1
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Cao Y, Qiu B, Ning X, Fan L, Qin Y, Yu D, Yang C, Ma H, Liao X, You C. Enhancing Machine-Learning Prediction of Enzyme Catalytic Temperature Optima through Amino Acid Conservation Analysis. Int J Mol Sci 2024; 25:6252. [PMID: 38892439 PMCID: PMC11173260 DOI: 10.3390/ijms25116252] [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: 04/02/2024] [Revised: 05/22/2024] [Accepted: 05/30/2024] [Indexed: 06/21/2024] Open
Abstract
Enzymes play a crucial role in various industrial production and pharmaceutical developments, serving as catalysts for numerous biochemical reactions. Determining the optimal catalytic temperature (Topt) of enzymes is crucial for optimizing reaction conditions, enhancing catalytic efficiency, and accelerating the industrial processes. However, due to the limited availability of experimentally determined Topt data and the insufficient accuracy of existing computational methods in predicting Topt, there is an urgent need for a computational approach to predict the Topt values of enzymes accurately. In this study, using phosphatase (EC 3.1.3.X) as an example, we constructed a machine learning model utilizing amino acid frequency and protein molecular weight information as features and employing the K-nearest neighbors regression algorithm to predict the Topt of enzymes. Usually, when conducting engineering for enzyme thermostability, researchers tend not to modify conserved amino acids. Therefore, we utilized this machine learning model to predict the Topt of phosphatase sequences after removing conserved amino acids. We found that the predictive model's mean coefficient of determination (R2) value increased from 0.599 to 0.755 compared to the model based on the complete sequences. Subsequently, experimental validation on 10 phosphatase enzymes with undetermined optimal catalytic temperatures shows that the predicted values of most phosphatase enzymes based on the sequence without conservative amino acids are closer to the experimental optimal catalytic temperature values. This study lays the foundation for the rapid selection of enzymes suitable for industrial conditions.
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Affiliation(s)
- Yinyin Cao
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, China; (Y.C.)
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; (B.Q.); (H.M.)
| | - Boyu Qiu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; (B.Q.); (H.M.)
- Department of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230022, China
| | - Xiao Ning
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; (B.Q.); (H.M.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lin Fan
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; (B.Q.); (H.M.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yanmei Qin
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; (B.Q.); (H.M.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dong Yu
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, China; (Y.C.)
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; (B.Q.); (H.M.)
| | - Chunhe Yang
- College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, China; (Y.C.)
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; (B.Q.); (H.M.)
| | - Hongwu Ma
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; (B.Q.); (H.M.)
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Xiaoping Liao
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; (B.Q.); (H.M.)
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Chun You
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; (B.Q.); (H.M.)
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
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2
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Ryzhkov FV, Ryzhkova YE, Elinson MN. Python tools for structural tasks in chemistry. Mol Divers 2024:10.1007/s11030-024-10889-7. [PMID: 38744790 DOI: 10.1007/s11030-024-10889-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 04/27/2024] [Indexed: 05/16/2024]
Abstract
In recent decades, the use of computational approaches and artificial intelligence in the scientific environment has become more widespread. In this regard, the popular and versatile programming language Python has attracted considerable attention from scientists in the field of chemistry. It is used to solve a variety of chemical and structural problems, including calculating descriptors, molecular fingerprints, graph construction, and computing chemical reaction networks. Python offers high-quality visualization tools for analyzing chemical spaces and compound libraries. This review is a list of tools for the above tasks, including scripts, libraries, ready-made programs, and web interfaces. Inevitably this manuscript does not claim to be an all-encompassing handbook including all the existing Python-based structural chemistry codes. The review serves as a starting point for scientists wishing to apply automatization or optimization to routine chemistry problems.
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Affiliation(s)
- Fedor V Ryzhkov
- N. D. Zelinsky Institute of Organic Chemistry Russian Academy of Sciences, 47 Leninsky Prospekt, Moscow, 119991, Russia.
| | - Yuliya E Ryzhkova
- N. D. Zelinsky Institute of Organic Chemistry Russian Academy of Sciences, 47 Leninsky Prospekt, Moscow, 119991, Russia
| | - Michail N Elinson
- N. D. Zelinsky Institute of Organic Chemistry Russian Academy of Sciences, 47 Leninsky Prospekt, Moscow, 119991, Russia
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3
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Arora S, Satija S, Mittal A, Solanki S, Mohanty SK, Srivastava V, Sengupta D, Rout D, Arul Murugan N, Borkar RM, Ahuja G. Unlocking The Mysteries of DNA Adducts with Artificial Intelligence. Chembiochem 2024; 25:e202300577. [PMID: 37874183 DOI: 10.1002/cbic.202300577] [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: 08/16/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 10/25/2023]
Abstract
Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.
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Affiliation(s)
- Sakshi Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Shiva Satija
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Aayushi Mittal
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Saveena Solanki
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Sanjay Kumar Mohanty
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Vaibhav Srivastava
- Division of Glycoscience, Department of Chemistry CBH School, Royal Institute of Technology (KTH) AlbaNova University Center, 10691, Stockholm, Sweden
| | - Debarka Sengupta
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Diptiranjan Rout
- Department of Transfusion Medicine National Cancer Institute, AIIMS, New Delhi, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110608, India
| | - Natarajan Arul Murugan
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Roshan M Borkar
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER)-Guwahati, Sila Katamur Halugurisuk P.O.: Changsari, Dist, Guwahati, Assam, 781101, India
| | - Gaurav Ahuja
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
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4
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McGibbon M, Shave S, Dong J, Gao Y, Houston DR, Xie J, Yang Y, Schwaller P, Blay V. From intuition to AI: evolution of small molecule representations in drug discovery. Brief Bioinform 2023; 25:bbad422. [PMID: 38033290 PMCID: PMC10689004 DOI: 10.1093/bib/bbad422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023] Open
Abstract
Within drug discovery, the goal of AI scientists and cheminformaticians is to help identify molecular starting points that will develop into safe and efficacious drugs while reducing costs, time and failure rates. To achieve this goal, it is crucial to represent molecules in a digital format that makes them machine-readable and facilitates the accurate prediction of properties that drive decision-making. Over the years, molecular representations have evolved from intuitive and human-readable formats to bespoke numerical descriptors and fingerprints, and now to learned representations that capture patterns and salient features across vast chemical spaces. Among these, sequence-based and graph-based representations of small molecules have become highly popular. However, each approach has strengths and weaknesses across dimensions such as generality, computational cost, inversibility for generative applications and interpretability, which can be critical in informing practitioners' decisions. As the drug discovery landscape evolves, opportunities for innovation continue to emerge. These include the creation of molecular representations for high-value, low-data regimes, the distillation of broader biological and chemical knowledge into novel learned representations and the modeling of up-and-coming therapeutic modalities.
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Affiliation(s)
- Miles McGibbon
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Steven Shave
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, China
| | - Yumiao Gao
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Douglas R Houston
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Jiancong Xie
- Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Yuedong Yang
- Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Philippe Schwaller
- Laboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vincent Blay
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
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5
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Panwar P, Yang Q, Martini A. PyL3dMD: Python LAMMPS 3D molecular descriptors package. J Cheminform 2023; 15:69. [PMID: 37507792 PMCID: PMC10385924 DOI: 10.1186/s13321-023-00737-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 07/16/2023] [Indexed: 07/30/2023] Open
Abstract
Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules, called 3-dimensional (3D) descriptors. When calculated from molecular dynamics (MD) simulation trajectories, 3D descriptors can also capture the effects of operating conditions such as temperature or pressure. However, extracting 3D descriptors from MD trajectories is non-trivial, which hinders their wide use by researchers developing advanced quantitative-structure-property-relationship models using machine learning. Here, we describe a suite of open-source Python-based post-processing routines, called PyL3dMD, for calculating 3D descriptors from MD simulations. PyL3dMD is compatible with the popular simulation package LAMMPS and enables users to compute more than 2000 3D molecular descriptors from atomic trajectories generated by MD simulations. PyL3dMD is freely available via GitHub and can be easily installed and used as a highly flexible Python package on all major platforms (Windows, Linux, and macOS). A performance benchmark study used descriptors calculated by PyL3dMD to develop a neural network and the results showed that PyL3dMD is fast and efficient in calculating descriptors for large and complex molecular systems with long simulation durations. PyL3dMD facilitates the calculation of 3D molecular descriptors using MD simulations, making it a valuable tool for cheminformatics studies.
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Affiliation(s)
- Pawan Panwar
- Department of Mechanical Engineering, University of California Merced, 5200 North Lake Road, Merced, CA, 95343, USA.
| | - Quanpeng Yang
- Department of Mechanical Engineering, University of California Merced, 5200 North Lake Road, Merced, CA, 95343, USA
| | - Ashlie Martini
- Department of Mechanical Engineering, University of California Merced, 5200 North Lake Road, Merced, CA, 95343, USA.
