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1.
Cell ; 187(3): 517-520, 2024 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-38306978

RESUMO

Structural biology, as powerful as it is, can be misleading. We highlight four fundamental challenges: interpreting raw experimental data; accounting for motion; addressing the misleading nature of in vitro structures; and unraveling interactions between drugs and "anti-targets." Overcoming these challenges will amplify the impact of structural biology on drug discovery.


Assuntos
Descoberta de Drogas , Biologia Molecular , Beleza
2.
Nat Commun ; 15(1): 1474, 2024 Feb 17.
Artigo em Inglês | MEDLINE | ID: mdl-38368416

RESUMO

α,α-Disubstituted α-amino acids (α-AAs) have improved properties compared to other types of amino acids. They serve as modifiers of peptide conformation and as precursors of bioactive compounds. Therefore, it has been a long-standing goal to construct this highly valuable scaffold efficiently in organic synthesis and drug discovery. However, access to α,α-disubstituted α-AAs is highly challenging and largely unexplored due to their steric constraints. To overcome these, remarkable advances have been made in the last decades. Emerging strategies such as synergistic enantioselective catalysis, visible-light-mediated photocatalysis, metal-free methodologies and CO2 fixation offer new avenues to access the challenging synthesis of α,α-disubstituted α-AAs and continuously bring additional contributions to this field. This review article aims to provide an overview of the recent advancements since 2015 and discuss existing challenges for the synthesis of α,α-disubstituted α-AAs and their derivatives.


Assuntos
Aminoácidos , Descoberta de Drogas , Aminoácidos/química , Conformação Molecular , Técnicas de Química Sintética , Catálise
3.
Nat Commun ; 15(1): 1071, 2024 Feb 05.
Artigo em Inglês | MEDLINE | ID: mdl-38316797

RESUMO

While significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding protein function and facilitating drug discovery. Traditional docking methods, frequently used in studying protein-ligand interactions, typically treat proteins as rigid. While molecular dynamics simulations can propose appropriate protein conformations, they're computationally demanding due to rare transitions between biologically relevant equilibrium states. In this study, we present DynamicBind, a deep learning method that employs equivariant geometric diffusion networks to construct a smooth energy landscape, promoting efficient transitions between different equilibrium states. DynamicBind accurately recovers ligand-specific conformations from unbound protein structures without the need for holo-structures or extensive sampling. Remarkably, it demonstrates state-of-the-art performance in docking and virtual screening benchmarks. Our experiments reveal that DynamicBind can accommodate a wide range of large protein conformational changes and identify cryptic pockets in unseen protein targets. As a result, DynamicBind shows potential in accelerating the development of small molecules for previously undruggable targets and expanding the horizons of computational drug discovery.


Assuntos
Simulação de Dinâmica Molecular , Proteínas , Ligantes , Proteínas/metabolismo , Conformação Proteica , Descoberta de Drogas , Ligação Proteica , Simulação de Acoplamento Molecular
4.
BMC Bioinformatics ; 25(1): 59, 2024 Feb 06.
Artigo em Inglês | MEDLINE | ID: mdl-38321386

RESUMO

The prediction of interactions between novel drugs and biological targets is a vital step in the early stage of the drug discovery pipeline. Many deep learning approaches have been proposed over the last decade, with a substantial fraction of them sharing the same underlying two-branch architecture. Their distinction is limited to the use of different types of feature representations and branches (multi-layer perceptrons, convolutional neural networks, graph neural networks and transformers). In contrast, the strategy used to combine the outputs (embeddings) of the branches has remained mostly the same. The same general architecture has also been used extensively in the area of recommender systems, where the choice of an aggregation strategy is still an open question. In this work, we investigate the effectiveness of three different embedding aggregation strategies in the area of drug-target interaction (DTI) prediction. We formally define these strategies and prove their universal approximator capabilities. We then present experiments that compare the different strategies on benchmark datasets from the area of DTI prediction, showcasing conditions under which specific strategies could be the obvious choice.


