Energetic and Dynamic Analysis of Inhibitor Binding to Drug-Resistant HIV-1 Proteases: A Dissertation

HIV-1 protease is a very important drug target for AIDS therapy. Nine protease inhibitors have been proved by FDA and used in AIDS treatment. Due to the high replication rate and the lack of fidelity of the HIV-1 reverse transcriptase, HIV-1 virus developed various drug-resistant variants. Although experimental methods such as crystallography and isothermal titration calorimetry provide structural and thermodynamic data on drug-resistant variants, they are unable to discern the mechanism by which the mutations confer resistance to inhibitors. Understanding the drug-resistance mechanism is crucial for developing new inhibitors more tolerant to the drug-resistant mutations. Computational methods such as free energy calculations and molecular dynamic simulations can provide insights to the drug resistance mechanism at an atomic level. In this thesis, I have focused on the elucidation of the energetic and dynamics of key drug-resistant variants of HIV-1 protease. Two multi-drug resistant variants, in comparison with wild-type HIV-1 protease were used for the comparisons: Flap+ (L10I, G48V, I54V, and V82A) which contains a combination of flap and active site mutations and ACT (V82T, I84V) that only contains active site mutations. In Chapter II, I applied free energy simulations and decomposition methods to study the differential mechanism of resistance to the two variants, Flap+ and ACT, to the recently FDA-approved protease inhibitor darunavir (DRV). In this study, the absolute and relative binding free energies of DRV with wild-type protease and the two protease variants were calculated with MM-PB/GBSA and thermodynamic integration methods, respectively. And the predicted results are in good agreement with the ITC experimental results. Free energy decomposition elucidates the mutations alter not only its own interaction with DRV but also other residues by changing the geometry of binding pocket. And the VdW interactions between the bis-THF group of DRV is predominant even in the drug-resistant variants. At the end of this chapter, I offer suggestions on developing new inhibitors that are based on DRV but might be less susceptible to drug-resistant mutations. In Chapter III, 20-ns MD simulations of the apo wildtype protease and the apo drug-resistant protease variant Flap+ are analyzed and compared. In these studies, these mutations have been found to decrease the protease flexibility in the apo form but increase the mobility when the protease is binding with inhibitor. In Chapter IV, more details of the free energy simulation and decomposition are discussed. NMR relaxation experiments were set up as a control for the MD simulation study of the dynamics of the Flap+ variant. The difficulty of finishing the NMR experiment is discussed and the solution and some preliminary results are shown. In summary, the scope of this thesis was to use computational methods to study drug-resistant protease variants’ thermodynamic and dynamic properties to illuminate the mechanism of protease drug resistance. This knowledge will contribute to rational design of new protease inhibitors which bind more tightly to the protease and hinder the development of drug-resistant mutations

Core genome components and lineage specific expansions in malaria parasites Plasmodium

<p>Abstract</p> <p>Background</p> <p>The increasing resistance of <it>Plasmodium,</it> the malaria parasites, to multiple commonly used drugs has underscored the urgent need to develop effective antimalarial drugs and vaccines. The new direction of genomics-driven target discovery has become possible with the completion of parasite genome sequencing, which can lead us to a better understanding of how the parasites develop the genetic variability that is associated with their response to environmental challenges and other adaptive phenotypes.</p> <p>Results</p> <p>We present the results of a comprehensive analysis of the genomes of six <it>Plasmodium</it> species, including two species that infect humans, one that infects monkeys, and three that infect rodents. The core genome shared by all six species is composed of 3,351 genes, which make up about 22%-65% of the genome repertoire. These components play important roles in fundamental functions as well as in parasite-specific activities. We further investigated the distribution and features of genes that have been expanded in specific Plasmodium lineage(s). Abundant duplicate genes are present in the six species, with 5%-9% of the whole genomes composed lineage specific radiations. The majority of these gene families are hypothetical proteins with unknown functions; a few may have predicted roles such as antigenic variation.</p> <p>Conclusions</p> <p>The core genome components in the malaria parasites have functions ranging from fundamental biological processes to roles in the complex networks that sustain the parasite-specific lifestyles appropriate to different hosts. They represent the minimum requirement to maintain a successful life cycle that spans vertebrate hosts and mosquito vectors. Lineage specific expansions (LSEs) have given rise to abundant gene families in <it>Plasmodium.</it> Although the functions of most families remain unknown, these LSEs could reveal components in parasite networks that, by their enhanced genetic variability, can contribute to pathogenesis, virulence, responses to environmental challenges, or interesting phenotypes.</p

Proteases in Malaria Parasites - A Phylogenomic Perspective

Malaria continues to be one of the most devastating global health problems due to the high morbidity and mortality it causes in endemic regions. The search for new antimalarial targets is of high priority because of the increasing prevalence of drug resistance in malaria parasites. Malarial proteases constitute a class of promising therapeutic targets as they play important roles in the parasite life cycle and it is possible to design and screen for specific protease inhibitors. In this mini-review, we provide a phylogenomic overview of malarial proteases. An evolutionary perspective on the origin and divergence of these proteases will provide insights into the adaptive mechanisms of parasite growth, development, infection, and pathogenesis.

