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McCone JAJ, Teesdale-Spittle PH, Flanagan JU, Harvey JE. A Structure-Activity Investigation of the Fungal Metabolite (-)-TAN-2483B: Inhibition of Bruton's Tyrosine Kinase. Chemistry 2024; 30:e202401051. [PMID: 38629656 DOI: 10.1002/chem.202401051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Indexed: 06/01/2024]
Abstract
The natural product (-)-TAN-2483B is a fungal secondary metabolite which displays promising anti-cancer and immunomodulatory activity. Our previous syntheses of (-)-TAN-2483B and sidechain analogues uncovered inhibitory activity against Bruton's tyrosine kinase (Btk), an established drug target for various leukaemia and immunological diseases. A structure-based computational study using ensemble docking and molecular dynamics was performed to determine plausible binding modes for (-)-TAN-2483B and analogues in the Btk binding site. These hypotheses guided the design of new analogues which were synthesised and their inhibitory activities determined, providing insights into the structural determinants of the furopyranone scaffold that confer both activity and selectivity for Btk. These findings offer new perspectives for generating optimised (-)-TAN-2483B-based kinase inhibitors for the treatment of leukaemia and immunological diseases.
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Affiliation(s)
- Jordan A J McCone
- School of Chemical and Physical Sciences, Centre for Biodiscovery, Victoria University of Wellington, Wellington, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
| | - Paul H Teesdale-Spittle
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
- School of Biological Sciences, Centre for Biodiscovery, Victoria University of Wellington, Wellington, New Zealand
| | - Jack U Flanagan
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
- Auckland Cancer Society Research Centre, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
- Department of Pharmacology and Clinical Pharmacology, School of Medical Sciences, The University of Auckland, Auckland, New Zealand
| | - Joanne E Harvey
- School of Chemical and Physical Sciences, Centre for Biodiscovery, Victoria University of Wellington, Wellington, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
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Zhuo H, Zhou Z, Chen X, Song Z, Shang Q, Huang H, Xiao Y, Wang X, Chen H, Yan X, Zhang P, Gong Y, Liu H, Liu Y, Wu Z, Liang D, Ren H, Jiang X. Constructing and validating a predictive nomogram for osteoporosis risk among Chinese single-center male population using the systemic immune-inflammation index. Sci Rep 2024; 14:12637. [PMID: 38825605 PMCID: PMC11144694 DOI: 10.1038/s41598-024-63193-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Accepted: 05/27/2024] [Indexed: 06/04/2024] Open
Abstract
Osteoporosis (OP) is a bone metabolism disease that is associated with inflammatory pathological mechanism. Nonetheless, rare studies have investigated the diagnostic effectiveness of immune-inflammation index in the male population. Therefore, it is interesting to achieve early diagnosis of OP in male population based on the inflammatory makers from blood routine examination. We developed a prediction model based on a training dataset of 826 Chinese male patients through a retrospective study, and the data was collected from January 2022 to May 2023. All participants underwent the dual-energy X-ray absorptiometry (DXEA) and blood routine examination. Inflammatory markers such as systemic immune-inflammation index (SII) and platelet-to-lymphocyte ratio (PLR) was calculated and recorded. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to optimize feature selection. Multivariable logistic regression analysis was applied to construct a predicting model incorporating the feature selected in the LASSO model. This predictive model was displayed as a nomogram. Receiver operating characteristic (ROC) curve, C-index, calibration curve, and clinical decision curve analysis (DCA) to evaluate model performance. Internal validation was test by the bootstrapping method. This study was approved by the Ethic Committee of the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine (Ethic No. JY2023012) and conducted in accordance with the relevant guidelines and regulations. The predictive factors included in the prediction model were age, BMI, cardiovascular diseases, cerebrovascular diseases, neuropathy, thyroid diseases, fracture history, SII, PLR, C-reactive protein (CRP). The model displayed well discrimination with a C-index of 0.822 (95% confidence interval: 0.798-0.846) and good calibration. Internal validation showed a high C-index value of 0.805. Decision curve analysis (DCA) showed that when the threshold probability was between 3 and 76%, the nomogram had a good clinical value. This nomogram can effectively predict the incidence of OP in male population based on SII and PLR, which would help clinicians rapidly and conveniently diagnose OP with men in the future.
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Affiliation(s)
- Hang Zhuo
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Zelin Zhou
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Xingda Chen
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Zefeng Song
- Medical Department, Dalian University of Technology, Dalian, 116024, China
| | - Qi Shang
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Hongwei Huang
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Yun Xiao
- The Third Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, Guangdong, China
| | - Xiaowen Wang
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Honglin Chen
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Xianwei Yan
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Peng Zhang
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Yan Gong
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Huiwen Liu
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Yu Liu
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Zixian Wu
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - De Liang
- The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, 510405, China
| | - Hui Ren
- The Spine Surgery Department, Second Affiliated Hospital of Guangzhou Medical University, 250 Changgang East Road, Haizhu District, Guangzhou, 510260, Guangdong, China.
| | - Xiaobing Jiang
- The Spine Surgery Department, Second Affiliated Hospital of Guangzhou Medical University, 250 Changgang East Road, Haizhu District, Guangzhou, 510260, Guangdong, China.
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Zhou J, Yuan X. Establishment of a risk prediction model for bowel necrosis in patients with incarcerated inguinal hernia. BMC Med Inform Decis Mak 2024; 24:39. [PMID: 38321399 PMCID: PMC10845797 DOI: 10.1186/s12911-024-02440-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/25/2024] [Indexed: 02/08/2024] Open
Abstract
INTRODUCTION Incarceration occurred in approximately 5% to 15% of inguinal hernia patients, with around 15% of incarcerated cases progressing to intestinal necrosis, necessitating bowel resection surgery. Patients with intestinal necrosis had significantly higher mortality and complication rates compared to those without necrosis.The primary objective of this study was to design and validate a diagnostic model capable of predicting intestinal necrosis in patients with incarcerated groin hernias. METHODS We screened the clinical records of patients who underwent emergency surgery for incarcerated inguinal hernia between January 1, 2015, and December 31, 2022. To ensure balanced representation, the enrolled patients were randomly divided into a training set (n = 180) and a validation set (n = 76) using a 2:1 ratio. Logistic regression analysis was conducted using the rms package in R software, incorporating selected features from the LASSO regression model, to construct a predictive model. RESULTS Based on the results of the LASSO regression analysis, a multivariate logistic regression model was developed to establish the predictive model. The predictors included in the model were Abdominal effusion, Hernia Sac Effusion, and Procalcitonin. The area under the receiver operating characteristic (ROC) curve for the nomogram graph in the training set was 0.977 (95% CI = 0.957-0.992). In the validation set, the AUC for the nomogram graph was 0.970. Calibration curve and decision curve analysis (DCA) verified the accuracy and practicability of the nomogram graph in our study. CONCLUSION Bowel necrosis in patients with incarcerated inguinal hernia was influenced by multiple factors. The nomogram predictive model constructed in this study could be utilized to predict and differentiate whether incarcerated inguinal hernia patients were at risk of developing bowel necrosis.
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Affiliation(s)
- Jiajie Zhou
- Department of General Surgery, Huai'an First People's Hospital, Nanjing Medical University, Huai'an, Jiangsu Province, China.
| | - Xiaoming Yuan
- Department of General Surgery, Huai'an First People's Hospital, Nanjing Medical University, Huai'an, Jiangsu Province, China
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Yu M, Li X, Zong L, Wang Z, Lv Q. A Novel Body Mass Index-Based Thromboembolic Risk Score for Overweight Patients with Nonvalvular Atrial Fibrillation. Anatol J Cardiol 2024; 28:35-43. [PMID: 37961898 PMCID: PMC10796238 DOI: 10.14744/anatoljcardiol.2023.3373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 09/15/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND A novel risk prediction model appears to be urgently required to improve the assessment of thrombotic risk in overweight patients with nonvalvular atrial fibrillation (NVAF). We developed a novel body mass index (BMI)-based thromboembolic risk score (namely AB2S score) for these patients. METHODS A total of 952 overweight patients with NVAF were retrospectively enrolled in this study with a 12-month follow-up. The primary endpoint was 1-year systemic thromboembolism and the time to thrombosis (TTT). The candidate risk variables identified by logistic regression analysis were included in the final nomogram model to construct AB2S score. The measures of model fit were evaluated using area under the curve (AUC), C-statistic, and calibration curve. The performance comparison of the AB2S score to the CHADS2 and CHA2DS2-VASc score was performed in terms of the AUC and decision analysis curve (DAC). RESULTS The AB2S score was constructed using 7 candidate risk variables, including a 3-category BMI (25 to 30, 30 to 34, or ≥35 kg/m2). It yielded a c-index of 0.885 (95% CI, 0.814-0.954) and an AUC of 0.885 (95% CI, 0.815-0.955) for predicting 1-year systemic thromboembolism in patients with NVAF. Compared to the CHADS2 score and CHA2DS2-VASc score, the AB2S score had greater AUC and DAC values in predicting the thromboembolic risk and better risk stratification in TTT (P <.0001, P =.082, respectively). CONCLUSION Our results highlighted the importance of a BMI-based AB2S score in determining systemic thromboembolism risk in overweight patients with NVAF, which may aid in decision-making for these patients to balance the effectiveness of anticoagulation from the underlying thrombotic risk.
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Affiliation(s)
- Meixiang Yu
- Department of Pharmacy, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiaoye Li
- Department of Pharmacy, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Liuliu Zong
- Department of Pharmacy, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zi Wang
- Department of Pharmacy, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qianzhou Lv
- Department of Pharmacy, Zhongshan Hospital, Fudan University, Shanghai, China
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Cao C, Xu Y, Jiang W, Wu S, Shen Y, Xia X, Wang L, Zhang H, Jiang H, Li X, Li X, Ye Y. Nomogram for predicting bleeding events in nonvalvular atrial fibrillation patients receiving rivaroxaban: A retrospective study. Health Sci Rep 2024; 7:e1792. [PMID: 38196572 PMCID: PMC10774492 DOI: 10.1002/hsr2.1792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/29/2023] [Accepted: 12/17/2023] [Indexed: 01/11/2024] Open
Abstract
Background and Aims To construct a bleeding events prediction model of nonvalvular atrial fibrillation (NVAF) patients receiving rivaroxaban. Methods We conducted a retrospective cohort study in patients with NVAF who received rivaroxaban from June 2017 to March 2019. Demographic information and clinical characteristics were obtained from the electronic medical system. Univariate analysis was used to find the primary predictive factors of bleeding events in patients receiving rivaroxaban. Multiple analysis was conducted to screen the primary independent predictive factors selected from the univariate analysis. Finally, the independent influencing factors were applied to build a prediction model by using R software; then, a nomogram was established according to the selected variables visually, and the sensitivity and specificity of the model was evaluated. Results Twelve primary predictive factors were selected by univariate analysis from 46 variables, and multivariate analysis showed that older age, higher prothrombin time (PT) values, history of heart failure and stroke were independent risk factors of bleeding events. The area under curve (AUC) for this novel nomogram model was 0.828 (95% CI: 0.763-0.894). The mean AUC over 10-fold stratified cross-validation was 0.787, and subgroup analysis validation also showed a satisfied AUC. In addition, the decision curve analysis showed that the PT in combination with CHA2DS2-VASc and HASBLED was more practical and accurate for predicting bleeding events than using CHA2DS2-VASc and HASBLED alone. Conclusions PT in combination with CHA2DS2-VASc and HASBLED could be considered as a more practical and accurate method for predicting bleeding events in patients taking rivaroxaban.
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Affiliation(s)
- Chang Cao
- Department of Pharmacy, Zhongshan Hospital (Xiamen)Fudan UniversityXiamenChina
- Department of Pharmacy, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Yijiao Xu
- Department of Respiration, Zhongshan Hospital (Xiamen)Fudan UniversityXiamenChina
| | - Weiwen Jiang
- Department of Pharmacy, Zhongshan Hospital (Xiamen)Fudan UniversityXiamenChina
- Department of Pharmacy, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Shujing Wu
- Department of Cardiology, Zhongshan Hospital (Xiamen)Fudan UniversityXiamenChina
| | - Yun Shen
- Department of Pharmacy, Zhongshan Hospital (Xiamen)Fudan UniversityXiamenChina
- Department of Pharmacy, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Xiaotong Xia
- Department of Pharmacy, Zhongshan Hospital (Xiamen)Fudan UniversityXiamenChina
| | - Lumin Wang
- Department of Pharmacy, Zhongshan Hospital (Xiamen)Fudan UniversityXiamenChina
| | - Huijun Zhang
- Department of Respiration, Zhongshan Hospital (Xiamen)Fudan UniversityXiamenChina
| | - Hongni Jiang
- Department of Respiration, Zhongshan Hospital (Xiamen)Fudan UniversityXiamenChina
| | - Xiaoyu Li
- Department of Pharmacy, Zhongshan Hospital (Xiamen)Fudan UniversityXiamenChina
- Department of Pharmacy, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Xiaoye Li
- Department of Pharmacy, Zhongshan HospitalFudan UniversityShanghaiChina
| | - Yanrong Ye
- Department of Pharmacy, Zhongshan Hospital (Xiamen)Fudan UniversityXiamenChina
- Department of Pharmacy, Zhongshan HospitalFudan UniversityShanghaiChina
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Barbosa DB, do Bomfim MR, de Oliveira TA, da Silva AM, Taranto AG, Cruz JN, de Carvalho PB, Campos JM, Santos CBR, Leite FHA. Development of Potential Multi-Target Inhibitors for Human Cholinesterases and Beta-Secretase 1: A Computational Approach. Pharmaceuticals (Basel) 2023; 16:1657. [PMID: 38139784 PMCID: PMC10748024 DOI: 10.3390/ph16121657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/04/2023] [Accepted: 11/13/2023] [Indexed: 12/24/2023] Open
Abstract
Alzheimer's disease causes chronic neurodegeneration and is the leading cause of dementia in the world. The causes of this disease are not fully understood but seem to involve two essential cerebral pathways: cholinergic and amyloid. The simultaneous inhibition of AChE, BuChE, and BACE-1, essential enzymes involved in those pathways, is a promising therapeutic approach to treat the symptoms and, hopefully, also halt the disease progression. This study sought to identify triple enzymatic inhibitors based on stereo-electronic requirements deduced from molecular modeling of AChE, BuChE, and BACE-1 active sites. A pharmacophore model was built, displaying four hydrophobic centers, three hydrogen bond acceptors, and one positively charged nitrogen, and used to prioritize molecules found in virtual libraries. Compounds showing adequate overlapping rates with the pharmacophore were subjected to molecular docking against the three enzymes and those with an adequate docking score (n = 12) were evaluated for physicochemical and toxicological parameters and commercial availability. The structure exhibiting the greatest inhibitory potential against all three enzymes was subjected to molecular dynamics simulations (100 ns) to assess the stability of the inhibitor-enzyme systems. The results of this in silico approach indicate ZINC1733 can be a potential multi-target inhibitor of AChE, BuChE, and BACE-1, and future enzymatic assays are planned to validate those results.
