Genome-wide prediction and validation of peptides that bind human prosurvival Bcl-2 proteins
Open accessAbstract
Programmed cell death is regulated by interactions between pro-apoptotic and prosurvival members of the Bcl-2 family. Pro-apoptotic family members contain a weakly conserved BH3 motif that can adopt an alpha-helical structure and bind to a groove on prosurvival partners Bcl-xL, Bcl-w, Bcl-2, Mcl-1 and Bfl-1. Peptides corresponding to roughly 13 reported BH3 motifs have been verified to bind in this manner. Due to their short lengths and low sequence conservation, BH3 motifs are not detected using standard sequence-based bioinformatics approaches. Thus, it is possible that many additional proteins harbor BH3-like sequences that can mediate interactions with the Bcl-2 family. In this work, we used structure-based and data-based Bcl-2 interaction models to find new BH3-like peptides in the human proteome. We used peptide SPOT arrays to test candidate peptides for interaction with one or more of the prosurvival proteins Bcl-xL, Bcl-w, Bcl-2, Mcl-1 and Bfl-1. For the 36 most promising array candidates, we quantified binding to all five human receptors using direct and competition binding assays in solution. All 36 peptides showed evidence of interaction with at least one prosurvival protein, and 22 peptides bound at least one prosurvival protein with a dissociation constant between 1 and 500 nM; many peptides had specificity profiles not previously observed. We also screened the full-length parent proteins of a subset of array-tested peptides for binding to Bcl-xL and Mcl-1. Finally, we used the peptide binding data, in conjunction with previously reported interactions, to assess the affinity and specificity prediction performance of different models.
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Empirical energy-based methods have accelerated prediction but suffer from insufficient conformational sampling, especially for mutations in flexible regions.
“The empirical energy-based methods, exemplified by STATIUM [12], FoldX [8] and Discovery Studio [13], have accelerated the prediction but suffered from insufficient conformational sampling, especially for mutations in flexible regions.”
From Deep geometric representations for modeling effects of mutations on protein-protein binding affinity (2021) · cites this paper as [12]
The six benchmark datasets have been widely used to test the predictive power of PPI prediction methods.
“These datasets have been widely used to test the predictive power of the PPI prediction methods [12, 13, 27–29, 29–35].”
From Deep geometric representations for modeling effects of mutations on protein-protein binding affinity (2021) · cites this paper as [12]
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References 51 · Cited by 39
Research works printed but not linked: 1
- [44]Denisov AY, Chen G, Sprules T, Moldoveanu T, Beauparlant P, et al (2006) Structural model of the BCL-w-BID peptide complex and its interactions with phospholipid micelles. Biochemistry 45: 2250– 2256. 16475813
- Authors
- DeBartolo, J., Taipale, M., & Keating, A. E.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Keating, Amy E.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-06-26
- Volume
- 10
- Issue
- 6
- Pages
- e1003693
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4072508
- OpenAlex
- W1994891218
- Cited by
- 39
- References
- 51
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 7 · 2022: 4 · 2021: 1 · 2020: 1 · 2019: 4 · 2018: 2 · 2017: 3 · 2016: 4 · 2015: 7 · 2014: 1
- FWCI
- 2.05
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Figshare (submittedVersion, cc-by)
- Funders
- National Science Foundation (0821391)
- American Cancer Society
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology
- National Institutes of Health (5r01gm084181-04, GM084181)
- National Institute of General Medical Sciences (R01-GM084181, GM084181)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Cell death mechanisms and regulation
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- Bcl-2 family proteins, Bcl-xL, Mcl-1, Bcl-w, Bfl-1, Bcl-2, programmed cell death, apoptosis, protein-peptide interactions, binding affinity prediction, structure-based models, data-driven modeling
- MeSH terms
- Amino Acid Sequence, Humans, Models, Molecular, Molecular Sequence Data, Peptides, Reproducibility of Results, Sequence Alignment, Proto-Oncogene Proteins c-bcl-2, Genomics
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