Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 101
Filtrar
1.
Eur J Med Chem ; 279: 116812, 2024 Aug 30.
Artigo em Inglês | MEDLINE | ID: mdl-39241668

RESUMO

Leucine-rich repeat kinase 2 (LRRK2) has been reported to be associated with familial and idiopathic Parkinson's disease (PD) risk and is a promising target for drug discovery against PD. To identify novel and effective LRRK2 inhibitors, an ensemble virtual screening strategy by combining fingerprint similarity, complex-based pharmacophore and structure-based molecular docking was proposed and applied. Using this strategy, we finally selected 25 compounds from ∼1.7 million compounds for in vitro and in vivo tests. Firstly, the kinase inhibitory activity tests of compounds based on ADP-Glo assay identified three most potent compounds LY2023-19, LY2023-24 and LY2023-25 with IC50 of 556.4 nM, 218.1 nM and 22.4 nM for LRRK2 G2019S mutant, respectively. The further cellular experiments also indicated that three hit compounds significantly inhibited Ser935 phosphorylation of both wide-type and G2019S LRRK2 with IC50 ranging from 27 nM to 1674 nM in HEK293T cells. The MD simulations of three compounds and G2019S LRRK2 showed the hydrogen bond formed by Glu1948 and Ala1950 is crucial for the binding of LRRK2. Afterwards, 6-OHDA-induced PD zebrafish model was constructed to evaluate the neuroprotective effects of hit compounds. The locomotion of the 6-OHDA treated zebrafish larvae was improved after treatment with LY2023-24. The obtained results can provide valuable guidance for the development of PD drugs by targeting LRRK2.

2.
Protein Sci ; 33(10): e5166, 2024 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-39291929

RESUMO

Mycobacterial membrane protein Large 3 (MmpL3) of Mycobacterium tuberculosis (Mtb) is crucial for the translocation of trehalose monomycolate (TMM) across the inner bacterial cell membrane, making it a promising target for anti-tuberculosis (TB) drug development. While several structural, microbiological, and in vitro studies have provided significant insights, the precise mechanisms underlying TMM transport by MmpL3 and its inhibition remain incompletely understood at the atomic level. In this study, molecular dynamic (MD) simulations for the apo form and seven inhibitor-bound forms of Mtb MmpL3 were carried out to obtain a thorough comprehension of the protein's dynamics and function. MD simulations revealed that the seven inhibitors in this work stably bind to the central channel of the transmembrane domain and primarily forming hydrogen bonds with ASP251, ASP640, or both residues. Through dynamical cross-correlation matrix and principal component analysis analyses, several types of coupled motions between different domains were observed in the apo state, and distinct conformational states were identified using Markov state model analysis. These coupled motions and varied conformational states likely contribute to the transport of TMM. However, simulations of inhibitor-bound MmpL3 showed an enlargement of the proton channel, potentially disrupting coupled motions. This indicates that inhibitors may impair MmpL3's transport function by directly blocking the proton channel, thereby hindering coordinated domain movements and indirectly affecting TMM translocation.


Assuntos
Proteínas de Bactérias , Simulação de Dinâmica Molecular , Mycobacterium tuberculosis , Mycobacterium tuberculosis/metabolismo , Mycobacterium tuberculosis/efeitos dos fármacos , Mycobacterium tuberculosis/química , Proteínas de Bactérias/química , Proteínas de Bactérias/metabolismo , Proteínas de Bactérias/genética , Trealose/química , Trealose/metabolismo , Proteínas de Membrana Transportadoras/química , Proteínas de Membrana Transportadoras/metabolismo , Transporte Biológico , Ligação Proteica , Fatores Corda
3.
Psychiatry Res Neuroimaging ; 344: 111862, 2024 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-39153232