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Affiliation(s)
- Deyse B. Barbosa
- Laboratório de Modelagem Molecular, Departamento de Saúde, Universidade Estadual de Feira de Santana, Feira de Santana 44036-900, BA, Brazil; (D.B.B.); (M.R.d.B.); (F.H.A.L.)
| | - Mayra R. do Bomfim
- Laboratório de Modelagem Molecular, Departamento de Saúde, Universidade Estadual de Feira de Santana, Feira de Santana 44036-900, BA, Brazil; (D.B.B.); (M.R.d.B.); (F.H.A.L.)
| | - Tiago A. de Oliveira
- Departamento de Informática, Gestão e Desenho, Centro Federal de Educação Tecnológica de Minas Gerais, Divinópolis 30575-180, MG, Brazil;
| | - Alisson M. da Silva
- Laboratório de Bioinformática e Desenho de Fármacos, Universidade Federal de São João del-Rei, São João del-Rei 36307-352, MG, Brazil; (A.M.d.S.); (A.G.T.)
| | - Alex G. Taranto
- Laboratório de Bioinformática e Desenho de Fármacos, Universidade Federal de São João del-Rei, São João del-Rei 36307-352, MG, Brazil; (A.M.d.S.); (A.G.T.)
| | - Jorddy N. Cruz
- Laboratório de Modelagem e Química Computacional, Departamento de Ciências Biológicas e de Saúde, Universidade Federal do Amapá, Macapá 68903-419, AP, Brazil;
| | - Paulo B. de Carvalho
- Feik School of Pharmacy, University of the Incarnate Word, San Antonio, TX 78209, USA;
| | - Joaquín M. Campos
- Departamento de Química Orgánica Farmacéutica, Facultad de Farmacia, Campus de la Cartuja, Universidad de Granada, 18012 Granada, Spain;
| | - Cleydson B. R. Santos
- Laboratório de Modelagem e Química Computacional, Departamento de Ciências Biológicas e de Saúde, Universidade Federal do Amapá, Macapá 68903-419, AP, Brazil;
- Programa de Pós-Graduação em Biodiversidade e Biotecnologia—Rede BIONORTE, Universidade Federal do Amapá, Macapá 68903-419, AP, Brazil
| | - Franco H. A. Leite
- Laboratório de Modelagem Molecular, Departamento de Saúde, Universidade Estadual de Feira de Santana, Feira de Santana 44036-900, BA, Brazil; (D.B.B.); (M.R.d.B.); (F.H.A.L.)
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Yang W, Wang X, Kang C, Yang L, Liu D, Zhao N, Zhang X. Establishment of a risk prediction model for suicide attempts in first-episode and drug naïve patients with major depressive disorder. Asian J Psychiatr 2023; 88:103732. [PMID: 37586124 DOI: 10.1016/j.ajp.2023.103732] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 08/03/2023] [Accepted: 08/09/2023] [Indexed: 08/18/2023]
Abstract
BACKGROUND Suicide is common in patients with major depressive disorder (MDD) and has serious consequences for individuals and families. This study aims to establish a risk prediction model for suicide attempts in MDD patients to make the detection of suicide risk more accurate and effective. METHODS A cross-sectional survey, clinical examination, and biochemical indicator tests were performed on 1718 first-episode and drug naïve patients with major depressive disorder. We used Machine Learning to establish a risk prediction model for suicide attempts in FEDN patients with MDD. RESULTS Five predictors were identified by LASSO regression analysis from a total of 20 variables studied, namely psychotic symptoms, anxiety symptoms, thyroid peroxidase antibodies (ATPO), total cholesterol (TC), and high-density lipoprotein-cholesterol (HDL-C). The model constructed using the five predictors displayed moderate predictive ability, with an area under the ROC of 0.771 in the training set and 0.720 in the validation set. The DCA curve showed that the nomogram could be applied clinically if the risk threshold was between 22 % and 60 %. The risk threshold was found to be between 20 % and 60 % in external validation. CONCLUSION Introducing psychotic symptoms, anxiety symptoms, ATPO, TC, and HDL-C to the risk nomogram increased its usefulness for predicting suicide risk in patients with MDD. It may be useful in clinical decision-making or in discussions with patients, especially in crisis interventions.
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Affiliation(s)
- Wanqiu Yang
- School of Ethnology and Sociology, Yunnan University, Kunming, China; School of Medicine, Yunnan University, Kunming, China
| | - Xiaohong Wang
- Department of Psychiatry, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Chuanyi Kang
- Department of Psychiatry, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Liying Yang
- Dalian Seventh People's Hospital (Dalian Mental Health Center), Dalian, China
| | - Di Liu
- School of Marxism, Harbin Medical University, China
| | - Na Zhao
- Department of Psychiatry, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
| | - Xiangyang Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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Shamsian S, Sokouti B, Dastmalchi S. Benchmarking different docking protocols for predicting the binding poses of ligands complexed with cyclooxygenase enzymes and screening chemical libraries. BIOIMPACTS : BI 2023; 14:29955. [PMID: 38505677 PMCID: PMC10945300 DOI: 10.34172/bi.2023.29955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/09/2023] [Accepted: 08/23/2023] [Indexed: 03/21/2024]
Abstract
Introduction Non-steroidal anti-inflammatory drugs (NSAIDs) constitute an important class of pharmaceuticals acting on cyclooxygenase COX-1 and COX-2 enzymes. Due to their numerous severe side effects, it is necessary to search for new selective, safe, and effective anti-inflammatory drugs. In silico design of novel therapeutics plays an important role in nowadays drug discovery pipelines. In most cases, the design strategies require the use of molecular docking calculations. The docking procedure may require case-specific condition for a successful result. Additionally, many different docking programs are available, which highlights the importance of identifying the most proper docking method and condition for a given problem. Methods In the current work, the performances of five popular molecular docking programs, namely, GOLD, AutoDock, FlexX, Molegro Virtual Docker (MVD) and Glide to predict the binding mode of co- crystallized inhibitors in the structures of known complexes available for cyclooxygenases were evaluated. Furthermore, the best performers, Glide, AutoDock, GOLD and FlexX, were further evaluated in docking-based virtual screening of libraries consisted of active ligands and decoy molecules for cyclooxygenase enzymes and the obtained docking scores were assessed by receiver operating characteristics (ROC) analysis. Results The results of docking experiments indicated that Glide program outperformed other docking programs by correctly predicting the binding poses (RMSD less than 2 Å) of all studied co-crystallized ligands of COX-1 and COX-2 enzymes (i.e., the performance was 100%). However, the performances of the other studied docking methods for correctly predicting the binding poses of the ligands were between 59% to 82%. Virtual screening results treated by ROC analysis revealed that all tested methods are useful tools for classification and enrichment of molecules targeting COX enzymes. The obtained AUCs range between 0.61-0.92 with enrichment factors of 8 - 40 folds. Conclusion The obtained results support the importance of choosing appropriate docking method for predicting ligand-receptor binding modes, and provide specific information about docking calculations on COXs ligands.
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Affiliation(s)
- Sara Shamsian
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, 5165665931, Iran
- Department of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, 5166414766, Iran
| | - Babak Sokouti
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, 5165665813, Iran
| | - Siavoush Dastmalchi
- Department of Medicinal Chemistry, School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, 5166414766, Iran
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, 5165665813, Iran
- Faculty of Pharmacy, Near East University, POBOX:99138, Nicosia, North Cyprus, Mersin 10, Turkey
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Xu Y, Shi Z, Sun D, Munivrana G, Liang M, István B, Radak Z, Baker JS, Gu Y. Establishment of hypertension risk nomograms based on physical fitness parameters for men and women: a cross-sectional study. Front Cardiovasc Med 2023; 10:1152240. [PMID: 37771672 PMCID: PMC10523331 DOI: 10.3389/fcvm.2023.1152240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 08/28/2023] [Indexed: 09/30/2023] Open
Abstract
Objective This study aims to establish hypertension risk nomograms for Chinese male and female adults, respectively. Method A series of questionnaire surveys, physical assessments, and biochemical indicator tests were performed on 18,367 adult participants in China. The optimization of variable selection was conducted by running cyclic coordinate descent with 10-fold cross-validation through the least absolute shrinkage and selection operator (LASSO) regression. The nomograms were built by including the predictors selected through multivariable logistic regression. Calibration plots, receiver operating characteristic curves (ROC), decision curve analysis (DCA), clinical impact curves (CIC), and net reduction curve plots (NRC) were used to validate the models. Results Out of a total of 18 variables, 5 predictors-namely age, body mass index, waistline, hipline, and resting heart rate-were identified for the hypertension risk predictive model for men with an area under the ROC of 0.693 in the training set and 0.707 in the validation set. Seven predictors-namely age, body mass index, body weight, cardiovascular disease history, waistline, resting heart rate, and daily activity level-were identified for the hypertension risk predictive model for women with an area under the ROC of 0.720 in the training set and 0.748 in the validation set. The nomograms for both men and women were externally well-validated. Conclusion Gender differences may induce heterogeneity in hypertension risk prediction between men and women. Besides basic demographic and anthropometric parameters, information related to the functional status of the cardiovascular system and physical activity appears to be necessary.
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Affiliation(s)
- Yining Xu
- Faculty of Sports Science, Ningbo University, Ningbo, China
| | - Zhiyong Shi
- Faculty of Sports Science, Ningbo University, Ningbo, China
| | - Dong Sun
- Faculty of Sports Science, Ningbo University, Ningbo, China
| | | | - Minjun Liang
- Faculty of Sports Science, Ningbo University, Ningbo, China
| | - Bíró István
- Faculty of Engineering, University of Szeged, Szeged, Hungary
| | - Zsolt Radak
- Research Institute of Sport Science, University of Physical Education, Budapest, Hungary
| | - Julien S. Baker
- Department of Sport and Physical Education, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China
| | - Yaodong Gu
- Faculty of Sports Science, Ningbo University, Ningbo, China
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Tang L, Wu M, Xu Y, Zhu T, Fang C, Ma K, Wang J. Multimodal data-driven prognostic model for predicting new-onset ST-elevation myocardial infarction following emergency percutaneous coronary intervention. Inflamm Res 2023; 72:1799-1809. [PMID: 37644338 DOI: 10.1007/s00011-023-01781-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/22/2023] [Accepted: 08/07/2023] [Indexed: 08/31/2023] Open
Abstract
OBJECTIVES We developed a nomogram model derived from inflammatory indices, clinical data, and imaging data to predict in-hospital major adverse cardiac and cerebrovascular events (MACCEs) following emergency percutaneous coronary intervention (PCI) in patients with new-onset ST-elevation myocardial infarction (STEMI). METHODS Patients with new-onset STEMI admitted between June 2020 and November 2022 were retrospectively reviewed. Data pertaining to coronary angiograms, clinical data, biochemical indices, and in-hospital clinical outcomes were derived from electronic medical records. Lasso regression model was employed to screen risk factors and construct a prediction model. RESULTS Overall, 547 patients with new-onset STEMI who underwent PCI were included and assigned to the training cohort (n = 384) and independent verification cohort (n = 163). Six clinical features (age, diabetes mellitus, current smoking, hyperuricemia, neutrophil-to-lymphocyte ratio, and Gensini score) were selected by LASSO regression to construct a nomogram to predict the risk of in-hospital MACCEs. The area-under-the-curve (AUC) values for in-hospital MACCEs risk in the training and independent verification cohorts were 0.921 (95% CI 0.881-0.961) and 0.898 (95% CI 0.821-0.976), respectively. It was adequately calibrated in both training cohort and independent verification cohorts, and predictions were correlated with actual outcomes. Decision curve analysis demonstrated that the nomogram was capable of predicting in-hospital MACCEs with good clinical benefit. CONCLUSIONS Our prediction nomogram based on multi-modal data (inflammatory indices, clinical and imaging data) reliably predicted in-hospital MACCEs in new-onset STEMI patients with emergency PCI. This prediction nomogram can enable individualized treatment strategies.
