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Tan LH, Kwoh CK, Mu Y. RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method. Brief Bioinform 2024; 25:bbae166. [PMID: 38695120 PMCID: PMC11063749 DOI: 10.1093/bib/bbae166] [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: 12/21/2023] [Revised: 03/15/2024] [Accepted: 03/19/2024] [Indexed: 05/04/2024] Open
Abstract
Small molecule drugs can be used to target nucleic acids (NA) to regulate biological processes. Computational modeling methods, such as molecular docking or scoring functions, are commonly employed to facilitate drug design. However, the accuracy of the scoring function in predicting the closest-to-native docking pose is often suboptimal. To overcome this problem, a machine learning model, RmsdXNA, was developed to predict the root-mean-square-deviation (RMSD) of ligand docking poses in NA complexes. The versatility of RmsdXNA has been demonstrated by its successful application to various complexes involving different types of NA receptors and ligands, including metal complexes and short peptides. The predicted RMSD by RmsdXNA was strongly correlated with the actual RMSD of the docked poses. RmsdXNA also outperformed the rDock scoring function in ranking and identifying closest-to-native docking poses across different structural groups and on the testing dataset. Using experimental validated results conducted on polyadenylated nuclear element for nuclear expression triplex, RmsdXNA demonstrated better screening power for the RNA-small molecule complex compared to rDock. Molecular dynamics simulations were subsequently employed to validate the binding of top-scoring ligand candidates selected by RmsdXNA and rDock on MALAT1. The results showed that RmsdXNA has a higher success rate in identifying promising ligands that can bind well to the receptor. The development of an accurate docking score for a NA-ligand complex can aid in drug discovery and development advancements. The code to use RmsdXNA is available at the GitHub repository https://github.com/laiheng001/RmsdXNA.
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Affiliation(s)
- Lai Heng Tan
- Interdisciplinary Graduate School, Nanyang Technological University, 61 Nanyang Drive, 637335 Singapore, Singapore
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Singapore, Singapore
| | - Yuguang Mu
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, 637551 Singapore, Singapore
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2
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Kravchenko A, de Vries SJ, Smaïl-Tabbone M, Chauvot de Beauchene I. HIPPO: HIstogram-based Pseudo-POtential for scoring protein-ssRNA fragment-based docking poses. BMC Bioinformatics 2024; 25:129. [PMID: 38532339 DOI: 10.1186/s12859-024-05733-6] [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: 05/25/2023] [Accepted: 03/06/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND The RNA-Recognition motif (RRM) is a protein domain that binds single-stranded RNA (ssRNA) and is present in as much as 2% of the human genome. Despite this important role in biology, RRM-ssRNA interactions are very challenging to study on the structural level because of the remarkable flexibility of ssRNA. In the absence of atomic-level experimental data, the only method able to predict the 3D structure of protein-ssRNA complexes with any degree of accuracy is ssRNA'TTRACT, an ssRNA fragment-based docking approach using ATTRACT. However, since ATTRACT parameters are not ssRNA-specific and were determined in 2010, there is substantial opportunity for enhancement. RESULTS Here we present HIPPO, a composite RRM-ssRNA scoring potential derived analytically from contact frequencies in near-native versus non-native docking models. HIPPO consists of a consensus of four distinct potentials, each extracted from a distinct reference pool of protein-trinucleotide docking decoys. To score a docking pose with one potential, for each pair of RNA-protein coarse-grained bead types, each contact is awarded or penalised according to the relative frequencies of this contact distance range among the correct and incorrect poses of the reference pool. Validated on a fragment-based docking benchmark of 57 experimentally solved RRM-ssRNA complexes, HIPPO achieved a threefold or higher enrichment for half of the fragments, versus only a quarter with the ATTRACT scoring function. In particular, HIPPO drastically improved the chance of very high enrichment (12-fold or higher), a scenario where the incremental modelling of entire ssRNA chains from fragments becomes viable. However, for the latter result, more research is needed to make it directly practically applicable. Regardless, our approach already improves upon the state of the art in RRM-ssRNA modelling and is in principle extendable to other types of protein-nucleic acid interactions.