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6
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Emonts J, Buyel J. An overview of descriptors to capture protein properties - Tools and perspectives in the context of QSAR modeling. Comput Struct Biotechnol J 2023; 21:3234-3247. [PMID: 38213891 PMCID: PMC10781719 DOI: 10.1016/j.csbj.2023.05.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 01/13/2024] Open
Abstract
Proteins are important ingredients in food and feed, they are the active components of many pharmaceutical products, and they are necessary, in the form of enzymes, for the success of many technical processes. However, production can be challenging, especially when using heterologous host cells such as bacteria to express and assemble recombinant mammalian proteins. The manufacturability of proteins can be hindered by low solubility, a tendency to aggregate, or inefficient purification. Tools such as in silico protein engineering and models that predict separation criteria can overcome these issues but usually require the complex shape and surface properties of proteins to be represented by a small number of quantitative numeric values known as descriptors, as similarly used to capture the features of small molecules. Here, we review the current status of protein descriptors, especially for application in quantitative structure activity relationship (QSAR) models. First, we describe the complexity of proteins and the properties that descriptors must accommodate. Then we introduce descriptors of shape and surface properties that quantify the global and local features of proteins. Finally, we highlight the current limitations of protein descriptors and propose strategies for the derivation of novel protein descriptors that are more informative.
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Affiliation(s)
- J. Emonts
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Germany
| | - J.F. Buyel
- University of Natural Resources and Life Sciences, Vienna (BOKU), Department of Biotechnology (DBT), Institute of Bioprocess Science and Engineering (IBSE), Muthgasse 18, 1190 Vienna, Austria
- Institute for Molecular Biotechnology, Worringerweg 1, RWTH Aachen University, 52074 Aachen, Germany
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7
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Sun J, Xu M, Ru J, James-Bott A, Xiong D, Wang X, Cribbs AP. Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications. Eur J Med Chem 2023; 257:115500. [PMID: 37262996 DOI: 10.1016/j.ejmech.2023.115500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/05/2023] [Accepted: 05/15/2023] [Indexed: 06/03/2023]
Abstract
Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.
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Affiliation(s)
- Jianfeng Sun
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
| | - Miaoer Xu
- Department of Biology, Emory University, Atlanta, GA, 30322, USA
| | - Jinlong Ru
- Chair of Prevention of Microbial Diseases, School of Life Sciences Weihenstephan, Technical University of Munich, Freising, 85354, Germany
| | - Anna James-Bott
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Dapeng Xiong
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Xia Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China.
| | - Adam P Cribbs
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
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8
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Yang SQ, Zhang LX, Ge YJ, Zhang JW, Hu JX, Shen CY, Lu AP, Hou TJ, Cao DS. In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences. J Cheminform 2023; 15:48. [PMID: 37088813 PMCID: PMC10123967 DOI: 10.1186/s13321-023-00720-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 04/08/2023] [Indexed: 04/25/2023] Open
Abstract
Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing.
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Affiliation(s)
- Su-Qing Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China
- Department of Pharmacy, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Liu-Xia Zhang
- The First Hospital of Hunan University of Chinese Medicine, Changsha, 410007, Hunan, People's Republic of China
| | - You-Jin Ge
- Department of Pharmacy, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Jin-Wei Zhang
- Departments of Biomedical Engineering and Pathology, School of Basic Medical Science, Central South University, Changsha, 410013, Hunan, People's Republic of China
| | - Jian-Xin Hu
- Department of Pharmacy, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Cheng-Ying Shen
- Department of Pharmacy, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, People's Republic of China
| | - Ting-Jun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, People's Republic of China.
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, People's Republic of China.
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, People's Republic of China.
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9
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Pande A, Patiyal S, Lathwal A, Arora C, Kaur D, Dhall A, Mishra G, Kaur H, Sharma N, Jain S, Usmani SS, Agrawal P, Kumar R, Kumar V, Raghava GPS. Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models. J Comput Biol 2023; 30:204-222. [PMID: 36251780 DOI: 10.1089/cmb.2022.0241] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
In the last three decades, a wide range of protein features have been discovered to annotate a protein. Numerous attempts have been made to integrate these features in a software package/platform so that the user may compute a wide range of features from a single source. To complement the existing methods, we developed a method, Pfeature, for computing a wide range of protein features. Pfeature allows to compute more than 200,000 features required for predicting the overall function of a protein, residue-level annotation of a protein, and function of chemically modified peptides. It has six major modules, namely, composition, binary profiles, evolutionary information, structural features, patterns, and model building. Composition module facilitates to compute most of the existing compositional features, plus novel features. The binary profile of amino acid sequences allows to compute the fraction of each type of residue as well as its position. The evolutionary information module allows to compute evolutionary information of a protein in the form of a position-specific scoring matrix profile generated using Position-Specific Iterative Basic Local Alignment Search Tool (PSI-BLAST); fit for annotation of a protein and its residues. A structural module was developed for computing of structural features/descriptors from a tertiary structure of a protein. These features are suitable to predict the therapeutic potential of a protein containing non-natural or chemically modified residues. The model-building module allows to implement various machine learning techniques for developing classification and regression models as well as feature selection. Pfeature also allows the generation of overlapping patterns and features from a protein. A user-friendly Pfeature is available as a web server python library and stand-alone package.
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Affiliation(s)
- Akshara Pande
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Anjali Lathwal
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Chakit Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Dilraj Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Gaurav Mishra
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.,Department of Electrical Engineering, Shiv Nadar University, Greater Noida, India
| | - Harpreet Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.,Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Neelam Sharma
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Shipra Jain
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Salman Sadullah Usmani
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.,Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Piyush Agrawal
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.,Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Rajesh Kumar
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.,Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Vinod Kumar
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.,Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
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10
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Mao J, Zeb A, Kim MS, Jeon HN, Wang J, Guan S, No KT. Development of an innovative data-driven system to generate descriptive prediction equation of dielectric constant on small sample sets. Heliyon 2022; 8:e10011. [PMID: 36016529 PMCID: PMC9396556 DOI: 10.1016/j.heliyon.2022.e10011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 04/13/2022] [Accepted: 07/15/2022] [Indexed: 11/29/2022] Open
Abstract
Dielectric constant (DC, ε) is a fundamental parameter in material sciences to measure polarizability of the system. In industrial processes, its value is an imperative indicator, which demonstrates the dielectric property of material and compiles information including separation information, chemical equilibrium, chemical reactivity analysis, and solubility modeling. Since, the available ε-prediction models are fairly primitive and frequently suffer from serious failures especially when deals with strong polar compounds. Therefore, we have developed a novel data-driven system to improve the efficiency and wide-range applicability of ε using in material sciences. This innovative scheme adopts the correlation distance and genetic algorithm to discriminate features’ combination and avoid overfitting. Herein, the prediction output of the single ML model as a coding to estimate the target value by simulating the layer-by-layer extraction in deep learning, and enabling instant search for the optimal combination of features is recruited. Our model established an improved correlation value of 0.956 with target as compared to the previously available best traditional ML result of 0.877. Our framework established a profound improvement, especially for material systems possessing ε value >50. In terms of interpretability, we have derived a conceptual computational equation from a minimum generating tree. Our innovative data-driven system is preferentially superior over other methods due to its application for the prediction of dielectric constants as well as for the prediction of overall micro and macro-properties of any multi-components complex.
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11
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Chen Z, Liu X, Zhao P, Li C, Wang Y, Li F, Akutsu T, Bain C, Gasser RB, Li J, Yang Z, Gao X, Kurgan L, Song J. iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets. Nucleic Acids Res 2022; 50:W434-W447. [PMID: 35524557 PMCID: PMC9252729 DOI: 10.1093/nar/gkac351] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 01/07/2023] Open
Abstract
The rapid accumulation of molecular data motivates development of innovative approaches to computationally characterize sequences, structures and functions of biological and chemical molecules in an efficient, accessible and accurate manner. Notwithstanding several computational tools that characterize protein or nucleic acids data, there are no one-stop computational toolkits that comprehensively characterize a wide range of biomolecules. We address this vital need by developing a holistic platform that generates features from sequence and structural data for a diverse collection of molecule types. Our freely available and easy-to-use iFeatureOmega platform generates, analyzes and visualizes 189 representations for biological sequences, structures and ligands. To the best of our knowledge, iFeatureOmega provides the largest scope when directly compared to the current solutions, in terms of the number of feature extraction and analysis approaches and coverage of different molecules. We release three versions of iFeatureOmega including a webserver, command line interface and graphical interface to satisfy needs of experienced bioinformaticians and less computer-savvy biologists and biochemists. With the assistance of iFeatureOmega, users can encode their molecular data into representations that facilitate construction of predictive models and analytical studies. We highlight benefits of iFeatureOmega based on three research applications, demonstrating how it can be used to accelerate and streamline research in bioinformatics, computational biology, and cheminformatics areas. The iFeatureOmega webserver is freely available at http://ifeatureomega.erc.monash.edu and the standalone versions can be downloaded from https://github.com/Superzchen/iFeatureOmega-GUI/ and https://github.com/Superzchen/iFeatureOmega-CLI/.
