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Rovenchak A, Druchok M. Machine learning-assisted search for novel coagulants: When machine learning can be efficient even if data availability is low. J Comput Chem 2024; 45:937-952. [PMID: 38174834 DOI: 10.1002/jcc.27292] [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: 10/30/2023] [Revised: 12/04/2023] [Accepted: 12/10/2023] [Indexed: 01/05/2024]
Abstract
Design of new drugs is a challenging process: a candidate molecule should satisfy multiple conditions to act properly and make the least side-effect-perfect candidates selectively attach to and influence only targets, leaving off-targets intact. The amount of experimental data about various properties of molecules constantly grows, promoting data-driven approaches. However, the applicability of typical predictive machine learning techniques can be substantially limited by a lack of experimental data about a particular target. For example, there are many known Thrombin inhibitors (acting as anticoagulants), but a very limited number of known Protein C inhibitors (coagulants). In this study, we present our approach to suggest new inhibitor candidates by building an effective representation of chemical space. For this aim, we developed a deep learning model-autoencoder, trained on a large set of molecules in the SMILES format to map the chemical space. Further, we applied different sampling strategies to generate novel coagulant candidates. Symmetrically, we tested our approach on anticoagulant candidates, where we were able to predict their inhibition towards Thrombin. We also compare our approach with MegaMolBART-another deep learning generative model, but exploiting similar principles of navigation in a chemical space.
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Affiliation(s)
- Andrij Rovenchak
- SoftServe, Inc., Lviv, Ukraine
- Professor Ivan Vakarchuk Department for Theoretical Physics, Ivan Franko National University of Lviv, Lviv, Ukraine
| | - Maksym Druchok
- SoftServe, Inc., Lviv, Ukraine
- Institute for Condensed Matter Physics, Lviv, Ukraine
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2
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Zhang C, Zhao X, Li F, Qin J, Yang L, Yin Q, Liu Y, Zhu Z, Zhang F, Wang Z, Liang H. Integrating single-cell and multi-omic approaches reveals Euphorbiae Humifusae Herba-dependent mitochondrial dysfunction in non-small-cell lung cancer. J Cell Mol Med 2024; 28:e18317. [PMID: 38801409 PMCID: PMC11129731 DOI: 10.1111/jcmm.18317] [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: 02/21/2024] [Revised: 03/25/2024] [Accepted: 04/03/2024] [Indexed: 05/29/2024] Open
Abstract
Euphorbiae Humifusae Herba (EHH) is a pivotal therapeutic agent with diverse pharmacological effects. However, a substantial gap exists in understanding its pharmacological properties and anti-tumour mechanisms. This study aimed to address this gap by exploring EHH's pharmacological properties, identifying NSCLC therapy-associated protein targets, and elucidating how EHH induces mitochondrial disruption in NSCLC cells, offering insights into novel NSCLC treatment strategies. String database was utilized to explore protein-protein interactions. Subsequently, single-cell analysis and multi-omics further unveiled the impact of EHH-targeted genes on the immune microenvironment of NSCLC, as well as their influence on immunotherapeutic responses. Finally, both in vivo and in vitro experiments elucidated the anti-tumour mechanisms of EHH, specifically through the assessment of mitochondrial ROS levels and alterations in mitochondrial membrane potential. EHH exerts its influence through engagement with a cluster of 10 genes, including the apoptotic gene CASP3. This regulatory impact on the immune milieu within NSCLC holds promise as an indicator for predicting responses to immunotherapy. Besides, EHH demonstrated the capability to induce mitochondrial ROS generation and perturbations in mitochondrial membrane potential in NSCLC cells, ultimately leading to mitochondrial dysfunction and consequent apoptosis of tumour cells. EHH induces mitochondrial disruption in NSCLC cells, leading to cell apoptosis to inhibit the progress of NSCLC.
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Affiliation(s)
- Chengcheng Zhang
- Department of Medical OncologyLonghua Hospital affiliated to Shanghai University of Traditional Chinese MedicineShanghaiChina
| | - Xiaoxue Zhao
- Department of Medical OncologyLonghua Hospital affiliated to Shanghai University of Traditional Chinese MedicineShanghaiChina
| | - Feng Li
- Department of RheumatologyLonghua Hospital affiliated to Shanghai University of Traditional Chinese MedicineShanghaiChina
| | - Jingru Qin
- Department of Medical OncologyLonghua Hospital affiliated to Shanghai University of Traditional Chinese MedicineShanghaiChina
| | - Lu Yang
- Department of Medical OncologyLonghua Hospital affiliated to Shanghai University of Traditional Chinese MedicineShanghaiChina
| | - Qianqian Yin
- Department of Medical OncologyLonghua Hospital affiliated to Shanghai University of Traditional Chinese MedicineShanghaiChina
| | - Yiyi Liu
- Department of Medical OncologyLonghua Hospital affiliated to Shanghai University of Traditional Chinese MedicineShanghaiChina
| | - Zhiyao Zhu
- Department of Medical OncologyLonghua Hospital affiliated to Shanghai University of Traditional Chinese MedicineShanghaiChina
| | - Fei Zhang
- Department of General SurgeryXinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
| | - Zhongqi Wang
- Department of Medical OncologyLonghua Hospital affiliated to Shanghai University of Traditional Chinese MedicineShanghaiChina
| | - Haibin Liang
- Department of General SurgeryXinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina
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3
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Gu S, Liu H, Liu L, Hou T, Kang Y. Artificial intelligence methods in kinase target profiling: Advances and challenges. Drug Discov Today 2023; 28:103796. [PMID: 37805065 DOI: 10.1016/j.drudis.2023.103796] [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: 07/12/2023] [Revised: 09/29/2023] [Accepted: 10/03/2023] [Indexed: 10/09/2023]
Abstract
Kinases have a crucial role in regulating almost the full range of cellular processes, making them essential targets for therapeutic interventions against various diseases. Accurate kinase-profiling prediction is vital for addressing the selectivity/specificity challenges in kinase drug discovery, which is closely related to lead optimization, drug repurposing, and the understanding of potential drug side effects. In this review, we provide an overview of the latest advancements in machine learning (ML)-based and deep learning (DL)-based quantitative structure-activity relationship (QSAR) models for kinase profiling. We highlight current trends in this rapidly evolving field and discuss the existing challenges and future directions regarding experimental data set construction and model architecture design. Our aim is to offer practical insights and guidance for the development and utilization of these approaches.
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Affiliation(s)
- Shukai Gu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao 999078
| | - Liwei Liu
- Advanced Computing and Storage Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co. Ltd, Nanjing 210000, Jiangsu, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
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4
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Delre P, Contino M, Alberga D, Saviano M, Corriero N, Mangiatordi GF. ALPACA: A machine Learning Platform for Affinity and selectivity profiling of CAnnabinoids receptors modulators. Comput Biol Med 2023; 164:107314. [PMID: 37572442 DOI: 10.1016/j.compbiomed.2023.107314] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/10/2023] [Accepted: 08/07/2023] [Indexed: 08/14/2023]
Abstract
The development of small molecules that selectively target the cannabinoid receptor subtype 2 (CB2R) is emerging as an intriguing therapeutic strategy to treat neurodegeneration, as well as to contrast the onset and progression of cancer. In this context, in-silico tools able to predict CB2R affinity and selectivity with respect to the subtype 1 (CB1R), whose modulation is responsible for undesired psychotropic effects, are highly desirable. In this work, we developed a series of machine learning classifiers trained on high-quality bioactivity data of small molecules acting on CB2R and/or CB1R extracted from ChEMBL v30. Our classifiers showed strong predictive power in accurately determining CB2R affinity, CB1R affinity, and CB2R/CB1R selectivity. Among the built models, those obtained using random forest as algorithm proved to be the top-performing ones (AUC in validation ≥0.96) and were made freely accessible through a user-friendly web platform developed ad hoc and called ALPACA (https://www.ba.ic.cnr.it/softwareic/alpaca/). Due to its user-friendly interface and robust predictive power, ALPACA can be a valuable tool in saving both time and resources involved in the design of selective CB2R modulators.
