435 research outputs found

    GPU-optimized approaches to molecular docking-based virtual screening in drug discovery: A comparative analysis

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    Finding a novel drug is a very long and complex procedure. Using computer simulations, it is possible to accelerate the preliminary phases by performing a virtual screening that filters a large set of drug candidates to a manageable number. This paper presents the implementations and comparative analysis of two GPU-optimized implementations of a virtual screening algorithm targeting novel GPU architectures. This work focuses on the analysis of parallel computation patterns and their mapping onto the target architecture. The first method adopts a traditional approach that spreads the computation for a single molecule across the entire GPU. The second uses a novel batched approach that exploits the parallel architecture of the GPU to evaluate more molecules in parallel. Experimental results showed a different behavior depending on the size of the database to be screened, either reaching a performance plateau sooner or having a more extended initial transient period to achieve a higher throughput (up to 5x), which is more suitable for extreme-scale virtual screening campaigns

    AI in drug discovery and its clinical relevance

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    The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.  Other InformationPublished in:HeliyonLicense: https://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://doi.org/10.1016/j.heliyon.2023.e17575 </p

    Solving an Old Puzzle: Elucidation and Evaluation of the Binding Mode of Salvinorin A at the Kappa Opioid Receptor

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    The natural product Salvinorin A (SalA) was the first nitrogen-lacking agonist discovered for the opioid receptors and exhibits high selectivity for the kappa opioid receptor (KOR) turning SalA into a promising analgesic to overcome the current opioid crisis. Since SalA’s suffers from poor pharmacokinetic properties, particularly the absence of gastrointestinal bioavailability, fast metabolic inactivation, and subsequent short duration of action, the rational design of new tailored analogs with improved clinical usability is highly desired. Despite being known for decades, the binding mode of SalA within the KOR remains elusive as several conflicting binding modes of SalA were proposed hindering the rational design of new analgesics. In this study, we rationally determined the binding mode of SalA to the active state KOR by in silico experiments (docking, molecular dynamics simulations, dynophores) in the context of all available mutagenesis studies and structure-activity relationship (SAR) data. To the best of our knowledge, this is the first comprehensive evaluation of SalA’s binding mode since the determination of the active state KOR crystal structure. SalA binds above the morphinan binding site with its furan pointing toward the intracellular core while the C2-acetoxy group is oriented toward the extracellular loop 2 (ECL2). SalA is solely stabilized within the binding pocket by hydrogen bonds (C210ECL2, Y3127.35, Y3137.36) and hydrophobic contacts (V1182.63, I1393.33, I2946.55, I3167.39). With the disruption of this interaction pattern or the establishment of additional interactions within the binding site, we were able to rationalize the experimental data for selected analogs. We surmise the C2-substituent interactions as important for SalA and its analogs to be experimentally active, albeit with moderate frequency within MD simulations of SalA. We further identified the non-conserved residues 2.63, 7.35, and 7.36 responsible for the KOR subtype selectivity of SalA. We are confident that the elucidation of the SalA binding mode will promote the understanding of KOR activation and facilitate the development of novel analgesics that are urgently needed

    Markov field models of molecular kinetics

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    Computer simulations such as molecular dynamics (MD) provide a possible means to understand protein dynamics and mechanisms on an atomistic scale. The resulting simulation data can be analyzed with Markov state models (MSMs), yielding a quantitative kinetic model that, e.g., encodes state populations and transition rates. However, the larger an investigated system, the more data is required to estimate a valid kinetic model. In this work, we show that this scaling problem can be escaped when decomposing a system into smaller ones, leveraging weak couplings between local domains. Our approach, termed independent Markov decomposition (IMD), is a first-order approximation neglecting couplings, i.e., it represents a decomposition of the underlying global dynamics into a set of independent local ones. We demonstrate that for truly independent systems, IMD can reduce the sampling by three orders of magnitude. IMD is applied to two biomolecular systems. First, synaptotagmin-1 is analyzed, a rapid calcium switch from the neurotransmitter release machinery. Within its C2A domain, local conformational switches are identified and modeled with independent MSMs, shedding light on the mechanism of its calcium-mediated activation. Second, the catalytic site of the serine protease TMPRSS2 is analyzed with a local drug-binding model. Equilibrium populations of different drug-binding modes are derived for three inhibitors, mirroring experimentally determined drug efficiencies. IMD is subsequently extended to an end-to-end deep learning framework called iVAMPnets, which learns a domain decomposition from simulation data and simultaneously models the kinetics in the local domains. We finally classify IMD and iVAMPnets as Markov field models (MFM), which we define as a class of models that describe dynamics by decomposing systems into local domains. Overall, this thesis introduces a local approach to Markov modeling that enables to quantitatively assess the kinetics of large macromolecular complexes, opening up possibilities to tackle current and future computational molecular biology questions