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Affiliation(s)
- Zhen Chen
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China
- Center for Crop Genome Engineering, Henan Agricultural University, Zhengzhou 450046, China
| | - Xuhan Liu
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, Leiden 2333 CC, The Netherlands
| | - Pei Zhao
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Chen Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Yanan Wang
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Fuyi Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto 611-0011, Japan
| | - Chris Bain
- Monash Data Future Institutes, Monash University, Melbourne, Victoria 3800, Australia
| | - Robin B Gasser
- Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - Junzhou Li
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450046, China
| | - Zuoren Yang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research of Chinese Academy of Agricultural Sciences (CAAS), Anyang 455000, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
- Monash Data Future Institutes, Monash University, Melbourne, Victoria 3800, Australia
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12
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Zhao Q, Yang M, Cheng Z, Li Y, Wang J. Biomedical Data and Deep Learning Computational Models for Predicting Compound-Protein Relations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2092-2110. [PMID: 33769935 DOI: 10.1109/tcbb.2021.3069040] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The identification of compound-protein relations (CPRs), which includes compound-protein interactions (CPIs) and compound-protein affinities (CPAs), is critical to drug development. A common method for compound-protein relation identification is the use of in vitro screening experiments. However, the number of compounds and proteins is massive, and in vitro screening experiments are labor-intensive, expensive, and time-consuming with high failure rates. Researchers have developed a computational field called virtual screening (VS) to aid experimental drug development. These methods utilize experimentally validated biological interaction information to generate datasets and use the physicochemical and structural properties of compounds and target proteins as input information to train computational prediction models. At present, deep learning has been widely used in computer vision and natural language processing and has experienced epoch-making progress. At the same time, deep learning has also been used in the field of biomedicine widely, and the prediction of CPRs based on deep learning has developed rapidly and has achieved good results. The purpose of this study is to investigate and discuss the latest applications of deep learning techniques in CPR prediction. First, we describe the datasets and feature engineering (i.e., compound and protein representations and descriptors) commonly used in CPR prediction methods. Then, we review and classify recent deep learning approaches in CPR prediction. Next, a comprehensive comparison is performed to demonstrate the prediction performance of representative methods on classical datasets. Finally, we discuss the current state of the field, including the existing challenges and our proposed future directions. We believe that this investigation will provide sufficient references and insight for researchers to understand and develop new deep learning methods to enhance CPR predictions.
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Zapadka M, Dekowski P, Kupcewicz B. HATS5m as an Example of GETAWAY Molecular Descriptor in Assessing the Similarity/Diversity of the Structural Features of 4-Thiazolidinone. Int J Mol Sci 2022; 23:6576. [PMID: 35743020 PMCID: PMC9223869 DOI: 10.3390/ijms23126576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 04/30/2022] [Accepted: 06/10/2022] [Indexed: 11/29/2022] Open
Abstract
Among the various methods for drug design, the approach using molecular descriptors for quantitative structure-activity relationships (QSAR) bears promise for the prediction of innovative molecular structures with bespoke pharmacological activity. Despite the growing number of successful potential applications, the QSAR models often remain hard to interpret. The difficulty arises from the use of advanced chemometric or machine learning methods on the one hand, and the complexity of molecular descriptors on the other hand. Thus, there is a need to interpret molecular descriptors for identifying the features of molecules crucial for desirable activity. For example, the development of structure-activity modeling of different molecule endpoints confirmed the usefulness of H-GETAWAY (H-GEometry, Topology, and Atom-Weights AssemblY) descriptors in molecular sciences. However, compared with other 3D molecular descriptors, H-GETAWAY interpretation is much more complicated. The present study provides insights into the interpretation of the HATS5m descriptor (H-GETAWAY) concerning the molecular structures of the 4-thiazolidinone derivatives with antitrypanosomal activity. According to the published study, an increase in antitrypanosomal activity is associated with both a decrease and an increase in HATS5m (leverage-weighted autocorrelation with lag 5, weighted by atomic masses) values. The substructure-based method explored how the changes in molecular features affect the HATS5m value. Based on this approach, we proposed substituents that translate into low and high HATS5m. The detailed interpretation of H-GETAWAY descriptors requires the consideration of three elements: weighting scheme, leverages, and the Dirac delta function. Particular attention should be paid to the impact of chemical compounds' size and shape and the leverage values of individual atoms.
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Affiliation(s)
- Mariusz Zapadka
- Department of Inorganic and Analytical Chemistry, Faculty of Pharmacy, Nicolaus Copernicus University in Toruń, Jurasza 2, 85-089 Bydgoszcz, Poland
| | - Przemysław Dekowski
- New Technologies Department, Softmaks.pl Sp. z o.o., Kraszewskiego 1, 85-241 Bydgoszcz, Poland;
| | - Bogumiła Kupcewicz
- Department of Inorganic and Analytical Chemistry, Faculty of Pharmacy, Nicolaus Copernicus University in Toruń, Jurasza 2, 85-089 Bydgoszcz, Poland
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PDAUG: a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling. BMC Bioinformatics 2022; 23:197. [PMID: 35643441 PMCID: PMC9148462 DOI: 10.1186/s12859-022-04727-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 05/11/2022] [Indexed: 11/28/2022] Open
Abstract
Background Computational methods based on initial screening and prediction of peptides for desired functions have proven to be effective alternatives to lengthy and expensive biochemical experimental methods traditionally utilized in peptide research, thus saving time and effort. However, for many researchers, the lack of expertise in utilizing programming libraries, access to computational resources, and flexible pipelines are big hurdles to adopting these advanced methods.
Results To address the above mentioned barriers, we have implemented the peptide design and analysis under Galaxy (PDAUG) package, a Galaxy-based Python powered collection of tools, workflows, and datasets for rapid in-silico peptide library analysis. In contrast to existing methods like standard programming libraries or rigid single-function web-based tools, PDAUG offers an integrated GUI-based toolset, providing flexibility to build and distribute reproducible pipelines and workflows without programming expertise. Finally, we demonstrate the usability of PDAUG in predicting anticancer properties of peptides using four different feature sets and assess the suitability of various ML algorithms. Conclusion PDAUG offers tools for peptide library generation, data visualization, built-in and public database peptide sequence retrieval, peptide feature calculation, and machine learning (ML) modeling. Additionally, this toolset facilitates researchers to combine PDAUG with hundreds of compatible existing Galaxy tools for limitless analytic strategies. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04727-6.
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15
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Sequeira AM, Lousa D, Rocha M. ProPythia: A Python package for protein classification based on machine and deep learning. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.07.102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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Ignacz G, Szekely G. Deep learning meets quantitative structure–activity relationship (QSAR) for leveraging structure-based prediction of solute rejection in organic solvent nanofiltration. J Memb Sci 2022. [DOI: 10.1016/j.memsci.2022.120268] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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17
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Machine Learning Approaches for Discriminating Bacterial and Viral Targeted Human Proteins. Processes (Basel) 2022. [DOI: 10.3390/pr10020291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Infectious diseases are one of the core biological complications for public health. It is important to recognize the pathogen-specific mechanisms to improve our understanding of infectious diseases. Differentiations between bacterial- and viral-targeted human proteins are important for improving both prognosis and treatment for the patient. Here, we introduce machine learning-based classifiers to discriminate between the two groups of human proteins. We used the sequence, network, and gene ontology features of human proteins. Among different classifiers and features, the deep neural network (DNN) classifier with amino acid composition (AAC), dipeptide composition (DC), and pseudo-amino acid composition (PAAC) (445 features) achieved the best area under the curve (AUC) value (0.939), F1-score (94.9%), and Matthews correlation coefficient (MCC) value (0.81). We found that each of the selected top 100 of the bacteria- and virus-targeted human proteins from a candidate pool of 1618 and 3916 proteins, respectively, were part of distinct enriched biological processes and pathways. Our proposed method will help to differentiate between the bacterial and viral infections based on the targeted human proteins on a global scale. Furthermore, identification of the crucial pathogen targets in the human proteome would help us to better understand the pathogen-specific infection strategies and develop novel therapeutics.
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Abstract
Artificial intelligence (AI) offers new possibilities for hit and lead finding in medicinal chemistry. Several instances of AI have been used for prospective de novo drug design. Among these, chemical language models have been shown to perform well in various experimental scenarios. In this study, we provide a hands-on introduction to chemical language modeling. A technique based on recurrent neural networks is discussed in detail, together with a step-by-step guide to applying this AI method for focused compound library design. The program code is freely available at URL: github.com/ETHmodlab/de_novo_design_RNN .
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Affiliation(s)
- Francesca Grisoni
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Zurich, Switzerland.
- Eindhoven University of Technology, Department of Biomedical Engineering, Eindhoven, Netherlands.
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Zurich, Switzerland.
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19
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Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum. Biomolecules 2021; 11:biom11121750. [PMID: 34944394 PMCID: PMC8698534 DOI: 10.3390/biom11121750] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/12/2021] [Accepted: 11/17/2021] [Indexed: 11/16/2022] Open
Abstract
Malaria remains by far one of the most threatening and dangerous illnesses caused by the plasmodium falciparum parasite. Chloroquine (CQ) and first-line artemisinin-based combination treatment (ACT) have long been the drug of choice for the treatment and controlling of malaria; however, the emergence of CQ-resistant and artemisinin resistance parasites is now present in most areas where malaria is endemic. In this work, we developed five machine learning models to predict antimalarial bioactivities of a drug against plasmodium falciparum from the features (i.e., molecular descriptors values) obtained from PaDEL software from SMILES of compounds and compare the machine learning models by experiments with our collected data of 4794 instances. As a consequence, we found that three models amongst the five, namely artificial neural network (ANN), extreme gradient boost (XGB), and random forest (RF), outperform the others in terms of accuracy while observing that, using roughly a quarter of the promising descriptors picked by the feature selection algorithm, the five models achieved equivalent and comparable performance. Nevertheless, the contribution of all molecular descriptors in the models was investigated through the comparison of their rank values by the feature selection algorithm and found that the most potent and relevant descriptors which come from the ‘Autocorrelation’ module contributed more while the ‘Atom type electrotopological state’ contributed the least to the model.
