Prediction of 3D RNA Structures from Sequence Using Energy Landscapes of RNA Dimers: Application to RNA Tetraloops
Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dyna...
Uložené v:
| Vydané v: | Journal of chemical theory and computation Ročník 20; číslo 10; s. 4363 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
United States
28.05.2024
|
| Predmet: | |
| ISSN: | 1549-9626, 1549-9626 |
| On-line prístup: | Zistit podrobnosti o prístupe |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dynamic and structurally complex properties of RNA, both experimental and traditional computational methods have difficulty in determining RNA's 3D structure. Herein, we introduce TAPERSS (Theoretical Analyses, Prediction, and Evaluation of RNA Structures from Sequence), a physics-based fragment assembly method for predicting 3D RNA structures from sequence. Using a fragment library created using discrete path sampling calculations of RNA dinucleoside monophosphates, TAPERSS can sample the physics-based energy landscapes of any RNA sequence with relatively low computational complexity. We have benchmarked TAPERSS on 21 RNA tetraloops, using a combinatorial algorithm as a proof-of-concept. We show that TAPERSS was successfully able to predict the apo-state structures of all 21 RNA hairpins, with 16 of those structures also having low predicted energies as well. We demonstrate that TAPERSS performs most accurately on GNRA-like tetraloops with mostly stacked loop-nucleotides, while having limited success with more dynamic UNCG and CUYG tetraloops, most likely due to the influence of the RNA force field used to create the fragment library. Moreover, we show that TAPERSS can successfully predict the majority of the experimental non-apo states, highlighting its potential in anticipating biologically significant yet unobserved states. This holds great promise for future applications in drug design and related studies. With discussed improvements and implementation of more efficient sampling algorithms, we believe TAPERSS may serve as a useful tool for a physics-based conformational sampling of large RNA structures. |
|---|---|
| AbstractList | Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dynamic and structurally complex properties of RNA, both experimental and traditional computational methods have difficulty in determining RNA's 3D structure. Herein, we introduce TAPERSS (Theoretical Analyses, Prediction, and Evaluation of RNA Structures from Sequence), a physics-based fragment assembly method for predicting 3D RNA structures from sequence. Using a fragment library created using discrete path sampling calculations of RNA dinucleoside monophosphates, TAPERSS can sample the physics-based energy landscapes of any RNA sequence with relatively low computational complexity. We have benchmarked TAPERSS on 21 RNA tetraloops, using a combinatorial algorithm as a proof-of-concept. We show that TAPERSS was successfully able to predict the apo-state structures of all 21 RNA hairpins, with 16 of those structures also having low predicted energies as well. We demonstrate that TAPERSS performs most accurately on GNRA-like tetraloops with mostly stacked loop-nucleotides, while having limited success with more dynamic UNCG and CUYG tetraloops, most likely due to the influence of the RNA force field used to create the fragment library. Moreover, we show that TAPERSS can successfully predict the majority of the experimental non-apo states, highlighting its potential in anticipating biologically significant yet unobserved states. This holds great promise for future applications in drug design and related studies. With discussed improvements and implementation of more efficient sampling algorithms, we believe TAPERSS may serve as a useful tool for a physics-based conformational sampling of large RNA structures.Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dynamic and structurally complex properties of RNA, both experimental and traditional computational methods have difficulty in determining RNA's 3D structure. Herein, we introduce TAPERSS (Theoretical Analyses, Prediction, and Evaluation of RNA Structures from Sequence), a physics-based fragment assembly method for predicting 3D RNA structures from sequence. Using a fragment library created using discrete path sampling calculations of RNA dinucleoside monophosphates, TAPERSS can sample the physics-based energy landscapes of any RNA sequence with relatively low computational complexity. We have benchmarked TAPERSS on 21 RNA tetraloops, using a combinatorial algorithm as a proof-of-concept. We show that TAPERSS was successfully able to predict the apo-state structures of all 21 RNA hairpins, with 16 of those structures also having low predicted energies as well. We demonstrate that TAPERSS performs most accurately on GNRA-like tetraloops with mostly stacked loop-nucleotides, while having limited success with more dynamic UNCG and CUYG tetraloops, most likely due to the influence of the RNA force field used to create the fragment library. Moreover, we show that TAPERSS can successfully predict the majority of the experimental non-apo states, highlighting its potential in anticipating biologically significant yet unobserved states. This holds great promise for future applications in drug design and related studies. With discussed improvements and implementation of more efficient sampling algorithms, we believe TAPERSS may serve as a useful tool for a physics-based conformational sampling of large RNA structures. Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dynamic and structurally complex properties of RNA, both experimental and traditional computational methods have difficulty in determining RNA's 3D structure. Herein, we introduce TAPERSS (Theoretical Analyses, Prediction, and Evaluation of RNA Structures from Sequence), a physics-based fragment assembly method for predicting 3D RNA structures from sequence. Using a fragment library created using discrete path sampling calculations of RNA dinucleoside monophosphates, TAPERSS can sample the physics-based energy landscapes of any RNA sequence with relatively low computational complexity. We have benchmarked TAPERSS on 21 RNA tetraloops, using a combinatorial algorithm as a proof-of-concept. We show that TAPERSS was successfully able to predict the apo-state structures of all 21 RNA hairpins, with 16 of those structures also having low predicted energies as well. We demonstrate that TAPERSS performs most accurately on GNRA-like tetraloops with mostly stacked loop-nucleotides, while having limited success with more dynamic UNCG and CUYG tetraloops, most likely due to the influence of the RNA force field used to create the fragment library. Moreover, we show that TAPERSS can successfully predict the majority of the experimental non-apo states, highlighting its potential in anticipating biologically significant yet unobserved states. This holds great promise for future applications in drug design and related studies. With discussed improvements and implementation of more efficient sampling algorithms, we believe TAPERSS may serve as a useful tool for a physics-based conformational sampling of large RNA structures. |
| Author | Yildirim, Ilyas Riveros, Ivan Isaac |
| Author_xml | – sequence: 1 givenname: Ivan Isaac orcidid: 0000-0002-1454-7125 surname: Riveros fullname: Riveros, Ivan Isaac organization: Department of Chemistry and Biochemistry, Florida Atlantic University, Jupiter, Florida 33458, United States – sequence: 2 givenname: Ilyas orcidid: 0000-0001-8357-1922 surname: Yildirim fullname: Yildirim, Ilyas organization: Department of Chemistry and Biochemistry, Florida Atlantic University, Jupiter, Florida 33458, United States |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38728627$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkElPwzAUhC1URBe4c0I-cknxEjsxt6oti1QBou05cpzXKlUSB9s59N8TSpE4vZHeN6PRjNGgsQ0gdEvJlBJGH7Tx04MJZhobQmiqLtCIilhFSjI5-KeHaOz9gRDOY8av0JCnCUslS0bIfTgoShNK22C7w3yBP99meB1cZ0LnwOOdszVew1cHjQG89WWzx8sG3P6IV7opvNFtT_XWH9-irMH5Rzxr26o0-pQa7Om1geB0ZW3rr9HlTlcebs53grZPy838JVq9P7_OZ6tI84SHiCkOEFMCiciZzjXjBgQFIROSg5JCpoUQJuFEkaLIlQSecqWKnLGckh3TbILuf3NbZ_v6PmR16Q1UlW7Adj7jRHCVECFJj96d0S6vochaV9baHbO_ndg3T7xsiA |