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Affiliation(s)
- Pietro Delre
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | - Marialessandra Contino
- Department of Pharmacy - Pharmaceutical Sciences, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125, Bari, Italy
| | - Domenico Alberga
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | - Michele Saviano
- CNR - Institute of Crystallography, Via Vivaldi 43, 81100, Caserta, Italy
| | - Nicola Corriero
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy.
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5
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Gu S, Shen C, Yu J, Zhao H, Liu H, Liu L, Sheng R, Xu L, Wang Z, Hou T, Kang Y. Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning? Brief Bioinform 2023; 24:6995375. [PMID: 36681903 DOI: 10.1093/bib/bbad008] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 12/04/2022] [Accepted: 12/30/2023] [Indexed: 01/23/2023] Open
Abstract
Binding affinity prediction largely determines the discovery efficiency of lead compounds in drug discovery. Recently, machine learning (ML)-based approaches have attracted much attention in hopes of enhancing the predictive performance of traditional physics-based approaches. In this study, we evaluated the impact of structural dynamic information on the binding affinity prediction by comparing the models trained on different dimensional descriptors, using three targets (i.e. JAK1, TAF1-BD2 and DDR1) and their corresponding ligands as the examples. Here, 2D descriptors are traditional ECFP4 fingerprints, 3D descriptors are the energy terms of the Smina and NNscore scoring functions and 4D descriptors contain the structural dynamic information derived from the trajectories based on molecular dynamics (MD) simulations. We systematically investigate the MD-refined binding affinity prediction performance of three classical ML algorithms (i.e. RF, SVR and XGB) as well as two common virtual screening methods, namely Glide docking and MM/PBSA. The outcomes of the ML models built using various dimensional descriptors and their combinations reveal that the MD refinement with the optimized protocol can improve the predictive performance on the TAF1-BD2 target with considerable structural flexibility, but not for the less flexible JAK1 and DDR1 targets, when taking docking poses as the initial structure instead of the crystal structures. The results highlight the importance of the initial structures to the final performance of the model through conformational analysis on the three targets with different flexibility.
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Affiliation(s)
- Shukai Gu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Chao Shen
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jiahui Yu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Hong Zhao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao, SAR, China
| | - Liwei Liu
- Advanced Computing and Storage Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co., Ltd., Shenzhen 518129, Guangdong, China
| | - Rong Sheng
- Health Technology Development Dept, Huawei Device Co., Ltd., Dongguan 523808, Guangdong, China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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6
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Sunsetting Binding MOAD with its last data update and the addition of 3D-ligand polypharmacology tools. Sci Rep 2023; 13:3008. [PMID: 36810894 PMCID: PMC9944886 DOI: 10.1038/s41598-023-29996-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 02/14/2023] [Indexed: 02/24/2023] Open
Abstract
Binding MOAD is a database of protein-ligand complexes and their affinities with many structured relationships across the dataset. The project has been in development for over 20 years, but now, the time has come to bring it to a close. Currently, the database contains 41,409 structures with affinity coverage for 15,223 (37%) complexes. The website BindingMOAD.org provides numerous tools for polypharmacology exploration. Current relationships include links for structures with sequence similarity, 2D ligand similarity, and binding-site similarity. In this last update, we have added 3D ligand similarity using ROCS to identify ligands which may not necessarily be similar in two dimensions but can occupy the same three-dimensional space. For the 20,387 different ligands present in the database, a total of 1,320,511 3D-shape matches between the ligands were added. Examples of the utility of 3D-shape matching in polypharmacology are presented. Finally, plans for future access to the project data are outlined.
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7
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Atiya A, Alhumaydhi FA, Sharaf SE, Al Abdulmonem W, Elasbali AM, Al Enazi MM, Shamsi A, Jawaid T, Alghamdi BS, Hashem AM, Ashraf GM, Shahwan M. Identification of 11-Hydroxytephrosin and Torosaflavone A as Potential Inhibitors of 3-Phosphoinositide-Dependent Protein Kinase 1 (PDPK1): Toward Anticancer Drug Discovery. BIOLOGY 2022; 11:1230. [PMID: 36009858 PMCID: PMC9405294 DOI: 10.3390/biology11081230] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/07/2022] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Abstract
The 3-phosphoinositide-dependent protein kinase 1 (PDPK1) has a significant role in cancer progression and metastasis as well as other inflammatory disorders, and has been proposed as a promising therapeutic target for several malignancies. In this work, we conducted a systematic virtual screening of natural compounds from the IMPPAT database to identify possible PDPK1 inhibitors. Primarily, the Lipinski rules, ADMET, and PAINS filter were applied and then the binding affinities, docking scores, and selectivity were carried out to find effective hits against PDPK1. Finally, we identified two natural compounds, 11-Hydroxytephrosin and Torosaflavone A, bearing substantial affinity with PDPK1. Both compounds showed drug-likeness as predicted by the ADMET analysis and their physicochemical parameters. These compounds preferentially bind to the ATP-binding pocket of PDPK1 and interact with functionally significant residues. The conformational dynamics and complex stability of PDPK1 with the selected compounds were then studied using interaction analysis and molecular dynamics (MD) simulations for 100 ns. The simulation results revealed that PDPK1 forms stable docked complexes with the elucidated compounds. The findings show that the newly discovered 11-Hydroxytephrosin and Torosaflavone A bind to PDPK1 in an ATP-competitive manner, suggesting that they could one day be used as therapeutic scaffolds against PDPK1-associated diseases including cancer.
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Affiliation(s)
- Akhtar Atiya
- Department of Pharmacognosy, College of Pharmacy, King Khalid University (KKU), Guraiger St., Abha 62529, Saudi Arabia
| | - Fahad A. Alhumaydhi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 52571, Saudi Arabia
| | - Sharaf E. Sharaf
- Pharmaceutical Chemistry Department, College of Pharmacy Umm Al-Qura University, Makkah 21961, Saudi Arabia
- Clinical Research Administration, Executive Administration of Research and Innovation, King Abdullah Medical City in the Holy Capital, Makkah 21955, Saudi Arabia
| | - Waleed Al Abdulmonem
- Department of Pathology, College of Medicine, Qassim University, P.O. Box 6655, Buraidah 51452, Saudi Arabia
| | - Abdelbaset Mohamed Elasbali
- Department of Clinical Laboratory Science, College of Applied Sciences-Qurayyat, Jouf University, Sakaka 72388, Saudi Arabia
| | - Maher M. Al Enazi
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Prince Sattam Bin Abdelaziz University, Al-Kharj 11942, Saudi Arabia
| | - Anas Shamsi
- Center of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman P.O. Box 346, United Arab Emirates
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India
| | - Talha Jawaid
- Department of Pharmacology, College of Medicine, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13317, Saudi Arabia
| | - Badrah S. Alghamdi
- Neuroscience Unit, Department of Physiology, Faculty of Medicine, King Abdulaziz University, Jeddah 22254, Saudi Arabia
- Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 22254, Saudi Arabia
| | - Anwar M. Hashem
- Department of Medical Microbiology and Parasitology, Faculty of Medicine, King Abdulaziz University, Jeddah 22254, Saudi Arabia
- Vaccines and Immunotherapy Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 22254, Saudi Arabia
| | - Ghulam Md. Ashraf
- Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 22254, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 22254, Saudi Arabia
| | - Moyad Shahwan
- Center of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman P.O. Box 346, United Arab Emirates
- College of Pharmacy, Ajman University, Ajman P.O. Box 346, United Arab Emirates
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8
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Nikolaienko T, Gurbych O, Druchok M. Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network. J Comput Chem 2022; 43:728-739. [PMID: 35201629 DOI: 10.1002/jcc.26831] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/04/2022] [Accepted: 02/09/2022] [Indexed: 12/12/2022]
Abstract
Drug discovery pipelines typically involve high-throughput screening of large amounts of compounds in a search of potential drugs candidates. As a chemical space of small organic molecules is huge, a "navigation" over it urges for fast and lightweight computational methods, thus promoting machine-learning approaches for processing huge pools of candidates. In this contribution, we present a graph-based deep neural network for prediction of protein-drug binding affinity and assess its predictive power under thorough testing conditions. Within the suggested approach, both protein and drug molecules are represented as graphs and passed to separate graph sub-networks, then concatenated and regressed towards a binding affinity. The neural network is trained on two binding affinity datasets-PDBbind and data imported from RCSB Protein Data Bank. In order to explore the generalization capabilities of the model we go beyond traditional random or leave-cluster-out techniques and demonstrate the need for more elaborate model performance assessment - six different strategies for test/train data partitioning (random, time- and property-arranged, protein- and ligand-clustered) with a k-fold cross-validation are engaged. Finally, we discuss the model performance in terms of a set of metrics for different split strategies and fold arrangement. Our code is available at https://github.com/SoftServeInc/affinity-by-GNN.