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Affiliation(s)
- Anna Kravchenko
- Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France
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3
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Rinaldi S, Moroni E, Rozza R, Magistrato A. Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation. J Chem Theory Comput 2024; 20:993-1018. [PMID: 38287883 DOI: 10.1021/acs.jctc.3c01239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.
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Affiliation(s)
- Silvia Rinaldi
- National Research Council of Italy (CNR) - Institute of Chemistry of OrganoMetallic Compounds (ICCOM), c/o Area di Ricerca CNR di Firenze Via Madonna del Piano 10, 50019 Sesto Fiorentino, Florence, Italy
| | - Elisabetta Moroni
- National Research Council of Italy (CNR) - Institute of Chemical Sciences and Technologies (SCITEC), via Mario Bianco 9, 20131 Milano, Italy
| | - Riccardo Rozza
- National Research Council of Italy (CNR) - Institute of Material Foundry (IOM) c/o International School for Advanced Studies (SISSA), Via Bonomea, 265, 34136 Trieste, Italy
| | - Alessandra Magistrato
- National Research Council of Italy (CNR) - Institute of Material Foundry (IOM) c/o International School for Advanced Studies (SISSA), Via Bonomea, 265, 34136 Trieste, Italy
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4
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Zsidó BZ, Bayarsaikhan B, Börzsei R, Hetényi C. Construction of Histone-Protein Complex Structures by Peptide Growing. Int J Mol Sci 2023; 24:13831. [PMID: 37762134 PMCID: PMC10530865 DOI: 10.3390/ijms241813831] [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: 08/21/2023] [Revised: 09/04/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
The structures of histone complexes are master keys to epigenetics. Linear histone peptide tails often bind to shallow pockets of reader proteins via weak interactions, rendering their structure determination challenging. In the present study, a new protocol, PepGrow, is introduced. PepGrow uses docked histone fragments as seeds and grows the full peptide tails in the reader-binding pocket, producing atomic-resolution structures of histone-reader complexes. PepGrow is able to handle the flexibility of histone peptides, and it is demonstrated to be more efficient than linking pre-docked peptide fragments. The new protocol combines the advantages of popular program packages and allows fast generation of solution structures. AutoDock, a force-field-based program, is used to supply the docked peptide fragments used as structural seeds, and the building algorithm of Modeller is adopted and tested as a peptide growing engine. The performance of PepGrow is compared to ten other docking methods, and it is concluded that in situ growing of a ligand from a seed is a viable strategy for the production of complex structures of histone peptides at atomic resolution.
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Affiliation(s)
| | | | | | - Csaba Hetényi
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti Út 12, 7624 Pécs, Hungary; (B.Z.Z.); (B.B.); (R.B.)
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5
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Mollica L, Cupaioli FA, Rossetti G, Chiappori F. An overview of structural approaches to study therapeutic RNAs. Front Mol Biosci 2022; 9:1044126. [PMID: 36387283 PMCID: PMC9649582 DOI: 10.3389/fmolb.2022.1044126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 10/18/2022] [Indexed: 11/07/2023] Open
Abstract
RNAs provide considerable opportunities as therapeutic agent to expand the plethora of classical therapeutic targets, from extracellular and surface proteins to intracellular nucleic acids and its regulators, in a wide range of diseases. RNA versatility can be exploited to recognize cell types, perform cell therapy, and develop new vaccine classes. Therapeutic RNAs (aptamers, antisense nucleotides, siRNA, miRNA, mRNA and CRISPR-Cas9) can modulate or induce protein expression, inhibit molecular interactions, achieve genome editing as well as exon-skipping. A common RNA thread, which makes it very promising for therapeutic applications, is its structure, flexibility, and binding specificity. Moreover, RNA displays peculiar structural plasticity compared to proteins as well as to DNA. Here we summarize the recent advances and applications of therapeutic RNAs, and the experimental and computational methods to analyze their structure, by biophysical techniques (liquid-state NMR, scattering, reactivity, and computational simulations), with a focus on dynamic and flexibility aspects and to binding analysis. This will provide insights on the currently available RNA therapeutic applications and on the best techniques to evaluate its dynamics and reactivity.