RESUMO

Puberty is a vulnerable period for the onset of major depressive disorder (MDD) due to considerable neurodevelopmental changes. Prior diffusion tensor imaging (DTI) studies in depressed youth have had heterogeneous participants, making assessment of early pathology challenging due to illness chronicity and medication confounds. This study leveraged whole-brain DTI and graph theory approaches to probe white matter (WM) abnormalities and disturbances in structural network topology related to first-episode, treatment-naïve pediatric MDD. Participants included 36 first-episode, unmedicated adolescents with MDD (mean age 15.8 years) and 29 age- and sex-matched healthy controls (mean age 15.2 years). Compared to controls, the MDD group showed reduced fractional anisotropy in the internal and external capsules, unveiling novel regions of WM disruption in early-onset depression. The right thalamus and superior temporal gyrus were identified as network hubs where betweenness centrality changes mediated links between WM anomalies and depression severity. A diagnostic model incorporating demographics, DTI, and network metrics achieved an AUROC of 0.88 and a F1 score of 0.80 using a neural network algorithm. By examining first-episode, treatment-naïve patients, this work identified novel WM abnormalities and a potential causal pathway linking WM damage to symptom severity via regional structural network alterations in brain hubs.


Assuntos
Transtorno Depressivo Maior , Imagem de Tensor de Difusão , Substância Branca , Humanos , Adolescente , Transtorno Depressivo Maior/diagnóstico por imagem , Transtorno Depressivo Maior/patologia , Feminino , Masculino , Substância Branca/patologia , Substância Branca/diagnóstico por imagem , Imagem de Tensor de Difusão/métodos , Encéfalo/diagnóstico por imagem , Encéfalo/patologia , Puberdade/psicologia , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/patologia , Criança
4.
JAMA Netw Open ; 7(6): e2417796, 2024 Jun 03.
Artigo em Inglês | MEDLINE | ID: mdl-38922618

RESUMO

Importance: Determining how individuals engage with digital health interventions over time is crucial to understand and optimize intervention outcomes. Objective: To identify the engagement trajectories with a mobile chat-based smoking cessation intervention and examine its association with biochemically validated abstinence. Design, Setting, and Participants: A secondary analysis of a pragmatic, cluster randomized clinical trial conducted in Hong Kong with 6-month follow-up. From June 18 to September 30, 2017, 624 adult daily smokers were recruited from 34 community sites randomized to the intervention group. Data were analyzed from March 6 to October 30, 2023. Intervention: Chat-based cessation support delivered by a live counselor via a mobile instant messaging app for 3 months from baseline. Main Outcomes and Measures: Group-based trajectory modeling was used to identify engagement trajectories using the participants' weekly responses to the messages from the counselor over the 3-month intervention period. The outcome measures were biochemically validated tobacco abstinence at 3-month (end of treatment) and 6-month follow-ups. Covariates included sex, age, educational level, nicotine dependence, past quit attempt, and intention to quit at baseline. Results: Of 624 participants included in the analysis, 479 were male (76.8%), and the mean (SD) age was 42.1 (16.2) years. Four distinct engagement trajectories were identified: low engagement group (447 [71.6%]), where participants maintained very low engagement throughout; rapid-declining group (86 [13.8%]), where participants began with moderate engagement and rapidly decreased to a low level; gradual-declining group (58 [9.3%]), where participants had high initial engagement and gradually decreased to a moderate level; and high engagement group (58 [5.3%]), where participants maintained high engagement throughout. Compared with the low engagement group, the 6-month validated abstinence rates were significantly higher in the rapid-declining group (adjusted relative risk [ARR], 3.30; 95% CI, 1.39-7.81), gradual-declining group (ARR, 5.17; 95% CI, 2.21-12.11), and high engagement group (ARR, 4.98; 95% CI, 1.82-13.60). The corresponding ARRs (95% CI) of 3-month validated abstinence were 4.03 (95% CI, 1.53-10.59), 5.25 (95% CI, 1.98-13.88), and 9.23 (95% CI, 3.29-25.86). Conclusions and Relevance: The findings of this study suggest that higher levels of engagement with the chat-based smoking cessation intervention were associated with greater biochemically validated tobacco abstinence. Improving engagement with digital interventions may increase intervention benefits. Trial Registration: ClinicalTrials.gov Identifier: NCT03182790.


Assuntos
Abandono do Hábito de Fumar , Humanos , Abandono do Hábito de Fumar/métodos , Abandono do Hábito de Fumar/psicologia , Masculino , Feminino , Adulto , Hong Kong , Pessoa de Meia-Idade , Envio de Mensagens de Texto , Aplicativos Móveis
5.
Brief Bioinform ; 25(4)2024 May 23.
Artigo em Inglês | MEDLINE | ID: mdl-38833322

RESUMO

Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.