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Affiliation(s)
- Tymofii Nikolaienko
- SoftServe, Inc., Lviv, Ukraine.,Faculty of Physics, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
| | - Oleksandr Gurbych
- Blackthorn AI Ltd., London, UK.,Department of Artificial Intelligence Systems, Lviv Polytechnic National University, Lviv, Ukraine
| | - Maksym Druchok
- SoftServe, Inc., Lviv, Ukraine.,Institute for Condensed Matter Physics, NAS of Ukraine, Lviv, Ukraine
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9
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Ilter M, Kasmer R, Jalalypour F, Atilgan C, Topcu O, Karakas N, Sensoy O. Inhibition of mutant RAS-RAF interaction by mimicking structural and dynamic properties of phosphorylated RAS. eLife 2022; 11:79747. [PMID: 36458814 PMCID: PMC9762712 DOI: 10.7554/elife.79747] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 11/30/2022] [Indexed: 12/03/2022] Open
Abstract
Undruggability of RAS proteins has necessitated alternative strategies for the development of effective inhibitors. In this respect, phosphorylation has recently come into prominence as this reversible post-translational modification attenuates sensitivity of RAS towards RAF. As such, in this study, we set out to unveil the impact of phosphorylation on dynamics of HRASWT and aim to invoke similar behavior in HRASG12D mutant by means of small therapeutic molecules. To this end, we performed molecular dynamics (MD) simulations using phosphorylated HRAS and showed that phosphorylation of Y32 distorted Switch I, hence the RAS/RAF interface. Consequently, we targeted Switch I in HRASG12D by means of approved therapeutic molecules and showed that the ligands enabled detachment of Switch I from the nucleotide-binding pocket. Moreover, we demonstrated that displacement of Switch I from the nucleotide-binding pocket was energetically more favorable in the presence of the ligand. Importantly, we verified computational findings in vitro where HRASG12D/RAF interaction was prevented by the ligand in HEK293T cells that expressed HRASG12D mutant protein. Therefore, these findings suggest that targeting Switch I, hence making Y32 accessible might open up new avenues in future drug discovery strategies that target mutant RAS proteins.
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Affiliation(s)
- Metehan Ilter
- Graduate School of Engineering and Natural Sciences, Istanbul Medipol UniversityIstanbulTurkey
| | - Ramazan Kasmer
- Medical Biology and Genetics Program, Graduate School for Health Sciences, Istanbul Medipol UniversityIstanbulTurkey,Cancer Research Center, Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol UniversityIstanbulTurkey
| | - Farzaneh Jalalypour
- Faculty of Engineering and Natural Sciences, Sabanci UniversityIstanbulTurkey
| | - Canan Atilgan
- Faculty of Engineering and Natural Sciences, Sabanci UniversityIstanbulTurkey
| | - Ozan Topcu
- Medical Biology and Genetics Program, Graduate School for Health Sciences, Istanbul Medipol UniversityIstanbulTurkey
| | - Nihal Karakas
- Medical Biology and Genetics Program, Graduate School for Health Sciences, Istanbul Medipol UniversityIstanbulTurkey,Department of Medical Biology, International School of Medicine, Istanbul Medipol UniversityIstanbulTurkey
| | - Ozge Sensoy
- Department of Computer Engineering, School of Engineering and Natural Sciences, Istanbul Medipol UniversityIstanbulTurkey,Regenerative and Restorative Medicine Research Center (REMER), Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol UniversityIstanbulTurkey
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10
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Venkateswaran MR, Vadivel TE, Jayabal S, Murugesan S, Rajasekaran S, Periyasamy S. A review on network pharmacology based phytotherapy in treating diabetes- An environmental perspective. ENVIRONMENTAL RESEARCH 2021; 202:111656. [PMID: 34265348 DOI: 10.1016/j.envres.2021.111656] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/19/2021] [Accepted: 07/04/2021] [Indexed: 06/13/2023]
Abstract
Diabetes has become common lifestyle disorder associated with obesity and cardiovascular diseases. Environmental factors like physical inactivity, polluted surroundings and unhealthy dieting also plays a vital role in diabetes pathogenesis. As the current anti-diabetic drugs possess unprecedented side effects, traditional herbal medicine can be used an alternative therapy. The paramount challenge with the herbal formulation usage is the lack of standardized procedure, entangled with little knowledge on drug safety and mechanism of drug action. Heavy metal contamination is a major environmental hazard where plants tend to accumulate toxic metals like nickel, chromium and lead through industrial and agricultural activities. It becomes inappropriate to use these plants for phytotherapy as it may affect the human health on long term consumption. This review discuss about the environmental risk factors related to diabetes and better implication of medicinal plants in anti-diabetic therapy using network pharmacology. It is an in silico analytical tool that helps to unravel the multi-targeted action of herbal formulations rich in secondary metabolites. Also, a special focus is attempted to pool the databases regarding the medicinal plants for diabetes and associated diseases, their bioactive compounds, possible diabetic targets, drug-target interaction and toxicology reports that may open an aisle in safer, effective and toxicity-free drug discovery.
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Affiliation(s)
- Meenakshi R Venkateswaran
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India
| | - Tamil Elakkiya Vadivel
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India
| | - Sasidharan Jayabal
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India
| | - Selvakumar Murugesan
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India
| | - Subbiah Rajasekaran
- Department of Biochemistry, ICMR-National Institute for Research in Environmental Health, Bhopal, India.
| | - Sureshkumar Periyasamy
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India.
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11
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Assessing the Anti-inflammatory Mechanism of Reduning Injection by Network Pharmacology. BIOMED RESEARCH INTERNATIONAL 2020; 2020:6134098. [PMID: 33381562 PMCID: PMC7758122 DOI: 10.1155/2020/6134098] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 10/30/2020] [Accepted: 12/04/2020] [Indexed: 12/13/2022]
Abstract
Reduning Injection (RDNI) is a traditional Chinese medicine formula indicated for the treatment of inflammatory diseases. However, the molecular mechanism of RDNI is unclear. The information of RDNI ingredients was collected from previous studies. Targets of them were obtained by data mining and molecular docking. The information of targets and related pathways was collected in UniProt and KEGG. Networks were constructed and analyzed by Cytoscape to identify key compounds, targets, and pathways. Data mining and molecular docking identified 11 compounds, 84 targets, and 201 pathways that are related to the anti-inflammatory activity of RDNI. Network analysis identified two key compounds (caffeic acid and ferulic acid), five key targets (Bcl-2, eNOS, PTGS2, PPARA, and MMPs), and four key pathways (estrogen signaling pathway, PI3K-AKT signaling pathway, cGMP-PKG signaling pathway, and calcium signaling pathway) which would play critical roles in the treatment of inflammatory diseases by RDNI. The cross-talks among pathways provided a deeper understanding of anti-inflammatory effect of RDNI. RDNI is capable of regulating multiple biological processes and treating inflammation at a systems level. Network pharmacology is a practical approach to explore the therapeutic mechanism of TCM for complex disease.