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Affiliation(s)
- Long Tang
- Department of Cardiology, People's Hospital of Xuancheng City, The Affiliated Xuancheng Hospital of Wannan Medical College, Anhui, 242000, China
| | - Min Wu
- Department of Oncology, Third People's Hospital of Honghe Prefecture, Gejiu, Yunnan, China
| | - Yanan Xu
- Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
| | - Tongjian Zhu
- Department of Cardiology, Institute of Cardiovascular Diseases, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Cunming Fang
- Department of Cardiology, People's Hospital of Xuancheng City, The Affiliated Xuancheng Hospital of Wannan Medical College, Anhui, 242000, China.
| | - Kezhong Ma
- Department of Cardiology, Institute of Cardiovascular Diseases, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China.
| | - Jun Wang
- Department of Cardiology, Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China.
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11
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Shi B, Shen L, Huang W, Cai L, Yang S, Zhang Y, Tou J, Lai D. A Nomogram for Predicting Surgical Timing in Neonates with Necrotizing Enterocolitis. J Clin Med 2023; 12:jcm12093062. [PMID: 37176503 PMCID: PMC10179100 DOI: 10.3390/jcm12093062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/02/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023] Open
Abstract
OBJECTIVE To explore the surgical risk variables in patients with necrotizing enterocolitis (NEC) and develop a nomogram model for predicting the surgical intervention timing of NEC. METHODS Infants diagnosed with NEC were enrolled in our study. We gathered information from clinical data, laboratory examinations, and radiological manifestations. Using LASSO (least absolute shrinkage and selection operator) regression analysis and multivariate logistic regression analysis, a clinical prediction model based on the logistic nomogram was developed. The performance of the nomogram model was evaluated using the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA). RESULTS A surgical intervention risk nomogram based on hypothermia, absent bowel sounds, WBC > 20 × 109/L or < 5 × 109/L, CRP > 50 mg/L, pneumatosis intestinalis, and ascites was practical, had a moderate predictive value (AUC > 0.8), improved calibration, and enhanced clinical benefit. CONCLUSIONS This simple and reliable clinical prediction nomogram model can help physicians evaluate children with NEC in a fast and effective manner, enabling the early identification and diagnosis of children at risk for surgery. It offers clinical revolutionary value for the development of medical or surgical treatment plans for children with NEC.
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Affiliation(s)
- Bo Shi
- Department of Neonatal Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
| | - Leiting Shen
- Department of Neonatal Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
| | - Wenchang Huang
- Department of Neonatal Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
| | - Linghao Cai
- Department of Neonatal Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
| | - Sisi Yang
- Department of Neonatal Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
| | - Yuanyuan Zhang
- Department of Pulmonology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
| | - Jinfa Tou
- Department of Neonatal Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
| | - Dengming Lai
- Department of Neonatal Surgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
- Binjiang Institute of Zhejiang University, Hangzhou 310053, China
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12
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Jokinen EM, Niemeläinen M, Kurkinen ST, Lehtonen JV, Lätti S, Postila PA, Pentikäinen OT, Niinivehmas SP. Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators. Molecules 2023; 28:molecules28083420. [PMID: 37110655 PMCID: PMC10145393 DOI: 10.3390/molecules28083420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/06/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023] Open
Abstract
Molecular docking is a key method used in virtual screening (VS) campaigns to identify small-molecule ligands for drug discovery targets. While docking provides a tangible way to understand and predict the protein-ligand complex formation, the docking algorithms are often unable to separate active ligands from inactive molecules in practical VS usage. Here, a novel docking and shape-focused pharmacophore VS protocol is demonstrated for facilitating effective hit discovery using retinoic acid receptor-related orphan receptor gamma t (RORγt) as a case study. RORγt is a prospective target for treating inflammatory diseases such as psoriasis and multiple sclerosis. First, a commercial molecular database was flexibly docked. Second, the alternative docking poses were rescored against the shape/electrostatic potential of negative image-based (NIB) models that mirror the target's binding cavity. The compositions of the NIB models were optimized via iterative trimming and benchmarking using a greedy search-driven algorithm or brute force NIB optimization. Third, a pharmacophore point-based filtering was performed to focus the hit identification on the known RORγt activity hotspots. Fourth, free energy binding affinity evaluation was performed on the remaining molecules. Finally, twenty-eight compounds were selected for in vitro testing and eight compounds were determined to be low μM range RORγt inhibitors, thereby showing that the introduced VS protocol generated an effective hit rate of ~29%.
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Affiliation(s)
- Elmeri M Jokinen
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
| | - Miika Niemeläinen
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland
| | - Sami T Kurkinen
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
| | - Jukka V Lehtonen
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, FI-20500 Turku, Finland
- InFLAMES Research Flagship Center, Åbo Akademi University, FI-20500 Turku, Finland
| | - Sakari Lätti
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
| | - Pekka A Postila
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
| | - Olli T Pentikäinen
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
| | - Sanna P Niinivehmas
- MedChem.fi, Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, FI-20014 Turku, Finland
- InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
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13
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Wang X, Ye CH, Li EM, Xu LY, Lin WQ, Chen GH. Discovery of octahydropyrrolo [3,2-b] pyridin derivative as a highly selective Type I inhibitor of FGFR3 over VEGFR2 by high-throughput virtual screening. J Cell Biochem 2023; 124:221-238. [PMID: 36502529 DOI: 10.1002/jcb.30357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 10/17/2022] [Accepted: 11/24/2022] [Indexed: 12/14/2022]
Abstract
Although the aberrant activity of fibroblast growth factor receptor 3 (FGFR3) is implicated in various cancers, the reported kinase inhibitors of FGFR3 tend to cause side effects resulting from the inhibitory activity on vascular endothelial growth factor receptor 2 (VEGFR2). Therefore, it is necessary to find a novel high-selective inhibitor of FGFR3 over VEGFR2 from the small-molecule compound database. In this study, integrated virtual screening protocols were established to screen for selective inhibitors of FGFR3 over VEGFR2 in Drugbank and Asinex databases by combining three-dimensional pharmacophore model, molecular docking, molecular dynamics (MD) simulation, and molecular mechanics Poisson-Boltzmann surface area (MMPBSA) calculations. Finally, it is found that Asinex-5082, as an octahydropyrrolo[3,2-b] pyridin derivative, has larger binding free energy with FGFR3 (-39.3 kcal/mol) than reference drug Erdafitinib (-29.9 kcal/mol), while cannot bind with VEGFR2, resulting in considerable inhibitory selectivity. This is because Asinex-5082, unlike Erdafitinib, has not m-dimethoxybenzene with large steric hindrance, thus can enter the larger ATP-binding pocket of FGFR3 with DFG-in conformation to form hydrophobic interaction with residues Met529, Ile539, and Tyr557 as well as hydrogen bond with Ala558. On the other hand, due to the fact that the benzodioxane and N-heterocyclic rings are connected by carbonyl (C=O), Asinex-5082 cannot rotate freely so as to enter the smaller ATP binding pocket of VEGFR2 on the DFG-out conformation. The lead molecule Asinex-5082 may facilitate the rational design and development of novel selective inhibitors of FGFR3 over VEGFR2 as anticancer drugs.
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Affiliation(s)
- Xin Wang
- Department of Chemistry, Shantou University, Shantou, China
| | - Cheng-Hao Ye
- Department of Chemistry, Shantou University, Shantou, China
| | - En-Min Li
- Medical Informatics Research Center, Shantou University Medical College, Shantou, China
| | - Li-Yan Xu
- Medical Informatics Research Center, Shantou University Medical College, Shantou, China
| | - Wang-Qiang Lin
- Department of Chemistry, Shantou University, Shantou, China
| | - Guang-Hui Chen
- Department of Chemistry, Shantou University, Shantou, China
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14
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Gniado N, Krawczyk-Balska A, Mehta P, Miszta P, Filipek S. Protein Homology Modeling for Effective Drug Design. Methods Mol Biol 2023; 2627:329-337. [PMID: 36959456 DOI: 10.1007/978-1-0716-2974-1_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
The effective drug design, especially for combating the multi-drug-resistant bacterial pathogens, requires more and more sophisticated procedures to obtain novel lead-like compounds. New classes of enzymes should be explored, especially those that help bacteria overcome existing treatments. The homology modeling is useful in obtaining the models of new enzymes; however, the active sites of them are sometimes present in closed conformations in the crystal structures, not suitable for drug design purposes. In such difficult cases, the combination of homology modeling, molecular dynamics simulations, and fragment screening can give satisfactory results.
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Affiliation(s)
- Natalia Gniado
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
- Department of Molecular Microbiology, Biological and Chemical Research Centre, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - Agata Krawczyk-Balska
- Department of Molecular Microbiology, Biological and Chemical Research Centre, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - Pakhuri Mehta
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
| | - Przemysław Miszta
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
| | - Sławomir Filipek
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland.
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15
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Wang J, Wu X, Sun J, Xu T, Zhu T, Yu F, Duan S, Deng Q, Liu Z, Guo F, Li X, Wang Y, Song L, Feng H, Zhou X, Jiang H. Prediction of major adverse cardiovascular events in patients with acute coronary syndrome: Development and validation of a non-invasive nomogram model based on autonomic nervous system assessment. Front Cardiovasc Med 2022; 9:1053470. [PMID: 36407419 PMCID: PMC9670131 DOI: 10.3389/fcvm.2022.1053470] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 10/13/2022] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Disruption of the autonomic nervous system (ANS) can lead to acute coronary syndrome (ACS). We developed a nomogram model using heart rate variability (HRV) and other data to predict major adverse cardiovascular events (MACEs) following emergency coronary angiography in patients with ACS. METHODS ACS patients admitted from January 2018 to June 2020 were examined. Holter monitors were used to collect HRV data for 24 h. Coronary angiograms, clinical data, and MACEs were recorded. A nomogram was developed using the results of Cox regression analysis. RESULTS There were 439 patients in a development cohort and 241 in a validation cohort, and the mean follow-up time was 22.80 months. The nomogram considered low-frequency/high-frequency ratio, age, diabetes, previous myocardial infarction, and current smoking. The area-under-the-curve (AUC) values for 1-year MACE-free survival were 0.790 (95% CI: 0.702-0.877) in the development cohort and 0.894 (95% CI: 0.820-0.967) in the external validation cohort. The AUCs for 2-year MACE-free survival were 0.802 (95% CI: 0.739-0.866) in the development cohort and 0.798 (95% CI: 0.693-0.902) in the external validation cohort. Development and validation were adequately calibrated and their predictions correlated with the observed outcome. Decision curve analysis (DCA) showed the model had good discriminative ability in predicting MACEs. CONCLUSION Our validated nomogram was based on non-invasive ANS assessment and traditional risk factors, and indicated reliable prediction of MACEs in patients with ACS. This approach has potential for use as a method for non-invasive monitoring of health that enables provision of individualized treatment strategies.
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Affiliation(s)
- Jun Wang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, China
- TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Xiaolin Wu
- Department of Cardiology, Institute of Cardiovascular Diseases, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Ji Sun
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, China
- TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Tianyou Xu
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, China
- TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Tongjian Zhu
- Department of Cardiology, Institute of Cardiovascular Diseases, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China
| | - Fu Yu
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, China
- TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Shoupeng Duan
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, China
- TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Qiang Deng
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, China
- TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Zhihao Liu
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, China
- TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Fuding Guo
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, China
- TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Xujun Li
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, China
- TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Yijun Wang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, China
- TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Lingpeng Song
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, China
- TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Hui Feng
- Information Center, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaoya Zhou
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, China
- TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Wuhan, China
| | - Hong Jiang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Cardiac Autonomic Nervous System Research Center of Wuhan University, Wuhan, China
- Hubei Key Laboratory of Autonomic Nervous System Modulation, Wuhan, China
- TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
- Cardiovascular Research Institute, Wuhan University, Wuhan, China
- Hubei Key Laboratory of Cardiology, Wuhan, China
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16
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Gantner ME, Prada Gori DN, Llanos MA, Talevi A, Angeli A, Vullo D, Supuran CT, Gavernet L. Identification of New Carbonic Anhydrase VII Inhibitors by Structure-Based Virtual Screening. J Chem Inf Model 2022; 62:4760-4770. [PMID: 36126250 DOI: 10.1021/acs.jcim.2c00910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Human carbonic anhydrase VII (hCA VII) constitutes a promising molecular target for the treatment of epileptic seizures and other central nervous system disorders due to its almost exclusive expression in neurons. Achieving isoform selectivity is one of the main challenges for the discovery of new hCA inhibitors, since nonspecific inhibition may lead to tolerance and side effects. In the present work, we report the development of a molecular docking protocol based on AutoDock4Zn for the search of new hCA VII inhibitors by virtual screening. The docking protocol was applied to the screening of two sets of compounds: a ZINC15 subset of sulfur-containing structures and an in-house library consisting of synthetic and commercial candidates (including approved drugs). Five compounds were selected from the first screening campaign and three from the second one, and they were tested in vitro against the enzyme. Among the eight selected structures, four showed Ki values in the low nanomolar range. These confirmed hits include three approved drugs: meloxicam, piroxicam, and nitrofurantoin, which also showed good selectivity for hCA VII versus hCA II.