Assuntos
Medicina de Precisão , Humanos , Medicina de Precisão/métodos , Neoplasias/genética , Neoplasias/classificação , Neoplasias/terapia , Biomarcadores Tumorais/genética , Biologia Computacional/métodos , Oncologia/métodos , Neoplasias da Mama/genética , Neoplasias da Mama/classificação , Neoplasias da Mama/terapia , Feminino
6.
Comput Struct Biotechnol J ; 23: 1408-1417, 2024 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-38616962

RESUMO

Utilizing α,ß-unsaturated carbonyl group as Michael acceptors to react with thiols represents a successful strategy for developing KRASG12C inhibitors. Despite this, the precise reaction mechanism between KRASG12C and covalent inhibitors remains a subject of debate, primarily due to the absence of an appropriate residue capable of deprotonating the cysteine thiol as a base. To uncover this reaction mechanism, we first discussed the chemical reaction mechanism in solvent conditions via density functional theory (DFT) calculation. Based on this, we then proposed and validated the enzymatic reaction mechanism by employing quantum mechanics/molecular mechanics (QM/MM) calculation. Our QM/MM analysis suggests that, in biological conditions, proton transfer and nucleophilic addition may proceed through a concerted process to form an enolate intermediate, bypassing the need for a base catalyst. This proposed mechanism differs from previous findings. Following the formation of the enolate intermediate, solvent-assisted tautomerization results in the final product. Our calculations indicate that solvent-assisted tautomerization is the rate-limiting step in the catalytic cycle under biological conditions. On the basis of this reaction mechanism, the calculated kinact/ki for two inhibitors is consistent well with the experimental results. Our findings provide new insights into the reaction mechanism between the cysteine of KRASG12C and the covalent inhibitors and may provide valuable information for designing effective covalent inhibitors targeting KRASG12C and other similar targets.

7.
J Chem Inf Model ; 64(9): 3630-3639, 2024 May 13.
Artigo em Inglês | MEDLINE | ID: mdl-38630855

RESUMO

The introduction of AlphaFold2 (AF2) has sparked significant enthusiasm and generated extensive discussion within the scientific community, particularly among drug discovery researchers. Although previous studies have addressed the performance of AF2 structures in virtual screening (VS), a more comprehensive investigation is still necessary considering the paramount importance of structural accuracy in drug design. In this study, we evaluate the performance of AF2 structures in VS across three common drug discovery scenarios: targets with holo, apo, and AF2 structures; targets with only apo and AF2 structures; and targets exclusively with AF2 structures. We utilized both the traditional physics-based Glide and the deep-learning-based scoring function RTMscore to rank the compounds in the DUD-E, DEKOIS 2.0, and DECOY data sets. The results demonstrate that, overall, the performance of VS on AF2 structures is comparable to that on apo structures but notably inferior to that on holo structures across diverse scenarios. Moreover, when a target has solely AF2 structure, selecting the holo structure of the target from different subtypes within the same protein family produces comparable results with the AF2 structure for VS on the data set of the AF2 structures, and significantly better results than the AF2 structures on its own data set. This indicates that utilizing AF2 structures for docking-based VS may not yield most satisfactory outcomes, even when solely AF2 structures are available. Moreover, we rule out the possibility that the variations in VS performance between the binding pockets of AF2 and holo structures arise from the differences in their biological assembly composition.


Assuntos
Descoberta de Drogas , Descoberta de Drogas/métodos , Proteínas/química , Proteínas/metabolismo , Conformação Proteica , Simulação de Acoplamento Molecular , Aprendizado Profundo , Humanos , Desenho de Fármacos
8.
Tob Control ; 2024 Mar 08.
Artigo em Inglês | MEDLINE | ID: mdl-38458757