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12
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A Computational Approach with Biological Evaluation: Combinatorial Treatment of Curcumin and Exemestane Synergistically Regulates DDX3 Expression in Cancer Cell Lines. Biomolecules 2020; 10:biom10060857. [PMID: 32512851 PMCID: PMC7355417 DOI: 10.3390/biom10060857] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 05/15/2020] [Accepted: 05/20/2020] [Indexed: 01/07/2023] Open
Abstract
DDX3 belongs to RNA helicase family that demonstrates oncogenic properties and has gained wider attention due to its role in cancer progression, proliferation and transformation. Mounting reports have evidenced the role of DDX3 in cancers making it a promising target to abrogate DDX3 triggered cancers. Dual pharmacophore models were generated and were subsequently validated. They were used as 3D queries to screen the InterBioScreen database, resulting in the selection of curcumin that was escalated to molecular dynamics simulation studies. In vitro anti-cancer analysis was conducted on three cell lines such as MCF-7, MDA-MB-231 and HeLa, which were evaluated along with exemestane. Curcumin was docked into the active site of the protein target (PDB code 2I4I) to estimate the binding affinity. The compound has interacted with two key residues and has displayed stable molecular dynamics simulation results. In vitro analysis has demonstrated that both the candidate compounds have reduced the expression of DDX3 in three cell lines. However, upon combinatorial treatment of curcumin (10 and 20 μM) and exemestane (50 μM) a synergism was exhibited, strikingly downregulating the DDX3 expression and has enhanced apoptosis in three cell lines. The obtained results illuminate the use of curcumin as an alternative DDX3 inhibitor and can serve as a chemical scaffold to design new small molecules.
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Tutone M, Virzì A, Almerico AM. Reverse screening on indicaxanthin from Opuntia ficus-indica as natural chemoactive and chemopreventive agent. J Theor Biol 2018; 455:147-160. [DOI: 10.1016/j.jtbi.2018.07.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 05/28/2018] [Accepted: 07/16/2018] [Indexed: 11/16/2022]
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Wang C, Kurgan L. Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome. Brief Bioinform 2018; 20:2066-2087. [DOI: 10.1093/bib/bby069] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 06/26/2018] [Accepted: 07/10/2018] [Indexed: 12/18/2022] Open
Abstract
AbstractDrug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.
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Affiliation(s)
- Chen Wang
- Computer Science Department, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Lukasz Kurgan
- Computer Science Department, Virginia Commonwealth University, Richmond, VA 23284, USA
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Bistrović A, Grbčić P, Harej A, Sedić M, Kraljević-Pavelić S, Koštrun S, Plavec J, Makuc D, Raić-Malić S. Small molecule purine and pseudopurine derivatives: synthesis, cytostatic evaluations and investigation of growth inhibitory effect in non-small cell lung cancer A549. J Enzyme Inhib Med Chem 2018; 33:271-285. [PMID: 29271659 PMCID: PMC6009932 DOI: 10.1080/14756366.2017.1414807] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Novel halogenated purines and pseudopurines with diverse aryl-substituted 1,2,3-triazoles were prepared. While p-(trifluoromethyl)-substituted 1,2,3-triazole in N-9 alkylated purine and 3-deazapurine was critical for strong albeit unselective activity on pancreatic adenocarcinoma cells CFPAC-1,1-(p-fluorophenyl)-1,2,3-triazole derivative of 7-deazapurine showed selective cytostatic effect on metastatic colon cancer cells SW620. Importantly, 1-(p-chlorophenyl)-1,2,3-triazole-tagged benzimidazole displayed the most pronounced and highly selective inhibitory effect in nM range on non-small cell lung cancer A549. This compound revealed to target molecular processes at the extracellular side and inside the plasma membrane regulated by GPLD1 and growth factor receptors PDGFR and IGF-1R leading to the inhibition of cell proliferation and induction of apoptosis mediated by p38 MAP kinase and NF-κB, respectively. Further optimisation of this compound as to reduce its toxicity in normal cells may lead to the development of novel agent effective against lung cancer.
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Affiliation(s)
- Andrea Bistrović
- a Department of Organic Chemistry, Faculty of Chemical Engineering and Technology , University of Zagreb , Zagreb , Croatia
| | - Petra Grbčić
- b Department of Biotechnology, Center for High-Throughput Technologies , University of Rijeka , Rijeka , Croatia
| | - Anja Harej
- b Department of Biotechnology, Center for High-Throughput Technologies , University of Rijeka , Rijeka , Croatia
| | - Mirela Sedić
- b Department of Biotechnology, Center for High-Throughput Technologies , University of Rijeka , Rijeka , Croatia
| | - Sandra Kraljević-Pavelić
- b Department of Biotechnology, Center for High-Throughput Technologies , University of Rijeka , Rijeka , Croatia
| | - Sanja Koštrun
- c Chemistry Department , Fidelta Ltd. , Zagreb , Croatia
| | - Janez Plavec
- d Slovenian NMR Centre , National Institute of Chemistry , Ljubljana , Slovenia.,e En-FIST Centre of Excellence , Ljubljana , Slovenia.,f Faculty of Chemistry and Chemical Technology , University of Ljubljana , Ljubljana , Slovenia
| | - Damjan Makuc
- d Slovenian NMR Centre , National Institute of Chemistry , Ljubljana , Slovenia.,e En-FIST Centre of Excellence , Ljubljana , Slovenia
| | - Silvana Raić-Malić
- a Department of Organic Chemistry, Faculty of Chemical Engineering and Technology , University of Zagreb , Zagreb , Croatia
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Design, synthesis and biological evaluation of novel benzimidazole amidines as potent multi-target inhibitors for the treatment of non-small cell lung cancer. Eur J Med Chem 2017; 143:1616-1634. [PMID: 29133046 DOI: 10.1016/j.ejmech.2017.10.061] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Revised: 10/18/2017] [Accepted: 10/20/2017] [Indexed: 01/05/2023]
Abstract
A series of novel amidino 2-substituted benzimidazoles linked to 1,4-disubstituted 1,2,3-triazoles were synthesized by implementation of microwave and ultrasound irradiation in click reaction and subsequent condensation of thus obtained 4-(1,2,3-triazol-1-yl)benzaldehyde with o-phenylenediamines. In vitro antiproliferative screening of compounds performed on human cancer cell lines revealed that p-chlorophenyl-substituted 1,2,3-triazolyl N-isopropylamidine 10c and benzyl-substituted 1,2,3-triazolyl imidazoline 11f benzimidazoles had selective and potent cytostatic activities in the low nM range against non-small cell lung cancer cell line A549, which could be attributed to induction of apoptosis and primary necrosis. Additional Western blot analyses showed different mechanisms of cytostatic activity between compounds 10c and 11f that could be associated with the nature of aromatic substituent at 1-(1,2,3-triazolyl) and amidino moiety at C-5 position of benzimidazole ring. Specifically, compound 11f abrogated the activity of several protein kinases including TGM2, CDK9, SK1 and p38 MAPK, whereas compound 10c did not have profound effect on the activities of CDK9 and TGM2, but instead showed moderate downregulation of SK1 activity concomitant with a significant reduction in p38 MAPK. Further in silico structural analysis demonstrated that compound 11f bound slightly better to the ATP binding site of p38 MAPK compared to 10c, which correlated well with observed stronger decrement in the expression level of phospho-p38 MAPK elicited by 11f in comparison with 10c.
