High-Resolution Tuning of Non-Natural and Cyclic Peptide Folding Landscapes against NMR Measurements Using Markov Models and Bayesian Inference of Conformational Populations
The rational design of stable and highly preorganized non-native and/or cyclic peptides is a challenging task that requires atomically detailed insight into folded and unfolded conformational ensembles. In this work, we demonstrate how Markov models constructed from collections of simulated trajecto...
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2025
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| _version_ | 1852019469107331072 |
|---|---|
| author | Thi Dung Nguyen (21513519) |
| author2 | Robert M. Raddi (17615168) Vincent A. Voelz (267369) |
| author2_role | author author |
| author_facet | Thi Dung Nguyen (21513519) Robert M. Raddi (17615168) Vincent A. Voelz (267369) |
| author_role | author |
| dc.creator.none.fl_str_mv | Thi Dung Nguyen (21513519) Robert M. Raddi (17615168) Vincent A. Voelz (267369) |
| dc.date.none.fl_str_mv | 2025-06-09T21:14:32Z |
| dc.identifier.none.fl_str_mv | 10.1021/acs.jctc.5c00489.s002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/media/High-Resolution_Tuning_of_Non-Natural_and_Cyclic_Peptide_Folding_Landscapes_against_NMR_Measurements_Using_Markov_Models_and_Bayesian_Inference_of_Conformational_Populations/29275074 |
| dc.rights.none.fl_str_mv | CC BY-NC 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biophysics Biochemistry Neuroscience Biotechnology Computational Biology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Physical Sciences not elsewhere classified side chain hydrogen scalar couplings ). scalar coupling constants refine karplus parameters purpose force fields 2 kj mol >< sub >< unfolded conformational ensembles reweight conformational ensembles subtle chemical modifications bonding group changes designing foldable non ></ sup >< optimal forward model markov models constructed experimental nmr observables >< sup >< peptide folding stability highly preorganized non reweighted landscapes predict obtain folding landscapes >< sup folding landscapes markov models folding stability conformational populations nmr measurements highly robust experimental trends erdelyi group chemical shifts folding landscape resolution tuning reliable pathway rational design previous estimates overall agreement noe distances n </ key features j </ h </ cyclic peptides challenging task bayesian inference 12 linear |
| dc.title.none.fl_str_mv | High-Resolution Tuning of Non-Natural and Cyclic Peptide Folding Landscapes against NMR Measurements Using Markov Models and Bayesian Inference of Conformational Populations |
| dc.type.none.fl_str_mv | Dataset Media info:eu-repo/semantics/publishedVersion dataset |
| description | The rational design of stable and highly preorganized non-native and/or cyclic peptides is a challenging task that requires atomically detailed insight into folded and unfolded conformational ensembles. In this work, we demonstrate how Markov models constructed from collections of simulated trajectories using general-purpose force fields can be reweighted against NMR measurements to produce accurate folding landscapes. Here, we model the folding landscapes of 12 linear and cyclic peptide β hairpin mimics studied by the Erdelyi group, with the goal of reproducing the effects of subtle chemical modifications on peptide folding stability. The Bayesian Inference of Conformational Populations (BICePs) algorithm was first used to refine Karplus parameters to obtain an optimal forward model for scalar coupling constants; then, BICePs was used to reweight conformational ensembles against experimental NMR observables (NOE distances, chemical shifts, and <sup>3</sup><i>J</i><sub><i>H</i><sup><i>N</i></sup><i>H</i><sup>α</sup></sub> scalar couplings). Before reweighting, Markov models of the folding dynamics reasonably capture the key features of the folding landscape. Only after reweighting, however, do we obtain folding landscapes that agree with experimental trends. Compared to previous estimates of folded populations made using the NAMFIS algorithm, BICePs-reweighted landscapes predict that the introduction of a side chain hydrogen- or halogen-bonding group changes the folding stability by no more than 2 kJ mol<sup>–1</sup>. The overall agreement between simulated and experimental NMR observables suggests that our approach is highly robust, offering a reliable pathway for designing foldable non-natural and cyclic peptides. |
| eu_rights_str_mv | openAccess |
| id | Manara_6fbbdfddbfa4e1ad9b0b4763950e0d2d |
| identifier_str_mv | 10.1021/acs.jctc.5c00489.s002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29275074 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY-NC 4.0 |
| spelling | High-Resolution Tuning of Non-Natural and Cyclic Peptide Folding Landscapes against NMR Measurements Using Markov Models and Bayesian Inference of Conformational PopulationsThi Dung Nguyen (21513519)Robert M. Raddi (17615168)Vincent A. Voelz (267369)BiophysicsBiochemistryNeuroscienceBiotechnologyComputational BiologyBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedPhysical Sciences not elsewhere classifiedside chain hydrogenscalar couplings ).scalar coupling constantsrefine karplus parameterspurpose force fields2 kj mol>< sub ><unfolded conformational ensemblesreweight conformational ensemblessubtle chemical modificationsbonding group changesdesigning foldable non></ sup ><optimal forward modelmarkov models constructedexperimental nmr observables>< sup ><peptide folding stabilityhighly preorganized nonreweighted landscapes predictobtain folding landscapes>< supfolding landscapesmarkov modelsfolding stabilityconformational populationsnmr measurementshighly robustexperimental trendserdelyi groupchemical