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20
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Timmons PB, Hewage CM. ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides. Brief Bioinform 2021; 22:bbab258. [PMID: 34297817 PMCID: PMC8575049 DOI: 10.1093/bib/bbab258] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/09/2021] [Accepted: 06/18/2021] [Indexed: 11/14/2022] Open
Abstract
Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore represent a promising field of novel antiviral drugs. As the quantity of sequencing data grows annually, the development of an accurate in silico method for the prediction of peptide antiviral activities is important. This study leverages advances in deep learning and cheminformatics to produce a novel sequence-based deep neural network classifier for the prediction of antiviral peptide activity. The method outperforms the existent best-in-class, with an external test accuracy of 93.9%, Matthews correlation coefficient of 0.87 and an Area Under the Curve of 0.93 on the dataset of experimentally validated peptide activities. This cutting-edge classifier is available as an online web server at https://research.timmons.eu/ennavia, facilitating in silico screening and design of peptide antiviral drugs by the wider research community.
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Affiliation(s)
- Patrick Brendan Timmons
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland
| | - Chandralal M Hewage
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland
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21
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Monteiro NRC, Ribeiro B, Arrais JP. Drug-Target Interaction Prediction: End-to-End Deep Learning Approach. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2364-2374. [PMID: 32142454 DOI: 10.1109/tcbb.2020.2977335] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The discovery of potential Drug-Target Interactions (DTIs) is a determining step in the drug discovery and repositioning process, as the effectiveness of the currently available antibiotic treatment is declining. Although putting efforts on the traditional in vivo or in vitro methods, pharmaceutical financial investment has been reduced over the years. Therefore, establishing effective computational methods is decisive to find new leads in a reasonable amount of time. Successful approaches have been presented to solve this problem but seldom protein sequences and structured data are used together. In this paper, we present a deep learning architecture model, which exploits the particular ability of Convolutional Neural Networks (CNNs) to obtain 1D representations from protein sequences (amino acid sequence) and compounds SMILES (Simplified Molecular Input Line Entry System) strings. These representations can be interpreted as features that express local dependencies or patterns that can then be used in a Fully Connected Neural Network (FCNN), acting as a binary classifier. The results achieved demonstrate that using CNNs to obtain representations of the data, instead of the traditional descriptors, lead to improved performance. The proposed end-to-end deep learning method outperformed traditional machine learning approaches in the correct classification of both positive and negative interactions.
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22
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Wani MA, Garg P, Roy KK. Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides. Med Biol Eng Comput 2021; 59:2397-2408. [PMID: 34632545 DOI: 10.1007/s11517-021-02443-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 09/14/2021] [Indexed: 10/20/2022]
Abstract
The ubiquitous antimicrobial peptides (AMPs), with a broad range of antimicrobial activities, represent a great promise for combating the multi-drug resistant infections. In this study, using a large and diverse set of AMPs (2638) and non-AMPs (3700), we have explored a variety of machine learning classifiers to build in silico models for AMP prediction, including Random Forest (RF), k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), and ensemble learning. Among the various models generated, the RF classifier-based model top-performed in both the internal [Accuracy: 91.40%, Precision: 89.37%, Sensitivity: 90.05%, and Specificity: 92.36%] and external validations [Accuracy: 89.43%, Precision: 88.92%, Sensitivity: 85.21%, and Specificity: 92.43%]. In addition, the RF classifier-based model correctly predicted the known AMPs and non-AMPs; those kept aside as an additional external validation set. The performance assessment revealed three features viz. ChargeD2001, PAAC12 (pseudo amino acid composition), and polarity T13 that are likely to play vital roles in the antimicrobial activity of AMPs. The developed RF-based classification model may further be useful in the design and prediction of the novel potential AMPs.
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Affiliation(s)
- Mushtaq Ahmad Wani
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Kolkata, 700054, West Bengal, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Mohali, 160062, Punjab, India
| | - Kuldeep K Roy
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Kolkata, 700054, West Bengal, India. .,Department of Pharmaceutical Sciences, School of Health Sciences, University of Petroleum and Energy Studies (UPES), P.O. Bidholi, Dehradun, 248007, Uttarakhand, India.
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23
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Moreira P, Sequeira AM, Pereira S, Rodrigues R, Rocha M, Lousa D. ViralFP: A Web Application of Viral Fusion Proteins. FRONTIERS IN MEDICAL TECHNOLOGY 2021; 3:722392. [PMID: 35047951 PMCID: PMC8757699 DOI: 10.3389/fmedt.2021.722392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 07/19/2021] [Indexed: 12/03/2022] Open
Abstract
Viral fusion proteins are attached to the membrane of enveloped viruses (a group that includes Coronaviruses, Dengue, HIV and Influenza) and catalyze fusion between the viral and host membranes, enabling the virus to insert its genetic material into the host cell. Given the importance of these biomolecules, this work presents a centralized database containing the most relevant information on viral fusion proteins, available through a free-to-use web server accessible through the URL https://viralfp.bio.di.uminho.pt/. This web application contains several bioinformatic tools, such as Clustal sequence alignment and Weblogo, including as well a machine learning-based tool capable of predicting the location of fusion peptides (the component of fusion proteins that inserts into the host's cell membrane) within the fusion protein sequence. Given the crucial role of these proteins in viral infection, their importance as natural targets of our immune system and their potential as therapeutic targets, this web application aims to foster our ability to fight pathogenic viruses.
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Affiliation(s)
- Pedro Moreira
- Centro de Engenharia Biológica, Escola de Engenharia da Universidade do Minho, Braga, Portugal
| | - Ana Marta Sequeira
- Centro de Engenharia Biológica, Escola de Engenharia da Universidade do Minho, Braga, Portugal
| | - Sara Pereira
- ITQB NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Rúben Rodrigues
- Centro de Engenharia Biológica, Escola de Engenharia da Universidade do Minho, Braga, Portugal
| | - Miguel Rocha
- Centro de Engenharia Biológica, Escola de Engenharia da Universidade do Minho, Braga, Portugal
| | - Diana Lousa
- ITQB NOVA, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal
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Wani MA, Roy KK. Development and validation of consensus machine learning-based models for the prediction of novel small molecules as potential anti-tubercular agents. Mol Divers 2021; 26:1345-1356. [PMID: 34110578 DOI: 10.1007/s11030-021-10238-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 05/27/2021] [Indexed: 11/30/2022]
Abstract
Tuberculosis (TB) is an infectious disease and the leading cause of death globally. The rapidly emerging cases of drug resistance among pathogenic mycobacteria have been a global threat urging the need of new drug discovery and development. However, considering the fact that the new drug discovery and development is commonly lengthy and costly processes, strategic use of the cutting-edge machine learning (ML) algorithms may be very supportive in reducing both the cost and time involved. Considering the urgency of new drugs for TB, herein, we have attempted to develop predictive ML algorithms-based models useful in the selection of novel potential small molecules for subsequent in vitro validation. For this purpose, we used the GlaxoSmithKline (GSK) TCAMS TB dataset comprising a total of 776 hits that were made publicly available to the wider scientific community through the ChEMBL Neglected Tropical Diseases (ChEMBL-NTD) database. After exploring the different ML classifiers, viz. decision trees (DT), support vector machine (SVM), random forest (RF), Bernoulli Naive Bayes (BNB), K-nearest neighbors (k-NN), and linear logistic regression (LLR), and ensemble learning models (bagging and Adaboost) for training the model using the GSK dataset, we concluded with three best models, viz. Adaboost decision tree (ABDT), RF classifier, and k-NN models that gave the top prediction results for both the training and test sets. However, during the prediction of the external set of known anti-tubercular compounds/drugs, it was realized that each of these models had some limitations. The ABDT model correctly predicted 22 molecules as actives, while both the RF and k-NN models predicted 18 molecules correctly as actives; a number of molecules were predicted as actives by two of these models, while the third model predicted these compounds as inactives. Therefore, we concluded that while deciding the anti-tubercular potential of a new molecule, one should rely on the use of consensus predictions using these three models; it may lessen the attrition rate during the in vitro validation. We believe that this study may assist the wider anti-tuberculosis research community by providing a platform for predicting small molecules with subsequent validation for drug discovery and development.
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Affiliation(s)
- Mushtaq Ahmad Wani
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Kolkata, West Bengal, 700054, India
| | - Kuldeep K Roy
- Department of Pharmaceutical Technology, School of Medical Sciences, Adamas University, Kolkata, West Bengal, 700126, India.
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Tanoli Z, Vähä-Koskela M, Aittokallio T. Artificial intelligence, machine learning, and drug repurposing in cancer. Expert Opin Drug Discov 2021; 16:977-989. [PMID: 33543671 DOI: 10.1080/17460441.2021.1883585] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means.Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication.Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.
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Affiliation(s)
- Ziaurrehman Tanoli
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland
| | - Markus Vähä-Koskela
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland.,Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway.,Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway
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26
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Cocrystal Prediction Using Machine Learning Models and Descriptors. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11031323] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Cocrystals are of much interest in industrial application as well as academic research, and screening of suitable coformers for active pharmaceutical ingredients is the most crucial and challenging step in cocrystal development. Recently, machine learning techniques are attracting researchers in many fields including pharmaceutical research such as quantitative structure-activity/property relationship. In this paper, we develop machine learning models to predict cocrystal formation. We extract descriptor values from simplified molecular-input line-entry system (SMILES) of compounds and compare the machine learning models by experiments with our collected data of 1476 instances. As a result, we found that artificial neural network shows great potential as it has the best accuracy, sensitivity, and F1 score. We also found that the model achieved comparable performance with about half of the descriptors chosen by feature selection algorithms. We believe that this will contribute to faster and more accurate cocrystal development.