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Affiliation(s)
- Melisa E Gantner
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata B1900ADU, Buenos Aires, Argentina
| | - Denis N Prada Gori
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata B1900ADU, Buenos Aires, Argentina
| | - Manuel A Llanos
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata B1900ADU, Buenos Aires, Argentina
| | - Alan Talevi
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata B1900ADU, Buenos Aires, Argentina
| | - Andrea Angeli
- Neurofarba Department, Sezione di Scienze Farmaceutiche e Nutraceutiche, Università degli Studi di Firenze, 50019 Sesto Fiorentino, Florence, Italy
| | - Daniela Vullo
- Dipartimento di Chimica Ugo Schiff, Università degli Studi di Firenze, 50019 Sesto Fiorentino, Florence, Italy
| | - Claudiu T Supuran
- Neurofarba Department, Sezione di Scienze Farmaceutiche e Nutraceutiche, Università degli Studi di Firenze, 50019 Sesto Fiorentino, Florence, Italy
| | - Luciana Gavernet
- Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47&115, La Plata B1900ADU, Buenos Aires, Argentina
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17
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Identification of Potential Insect Growth Inhibitor against Aedes aegypti: A Bioinformatics Approach. Int J Mol Sci 2022; 23:ijms23158218. [PMID: 35897792 PMCID: PMC9332482 DOI: 10.3390/ijms23158218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/30/2022] [Accepted: 07/11/2022] [Indexed: 02/04/2023] Open
Abstract
Aedes aegypti is the main vector that transmits viral diseases such as dengue, hemorrhagic dengue, urban yellow fever, zika, and chikungunya. Worldwide, many cases of dengue have been reported in recent years, showing significant growth. The best way to manage diseases transmitted by Aedes aegypti is to control the vector with insecticides, which have already been shown to be toxic to humans; moreover, insects have developed resistance. Thus, the development of new insecticides is considered an emergency. One way to achieve this goal is to apply computational methods based on ligands and target information. In this study, sixteen compounds with acceptable insecticidal activities, with 100% larvicidal activity at low concentrations (2.0 to 0.001 mg·L−1), were selected from the literature. These compounds were used to build up and validate pharmacophore models. Pharmacophore model 6 (AUC = 0.78; BEDROC = 0.6) was used to filter 4793 compounds from the subset of lead-like compounds from the ZINC database; 4142 compounds (dG < 0 kcal/mol) were then aligned to the active site of the juvenile hormone receptor Aedes aegypti (PDB: 5V13), 2240 compounds (LE < −0.40 kcal/mol) were prioritized for molecular docking from the construction of a chitin deacetylase model of Aedes aegypti by the homology modeling of the Bombyx mori species (PDB: 5ZNT), which aligned 1959 compounds (dG < 0 kcal/mol), and 20 compounds (LE < −0.4 kcal/mol) were predicted for pharmacokinetic and toxicological prediction in silico (Preadmet, SwissADMET, and eMolTox programs). Finally, the theoretical routes of compounds M01, M02, M03, M04, and M05 were proposed. Compounds M01−M05 were selected, showing significant differences in pharmacokinetic and toxicological parameters in relation to positive controls and interaction with catalytic residues among key protein sites reported in the literature. For this reason, the molecules investigated here are dual inhibitors of the enzymes chitin synthase and juvenile hormonal protein from insects and humans, characterizing them as potential insecticides against the Aedes aegypti mosquito.
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McGibbon M, Money-Kyrle S, Blay V, Houston DR. SCORCH: Improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation. J Adv Res 2022; 46:135-147. [PMID: 35901959 PMCID: PMC10105235 DOI: 10.1016/j.jare.2022.07.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 07/08/2022] [Accepted: 07/09/2022] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION The discovery of a new drug is a costly and lengthy endeavour. The computational prediction of which small molecules can bind to a protein target can accelerate this process if the predictions are fast and accurate enough. Recent machine-learning scoring functions re-evaluate the output of molecular docking to achieve more accurate predictions. However, previous scoring functions were trained on crystalised protein-ligand complexes and datasets of decoys. The limited availability of crystal structures and biases in the decoy datasets can lower the performance of scoring functions. OBJECTIVES To address key limitations of previous scoring functions and thus improve the predictive performance of structure-based virtual screening. METHODS A novel machine-learning scoring function was created, named SCORCH (Scoring COnsensus for RMSD-based Classification of Hits). To develop SCORCH, training data is augmented by considering multiple ligand poses and labelling poses based on their RMSD from the native pose. Decoy bias is addressed by generating property-matched decoys for each ligand and using the same methodology for preparing and docking decoys and ligands. A consensus of 3 different machine learning approaches is also used to improve performance. RESULTS We find that multi-pose augmentation in SCORCH improves its docking power and screening power on independent benchmark datasets. SCORCH outperforms an equivalent scoring function trained on single poses, with a 1% enrichment factor (EF) of 13.78 vs. 10.86 on 18 DEKOIS 2.0 targets and a mean native pose rank of 5.9 vs 30.4 on CSAR 2014. Additionally, SCORCH outperforms widely used scoring functions in virtual screening and pose prediction on independent benchmark datasets. CONCLUSION By rationally addressing key limitations of previous scoring functions, SCORCH improves the performance of virtual screening. SCORCH also provides an estimate of its uncertainty, which can help reduce the cost and time required for drug discovery.
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Affiliation(s)
- Miles McGibbon
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, UK
| | - Sam Money-Kyrle
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, UK
| | - Vincent Blay
- Department of Microbiology and Environmental Toxicology, University of California at Santa Cruz, Santa Cruz, CA 95064, USA; Institute for Integrative Systems Biology (I(2)SysBio), Universitat de València and Spanish Research Council (CSIC), 46980 Valencia, Spain.
| | - Douglas R Houston
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, UK.
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Ligand-Enhanced Negative Images Optimized for Docking Rescoring. Int J Mol Sci 2022; 23:ijms23147871. [PMID: 35887220 PMCID: PMC9323918 DOI: 10.3390/ijms23147871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 07/14/2022] [Accepted: 07/15/2022] [Indexed: 12/04/2022] Open
Abstract
Despite the pivotal role of molecular docking in modern drug discovery, the default docking scoring functions often fail to recognize active ligands in virtual screening campaigns. Negative image-based rescoring improves docking enrichment by comparing the shape/electrostatic potential (ESP) of the flexible docking poses against the target protein’s inverted cavity volume. By optimizing these negative image-based (NIB) models using a greedy search, the docking rescoring yield can be improved massively and consistently. Here, a fundamental modification is implemented to this shape-focused pharmacophore modelling approach—actual ligand 3D coordinates are incorporated into the NIB models for the optimization. This hybrid approach, labelled as ligand-enhanced brute-force negative image-based optimization (LBR-NiB), takes the best from both worlds, i.e., the all-roundedness of the NIB models and the difficult to emulate atomic arrangements of actual protein-bound small-molecule ligands. Thorough benchmarking, focused on proinflammatory targets, shows that the LBR-NiB routinely improves the docking enrichment over prior iterations of the R-NiB methodology. This boost can be massive, if the added ligand information provides truly essential binding information that was lacking or completely missing from the cavity-based NIB model. On a practical level, the results indicate that the LBR-NiB typically works well when the added ligand 3D data originates from a high-quality source, such as X-ray crystallography, and, yet, the NIB model compositions can also sometimes be improved by fusing into them, for example, with flexibly docked solvent molecules. In short, the study demonstrates that the protein-bound ligands can be used to improve the shape/ESP features of the negative images for effective docking rescoring use in virtual screening.
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20
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Novel Insights into the Predictors of Obstructive Sleep Apnea Syndrome in Patients with Chronic Coronary Syndrome: Development of a Predicting Model. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:5497134. [PMID: 35795859 PMCID: PMC9252843 DOI: 10.1155/2022/5497134] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 01/15/2023]
Abstract
Background Obstructive sleep apnea syndrome (OSAS) is common in patients with chronic coronary syndrome (CCS); however, a predictive model of OSAS in patients with CCS remains rarely reported. The study aimed to construct a novel nomogram scoring system to predict OSAS comorbidity in patients with CCS. Methods Consecutive CCS patients scheduled for sleep monitoring at our hospital from January 2019 to September 2020 were enrolled in the current study. Coronary CT angiography or coronary angiography was used for the diagnosis of CCS, and clinical characteristics of the patients were collected. Significant predictors for OSAS in patients with moderate/severe CCS were estimated via logistic regression analysis, and a clinical nomogram was constructed. A calibration plot, examining discrimination (Harrell's concordance index) and decision curve analysis (DCA), was applied to validate the nomogram's predictive performance. Internal validity of the predictive model was assessed using bootstrapping (1000 replications). Results The nomograms were constructed based on available clinical variables from 527 patients which were significantly associated with moderate/severe OSAS in patients with CCS, including body mass index, impaired glucose tolerance, hypertension, diabetes mellitus, nonalcoholic fatty liver disease, and routine laboratory indices such as neutrophil to lymphocyte ratio, platelet-to-lymphocyte ratio, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The C-index (0.793) and AUC (0.771, 95% CI: 0.731–0.811) demonstrated a favorable discriminative ability of the nomogram. Moreover, calibration plots revealed consistency between moderate/severe OSAS predicted by the nomogram and validated by the results of sleep monitoring. Clinically, DCA showed that the nomogram had good discriminative ability to predict moderate/severe OSAS in patients with CCS. Conclusions The risk nomogram constructed via the routinely available clinical variables in patients with CCS showed satisfying discriminative ability to predict comorbid moderate/severe OSAS, which may be useful for identification of high-risk patients with OSAS in patients with CCS.
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21
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de Oliveira MVD, Bittencourt Fernandes GM, da Costa KS, Vakal S, Lima AH. Virtual screening of natural products against 5-enolpyruvylshikimate-3-phosphate synthase using the Anagreen herbicide-like natural compound library. RSC Adv 2022; 12:18834-18847. [PMID: 35873314 PMCID: PMC9240924 DOI: 10.1039/d2ra02645g] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/14/2022] [Indexed: 11/21/2022] Open
Abstract
The shikimate pathway enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) catalyzes a reaction involved in the production of amino acids essential for plant growth and survival. EPSPS is the main target of glyphosate, a broad-spectrum herbicide that acts as a competitive inhibitor concerning phosphoenolpyruvate (PEP), which is the natural substrate of EPSPS. In the present study, we introduce a natural compound library, named Anagreen, which is a compendium of herbicide-like compounds obtained from different natural product databases. Herein, we combined the structure- and ligand-based virtual screening strategies to explore Anagreen against EPSPS using the structure of glyphosate complexed with a T102I/P106S mutant of EPSPS from Eleusine indica (EiEPSPS) as a starting point. First, ligand-based pharmacophore screening was performed to select compounds with a similar pharmacophore to glyphosate. Then, structure-based pharmacophore modeling was applied to build a model which represents the molecular features of glyphosate. Then, consensus docking was performed to rank the best poses of the natural compounds against the PEP binding site, and then molecular dynamics simulations were performed to analyze the stability of EPSPS complexed with the selected ligands. Finally, we have investigated the binding affinity of the complexes using free energy calculations. The selected hit compound, namely AG332841, showed a stable conformation and binding affinity to the EPSPS structure and showed no structural similarity to the already known weed EPSPS inhibitors. Our computational study aims to clarify the inhibition of the mutant EiEPSPS, which is resistant to glyphosate, and identify new potential herbicides from natural products.
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Affiliation(s)
- Maycon Vinicius Damasceno de Oliveira
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará 66075-110 Belém Pará Brazil
| | - Gilson Mateus Bittencourt Fernandes
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará 66075-110 Belém Pará Brazil
| | - Kauê S da Costa
- Institute of Biodiversity, Federal University of Western Pará Santarém Pará Brazil
| | - Serhii Vakal
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University Turku Finland
| | - Anderson H Lima
- Laboratório de Planejamento e Desenvolvimento de Fármacos, Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará 66075-110 Belém Pará Brazil
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22
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Yau MQ, Loo JSE. Consensus scoring evaluated using the GPCR-Bench dataset: Reconsidering the role of MM/GBSA. J Comput Aided Mol Des 2022; 36:427-441. [PMID: 35581483 DOI: 10.1007/s10822-022-00456-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 04/28/2022] [Indexed: 01/09/2023]
Abstract
The recent availability of large numbers of GPCR crystal structures has provided an unprecedented opportunity to evaluate their performance in virtual screening protocols using established benchmarking datasets. In this study, we evaluated the ability of MM/GBSA in consensus scoring-based virtual screening enrichment together with nine classical scoring functions, using the GPCR-Bench dataset consisting of 24 GPCR crystal structures and 254,646 actives and decoys. While the performance of consensus scoring was modest overall, combinations which included MM/GBSA performed relatively well compared to combinations of classical scoring functions. Combinations of MM/GBSA and good-performing scoring functions provided the highest proportion of improvements, with improvements observed in 32% and 19% of all combinations across all targets at the EF1% and EF5% levels respectively. Combinations of MM/GBSA and poor-performing scoring functions still outperformed classical scoring functions, with improvements observed in 26% and 17% of all combinations at the EF1% and EF5% levels. In comparison, only 14-22% and 6-11% of combinations of classical scoring functions produced improvements at EF1% and EF5% respectively. Efforts to improve performance by increasing the number of scoring functions in consensus scoring to three were mostly ineffective. We also observed that consensus scoring performed better for individual scoring functions possessing initially low enrichment factors, potentially implying their benefits are more relevant in such scenarios. Overall, this study demonstrated the first implementation of MM/GBSA in consensus scoring using the GPCR-Bench dataset and could provide a valuable benchmark of the performance of MM/GBSA in comparison to classical scoring functions in consensus scoring for GPCRs.
