1
|
Morishita EC, Nakamura S. Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery. Expert Opin Drug Discov 2024; 19:415-431. [PMID: 38321848 DOI: 10.1080/17460441.2024.2313455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 01/30/2024] [Indexed: 02/08/2024]
Abstract
INTRODUCTION Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted in large amounts of data on disease associated RNAs, as well as on small molecules that bind to such RNAs. Artificial intelligence (AI) approaches, including machine learning and deep learning, present an opportunity to speed up the discovery of RNA-targeted small molecules by improving decision-making efficiency and quality. AREAS COVERED The topics described in this review include the recent applications of AI in the identification of RNA targets, RNA structure determination, screening of chemical compound libraries, and hit-to-lead optimization. The impact and limitations of the recent AI applications are discussed, along with an outlook on the possible applications of next-generation AI tools for the discovery of novel RNA-targeted small molecule drugs. EXPERT OPINION Key areas for improvement include developing AI tools for understanding RNA dynamics and RNA - small molecule interactions. High-quality and comprehensive data still need to be generated especially on the biological activity of small molecules that target RNAs.
Collapse
|
2
|
Xiao H, Yang X, Zhang Y, Zhang Z, Zhang G, Zhang BT. RNA-targeted small-molecule drug discoveries: a machine-learning perspective. RNA Biol 2023; 20:384-397. [PMID: 37337437 PMCID: PMC10283424 DOI: 10.1080/15476286.2023.2223498] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/06/2023] [Indexed: 06/21/2023] Open
Abstract
In the past two decades, machine learning (ML) has been extensively adopted in protein-targeted small molecule (SM) discovery. Once trained, ML models could exert their predicting abilities on large volumes of molecules within a short time. However, applying ML approaches to discover RNA-targeted SMs is still in its early stages. This is primarily because of the intrinsic structural instability of RNA molecules that impede the structure-based screening or designing of RNA-targeted SMs. Recently, with more studies revealing RNA structures and a growing number of RNA-targeted ligands being identified, it resulted in an increased interest in the field of drugging RNA. Undeniably, intracellular RNA is much more abundant than protein and, if successfully targeted, will be a major alternative target for therapeutics. Therefore, in this context, as well as under the premise of having RNA-related research data, ML-based methods can get involved in improving the speed of traditional experimental processes. [Figure: see text].
Collapse
Affiliation(s)
- Huan Xiao
- School of Chinese Medicine, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Xin Yang
- School of Chinese Medicine, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Yihao Zhang
- School of Chinese Medicine, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Zongkang Zhang
- School of Chinese Medicine, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Ge Zhang
- Law Sau Fai Institute for Advancing Translational Medicine in Bone & Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
- Institute of Integrated Bioinformedicine and Translational Science, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
- Institute of Precision Medicine and Innovative Drug Discovery, HKBU Institute for Research and Continuing Education, Shenzhen, China
| | - Bao-Ting Zhang
- School of Chinese Medicine, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| |
Collapse
|
3
|
Reinharz V, Sarrazin-Gendron R, Waldispühl J. Modeling and Predicting RNA Three-Dimensional Structures. Methods Mol Biol 2021; 2284:17-42. [PMID: 33835435 DOI: 10.1007/978-1-0716-1307-8_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Modeling the three-dimensional structure of RNAs is a milestone toward better understanding and prediction of nucleic acids molecular functions. Physics-based approaches and molecular dynamics simulations are not tractable on large molecules with all-atom models. To address this issue, coarse-grained models of RNA three-dimensional structures have been developed. In this chapter, we describe a graphical modeling based on the Leontis-Westhof extended base pair classification. This representation of RNA structures enables us to identify highly conserved structural motifs with complex nucleotide interactions in structure databases. We show how to take advantage of this knowledge to quickly predict three-dimensional structures of large RNA molecules and present the RNA-MoIP web server (http://rnamoip.cs.mcgill.ca) that streamlines the computational and visualization processes. Finally, we show recent advances in the prediction of local 3D motifs from sequence data with the BayesPairing software and discuss its impact toward complete 3D structure prediction.