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27
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Kim H, Kim E, Lee I, Bae B, Park M, Nam H. Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches. BIOTECHNOL BIOPROC E 2021; 25:895-930. [PMID: 33437151 PMCID: PMC7790479 DOI: 10.1007/s12257-020-0049-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 05/27/2020] [Accepted: 06/03/2020] [Indexed: 02/07/2023]
Abstract
As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.
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Affiliation(s)
- Hyunho Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Eunyoung Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Ingoo Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Bongsung Bae
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Minsu Park
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Hojung Nam
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
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28
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Abstract
Molecular descriptors encode a variety of molecular representations for computer-assisted drug discovery. Here, we focus on the Weighted Holistic Atom Localization and Entity Shape (WHALES) descriptors, which were originally designed for scaffold hopping from natural products to synthetic molecules. WHALES descriptors capture molecular shape and partial charges simultaneously. We introduce the key aspects of the WHALES concept and provide a step-by-step guide on how to use these descriptors for virtual compound screening and scaffold hopping. The results presented can be reproduced by using the code freely available from URL: github.com/ETHmodlab/scaffold_hopping_whales .
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Affiliation(s)
- Francesca Grisoni
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Zurich, Switzerland.
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Zurich, Switzerland
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29
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Timmons PB, Hewage CM. ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides. Biomed Pharmacother 2020; 133:111051. [PMID: 33254015 DOI: 10.1016/j.biopha.2020.111051] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/08/2020] [Accepted: 11/19/2020] [Indexed: 12/12/2022] Open
Abstract
The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact the human body unfavourably. Anticancer peptides represent a promising emerging field of anticancer therapeutics, which are characterized by favourable toxicity profile. The development of accurate in silico methods for anticancer peptide prediction is of paramount importance, as the amount of available sequence data is growing each year. This study leverages advances in machine learning research to produce a novel sequence-based deep neural network classifier for anticancer peptide activity. The classifier achieves performance comparable to the best-in-class, with a cross-validated accuracy of 98.3%, Matthews correlation coefficient of 0.91 and an Area Under the Curve of 0.95. This innovative classifier is available as a web server at https://research.timmons.eu/ennaact, facilitating in silico screening and design of new anticancer peptide chemotherapeutics by the research community.
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Affiliation(s)
- Patrick Brendan Timmons
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland
| | - Chandralal M Hewage
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland.
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30
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Yang ZY, Dong J, Yang ZJ, Yin M, Jiang HL, Lu AP, Chen X, Hou TJ, Cao DS. ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds. Brief Bioinform 2020; 22:5985287. [PMID: 33201188 DOI: 10.1093/bib/bbaa282] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 09/12/2020] [Accepted: 09/25/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescence interference is especially difficult to identify as it is reproducible and possesses concentration-dependent characteristic. Therefore, the development of a credible screening tool to detect fluorescent compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS In this study, we developed a webserver ChemFLuo for fluorescent compound detection, based on two large and high-quality training datasets containing 4906 blue and 8632 green fluorescent compounds. These molecules were used to construct a group of prediction models based on the combination of three machine learning algorithms and seven types of molecular representations. The best blue fluorescence prediction model achieved with balanced accuracy (BA) = 0.858 and area under the receiver operating characteristic curve (AUC) = 0.931 for the validation set, and BA = 0.823 and AUC = 0.903 for the test set. The best green fluorescence prediction model achieved the prediction accuracy with BA = 0.810 and AUC = 0.887 for the validation set, and BA = 0.771 and AUC = 0.852 for the test set. Besides prediction model, 22 blue and 16 green representative fluorescent substructures were summarized for the screening of potential fluorescent compounds. The comparison with other fluorescence detection tools and theapplication to external validation sets and large molecule libraries have demonstrated the reliability of prediction model for fluorescent compound detection. CONCLUSION ChemFLuo is a public webserver to filter out compounds with undesirable fluorescent properties, which will benefit the design of high-quality chemical libraries for drug discovery. It is freely available at http://admet.scbdd.com/chemfluo/index/.
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Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P. R. China
| | - Jie Dong
- Central South University of Forestry and Technology, Changsha, 410004, P.R. China
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P. R. China
| | - Mingzhu Yin
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Hong-Li Jiang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P. R. China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P.R. China
| | - Xiang Chen
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Ting-Jun Hou
- Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P. R. China.,Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P.R. China
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31
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Foroozandeh Shahraki M, Farhadyar K, Kavousi K, Azarabad MH, Boroomand A, Ariaeenejad S, Hosseini Salekdeh G. A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: A xylanase temperature dependence case study. Biotechnol Bioeng 2020; 118:759-769. [PMID: 33095441 DOI: 10.1002/bit.27608] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 09/23/2020] [Accepted: 10/11/2020] [Indexed: 11/08/2022]
Abstract
Growing industrial utilization of enzymes and the increasing availability of metagenomic data highlight the demand for effective methods of targeted identification and verification of novel enzymes from various environmental microbiota. Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose, a component of lignocellulose. The optimum temperature of enzymes is an essential factor to be considered when choosing appropriate biocatalysts for a particular purpose. Therefore, in silico prediction of this attribute is a significant cost and time-effective step in the effort to characterize novel enzymes. The objective of this study was to develop a computational method to predict the thermal dependence of xylanases. This tool was then implemented for targeted screening of putative xylanases with specific thermal dependencies from metagenomic data and resulted in the identification of three novel xylanases from sheep and cow rumen microbiota. Here we present thermal activity prediction for xylanase, a new sequence-based machine learning method that has been trained using a selected combination of various protein features. This random forest classifier discriminates non-thermophilic, thermophilic, and hyper-thermophilic xylanases. The model's performance was evaluated through multiple iterations of sixfold cross-validations as well as holdout tests, and it is freely accessible as a web-service at arimees.com.
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Affiliation(s)
- Mehdi Foroozandeh Shahraki
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Kiana Farhadyar
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Mohammad H Azarabad
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Amin Boroomand
- School of Natural Sciences, University of California Merced, Merced, California, USA
| | - Shohreh Ariaeenejad
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
| | - Ghasem Hosseini Salekdeh
- Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.,Department of Molecular Sciences, Macquarie University, Sydney, New South Wales, Australia
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32
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Kyaw Zin PP, Borrel A, Fourches D. Benchmarking 2D/3D/MD-QSAR Models for Imatinib Derivatives: How Far Can We Predict? J Chem Inf Model 2020; 60:3342-3360. [PMID: 32623886 DOI: 10.1021/acs.jcim.0c00200] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Imatinib, a 2-phenylaminopyridine-based BCR-ABL tyrosine kinase inhibitor, is a highly effective drug for treating Chronic Myeloid Leukemia (CML). However, cases of drug resistance are constantly emerging due to various mutations in the ABL kinase domain; thus, it is crucial to identify novel bioactive analogues. Reliable QSAR models and molecular docking protocols have been shown to facilitate the discovery of new compounds from chemical libraries prior to experimental testing. However, as the vast majority of QSAR models strictly relies on 2D descriptors, the rise of 3D descriptors directly computed from molecular dynamics simulations offers new opportunities to potentially augment the reliability of QSAR models. Herein, we employed molecular docking and molecular dynamics on a large series of Imatinib derivatives and developed an ensemble of QSAR models relying on deep neural nets (DNN) and hybrid sets of 2D/3D/MD descriptors in order to predict the binding affinity and inhibition potencies of those compounds. Through rigorous validation tests, we showed that our DNN regression models achieved excellent external prediction performances for the pKi data set (n = 555, R2 ≥ 0.71. and MAE ≤ 0.85), and the pIC50 data set (n = 306, R2 ≥ 0.54. and MAE ≤ 0.71) with strict validation protocols based on external test sets and 10-fold native and nested cross validations. Interestingly, the best DNN and random forest models performed similarly across all descriptor sets. In fact, for this particular series of compounds, our external test results suggest that incorporating additional 3D protein-ligand binding site fingerprint, descriptors, or even MD time-series descriptors did not significantly improve the overall R2 but lowered the MAE of DNN QSAR models. Those augmented models could still help in identifying and understanding the key dynamic protein-ligand interactions to be optimized for further molecular design.
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Affiliation(s)
- Phyo Phyo Kyaw Zin
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Alexandre Borrel
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27695, United States
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33
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Timmons PB, Hewage CM. HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks. Sci Rep 2020; 10:10869. [PMID: 32616760 PMCID: PMC7331684 DOI: 10.1038/s41598-020-67701-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 06/09/2020] [Indexed: 12/11/2022] Open
Abstract
The growing prevalence of resistance to antibiotics motivates the search for new antibacterial agents. Antimicrobial peptides are a diverse class of well-studied membrane-active peptides which function as part of the innate host defence system, and form a promising avenue in antibiotic drug research. Some antimicrobial peptides exhibit toxicity against eukaryotic membranes, typically characterised by hemolytic activity assays, but currently, the understanding of what differentiates hemolytic and non-hemolytic peptides is limited. This study leverages advances in machine learning research to produce a novel artificial neural network classifier for the prediction of hemolytic activity from a peptide's primary sequence. The classifier achieves best-in-class performance, with cross-validated accuracy of [Formula: see text] and Matthews correlation coefficient of 0.71. This innovative classifier is available as a web server at https://research.timmons.eu/happenn , allowing the research community to utilise it for in silico screening of peptide drug candidates for high therapeutic efficacies.
