotic, which inhibits the bacterial cell wall through binding with peptidoglycan (PG). The key interactions of vancomycin and cell wall structure are identified by the conformational distributions of vancomycin and its three derivatives with PG complexes. TEM-1 is a serine-based β-lactamase and can hydrolyze the benzyl penicillin antibiotic. The key residues on TEM-1 are identified by random forest classification models. Moreover, the dynamical motions of four antibiotic resistance related proteins TEM-1, TOHO-1, PBP-A and DD-transpeptidase with a benzyl penicillin are analyzed and compared to explore their evolutionary correlation. I also investigated the petroleum thermal cracking mechanism through quantum chemistry calculations, and provided a quantitative and insightful understanding of thermal cracking processes

Dynamical Behavior of β-Lactamases and Penicillin- Binding Proteins in Different Functional States and Its Potential Role in Evolution

β-Lactamases are enzymes produced by bacteria to hydrolyze β-lactam-based antibiotics, and pose serious threat to public health through related antibiotic resistance. Class A β-lactamases are structurally and functionally related to penicillin-binding proteins (PBPs). Despite the extensive studies of the structures, catalytic mechanisms and dynamics of both β-lactamases and PBPs, the potentially different dynamical behaviors of these proteins in different functional states still remain elusive in general. In this study, four evolutionarily related proteins, including TEM-1 and TOHO-1 as class A β-lactamases, PBP-A and DD-transpeptidase as two PBPs, are subjected to molecular dynamics simulations and various analyses to characterize their dynamical behaviors in different functional states. Penicillin G and its ring opening product serve as common ligands for these four proteins of interest. The dynamic analyses of overall structures, the active sites with penicillin G, and three catalytically important residues commonly shared by all four proteins reveal unexpected cross similarities between Class A β-lactamases and PBPs. These findings shed light on both the hidden relations among dynamical behaviors of these proteins and the functional and evolutionary relations among class A β-lactamases and PBPs

Use of Computational Methods To Understand The Pattern Of Antimicrobial Resistance

Antimicrobial resistance (AMR) as one of the most serious problems in the world is especially urgent with the increase in antibiotic resistance of bacteria across the world. Antibiotics reach the environment via excretions from humans and agriculture, and industrial and hospital waste products. The environmental concentrations of antibiotics are usually much lower than the minimal inhibitory concentrations and most often lower than concentrations predicted to select for resistant strains in the laboratory. However, exposure to low levels of antibiotics has also been shown to increase resistance, resulting in the increase of selective pressure. The resistance pattern of the AMR in different environments has been identified by many studies but the connection between the antibiotics present in the environment and the resistance pattern remains uncertain. To understand how different patterns of resistance emerge, computational method is essential for processing and analyzing the molecular interaction model to estimate the bioactivity of the metabolites of antibiotics and evaluated methods for visualizing high dimensional resistance data, in order to be able to better ascertain patterns of resistance. Through molecular docking and molecular dynamics, the metabolites (5R) pseudopenicillin, (5S)-penicilloic acid and 6APA are found to be potentially bioactive towards target protein penicillin binding protein. T-SNE has been suggested to be the most suitable for analyzing AMR data compared with other methods (PCA, MDS, isomap and PHATE) and this helps to have a better understanding of correlative of the AMR development. Therefore, some undetected compounds (metabolites of antibiotics) may cause selective pressure and increase resistance. These compounds may also be involved in developing bacteria resistance within environments. This could have considerable significance for environmental surveillance for antibiotics to reduce antimicrobial resist

Use of Computational Methods To Understand The Pattern Of Antimicrobial Resistance

Antimicrobial resistance (AMR) as one of the most serious problems in the world is especially urgent with the increase in antibiotic resistance of bacteria across the world. Antibiotics reach the environment via excretions from humans and agriculture, and industrial and hospital waste products. The environmental concentrations of antibiotics are usually much lower than the minimal inhibitory concentrations and most often lower than concentrations predicted to select for resistant strains in the laboratory. However, exposure to low levels of antibiotics has also been shown to increase resistance, resulting in the increase of selective pressure. The resistance pattern of the AMR in different environments has been identified by many studies but the connection between the antibiotics present in the environment and the resistance pattern remains uncertain. To understand how different patterns of resistance emerge, computational method is essential for processing and analyzing the molecular interaction model to estimate the bioactivity of the metabolites of antibiotics and evaluated methods for visualizing high dimensional resistance data, in order to be able to better ascertain patterns of resistance. Through molecular docking and molecular dynamics, the metabolites (5R) pseudopenicillin, (5S)-penicilloic acid and 6APA are found to be potentially bioactive towards target protein penicillin binding protein. T-SNE has been suggested to be the most suitable for analyzing AMR data compared with other methods (PCA, MDS, isomap and PHATE) and this helps to have a better understanding of correlative of the AMR development. Therefore, some undetected compounds (metabolites of antibiotics) may cause selective pressure and increase resistance. These compounds may also be involved in developing bacteria resistance within environments. This could have considerable significance for environmental surveillance for antibiotics to reduce antimicrobial resist