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Affiliation(s)
- Luca Mollica
- Department of Medical Biotechnologies and Translational Medicine, L.I.T.A/University of Milan, Milan, Italy
| | | | | | - Federica Chiappori
- National Research Council—Institute for Biomedical Technologies, Milan, Italy
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Yin B, Zhang Q, Xia X, Li C, Ho WKH, Yan J, Huang Y, Wu H, Wang P, Yi C, Hao J, Wang J, Chen H, Wong SHD, Yang M. A CRISPR-Cas12a integrated SERS nanoplatform with chimeric DNA/RNA hairpin guide for ultrasensitive nucleic acid detection. Am J Cancer Res 2022; 12:5914-5930. [PMID: 35966585 PMCID: PMC9373821 DOI: 10.7150/thno.75816] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 08/01/2022] [Indexed: 11/05/2022] Open
Abstract
Background: CRISPR-Cas12a has been integrated with nanomaterial-based optical techniques, such as surface-enhanced Raman scattering (SERS), to formulate a powerful amplification-free nucleic acid detection system. However, nanomaterials impose steric hindrance to limit the accessibility of CRISPR-Cas12a to the narrow gaps (SERS hot spots) among nanoparticles (NPs) for producing a significant change in signals after nucleic acid detection. Methods: To overcome this restriction, we specifically design chimeric DNA/RNA hairpins (displacers) that can be destabilized by activated CRISPR-Cas12a in the presence of target DNA, liberating excessive RNA that can disintegrate a core-satellite nanocluster via toehold-mediated strand displacement for orchestrating a promising "on-off" nucleic acid biosensor. The core-satellite nanocluster comprises a large gold nanoparticle (AuNP) core surrounded by small AuNPs with Raman tags via DNA hybridization as an ultrabright Raman reporter, and its disassembly leads to a drastic decrease of SERS intensity as signal readouts. We further introduce a magnetic core to the large AuNPs that can facilitate their separation from the disassembled nanostructures to suppress the background for improving detection sensitivity. Results: As a proof-of-concept study, our findings showed that the application of displacers was more effective in decreasing the SERS intensity of the system and attained a better limit of detection (LOD, 10 aM) than that by directly using activated CRISPR-Cas12a, with high selectivity and stability for nucleic acid detection. Introducing magnetic-responsive functionality to our system further improves the LOD to 1 aM. Conclusion: Our work not only offers a platform to sensitively and selectively probe nucleic acids without pre-amplification but also provides new insights into the design of the CRISPR-Cas12a/SERS integrated system to resolve the steric hindrance of nanomaterials for constructing biosensors.
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Affiliation(s)
- Bohan Yin
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
| | - Qin Zhang
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
| | - Xinyue Xia
- Department of Physics, The Chinese University of Hong Kong, Shatin, Hong Kong 999077, China
| | - Chuanqi Li
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
| | - Willis Kwun Hei Ho
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
| | - Jiaxiang Yan
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
| | - Yingying Huang
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
| | - Honglian Wu
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
| | - Pui Wang
- Department of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong 999077, China
| | - Changqing Yi
- Key Laboratory of Sensing Technology and Biomedical Instruments (Guangdong Province), School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, 510006, P. R. China
| | - Jianhua Hao
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
| | - Jianfang Wang
- Department of Physics, The Chinese University of Hong Kong, Shatin, Hong Kong 999077, China
| | - Honglin Chen
- Department of Microbiology, The University of Hong Kong, Pokfulam, Hong Kong 999077, China
| | - Siu Hong Dexter Wong
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China.,Research Institute for Sports Science and Technology, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
| | - Mo Yang
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
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7
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Mias-Lucquin D, Chauvot de Beauchene I. Conformational variability in proteins bound to single-stranded DNA: A new benchmark for new docking perspectives. Proteins 2021; 90:625-631. [PMID: 34617336 PMCID: PMC9292434 DOI: 10.1002/prot.26258] [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: 03/25/2021] [Revised: 09/15/2021] [Accepted: 09/27/2021] [Indexed: 12/19/2022]
Abstract
We explored the Protein Data Bank (PDB) to collect protein-ssDNA structures and create a multi-conformational docking benchmark including both bound and unbound protein structures. Due to ssDNA high flexibility when not bound, no ssDNA unbound structure is included in the benchmark. For the 91 sequence-identity groups identified as bound-unbound structures of the same protein, we studied the conformational changes in the protein induced by the ssDNA binding. Moreover, based on several bound or unbound protein structures in some groups, we also assessed the intrinsic conformational variability in either bound or unbound conditions and compared it to the supposedly binding-induced modifications. To illustrate a use case of this benchmark, we performed docking experiments using ATTRACT docking software. This benchmark is, to our knowledge, the first one made to peruse available structures of ssDNA-protein interactions to such an extent, aiming to improve computational docking tools dedicated to this kind of molecular interactions.
