Engineering the Fullerene-protein Interface by Computational Design: The Sum is More than its Parts

Of all the amino acids, the surface of \u3c0-electron conjugated carbon nanoparticles has the largest affinity for tryptophan, followed by tyrosine, phenylalanine, and histidine. In order to increase the binding of a protein to a fullerene, it should suffice to mutate a residue of the site that binds to the fullerene to tryptophan, Trp. Computational chemistry shows that this intuitive approach is fraught with danger. Mutation of a binding residue to Trp may even destabilize the binding because of the complicated balance between van der Waals, polar and non-polar solvation interactions

Structural characterization of the Hepatitis C Virus NS3 protease from genotype 3a: The basis of the genotype 1b vs. 3a inhibitor potency shift

AbstractThe first structural characterization of the genotype 3a Hepatitis C Virus NS3 protease is reported, providing insight into the differential susceptibility of 1b and 3a proteases to certain inhibitors. Interaction of the 3a NS3 protease with a P2–P4 macrocyclic and a linear phenethylamide inhibitor was investigated. In addition, the effect of the NS4A cofactor binding on the conformation of the protease was analyzed. Complexation of NS3 with the phenethylamide inhibitor significantly stabilizes the protease but binding does not involve residues 168 and 123, two key amino acids underlying the different inhibition of genotype 1b vs. 3a proteases by P2–P4 macrocycles. Therefore, we studied the dynamic behavior of these two residues in the phenethylamide complex, serving as a model of the situation in the apo 3a protein, in order to explore the structural basis of the inhibition potency shift between the proteases of the genotypes 1b and 3a

Mechanistic Insights into Protein Allostery in LOV Domains and ACE2 PD via Computational Approaches

To decipher how proteins perform their roles within biology, it is necessary to investigate their dynamical nature. Proteins’ functional movements can be altered in a process called allostery, a phenomenon ubiquitous in nature and leveraged in biotechnology. Thanks to advancements in computing power, computational scientists can now investigate protein allostery at an atomistic level of detail via molecular simulations. In this work, various studies on protein allostery, based on molecular dynamics simulations data, that leverage advanced computational techniques, from machine learning to stochastic modeling and structural analyses, are presented. The first part of this dissertation focuses on examining allostery in light-oxygen-voltage (LOV) domain proteins, a class of protein sensors used by a variety of organisms to couple and integrate environmental stimuli to cell responses. Specifically, the unique dynamical nature of light-induced allostery activation in the LOV domain of the circadian photoreceptor ZEITLUPE is delineated and an alternative activation mechanism that does not rely on the active Gln is explored. The second part of this work focuses on studying the allosteric potential of the angiotensin-converting enzyme 2, a regulator of blood pressure and a main target for a variety of coronaviruses. The ability of surface allosteric binders to affect the conformational space explored by the protein is illustrated. The custom deep learning model, named CV-CNN, in conjunction with the REDAN analysis, unraveled the path of allosteric propagation within the protein domain at an atomistic level of detail. Lastly, the feasibility of a recently developed dimensionality reduction technique called UMAP in studying protein dynamics and allostery is evaluated

Mechanistic Insights into Protein Allostery in LOV Domains and ACE2 PD via Computational Approaches

To decipher how proteins perform their roles within biology, it is necessary to investigate their dynamical nature. Proteins’ functional movements can be altered in a process called allostery, a phenomenon ubiquitous in nature and leveraged in biotechnology. Thanks to advancements in computing power, computational scientists can now investigate protein allostery at an atomistic level of detail via molecular simulations. In this work, various studies on protein allostery, based on molecular dynamics simulations data, that leverage advanced computational techniques, from machine learning to stochastic modeling and structural analyses, are presented. The first part of this dissertation focuses on examining allostery in light-oxygen-voltage (LOV) domain proteins, a class of protein sensors used by a variety of organisms to couple and integrate environmental stimuli to cell responses. Specifically, the unique dynamical nature of light-induced allostery activation in the LOV domain of the circadian photoreceptor ZEITLUPE is delineated and an alternative activation mechanism that does not rely on the active Gln is explored. The second part of this work focuses on studying the allosteric potential of the angiotensin-converting enzyme 2, a regulator of blood pressure and a main target for a variety of coronaviruses. The ability of surface allosteric binders to affect the conformational space explored by the protein is illustrated. The custom deep learning model, named CV-CNN, in conjunction with the REDAN analysis, unraveled the path of allosteric propagation within the protein domain at an atomistic level of detail. Lastly, the feasibility of a recently developed dimensionality reduction technique called UMAP in studying protein dynamics and allostery is evaluated

Deciphering the Allosteric Process of Phaeodactylum tricornutum Aureochrome 1a LOV Domain

