Towards rational computational peptide design
Peptides are prevalent in biology, mediating as many as 40% of protein-protein interactions, and involved in other cellular functions such as transport and signaling. Their ability to bind with high specificity make them promising therapeutical agents with intermediate properties between small molec...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Bioinformatics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2022.1046493/full |
| _version_ | 1811337145337511936 |
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| author | Liwei Chang Liwei Chang Arup Mondal Arup Mondal Alberto Perez Alberto Perez |
| author_facet | Liwei Chang Liwei Chang Arup Mondal Arup Mondal Alberto Perez Alberto Perez |
| author_sort | Liwei Chang |
| collection | DOAJ |
| description | Peptides are prevalent in biology, mediating as many as 40% of protein-protein interactions, and involved in other cellular functions such as transport and signaling. Their ability to bind with high specificity make them promising therapeutical agents with intermediate properties between small molecules and large biologics. Beyond their biological role, peptides can be programmed to self-assembly, and they are already being used for functions as diverse as oligonuclotide delivery, tissue regeneration or as drugs. However, the transient nature of their interactions has limited the number of structures and knowledge of binding affinities available–and their flexible nature has limited the success of computational pipelines that predict the structures and affinities of these molecules. Fortunately, recent advances in experimental and computational pipelines are creating new opportunities for this field. We are starting to see promising predictions of complex structures, thermodynamic and kinetic properties. We believe in the following years this will lead to robust rational peptide design pipelines with success similar to those applied for small molecule drug discovery. |
| first_indexed | 2024-04-13T17:50:38Z |
| format | Article |
| id | doaj.art-88f2c7464594410382f9730fc29bfafc |
| institution | Directory Open Access Journal |
| issn | 2673-7647 |
| language | English |
| last_indexed | 2024-04-13T17:50:38Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj.art-88f2c7464594410382f9730fc29bfafc2022-12-22T02:36:44ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472022-10-01210.3389/fbinf.2022.10464931046493Towards rational computational peptide designLiwei Chang0Liwei Chang1Arup Mondal2Arup Mondal3Alberto Perez4Alberto Perez5Department of Chemistry, University of Florida, Gainesville, FL, United StatesQuantum Theory Project, University of Florida, Gainesville, FL, United StatesDepartment of Chemistry, University of Florida, Gainesville, FL, United StatesQuantum Theory Project, University of Florida, Gainesville, FL, United StatesDepartment of Chemistry, University of Florida, Gainesville, FL, United StatesQuantum Theory Project, University of Florida, Gainesville, FL, United StatesPeptides are prevalent in biology, mediating as many as 40% of protein-protein interactions, and involved in other cellular functions such as transport and signaling. Their ability to bind with high specificity make them promising therapeutical agents with intermediate properties between small molecules and large biologics. Beyond their biological role, peptides can be programmed to self-assembly, and they are already being used for functions as diverse as oligonuclotide delivery, tissue regeneration or as drugs. However, the transient nature of their interactions has limited the number of structures and knowledge of binding affinities available–and their flexible nature has limited the success of computational pipelines that predict the structures and affinities of these molecules. Fortunately, recent advances in experimental and computational pipelines are creating new opportunities for this field. We are starting to see promising predictions of complex structures, thermodynamic and kinetic properties. We believe in the following years this will lead to robust rational peptide design pipelines with success similar to those applied for small molecule drug discovery.https://www.frontiersin.org/articles/10.3389/fbinf.2022.1046493/fullpeptidecomputational modelingstructure predictionpeptide-protein interactionspeptide self-assembly |
| spellingShingle | Liwei Chang Liwei Chang Arup Mondal Arup Mondal Alberto Perez Alberto Perez Towards rational computational peptide design Frontiers in Bioinformatics peptide computational modeling structure prediction peptide-protein interactions peptide self-assembly |
| title | Towards rational computational peptide design |
| title_full | Towards rational computational peptide design |
| title_fullStr | Towards rational computational peptide design |
| title_full_unstemmed | Towards rational computational peptide design |
| title_short | Towards rational computational peptide design |
| title_sort | towards rational computational peptide design |
| topic | peptide computational modeling structure prediction peptide-protein interactions peptide self-assembly |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2022.1046493/full |
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