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Affiliation(s)
- Mei Qian Yau
- Centre for Drug Discovery and Molecular Pharmacology, Faculty of Health and Medical Sciences, Taylor's University, No. 1 Jalan Taylor's, 47500, Subang Jaya, Selangor, Malaysia.,School of Pharmacy, Faculty of Health and Medical Sciences, Taylor's University, No. 1 Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia
| | - Jason S E Loo
- Centre for Drug Discovery and Molecular Pharmacology, Faculty of Health and Medical Sciences, Taylor's University, No. 1 Jalan Taylor's, 47500, Subang Jaya, Selangor, Malaysia. .,School of Pharmacy, Faculty of Health and Medical Sciences, Taylor's University, No. 1 Jalan Taylors, 47500, Subang Jaya, Selangor, Malaysia.
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23
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Yang J, Jiang S. Development and Validation of a Model That Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Cross-Sectional Study. Int J Gen Med 2022; 15:5089-5101. [PMID: 35645579 PMCID: PMC9130557 DOI: 10.2147/ijgm.s363474] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 05/12/2022] [Indexed: 12/19/2022] Open
Abstract
Purpose To develop a nomogram model that predicts the risk of diabetic nephropathy (DN) incidence in type 2 diabetes mellitus (T2DM) patients. Methods We collect information from electronic medical record systems. The data were split into a training set (n=521) containing 73.8% of patients and a validation set (n=185) holding the remaining 26.2% of patients based on the date of data collection. Stepwise and multivariable logistic regression analyses were used to screen out DN risk factors. A predictive model including selected risk factors was developed by logistic regression analysis. The results of binary logistic regression are presented through forest plots and nomogram. Lastly, the c-index, calibration plots, and receiver operating characteristic (ROC) curves were used to assess the accuracy of the nomogram in internal and external validation. The clinical benefit of the model was evaluated by decision curve analysis. Results Predictors included serum creatinine (Scr), hypertension, glycosylated hemoglobin A1c (HbA1c), blood urea nitrogen (BUN), body mass index (BMI), triglycerides (TG), and Diabetic peripheral neuropathy (DPN). Harrell’s C-indexes were 0.773 (95% CI:0.726–0.821) and 0.758 (95% CI:0.679–0.837) in the training and validation sets, respectively. Decision curve analysis (DCA) demonstrated that the novel nomogram was clinically valuable. Conclusion Our simple nomogram with seven factors may help clinicians predict the risk of DN incidence in patients with T2DM.
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Affiliation(s)
- Jing Yang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia; Department of Endocrinology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830017, People’s Republic of China
| | - Sheng Jiang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia; Department of Endocrinology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830017, People’s Republic of China
- Correspondence: Sheng Jiang, Email
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24
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do Bomfim MR, Barbosa DB, de Carvalho PB, da Silva AM, de Oliveira TA, Taranto AG, Leite FHA. Identification of potential human beta-secretase 1 inhibitors by hierarchical virtual screening and molecular dynamics. J Biomol Struct Dyn 2022:1-15. [DOI: 10.1080/07391102.2022.2069155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Mayra Ramos do Bomfim
- Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Estadual de Feira de Santana, Feira de Santana, Brazil
| | - Deyse Brito Barbosa
- Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Estadual de Feira de Santana, Feira de Santana, Brazil
| | | | - Alisson Marques da Silva
- Departamento de Informática, Gestão e Design, Centro Federal de Educação Tecnológica de Minas Gerais, Divinópolis, Brazil
| | - Tiago Alves de Oliveira
- Departamento de Informática, Gestão e Design, Centro Federal de Educação Tecnológica de Minas Gerais, Divinópolis, Brazil
- Departamento de Bioengenharia, Universidade Federal de São João del-Rei, São João del-Rei, Brazil
| | - Alex Gutterres Taranto
- Departamento de Bioengenharia, Universidade Federal de São João del-Rei, São João del-Rei, Brazil
- Faculty of Computing, University of Latvia (UL), Riga, Latvia
| | - Franco Henrique Andrade Leite
- Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Estadual de Feira de Santana, Feira de Santana, Brazil
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Kurkinen ST, Lehtonen JV, Pentikäinen OT, Postila PA. Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening. J Chem Inf Model 2022; 62:1100-1112. [PMID: 35133138 PMCID: PMC8889583 DOI: 10.1021/acs.jcim.1c01145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Molecular docking is a key in silico method used routinely in modern drug discovery projects. Although docking provides high-quality ligand binding predictions, it regularly fails to separate the active compounds from the inactive ones. In negative image-based rescoring (R-NiB), the shape/electrostatic potential (ESP) of docking poses is compared to the negative image of the protein's ligand binding cavity. While R-NiB often improves the docking yield considerably, the cavity-based models do not reach their full potential without expert editing. Accordingly, a greedy search-driven methodology, brute force negative image-based optimization (BR-NiB), is presented for optimizing the models via iterative editing and benchmarking. Thorough and unbiased training, testing and stringent validation with a multitude of drug targets, and alternative docking software show that BR-NiB ensures excellent docking efficacy. BR-NiB can be considered as a new type of shape-focused pharmacophore modeling, where the optimized models contain only the most vital cavity information needed for effectively filtering docked actives from the inactive or decoy compounds. Finally, the BR-NiB code for performing the automated optimization is provided free-of-charge under MIT license via GitHub (https://github.com/jvlehtonen/brutenib) for boosting the success rates of docking-based virtual screening campaigns.
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Affiliation(s)
- Sami T Kurkinen
- Institute of Biomedicine, Integrative Physiology and Pharmacy, University of Turku, FI-20014 Turku, Finland.,Aurlide Ltd., FI-21420 Lieto, Finland.,InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
| | - Jukka V Lehtonen
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, FI-20500 Turku, Finland.,InFLAMES Research Flagship Center, Åbo Akademi University, FI-20500 Turku, Finland
| | - Olli T Pentikäinen
- Institute of Biomedicine, Integrative Physiology and Pharmacy, University of Turku, FI-20014 Turku, Finland.,Aurlide Ltd., FI-21420 Lieto, Finland.,InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
| | - Pekka A Postila
- Institute of Biomedicine, Integrative Physiology and Pharmacy, University of Turku, FI-20014 Turku, Finland.,Aurlide Ltd., FI-21420 Lieto, Finland.,InFLAMES Research Flagship Center, University of Turku, FI-20014 Turku, Finland
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26
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de Oliveira TA, Medaglia LR, Maia EHB, Assis LC, de Carvalho PB, da Silva AM, Taranto AG. Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand Systems. Pharmaceuticals (Basel) 2022; 15:ph15020132. [PMID: 35215245 PMCID: PMC8874395 DOI: 10.3390/ph15020132] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/06/2022] [Accepted: 01/17/2022] [Indexed: 12/01/2022] Open
Abstract
DNA is a molecular target for the treatment of several diseases, including cancer, but there are few docking methodologies exploring the interactions between nucleic acids with DNA intercalating agents. Different docking methodologies, such as AutoDock Vina, DOCK 6, and Consensus, implemented into Molecular Architect (MolAr), were evaluated for their ability to analyze those interactions, considering visual inspection, redocking, and ROC curve. Ligands were refined by Parametric Method 7 (PM7), and ligands and decoys were docked into the minor DNA groove (PDB code: 1VZK). As a result, the area under the ROC curve (AUC-ROC) was 0.98, 0.88, and 0.99 for AutoDock Vina, DOCK 6, and Consensus methodologies, respectively. In addition, we proposed a machine learning model to determine the experimental ∆Tm value, which found a 0.84 R2 score. Finally, the selected ligands mono imidazole lexitropsin (42), netropsin (45), and N,N′-(1H-pyrrole-2,5-diyldi-4,1-phenylene)dibenzenecarboximidamide (51) were submitted to Molecular Dynamic Simulations (MD) through NAMD software to evaluate their equilibrium binding pose into the groove. In conclusion, the use of MolAr improves the docking results obtained with other methodologies, is a suitable methodology to use in the DNA system and was proven to be a valuable tool to estimate the ∆Tm experimental values of DNA intercalating agents.
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Affiliation(s)
- Tiago Alves de Oliveira
- Department of Bioengineering, Federal University of Sao Joao del-Rei, Praça Dom Helvecio, 74, Fabricas, Sao Joao del-Rei 36301-1601, MG, Brazil; (L.R.M.); (L.C.A.)
- Federal Center for Technological Education of Minas Gerais, Department of Informatics, Management and Design, CEFET MG, Campus Divinopolis, Rua Alvares de Azevedo, 400, Bela Vista, Divinopolis 35503-822, MG, Brazil; (E.H.B.M.); (A.M.d.S.)
- Correspondence: (T.A.d.O.); (A.G.T.); Tel.: +55-(37)99969-6735 (T.A.d.O.); +55-(37)98808-6168 (A.G.T.)
| | - Lucas Rolim Medaglia
- Department of Bioengineering, Federal University of Sao Joao del-Rei, Praça Dom Helvecio, 74, Fabricas, Sao Joao del-Rei 36301-1601, MG, Brazil; (L.R.M.); (L.C.A.)
| | - Eduardo Habib Bechelane Maia
- Federal Center for Technological Education of Minas Gerais, Department of Informatics, Management and Design, CEFET MG, Campus Divinopolis, Rua Alvares de Azevedo, 400, Bela Vista, Divinopolis 35503-822, MG, Brazil; (E.H.B.M.); (A.M.d.S.)
| | - Letícia Cristina Assis
- Department of Bioengineering, Federal University of Sao Joao del-Rei, Praça Dom Helvecio, 74, Fabricas, Sao Joao del-Rei 36301-1601, MG, Brazil; (L.R.M.); (L.C.A.)
| | - Paulo Batista de Carvalho
- Feik School of Pharmacy, University of the Incarnate Word, 4301 Broadway, San Antonio, TX 78209, USA;
| | - Alisson Marques da Silva
- Federal Center for Technological Education of Minas Gerais, Department of Informatics, Management and Design, CEFET MG, Campus Divinopolis, Rua Alvares de Azevedo, 400, Bela Vista, Divinopolis 35503-822, MG, Brazil; (E.H.B.M.); (A.M.d.S.)
| | - Alex Gutterres Taranto
- Department of Bioengineering, Federal University of Sao Joao del-Rei, Praça Dom Helvecio, 74, Fabricas, Sao Joao del-Rei 36301-1601, MG, Brazil; (L.R.M.); (L.C.A.)
- Faculty of Computing, University of Latvia (UL), Raina Boulevard 19 Center District, LV-1050 Riga, Latvia
- Correspondence: (T.A.d.O.); (A.G.T.); Tel.: +55-(37)99969-6735 (T.A.d.O.); +55-(37)98808-6168 (A.G.T.)
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Mao Y, Xu L, Xue T, Liang J, Lin W, Wen J, Huang H, Li L, Chen G. Novel nomogram for predicting the 3-year incidence risk of osteoporosis in a Chinese male population. Endocr Connect 2021; 10:1111-1124. [PMID: 34414899 PMCID: PMC8494413 DOI: 10.1530/ec-21-0330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 08/17/2021] [Indexed: 11/08/2022]
Abstract
OBJECTIVE To establish a rapid, cost-effective, accurate, and acceptable osteoporosis (OP) screening model for the Chinese male population (age ≥ 40 years) based on data mining technology. MATERIALS AND METHODS This was a 3-year retrospective cohort study, which belonged to the sub-cohort of the Chinese Reaction Study. The research period was from March 2011 to December 2014. A total of 1834 subjects who did not have OP at the baseline and completed a 3-year follow-up were included in this study. All subjects underwent quantitative ultrasound examinations for calcaneus at the baseline and follow-ups that lasted for 3 years. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to select feature variables. The characteristic variables selected in the LASSO regression were analyzed by multivariable logistic regression (MLR) to construct the predictive model. This predictive model was displayed through a nomogram. We used the receiver operating characteristic (ROC) curve, C-index, calibration curve, and clinical decision curve analysis (DCA) to evaluate model performance and the bootstrapping validation to internally validate the model. RESULTS The predictive factors included in the prediction model were age, neck circumference, waist-to-height ratio, BMI, triglyceride, impaired fasting glucose, dyslipidemia, osteopenia, smoking history, and strenuous exercise. The area under the ROC (AUC) curve of the risk nomogram was 0.882 (95% CI, 0.858-0.907), exhibiting good predictive ability and performance. The C-index for the risk nomogram was 0.882 in the prediction model, which presented good refinement. In addition, the nomogram calibration curve indicated that the prediction model was consistent. The DCA showed that when the threshold probability was between 1 and 100%, the nomogram had a good clinical application value. More importantly, the internally verified C-index of the nomogram was still very high, at 0.870. CONCLUSIONS This novel nomogram can effectively predict the 3-year incidence risk of OP in the male population. It also helps clinicians to identify groups at high risk of OP early and formulate personalized intervention measures.
