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A Comprehensive Review on Current Advances in Peptide Drug Development and Design
Open accessAbstract
Protein–protein interactions (PPIs) execute many fundamental cellular functions and have served as prime drug targets over the last two decades. Interfering intracellular PPIs with small molecules has been extremely difficult for larger or flat binding sites, as antibodies cannot cross the cell membrane to reach such target sites. In recent years, peptides smaller size and balance of conformational rigidity and flexibility have made them promising candidates for targeting challenging binding interfaces with satisfactory binding affinity and specificity. Deciphering and characterizing peptide–protein recognition mechanisms is thus central for the invention of peptide-based strategies to interfere with endogenous protein interactions, or improvement of the binding affinity and specificity of existing approaches. Importantly, a variety of computation-aided rational designs for peptide therapeutics have been developed, which aim to deliver comprehensive docking for peptide–protein interaction interfaces. Over 60 peptides have been approved and administrated globally in clinics. Despite this, advances in various docking models are only on the merge of making their contribution to peptide drug development. In this review, we provide (i) a holistic overview of peptide drug development and the fundamental technologies utilized to date, and (ii) an updated review on key developments of computational modeling of peptide–protein interactions (PepPIs) with an aim to assist experimental biologists exploit suitable docking methods to advance peptide interfering strategies against PPIs.
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Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Strategic Approaches to Optimizing Peptide ADME Properties
Di, L. (2015) · The AAPS Journal · doi:10.1208/s12248-014-9687-3 · Cited by 669
Modeling protein–protein and protein–peptide complexes: CAPRI 6th edition
Lensink, M. F., Velankar, S., & Wodak, S. J. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25215 · Cited by 222 · cited 3×
“A community-wide experiment CAPRI compared computationally predicted protein complex structures with experimentally determined structures to evaluate and rank the best-performing servers based on prediction accuracy [78].”
BackgroundMethodQian, Z., LaRochelle, J. R., Jiang, B., Lian, W., Hard, R. L., Selner, N. G., Luechapanichkul, R., Barrios, A. M., & Pei, D. (2014) · Biochemistry · doi:10.1021/bi5004102 · Cited by 176
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Metadata
- Authors
- Lee, A. C.-L., Harris, J. L., Khanna, K. K., & Hong, J.-H.
- Venue
- International Journal of Molecular Sciences
- Publisher
- MDPI AG
- Published
- 2019-05-14
- Volume
- 20
- Issue
- 10
- Pages
- 2383
- ISSN
- 1422-0067 (ISSN-L)
- OpenAlex
- W2945669687
- Cited by
- 686
- References
- 4
- FWCI
- 25.28
- Type
- journal article
- Language
- en
- Open access
- gold
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