Multi-scale modelling of the human left ventricle

In this paper, a multi-scale computational frame is proposed to simulate dynamics of the human left ventricle. First of all, a modified Level Set method is used to segment the cardiac magnetic resonance imaging and then reconstruct the 3D computational domain of the left ventricle. The Holzapfel-Ogden's nonlinear and anisotropic model is imposed to calculate the passive elastic response. The Fenton-Karma model with stimulus current is optimized to produce the reasonable membrane potential and intracellular calcium concentration. Based on the obtained calcium concentration, the active tension is calculated. Finally, the passive elastic response and the active tension of the left ventricle are coupled with the blood and the obtained fluid structure interaction is solved by the immersed boundary method. Our numerical results at end-diastole and end-systole are generally in good agreement with the clinical measurement and the earlier studies, which verifies the efficiency of the method

Protease-associated cellular networks in malaria parasite Plasmodium falciparum

Abstract Background Malaria continues to be one of the most severe global infectious diseases, responsible for 1-2 million deaths yearly. The rapid evolution and spread of drug resistance in parasites has led to an urgent need for the development of novel antimalarial targets. Proteases are a group of enzymes that play essential roles in parasite growth and invasion. The possibility of designing specific inhibitors for proteases makes them promising drug targets. Previously, combining a comparative genomics approach and a machine learning approach, we identified the complement of proteases (degradome) in the malaria parasite Plasmodium falciparum and its sibling species 123, providing a catalog of targets for functional characterization and rational inhibitor design. Network analysis represents another route to revealing the role of proteins in the biology of parasites and we use this approach here to expand our understanding of the systems involving the proteases of P. falciparum. Results We investigated the roles of proteases in the parasite life cycle by constructing a network using protein-protein association data from the STRING database 4, and analyzing these data, in conjunction with the data from protein-protein interaction assays using the yeast 2-hybrid (Y2H) system 5, blood stage microarray experiments 678, proteomics 9101112, literature text mining, and sequence homology analysis. Seventy-seven (77) out of 124 predicted proteases were associated with at least one other protein, constituting 2,431 protein-protein interactions (PPIs). These proteases appear to play diverse roles in metabolism, cell cycle regulation, invasion and infection. Their degrees of connectivity (i.e., connections to other proteins), range from one to 143. The largest protease-associated sub-network is the ubiquitin-proteasome system which is crucial for protein recycling and stress response. Proteases are also implicated in heat shock response, signal peptide processing, cell cycle progression, transcriptional regulation, and signal transduction networks. Conclusions Our network analysis of proteases from P. falciparum uses a so-called guilt-by-association approach to extract sets of proteins from the proteome that are candidates for further study. Novel protease targets and previously unrecognized members of the protease-associated sub-systems provide new insights into the mechanisms underlying parasitism, pathogenesis and virulence.</p

Comparative genomics of the family Vibrionaceae reveals the wide distribution of genes encoding virulence-associated proteins

Background Species of the family Vibrionaceae are ubiquitous in marine environments. Several of these species are important pathogens of humans and marine species. Evidence indicates that genetic exchange plays an important role in the emergence of new pathogenic strains within this family. Data from the sequenced genomes of strains in this family could show how the genes encoded by all these strains, known as the pangenome, are distributed. Information about the core, accessory and panproteome of this family can show how, for example, genes encoding virulence-associated proteins are distributed and help us understand how virulence emerges. Results We deduced the complete set of orthologs for eleven strains from this family. The core proteome consists of 1,882 orthologous groups, which is 28% of the 6,629 orthologous groups in this family. There were 4,411 accessory orthologous groups (i.e., proteins that occurred in from 2 to 10 proteomes) and 5,584 unique proteins (encoded once on only one of the eleven genomes). Proteins that have been associated with virulence in V. cholerae were widely distributed across the eleven genomes, but the majority was found only on the genomes of the two V. cholerae strains examined. Conclusions The proteomes are reflective of the differing evolutionary trajectories followed by different strains to similar phenotypes. The composition of the proteomes supports the notion that genetic exchange among species of the Vibrionaceae is widespread and that this exchange aids these species in adapting to their environments