    Multi-Fidelity Bayesian Optimization for Efficient Materials Design

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    Materials design is a process of identifying compositions and structures to achieve desirable properties. Usually, costly experiments or simulations are required to evaluate the objective function for a design solution. Therefore, one of the major challenges is how to reduce the cost associated with sampling and evaluating the objective. Bayesian optimization is a new global optimization method which can increase the sampling efficiency with the guidance of the surrogate of the objective. In this work, a new acquisition function, called consequential improvement, is proposed for simultaneous selection of the solution and fidelity level of sampling. With the new acquisition function, the subsequent iteration is considered for potential selections at low-fidelity levels, because evaluations at the highest fidelity level are usually required to provide reliable objective values. To reduce the number of samples required to train the surrogate for molecular design, a new recursive hierarchical similarity metric is proposed. The new similarity metric quantifies the differences between molecules at multiple levels of hierarchy simultaneously based on the connections between multiscale descriptions of the structures. The new methodologies are demonstrated with simulation-based design of materials and structures based on fully atomistic and coarse-grained molecular dynamics simulations, and finite-element analysis. The new similarity metric is demonstrated in the design of tactile sensors and biodegradable oligomers. The multi-fidelity Bayesian optimization method is also illustrated with the multiscale design of a piezoelectric transducer by concurrently optimizing the atomic composition of the aluminum titanium nitride ceramic and the device’s porous microstructure at the micrometer scale.Ph.D

    Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design

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    The efficient exploration of chemical space to design molecules with intended properties enables the accelerated discovery of drugs, materials, and catalysts, and is one of the most important outstanding challenges in chemistry. Encouraged by the recent surge in computer power and artificial intelligence development, many algorithms have been developed to tackle this problem. However, despite the emergence of many new approaches in recent years, comparatively little progress has been made in developing realistic benchmarks that reflect the complexity of molecular design for real-world applications. In this work, we develop a set of practical benchmark tasks relying on physical simulation of molecular systems mimicking real-life molecular design problems for materials, drugs, and chemical reactions. Additionally, we demonstrate the utility and ease of use of our new benchmark set by demonstrating how to compare the performance of several well-established families of algorithms. Surprisingly, we find that model performance can strongly depend on the benchmark domain. We believe that our benchmark suite will help move the field towards more realistic molecular design benchmarks, and move the development of inverse molecular design algorithms closer to designing molecules that solve existing problems in both academia and industry alike.Comment: 29+21 pages, 6+19 figures, 6+2 table

    Computational method development for drug discovery

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    Protein-small molecule interactions play a central role in various aspects of the structural and functional organization of the cell and are therefore integral for drug discovery. The most comprehensive structural characterization of small molecule binding sites is provided by X-ray crystallography. However, it is often time-consuming and challenging to perform direct experimental analysis. Therefore, it is necessary to have computational methods that can predict binding site locations on unbound structures with accuracy close to that provided by X-ray crystallography. This thesis details four projects which involve the development of a fragment benchmark set, evaluation of allosteric sites in G Protein-Coupled Receptors (GPCRs), computational modeling of binding pocket dynamics, and the development of an Application Program Interface (API) framework for High-Performance Computing (HPC) centers. The first project provides a benchmark set for testing hot spot identification methods, emphasizing application to fragment-based drug discovery. Using the solvent mapping server, FTMap, which finds small molecule binding hot spots on proteins, we compared our benchmark set to an existing benchmark set that with a different method of construction. The second project details the effort to identify allosteric binding sites on GPCRs. We demonstrate that FTMap successfully identifies structurally determined allosteric sites in bound crystal structures and unbound structures. The project was further expanded to evaluate the conservation of allosteric sites across different classes, families, and types of GPCRs. The third project provides a structure-based analysis of cryptic site openings. Cryptic sites are pockets formed in ligand-bound proteins but not observed in unbound protein structures. Through analysis of crystal structures supplemented by molecular dynamics (MD) with enhanced sampling techniques, it was shown that cryptic sites can be grouped into three types: 1) “genuine” cryptic sites, which do not form without ligand binding, 2) spontaneously forming cryptic sites, and 3) cryptic sites impacted by mutations or off-site ligand binding. The fourth project presents an API framework for increasing the accessibility of HPC resources

    Thermal titration molecular dynamics (TTMD): shedding light on the stability of RNA-small molecule complexes

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    Ribonucleic acids are gradually becoming relevant players among putative drug targets, thanks to the increasing amount of structural data exploitable for the rational design of selective and potent binders that can modulate their activity. Mainly, this information allows employing different computational techniques for predicting how well would a ribonucleic-targeting agent fit within the active site of its target macromolecule. Due to some intrinsic peculiarities of complexes involving nucleic acids, such as structural plasticity, surface charge distribution, and solvent-mediated interactions, the application of routinely adopted methodologies like molecular docking is challenged by scoring inaccuracies, while more physically rigorous methods such as molecular dynamics require long simulation times which hamper their conformational sampling capabilities. In the present work, we present the first application of Thermal Titration Molecular Dynamics (TTMD), a recently developed method for the qualitative estimation of unbinding kinetics, to characterize RNA-ligand complexes. In this article, we explored its applicability as a post-docking refinement tool on RNA in complex with small molecules, highlighting the capability of this method to identify the native binding mode among a set of decoys across various pharmaceutically relevant test cases

    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
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