RESUMO

OBJECTIVES: To examine the associations between tobacco industry denormalisation (TID) beliefs and support for tobacco endgame policies. METHODS: A total of 2810 randomly selected adult respondents of population-based tobacco policy-related surveys (2018-2019) were included. TID beliefs (agree vs disagree/unsure) were measured by seven items: tobacco manufacturers ignore health, induce addiction, hide harm, spread false information, lure smoking, interfere with tobacco control policies and should be responsible for health problems. Score of each item was summed up and dichotomised (median=5, >5 strong beliefs; ≤5 weak beliefs). Support for tobacco endgame policies on total bans of tobacco sales (yes/no) and use (yes/no) was reported. Associations between TID beliefs and tobacco endgame policies support across various smoking status were analysed, adjusting for sociodemographics. RESULTS: Fewer smokers (23.3%) had strong beliefs of TID than ex-smokers (48.4%) and never smokers (48.5%) (p<0.001). Support for total bans on tobacco sales (74.6%) and use (76.9%) was lower in smokers (33.3% and 35.3%) than ex-smokers (74.3% and 77.9%) and never smokers (76.0% and 78.3%) (all p values<0.001). An increase in the number of TID beliefs supported was positively associated with support for a total ban on sales (adjusted risk ratio 1.06, 95% CI 1.05 to 1.08, p<0.001) and use (1.06, 95% CI 1.05 to 1.07, p<0.001). The corresponding associations were stronger in smokers than non-smokers (sales: 1.87 vs 1.25, p value for interaction=0.03; use: 1.78 vs 1.21, p value for interaction=0.03). CONCLUSION: Stronger TID belief was associated with greater support for total bans on tobacco sales and use. TID intervention may increase support for tobacco endgame, especially in current smokers.

9.
J Biomol Struct Dyn ; : 1-13, 2024 Mar 18.
Artigo em Inglês | MEDLINE | ID: mdl-38497736

RESUMO

The production of penicillin-binding protein 2a (PBP2a), a cell wall synthesis protein, is primarily responsible for the high-level resistance observed in methicillin-resistant Staphylococcus aureus (MRSA). PBP2a exhibits a significantly reduced affinity for most ß-lactam antibiotics owing to its tightly closed active site. Quinazolinones (QNE), a novel class of non-ß-lactam antibiotics, could initiate the allosteric regulation of PBP2a, resulting in the opening of the initially closed active pocket. Based on our previous study, we have a basic understanding of the dual-site inhibitor ceftaroline (CFT) induced allosteric regulation of PBP2a. However, there are still limitations in the knowledge of how combining medicines, QNE and piperacillin (PIP), induce the allosteric response of PBP2a and inhibit its function. Herein, molecular dynamics (MD) simulations were performed to elucidate the intricate mechanisms underlying the combination mode of QNE and PIP. Our study successfully captured the opening process of the active pocket upon the binding of the QNE at the allosteric site, which alters the signaling pathways with a favorable transmission to the active site. Subsequent docking experiments with different conformational states of the active pocket indicated that all three inhibitors, PIP, QNE, and CFT, exhibited higher docking scores and more favorable docking poses to the open active pocket. These findings reveal the implied mechanism of QNE-mediated allostery underlying combination therapy and provide novel insights into developing innovative therapeutic modalities against MRSA.Communicated by Ramaswamy H. Sarma.

10.
Tob Induc Dis ; 222024.
Artigo em Inglês | MEDLINE | ID: mdl-38375095

RESUMO

INTRODUCTION: Smoking prevalence among people in custody (PIC) is extremely high, and prison-based smoking cessation interventions are needed. The study explored the quitting experiences of PIC who participated in the 'Quit to Win' contest (QTW). METHODS: This qualitative study, conducted from 2019 to 2021 in two Hong Kong prisons, included semi-structured individual interviews with 26 PIC (13 men and 13 women) who were participants in QTW and two correctional staff who coordinated QTW. A semi-structured interview guide with open-ended questions was developed to examine multilevel factors that promote or impede smoking cessation in prisons. Maximum variation sampling was used to ensure a diverse range of social, demographic, and smoking profiles. Data were managed and analyzed using thematic analysis. RESULTS: Two themes were identified from the data: 1) quitting in prison: barriers and facilitators; and 2) QTW in prison: a trigger for behavior change. Barriers (i.e. stress, boredom, isolation, lack of self-autonomy, nicotine dependence and lack of cessation medication, barriers to moving to a different wing) and facilitators (i.e. concerns about health, money savings, and the smoke-free wing) that impeded or supported smoking cessation during incarceration were identified. QTW provided health education, quitting incentives, and social support that helped PIC overcome the barriers of quitting by serving as a trigger for behavior change. Notably, social visits with family were identified as key drivers of PIC's quitting success, whereas their suspension during the COVID-19 pandemic disincentivized their abstinence. CONCLUSIONS: This study introduced the QTW contest to prisons and provided qualitative evidence on the multilevel factors promoting or impeding smoking cessation in prison. QTW helped PIC overcome the barriers of quitting by serving as a trigger for behavior change. Future prison-based interventions should leverage social support, enhance stress-coping skills, facilitate access to pharmacotherapy, and collaborate with correctional services agencies.