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8
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González-Alemán R, Chevrollier N, Simoes M, Montero-Cabrera L, Leclerc F. MCSS-Based Predictions of Binding Mode and Selectivity of Nucleotide Ligands. J Chem Theory Comput 2021; 17:2599-2618. [PMID: 33764770 DOI: 10.1021/acs.jctc.0c01339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Computational fragment-based approaches are widely used in drug design and discovery. One of their limitations is the lack of performance of docking methods, mainly the scoring functions. With the emergence of fragment-based approaches for single-stranded RNA ligands, we analyze the performance in docking and screening powers of an MCSS-based approach. The performance is evaluated on a benchmark of protein-nucleotide complexes where the four RNA residues are used as fragments. The screening power can be considered the major limiting factor for the fragment-based modeling or design of sequence-selective oligonucleotides. We show that the MCSS sampling is efficient even for such large and flexible fragments. Hybrid solvent models based on some partial explicit representations improve both the docking and screening powers. Clustering of the n best-ranked poses can also contribute to a lesser extent to better performance. A detailed analysis of molecular features suggests various ways to optimize the performance further.
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Affiliation(s)
- Roy González-Alemán
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Saclay, Gif-sur-Yvette F-91198, France.,Laboratorio de Química Computacional y Teórica (LQCT), Facultad de Química, Universidad de La Habana, 10400 La Habana, Cuba
| | - Nicolas Chevrollier
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Saclay, Gif-sur-Yvette F-91198, France
| | - Manuel Simoes
- CPC Manufacturing Analytics, 67000 Strasbourg, France
| | - Luis Montero-Cabrera
- Laboratorio de Química Computacional y Teórica (LQCT), Facultad de Química, Universidad de La Habana, 10400 La Habana, Cuba
| | - Fabrice Leclerc
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Saclay, Gif-sur-Yvette F-91198, France
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9
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He J, Tao H, Huang SY. Protein-ensemble-RNA docking by efficient consideration of protein flexibility through homology models. Bioinformatics 2020; 35:4994-5002. [PMID: 31086984 DOI: 10.1093/bioinformatics/btz388] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Revised: 04/28/2019] [Accepted: 05/03/2019] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION Given the importance of protein-ribonucleic acid (RNA) interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble-RNA docking strategy to explicitly consider the protein flexibility in protein-RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB). RESULTS Our approach can not only avoid the reliability issue of structures from MD simulations but also circumvent the limited number of experimental structures for a target protein in the PDB. Tested on 68 unbound-bound and 18 unbound-unbound protein-RNA complexes, our MPRDock/DITScorePR considerably improved the docking performance and achieved a significantly higher success rate than single-protein rigid docking whether pseudo-unbound templates are included or not. Similar improvements were also observed when combining our ensemble docking strategy with other scoring functions. The present homology model-based ensemble docking approach will have a general application in molecular docking for other interactions. AVAILABILITY AND IMPLEMENTATION http://huanglab.phys.hust.edu.cn/mprdock/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jiahua He
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Huanyu Tao
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sheng-You Huang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
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10
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Pal A, Levy Y. Structure, stability and specificity of the binding of ssDNA and ssRNA with proteins. PLoS Comput Biol 2019; 15:e1006768. [PMID: 30933978 PMCID: PMC6467422 DOI: 10.1371/journal.pcbi.1006768] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 04/16/2019] [Accepted: 01/01/2019] [Indexed: 02/06/2023] Open
Abstract