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Affiliation(s)
- Yaqian Mao
- Shengli Clinical Medical College of Fujian Medical University, Fujian, China
- Department of Endocrinology, Fujian Provincial Hospital, Fujian, China
| | - Lizhen Xu
- Shengli Clinical Medical College of Fujian Medical University, Fujian, China
| | - Ting Xue
- Shengli Clinical Medical College of Fujian Medical University, Fujian, China
| | - Jixing Liang
- Department of Endocrinology, Fujian Provincial Hospital, Fujian, China
| | - Wei Lin
- Department of Endocrinology, Fujian Provincial Hospital, Fujian, China
| | - Junping Wen
- Department of Endocrinology, Fujian Provincial Hospital, Fujian, China
| | - Huibin Huang
- Department of Endocrinology, Fujian Provincial Hospital, Fujian, China
| | - Liantao Li
- Department of Endocrinology, Fujian Provincial Hospital, Fujian, China
| | - Gang Chen
- Shengli Clinical Medical College of Fujian Medical University, Fujian, China
- Department of Endocrinology, Fujian Provincial Hospital, Fujian, China
- Fujian Provincial Key Laboratory of Medical Analysis, Fujian Academy of Medical, Fujian, China
- Correspondence should be addressed to G Chen:
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Alice JI, Bellera CL, Benítez D, Comini MA, Duchowicz PR, Talevi A. Ensemble learning application to discover new trypanothione synthetase inhibitors. Mol Divers 2021; 25:1361-1373. [PMID: 34264440 DOI: 10.1007/s11030-021-10265-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 06/24/2021] [Indexed: 11/28/2022]
Abstract
Trypanosomatid-caused diseases are among the neglected infectious diseases with the highest disease burden, affecting about 27 million people worldwide and, in particular, socio-economically vulnerable populations. Trypanothione synthetase (TryS) is considered one of the most attractive drug targets within the thiol-polyamine metabolism of typanosomatids, being unique, essential and druggable. Here, we have compiled a dataset of 401 T. brucei TryS inhibitors that includes compounds with inhibitory data reported in the literature, but also in-house acquired data. QSAR classifiers were derived and validated from such dataset, using publicly available and open-source software, thus assuring the portability of the obtained models. The performance and robustness of the resulting models were substantially improved through ensemble learning. The performance of the individual models and the model ensembles was further assessed through retrospective virtual screening campaigns. At last, as an application example, the chosen model-ensemble has been applied in a prospective virtual screening campaign on DrugBank 5.1.6 compound library. All the in-house scripts used in this study are available on request, whereas the dataset has been included as supplementary material.
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Affiliation(s)
- Juan I Alice
- Laboratorio de Investigación y Desarrollo de Bioactivos (LIDeB), Facultad de Ciencias Exactas, Universidad Nacional de La Plata (UNLP), La Plata, Buenos Aires, Argentina.,Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), CCT La Plata, La Plata, Argentina
| | - Carolina L Bellera
- Laboratorio de Investigación y Desarrollo de Bioactivos (LIDeB), Facultad de Ciencias Exactas, Universidad Nacional de La Plata (UNLP), La Plata, Buenos Aires, Argentina.,Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), CCT La Plata, La Plata, Argentina
| | - Diego Benítez
- Group Redox Biology of Trypanosomes, Institut Pasteur Montevideo, Montevideo, Uruguay
| | - Marcelo A Comini
- Group Redox Biology of Trypanosomes, Institut Pasteur Montevideo, Montevideo, Uruguay
| | - Pablo R Duchowicz
- Facultad de Ciencias Exactas, Universidad Nacional de La Plata (UNLP)-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), La Plata, Argentina
| | - Alan Talevi
- Laboratorio de Investigación y Desarrollo de Bioactivos (LIDeB), Facultad de Ciencias Exactas, Universidad Nacional de La Plata (UNLP), La Plata, Buenos Aires, Argentina. .,Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), CCT La Plata, La Plata, Argentina.
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29
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Combination of consensus and ensemble docking strategies for the discovery of human dihydroorotate dehydrogenase inhibitors. Sci Rep 2021; 11:11417. [PMID: 34075175 PMCID: PMC8169699 DOI: 10.1038/s41598-021-91069-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 05/21/2021] [Indexed: 02/06/2023] Open
Abstract
The inconsistencies in the performance of the virtual screening (VS) process, depending on the used software and structural conformation of the protein, is a challenging issue in the drug design and discovery field. Varying performance, especially in terms of early recognition of the potential hit compounds, negatively affects the whole process and leads to unnecessary waste of the time and resources. Appropriate application of the ensemble docking and consensus-scoring approaches can significantly increase reliability of the VS results. Dihydroorotate dehydrogenase (DHODH) is a key enzyme in the pyrimidine biosynthesis pathway. It is considered as a valuable therapeutic target in cancer, autoimmune and viral diseases. Based on the conducted benchmark study and analysis of the effect of different combinations of the applied methods and approaches, here we suggested a structure-based virtual screening (SBVS) workflow that can be used to increase the reliability of VS.
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30
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Sahakyan H. Improving virtual screening results with MM/GBSA and MM/PBSA rescoring. J Comput Aided Mol Des 2021; 35:731-736. [PMID: 33983518 DOI: 10.1007/s10822-021-00389-3] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 05/08/2021] [Indexed: 11/25/2022]
Abstract
Virtual screening (VS) based on molecular docking is one of the most useful methods in computer-aided drug design. By allowing to identify computationally putative ligands binding to the proteins of interest, VS dramatically reduces the time and expense of the development of novel therapeutics. Among the limitations of the VS approaches is the low accuracy of scoring functions implemented in docking methods for assessing binding affinity. Many such scoring functions are developed for rapid, high-throughput evaluation of binding energy of multiple conformations generated by a searching algorithm. The methods for more rigorous calculation of binding affinity calculation are generally time-consuming. Even so, in many studies more accurate methods were used for rescoring of the final poses and false-positive hits evaluation. We performed VS for three benchmark sets and used energy minimization with MM/PB(GB)SA methods (molecular mechanics energies combined with the Poisson-Boltzmann or generalized Born and surface area) to rescore binding affinities. The comparison of the area under the curve (AUC), enrichment factor (EF), and Boltzmann-enhanced discrimination of receiver operating characteristics (BEDROC) showed essential improvements in the binding energy prediction after the rescoring. Finally, we provide a program for minimization and rescoring VS results based on freely available AmberTools. The code requires just the final binding poses of the ligand as the input and can be used with any docking program.
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Affiliation(s)
- Harutyun Sahakyan
- Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia, 0014, Yerevan, Armenia.
- Denovo Sciences, 0033, Yerevan, Armenia.
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31
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Ansar S, Vetrivel U. Structure-based design of small molecule and peptide inhibitors for selective targeting of ROCK1: an integrative computational approach. J Biomol Struct Dyn 2021; 40:7450-7468. [PMID: 33715594 DOI: 10.1080/07391102.2021.1898470] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Rho-associated, coiled-coil-containing protein kinase (ROCK1) regulates cell contraction, morphology, and motility by phosphorylating its downstream targets. ROCK1 is a proven target for many pathological conditions like cancer, atherosclerosis, glaucoma, neuro-degeneration, etc. Though many kinase inhibitors are available, there is a dearth of studies on repurposing approved drugs and novel peptide inhibitors that could potentially target ROCK1. Hence, in this study, an extensive integration of open-source pipelines was employed to probe the potential inhibitors (ligand/peptide) for targeting ROCK1. To start with, a systematic enrichment analysis was performed to delineate the most optimal ROCK1 crystal structure that can be harnessed for drug design. A comparative analysis of conformational flexibility between monomeric and dimeric forms was also performed to prioritize the optimal assembly for structural studies. Subsequently, Virtual screening of FDA-approved drugs in Drugbank was performed using POAP pipeline. Further, the top hits were probed for binding affinity, crucial interaction fingerprints, and complex stability during MD simulation. In parallel, a combinatorial tetrapeptide library was also virtually screened against ROCK1 using the PepVis pipeline. Following which, all these shortlisted inhibitors (compounds/peptides) were subjected to Kinomerun analysis to infer other potential kinase targets. Finally, Polydatin and conivaptan were prioritized as the most potential repurposable inhibitors, and WWWF, WWVW as potential inhibitory peptides for targeting ROCK1. The prioritized inhibitors are highly promising for use in therapeutics, as these are resultants of the multilevel stringent filtration process. The computational strategies implemented in this study could potentially serve as a scaffold towards selective inhibitor design for other kinases.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Samdani Ansar
- Centre for Bioinformatics, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Vision Research Foundation, Chennai, Tamil Nadu, India.,School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, Tamil Nadu, India
| | - Umashankar Vetrivel
- Centre for Bioinformatics, Kamalnayan Bajaj Institute for Research in Vision and Ophthalmology, Vision Research Foundation, Chennai, Tamil Nadu, India.,Department of Health Research, (Govt. of India), National Institute of Traditional Medicine, Indian Council of Medical Research, Belagavi, Karnataka, India
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32
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Guedes IA, Barreto AMS, Marinho D, Krempser E, Kuenemann MA, Sperandio O, Dardenne LE, Miteva MA. New machine learning and physics-based scoring functions for drug discovery. Sci Rep 2021; 11:3198. [PMID: 33542326 PMCID: PMC7862620 DOI: 10.1038/s41598-021-82410-1] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022] Open
Abstract
Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein–ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein–protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein–protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br.
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Affiliation(s)
- Isabella A Guedes
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil.,Inserm U973, Université Paris Diderot, Paris, France
| | - André M S Barreto
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil
| | - Diogo Marinho
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil
| | | | | | - Olivier Sperandio
- Inserm U973, Université Paris Diderot, Paris, France.,Structural Bioinformatics Unit, CNRS UMR3528, Institut Pasteur, 75015, Paris, France
| | - Laurent E Dardenne
- Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil.
| | - Maria A Miteva
- Inserm U973, Université Paris Diderot, Paris, France. .,Inserm U1268 "Medicinal Chemistry and Translational Research", CiTCoM, UMR 8038, CNRS, Université de Paris, 75006, Paris, France.
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33
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Thesnaar L, Bezuidenhout JJ, Petzer A, Petzer JP, Cloete TT. Methylene blue analogues: In vitro antimicrobial minimum inhibitory concentrations and in silico pharmacophore modelling. Eur J Pharm Sci 2021; 157:105603. [DOI: 10.1016/j.ejps.2020.105603] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 10/13/2020] [Accepted: 10/14/2020] [Indexed: 01/05/2023]
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34
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Khalid RR, Maryam A, Çınaroğlu SS, Siddiqi AR, Sezerman OU. A recursive molecular docking coupled with energy-based pose-rescoring and MD simulations to identify hsGC βH-NOX allosteric modulators for cardiovascular dysfunctions. J Biomol Struct Dyn 2021; 40:6128-6150. [PMID: 33522438 DOI: 10.1080/07391102.2021.1877818] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Modulating the activity of human soluble guanylate cyclase (hsGC) through allosteric regulation of the βH-NOX domain has been considered as an immediate treatment for cardiovascular disorder (CVDs). Currently available βH-NOX domain-specific agonists including cinaciguat are unable to deal with the conundrum raised due to oxidative stress in the case of CVDs and their associated comorbidities. Therefore, the idea of investigating novel compounds for allosteric regulation of hsGC activation has been rekindled to circumvent CVDs. Current study aims to identify novel βH-NOX domain-specific compounds that can selectively turn on sGC functions by modulating the conformational dynamics of the target protein. Through a comprehensive computational drug-discovery approach, we first executed a target-based performance assessment of multiple docking (PLANTS, QVina, LeDock, Vinardo, Smina) scoring functions based on multiple performance metrices. QVina showed the highest capability of selecting true-positive ligands over false positives thus, used to screen 4.8 million ZINC15 compounds against βH-NOX domain. The docked ligands were further probed in terms of contact footprint and pose reassessment through clustering analysis and PLANTS docking, respectively. Subsequently, energy-based AMBER rescoring of top 100 low-energy complexes, per-residue energy decomposition analysis, and ADME-Tox analysis yielded the top three compounds i.e. ZINC000098973660, ZINC001354120371, and ZINC000096022607. The impact of three selected ligands on the internal structural dynamics of the βH-NOX domain was also investigated through molecular dynamics simulations. The study revealed potential electrostatic interactions for better conformational dialogue between βH-NOX domain and allosteric ligands that are critical for the activation of hsGC as compared to the reference compound.
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Affiliation(s)
- Rana Rehan Khalid
- Department of Biosciences, COMSATS University, Islamabad, Pakistan.,Department of Biostatistics and Medical Informatics, Acibadem M. A. A. University, Istanbul, Turkey
| | - Arooma Maryam
- Department of Biosciences, COMSATS University, Islamabad, Pakistan
| | - Süleyman Selim Çınaroğlu
- Department of Biostatistics and Medical Informatics, Acibadem M. A. A. University, Istanbul, Turkey.,Department of Biochemistry, University of Oxford, Oxford, UK
| | | | - Osman Ugur Sezerman
- Department of Biostatistics and Medical Informatics, Acibadem M. A. A. University, Istanbul, Turkey
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35
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Stein RM, Yang Y, Balius TE, O'Meara MJ, Lyu J, Young J, Tang K, Shoichet BK, Irwin JJ. Property-Unmatched Decoys in Docking Benchmarks. J Chem Inf Model 2021; 61:699-714. [PMID: 33494610 DOI: 10.1021/acs.jcim.0c00598] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one's confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.