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Affiliation(s)
- Patrick Brendan Timmons
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland
| | - Chandralal M Hewage
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland.
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34
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Korkmaz S. Deep Learning-Based Imbalanced Data Classification for Drug Discovery. J Chem Inf Model 2020; 60:4180-4190. [DOI: 10.1021/acs.jcim.9b01162] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Selçuk Korkmaz
- Trakya University Faculty of Medicine, Department of Biostatistics and Medical Informatics, Edirne, Turkey
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35
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Fallah Atanaki F, Behrouzi S, Ariaeenejad S, Boroomand A, Kavousi K. BIPEP: Sequence-based Prediction of Biofilm Inhibitory Peptides Using a Combination of NMR and Physicochemical Descriptors. ACS OMEGA 2020; 5:7290-7297. [PMID: 32280870 PMCID: PMC7144140 DOI: 10.1021/acsomega.9b04119] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 03/12/2020] [Indexed: 05/26/2023]
Abstract
Biofilms are biological systems that are formed by a community of microorganisms in which microbial cells are connected on a surface within a self-produced matrix of an extracellular polymeric substance. On some occasions, microorganisms use biofilms to protect themselves against the harmful effects of the host body immune system and the surrounding environment, hence increasing their chances of survival against the various anti-microbial agents. Biofilms play a crucial role in medicine and industry because of the problems they cause. Designing agents that inhibit bacterial biofilm formation is very costly and takes too much time in the laboratory to be discovered and validated. Therefore, developing computational tools for the prediction of biofilm inhibitor peptides is inevitable and important. Here, we present a computational prediction tool to screen the vast number of peptide sequences and select potential candidate peptides for further lab experiments and validation. In this learning model, different feature vectors, extracted from the peptide primary structure, are exploited to learn patterns from the sequence of biofilm inhibitory peptides. Various classification algorithms including SVM, random forest, and k-nearest neighbor have been examined to evaluate their performance. Overall, our approach showed better prediction in comparison with other prediction methods. In this study, for the first time, we applied features extracted from NMR spectra of amino acids along with physicochemical features. Although each group of features showed good discrimination potential alone, we used a combination of features to enhance the performance of our method. Our prediction tool is freely available.
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Affiliation(s)
- Fereshteh Fallah Atanaki
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 1417466191, Iran
| | - Saman Behrouzi
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 1417466191, Iran
| | - Shohreh Ariaeenejad
- Department
of Systems and Synthetic Biology, Agricultural
Biotechnology Research Institute of Iran (ABRII), Agricultural Research,
Education, and Extension Organization (AREEO), Karaj 31535-1897, Iran
| | - Amin Boroomand
- School
of Natural Sciences, University of California
Merced, Merced 95343-5001, California, United States of America
| | - Kaveh Kavousi
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 1417466191, Iran
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36
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High-Throughput Screening to Predict Chemical-Assay Interference. Sci Rep 2020; 10:3986. [PMID: 32132587 PMCID: PMC7055224 DOI: 10.1038/s41598-020-60747-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 01/31/2020] [Indexed: 12/31/2022] Open
Abstract
The U.S. federal consortium on toxicology in the 21st century (Tox21) produces quantitative, high-throughput screening (HTS) data on thousands of chemicals across a wide range of assays covering critical biological targets and cellular pathways. Many of these assays, and those used in other in vitro screening programs, rely on luciferase and fluorescence-based readouts that can be susceptible to signal interference by certain chemical structures resulting in false positive outcomes. Included in the Tox21 portfolio are assays specifically designed to measure interference in the form of luciferase inhibition and autofluorescence via multiple wavelengths (red, blue, and green) and under various conditions (cell-free and cell-based, two cell types). Out of 8,305 chemicals tested in the Tox21 interference assays, percent actives ranged from 0.5% (red autofluorescence) to 9.9% (luciferase inhibition). Self-organizing maps and hierarchical clustering were used to relate chemical structural clusters to interference activity profiles. Multiple machine learning algorithms were applied to predict assay interference based on molecular descriptors and chemical properties. The best performing predictive models (accuracies of ~80%) have been included in a web-based tool called InterPred that will allow users to predict the likelihood of assay interference for any new chemical structure and thus increase confidence in HTS data by decreasing false positive testing results.
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37
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Contreras-Torres E, Marrero-Ponce Y, Terán JE, García-Jacas CR, Brizuela CA, Sánchez-Rodríguez JC. MuLiMs-MCoMPAs: A Novel Multiplatform Framework to Compute Tensor Algebra-Based Three-Dimensional Protein Descriptors. J Chem Inf Model 2020; 60:1042-1059. [PMID: 31663741 DOI: 10.1021/acs.jcim.9b00629] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
This report introduces the MuLiMs-MCoMPAs software (acronym for Multi-Linear Maps based on N-Metric and Contact Matrices of 3D Protein and Amino-acid weightings), designed to compute tensor-based 3D protein structural descriptors by applying two- and three-linear algebraic forms. Moreover, these descriptors contemplate generalizing components such as novel 3D protein structural representations, (dis)similarity metrics, and multimetrics to extract geometrical related information between two and three amino acids, weighting schemes based on amino acid properties, matrix normalization procedures that consider simple-stochastic and mutual probability transformations, topological and geometrical cutoffs, amino acid, and group-based MD calculations, and aggregation operators for merging amino acidic and group MDs. The MuLiMs-MCoMPAs software, which belongs to the ToMoCoMD-CAMPS suite, was developed in Java (version 1.8) using the Chemistry Development Kit (CDK) (version 1.4.19) and the Jmol libraries. This software implemented a divide-and-conquer strategy to parallelize the computation of the indices as well as modules for data preprocessing and batch computing functionalities. Furthermore, it consists of two components: (i) a desktop-graphical user interface (GUI) and (ii) an API library. The relevance of this novel approach is demonstrated through two analyses that considered Shannon's entropy-based variability and a principal component analysis. These studies showed that the MuLiMs-MCoMPAs' three-linear descriptor family contains higher informational entropy than several other descriptors generated with available computation tools. Moreover, the MuLiMs-MCoMPAs indices capture additional orthogonal information to the one codified by the available calculation approaches. As a result, two sets of suggested theoretical configurations that contain 13648 two-linear indices and 20263 three-linear indices are available for download at tomocomd.com . Furthermore, as a demonstration of the applicability and easy integration of the MuLiMs library into a QSAR-based expert system, a software application (ProStAF) was generated to predict SCOP protein structural classes and folding rate. It can thus be anticipated that the MuLiMs-MCoMPAs framework will turn into a valuable contribution to the chem- and bioinformatics research fields.
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Affiliation(s)
- Ernesto Contreras-Torres
- Computer-Aided Molecular "Biosilico" Discovery and Bioinformatics Research International Network (CAMD-BIR IN) , Cumbayá, Quito , Ecuador.,Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas; and Instituto de Simulación Computacional (ISC-USFQ) , Universidad San Francisco de Quito (USFQ) , Diego de Robles y vía Interoceánica , Quito 170157 , Pichincha , Ecuador
| | - Yovani Marrero-Ponce
- Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas; and Instituto de Simulación Computacional (ISC-USFQ) , Universidad San Francisco de Quito (USFQ) , Diego de Robles y vía Interoceánica , Quito 170157 , Pichincha , Ecuador.,Grupo GINUMED, Facultad de Salud, Programa de Medicina , Corporacion Universitaria Rafal Nuñez , Cartagena , Colombia.,Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia , Universitat de València , 46010 Valéncia , Spain
| | - Julio E Terán
- Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas; and Instituto de Simulación Computacional (ISC-USFQ) , Universidad San Francisco de Quito (USFQ) , Diego de Robles y vía Interoceánica , Quito 170157 , Pichincha , Ecuador.,Grupo de Química Computacional y Teórica, Departamento de Ingeniería Química , Universidad San Francisco de Quito (USFQ) , Diego de Robles y vía Interoceánica , Quito 170157 , Pichincha Ecuador
| | - César R García-Jacas
- Cátedras Conacyt-Departamento de Ciencias de la Computación , Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE) , Ensenada , Baja California , México
| | - Carlos A Brizuela
- Departamento de Ciencias de la Computación , Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE) , Ensenada , Baja California , México
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38
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Guan C, Lu M, Zeng W, Yang D, Han D. Prediction of Standard Enthalpies of Formation Based on Hydrocarbon Molecular Descriptors and Active Subspace Methodology. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.9b06319] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Cheng Guan
- Key Laboratory for Power Machinery and Engineering, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Mingrui Lu
- Key Laboratory for Power Machinery and Engineering, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Wei Zeng
- Key Laboratory for Power Machinery and Engineering, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dongjin Yang
- Key Laboratory for Power Machinery and Engineering, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dong Han
- Key Laboratory for Power Machinery and Engineering, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
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39
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Abstract
Background:
Identifying Drug-Target Interactions (DTIs) is a major challenge for
current drug discovery and drug repositioning. Compared to traditional experimental approaches,
in silico methods are fast and inexpensive. With the increase in open-access experimental data,
numerous computational methods have been applied to predict DTIs.
Methods:
In this study, we propose an end-to-end learning model of Factorization Machine and
Deep Neural Network (FM-DNN), which emphasizes both low-order (first or second order) and
high-order (higher than second order) feature interactions without any feature engineering other
than raw features. This approach combines the power of FM and DNN learning for feature
learning in a new neural network architecture.
Results:
The experimental DTI basic features include drug characteristics (609), target
characteristics (1819), plus drug ID, target ID, total 2430. We compare 8 models such as SVM,
GBDT, WIDE-DEEP etc, the FM-DNN algorithm model obtains the best results of AUC(0.8866)
and AUPR(0.8281).
Conclusion:
Feature engineering is a job that requires expert knowledge, it is often difficult and
time-consuming to achieve good results. FM-DNN can auto learn a lower-order expression by FM
and a high-order expression by DNN.FM-DNN model has outstanding advantages over other
commonly used models.
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Affiliation(s)
- Jihong Wang
- School of Data and Computer Science, Sun Yat-Sen University, No.132 Waihuan East Road, 510000 Guangzhou, China
| | - Hao Wang
- School of Data and Computer Science, Sun Yat-Sen University, No.132 Waihuan East Road, 510000 Guangzhou, China
| | - Xiaodan Wang
- School of Pharmaceutical Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, No. 9- 13 Wuguishan Avenue of Life Street, 528458, Zhongshan, China
| | - Huiyou Chang
- School of Data and Computer Science, Sun Yat-Sen University, No.132 Waihuan East Road, 510000 Guangzhou, China
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40
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Bagherian M, Sabeti E, Wang K, Sartor MA, Nikolovska-Coleska Z, Najarian K. Machine learning approaches and databases for prediction of drug-target interaction: a survey paper. Brief Bioinform 2020; 22:247-269. [PMID: 31950972 PMCID: PMC7820849 DOI: 10.1093/bib/bbz157] [Citation(s) in RCA: 161] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/01/2019] [Accepted: 11/07/2019] [Indexed: 12/12/2022] Open
Abstract
The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.
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Affiliation(s)
- Maryam Bagherian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Elyas Sabeti
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kai Wang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Maureen A Sartor
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | - Kayvan Najarian
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
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41
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Dong J, Zhu MF, Yun YH, Lu AP, Hou TJ, Cao DS. BioMedR: an R/CRAN package for integrated data analysis pipeline in biomedical study. Brief Bioinform 2019; 22:474-484. [PMID: 31885044 DOI: 10.1093/bib/bbz150] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 10/22/2019] [Accepted: 10/30/2019] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND With the increasing development of biotechnology and information technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these resources needs to be extracted and then transformed to useful knowledge by various data mining methods. However, a main computational challenge is how to effectively represent or encode molecular objects under investigation such as chemicals, proteins, DNAs and even complicated interactions when data mining methods are employed. To further explore these complicated data, an integrated toolkit to represent different types of molecular objects and support various data mining algorithms is urgently needed. RESULTS We developed a freely available R/CRAN package, called BioMedR, for molecular representations of chemicals, proteins, DNAs and pairwise samples of their interactions. The current version of BioMedR could calculate 293 molecular descriptors and 13 kinds of molecular fingerprints for small molecules, 9920 protein descriptors based on protein sequences and six types of generalized scale-based descriptors for proteochemometric modeling, more than 6000 DNA descriptors from nucleotide sequences and six types of interaction descriptors using three different combining strategies. Moreover, this package realized five similarity calculation methods and four powerful clustering algorithms as well as several useful auxiliary tools, which aims at building an integrated analysis pipeline for data acquisition, data checking, descriptor calculation and data modeling. CONCLUSION BioMedR provides a comprehensive and uniform R package to link up different representations of molecular objects with each other and will benefit cheminformatics/bioinformatics and other biomedical users. It is available at: https://CRAN.R-project.org/package=BioMedR and https://github.com/wind22zhu/BioMedR/.
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Affiliation(s)
- Jie Dong
- National Engineering Laboratory for Deep Processing of Rice and Byproducts, Hunan Key Laboratory of Processed Food for Special Medical Purpose, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha,410003 P. R. China.,Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003 P. R. China
| | - Min-Feng Zhu
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003 P. R. China
| | - Yong-Huan Yun
- College of Food Science and Engineering, Hainan University, Haikou, 570228 PR China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P. R. China
| | - Ting-Jun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003 P. R. China.,Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P. R. China
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42
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McFarlane J, Henderson B, Donnecke S, McIndoe JS. An Information-Rich Graphical Representation of Catalytic Cycles. Organometallics 2019. [DOI: 10.1021/acs.organomet.9b00563] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- James McFarlane
- Department of Chemistry, University of Victoria, PO Box 1700
STN CSC, Victoria, British Columbia V8W 2Y2, Canada
| | - Brett Henderson
- Department of Chemistry, University of Victoria, PO Box 1700
STN CSC, Victoria, British Columbia V8W 2Y2, Canada
| | - Sofia Donnecke
- Department of Chemistry, University of Victoria, PO Box 1700
STN CSC, Victoria, British Columbia V8W 2Y2, Canada
| | - J. Scott McIndoe
- Department of Chemistry, University of Victoria, PO Box 1700
STN CSC, Victoria, British Columbia V8W 2Y2, Canada
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43
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Borrel A, Kleinstreuer NC, Fourches D. Exploring drug space with ChemMaps.com. Bioinformatics 2019; 34:3773-3775. [PMID: 29790904 DOI: 10.1093/bioinformatics/bty412] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 05/18/2018] [Indexed: 11/12/2022] Open
Abstract
Motivation Easily navigating chemical space has become more important due to the increasing size and diversity of publicly-accessible databases such as DrugBank, ChEMBL or Tox21. To do so, modelers typically rely on complex projection techniques using molecular descriptors computed for all the chemicals to be visualized. However, the multiple cheminformatics steps required to prepare, characterize, compute and explore those molecules, are technical, typically necessitate scripting skills, and thus represent a real obstacle for non-specialists. Results We developed the ChemMaps.com webserver to easily browse, navigate and mine chemical space. The first version of ChemMaps.com features more than 8000 approved, in development, and rejected drugs, as well as over 47 000 environmental chemicals. Availability and implementation The webserver is freely available at http://www.chemmaps.com.
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Affiliation(s)
- Alexandre Borrel
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.,Division of Intramural Research/Biostatistics and Computational Biology Branch
| | - Nicole C Kleinstreuer
- Division of Intramural Research/Biostatistics and Computational Biology Branch.,National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, NIEHS, RTP, Research Triangle, NC, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
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Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Doğan T. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform 2019; 20:1878-1912. [PMID: 30084866 PMCID: PMC6917215 DOI: 10.1093/bib/bby061] [Citation(s) in RCA: 223] [Impact Index Per Article: 44.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 05/25/2018] [Indexed: 01/16/2023] Open
Abstract
The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.
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Affiliation(s)
- Ahmet Sureyya Rifaioglu
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
- Department of Computer Engineering, İskenderun Technical University, Hatay, Turkey
| | - Heval Atas
- Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
| | - Maria Jesus Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Cambridge, Hinxton, UK
| | - Rengul Cetin-Atalay
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Volkan Atalay
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Tunca Doğan
- Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey and European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Cambridge, Hinxton, UK
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45
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Yang ZY, Yang ZJ, Dong J, Wang LL, Zhang LX, Ding JJ, Ding XQ, Lu AP, Hou TJ, Cao DS. Structural Analysis and Identification of Colloidal Aggregators in Drug Discovery. J Chem Inf Model 2019; 59:3714-3726. [DOI: 10.1021/acs.jcim.9b00541] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, People’s Republic of China
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, People’s Republic of China
| | - Jie Dong
- Central South University of Forestry and Technology, Changsha 410004, People’s Republic of China
| | - Liang-Liang Wang
- Beijing Institute of Pharmaceutical Chemistry, Beijing 102205, People’s Republic of China
| | - Liu-Xia Zhang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, People’s Republic of China
| | - Jun-Jie Ding
- Beijing Institute of Pharmaceutical Chemistry, Beijing 102205, People’s Republic of China
| | - Xiao-Qin Ding
- Beijing Institute of Pharmaceutical Chemistry, Beijing 102205, People’s Republic of China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong Special Administrative Region, People’s Republic of China
| | - Ting-Jun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, People’s Republic of China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, People’s Republic of China
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong Special Administrative Region, People’s Republic of China
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46
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Liu L, Yang H, Cai Y, Cao Q, Sun L, Wang Z, Li W, Liu G, Lee PW, Tang Y. In silico prediction of chemical aquatic toxicity for marine crustaceans via machine learning. Toxicol Res (Camb) 2019; 8:341-352. [PMID: 31160968 PMCID: PMC6505403 DOI: 10.1039/c8tx00331a] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Accepted: 01/24/2019] [Indexed: 12/30/2022] Open
Abstract
Aquatic toxicity is a crucial endpoint for evaluating chemically adverse effects on ecosystems. Therefore, we developed in silico methods for the prediction of chemical aquatic toxicity in marine environment. At first, a diverse data set including different crustacean species was constructed. We then built local binary models using Mysidae data and global binary models using Mysidae, Palaemonidae, and Penaeidae data. Molecular fingerprints and descriptors were employed to represent chemical structures separately. All the models were built by six machine learning methods. The AUC (area under the receiver operating characteristic curve) values of the better local and global models were around 0.8 and 0.9 for the test sets, respectively. We also identified several chemicals with selective toxicity on different species. The analysis of selective toxicity would promote to design greener chemicals in a specific environment. Finally, to understand and interpret the models, we explored the relationships between chemical aquatic toxicity and the molecular descriptors. Our study would be helpful in gaining further insights into marine organisms, prediction of chemical aquatic toxicity and prioritization of environmental hazard assessment.
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Affiliation(s)
- Lin Liu
- Shanghai Key Laboratory of New Drug Design , School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China .
| | - Hongbin Yang
- Shanghai Key Laboratory of New Drug Design , School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China .
| | - Yingchun Cai
- Shanghai Key Laboratory of New Drug Design , School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China .
| | - Qianqian Cao
- Shanghai Key Laboratory of New Drug Design , School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China .
| | - Lixia Sun
- Shanghai Key Laboratory of New Drug Design , School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China .
| | - Zhuang Wang
- Shanghai Key Laboratory of New Drug Design , School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China .
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design , School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China .
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design , School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China .
| | - Philip W Lee
- Shanghai Key Laboratory of New Drug Design , School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China .
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design , School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China .
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47
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Tangadpalliwar SR, Vishwakarma S, Nimbalkar R, Garg P. ChemSuite: A package for chemoinformatics calculations and machine learning. Chem Biol Drug Des 2019; 93:960-964. [PMID: 30637953 DOI: 10.1111/cbdd.13479] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2018] [Revised: 12/12/2018] [Accepted: 12/22/2018] [Indexed: 01/18/2023]
Abstract
Prediction of biological and toxicological properties of small molecules using in silico approaches has become a wide practice in pharmaceutical research to lessen the cost and enhance productivity. The development of a tool "ChemSuite," a stand-alone application for chemoinformatics calculations and machine-learning model development, is reported. Availability of multi-functional features makes it widely acceptable in various fields. Force field such as UFF is incorporated in tool for optimization of molecules. Packages like RDKit, PyDPI and PaDEL help to calculate 1D, 2D and 3D descriptors and more than 10 types of fingerprints. MinMax Scaler and Z-Score algorithms are available to normalize descriptor values. Varied descriptor selection and machine-learning algorithms are available for model development. It allows the user to add their own algorithm or extend the software for various scientific purposes. It is free, open source and has user-friendly graphical interface, and it can work on all major platforms.
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Affiliation(s)
- Sujit R Tangadpalliwar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Mohali, Punjab, India
| | - Sachin Vishwakarma
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Mohali, Punjab, India
| | - Rakesh Nimbalkar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Mohali, Punjab, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, Mohali, Punjab, India
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48
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Seal A, Wild DJ. Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links. BMC Bioinformatics 2018; 19:265. [PMID: 30012095 PMCID: PMC6047136 DOI: 10.1186/s12859-018-2254-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 06/18/2018] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Netpredictor is an R package for prediction of missing links in any given unipartite or bipartite network. The package provides utilities to compute missing links in a bipartite and well as unipartite networks using Random Walk with Restart and Network inference algorithm and a combination of both. The package also allows computation of Bipartite network properties, visualization of communities for two different sets of nodes, and calculation of significant interactions between two sets of nodes using permutation based testing. The application can also be used to search for top-K shortest paths between interactome and use enrichment analysis for disease, pathway and ontology. The R standalone package (including detailed introductory vignettes) and associated R Shiny web application is available under the GPL-2 Open Source license and is freely available to download. RESULTS We compared different algorithms performance in different small datasets and found random walk supersedes rest of the algorithms. The package is developed to perform network based prediction of unipartite and bipartite networks and use the results to understand the functionality of proteins in an interactome using enrichment analysis. CONCLUSION The rapid application development envrionment like shiny, helps non programmers to develop fast rich visualization apps and we beleieve it would continue to grow in future with further enhancements. We plan to update our algorithms in the package in near future and help scientist to analyse data in a much streamlined fashion.
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Affiliation(s)
- Abhik Seal
- School of Informatics and Computing, Indiana University Bloomington, Informatics West, Bloomington, 47408, Indiana, USA
| | - David J Wild
- School of Informatics and Computing, Indiana University Bloomington, Informatics West, Bloomington, 47408, Indiana, USA.
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49
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Dong J, Yao ZJ, Zhang L, Luo F, Lin Q, Lu AP, Chen AF, Cao DS. PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions. J Cheminform 2018; 10:16. [PMID: 29556758 PMCID: PMC5861255 DOI: 10.1186/s13321-018-0270-2] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 03/12/2018] [Indexed: 11/15/2022] Open
Abstract
Background
With the increasing development of biotechnology and informatics technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these data needs to be extracted and transformed to useful knowledge by various data mining methods. Considering the amazing rate at which data are accumulated in chemistry and biology fields, new tools that process and interpret large and complex interaction data are increasingly important. So far, there are no suitable toolkits that can effectively link the chemical and biological space in view of molecular representation. To further explore these complex data, an integrated toolkit for various molecular representation is urgently needed which could be easily integrated with data mining algorithms to start a full data analysis pipeline. Results Herein, the python library PyBioMed is presented, which comprises functionalities for online download for various molecular objects by providing different IDs, the pretreatment of molecular structures, the computation of various molecular descriptors for chemicals, proteins, DNAs and their interactions. PyBioMed is a feature-rich and highly customized python library used for the characterization of various complex chemical and biological molecules and interaction samples. The current version of PyBioMed could calculate 775 chemical descriptors and 19 kinds of chemical fingerprints, 9920 protein descriptors based on protein sequences, more than 6000 DNA descriptors from nucleotide sequences, and interaction descriptors from pairwise samples using three different combining strategies. Several examples and five real-life applications were provided to clearly guide the users how to use PyBioMed as an integral part of data analysis projects. By using PyBioMed, users are able to start a full pipelining from getting molecular data, pretreating molecules, molecular representation to constructing machine learning models conveniently. Conclusion PyBioMed provides various user-friendly and highly customized APIs to calculate various features of biological molecules and complex interaction samples conveniently, which aims at building integrated analysis pipelines from data acquisition, data checking, and descriptor calculation to modeling. PyBioMed is freely available at http://projects.scbdd.com/pybiomed.html.![]()
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Affiliation(s)
- Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China.,College of Food Science and Engineering, National Engineering Laboratory for Deep Processing of Rice and Byproducts, Central South University of Forestry and Technology, Changsha, China
| | - Zhi-Jiang Yao
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China
| | - Lin Zhang
- College of Food Science and Engineering, National Engineering Laboratory for Deep Processing of Rice and Byproducts, Central South University of Forestry and Technology, Changsha, China
| | - Feijun Luo
- College of Food Science and Engineering, National Engineering Laboratory for Deep Processing of Rice and Byproducts, Central South University of Forestry and Technology, Changsha, China
| | - Qinlu Lin
- College of Food Science and Engineering, National Engineering Laboratory for Deep Processing of Rice and Byproducts, Central South University of Forestry and Technology, Changsha, China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China
| | - Alex F Chen
- Center for Vascular Disease and Translational Medicine, Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China. .,Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, China. .,Center for Vascular Disease and Translational Medicine, Third Xiangya Hospital, Central South University, Changsha, People's Republic of China.
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50
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Titaley IA, Ogba OM, Chibwe L, Hoh E, Cheong PHY, Simonich SLM. Automating data analysis for two-dimensional gas chromatography/time-of-flight mass spectrometry non-targeted analysis of comparative samples. J Chromatogr A 2018; 1541:57-62. [PMID: 29448996 PMCID: PMC5909067 DOI: 10.1016/j.chroma.2018.02.016] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 02/03/2018] [Accepted: 02/06/2018] [Indexed: 12/19/2022]
Abstract
Non-targeted analysis of environmental samples, using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC/ToF-MS), poses significant data analysis challenges due to the large number of possible analytes. Non-targeted data analysis of complex mixtures is prone to human bias and is laborious, particularly for comparative environmental samples such as contaminated soil pre- and post-bioremediation. To address this research bottleneck, we developed OCTpy, a Python™ script that acts as a data reduction filter to automate GC × GC/ToF-MS data analysis from LECO® ChromaTOF® software and facilitates selection of analytes of interest based on peak area comparison between comparative samples. We used data from polycyclic aromatic hydrocarbon (PAH) contaminated soil, pre- and post-bioremediation, to assess the effectiveness of OCTpy in facilitating the selection of analytes that have formed or degraded following treatment. Using datasets from the soil extracts pre- and post-bioremediation, OCTpy selected, on average, 18% of the initial suggested analytes generated by the LECO® ChromaTOF® software Statistical Compare feature. Based on this list, 63-100% of the candidate analytes identified by a highly trained individual were also selected by OCTpy. This process was accomplished in several minutes per sample, whereas manual data analysis took several hours per sample. OCTpy automates the analysis of complex mixtures of comparative samples, reduces the potential for human error during heavy data handling and decreases data analysis time by at least tenfold.
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Affiliation(s)
- Ivan A Titaley
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA
| | - O Maduka Ogba
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA; Department of Chemistry, Pomona College, Claremont, CA, 91711, USA
| | - Leah Chibwe
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA
| | - Eunha Hoh
- Graduate School of Public Health, San Diego State University, San Diego, CA, 92182, USA
| | - Paul H-Y Cheong
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA.
| | - Staci L Massey Simonich
- Department of Chemistry, Oregon State University, Corvallis, OR, 97331, USA; Department of Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR, 97331, USA.
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