951 research outputs found

    Machine Learning Model for Repurposing Drugs to Target Viral Diseases

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    With recent events, such as the Covid-19 pandemic, it is increasingly important to develop strategies to combat viral diseases. Due to technological advancements, computer-aided drug design and machine learning (ML)-based hit identification strategies have gained popularity. Applying these techniques to identify novel scaffolds and/or repurpose existing therapeutics for viral diseases is a promising approach. As an avenue to improve existing classification models for antiviral applications, this thesis aimed to make improvements to non-binding data selection within these models. We created a classification model using molecular fingerprints to assess the performance of machine learning predictions when the model is trained using randomly selected and rationally selected non-binding datasets. Our analyses revealed that machine learning predictions can be improved using a rational selection approach. We further used this approach and trained three machine learning models based on XGBoost, Random Forest, and Support Vector Machine to predict potential inhibitors for the SARS-CoV2 main protease (Mpro) enzyme. Probability-ranked hits from the combined model were further analyzed using classical structure-based methods. The binding modes and affinities of the hits were identified using AutoDock Vina, and molecular dynamics simulations-enabled MM-GBSA calculations. The top hits identified from this multi-step screening approach revealed potential candidates that show improved affinity and stability than existing non-covalent Mpro inhibitors. Thus, our approach and the model could be useful for screening large ligand libraries

    Multiscale modeling of synthetic and biological supramolecular systems.

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    In this thesis, we exploited the synergistic combination of multiscale molecular modeling, molecular dynamics (MD), and enhanced sampling to tackle two complex systems. In the first case study, we investigated the intrinsic dynamic behavior of a Benzene 1,3,5-TricarboxAmide (BTA) supramolecular polymer in water. In the second case study, we inquired about the effect of functionalized amphiphilic gold nanoparticles (Au NPs) on the phase behavior of a multi-component lipid membrane. Through our simulations, we gained a deeper understanding of the structure and dynamics of a class of supramolecular polymers. Additionally, we identified the factors that control the exchange of monomers between the different fibers, which can be used to inform the design of novel supramolecular materials in the future. Our simulations provided insights into the mechanisms underlying the interaction between functionalized nanoparticles and lipid membranes, extrapolating the factors that influence the stability of the membrane phase separation. The acquired knowledge can be applied in drug delivery systems or to create new hybrid materials containing ordered two-dimensional NP lattices. In particular, it is worth noting that in both studies, using coarse-grained models with the proper (sub-molecular) resolution was crucial to overcoming the limitations of classic all-atom force fields while maintaining the needed chemical specificity. Overall, the results of these studies have broad implications for materials science and biophysics and demonstrate the potential of computational modeling to inform the design of novel materials and systems

    Long Time Scale Ensemble Methods in Molecular Dynamics: Ligand–Protein Interactions and Allostery in SARS-CoV-2 Targets

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    We subject a series of five protein-ligand systems which contain important SARS-CoV-2 targets, 3-chymotrypsin-like protease (3CLPro), papain-like protease, and adenosine ribose phosphatase, to long time scale and adaptive sampling molecular dynamics simulations. By performing ensembles of ten or twelve 10 μs simulations for each system, we accurately and reproducibly determine ligand binding sites, both crystallographically resolved and otherwise, thereby discovering binding sites that can be exploited for drug discovery. We also report robust, ensemble-based observation of conformational changes that occur at the main binding site of 3CLPro due to the presence of another ligand at an allosteric binding site explaining the underlying cascade of events responsible for its inhibitory effect. Using our simulations, we have discovered a novel allosteric mechanism of inhibition for a ligand known to bind only at the substrate binding site. Due to the chaotic nature of molecular dynamics trajectories, regardless of their temporal duration individual trajectories do not allow for accurate or reproducible elucidation of macroscopic expectation values. Unprecedentedly at this time scale, we compare the statistical distribution of protein-ligand contact frequencies for these ten/twelve 10 μs trajectories and find that over 90% of trajectories have significantly different contact frequency distributions. Furthermore, using a direct binding free energy calculation protocol, we determine the ligand binding free energies for each of the identified sites using long time scale simulations. The free energies differ by 0.77 to 7.26 kcal/mol across individual trajectories depending on the binding site and the system. We show that, although this is the standard way such quantities are currently reported at long time scale, individual simulations do not yield reliable free energies. Ensembles of independent trajectories are necessary to overcome the aleatoric uncertainty in order to obtain statistically meaningful and reproducible results. Finally, we compare the application of different free energy methods to these systems and discuss their advantages and disadvantages. Our findings here are generally applicable to all molecular dynamics based applications and not confined to the free energy methods used in this study