Recognition of single-stranded DNA (ssDNA) or single-stranded RNA (ssRNA) is important for many fundamental cellular functions. A variety of single-stranded DNA-binding proteins (ssDBPs) and single-stranded RNA-binding proteins (ssRBPs) have evolved that bind ssDNA and ssRNA, respectively, with varying degree of affinities and specificities to form complexes. Structural studies of these complexes provide key insights into their recognition mechanism. However, computational modeling of the specific recognition process and to predict the structure of the complex is challenging, primarily due to the heterogeneity of their binding energy landscape and the greater flexibility of ssDNA or ssRNA compared with double-stranded nucleic acids. Consequently, considerably fewer computational studies have explored interactions between proteins and single-stranded nucleic acids compared with protein interactions with double-stranded nucleic acids. Here, we report a newly developed energy-based coarse-grained model to predict the structure of ssDNA–ssDBP and ssRNA–ssRBP complexes and to assess their sequence-specific interactions and stabilities. We tuned two factors that can modulate specific recognition: base–aromatic stacking strength and the flexibility of the single-stranded nucleic acid. The model was successfully applied to predict the binding conformations of 12 distinct ssDBP and ssRBP structures with their cognate ssDNA and ssRNA partners having various sequences. Estimated binding energies agreed well with the corresponding experimental binding affinities. Bound conformations from the simulation showed a funnel-shaped binding energy distribution where the native-like conformations corresponded to the energy minima. The various ssDNA–protein and ssRNA–protein complexes differed in the balance of electrostatic and aromatic energies. The lower affinity of the ssRNA–ssRBP complexes compared with the ssDNA–ssDBP complexes stems from lower flexibility of ssRNA compared to ssDNA, which results in higher rate constants for the dissociation of the complex (koff) for complexes involving the former. Quantifying bimolecular self-assembly is pivotal to understanding cellular function. In recent years, a large progress has been made in understanding the structure and biophysics of protein-protein interactions. Particularly, various computational tools are available for predicting these structures and to estimate their stability and the driving forces of their formation. The understating of the interactions between proteins and nucleic acids, however, is still limited, presumably due to the involvement of non-specific interactions as well as the high conformational plasticity that may demand an induced-fit mechanism. In particular, the interactions between proteins and single-stranded nucleic acids (i.e., single-stranded DNA and RNA) is very challenging due to their high flexibility. Furthermore, the interface between proteins and single-stranded nucleic acids is often chemically more heterogeneous than the interface between proteins and double-stranded DNA. In this study, we developed a coarse-grained computational model to predict the structure of complexes between proteins and single-stranded nucleic acids. The model was applied to estimate binding affinities and the estimated binding energies agreed well with the corresponding experimental binding affinities. The kinetics of association as well as the specificity of the complexes between proteins and ssDNA are different than those with ssRNA, mostly due to differences in their conformational flexibility.
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Affiliation(s)
- Arumay Pal
- Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Yaakov Levy
- Department of Structural Biology, Weizmann Institute of Science, Rehovot, Israel
- * E-mail:
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11
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Samsonov SA, Zacharias M, Chauvot de Beauchene I. Modeling large protein-glycosaminoglycan complexes using a fragment-based approach. J Comput Chem 2019; 40:1429-1439. [PMID: 30768805 DOI: 10.1002/jcc.25797] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 01/10/2019] [Accepted: 01/16/2019] [Indexed: 11/07/2022]
Abstract
Glycosaminoglycans (GAGs), a major constituent of the extracellular matrix, participate in cell-signaling by binding specific proteins. Structural data on protein-GAG interactions are crucial to understand and modulate these signaling processes, with potential applications in regenerative medicine. However, experimental and theoretical approaches used to study GAG-protein systems are challenged by GAGs high flexibility limiting the conformational sampling above a certain size, and by the scarcity of GAG-specific docking tools compared to protein-protein or protein-drug docking approaches. We present for the first time an automated fragment-based method for docking GAGs on a protein binding site. In this approach, trimeric GAG fragments are flexibly docked to the protein, assembled based on their spacial overlap, and refined by molecular dynamics. The method appeared more successful than the classical full-ligand approach for most of 13 tested complexes with known structure. The approach is particularly promising for docking of long GAG chains, which represents a bottleneck for classical docking approaches applied to these systems. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Sergey A Samsonov
