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Li Z, Casteleiro Costa P, Guang Z, Filan C, Robles FE. GAN-based quantitative oblique back-illumination microscopy enables computationally efficient epi-mode refractive index tomography. BIOMEDICAL OPTICS EXPRESS 2024; 15:4764-4774. [PMID: 39346989 PMCID: PMC11427205 DOI: 10.1364/boe.528968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 06/09/2024] [Accepted: 06/10/2024] [Indexed: 10/01/2024]
Abstract
Quantitative oblique back-illumination microscopy (qOBM) is a novel imaging technology that enables epi-mode 3D quantitative phase imaging and refractive index (RI) tomography of thick scattering samples. The technology uses four oblique back illumination images captured at the same focal plane and a fast 2D deconvolution reconstruction algorithm to reconstruct 2D phase cross-sections of thick samples. Alternatively, a through-focus z-stack of oblique back illumination images can be used to recover 3D RI tomograms with improved RI quantitative fidelity at the cost of a more computationally expensive reconstruction algorithm. Here, we report on a generative adversarial network (GAN) assisted approach to reconstruct 3D RI tomograms with qOBM that achieves high fidelity and greatly reduces processing time. The proposed approach achieves high-fidelity 3D RI tomography using differential phase contrast images from three adjacent z-planes. A ∼9-fold improvement in volumetric reconstruction time is achieved. We further show that this technique provides high SNR RI tomograms with high quantitative fidelity, reduces motion artifacts, and generalizes to different tissue types. This work can lead to real-time, high-fidelity RI tomographic imaging for in-vivo pre-clinical and clinical applications.
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Affiliation(s)
- Zhenmin Li
- School of Electrical and Computer Engineering, Georgia Institute of Technology, USA
| | | | - Zhe Guang
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, USA
| | - Caroline Filan
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, USA
| | - Francisco E Robles
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, USA
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2
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Torabi M, Yasami-Khiabani S, Sardari S, Golkar M, Pérez-Sánchez H, Ghasemi F. Identification of new potential candidates to inhibit EGF via machine learning algorithm. Eur J Pharmacol 2024; 963:176176. [PMID: 38000720 DOI: 10.1016/j.ejphar.2023.176176] [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: 06/19/2023] [Revised: 10/26/2023] [Accepted: 10/31/2023] [Indexed: 11/26/2023]
Abstract
One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The advantage of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerate the drug discovery process. Hence, an ensemble computational-experimental approach, consisting of three steps, a machine learning model, simulation of drug-target interaction and experimental characterization, was developed. The machine learning type used here was a different tree classification method, which is one of the best randomize machine learning model to identify potential inhibitors from weak inhibitors. This model was trained more than one-hundred times, and forty top trained models were extracted for the drug repositioning step. The machine learning step aimed to discover the approved drugs with the highest possible success rate in the experimental step. Therefore, among all the identified molecules with more than 0.9 probability in more than 70% of the models, nine compounds, were selected. Besides, out of the nine chosen drugs, seven compounds have been confirmed to inhibit EGF in the published articles since 2019. Hence, two identified compounds, in addition to gefitinib, as a positive control, five weak-inhibitors and one neutral, were considered via molecular docking study. Finally, the eight proposed drugs, including gefitinib, were investigated using MTT assay and In-Cell ELISA to characterize the drugs' effect on A431 cell growth and EGF-signaling. From our experiments, we could conclude that salicylic acid and piperazine could play an EGF-inhibitor role like gefitinib.
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Affiliation(s)
- Mohammadreza Torabi
- Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Iran
| | | | - Soroush Sardari
- Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Majid Golkar
- Department of Parasitology, Pasteur Institute of Iran, Tehran, Iran
| | - Horacio Pérez-Sánchez
- Structural Bioinformatics and High Performance Computing Reseach Group (BIO-HPC), Computer Engineering Department, UCAM Universidad Católica de Murcia, Murcia, E30107, Spain
| | - Fahimeh Ghasemi
- Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Iran; Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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Popov P, Kalinin R, Buslaev P, Kozlovskii I, Zaretckii M, Karlov D, Gabibov A, Stepanov A. Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites. Brief Bioinform 2023; 25:bbad459. [PMID: 38113077 PMCID: PMC10783863 DOI: 10.1093/bib/bbad459] [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: 08/07/2023] [Revised: 11/10/2023] [Accepted: 11/22/2023] [Indexed: 12/21/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has spurred a wide range of approaches to control and combat the disease. However, selecting an effective antiviral drug target remains a time-consuming challenge. Computational methods offer a promising solution by efficiently reducing the number of candidates. In this study, we propose a structure- and deep learning-based approach that identifies vulnerable regions in viral proteins corresponding to drug binding sites. Our approach takes into account the protein dynamics, accessibility and mutability of the binding site and the putative mechanism of action of the drug. We applied this technique to validate drug targeting toward severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein S. Our findings reveal a conformation- and oligomer-specific glycan-free binding site proximal to the receptor binding domain. This site comprises topologically important amino acid residues. Molecular dynamics simulations of Spike in complex with candidate drug molecules bound to the potential binding sites indicate an equilibrium shifted toward the inactive conformation compared with drug-free simulations. Small molecules targeting this binding site have the potential to prevent the closed-to-open conformational transition of Spike, thereby allosterically inhibiting its interaction with human angiotensin-converting enzyme 2 receptor. Using a pseudotyped virus-based assay with a SARS-CoV-2 neutralizing antibody, we identified a set of hit compounds that exhibited inhibition at micromolar concentrations.
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Affiliation(s)
- Petr Popov
- Tetra-d, Rheinweg 9, Schaffhausen, 8200, Switzerland
- School of Science, Constructor University Bremen gGmbH, 28759, Bremen, Germany
| | - Roman Kalinin
- M.M. Shemyakin and Yu.A. Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, 117997, Russia
| | - Pavel Buslaev
- Nanoscience Center and Department of Chemistry, University of Jyväskylä, 40014, Jyväskylä, Finland
| | - Igor Kozlovskii
- Tetra-d, Rheinweg 9, Schaffhausen, 8200, Switzerland
- School of Science, Constructor University Bremen gGmbH, 28759, Bremen, Germany
| | - Mark Zaretckii
- Tetra-d, Rheinweg 9, Schaffhausen, 8200, Switzerland
- School of Science, Constructor University Bremen gGmbH, 28759, Bremen, Germany
| | - Dmitry Karlov
- School of Pharmacy, Medical Biology Centre, Queen’s University Belfast, Street, Belfast, BT9 7BL Northern Ireland, U.K
| | - Alexander Gabibov
- M.M. Shemyakin and Yu.A. Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, 117997, Russia
| | - Alexey Stepanov
- Department of Chemistry, The Scripps Research Institute, 10550 North Torrey Pines Road MB-10, La Jolla, 92037, CA, USA
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Khan U, Yasin A. Plane invariant segmentation of computed tomography images through weighted cross entropy optimized conditional GANs in compressed formats. Med Biol Eng Comput 2023; 61:2677-2697. [PMID: 37428300 DOI: 10.1007/s11517-023-02846-7] [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: 11/25/2022] [Accepted: 05/12/2023] [Indexed: 07/11/2023]
Abstract
Computed tomography (CT) scan provides first-hand knowledge to doctors to identify an ailment. Deep neural networks help enhance image understanding through segmentation and labeling. In this work, we implement two variants of Pix2Pix generative adversarial networks (GANs) with varying complexities of generator and discriminator networks for plane invariant segmentation of CT scan images and subsequently propose an effective generative adversarial network with a suitably weighted binary cross-entropy loss function followed by image processing layer necessary for getting high-quality output segmentation. Our conditional GAN is powered by a unique set of an encoder-decoder network that coupled with the image processing layer produces enhanced segmentation. The network can be extended to the complete set of Hounsfield units and can also be implemented on smartphones. Furthermore, we also demonstrate effects on accuracy, F-1 score, and Jaccard index by using the conditional GAN networks on the spine vertebrae dataset, thus achieving an average of 86.28 % accuracy, 90.5 % Jaccard index score, and 89.9 % F-1 score in predicting segmented maps for validation input images. In addition, an overall lifting of accuracy, F-1 score, and Jaccard index graph for validation images with better continuity has also been highlighted.
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Affiliation(s)
- Usman Khan
- SS-CASE-IT Islamabad, Islamabad, Pakistan.
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Dou B, Zhu Z, Merkurjev E, Ke L, Chen L, Jiang J, Zhu Y, Liu J, Zhang B, Wei GW. Machine Learning Methods for Small Data Challenges in Molecular Science. Chem Rev 2023; 123:8736-8780. [PMID: 37384816 PMCID: PMC10999174 DOI: 10.1021/acs.chemrev.3c00189] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.
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Affiliation(s)
- Bozheng Dou
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Zailiang Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Ekaterina Merkurjev
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Lu Ke
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Long Chen
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jian Jiang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yueying Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jie Liu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Bengong Zhang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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Dehnavi A, Nazem F, Ghasemi F, Fassihi A, Rasti R. A GU-Net-based architecture predicting ligand–Protein-binding atoms. JOURNAL OF MEDICAL SIGNALS & SENSORS 2023. [DOI: 10.4103/jmss.jmss_142_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Sun Y, Jiao Y, Shi C, Zhang Y. Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2. Comput Struct Biotechnol J 2022; 20:5014-5027. [PMID: 36091720 PMCID: PMC9448712 DOI: 10.1016/j.csbj.2022.09.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 08/03/2022] [Accepted: 09/03/2022] [Indexed: 11/26/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.
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Affiliation(s)
- Yao Sun
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yanqi Jiao
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Chengcheng Shi
- State Key Lab of Urban Water Resource and Environment, School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yang Zhang
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
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Automated pancreas segmentation and volumetry using deep neural network on computed tomography. Sci Rep 2022; 12:4075. [PMID: 35260710 PMCID: PMC8904764 DOI: 10.1038/s41598-022-07848-3] [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: 09/30/2021] [Accepted: 02/23/2022] [Indexed: 12/14/2022] Open
Abstract
Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis. Therefore, various studies have designed segmentation models based on convolutional neural networks for pancreas segmentation. However, the deep learning approach is limited by a lack of data, and studies conducted on a large computed tomography dataset are scarce. Therefore, this study aims to perform deep-learning-based semantic segmentation on 1006 participants and evaluate the automatic segmentation performance of the pancreas via four individual three-dimensional segmentation networks. In this study, we performed internal validation with 1,006 patients and external validation using the cancer imaging archive pancreas dataset. We obtained mean precision, recall, and dice similarity coefficients of 0.869, 0.842, and 0.842, respectively, for internal validation via a relevant approach among the four deep learning networks. Using the external dataset, the deep learning network achieved mean precision, recall, and dice similarity coefficients of 0.779, 0.749, and 0.735, respectively. We expect that generalized deep-learning-based systems can assist clinical decisions by providing accurate pancreatic segmentation and quantitative information of the pancreas for abdominal computed tomography.
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