Assuntos
Benchmarking , Descoberta de Drogas , Fontes de Energia Elétrica , Redes Neurais de Computação
5.
Brief Bioinform ; 25(2)2024 Jan 22.
Artigo em Inglês | MEDLINE | ID: mdl-38305454

RESUMO

This opinion article addresses a major issue in molecular biology and drug discovery by highlighting the complications that arise from combining polyproteins and their functional products within the same database entry. This problem, exemplified by the discovery of novel inhibitors for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease, has an influence on our ability to retrieve precise data and hinders the development of targeted therapies. It also emphasizes the need for improved database practices and underscores their significance in advancing scientific research. Furthermore, it emphasizes the need of learning from the SARS-CoV-2 pandemic in order to improve global preparedness for future health crises.


Assuntos
COVID-19 , Humanos , Poliproteínas/metabolismo , Cisteína Endopeptidases/metabolismo , SARS-CoV-2/metabolismo , Descoberta de Drogas , Simulação de Acoplamento Molecular
6.
Cell ; 187(3): 521-525, 2024 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-38306979

RESUMO

High-quality predicted structures enable structure-based approaches to an expanding number of drug discovery programs. We propose that by utilizing free energy perturbation (FEP), predicted structures can be confidently employed to achieve drug design goals. We use structure-based modeling of hERG inhibition to illustrate this value of FEP.


Assuntos
Desenho de Fármacos , Descoberta de Drogas , Termodinâmica , Entropia
7.
Proc Natl Acad Sci U S A ; 121(7): e2315069121, 2024 Feb 13.
Artigo em Inglês | MEDLINE | ID: mdl-38315851

RESUMO

A key step in drug discovery, common to many disease areas, is preclinical demonstration of efficacy in a mouse model of disease. However, this demonstration and its translation to the clinic can be impeded by mouse-specific pathways of drug metabolism. Here, we show that a mouse line extensively humanized for the cytochrome P450 gene superfamily ("8HUM") can circumvent these problems. The pharmacokinetics, metabolite profiles, and magnitude of drug-drug interactions of a test set of approved medicines were in much closer alignment with clinical observations than in wild-type mice. Infection with Mycobacterium tuberculosis, Leishmania donovani, and Trypanosoma cruzi was well tolerated in 8HUM, permitting efficacy assessment. During such assessments, mouse-specific metabolic liabilities were bypassed while the impact of clinically relevant active metabolites and DDI on efficacy were well captured. Removal of species differences in metabolism by replacement of wild-type mice with 8HUM therefore reduces compound attrition while improving clinical translation, accelerating drug discovery.


Assuntos
Doenças Transmissíveis , Descoberta de Drogas , Camundongos , Animais , Interações Medicamentosas , Modelos Animais de Doenças , Sistema Enzimático do Citocromo P-450/metabolismo , Aceleração
8.
Brief Bioinform ; 25(2)2024 Jan 22.
Artigo em Inglês | MEDLINE | ID: mdl-38348746

RESUMO

The prediction of molecular interactions is vital for drug discovery. Existing methods often focus on individual prediction tasks and overlook the relationships between them. Additionally, certain tasks encounter limitations due to insufficient data availability, resulting in limited performance. To overcome these limitations, we propose KGE-UNIT, a unified framework that combines knowledge graph embedding (KGE) and multi-task learning, for simultaneous prediction of drug-target interactions (DTIs) and drug-drug interactions (DDIs) and enhancing the performance of each task, even when data availability is limited. Via KGE, we extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, thereby improving the quality of features. Additionally, employing multi-task learning, we introduce an innovative predictor that comprises the task-aware Convolutional Neural Network-based (CNN-based) encoder and the task-aware attention decoder which can fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance. Experiments on two imbalanced datasets for DTIs and DDIs demonstrate the superiority of KGE-UNIT, achieving high area under the receiver operating characteristics curves (AUROCs) (0.942, 0.987) and area under the precision-recall curve ( AUPRs) (0.930, 0.980) for DTIs and high AUROCs (0.975, 0.989) and AUPRs (0.966, 0.988) for DDIs. Notably, on the LUO dataset where the data were more limited, KGE-UNIT exhibited a more pronounced improvement, with increases of 4.32$\%$ in AUROC and 3.56$\%$ in AUPR for DTIs and 6.56$\%$ in AUROC and 8.17$\%$ in AUPR for DDIs. The scalability of KGE-UNIT is demonstrated through its extension to protein-protein interactions prediction, ablation studies and case studies further validate its effectiveness.