The conformational-driven allosteric protein diatom Phaeodactylum tricornutum aureochrome 1a (PtAu1a) di ers from other light-oxygen-voltage (LOV) proteins for its uncommon structural topology. The mechanism of signaling transduction in PtAu1a LOV domain (AuLOV) including flanking helices remains unclear because of this dissimilarity, which hinders the study of PtAu1a as an optogenetic tool. To clarify this mechanism, we employed a combination of tree-based machine learning models, Markov state models, machine learning based community analysis and transition path theory to quantitatively analyze the allosteric process. Our results are in good agreement with reported experimental findings and revealed a previously overlooked C-alpha helix and linkers as important in promoting the protein conformational change. This integrated approach can be considered as a general workflow and applied on other allosteric proteins to provide detailed information about their allosteric mechanisms

Predicting Potential SARS-COV-2 Drugs—In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking

Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. A concerted effort from research labs around the world resulted in the identification of potential pharmaceutical treatments for CoVID-19 using existing drugs, as well as the discovery of multiple vaccines. During an urgent crisis, rapidly identifying potential new treatments requires global and cross-discipline cooperation, together with an enhanced open-access research model to distribute new ideas and leads. Herein, we introduce an application of a deep neural network based drug screening method, validating it using a docking algorithm on approved drugs for drug repurposing efforts, and extending the screen to a large library of 750,000 compounds for de novo drug discovery effort. The results of large library screens are incorporated into an open-access web interface to allow researchers from diverse fields to target molecules of interest. Our combined approach allows for both the identification of existing drugs that may be able to be repurposed and de novo design of ACE2-regulatory compounds. Through these efforts we demonstrate the utility of a new machine learning algorithm for drug discovery, SSnet, that can function as a tool to triage large molecular libraries to identify classes of molecules with possible efficacy

Dimeric Allostery Mechanism of the Plant Circadian Clock Photoreceptor ZEITLUPE

In Arabidopsis thaliana, the Light-Oxygen-Voltage (LOV) domain containing protein ZEITLUPE (ZTL) integrates light quality, intensity, and duration into regulation of the circadian clock. Recent structural and biochemical studies of ZTL indicate that the protein diverges from other members of the LOV superfamily in its allosteric mechanism, and that the divergent allosteric mechanism hinges upon conservation of two signaling residues G46 and V48 that alter dynamic motions of a Gln residue implicated in signal transduction in all LOV proteins. Here, we delineate the allosteric mechanism of ZTL via an integrated computational approach that employs atomistic simulations of wild type and allosteric variants of ZTL in the functional dark and light states, together with Markov state and supervised machine learning classification models. This approach has unveiled key factors of the ZTL allosteric mechanisms, and identified specific interactions and residues implicated in functional allosteric changes. The final results reveal atomic level insights into allosteric mechanisms of ZTL function that operate via a non-trivial combination of population-shift and dynamics-driven allosteric pathways

Explore protein conformational space with variational autoencoder

Molecular dynamic (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational resources and takes a prohibitive amount of time. In this study, we demonstrated that variational autoencoders (VAEs), a type of deep learning model, can be employed to explore the conformational space of a protein through MD simulations. VAEs are shown to be superior to autoencoders (AEs) through a benchmark study, with low deviation between the training and decoded conformations. Moreover, we show that the learned latent space in the VAE can be used to generate unsampled protein conformations. Additional simulations starting from these generated conformations accelerated the sampling process and explored hidden spaces in the conformational landscape

Predicting Potential SARS-COV-2 Drugs—In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking

Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. A concerted effort from research labs around the world resulted in the identification of potential pharmaceutical treatments for CoVID-19 using existing drugs, as well as the discovery of multiple vaccines. During an urgent crisis, rapidly identifying potential new treatments requires global and cross-discipline cooperation, together with an enhanced open-access research model to distribute new ideas and leads. Herein, we introduce an application of a deep neural network based drug screening method, validating it using a docking algorithm on approved drugs for drug repurposing efforts, and extending the screen to a large library of 750,000 compounds for de novo drug discovery effort. The results of large library screens are incorporated into an open-access web interface to allow researchers from diverse fields to target molecules of interest. Our combined approach allows for both the identification of existing drugs that may be able to be repurposed and de novo design of ACE2-regulatory compounds. Through these efforts we demonstrate the utility of a new machine learning algorithm for drug discovery, SSnet, that can function as a tool to triage large molecular libraries to identify classes of molecules with possible efficacy

Predicting Potential SARS-COV-2 Drugs - In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking

Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. Currently, research labs around the world are looking for new pharmaceutical treatments by repurposing existing drugs, identifying potential antibody-based therapeutics, as well as the design of new pharmaceutical products and vaccines. To be able to rapidly identify potentional new treatments we require global cooperation and an enhanced open-access research model to distribute new ideas and leads. Herein, we employ a combined machine learning and drug docking approach to evaluate the potential efficacy of existing FDA and World approved drugs to impact the ACE2-Spike complex necessary for viral entry and replication. Further, we extend the machine learning approach to databases containing between 700,000-1 billion compounds. The results of large library screens are incorporated into a open-access web interface to allow researchers from diverse fields to target molecules of interest. Our combined approach allows for both the identification of existing drugs that may be able to be repurposed, and de novo design of ACE2-regulatory compounds. Through these efforts we identify intriguing links between COVID-19 pathologies, particularly in regard to possible sex-differences in disease outcomes