| ContentType | Journal Article |
| DBID | CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1021/acs.jctc.4c00189 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Chemistry |
| EISSN | 1549-9626 |
| ExternalDocumentID | 38728627 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NIGMS NIH HHS grantid: R15 GM146199 |
| GroupedDBID | 4.4 53G 55A 5GY 5VS 7~N AABXI ABBLG ABJNI ABMVS ABQRX ABUCX ACGFS ACIWK ACS ADHLV AEESW AENEX AFEFF AHGAQ ALMA_UNASSIGNED_HOLDINGS AQSVZ BAANH CGR CS3 CUPRZ CUY CVF D0L DU5 EBS ECM ED~ EIF F5P GGK GNL IH9 J9A JG~ NPM P2P RNS ROL UI2 VF5 VG9 W1F 7X8 ABLBI |
| ID | FETCH-LOGICAL-a373t-293ee410e75b2aba23ce51e5670be96568d55c73090ddb96e38399db22b10f2a2 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001225189400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1549-9626 |
| IngestDate | Thu Jul 10 19:19:24 EDT 2025 Sat May 31 02:13:25 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 10 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a373t-293ee410e75b2aba23ce51e5670be96568d55c73090ddb96e38399db22b10f2a2 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0001-8357-1922 0000-0002-1454-7125 |
| PMID | 38728627 |
| PQID | 3053970560 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_3053970560 pubmed_primary_38728627 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-05-28 |
| PublicationDateYYYYMMDD | 2024-05-28 |
| PublicationDate_xml | – month: 05 year: 2024 text: 2024-05-28 day: 28 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Journal of chemical theory and computation |
| PublicationTitleAlternate | J Chem Theory Comput |
| PublicationYear | 2024 |
| SSID | ssj0033423 |
| Score | 2.4303236 |
| Snippet | Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 4363 |
| SubjectTerms | Algorithms Dimerization Nucleic Acid Conformation RNA - chemistry Thermodynamics |
| Title | Prediction of 3D RNA Structures from Sequence Using Energy Landscapes of RNA Dimers: Application to RNA Tetraloops |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/38728627 https://www.proquest.com/docview/3053970560 |
| Volume | 20 |
| WOSCitedRecordID | wos001225189400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEF7UCnrx_agvVvCaNt1NmsSLlD7woKXYCr2F3c0E6iGpTevvd2ab2JsIXnIJA2F2M69vZj7GHhIl_TASyolSYWgkBxzMQ5QDCejESIv0WLKJYDgMp9NoVBbcirKtsrKJ1lAnuaEaeRPvJbpOdNfu0_zTIdYoQldLCo1tVpMYylBLVzD9QREkbbez-1I92kIpKpgS3VpTmaLxYZam4RnipfslwLSOZnD43088YgdliMk76ztxzLYgO2F73YrZ7ZQtRguCZ-hIeJ5y2eNvww4f202yK0y_Oc2c8HHZZM1tVwHv2yFB_kKjwdQ0VZAoyfVmVPp-5J0NFM6XuX01Aaqj5Pm8OGPvg_6k--yU3AuOkoFcOhgFAHgtFwJfC6WVkAb8FvjtwNUQYRAYJr5v0DxEbpLoqA2Y6UZRooXQLTcVSpyznSzP4JJxNAla6DaIdhh6KTpHEJiRt1IZatA-6Dq7r9QZoyIIsFAZ5Ksi3ii0zi7WZxLP10s4YhkGArOx4OoP0tdsX2AsQqC_CG9YLcU_G27ZrvlazorFnb00-ByOXr8BA7XMPQ |
| linkProvider | ProQuest |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Prediction+of+3D+RNA+Structures+from+Sequence+Using+Energy+Landscapes+of+RNA+Dimers%3A+Application+to+RNA+Tetraloops&rft.jtitle=Journal+of+chemical+theory+and+computation&rft.au=Riveros%2C+Ivan+Isaac&rft.au=Yildirim%2C+Ilyas&rft.date=2024-05-28&rft.issn=1549-9626&rft.eissn=1549-9626&rft.volume=20&rft.issue=10&rft.spage=4363&rft_id=info:doi/10.1021%2Facs.jctc.4c00189&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1549-9626&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1549-9626&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1549-9626&client=summon |