11.
Anal Chem ; 2024 Feb 07.
Artigo em Inglês | MEDLINE | ID: mdl-38324756

RESUMO

Clinical metabolomics is growing as an essential tool for precision medicine. However, classical machine learning algorithms struggle to comprehensively encode and analyze the metabolomics data due to their high dimensionality and complex intercorrelations. This article introduces a new method called MetDIT, designed to analyze intricate metabolomics data effectively using deep convolutional neural networks (CNN). MetDIT comprises two components: TransOmics and NetOmics. Since CNN models have difficulty in processing one-dimensional (1D) sequence data efficiently, we developed TransOmics, a framework that transforms sequence data into two-dimensional (2D) images while maintaining a one-to-one correspondence between the sequences and images. NetOmics, the second component, leverages a CNN architecture to extract more discriminative representations from the transformed samples. To overcome the overfitting due to the small sample size and class imbalance, we introduced a feature augmentation module (FAM) and a loss function to improve the model performance. Furthermore, we systematically optimized the model backbone and image resolution to balance the model parameters and computational costs. To demonstrate the performance of the proposed MetDIT, we conducted extensive experiments using three different clinical metabolomics data sets and achieved better classification performance than classical machine learning methods used in metabolomics, including Random Forest, SVM, XGBoost, and LightGBM. The source code is available at the GitHub repository at https://github.com/Li-OmicsLab/MetDIT, and the WebApp can be found at http://metdit.bioinformatics.vip/.

12.
Mini Rev Med Chem ; 24(14): 1353-1367, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38243944

RESUMO

Drug discovery is a complex and iterative process, making it ideal for using artificial intelligence (AI). This paper uses a bibliometric approach to reveal AI's trend and underlying structure in drug discovery (AIDD). A total of 4310 journal articles and reviews indexed in Scopus were analyzed, revealing that AIDD has been rapidly growing over the past two decades, with a significant increase after 2017. The United States, China, and the United Kingdom were the leading countries in research output, with academic institutions, particularly the Chinese Academy of Sciences and the University of Cambridge, being the most productive. In addition, industrial companies, including both pharmaceutical and high-tech ones, also made significant contributions. Additionally, this paper thoroughly discussed the evolution and research frontiers of AIDD, which were uncovered through co-occurrence analyses of keywords using VOSviewer. Our findings highlight that AIDD is an interdisciplinary and promising research field that has the potential to revolutionize drug discovery. The comprehensive overview provided here will be of significant interest to researchers, practitioners, and policy-makers in related fields. The results emphasize the need for continued investment and collaboration in AIDD to accelerate drug discovery, reduce costs, and improve patient outcomes.


Assuntos
Inteligência Artificial , Bibliometria , Descoberta de Drogas , Descoberta de Drogas/métodos , Humanos
13.
J Cheminform ; 16(1): 5, 2024 Jan 11.
Artigo em Inglês | MEDLINE | ID: mdl-38212855

RESUMO

Probing the surface of proteins to predict the binding site and binding affinity for a given small molecule is a critical but challenging task in drug discovery. Blind docking addresses this issue by performing docking on binding regions randomly sampled from the entire protein surface. However, compared with local docking, blind docking is less accurate and reliable because the docking space is too largetly sampled. Cavity detection-guided blind docking methods improved the accuracy by using cavity detection (also known as binding site detection) tools to guide the docking procedure. However, it is worth noting that the performance of these methods heavily relies on the quality of the cavity detection tool. This constraint, namely the dependence on a single cavity detection tool, significantly impacts the overall performance of cavity detection-guided methods. To overcome this limitation, we proposed Consensus Blind Dock (CoBDock), a novel blind, parallel docking method that uses machine learning algorithms to integrate docking and cavity detection results to improve not only binding site identification but also pose prediction accuracy. Our experiments on several datasets, including PDBBind 2020, ADS, MTi, DUD-E, and CASF-2016, showed that CoBDock has better binding site and binding mode performance than other state-of-the-art cavity detector tools and blind docking methods.