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Wang T, Lu M, Du Q, Yao X, Zhang P, Chen X, Xie W, Li Z, Ma Y, Zhu Y. An integrated anti-arrhythmic target network of a Chinese medicine compound, Wenxin Keli, revealed by combined machine learning and molecular pathway analysis. MOLECULAR BIOSYSTEMS 2017; 13:1018-1030. [DOI: 10.1039/c7mb00003k] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Deciphering the compound Wenxin Keli's anti-arrhythmic pharmacological mechanism by integrating network pharmacology and experimental verification methods.
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18
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Spyrakis F, Cozzini P, Eugene Kellogg G. Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors. NUCLEAR RECEPTOR RESEARCH 2016. [DOI: 10.11131/2016/101202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Francesca Spyrakis
- University of Parma, Department of Food Science, Molecular Modelling Laboratory, Parma, Italy
| | - Pietro Cozzini
- University of Parma, Department of Food Science, Molecular Modelling Laboratory, Parma, Italy
| | - Glen Eugene Kellogg
- Virginia Commonwealth University, Department of Medicinal Chemistry & Institute for Structural Biology, Drug Discovery and Development Richmond, Virginia, USA
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Pathan AAK, Panthi B, Khan Z, Koppula PR, Alanazi MS, Sachchidanand, Parine NR, Chourasia M. Lead identification for the K-Ras protein: virtual screening and combinatorial fragment-based approaches. Onco Targets Ther 2016; 9:2575-84. [PMID: 27217775 PMCID: PMC4861002 DOI: 10.2147/ott.s99671] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE Kirsten rat sarcoma (K-Ras) protein is a member of Ras family belonging to the small guanosine triphosphatases superfamily. The members of this family share a conserved structure and biochemical properties, acting as binary molecular switches. The guanosine triphosphate-bound active K-Ras interacts with a range of effectors, resulting in the stimulation of downstream signaling pathways regulating cell proliferation, differentiation, and apoptosis. Efforts to target K-Ras have been unsuccessful until now, placing it among high-value molecules against which developing a therapy would have an enormous impact. K-Ras transduces signals when it binds to guanosine triphosphate by directly binding to downstream effector proteins, but in case of guanosine diphosphate-bound conformation, these interactions get disrupted. METHODS In the present study, we targeted the nucleotide-binding site in the "on" and "off" state conformations of the K-Ras protein to find out suitable lead compounds. A structure-based virtual screening approach has been used to screen compounds from different databases, followed by a combinatorial fragment-based approach to design the apposite lead for the K-Ras protein. RESULTS Interestingly, the designed compounds exhibit a binding preference for the "off" state over "on" state conformation of K-Ras protein. Moreover, the designed compounds' interactions are similar to guanosine diphosphate and, thus, could presumably act as a potential lead for K-Ras. The predicted drug-likeness properties of these compounds suggest that these compounds follow the Lipinski's rule of five and have tolerable absorption, distribution, metabolism, excretion and toxicity values. CONCLUSION Thus, through the current study, we propose targeting only "off" state conformations as a promising strategy for the design of reversible inhibitors to pharmacologically inhibit distinct conformations of K-Ras protein.
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Affiliation(s)
- Akbar Ali Khan Pathan
- Genome Research Chair (GRC), Department of Biochemistry, College of Science, King Saud University, Kingdom of Saudi Arabia; Integrated Gulf Biosystems, Riyadh, Kingdom of Saudi Arabia
| | - Bhavana Panthi
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Hajipur, India
| | - Zahid Khan
- Genome Research Chair (GRC), Department of Biochemistry, College of Science, King Saud University, Kingdom of Saudi Arabia
| | - Purushotham Reddy Koppula
- Department of Internal Medicine, School of Medicine, Columbia, MO, USA; Harry S. Truman Memorial Veterans Affairs Hospital, School of Medicine, Columbia, MO, USA; Department of Radiology, School of Medicine, Columbia, MO, USA
| | - Mohammed Saud Alanazi
- Genome Research Chair (GRC), Department of Biochemistry, College of Science, King Saud University, Kingdom of Saudi Arabia
| | - Sachchidanand
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Hajipur, India
| | - Narasimha Reddy Parine
- Genome Research Chair (GRC), Department of Biochemistry, College of Science, King Saud University, Kingdom of Saudi Arabia
| | - Mukesh Chourasia
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Hajipur, India
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20
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In silico exploration of c-KIT inhibitors by pharmaco-informatics methodology: pharmacophore modeling, 3D QSAR, docking studies, and virtual screening. Mol Divers 2015; 20:41-53. [DOI: 10.1007/s11030-015-9635-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Accepted: 09/14/2015] [Indexed: 10/23/2022]
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21
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Nicola G, Berthold MR, Hedrick MP, Gilson MK. Connecting proteins with drug-like compounds: Open source drug discovery workflows with BindingDB and KNIME. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015; 2015:bav087. [PMID: 26384374 PMCID: PMC4572361 DOI: 10.1093/database/bav087] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Accepted: 08/17/2015] [Indexed: 12/24/2022]
Abstract
Today's large, public databases of protein-small molecule interaction data are creating important new opportunities for data mining and integration. At the same time, new graphical user interface-based workflow tools offer facile alternatives to custom scripting for informatics and data analysis. Here, we illustrate how the large protein-ligand database BindingDB may be incorporated into KNIME workflows as a step toward the integration of pharmacological data with broader biomolecular analyses. Thus, we describe a collection of KNIME workflows that access BindingDB data via RESTful webservices and, for more intensive queries, via a local distillation of the full BindingDB dataset. We focus in particular on the KNIME implementation of knowledge-based tools to generate informed hypotheses regarding protein targets of bioactive compounds, based on notions of chemical similarity. A number of variants of this basic approach are tested for seven existing drugs with relatively ill-defined therapeutic targets, leading to replication of some previously confirmed results and discovery of new, high-quality hits. Implications for future development are discussed. Database URL: www.bindingdb.org.
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Affiliation(s)
- George Nicola
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA,
| | - Michael R Berthold
- Department of Computer and Information Science, Konstanz University, 78457 Konstanz, Germany, and
| | | | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA,
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22
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Targeting disordered proteins with small molecules using entropy. Trends Biochem Sci 2015; 40:491-6. [DOI: 10.1016/j.tibs.2015.07.004] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 07/06/2015] [Accepted: 07/07/2015] [Indexed: 12/31/2022]
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23
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Pérez-Villanueva J, Méndez-Lucio O, Soria-Arteche O, Medina-Franco JL. Activity cliffs and activity cliff generators based on chemotype-related activity landscapes. Mol Divers 2015; 19:1021-35. [PMID: 26150300 DOI: 10.1007/s11030-015-9609-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2015] [Accepted: 06/24/2015] [Indexed: 12/26/2022]
Abstract
Activity cliffs have large impact in drug discovery; therefore, their detection and quantification are of major importance. This work introduces the metric activity cliff enrichment factor and expands the previously reported activity cliff generator concept by adding chemotype information to representations of the activity landscape. To exemplify these concepts, three molecular databases with multiple biological activities were characterized. Compounds in each database were grouped into chemotype classes. Then, pairwise comparisons of structure similarities and activity differences were calculated for each compound and used to construct chemotype-based structure-activity similarity (SAS) maps. Different landscape distributions among four major regions of the SAS maps were observed for different subsets of molecules grouped in chemotypes. Based on this observation, the activity cliff enrichment factor was calculated to numerically detect chemotypes enriched in activity cliffs. Several chemotype classes were detected having major proportion of activity cliffs than the entire database. In addition, some chemotype classes comprising compounds with smooth structure activity relationships (SAR) were detected. Finally, the activity cliff generator concept was applied to compounds grouped in chemotypes to extract valuable SAR information.
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Affiliation(s)
- Jaime Pérez-Villanueva
- División de Ciencias Biológicas y de la Salud, Departamento de Sistemas Biológicos, Universidad Autónoma Metropolitana Unidad Xochimilco (UAM-X), 04960, Mexico, DF, Mexico.
| | - Oscar Méndez-Lucio
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México (UNAM), 04510, Mexico, DF, Mexico.,Unilever Centre for Molecular Science Informatics Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Olivia Soria-Arteche
- División de Ciencias Biológicas y de la Salud, Departamento de Sistemas Biológicos, Universidad Autónoma Metropolitana Unidad Xochimilco (UAM-X), 04960, Mexico, DF, Mexico
| | - José L Medina-Franco
- Departamento de Farmacia, Facultad de Química, Universidad Nacional Autónoma de México (UNAM), 04510, Mexico, DF, Mexico
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24
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Kooistra AJ, Kuhne S, de Esch IJP, Leurs R, de Graaf C. A structural chemogenomics analysis of aminergic GPCRs: lessons for histamine receptor ligand design. Br J Pharmacol 2014; 170:101-26. [PMID: 23713847 DOI: 10.1111/bph.12248] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2012] [Revised: 04/26/2013] [Accepted: 05/03/2013] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND AND PURPOSE Chemogenomics focuses on the discovery of new connections between chemical and biological space leading to the discovery of new protein targets and biologically active molecules. G-protein coupled receptors (GPCRs) are a particularly interesting protein family for chemogenomics studies because there is an overwhelming amount of ligand binding affinity data available. The increasing number of aminergic GPCR crystal structures now for the first time allows the integration of chemogenomics studies with high-resolution structural analyses of GPCR-ligand complexes. EXPERIMENTAL APPROACH In this study, we have combined ligand affinity data, receptor mutagenesis studies, and amino acid sequence analyses to high-resolution structural analyses of (hist)aminergic GPCR-ligand interactions. This integrated structural chemogenomics analysis is used to more accurately describe the molecular and structural determinants of ligand affinity and selectivity in different key binding regions of the crystallized aminergic GPCRs, and histamine receptors in particular. KEY RESULTS Our investigations highlight interesting correlations and differences between ligand similarity and ligand binding site similarity of different aminergic receptors. Apparent discrepancies can be explained by combining detailed analysis of crystallized or predicted protein-ligand binding modes, receptor mutation studies, and ligand structure-selectivity relationships that identify local differences in essential pharmacophore features in the ligand binding sites of different receptors. CONCLUSIONS AND IMPLICATIONS We have performed structural chemogenomics studies that identify links between (hist)aminergic receptor ligands and their binding sites and binding modes. This knowledge can be used to identify structure-selectivity relationships that increase our understanding of ligand binding to (hist)aminergic receptors and hence can be used in future GPCR ligand discovery and design.
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Affiliation(s)
- A J Kooistra
- Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems, VU University Amsterdam, The Netherlands
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25
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Pérez-Villanueva J, Méndez-Lucio O, Soria-Arteche O, Izquierdo T, Concepción Lozada M, Gloria-Greimel WA, Medina-Franco JL. Cyclic Systems Distribution Along Similarity Measures: Insights for an Application to Activity Landscape Modeling. Mol Inform 2013; 32:179-90. [DOI: 10.1002/minf.201200127] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2012] [Accepted: 12/21/2012] [Indexed: 12/12/2022]
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26
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Sirci F, Istyastono EP, Vischer HF, Kooistra AJ, Nijmeijer S, Kuijer M, Wijtmans M, Mannhold R, Leurs R, de Esch IJP, de Graaf C. Virtual Fragment Screening: Discovery of Histamine H3 Receptor Ligands Using Ligand-Based and Protein-Based Molecular Fingerprints. J Chem Inf Model 2012; 52:3308-24. [DOI: 10.1021/ci3004094] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Francesco Sirci
- Laboratory for Chemometrics
and Chemoinformatics, Chemistry Department, University of Perugia, Via Elce di Sotto, 10, I-06123 Perugia Italy
| | - Enade P. Istyastono
- Division of Medicinal Chemistry,
Faculty of Sciences, Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
- Molecular Modeling Division, Pharmaceutical
Technology Laboratory, Universitas Sanata Dharma, Yogyakarta, Indonesia
| | - Henry F. Vischer
- Division of Medicinal Chemistry,
Faculty of Sciences, Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Albert J. Kooistra
- Division of Medicinal Chemistry,
Faculty of Sciences, Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Saskia Nijmeijer
- Division of Medicinal Chemistry,
Faculty of Sciences, Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Martien Kuijer
- Division of Medicinal Chemistry,
Faculty of Sciences, Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Maikel Wijtmans
- Division of Medicinal Chemistry,
Faculty of Sciences, Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Raimund Mannhold
- Department of Laser Medicine,
Molecular Drug Research Group, Heinrich-Heine-Universität, Universitätsstrasse 1, D-40225 Düsseldorf, Germany
| | - Rob Leurs
- Division of Medicinal Chemistry,
Faculty of Sciences, Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Iwan J. P. de Esch
- Division of Medicinal Chemistry,
Faculty of Sciences, Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Chris de Graaf
- Division of Medicinal Chemistry,
Faculty of Sciences, Amsterdam Institute for Molecules, Medicines
and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
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Pérez-Villanueva J, Medina-Franco JL, Méndez-Lucio O, Yoo J, Soria-Arteche O, Izquierdo T, Lozada MC, Castillo R. CASE plots for the chemotype-based activity and selectivity analysis: a CASE study of cyclooxygenase inhibitors. Chem Biol Drug Des 2012; 80:752-62. [PMID: 22883137 DOI: 10.1111/cbdd.12019] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Structure-activity characterization of molecular databases plays a central role in drug discovery. However, the characterization of large databases containing structurally diverse molecules with several end-points represents a major challenge. For this purpose, the use of chemoinformatic methods plays an important role to elucidate structure-activity relationships. Herein, a general methodology, namely Chemotype Activity and Selectivity Enrichment plots, is presented. Chemotype Activity and Selectivity Enrichment plots provide graphical information concerning the activity and selectivity patterns of particular chemotypes contained in structurally diverse databases. As a case study, we analyzed a set of 658 compounds screened against cyclooxygenase-1 and cyclooxygenase-2. Chemotype Activity and Selectivity Enrichment plots analysis highlighted chemotypes enriched with active and selective molecules against cyclooxygenase-2; all this in a simple 2D graphical representation. Additionally, the most active and selective chemotypes detected in Chemotype Activity and Selectivity Enrichment plots were analyzed separately using the previously reported dual activity-difference maps. These findings indicate that Chemotype Activity and Selectivity Enrichment plots and dual activity-difference maps are complementary chemoinformatic tools to explore the structure-activity relationships of structurally diverse databases screened against two biological end-points.
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Affiliation(s)
- Jaime Pérez-Villanueva
- Departamento de Sistemas Biológicos, División de Ciencias Biológicas y de la Salud, UAM-X, México, DF 04960, Mexico.
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28
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Sanders MPA, Roumen L, van der Horst E, Lane JR, Vischer HF, van Offenbeek J, de Vries H, Verhoeven S, Chow KY, Verkaar F, Beukers MW, McGuire R, Leurs R, Ijzerman AP, de Vlieg J, de Esch IJP, Zaman GJR, Klomp JPG, Bender A, de Graaf C. A prospective cross-screening study on G-protein-coupled receptors: lessons learned in virtual compound library design. J Med Chem 2012; 55:5311-25. [PMID: 22563707 DOI: 10.1021/jm300280e] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
We present the systematic prospective evaluation of a protein-based and a ligand-based virtual screening platform against a set of three G-protein-coupled receptors (GPCRs): the β-2 adrenoreceptor (ADRB2), the adenosine A(2A) receptor (AA2AR), and the sphingosine 1-phosphate receptor (S1PR1). Novel bioactive compounds were identified using a consensus scoring procedure combining ligand-based (frequent substructure ranking) and structure-based (Snooker) tools, and all 900 selected compounds were screened against all three receptors. A striking number of ligands showed affinity/activity for GPCRs other than the intended target, which could be partly attributed to the fuzziness and overlap of protein-based pharmacophore models. Surprisingly, the phosphodiesterase 5 (PDE5) inhibitor sildenafil was found to possess submicromolar affinity for AA2AR. Overall, this is one of the first published prospective chemogenomics studies that demonstrate the identification of novel cross-pharmacology between unrelated protein targets. The lessons learned from this study can be used to guide future virtual ligand design efforts.