Comparative genomics of the family Vibrionaceae reveals the wide distribution of genes encoding virulence-associated proteins

<p>Abstract</p> <p>Background</p> <p>Species of the family <it>Vibrionaceae </it>are ubiquitous in marine environments. Several of these species are important pathogens of humans and marine species. Evidence indicates that genetic exchange plays an important role in the emergence of new pathogenic strains within this family. Data from the sequenced genomes of strains in this family could show how the genes encoded by all these strains, known as the pangenome, are distributed. Information about the core, accessory and panproteome of this family can show how, for example, genes encoding virulence-associated proteins are distributed and help us understand how virulence emerges.</p> <p>Results</p> <p>We deduced the complete set of orthologs for eleven strains from this family. The core proteome consists of 1,882 orthologous groups, which is 28% of the 6,629 orthologous groups in this family. There were 4,411 accessory orthologous groups (i.e., proteins that occurred in from 2 to 10 proteomes) and 5,584 unique proteins (encoded once on only one of the eleven genomes). Proteins that have been associated with virulence in <it>V. cholerae </it>were widely distributed across the eleven genomes, but the majority was found only on the genomes of the two <it>V. cholerae </it>strains examined.</p> <p>Conclusions</p> <p>The proteomes are reflective of the differing evolutionary trajectories followed by different strains to similar phenotypes. The composition of the proteomes supports the notion that genetic exchange among species of the <it>Vibrionaceae </it>is widespread and that this exchange aids these species in adapting to their environments.</p

Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory

Temporal-difference and Q-learning play a key role in deep reinforcement learning, where they are empowered by expressive nonlinear function approximators such as neural networks. At the core of their empirical successes is the learned feature representation, which embeds rich observations, e.g., images and texts, into the latent space that encodes semantic structures. Meanwhile, the evolution of such a feature representation is crucial to the convergence of temporal-difference and Q-learning. In particular, temporal-difference learning converges when the function approximator is linear in a feature representation, which is fixed throughout learning, and possibly diverges otherwise. We aim to answer the following questions: When the function approximator is a neural network, how does the associated feature representation evolve? If it converges, does it converge to the optimal one? We prove that, utilizing an overparameterized two-layer neural network, temporal-difference and Q-learning globally minimize the mean-squared projected Bellman error at a sublinear rate. Moreover, the associated feature representation converges to the optimal one, generalizing the previous analysis of Cai et al. (2019) in the neural tangent kernel regime, where the associated feature representation stabilizes at the initial one. The key to our analysis is a mean-field perspective, which connects the evolution of a finite-dimensional parameter to its limiting counterpart over an infinite-dimensional Wasserstein space. Our analysis generalizes to soft Q-learning, which is further connected to policy gradient

An Analysis of Attention via the Lens of Exchangeability and Latent Variable Models

With the attention mechanism, transformers achieve significant empirical successes. Despite the intuitive understanding that transformers perform relational inference over long sequences to produce desirable representations, we lack a rigorous theory on how the attention mechanism achieves it. In particular, several intriguing questions remain open: (a) What makes a desirable representation? (b) How does the attention mechanism infer the desirable representation within the forward pass? (c) How does a pretraining procedure learn to infer the desirable representation through the backward pass? We observe that, as is the case in BERT and ViT, input tokens are often exchangeable since they already include positional encodings. The notion of exchangeability induces a latent variable model that is invariant to input sizes, which enables our theoretical analysis. - To answer (a) on representation, we establish the existence of a sufficient and minimal representation of input tokens. In particular, such a representation instantiates the posterior distribution of the latent variable given input tokens, which plays a central role in predicting output labels and solving downstream tasks. - To answer (b) on inference, we prove that attention with the desired parameter infers the latent posterior up to an approximation error, which is decreasing in input sizes. In detail, we quantify how attention approximates the conditional mean of the value given the key, which characterizes how it performs relational inference over long sequences. - To answer (c) on learning, we prove that both supervised and self-supervised objectives allow empirical risk minimization to learn the desired parameter up to a generalization error, which is independent of input sizes. Particularly, in the self-supervised setting, we identify a condition number that is pivotal to solving downstream tasks.Comment: 85 pages, 7 figures, add acknowledgemen