Collapse
Affiliation(s)
- Vladimir Reinharz
- Department of Computer Science, Université du Québec à Montréal, Montréal, QC, Canada
| | | | - Jérôme Waldispühl
- School of Computer Science, McGill University, Montréal, QC, Canada.
| |
Collapse
|
4
|
Yao J, Reinharz V, Major F, Waldispühl J. RNA-MoIP: prediction of RNA secondary structure and local 3D motifs from sequence data. Nucleic Acids Res 2019; 45:W440-W444. [PMID: 28525607 PMCID: PMC5793723 DOI: 10.1093/nar/gkx429] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 05/12/2017] [Indexed: 11/13/2022] Open
Abstract
RNA structures are hierarchically organized. The secondary structure is articulated around sophisticated local three-dimensional (3D) motifs shaping the full 3D architecture of the molecule. Recent contributions have identified and organized recurrent local 3D motifs, but applications of this knowledge for predictive purposes is still in its infancy. We recently developed a computational framework, named RNA-MoIP, to reconcile RNA secondary structure and local 3D motif information available in databases. In this paper, we introduce a web service using our software for predicting RNA hybrid 2D–3D structures from sequence data only. Optionally, it can be used for (i) local 3D motif prediction or (ii) the refinement of user-defined secondary structures. Importantly, our web server automatically generates a script for the MC-Sym software, which can be immediately used to quickly predict all-atom RNA 3D models. The web server is available at http://rnamoip.cs.mcgill.ca.
Collapse
Affiliation(s)
- Jason Yao
- School of Computer Science, McGill University, 3480 University Street, Montreal, QC H3A 0E9, Canada
| | - Vladimir Reinharz
- Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - François Major
- Institute for Research in Immunology and Cancer and Department of Computer Science and Operations Research, Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Jérôme Waldispühl
- School of Computer Science, McGill University, 3480 University Street, Montreal, QC H3A 0E9, Canada
| |
Collapse
|
5
|
Wang YZ, Li J, Zhang S, Huang B, Yao G, Zhang J. An RNA Scoring Function for Tertiary Structure Prediction Based on Multi-Layer Neural Networks. Mol Biol 2019. [DOI: 10.1134/s0026893319010175] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
6
|
Dans PD, Gallego D, Balaceanu A, Darré L, Gómez H, Orozco M. Modeling, Simulations, and Bioinformatics at the Service of RNA Structure. Chem 2019. [DOI: 10.1016/j.chempr.2018.09.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
|
7
|
Waldispühl J, Reinharz V. Modeling and predicting RNA three-dimensional structures. Methods Mol Biol 2015; 1269:101-21. [PMID: 25577374 DOI: 10.1007/978-1-4939-2291-8_6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Modeling the three-dimensional structure of RNAs is a milestone toward better understanding and prediction of nucleic acids molecular functions. Physics-based approaches and molecular dynamics simulations are not tractable on large molecules with all-atom models. To address this issue, coarse-grained models of RNA three-dimensional structures have been developed. In this chapter, we describe a graphical modeling based on the Leontis-Westhof extended base-pair classification. This representation of RNA structures enables us to identify highly conserved structural motifs with complex nucleotide interactions in structure databases. Further, we show how to take advantage of this knowledge to quickly and simply predict three-dimensional structures of large RNA molecules.
Collapse
Affiliation(s)
- Jérôme Waldispühl
- School of Computer Science, McGill University, 3480 University Street, Room 318, Montreal, QC, Canada, H3A 0E9,
| | | |
Collapse
|
8
|
Altaf-Ul-Amin M, Afendi FM, Kiboi SK, Kanaya S. Systems biology in the context of big data and networks. BIOMED RESEARCH INTERNATIONAL 2014; 2014:428570. [PMID: 24982882 PMCID: PMC4058291 DOI: 10.1155/2014/428570] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2014] [Revised: 04/08/2014] [Accepted: 05/01/2014] [Indexed: 12/02/2022]
Abstract
Science is going through two rapidly changing phenomena: one is the increasing capabilities of the computers and software tools from terabytes to petabytes and beyond, and the other is the advancement in high-throughput molecular biology producing piles of data related to genomes, transcriptomes, proteomes, metabolomes, interactomes, and so on. Biology has become a data intensive science and as a consequence biology and computer science have become complementary to each other bridged by other branches of science such as statistics, mathematics, physics, and chemistry. The combination of versatile knowledge has caused the advent of big-data biology, network biology, and other new branches of biology. Network biology for instance facilitates the system-level understanding of the cell or cellular components and subprocesses. It is often also referred to as systems biology. The purpose of this field is to understand organisms or cells as a whole at various levels of functions and mechanisms. Systems biology is now facing the challenges of analyzing big molecular biological data and huge biological networks. This review gives an overview of the progress in big-data biology, and data handling and also introduces some applications of networks and multivariate analysis in systems biology.