Assuntos
Aprendizagem , Reconhecimento Automatizado de Padrão , Descoberta de Drogas , Área Sob a Curva , Redes Neurais de Computação , Interações Medicamentosas
9.
Drug Dev Res ; 85(1): e22154, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-38349259

RESUMO

Non-nucleoside reverse transcriptase inhibitors (NNRTIs) have significantly impacted the HIV-1 wild-type due to their high specificity and superior potency. As well as different combinations of NNRTIs have been used on clinically approved combining highly active antiretroviral therapy (HAART) to resist the growth of HIV-1 and decrease the mortality rate of HIV/AIDS. Although the feeble strength against the drug-resistant mutant strains and the long-term damaging effects have been reducing the effectiveness of HAART, it could be a crucial challenge to develop novel Anti-HIV leads with a vital mode of action and the least side effects. The extensive chemical reactivity and the diverse chemotherapeutic applications of the 1,3,5-triazine have provided a wide scope of research in medicinal chemistry via a structural modification. In this review, we focused on the Anti-HIV profile of the tri-substituted s-triazine derivatives with structure-based features and also discussed the active mode of action to evaluate the significant findings. The tri-substituted 1,3,5-triazine derivatives have been found more promising to inhibit the growth of the drug-sensitive and drug-resistant variants of HIV-1, especially HIV-1 wild-type, HIV-1 K103N/Y181C, and HIV-1 Tyr181Cys. It has been observed that these derivatives have interacted with the enzyme protein residues via a significant π $\pi $ - π $\pi $ interaction and hydrogen bonding to resist the proliferation of the viral genomes. Further, the SAR and the active binding modes are critically described and highlight the role of structural variations with functional groups along with the binding affinity of targeted enzymes, which may be beneficial for rational drug discovery to develop highly dynamic Anti-HIV agents.


Assuntos
HIV-1 , Triazinas , Triazinas/farmacologia , Triazinas/uso terapêutico , Inibidores da Transcriptase Reversa/farmacologia , Inibidores da Transcriptase Reversa/uso terapêutico , Química Farmacêutica , Descoberta de Drogas
10.
Cells ; 13(3)2024 Jan 29.
Artigo em Inglês | MEDLINE | ID: mdl-38334642

RESUMO

The human heart lacks significant regenerative capacity; thus, the solution to heart failure (HF) remains organ donation, requiring surgery and immunosuppression. The demand for constructed cardiac tissues (CCTs) to model and treat disease continues to grow. Recent advances in induced pluripotent stem cell (iPSC) manipulation, CRISPR gene editing, and 3D tissue culture have enabled a boom in iPSC-derived CCTs (iPSC-CCTs) with diverse cell types and architecture. Compared with 2D-cultured cells, iPSC-CCTs better recapitulate heart biology, demonstrating the potential to advance organ modeling, drug discovery, and regenerative medicine, though iPSC-CCTs could benefit from better methods to faithfully mimic heart physiology and electrophysiology. Here, we summarize advances in iPSC-CCTs and future developments in the vascularization, immunization, and maturation of iPSC-CCTs for study and therapy.


Assuntos
Células-Tronco Pluripotentes Induzidas , Humanos , Células-Tronco Pluripotentes Induzidas/metabolismo , Coração/fisiologia , Medicina Regenerativa , Descoberta de Drogas
11.
Sci Data ; 11(1): 180, 2024 Feb 09.
Artigo em Inglês | MEDLINE | ID: mdl-38336857

RESUMO

Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski's rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery.


Assuntos
Ligantes , Proteínas , Descoberta de Drogas , Aprendizado de Máquina , Ligação Proteica , Proteínas/química , Humanos , Animais
12.
Brief Bioinform ; 25(2)2024 Jan 22.
Artigo em Inglês | MEDLINE | ID: mdl-38340091

RESUMO

Discovering effective anti-tumor drug combinations is crucial for advancing cancer therapy. Taking full account of intricate biological interactions is highly important in accurately predicting drug synergy. However, the extremely limited prior knowledge poses great challenges in developing current computational methods. To address this, we introduce SynergyX, a multi-modality mutual attention network to improve anti-tumor drug synergy prediction. It dynamically captures cross-modal interactions, allowing for the modeling of complex biological networks and drug interactions. A convolution-augmented attention structure is adopted to integrate multi-omic data in this framework effectively. Compared with other state-of-the-art models, SynergyX demonstrates superior predictive accuracy in both the General Test and Blind Test and cross-dataset validation. By exhaustively screening combinations of approved drugs, SynergyX reveals its ability to identify promising drug combination candidates for potential lung cancer treatment. Another notable advantage lies in its multidimensional interpretability. Taking Sorafenib and Vorinostat as an example, SynergyX serves as a powerful tool for uncovering drug-gene interactions and deciphering cell selectivity mechanisms. In summary, SynergyX provides an illuminating and interpretable framework, poised to catalyze the expedition of drug synergy discovery and deepen our comprehension of rational combination therapy.