- Faculty of Chemistry, University of Gdańsk, ul. Wita Stwosza 63, 80-308, Gdańsk, Poland
| | - Martin Zacharias
- Physics Department, Technical University of Munich, James-Franck Strasse 1, 85748, Garching, Germany
| | - Isaure Chauvot de Beauchene
- CNRS, LORIA (CNRS, Inria NGE, Université de Lorraine), Campus Scientifique, 615 rue du Jardin Botanique, Vandœuvre-lès-Nancy, F-54506, France
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12
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Kappel K, Das R. Sampling Native-like Structures of RNA-Protein Complexes through Rosetta Folding and Docking. Structure 2018; 27:140-151.e5. [PMID: 30416038 DOI: 10.1016/j.str.2018.10.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 08/27/2018] [Accepted: 10/05/2018] [Indexed: 10/27/2022]
Abstract
RNA-protein complexes underlie numerous cellular processes including translation, splicing, and posttranscriptional regulation of gene expression. The structures of these complexes are crucial to their functions but often elude high-resolution structure determination. Computational methods are needed that can integrate low-resolution data for RNA-protein complexes while modeling de novo the large conformational changes of RNA components upon complex formation. To address this challenge, we describe RNP-denovo, a Rosetta method to simultaneously fold-and-dock RNA to a protein surface. On a benchmark set of diverse RNA-protein complexes not solvable with prior strategies, RNP-denovo consistently sampled native-like structures with better than nucleotide resolution. We revisited three past blind modeling challenges involving the spliceosome, telomerase, and a methyltransferase-ribosomal RNA complex in which previous methods gave poor results. When coupled with the same sparse FRET, crosslinking, and functional data used previously, RNP-denovo gave models with significantly improved accuracy. These results open a route to modeling global folds of RNA-protein complexes from low-resolution data.
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Affiliation(s)
- Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA 94305, USA
| | - Rhiju Das
- Biophysics Program, Stanford University, Stanford, CA 94305, USA; Department of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Physics, Stanford University, Stanford, CA 94305, USA.
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13
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Orr AA, Gonzalez-Rivera JC, Wilson M, Bhikha PR, Wang D, Contreras LM, Tamamis P. A high-throughput and rapid computational method for screening of RNA post-transcriptional modifications that can be recognized by target proteins. Methods 2018; 143:34-47. [DOI: 10.1016/j.ymeth.2018.01.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 01/14/2018] [Accepted: 01/26/2018] [Indexed: 12/25/2022] Open
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14
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Rajagopalan M, Balasubramanian S, Ramaswamy A. Insights into the RNA binding mechanism of human L1-ORF1p: a molecular dynamics study. MOLECULAR BIOSYSTEMS 2018; 13:1728-1743. [PMID: 28714502 DOI: 10.1039/c7mb00358g] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The recognition and binding of nucleic acids by ORF1p, an L1 retrotransposon protein, have not yet been clearly understood due to the lack of structural knowledge. The present study attempts to identify the probable single-stranded RNA binding pathway of trimeric ORF1p using computational methods like ligand mapping methodology combined with molecular dynamics simulations. Using the ligand mapping methodology, the possible RNA interacting sites on the surface of the trimeric ORF1p were identified. The crystal structure of the ORF1p timer and an RNA molecule of 29 nucleotide bases in length were used to generate the structure of the ORF1p complex based on information on predicted binding sites as well as the functional states of the CTD. The various complexes of ORF1p-RNA were generated using polyU, polyA and L1RNA sequences and were simulated for a period of 75 ns. The observed stable interaction pattern was used to propose the possible binding pathway. Based on the binding free energy for complex formation, both polyU and L1RNA complexes were identified as stable complexes, while the complex formed with polyA was the least stable one. Furthermore, the importance of the residues in the CC domain (Lys137 and Arg141), the RRM loop (Arg206, Arg210 and Arg211) and the CTD (Arg 261 and Arg262) of all three chains in stabilizing the wrapped RNA has been highlighted in this study. The presence of several electrostatic interactions including H-bond interactions increases the affinity towards RNA and hence plays a vital role in retaining the wrapped position of RNA around ORF1p. Altogether, this study presents one of the possible RNA binding pathways of ORF1p and clearly highlights the functional state of ORF1p visited during RNA binding.