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Affiliation(s)
- Reed M Stein
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Ying Yang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Trent E Balius
- Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., P.O. Box B, Frederick, Maryland 21702, United States
| | - Matt J O'Meara
- Department of Computational Medicine and Bioinformatics, University of Michigan, Palmer Commons, 100 Washtenaw Ave. #2017, Ann Arbor, Michigan 48109, United States
| | - Jiankun Lyu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Jennifer Young
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Khanh Tang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
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36
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Pentikäinen OT, Postila PA. Negative Image-Based Rescoring: Using Cavity Information to Improve Docking Screening. Methods Mol Biol 2021; 2266:141-154. [PMID: 33759125 DOI: 10.1007/978-1-0716-1209-5_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Molecular docking produces often lackluster results in real-life virtual screening assays that aim to discover novel drug candidates or hit compounds. The problem lies in the inability of the default docking scoring to properly estimate the Gibbs free energy of binding, which impairs the recognition of the best binding poses and the separation of active ligands from inactive compounds. Negative image-based rescoring (R-NiB) provides both effective and efficient way for re-ranking the outputted flexible docking poses to improve the virtual screening yield. Importantly, R-NiB has been shown to work with multiple genuine drug targets and six popular docking algorithms using demanding benchmark test sets. The effectiveness of the R-NiB methodology relies on the shape/electrostatics similarity between the target protein's ligand-binding cavity and the docked ligand poses. In this chapter, the R-NiB method is described with practical usability in mind.
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Affiliation(s)
- Olli T Pentikäinen
- Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, Turku, Finland
- Aurlide Ltd., Turku, Finland
| | - Pekka A Postila
- Institute of Biomedicine, Integrative Physiology and Pharmacology, University of Turku, Turku, Finland.
- Aurlide Ltd., Turku, Finland.
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37
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Zhao G, Tian X, Wang J, Cheng M, Zhang T, Wang Z. The structure-based virtual screening of non-benzofuran inhibitors against M. tuberculosis Pks13-TE for anti-tuberculosis phenotypic discovery. NEW J CHEM 2021. [DOI: 10.1039/d0nj03828h] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Structure-based virtual screening against M. tuberculosis Pks13-TE was performed for anti-tuberculosis phenotypic discovery.
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Affiliation(s)
- Guode Zhao
- Key Laboratory of Structure-Based Drug Design & Discovery
- Ministry of Education
- Shenyang Pharmaceutical University
- Shenyang 110016
- P. R. China
| | - Xirong Tian
- State Key Laboratory of Respiratory Disease
- Guangzhou Institutes of Biomedicine and Health
- Chinese Academy of Sciences
- Guangzhou 510530
- China
| | - Jian Wang
- Key Laboratory of Structure-Based Drug Design & Discovery
- Ministry of Education
- Shenyang Pharmaceutical University
- Shenyang 110016
- P. R. China
| | - Maosheng Cheng
- Key Laboratory of Structure-Based Drug Design & Discovery
- Ministry of Education
- Shenyang Pharmaceutical University
- Shenyang 110016
- P. R. China
| | - Tianyu Zhang
- State Key Laboratory of Respiratory Disease
- Guangzhou Institutes of Biomedicine and Health
- Chinese Academy of Sciences
- Guangzhou 510530
- China
| | - Zihou Wang
- Center for Drug Evaluation
- National Medical Products Administration
- Beijing 100022
- China
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38
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Negative Image-Based Screening: Rigid Docking Using Cavity Information. Methods Mol Biol 2021; 2266:125-140. [PMID: 33759124 DOI: 10.1007/978-1-0716-1209-5_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Rational drug discovery relies heavily on molecular docking-based virtual screening, which samples flexibly the ligand binding poses against the target protein's structure. The upside of flexible docking is that the geometries of the generated docking poses are adjusted to match the residue alignment inside the target protein's ligand-binding pocket. The downside is that the flexible docking requires plenty of computing resources and, regardless, acquiring a decent level of enrichment typically demands further rescoring or post-processing. Negative image-based screening is a rigid docking technique that is ultrafast and computationally light but also effective as proven by vast benchmarking and screening experiments. In the NIB screening, the target protein cavity's shape/electrostatics is aligned and compared against ab initio-generated ligand 3D conformers. In this chapter, the NIB methodology is explained at the practical level and both its weaknesses and strengths are discussed candidly.
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39
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Bélgamo JA, Alberca LN, Pórfido JL, Romero FNC, Rodriguez S, Talevi A, Córsico B, Franchini GR. Application of target repositioning and in silico screening to exploit fatty acid binding proteins (FABPs) from Echinococcus multilocularis as possible drug targets. J Comput Aided Mol Des 2020; 34:1275-1288. [PMID: 33067653 DOI: 10.1007/s10822-020-00352-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 10/09/2020] [Indexed: 10/23/2022]
Abstract
Fatty acid binding proteins (FABPs) are small intracellular proteins that reversibly bind fatty acids and other hydrophobic ligands. In cestodes, due to their inability to synthesise fatty acids and cholesterol de novo, FABPs, together with other lipid binding proteins, have been proposed as essential, involved in the trafficking and delivery of such lipophilic metabolites. Pharmacological agents that modify specific parasite FABP function may provide control of lipid signalling pathways, inflammatory responses and metabolic regulation that could be of crucial importance for the parasite development and survival. Echinococcus multilocularis and Echinococcus granulosus are, respectively, the causative agents of alveolar and cystic echinococcosis (or hydatidosis). These diseases are included in the World Health Organization's list of priority neglected tropical diseases. Here, we explore the potential of FABPs from cestodes as drug targets. To this end, we have applied a target repurposing approach to identify novel inhibitors of Echinococcus spp. FABPs. An ensemble of computational models was developed and applied in a virtual screening campaign of DrugBank library. 21 hits belonging to the applicability domain of the ensemble models were identified, and 3 of the hits were assayed against purified E. multilocularis FABP, experimentally confirming the model's predictions. Noteworthy, this is to our best knowledge the first report on isolation and purification of such four FABP, for which initial structural and functional characterization is reported here.
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Affiliation(s)
- Julián A Bélgamo
- Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP), Facultad de Ciencias Médicas, Universidad Nacional de La Plata (UNLP)-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), La Plata, Argentina.,Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Lucas N Alberca
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, Universidad Nacional de La Plata (UNLP), Buenos Aires, Argentina.,Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Jorge L Pórfido
- Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP), Facultad de Ciencias Médicas, Universidad Nacional de La Plata (UNLP)-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), La Plata, Argentina.,Institut Pasteur Montevideo, Montevideo, Uruguay
| | - Franco N Caram Romero
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, Universidad Nacional de La Plata (UNLP), Buenos Aires, Argentina
| | - Santiago Rodriguez
- Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP), Facultad de Ciencias Médicas, Universidad Nacional de La Plata (UNLP)-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), La Plata, Argentina.,Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Alan Talevi
- Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, Universidad Nacional de La Plata (UNLP), Buenos Aires, Argentina.,Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Betina Córsico
- Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP), Facultad de Ciencias Médicas, Universidad Nacional de La Plata (UNLP)-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), La Plata, Argentina.,Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Gisela R Franchini
- Instituto de Investigaciones Bioquímicas de La Plata (INIBIOLP), Facultad de Ciencias Médicas, Universidad Nacional de La Plata (UNLP)-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), La Plata, Argentina. .,Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
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40
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Zhang Y, Shi R, Yu L, Ji L, Li M, Hu F. Establishment of a Risk Prediction Model for Non-alcoholic Fatty Liver Disease in Type 2 Diabetes. Diabetes Ther 2020; 11:2057-2073. [PMID: 32725485 PMCID: PMC7434817 DOI: 10.1007/s13300-020-00893-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION Non-alcoholic fatty liver disease (NAFLD) is becoming more prevalent in patients with type 2 diabetes mellitus (T2DM) and can contribute to serious liver damage in this patient population. The aim of this study was to develop a risk nomogram for NAFLD in a Chinese population with T2DM. METHODS A questionnaire survey, physical examination and biochemical indicator testing were performed on 874 patients with T2DM, and the collected data were used to evaluate the risk to develop NAFLD in T2DM patients. The least absolute shrinkage and selection operator (LASSO) regression analysis method was used to optimize variable selection by running cyclic coordinate descent with k-fold (tenfold in this case) cross-validation. Multivariable logistic regression analysis was applied to build a predictive model by introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. A calibration plot, receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to validate the model, with further assessment by external validation. RESULTS A total of nine predictors, namely sex, age, total cholesterol (TC), body mass index (BMI), waistline, diastolic blood pressure (DBP), serum uric acid (SUA), course of disease and high-density lipoprotein-cholesterol (HDL-C), were identified by LASSO regression analysis from a total of 24 variables studied. The model constructed using these nine predictors displayed medium prediction ability, with an area under the ROC of 0.848 in the training set and 0.809 in the validation set. The DCA curve showed that the nomogram could be applied clinically if the risk threshold was between 48 and 91%, which was found to be between 44 and 82% in the external validation. CONCLUSION Introducing sex, age, TC, BMI, waistline, DBP, SUA, course of disease and HDL-C into the risk nomogram increased its usefulness for predicting NAFLD risk in patients with T2DM.
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Affiliation(s)
- Yali Zhang
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Rong Shi
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Liang Yu
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Liping Ji
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Min Li
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Fan Hu
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
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Mascarenhas AMS, de Almeida RBM, de Araujo Neto MF, Mendes GO, da Cruz JN, dos Santos CBR, Botura MB, Leite FHA. Pharmacophore-based virtual screening and molecular docking to identify promising dual inhibitors of human acetylcholinesterase and butyrylcholinesterase. J Biomol Struct Dyn 2020; 39:6021-6030. [DOI: 10.1080/07391102.2020.1796791] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Affiliation(s)
- Ana Mércia Silva Mascarenhas
- Laboratório de Modelagem Molecular, Departamento de Saúde, Universidade Estadual de Feira de Santana, Bahia, Brasil
| | | | | | - Géssica Oliveira Mendes
- Laboratório de Modelagem Molecular, Departamento de Saúde, Universidade Estadual de Feira de Santana, Bahia, Brasil
| | - Jorddy Neves da Cruz
- Laboratório de Modelagem e Química Computacional, Departamento de Ciências Biológicas e da Saúde, Universidade Federal do Amapá, Macapá, Brasil
| | - Cleydson Breno Rodrigues dos Santos
- Laboratório de Modelagem e Química Computacional, Departamento de Ciências Biológicas e da Saúde, Universidade Federal do Amapá, Macapá, Brasil
| | - Mariana Borges Botura
- Laboratório de Toxicologia, Departamento de Saúde, Universidade Estadual de Feira de Santana, Bahia, Brasil
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Fresnais L, Ballester PJ. The impact of compound library size on the performance of scoring functions for structure-based virtual screening. Brief Bioinform 2020; 22:5855396. [PMID: 32568385 DOI: 10.1093/bib/bbaa095] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 04/20/2020] [Accepted: 04/28/2020] [Indexed: 12/20/2022] Open
Abstract
Larger training datasets have been shown to improve the accuracy of machine learning (ML)-based scoring functions (SFs) for structure-based virtual screening (SBVS). In addition, massive test sets for SBVS, known as ultra-large compound libraries, have been demonstrated to enable the fast discovery of selective drug leads with low-nanomolar potency. This proof-of-concept was carried out on two targets using a single docking tool along with its SF. It is thus unclear whether this high level of performance would generalise to other targets, docking tools and SFs. We found that screening a larger compound library results in more potent actives being identified in all six additional targets using a different docking tool along with its classical SF. Furthermore, we established that a way to improve the potency of the retrieved molecules further is to rank them with more accurate ML-based SFs (we found this to be true in four of the six targets; the difference was not significant in the remaining two targets). A 3-fold increase in average hit rate across targets was also achieved by the ML-based SFs. Lastly, we observed that classical and ML-based SFs often find different actives, which supports using both types of SFs on those targets.
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Škuta C, Cortés-Ciriano I, Dehaen W, Kříž P, van Westen GJP, Tetko IV, Bender A, Svozil D. QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping. J Cheminform 2020; 12:39. [PMID: 33431038 PMCID: PMC7260783 DOI: 10.1186/s13321-020-00443-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 05/16/2020] [Indexed: 02/11/2023] Open
Abstract
An affinity fingerprint is the vector consisting of compound’s affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.![]()
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Affiliation(s)
- C Škuta
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague 4, Czech Republic
| | - I Cortés-Ciriano
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - W Dehaen
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague 4, Czech Republic.,CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic
| | - P Kříž
- Department of Mathematics, Faculty of Chemical Engineering, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic
| | - G J P van Westen
- Computational Drug Discovery, Drug Discovery and Safety, LACDR, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - I V Tetko
- Helmholtz Zentrum Muenchen - German Research Center for Environmental Health (GmbH) and BIGCHEM GmbH, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany
| | - A Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - D Svozil
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20, Prague 4, Czech Republic. .,CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic.