Assuntos
Descoberta de Drogas , Neoplasias Pulmonares , Humanos , Catálise , Terapia Combinada , Projetos de Pesquisa
14.
Molecules ; 29(3)2024 Feb 02.
Artigo em Inglês | MEDLINE | ID: mdl-38338433

RESUMO

Natural bioactive compounds are valuable resources for drug discovery due to their diverse and unique structures. However, these compounds often lack optimal drug-like properties. Therefore, structural optimization is a crucial step in the drug development process. By employing medicinal chemistry principles, targeted molecular operations can be applied to natural products while considering their size and complexity. Various strategies, including structural fragmentation, elimination of redundant atoms or groups, and exploration of structure-activity relationships, are utilized. Furthermore, improvements in physicochemical properties, chemical and metabolic stability, biophysical properties, and pharmacokinetic properties are sought after. This article provides a concise analysis of the process of modifying a few marketed drugs as illustrative examples.


Assuntos
Produtos Biológicos , Desenho de Fármacos , Produtos Biológicos/farmacologia , Produtos Biológicos/química , Química Farmacêutica , Descoberta de Drogas , Relação Estrutura-Atividade
15.
Int J Mol Sci ; 25(3)2024 Jan 23.
Artigo em Inglês | MEDLINE | ID: mdl-38338643

RESUMO

The process of drug discovery constitutes a highly intricate and formidable undertaking, encompassing the identification and advancement of novel therapeutic entities [...].


Assuntos
Desenho de Fármacos , Descoberta de Drogas
16.
BMC Bioinformatics ; 25(1): 75, 2024 Feb 16.
Artigo em Inglês | MEDLINE | ID: mdl-38365583

RESUMO

BACKGROUND: High-performance computing plays a pivotal role in computer-aided drug design, a field that holds significant promise in pharmaceutical research. The prediction of drug-target affinity (DTA) is a crucial stage in this process, potentially accelerating drug development through rapid and extensive preliminary compound screening, while also minimizing resource utilization and costs. Recently, the incorporation of deep learning into DTA prediction and the enhancement of its accuracy have emerged as key areas of interest in the research community. Drugs and targets can be characterized through various methods, including structure-based, sequence-based, and graph-based representations. Despite the progress in structure and sequence-based techniques, they tend to provide limited feature information. Conversely, graph-based approaches have risen to prominence, attracting considerable attention for their comprehensive data representation capabilities. Recent studies have focused on constructing protein and drug molecular graphs using sequences and SMILES, subsequently deriving representations through graph neural networks. However, these graph-based approaches are limited by the use of a fixed adjacent matrix of protein and drug molecular graphs for graph convolution. This limitation restricts the learning of comprehensive feature representations from intricate compound and protein structures, consequently impeding the full potential of graph-based feature representation in DTA prediction. This, in turn, significantly impacts the models' generalization capabilities in the complex realm of drug discovery. RESULTS: To tackle these challenges, we introduce GLCN-DTA, a model specifically designed for proficiency in DTA tasks. GLCN-DTA innovatively integrates a graph learning module into the existing graph architecture. This module is designed to learn a soft adjacent matrix, which effectively and efficiently refines the contextual structure of protein and drug molecular graphs. This advancement allows for learning richer structural information from protein and drug molecular graphs via graph convolution, specifically tailored for DTA tasks, compared to the conventional fixed adjacent matrix approach. A series of experiments have been conducted to validate the efficacy of the proposed GLCN-DTA method across diverse scenarios. The results demonstrate that GLCN-DTA possesses advantages in terms of robustness and high accuracy. CONCLUSIONS: The proposed GLCN-DTA model enhances DTA prediction performance by introducing a novel framework that synergizes graph learning operations with graph convolution operations, thereby achieving richer representations. GLCN-DTA does not distinguish between different protein classifications, including structurally ordered and intrinsically disordered proteins, focusing instead on improving feature representation. Therefore, its applicability scope may be more effective in scenarios involving structurally ordered proteins, while potentially being limited in contexts with intrinsically disordered proteins.


Assuntos
Proteínas Intrinsicamente Desordenadas , Desenvolvimento de Medicamentos , Descoberta de Drogas , Sistemas de Liberação de Medicamentos , Desenho de Fármacos
17.
Protein Sci ; 33(3): e4902, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-38358129

RESUMO

The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.


Assuntos
Medicina de Precisão , Proteínas , Humanos , Proteínas/química , Simulação por Computador , Física , Descoberta de Drogas , Simulação de Dinâmica Molecular
18.
PLoS Comput Biol ; 20(1): e1011151, 2024 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-38190398

RESUMO

The mammalian cell cycle is regulated by a well-studied but complex biochemical reaction system. Computational models provide a particularly systematic and systemic description of the mechanisms governing mammalian cell cycle control. By combining both state-of-the-art multiplexed experimental methods and powerful computational tools, this work aims at improving on these models along four dimensions: model structure, validation data, validation methodology and model reusability. We developed a comprehensive model structure of the full cell cycle that qualitatively explains the behaviour of human retinal pigment epithelial-1 cells. To estimate the model parameters, time courses of eight cell cycle regulators in two compartments were reconstructed from single cell snapshot measurements. After optimisation with a parallel global optimisation metaheuristic we obtained excellent agreements between simulations and measurements. The PEtab specification of the optimisation problem facilitates reuse of model, data and/or optimisation results. Future perturbation experiments will improve parameter identifiability and allow for testing model predictive power. Such a predictive model may aid in drug discovery for cell cycle-related disorders.


Assuntos
Descoberta de Drogas , Neurônios , Humanos , Animais , Divisão Celular , Ciclo Celular , Projetos de Pesquisa , Mamíferos
19.
BMC Bioinformatics ; 25(1): 10, 2024 Jan 04.
Artigo em Inglês | MEDLINE | ID: mdl-38177981

RESUMO

Examining potential drug-target interactions (DTIs) is a pivotal component of drug discovery and repurposing. Recently, there has been a significant rise in the use of computational techniques to predict DTIs. Nevertheless, previous investigations have predominantly concentrated on assessing either the connections between nodes or the consistency of the network's topological structure in isolation. Such one-sided approaches could severely hinder the accuracy of DTI predictions. In this study, we propose a novel method called TTGCN, which combines heterogeneous graph convolutional neural networks (GCN) and graph attention networks (GAT) to address the task of DTI prediction. TTGCN employs a two-tiered feature learning strategy, utilizing GAT and residual GCN (R-GCN) to extract drug and target embeddings from the diverse network, respectively. These drug and target embeddings are then fused through a mean-pooling layer. Finally, we employ an inductive matrix completion technique to forecast DTIs while preserving the network's node connectivity and topological structure. Our approach demonstrates superior performance in terms of area under the curve and area under the precision-recall curve in experimental comparisons, highlighting its significant advantages in predicting DTIs. Furthermore, case studies provide additional evidence of its ability to identify potential DTIs.


Assuntos
Descoberta de Drogas , Aprendizagem , Interações Medicamentosas , Redes Neurais de Computação
20.
Proc Natl Acad Sci U S A ; 121(5): e2308776121, 2024 Jan 30.
Artigo em Inglês | MEDLINE | ID: mdl-38252831

RESUMO

We present a drug design strategy based on structural knowledge of protein-protein interfaces selected through virus-host coevolution and translated into highly potential small molecules. This approach is grounded on Vinland, the most comprehensive atlas of virus-human protein-protein interactions with annotation of interacting domains. From this inspiration, we identified small viral protein domains responsible for interaction with human proteins. These peptides form a library of new chemical entities used to screen for replication modulators of several pathogens. As a proof of concept, a peptide from a KSHV protein, identified as an inhibitor of influenza virus replication, was translated into a small molecule series with low nanomolar antiviral activity. By targeting the NEET proteins, these molecules turn out to be of therapeutic interest in a nonalcoholic steatohepatitis mouse model with kidney lesions. This study provides a biomimetic framework to design original chemistries targeting cellular proteins, with indications going far beyond infectious diseases.


Assuntos
Influenza Humana , Vírus , Animais , Camundongos , Humanos , Proteoma , Peptídeos/farmacologia , Descoberta de Drogas
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