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Affiliation(s)
- Muthukumaran Rajagopalan
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry 605014, India.
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15
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de Vries SJ, Zacharias M. Fast and accurate grid representations for atom-based docking with partner flexibility. J Comput Chem 2017; 38:1538-1546. [DOI: 10.1002/jcc.24795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Revised: 01/18/2017] [Accepted: 01/19/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Sjoerd J. de Vries
- MTi, UMR-S 973, Physics Department T38; Technische Universität München; James-Franck-Strasse 1 85748 Garching Germany
| | - Martin Zacharias
- MTi, UMR-S 973, Physics Department T38; Technische Universität München; James-Franck-Strasse 1 85748 Garching Germany
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16
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Protein-RNA interactions: structural biology and computational modeling techniques. Biophys Rev 2016; 8:359-367. [PMID: 28510023 DOI: 10.1007/s12551-016-0223-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 09/20/2016] [Indexed: 12/30/2022] Open
Abstract
RNA-binding proteins are functionally diverse within cells, being involved in RNA-metabolism, translation, DNA damage repair, and gene regulation at both the transcriptional and post-transcriptional levels. Much has been learnt about their interactions with RNAs through structure determination techniques and computational modeling. This review gives an overview of the structural data currently available for protein-RNA complexes, and discusses the technical issues facing structural biologists working to solve their structures. The review focuses on three techniques used to solve the 3-dimensional structure of protein-RNA complexes at atomic resolution, namely X-ray crystallography, solution nuclear magnetic resonance (NMR) and cryo-electron microscopy (cryo-EM). The review then focuses on the main computational modeling techniques that use these atomic resolution data: discussing the prediction of RNA-binding sites on unbound proteins, docking proteins, and RNAs, and modeling the molecular dynamics of the systems. In conclusion, the review looks at the future directions this field of research might take.
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17
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Wang M, Yu Y, Liang C, Lu A, Zhang G. Recent Advances in Developing Small Molecules Targeting Nucleic Acid. Int J Mol Sci 2016; 17:ijms17060779. [PMID: 27248995 PMCID: PMC4926330 DOI: 10.3390/ijms17060779] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Revised: 05/01/2016] [Accepted: 05/09/2016] [Indexed: 12/19/2022] Open
Abstract
Nucleic acids participate in a large number of biological processes. However, current approaches for small molecules targeting protein are incompatible with nucleic acids. On the other hand, the lack of crystallization of nucleic acid is the limiting factor for nucleic acid drug design. Because of the improvements in crystallization in recent years, a great many structures of nucleic acids have been reported, providing basic information for nucleic acid drug discovery. This review focuses on the discovery and development of small molecules targeting nucleic acids.
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Affiliation(s)
- Maolin Wang
- Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong 999077, China.
- Shenzhen Lab of Combinatorial Compounds and Targeted Drug Delivery, HKBU Institute of Research and Continuing Education, Shenzhen 518000, China.
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong 999077, China.
| | - Yuanyuan Yu
- Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong 999077, China.
- Shenzhen Lab of Combinatorial Compounds and Targeted Drug Delivery, HKBU Institute of Research and Continuing Education, Shenzhen 518000, China.
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong 999077, China.
| | - Chao Liang
- Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong 999077, China.
- Shenzhen Lab of Combinatorial Compounds and Targeted Drug Delivery, HKBU Institute of Research and Continuing Education, Shenzhen 518000, China.
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong 999077, China.
| | - Aiping Lu
- Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong 999077, China.
- Shenzhen Lab of Combinatorial Compounds and Targeted Drug Delivery, HKBU Institute of Research and Continuing Education, Shenzhen 518000, China.
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong 999077, China.
| | - Ge Zhang
- Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong 999077, China.
- Shenzhen Lab of Combinatorial Compounds and Targeted Drug Delivery, HKBU Institute of Research and Continuing Education, Shenzhen 518000, China.
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong 999077, China.
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