    The interactions between gold nanoparticles and their self-assembly

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    Gold nanoparticles (AuNPs) are one of the most promising building blocks to fabricate versatile nanostructures. Such nanostructures have the great potential to enable new gold-based nanomaterials or nanocomposites with specific properties by precisely controlling the interactions (potential energies and/or forces) between them. In other words, the interactions between AuNPs are therefore regarded as one of the key factors governing particles’ self-assembly process that can drive multiple AuNPs to form ordered structures as required. Quantifying the interactions between them and understanding of their self-assembly process are of great importance and yet still challenging. In this study, molecular dynamics (MD) simulations are performed to calculate the interactions (e.g., potential energies) between AuNPs. The MD results reveal that a more effective force model between AuNPs can be developed as a function of their surface separation compared with the conventional Hamaker equation. In addition, MD simulations examine several effects (i.e., particle size, shape, rotation, surface patch, surfactant, as well as configuration) on their interactions. The results demonstrate that the different impacts of these factors (e.g., the hindrance of surfactant). Apart from spherical gold nanoparticles, interactions between gold nanorods (AuNRs) are also be quantified by MD simulations. The interparticle forces of AuNRs can be expressed as a function of their surface separation and the rotation angle since the rotational movement is applied on AuNR. Further, the MD-derived interparticle force models of gold nanospheres are integrated into discrete element method (DEM) to explore their self-assembly process. To the best of our knowledge, this might be the first time that the MD-based interparticle force models are integrated into DEM to explore the self-assembly process of gold nanoparticles. The results show that ordered nanostructures are ultimately constructed. Specifically, the mean coordination number (CN) of AuNPs (3 nm in size) is up to 5.99 and two major large clusters is observed under the simulation conditions at the equilibrated state. The completion of this study not only allows us to evaluate the interactions between AuNPs by MD simulation, but profoundly, the MD-DEM coupling approach opens a new window to unfold the self-assembly process of AuNPs

    Modeling the Stability of Protein Solutions and of Hepatitis B Virus-Like Particles

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    Effect of Oriented Electric Fields on Biologically Relevant Iron–Sulfur Clusters and Bioinformatics Investigations of Biotin Synthase

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    Enzymes, as biological catalysts, enjoy several benefits over the more commonly used metal catalysts in chemistry, particularly in terms of sustainability. They can, however, be more complicated to utilise and manipulate and research tends to focus on engineering enzymes for specific tasks where the complexity is reduced and a by-product of this is increased understanding of sequence-structure-function relationship. An alternative approach is to find broader problems whose solutions could be applied to the engineering of many enzymes, or at least a large, multipurpose superfamily of them. An excellent target for this type of approach is the radical S-adenosylmethionine (rSAM) superfamily, particularly due to its common mechanism of generating a radical species and using careful substrate control to dictate the reaction products across the different enzymes. This common mechanism includes an iron-sulfur cluster which can be influenced by the electrostatic environment, providing a clear path for study and promising powerful engineering opportunities. The focus of this research is an analysis of the effect of oriented electric fields on several relevant iron-sulfur clusters using a systematic, high throughput density-functional theory (DFT) study to gain both quantitative and qualitative information on the relative energies of spin states, orbitals, vertical electron affinities and spin-coupling constants. In addition, methods are identified for coupling this type of study with bioinformatic information for the purpose of enzyme engineering. Applying this to an exemplar of the rSAM superfamily, biotin synthase (BioB), indicates promising scope for variation at iron-sulfur cluster binding sites, whilst retaining functionality. Both the DFT results and the bioinformatics analysis represent a promising step towards the potential automation of enzyme engineering and is not limited to biotin synthase or even rSAM enzymes. This could result in improved development of a wide variety of chemical products in sustainable, efficient, and low-carbon syntheses, with the concomitant contributions to mitigating climate change

    Molecular modeling of drug delivery systems based on carbon nanostructures: structure, function, and potential applications for anticancer complexes of Pt(II)