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Affiliation(s)
- Marijn P A Sanders
- Computational Drug Discovery Group, Radboud University Nijmegen Medical Centre, Geert Grooteplein, Nijmegen, The Netherlands
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29
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Sharma S, Bagchi B, Mukhopadhyay S, Bothra AK. 2D QSAR Studies of Several Potent Aminopyridine, Anilinopyrimidine and Pyridine Carboxamide-based JNK Inhibitors. Indian J Pharm Sci 2012; 73:165-70. [PMID: 22303059 PMCID: PMC3267300 DOI: 10.4103/0250-474x.91584] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2010] [Revised: 04/06/2011] [Accepted: 04/08/2011] [Indexed: 11/23/2022] Open
Abstract
The c-Jan N-terminal kinases are members of the mitogen activated protein kinase family of signaling proteins. Amino pyridine based compounds, 4-anilino pyrimidine derivatives, and 2-pyridine carboxamide derivatives have been identified as potent JNK inhibitors with good cellular activity. In this study we calculated molecular topological and quantum chemical descriptors of 15 training compounds and three quantitative structure activity relationships models have been constructed. The significance of three models is judged on the basis of correlation, Fischer F test and quality factor (Q). This study is helpful for screening potent inhibitors of protein kinases.
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Affiliation(s)
- S Sharma
- Cheminformatics Bioinformatics Laboratory, Department of Chemistry, Raiganj College (University College), Raiganj P.O. Uttar Dinajpur-733 134, India
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Sanders MPA, Verhoeven S, de Graaf C, Roumen L, Vroling B, Nabuurs SB, de Vlieg J, Klomp JPG. Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs. J Chem Inf Model 2011; 51:2277-92. [PMID: 21866955 DOI: 10.1021/ci200088d] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ∼75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010.
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Affiliation(s)
- Marijn P A Sanders
- Computational Drug Discovery Group, CMBI, Radboud University Nijmegen, Nijmegen, The Netherlands
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31
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Abstract
There is a critical need for improving the level of chemistry awareness in systems biology. The data and information related to modulation of genes and proteins by small molecules continue to accumulate at the same time as simulation tools in systems biology and whole body physiologically based pharmacokinetics (PBPK) continue to evolve. We called this emerging area at the interface between chemical biology and systems biology systems chemical biology (SCB) (Nat Chem Biol 3: 447-450, 2007).The overarching goal of computational SCB is to develop tools for integrated chemical-biological data acquisition, filtering and processing, by taking into account relevant information related to interactions between proteins and small molecules, possible metabolic transformations of small molecules, as well as associated information related to genes, networks, small molecules, and, where applicable, mutants and variants of those proteins. There is yet an unmet need to develop an integrated in silico pharmacology/systems biology continuum that embeds drug-target-clinical outcome (DTCO) triplets, a capability that is vital to the future of chemical biology, pharmacology, and systems biology. Through the development of the SCB approach, scientists will be able to start addressing, in an integrated simulation environment, questions that make the best use of our ever-growing chemical and biological data repositories at the system-wide level. This chapter reviews some of the major research concepts and describes key components that constitute the emerging area of computational systems chemical biology.
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Affiliation(s)
- Tudor I Oprea
- Department of Biochemistry and Molecular Biology, School of Medicine, University of New Mexico, Albuquerque, NM, USA
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32
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Ligand efficiency indices for an effective mapping of chemico-biological space: the concept of an atlas-like representation. Drug Discov Today 2010; 15:804-11. [PMID: 20727982 DOI: 10.1016/j.drudis.2010.08.004] [Citation(s) in RCA: 88] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2010] [Revised: 07/08/2010] [Accepted: 08/11/2010] [Indexed: 01/08/2023]
Abstract
We propose a numerical framework that permits an effective atlas-like representation of chemico-biological space based on a series of Cartesian planes mapping the ligands with the corresponding targets connected by an affinity parameter (K(i) or related). The numerical framework is derived from the concept of ligand efficiency indices, which provide a natural coordinate system combining the potency toward the target (biological space) with the physicochemical properties of the ligand (chemical space). This framework facilitates navigation in the multidimensional drug discovery space using map-like representations based on pairs of combined variables related to the efficiency of the ligands per Dalton (molecular weight or number of non-hydrogen atoms) and per unit of polar surface area (or number of polar atoms).
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33
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Abstract
Supramolecular chemistry has expanded dramatically in recent years both in terms of potential applications and in its relevance to analogous biological systems. The formation and function of supramolecular complexes occur through a multiplicity of often difficult to differentiate noncovalent forces. The aim of this Review is to describe the crucial interaction mechanisms in context, and thus classify the entire subject. In most cases, organic host-guest complexes have been selected as examples, but biologically relevant problems are also considered. An understanding and quantification of intermolecular interactions is of importance both for the rational planning of new supramolecular systems, including intelligent materials, as well as for developing new biologically active agents.
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Affiliation(s)
- Hans-Jörg Schneider
- Organische Chemie, Universität des Saarlandes, 66041 Saarbrücken, Deutschland.
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Moghaddam S, Inoue Y, Gilson MK. Host-guest complexes with protein-ligand-like affinities: computational analysis and design. J Am Chem Soc 2009; 131:4012-21. [PMID: 19133781 DOI: 10.1021/ja808175m] [Citation(s) in RCA: 103] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
It has recently been discovered that guests combining a nonpolar core with cationic substituents bind cucurbit[7]uril (CB[7]) in water with ultrahigh affinities. The present study uses the Mining Minima algorithm to study the physics of these extraordinary associations and to computationally test a new series of CB[7] ligands designed to bind with similarly high affinity. The calculations reproduce key experimental observations regarding the affinities of ferrocene-based guests with CB[7] and beta-cyclodextrin and provide a coherent view of the roles of electrostatics and configurational entropy as determinants of affinity in these systems. The newly designed series of compounds is based on a bicyclo[2.2.2]octane core, which is similar in size and polarity to the ferrocene core of the existing series. Mining Minima predicts that these new compounds will, like the ferrocenes, bind CB[7] with extremely high affinities.
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Affiliation(s)
- Sarvin Moghaddam
- Center for Advanced Research in Biotechnology, University of Maryland Biotechnology Institute, 9600 Gudelsky Drive, Rockville, Maryland 20850, USA
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35
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36
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Abstract
Computational biology/chemistry tools are used in most areas of life/health science research. These methods are continually being developed and their use can present difficulties for both experienced and novice investigators. To facilitate the use of these applications, many packages have been implemented online during these last 5 years. This unit focuses on online computational methods with a special emphasis on structural refinement/atomic simulations, protein electrostatic calculations, searches for functional sites, searches for druggable pockets, protein docking and small molecule docking, and prediction of potential impact of amino acid variations on the structure and function of the protein molecules.
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37
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Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 2006; 35:D198-201. [PMID: 17145705 PMCID: PMC1751547 DOI: 10.1093/nar/gkl999] [Citation(s) in RCA: 1216] [Impact Index Per Article: 67.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
BindingDB () is a publicly accessible database currently containing ∼20 000 experimentally determined binding affinities of protein–ligand complexes, for 110 protein targets including isoforms and mutational variants, and ∼11 000 small molecule ligands. The data are extracted from the scientific literature, data collection focusing on proteins that are drug-targets or candidate drug-targets and for which structural data are present in the Protein Data Bank. The BindingDB website supports a range of query types, including searches by chemical structure, substructure and similarity; protein sequence; ligand and protein names; affinity ranges and molecular weight. Data sets generated by BindingDB queries can be downloaded in the form of annotated SDfiles for further analysis, or used as the basis for virtual screening of a compound database uploaded by the user. The data in BindingDB are linked both to structural data in the PDB via PDB IDs and chemical and sequence searches, and to the literature in PubMed via PubMed IDs.