shiftsfolding landscaperesolution tuningreliable pathwayrational designprevious estimatesoverall agreementnoe distancesn </key featuresj </h </cyclic peptideschallenging taskbayesian inference12 linearThe rational design of stable and highly preorganized non-native and/or cyclic peptides is a challenging task that requires atomically detailed insight into folded and unfolded conformational ensembles. In this work, we demonstrate how Markov models constructed from collections of simulated trajectories using general-purpose force fields can be reweighted against NMR measurements to produce accurate folding landscapes. Here, we model the folding landscapes of 12 linear and cyclic peptide β hairpin mimics studied by the Erdelyi group, with the goal of reproducing the effects of subtle chemical modifications on peptide folding stability. The Bayesian Inference of Conformational Populations (BICePs) algorithm was first used to refine Karplus parameters to obtain an optimal forward model for scalar coupling constants; then, BICePs was used to reweight conformational ensembles against experimental NMR observables (NOE distances, chemical shifts, and <sup>3</sup><i>J</i><sub><i>H</i><sup><i>N</i></sup><i>H</i><sup>α</sup></sub> scalar couplings). Before reweighting, Markov models of the folding dynamics reasonably capture the key features of the folding landscape. Only after reweighting, however, do we obtain folding landscapes that agree with experimental trends. Compared to previous estimates of folded populations made using the NAMFIS algorithm, BICePs-reweighted landscapes predict that the introduction of a side chain hydrogen- or halogen-bonding group changes the folding stability by no more than 2 kJ mol<sup>–1</sup>. The overall agreement between simulated and experimental NMR observables suggests that our approach is highly robust, offering a reliable pathway for designing foldable non-natural and cyclic peptides.2025-06-09T21:14:32ZDatasetMediainfo:eu-repo/semantics/publishedVersiondataset10.1021/acs.jctc.5c00489.s002https://figshare.com/articles/media/High-Resolution_Tuning_of_Non-Natural_and_Cyclic_Peptide_Folding_Landscapes_against_NMR_Measurements_Using_Markov_Models_and_Bayesian_Inference_of_Conformational_Populations/29275074CC BY-NC 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/292750742025-06-09T21:14:32Z |
| spellingShingle | High-Resolution Tuning of Non-Natural and Cyclic Peptide Folding Landscapes against NMR Measurements Using Markov Models and Bayesian Inference of Conformational Populations Thi Dung Nguyen (21513519) Biophysics Biochemistry Neuroscience Biotechnology Computational Biology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Physical Sciences not elsewhere classified side chain hydrogen scalar couplings ). scalar coupling constants refine karplus parameters purpose force fields 2 kj mol >< sub >< unfolded conformational ensembles reweight conformational ensembles subtle chemical modifications bonding group changes designing foldable non ></ sup >< optimal forward model markov models constructed experimental nmr observables >< sup >< peptide folding stability highly preorganized non reweighted landscapes predict obtain folding landscapes >< sup folding landscapes markov models folding stability conformational populations nmr measurements highly robust experimental trends erdelyi group chemical shifts folding landscape resolution tuning reliable pathway rational design previous estimates overall agreement noe distances n </ key features j </ h </ cyclic peptides challenging task bayesian inference 12 linear |
| status_str | publishedVersion |
| title | High-Resolution Tuning of Non-Natural and Cyclic Peptide Folding Landscapes against NMR Measurements Using Markov Models and Bayesian Inference of Conformational Populations |
| title_full | High-Resolution Tuning of Non-Natural and Cyclic Peptide Folding Landscapes against NMR Measurements Using Markov Models and Bayesian Inference of Conformational Populations |
| title_fullStr | High-Resolution Tuning of Non-Natural and Cyclic Peptide Folding Landscapes against NMR Measurements Using Markov Models and Bayesian Inference of Conformational Populations |
| title_full_unstemmed | High-Resolution Tuning of Non-Natural and Cyclic Peptide Folding Landscapes against NMR Measurements Using Markov Models and Bayesian Inference of Conformational Populations |
| title_short | High-Resolution Tuning of Non-Natural and Cyclic Peptide Folding Landscapes against NMR Measurements Using Markov Models and Bayesian Inference of Conformational Populations |
| title_sort | High-Resolution Tuning of Non-Natural and Cyclic Peptide Folding Landscapes against NMR Measurements Using Markov Models and Bayesian Inference of Conformational Populations |
| topic | Biophysics Biochemistry Neuroscience Biotechnology Computational Biology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Physical Sciences not elsewhere classified side chain hydrogen scalar couplings ). scalar coupling constants refine karplus parameters purpose force fields 2 kj mol >< sub >< unfolded conformational ensembles reweight conformational ensembles subtle chemical modifications bonding group changes designing foldable non ></ sup >< optimal forward model markov models constructed experimental nmr observables >< sup >< peptide folding stability highly preorganized non reweighted landscapes predict obtain folding landscapes >< sup folding landscapes markov models folding stability conformational populations nmr measurements highly robust experimental trends erdelyi group chemical shifts folding landscape resolution tuning reliable pathway rational design previous estimates overall agreement noe distances n </ key features j </ h </ cyclic peptides challenging task bayesian inference 12 linear |