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    The medication with Pt(II) drugs (cisplatin, carboplatin, and oxaliplatin) has been an effective alternative for treating cancers due to their notable inhibition of cancer cells growth and the prevention of metastasis. Nevertheless, the low selectivity of these metallodrugs for malignant cells produces severe side effects, which limit this chemotherapy. In this context, carbon nanohorns (CNHs) have been considered potential nanovectors for drugs, since they present low toxicity, drug-loading capacity, biodegradation routes, and biocompatibility when oxidized. However, there is still a lack of studies regarding the molecular behavior of these nanocarriers on cell membranes. The present work aims to characterize the interactions between inclusion complexes drug@CNH, which are formed by platinum drugs encapsulated in CNHs, and plasma membranes by using molecular dynamics simulations. The results demonstrated that the van der Waals contribution played a primary role (∼74%) for the complex stability, which explain the confined dynamics of drugs inside the CNHs. The free energy profiles revealed an endergonic character of the drug release processes from CNHs, in which the energy barrier for oxaliplatin release (~24 kcal mol–1 ) was ~30% larger than those for carboplatin and cisplatin (~18 kcal mol-1 ). The simulations also showed four stages of the interaction mechanism CNH--membrane: approach, insertion, permeation, and internalization. Despite the low structural disturbance of the membranes, the free energy barrier of ∼55 kcal mol-1 for the CNHs translocation indicated that this transport is kinetically unfavorable by passive process. The in silico experiments evidenced that the most likely mechanism of cisplatin delivery from CNHs involve the approach and insertion stages, where the nanovector adheres on the surface of cancer cells, as reported in in vitro studies. After this retention, the drug load may be slowly released in the tumor site. Finally, simulations of the cellular uptake of Pt(II) drugs also pointed out significant energy barriers (~30 kcal mol-1 ) for this process, which reflects their low permeability in membranes as discussed in experimental studies. In addition to reinforcing the potential of CNH as nanovector of Pt(II) drugs, the results presented in this thesis may assist and drive new experimental studies with CNHs, focusing on the development of less aggressive formulations for cancer treatments.A medicação com fármacos a base de Pt(II) (cisplatina, carboplatina e oxaliplatina) tem sido uma alternativa efetiva para tratar cânceres devido à sua notável inibição do crescimento de células cancerosas e a prevenção de metástases. No entanto, a baixa seletividade dessas metalodrogas por células cancerosas gera severos efeitos colaterais. Nesse contexto, nanohorns de carbono (CNHs) têm sido considerados potenciais nanovetores de fármacos, devido a baixa toxicidade, capacidade de carreamento de fármacos, rotas de biodegradação, e biocompatibilidade quando oxidados. Porém, existe uma carência de estudos tratando o comportamento desses nanocarreadores em biomembranas. Esse trabalho tem como objetivo caracterizar as interações entre complexos de inclusão fármaco@CNH, formados por fármacos de Pt(II) encapsulados em CNHs, e membranas usando simulações por dinâmica molecular. Os resultados demonstraram que a contribuição de van der Waals teve um papel primário (∼74%) na estabilidade dos complexos, o que explica a dinâmica confinada dos fármacos dentro dos CNHs. Os perfis de energia livre revelaram o caráter endergônico da liberação dos fármacos a partir de CNHs, nos quais a barreira de energia para a liberação da oxaliplatina (~24 kcal mol– 1 ) é ~30% maior do que aquelas para carboplatina e cisplatina. As simulações mostraram quatro estágios do mecanismo de interação CNH-membrana: aproximação, inserção, permeação e internalização. Apesar do baixo distúrbio estrutural das membranas, a barreira de energia livre de ∼55 kcal mol-1 para a translocação de CNHs indicou que esse transporte é desfavorável cineticamente via o processo passivo. Os experimentos in silico evidenciam que o mecanismo mais provável de entrega de cisplatina a partir de CNHs envolve a aproximação e inserção, onde o nanovetor adere na superfície de células cancerosas, como reportado em estudos in vitro. Após essa retenção, a carga de fármaco deve ser ligeiramente liberada no tumor. As simulações de captação celular de fármacos de Pt(II) também apontaram barreiras de energia significativas (∼30 kcal mol-1 ) para esse processo, o que reflete a baixa permeabilidade deles em membranas como discutido em estudos experimentais. Além de reforçar o potencial de CNHs como nanovetores de fármacos de Pt(II), os resultados apresentados nessa tese podem auxiliar e impulsionar novos estudos com CNHs, focando no desenvolvimento de formulações menos agressivas para tratamentos de câncer.FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerai

    MULTISCALE MOLECULAR MODELING STUDIES OF THE DYNAMICS AND CATALYTIC MECHANISMS OF IRON(II)- AND ZINC(II)-DEPENDENT METALLOENZYMES

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    Enzymes are biological systems that aid in specific biochemical reactions. They lower the reaction barrier, thus speeding up the reaction rate. A detailed knowledge of enzymes will not be achievable without computational modeling as it offers insight into atomistic details and catalytic species, which are crucial to designing enzyme-specific inhibitors and impossible to gain experimentally. This dissertation employs advanced multiscale computational approaches to study the dynamics and reaction mechanisms of non-heme Fe(II) and 2-oxoglutarate (2OG) dependent oxygenases, including AlkB, AlkBH2, TET2, and KDM4E, involved in DNA and histone demethylation. It also focuses on Zn(II) dependent matrix metalloproteinase-1 (MMP-1), which helps collagen degradation. Chapter 2 investigates the substrate selectivity and dynamics on the enzyme-substrate complexes of DNA repair enzymes, AlkB and FTO. Chapter 3 unravels the mechanisms and effects of dynamics on the demethylation of 3-methylcytosine substrate by AlkB and AlkBH2 enzymes. The results imply that the nature of DNA and conformational dynamics influence the electronic structure of the iron center during demethylation. Chapter 4 delineates how second-coordination and long-range residue mutations affect the oxidation of 5-methylcytosine substrate to 5-hydroxymethylcytosine by TET2 enzyme. The results reveal that mutations affect DNA binding/interactions and the energetic contributions of residues stabilizing key catalytic species. Chapter 5 describes the reparation of unnatural alkylated substrates by TET2, their effects on second-coordination interactions and long-range correlated motions in TET2. The study reveals that post-hydroxylation reactions occur in aqueous solution outside the enzyme environment. Chapter 6 establishes how applying external electric fields (EEFs) enhances specificity of KDM4E for C—H over N—H activation during dimethylated arginine substrate demethylation. The results reveal that applying positive EEFs parallel to Fe=O bond enhances C—H activation rate, while inhibiting the N—H one. Chapter 7 addresses the formation of catalytically competent MMP-1·THP complex of MMP-1. The studies reveal the role of MMP-1’s catalytic domain a-helices, the linker, and changes in coordination states of catalytic Zn(II) during the transition. Overall, the presented results contribute to the in-depth understanding of the fundamental mechanisms of the studied enzymes and provide a background for developing enzyme-specific inhibitors against the associated disorders and diseases

    Predicting and Understanding Binding Affinities of Synthetic Anion Receptors

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    Anion receptors are molecules that can recognise and bind anions. They have applications in organocatalysis, anion sensing and the removal of anions from wastewater. Some anion receptors are also able to transport anions across cell membranes and show promise for the treatment of diseases such as cystic fibrosis and cancer. As such, it is of interest to develop computational methods that can reliably predict the physicochemical properties and anion binding affinities of these molecules. However, efforts to computationally model these molecules are hampered by the sheer size of typical receptors, making them too expensive to treat using accurate quantum chemical methods. Whilst efficient approximations such as local-correlation methods have been developed, the broader accuracy of these methods, particularly in their application to ionic non-covalent systems remains unclear. To address this gap, this thesis has carried out an extensive validation of local-correlation methods, and economical density functional theory (DFT) methods for receptors with different binding motifs. Additionally, multiscale models have also been examined with the view to extending the scope of these methods to model very large anion receptors. DFT methods giving good agreement with highly accurate calculations at a fraction of the cost were identified. The use of semiempirical methods combined with DFT in a multiscale model for calculating anion binding affinities lead to unexpectedly large errors with modest savings of computational time, while some "three-fold corrected" methods show promise in reducing the cost of geometry optimisations of large receptors. These validated protocols were subsequently applied to investigate the structure-binding relationships of a wide range of dual-hydrogen bonding receptors. Notably, different receptor motifs were found to have different conformational preferences, which could explain why experimentally, thioureas, thiosquaramides and croconamides show weaker chloride binding affinities than would be expected based on their acidity. The results suggest that pre-organising anion receptors in the conformer that facilitates hydrogen bond formation could be a promising strategy for the development of anion receptors. It is envisaged that these findings will aid in the design and screening of novel anion receptors with increased binding affinity and selectivity

    From Static to Dynamic Structures: Improving Binding Affinity Prediction with a Graph-Based Deep Learning Model

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    Accurate prediction of the protein-ligand binding affinities is an essential challenge in the structure-based drug design. Despite recent advance in data-driven methods in affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally depicted by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, we curated an MD dataset containing 3,218 different protein-ligand complexes, and further developed Dynaformer, which is a graph-based deep learning model. Dynaformer was able to accurately predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that our model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, we performed a virtual screening on the heat shock protein 90 (HSP90) using Dynaformer that identified 20 candidates and further experimentally validated their binding affinities. We demonstrated that our approach is more efficient, which can identify 12 hit compounds (two were in the submicromolar range), including several newly discovered scaffolds. We anticipate this new synergy between large-scale MD datasets and deep learning models will provide a new route toward accelerating the early drug discovery process.Comment: totally reorganize the texts and figure
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