14.
J Biomol Struct Dyn ; 42(5): 2424-2436, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-37144732

RESUMO

Prion diseases are a group of fatal neurodegenerative diseases caused by the misfolding and aggregation of prion protein (PrP), and the inhibition of PrP aggregation is one of the most effective therapeutic strategies. Proanthocyanidin B2 (PB2) and B3 (PB3), the effective natural antioxidants have been evaluated for the inhibition of amyloid-related protein aggregation. Since PrP has similar aggregation mechanism with other amyloid-related proteins, will PB2 and PB3 affect the aggregation of PrP? In this paper, experimental and molecular dynamics (MD) simulation methods were combined to investigate the influence of PB2 and PB3 on PrP aggregation. Thioflavin T assays showed PB2 and PB3 could inhibit PrP aggregation in a concentrate-dependent manner in vitro. To understand the underlying mechanism, we performed 400 ns all-atom MD simulations. The results suggested PB2 could stabilize the α2 C-terminus and the hydrophobic core of protein by stabilizing two important salt bridges R156-E196 and R156-D202, and consequently made global structure of protein more stable. Surprisingly, PB3 could not stabilize PrP, which may inhibit PrP aggregation through a different mechanism. Since dimerization is the first step of aggregation, will PB3 inhibit PrP aggregation by inhibiting the dimerization? To verify our assumption, we then explored the effect of PB3 on protein dimerization by performing 800 ns MD simulations. The results suggested PB3 could reduce the residue contacts and hydrogen bonds between two monomers, preventing dimerization process of PrP. The possible inhibition mechanism of PB2 and PB3 on PrP aggregation could provide useful information for drug development against prion diseases.Communicated by Ramaswamy H. Sarma.


Assuntos
Doenças Priônicas , Príons , Proantocianidinas , Humanos , Simulação de Dinâmica Molecular , Proantocianidinas/farmacologia , Proteínas Priônicas/química
15.
Antimicrob Agents Chemother ; 67(12): e0089523, 2023 12 14.
Artigo em Inglês | MEDLINE | ID: mdl-37971241

RESUMO

Methicillin-resistant Staphylococcus aureus (MRSA) acquires high-level resistance against ß-lactam antibiotics by expressing penicillin-binding protein 2a (PBP2a). PBP2a is a cell wall-synthesizing protein whose closed active site exhibits a reduced binding affinity toward ß-lactam antibiotics. Ceftaroline (CFT), a fifth-generation cephalosporin, can effectively inhibit the PBP2a activity by binding to an allosteric site to trigger the active site opening, allowing a second CFT to access the active site. However, the essential mechanism behind the allosteric behavior of PBP2a remains unclear. Herein, computational simulations are employed to elucidate how CFT allosterically regulates the conformation and dynamics of the active site of PBP2a. While CFT stabilizes the allosteric domain surrounding it, it simultaneously enhances the dynamics of the catalytic domain. Specifically, the study successfully captured the opening process of the active pocket in the allosteric CFT-bound systems and discovered that CFT alters the potential signal-propagating pathways from the allosteric site to the active site. These findings reveal the implied mechanism of the CFT-mediated allostery in PBP2a and provide new insights into dual-site drug design or combination therapy against MRSA targeting PBP2a.