Collapse
|
9
|
YANG FAN, XU JINBO, ZENG JIANYANG. Drug-target interaction prediction by integrating chemical, genomic, functional and pharmacological data. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2014:148-159. [PMID: 24297542 PMCID: PMC4730876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
In silico prediction of unknown drug-target interactions (DTIs) has become a popular tool for drug repositioning and drug development. A key challenge in DTI prediction lies in integrating multiple types of data for accurate DTI prediction. Although recent studies have demonstrated that genomic, chemical and pharmacological data can provide reliable information for DTI prediction, it remains unclear whether functional information on proteins can also contribute to this task. Little work has been developed to combine such information with other data to identify new interactions between drugs and targets. In this paper, we introduce functional data into DTI prediction and construct biological space for targets using the functional similarity measure. We present a probabilistic graphical model, called conditional random field (CRF), to systematically integrate genomic, chemical, functional and pharmacological data plus the topology of DTI networks into a unified framework to predict missing DTIs. Tests on two benchmark datasets show that our method can achieve excellent prediction performance with the area under the precision-recall curve (AUPR) up to 94.9. These results demonstrate that our CRF model can successfully exploit heterogeneous data to capture the latent correlations of DTIs, and thus will be practically useful for drug repositioning. Supplementary Material is available at http://iiis.tsinghua.edu.cn/~compbio/papers/psb2014/psb2014_sm.pdf.
Collapse
Affiliation(s)
- FAN YANG
- Department of Mathematical Sciences Tsinghua University Beijing, 100084, P. R. China
| | - JINBO XU
- Toyota Technological Institute at Chicago 6045 S. Kenwood Ave. Chicago, IL 60637, USA
| | - JIANYANG ZENG
- Institute for Interdisciplinary Information Sciences Tsinghua University Beijing, 100084, P. R. China
| |
Collapse
|
10
|
Johansson KE, Hamelryck T. A simple probabilistic model of multibody interactions in proteins. Proteins 2013; 81:1340-50. [DOI: 10.1002/prot.24277] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2012] [Revised: 01/31/2013] [Accepted: 02/18/2013] [Indexed: 11/10/2022]
Affiliation(s)
- Kristoffer Enøe Johansson
- Section for Biomolecular Sciences; Department of Biology, University of Copenhagen; Ole Maal⊘es Vej 5, DK-2200 Copenhagen N Denmark
| | - Thomas Hamelryck
- Section for Computational and RNA biology; Department of Biology, University of Copenhagen; Room 1.2.22, Ole Maal⊘es Vej 5 DK-2200 Copenhagen N Denmark
| |
Collapse
|
11
|
Reinharz V, Major F, Waldispühl J. Towards 3D structure prediction of large RNA molecules: an integer programming framework to insert local 3D motifs in RNA secondary structure. Bioinformatics 2013; 28:i207-14. [PMID: 22689763 PMCID: PMC3371858 DOI: 10.1093/bioinformatics/bts226] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Motivation: The prediction of RNA 3D structures from its sequence only is a milestone to RNA function analysis and prediction. In recent years, many methods addressed this challenge, ranging from cycle decomposition and fragment assembly to molecular dynamics simulations. However, their predictions remain fragile and limited to small RNAs. To expand the range and accuracy of these techniques, we need to develop algorithms that will enable to use all the structural information available. In particular, the energetic contribution of secondary structure interactions is now well documented, but the quantification of non-canonical interactions—those shaping the tertiary structure—is poorly understood. Nonetheless, even if a complete RNA tertiary structure energy model is currently unavailable, we now have catalogues of local 3D structural motifs including non-canonical base pairings. A practical objective is thus to develop techniques enabling us to use this knowledge for robust RNA tertiary structure predictors. Results: In this work, we introduce RNA-MoIP, a program that benefits from the progresses made over the last 30 years in the field of RNA secondary structure prediction and expands these methods to incorporate the novel local motif information available in databases. Using an integer programming framework, our method refines predicted secondary structures (i.e. removes incorrect canonical base pairs) to accommodate the insertion of RNA 3D motifs (i.e. hairpins, internal loops and k-way junctions). Then, we use predictions as templates to generate complete 3D structures with the MC-Sym program. We benchmarked RNA-MoIP on a set of 9 RNAs with sizes varying from 53 to 128 nucleotides. We show that our approach (i) improves the accuracy of canonical base pair predictions; (ii) identifies the best secondary structures in a pool of suboptimal structures; and (iii) predicts accurate 3D structures of large RNA molecules. Availability:RNA-MoIP is publicly available at: http://csb.cs.mcgill.ca/RNAMoIP. Contact:jeromew@cs.mcgill.ca
Collapse
Affiliation(s)
- Vladimir Reinharz
- School of Computer Science & McGill Centre for Bioinformatics, McGill University, Montréal, Canada
| | | | | |
Collapse
|