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Costa Júnior DB, Araújo JSC, de Mattos Oliveira L, Neri FSM, Moreira POL, Taranto AG, Fonseca AL, de Pilla Varotti F, Leite FHA. Identification of novel antiplasmodial compound by hierarquical virtual screening and in vitro assays. J Biomol Struct Dyn 2020; 39:3378-3386. [PMID: 32364060 DOI: 10.1080/07391102.2020.1763837] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Malaria is an infectious disease caused by protozoa of the genus Plasmodium spp. with approximately 219 million cases in 2017. P. falciparum is main responsible for the most severe form of the disease, cerebral malaria. Despite of public health impacts, chemotherapy against malaria is still limited due to the emergence of drug resistance cases used in monotherapy and combination therapies. Thus, the development of new antimalarial drugs becomes emergency. One way of achieve this goal is to explore essential and/or unique therapeutic targets of the parasite, or at least sufficiently different to ensure selective inhibition. Enoil-ACP reductase (ENR) is a NADH-dependent enzyme responsible for the limiting step of the type II fatty acid biosynthetic pathway (FAS II). Thus, pharmacophore and docking based virtual screening were applied to prioritize molecules for in vitro assays against P. falciparum W2 strain. The application of successive filters at OOCC database (n = 618) resulted in the identification of one molecule (13) (EC50 = 0.098 ± 0.021 μM) with similar biological activity to artemether. The molecule 13 is a typical drug repurposing case due to previous other approved therapeutic uses on Chinese medicine as a non-specific cholinergic antagonist, thus it could be accelerated the drug development process. Additionally, molecular dynamics studies were used to confirm stability of the molecular interactions identified by molecular docking. Thus, representative structures of P. falciparum ENR can be used in a study to propose new derivatives for evaluation of biological activity in vitro and in vivo. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- David Bacelar Costa Júnior
- Programa de pós-graduação em Ciências Farmacêuticas, Universidade Estadual de Feira de Santana, Feira de Santana, Brazil
| | | | - Larissa de Mattos Oliveira
- Programa de pós-graduação em Biotecnologia, Universidade Estadual de Feira de Santana, Feira de Santana, Brazil
| | - Flávio Simas Moreira Neri
- Programa de pós-graduação em Ciências Farmacêuticas, Universidade Estadual de Feira de Santana, Feira de Santana, Brazil
| | | | - Alex Gutterres Taranto
- Laboratório de Química Farmacêutica Medicinal, Universidade Federal de São João Del-Rei, Sao Joao del-Rei, Brazil
| | - Amanda Luisa Fonseca
- Laboratório de Bioquímica Medicinal, Universidade Federal de São João Del-Rei, Sao Joao del-Rei, Brazil
| | - Fernando de Pilla Varotti
- Laboratório de Bioquímica Medicinal, Universidade Federal de São João Del-Rei, Sao Joao del-Rei, Brazil
| | - Franco Henrique Andrade Leite
- Programa de pós-graduação em Ciências Farmacêuticas, Universidade Estadual de Feira de Santana, Feira de Santana, Brazil.,Programa de pós-graduação em Biotecnologia, Universidade Estadual de Feira de Santana, Feira de Santana, Brazil.,Laboratório de Modelagem Molecular, Universidade Estadual de Feira de Santana, Feira de Santana, Brazil
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Maia EHB, Assis LC, de Oliveira TA, da Silva AM, Taranto AG. Structure-Based Virtual Screening: From Classical to Artificial Intelligence. Front Chem 2020; 8:343. [PMID: 32411671 PMCID: PMC7200080 DOI: 10.3389/fchem.2020.00343] [Citation(s) in RCA: 203] [Impact Index Per Article: 50.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 04/01/2020] [Indexed: 12/15/2022] Open
Abstract
The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.
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Affiliation(s)
- Eduardo Habib Bechelane Maia
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil.,Federal Center for Technological Education of Minas Gerais-CEFET-MG, Belo Horizonte, Brazil
| | - Letícia Cristina Assis
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
| | | | | | - Alex Gutterres Taranto
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
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Maia EHB, Medaglia LR, da Silva AM, Taranto AG. Molecular Architect: A User-Friendly Workflow for Virtual Screening. ACS OMEGA 2020; 5:6628-6640. [PMID: 32258898 PMCID: PMC7114615 DOI: 10.1021/acsomega.9b04403] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 03/06/2020] [Indexed: 05/02/2023]
Abstract
Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA-ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA-ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.
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Affiliation(s)
- Eduardo H. B. Maia
- Laboratório
de Quêmica Farmaĉutica Medicinal, Universidade Federal de São João Del-Rei, Divinópolis 35501-296, Minas Gerais, Brazil
- Centro
Federal de Educação Tecnológica de Minas Gerais,
CEFET-MG, Campus Divinópolis, Divinópolis 35503-822, MG, Brazil
| | | | - Alisson Marques da Silva
- Centro
Federal de Educação Tecnológica de Minas Gerais,
CEFET-MG, Campus Divinópolis, Divinópolis 35503-822, MG, Brazil
| | - Alex G. Taranto
- Laboratório
de Quêmica Farmaĉutica Medicinal, Universidade Federal de São João Del-Rei, Divinópolis 35501-296, Minas Gerais, Brazil
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47
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Djikic T, Vucicevic J, Laurila J, Radi M, Veljkovic N, Xhaard H, Nikolic K. Deciphering Imidazoline Off‐targets by Fishing in the Class A of GPCR field. Mol Inform 2020; 39:e1900165. [DOI: 10.1002/minf.201900165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 02/20/2020] [Indexed: 12/13/2022]
Affiliation(s)
- Teodora Djikic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of Belgrade Vojvode Stepe 450 11000 Belgrade Serbia
| | - Jelica Vucicevic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of Belgrade Vojvode Stepe 450 11000 Belgrade Serbia
| | - Jonne Laurila
- Research Center for Integrative Physiology and Pharmacology, Institute of BiomedicineUniversity of Turku FI-20014 Turun yliopisto, Turku Finland
| | - Marco Radi
- Dipartimento di Scienze degli Alimenti e del FarmacoUniversità degli Studi di Parma Viale delle Scienze, 27/A 43124 Parma Italy
| | - Nevena Veljkovic
- Laboratory for bioinformatics and computational chemistry, Institute of Nuclear Sciences VincaUniversity of Belgrade Mihaila Petrovica Alasa 14 11001 Belgrade Serbia
| | - Henri Xhaard
- Division of Pharmaceutical Chemistry, Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of PharmacyUniversity of Helsinki P.O. Box 56 FI-00014 Helsinki Finland
| | - Katarina Nikolic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of Belgrade Vojvode Stepe 450 11000 Belgrade Serbia
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Mo R, Shi R, Hu Y, Hu F. Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study. J Diabetes Res 2020; 2020:7261047. [PMID: 32587869 PMCID: PMC7298262 DOI: 10.1155/2020/7261047] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 04/15/2020] [Accepted: 04/28/2020] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVES This study is aimed at developing a risk nomogram of diabetic retinopathy (DR) in a Chinese population with type 2 diabetes mellitus (T2DM). METHODS A questionnaire survey, biochemical indicator examination, and physical examination were performed on 4170 T2DM patients, and the collected data were used to evaluate the DR risk in T2DM patients. By operating R software, firstly, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running cyclic coordinate descent with 10 times K cross-validation. Secondly, multivariable logistic regression analysis was applied to build a predicting model introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. Thirdly, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis were used to validate the model, and further assessment was running by external validation. RESULTS Seven predictors were selected by LASSO from 19 variables, including age, course of disease, postprandial blood glucose (PBG), glycosylated haemoglobin A1c (HbA1c), uric creatinine (UCR), urinary microalbumin (UMA), and systolic blood pressure (SBP). The model built by these 7 predictors displayed medium prediction ability with the area under the ROC curve of 0.700 in the training set and 0.715 in the validation set. The decision curve analysis curve showed that the nomogram could be applied clinically if the risk threshold is between 21% and 57% and 21%-51% in external validation. CONCLUSION Introducing age, course of disease, PBG, HbA1c, UCR, UMA, and SBP, the risk nomogram is useful for prediction of DR risk in T2DM individuals.
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Affiliation(s)
- Ruohui Mo
- School of Public Health, Shanghai University of Traditional Chinese Medicine, 201203, China
| | - Rong Shi
- School of Public Health, Shanghai University of Traditional Chinese Medicine, 201203, China
| | - Yuhong Hu
- School of Public Health, Shanghai University of Traditional Chinese Medicine, 201203, China
| | - Fan Hu
- School of Public Health, Shanghai University of Traditional Chinese Medicine, 201203, China
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Shi R, Niu Z, Wu B, Zhang T, Cai D, Sun H, Hu Y, Mo R, Hu F. Nomogram for the Risk of Diabetic Nephropathy or Diabetic Retinopathy Among Patients with Type 2 Diabetes Mellitus Based on Questionnaire and Biochemical Indicators: A Cross-Sectional Study. Diabetes Metab Syndr Obes 2020; 13:1215-1229. [PMID: 32368114 PMCID: PMC7182465 DOI: 10.2147/dmso.s244061] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 04/08/2020] [Indexed: 12/13/2022] Open
Abstract
PURPOSE This study aimed to develop a diabetic nephropathy (DN) or diabetic retinopathy (DR) incidence risk nomogram in China's population with type 2 diabetes mellitus (T2DM) based on a community-based sample. METHODS We carried out questionnaire evaluations, physical examinations and biochemical tests among 4219 T2DM patients in Shanghai. According to the incidence of DN and DR, 4219 patients in our study were divided into groups of T2DM patients with DN or DR, patients with both, and patients without any complications. We successively used least absolute shrinkage and selection operator regression analysis and logistic regression analysis to optimize the feature selection for DN and DR. To ensure the accuracy of the results, we carried out multivariable logistic regression analysis of the above significant risk factors on the sample data for both DN and DR. The selected features were included to establish a prediction model. The C-index, calibration plot, curve analysis and internal validation were used to validate the distinction, calibration, and clinical practicality of the model. RESULTS The predictors in the prediction model included disease course, body mass index (BMI), total triglycerides (TGs), systolic blood pressure (SBP), postprandial blood glucose (PBG), haemoglobin A1C (HbA1c) and blood urea nitrogen (BUN). The model displayed moderate predictive power with a C-index of 0.807 and an area under the receiver operating characteristic curve of 0.807. In internal verification, the C-index reached 0.804. The risk threshold was 16-75% according to the analysis of the decision curve, and the nomogram could be applied in clinical practice. CONCLUSION This DN or DR incidence risk nomogram incorporating disease course, BMI, TGs, SBP, PBG, HbA1c and BUN can be used to predict DN or DR incidence risk in T2DM patients. The research team has developed an online app based on a clinical prediction model incorporating risk factors for rapid and simple prediction.
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Affiliation(s)
- Rong Shi
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Zheyun Niu
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Birong Wu
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Taotao Zhang
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Dujie Cai
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Hui Sun
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Yuhong Hu
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Ruohui Mo
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
| | - Fan Hu
- School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
- Correspondence: Fan Hu School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of ChinaTel +8613585828140Fax +862151322466 Email
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50
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Çınaroğlu SS, Timuçin E. Comparative Assessment of Seven Docking Programs on a Nonredundant Metalloprotein Subset of the PDBbind Refined. J Chem Inf Model 2019; 59:3846-3859. [PMID: 31460757 DOI: 10.1021/acs.jcim.9b00346] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Extensive usage of molecular docking for computer-aided drug discovery resulted in development of numerous programs with versatile scoring and posing algorithms. Selection of the docking program among these vast number of options is central to the outcome of drug discovery. To this end, comparative assessment studies of docking offer valuable insights into the selection of the optimal tool. Despite the availability of various docking assessment studies, the performance difference of docking programs has not been well addressed on metalloproteins which comprise a substantial portion of the human proteome and have been increasingly targeted for treatment of a wide variety of diseases. This study reports comparative assessment of seven docking programs on a diverse metalloprotein set which was compiled for this study. The refined set of the PDBbind (2017) was screened to gather 710 complexes with metal ion(s) closely located to the ligands (<4 Å). The redundancy was eliminated by clustering and overall 213 complexes were compiled as the nonredundant metalloprotein subset of the PDBbind refined. The scoring, ranking, and posing powers of seven noncommercial docking programs, namely, AutoDock4, AutoDock4Zn, AutoDock Vina, Quick Vina 2, LeDock, PLANTS, and UCSF DOCK6, were comprehensively evaluated on this nonredundant set. Results indicated that PLANTS (80%) followed by LeDock (77%), QVina (76%), and Vina (73%) had the most accurate posing algorithms while AutoDock4 (48%) and DOCK6 (56%) were the least successful in posing. Contrary to their moderate-to-high level of posing success, none of the programs was successful in scoring or ranking of the binding affinities (r2 ≈ 0). Screening power was further evaluated by using active-decoy ligand sets for a large compilation of metalloprotein targets. PLANTS stood out among other programs to be able to enrich the active ligand for every target, underscoring its robustness for screening of metalloprotein inhibitors. This study provides useful information for drug discovery studies targeting metalloproteins.
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Affiliation(s)
- Süleyman Selim Çınaroğlu
- Department of Biostatistics and Medical Informatics, School of Medicine , Acibadem Mehmet Ali Aydinlar University , Istanbul 34752 , Turkey
| | - Emel Timuçin
- Department of Biostatistics and Medical Informatics, School of Medicine , Acibadem Mehmet Ali Aydinlar University , Istanbul 34752 , Turkey
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