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Affiliation(s)
| | | | | | | | - Michael K. Gilson
- To whom correspondence should be addressed. Tel: +1 240 314 6217; Fax: +1 240 314 6255;
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Yao LX, Wu ZC, Ji ZL, Chen YZ, Chen X. Internet resources related to drug action and human response: a review. ACTA ACUST UNITED AC 2006; 5:131-9. [PMID: 16922594 DOI: 10.2165/00822942-200605030-00001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
It has been demonstrated that numerous proteins interact with drugs or their metabolites. Knowledge of these proteins is necessary to understand the mechanisms of drug action and human response. Progress in modern genetics, molecular biology, biochemistry and pharmacology is generating a comprehensive mechanistic understanding of drug-target interaction on the molecular level. This is valuable for researchers and pharmaceutical companies in their efforts to improve the efficacy of existing drugs and to discover new ones. Most recently, the integration of a systems biology approach into drug discovery processes calls for more holistic knowledge and easily accessible resources of the proteins that are important in drug action and human response. We have reviewed many publicly accessible internet resources of these proteins, according to their roles in drug action and human response, such as therapeutic effect, adverse reaction, absorption, distribution, metabolism and excretion.
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Affiliation(s)
- L X Yao
- College of Life Science, Zhejiang University, Hangzhou, Zhejiang, China
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Rohl C, Price Y, Fischer TB, Paczkowski M, Zettel MF, Tsai J. Cataloging the relationships between proteins: a review of interaction databases. Mol Biotechnol 2006; 34:69-93. [PMID: 16943573 DOI: 10.1385/mb:34:1:69] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/1999] [Revised: 11/30/1999] [Accepted: 11/30/1999] [Indexed: 11/11/2022]
Abstract
By organizing and making widely accessible the increasing amounts of data from high-throughput analyses, protein interaction databases have become an integral resource for the biological community in relating sequence data with higher-order function. To provide a sense of the use and applicability of these databases, we describe each of the major comprehensive interaction databases as well as some of the more specialized ones. Content description, search/browse functionalities, and data presentation are discussed. A succinct explanation of database contents helps the user quickly identify whether the database contains applicable information to their research interest. Broad levels of search/browse functions as well as descriptions/examples allow users to quickly find and access pertinent data. At this point, clear presentation of search results as well as the primary content is necessary. Many databases display information graphically or divided into smaller digestible parts over a number of tabbed/linked pages. In addition, cross-linking between the databases promotes interconnectivity of the data and is an added layer of relational data for the user. Overall, although these protein interaction databases are under continual improvement, their current state shows that much time and effort has gone into organizing and presenting these large sets of data-describing protein interactions.
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Affiliation(s)
- Carol Rohl
- Rosetta Inpharmatics LLC, Seattle, WA, USA
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40
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Abstract
Efficient library design is an ongoing challenge for investigators seeking novel ligands for proteins, whether for drug discovery or chemical biology. Strategies that add neglected chemistry or exclude unproductive compounds are two dominant recent themes, as is a growing awareness of molecular complexity and its implications. The choice of how complex molecules in screening libraries should be often amounts to how big they should be. Small, simple molecules have lower affinities and must be screened at high concentration, but they will also have higher hit rates. Larger compounds, on the other hand, will often more closely resemble final drugs, but because they are more highly functionalized and specific, they will have much lower hit rates. The best general-purpose screening libraries may well be those of intermediate complexity that are free of artifact-causing nuisance compounds.
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Affiliation(s)
- John J Irwin
- Department of Pharmaceutical Chemistry, University of California San Francisco, 1700 4th St, San Francisco, CA 94143-2550, USA.
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41
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Abstract
Binding MOAD (Mother of All Databases) is the largest collection of high-quality, protein-ligand complexes available from the Protein Data Bank. At this time, Binding MOAD contains 5331 protein-ligand complexes comprised of 1780 unique protein families and 2630 unique ligands. We have searched the crystallography papers for all 5000+ structures and compiled binding data for 1375 (26%) of the protein-ligand complexes. The binding-affinity data ranges 13 orders of magnitude. This is the largest collection of binding data reported to date in the literature. We have also addressed the issue of redundancy in the data. To create a nonredundant dataset, one protein from each of the 1780 protein families was chosen as a representative. Representatives were chosen by tightest binding, best resolution, etc. For the 1780 "best" complexes that comprise the nonredundant version of Binding MOAD, 475 (27%) have binding data. This significant collection of protein-ligand complexes will be very useful in elucidating the biophysical patterns of molecular recognition and enzymatic regulation. The complexes with binding-affinity data will help in the development of improved scoring functions and structure-based drug discovery techniques. The dataset can be accessed at http://www.BindingMOAD.org.
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Affiliation(s)
- Liegi Hu
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109-1065, USA
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42
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Cliff MJ, Ladbury JE. A survey of the year 2002 literature on applications of isothermal titration calorimetry. J Mol Recognit 2004; 16:383-91. [PMID: 14732929 DOI: 10.1002/jmr.648] [Citation(s) in RCA: 48] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Isothermal titration calorimetry (ITC) is becoming widely accepted as a key instrument in any laboratory in which quantification of biomolecular interactions is a requisite. The method has matured with respect to general acceptance and application development over recent years. The number of publications on ITC has grown exponentially over the last 10 years, reflecting the general utility of the method. Here all the published works of the year 2002 in this area have been surveyed. We review the broad range of systems to which ITC is being directed and classify these into general areas highlighting key publications of interest. This provides an overview of what can be achieved using this method and what developments are likely to occur in the near future.
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Affiliation(s)
- Matthew J Cliff
- Department of Biochemistry and Molecular Biology, University College London, Gower Street, London WC1E 6BT, UK
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43
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Abstract
The problem of assigning a biochemical function to newly discovered proteins has been traditionally approached by expert enzymological analysis, sequence analysis, and structural modeling. In recent years, the appearance of databases containing protein-ligand interaction data for large numbers of protein classes and chemical compounds have provided new ways of investigating proteins for which the biochemical function is not completely understood. In this work, we introduce a method that utilizes ligand-binding data for functional classification of enzymes. The method makes use of the existing Enzyme Commission (EC) classification scheme and the data on interactions of small molecules with enzymes from the BRENDA database. A set of ligands that binds to an enzyme with unknown biochemical function serves as a query to search a protein-ligand interaction database for enzyme classes that are known to interact with a similar set of ligands. These classes provide hypotheses of the query enzyme's function and complement other computational annotations that take advantage of sequence and structural information. Similarity between sets of ligands is computed using point set similarity measures based upon similarity between individual compounds. We present the statistics of classification of the enzymes in the database by a cross-validation procedure and illustrate the application of the method on several examples.
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Affiliation(s)
- Sergei Izrailev
- Johnson & Johnson Pharmaceutical Research and Development, Cranbury, New Jersey 08512, USA.
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44
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Abstract
Starting with the Protein Data Bank (PDB) as a common ancestor, the evolution of structural databases has been driven by the rapprochement of the structural world and the practical applications. The result is an impressive number of secondary structural databases that is welcomed by structural biologists and bioinformaticians but runs the risk of producing an embarrassment of riches among non-specialist users. Given that any profit depends on the number of customers, efficient interfaces between many structural data banks must be available to make their contents easily accessible. Increasing the information content of central structural repositories might be the best way to guide users through the many, sometimes overlapping databases.
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Affiliation(s)
- Oliviero Carugo
- Protein Structure and Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Area Science Park, Padriciano 99, 34012 Trieste, Italy.
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