Assuntos
Antibacterianos , Staphylococcus aureus Resistente à Meticilina , Antibacterianos/química , Proteínas de Ligação às Penicilinas , Regulação Alostérica , Proteínas de Bactérias/metabolismo , Testes de Sensibilidade Microbiana
16.
Front Oncol ; 13: 1091958, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-37954072

RESUMO

Purpose: While observational studies have identified obesity as a potential risk factor for gastric cancer, the causality remains uncertain. This study aimed to evaluate the causal relationship between obesity and gastric cancer and identify the shared molecular signatures linking obesity to gastric cancer. Methods: A two-sample Mendelian randomization (MR) analysis was conducted using the GWAS data of body fat percentage (exposure, n = 331,117) and gastric cancer (outcome, n = 202,308). Bioinformatics and meta-analysis of multi-omics data were performed to identify key molecules mediating the causality. The meta-analysis of the plasma/serum proteome included 1,662 obese and 3,153 gastric cancer patients. Obesity and gastric cancer-associated genes were identified using seven common gene ontology databases. The transcriptomic data were obtained from TCGA and GEO databases. The Bioinformatic findings were clinically validated in plasma from 220 obese and 400 gastric cancer patients across two hospitals. Finally, structural-based virtual screening (SBVS) was performed to explore the potential FDA-approved drugs targeting the identified mediating molecules. Results: The MR analysis revealed a significant causal association between obesity and gastric cancer (IVW, OR = 1.37, 95% CI:1.12-1.69, P = 0.0028), without pleiotropy or heterogeneity. Bioinformatic and meta-analysis of multi-omics data revealed shared TNF, PI3K-AKT, and cytokine signaling dysregulation, with significant upregulation of AKT1, IL-6, and TNF. The clinical study confirmed widespread upregulation of systemic inflammatory markers in the plasma of both diseases. SBVS identified six novel potent AKT1 inhibitors, including the dietary supplement adenosine, representing a potentially preventive drug with low toxicity. Conclusion: Obesity causally increases gastric cancer, likely mediated by persistent AKT1/IL-6/TNF upregulation. As a potential AKT1 inhibitor, adenosine may mitigate the obesity-to-gastric cancer transition. These findings could inform preventive drug development to reduce gastric cancer risk in obesity.

17.
Cell Rep Methods ; 3(11): 100643, 2023 Nov 20.
Artigo em Inglês | MEDLINE | ID: mdl-37989083

RESUMO

A deep understanding of immunotherapy response/resistance mechanisms and a highly reliable therapy response prediction are vital for cancer treatment. Here, we developed scCURE (single-cell RNA sequencing [scRNA-seq] data-based Changed and Unchanged cell Recognition during immunotherapy). Based on Gaussian mixture modeling, Kullback-Leibler (KL) divergence, and mutual nearest-neighbors criteria, scCURE can faithfully discriminate between cells affected or unaffected by immunotherapy intervention. By conducting scCURE analyses in melanoma and breast cancer immunotherapy scRNA-seq data, we found that the baseline profiles of specific CD8+ T and macrophage cells (identified by scCURE) can determine the way in which tumor microenvironment immune cells respond to immunotherapy, e.g., antitumor immunity activation or de-activation; therefore, these cells could be predictive factors for treatment response. In this work, we demonstrated that the immunotherapy-associated cell-cell heterogeneities revealed by scCURE can be utilized to integrate the therapy response mechanism study and prediction model construction.


Assuntos
Neoplasias da Mama , Melanoma , Humanos , Feminino , Melanoma/terapia , Prognóstico , Neoplasias da Mama/terapia , Imunoterapia , Macrófagos/patologia , Microambiente Tumoral/genética
18.
J Comput Aided Mol Des ; 37(12): 695-706, 2023 12.
Artigo em Inglês | MEDLINE | ID: mdl-37642861

RESUMO

Multidrug-resistant tuberculosis (MDR-TB) continues to spread worldwide and remains one of the leading causes of death among infectious diseases. The enoyl-acyl carrier protein reductase (InhA) belongs to FAS-II family and is essential for the formation of the Mycobacterium tuberculosis cell wall. Recent years, InhA direct inhibitors have been extensively studied to overcome MDR-TB. However, there are still no inhibitors that have entered clinical research. Here, the ensemble docking-based virtual screening along with biological assay were used to identify potent InhA direct inhibitors from Chembridge, Chemdiv, and Specs. Ultimately, 34 compounds were purchased and first assayed for the binding affinity, of which four compounds can bind InhA well with KD values ranging from 48.4 to 56.2 µM. Among them, compound 9,222,034 has the best inhibitory activity against InhA enzyme with an IC50 value of 18.05 µM. In addition, the molecular dynamic simulation and binding free energy calculation indicate that the identified compounds bind to InhA with "extended" conformation. Residue energy decomposition shows that residues such as Tyr158, Met161, and Met191 have higher energy contributions in the binding of compounds. By analyzing the binding modes, we found that these compounds can bind to a hydrophobic sub-pocket formed by residues Tyr158, Phe149, Ile215, Leu218, etc., resulting in extensive van der Waals interactions. In summary, this study proposed an efficient strategy for discovering InhA direct inhibitors through ensemble docking-based virtual screening, and finally identified four active compounds with new skeletons, which can provide valuable information for the discovery and optimization of InhA direct inhibitors.


Assuntos
Mycobacterium tuberculosis , Tuberculose Resistente a Múltiplos Medicamentos , Humanos , Antituberculosos/farmacologia , Antituberculosos/química , Simulação de Dinâmica Molecular , Conformação Molecular , Proteínas de Bactérias/química , Simulação de Acoplamento Molecular , Inibidores Enzimáticos/farmacologia , Inibidores Enzimáticos/química
19.
Brief Bioinform ; 24(5)2023 09 20.
Artigo em Inglês | MEDLINE | ID: mdl-37651610

RESUMO

The accurate prediction of the effect of amino acid mutations for protein-protein interactions (PPI $\Delta \Delta G$) is a crucial task in protein engineering, as it provides insight into the relevant biological processes underpinning protein binding and provides a basis for further drug discovery. In this study, we propose MpbPPI, a novel multi-task pre-training-based geometric equivariance-preserving framework to predict PPI  $\Delta \Delta G$. Pre-training on a strictly screened pre-training dataset is employed to address the scarcity of protein-protein complex structures annotated with PPI $\Delta \Delta G$ values. MpbPPI employs a multi-task pre-training technique, forcing the framework to learn comprehensive backbone and side chain geometric regulations of protein-protein complexes at different scales. After pre-training, MpbPPI can generate high-quality representations capturing the effective geometric characteristics of labeled protein-protein complexes for downstream $\Delta \Delta G$ predictions. MpbPPI serves as a scalable framework supporting different sources of mutant-type (MT) protein-protein complexes for flexible application. Experimental results on four benchmark datasets demonstrate that MpbPPI is a state-of-the-art framework for PPI $\Delta \Delta G$ predictions. The data and source code are available at https://github.com/arantir123/MpbPPI.


Assuntos
Aminoácidos , Benchmarking , Mutação , Descoberta de Drogas , Aprendizagem
20.
Tob Induc Dis ; 21: 77, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-37323509

RESUMO

INTRODUCTION: Observational and experimental studies have suggested that messaging on smoking-related COVID-19 risk may promote smoking abstinence, but evidence from randomized clinical trials (RCTs) is lacking. METHODS: This was a pragmatic RCT in Hong Kong, China, to compare the effectiveness of communicating smoking-related COVID-19 risk with generic cessation support on abstinence. Both groups received brief cessation advice at baseline. The intervention group received messaging on smoking-related COVID-19 risk and cessation support via instant messaging for three months (16 messages in total), which highlighted the increased risk of severe COVID-19 and deaths, and potentially higher risk of viral exposure (e.g. due to mask removal) for smokers. The control group received generic text messaging support for three months (16 messages). The primary outcomes were biochemically validated 7-day point prevalence abstinence (PPA) at 3 and 6 months. Intention to treat analyses was used. RESULTS: Between 13 June and 30 October 2020, 1166 participants were randomly assigned to an intervention (n=583) or control (n=583) group. By intention-to-treat, validated 7-day PPA did not significantly differ between the intervention and control groups at three months (9.6% and 11.8%, relative risk, RR=0.81; 95% CI: 0.58-1.13, p=0.22) or six months (9.3% and 11.7%, RR=0.79; 95% CI: 0.57-1.11, p=0.18). A higher perceived severity of COVID-19 in smokers at baseline was associated with a greater validated 7-day PPA at six months, and a marginally significant intervention effect on changes in perceived severity from baseline through 6 months was found (p for group × time interaction = 0.08). CONCLUSIONS: Communicating smoking-related COVID-19 risk via instant messaging was not more effective in increasing smoking abstinence than generic cessation support. TRIAL REGISTRATION: The study is registered on ClinicalTrials.gov Identifier: NCT04399967.

SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA