Dataset Information

AlphaFold2-based prediction of the co-condensation propensity of proteins.

ABSTRACT: The process of protein phase separation into liquid condensates has been implicated in the formation of membraneless organelles (MLOs), which selectively concentrate biomolecules to perform essential cellular functions. Although the importance of this process in health and disease is increasingly recognized, the experimental identification of proteins forming MLOs remains a complex challenge. In this study, we addressed this problem by harnessing the power of AlphaFold2 to perform computational predictions of the conformational properties of proteins from their amino acid sequences. We thus developed the CoDropleT (co-condensation into droplet transformer) method of predicting the propensity of co-condensation of protein pairs. The method was trained by combining experimental datasets of co-condensing proteins from the CD-CODE database with curated negative datasets of non-co-condensing proteins. To illustrate the performance of the method, we applied it to estimate the propensity of proteins to co-condense into MLOs. Our results suggest that CoDropleT could facilitate functional and therapeutic studies on protein condensation by predicting the composition of protein condensates.

SUBMITTER: Zhang S

PROVIDER: S-EPMC11348322 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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AlphaFold2-based prediction of the co-condensation propensity of proteins.

Zhang Shengyu S Lim Christine M CM Occhetta Martina M Vendruscolo Michele M

Proceedings of the National Academy of Sciences of the United States of America 20240812 34

The process of protein phase separation into liquid condensates has been implicated in the formation of membraneless organelles (MLOs), which selectively concentrate biomolecules to perform essential cellular functions. Although the importance of this process in health and disease is increasingly recognized, the experimental identification of proteins forming MLOs remains a complex challenge. In this study, we addressed this problem by harnessing the power of AlphaFold2 to perform computational ...[more]

PMID: 39133858

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Project description:Bioluminescent proteins (BLPs) are a class of proteins with various mechanisms of light emission such as bioluminescence and fluorescence from luminous organisms. While valuable for commercial and medical applications, identification of BLPs, including luciferases and fluorescent proteins (FPs), is rather challenging, owing to their high variety of protein sequences. Moreover, characterization of BLPs facilitates mutagenesis analysis to enhance bioluminescence and fluorescence. Therefore, this study proposes a novel methodological approach to estimating the propensity scores of 400 dipeptides and 20 amino acids in order to design two prediction methods and characterize BLPs based on a scoring card method (SCM). The SCMBLP method for predicting BLPs achieves an accuracy of 90.83% for 10-fold cross-validation higher than existing support vector machine based methods and a test accuracy of 82.85%. A dataset consisting of 269 luciferases and 216 FPs is also established to design the SCMLFP prediction method, which achieves training and test accuracies of 97.10% and 96.28%, respectively. Additionally, four informative physicochemical properties of 20 amino acids are identified using the estimated propensity scores to characterize BLPs as follows: 1) high transfer free energy from inside to the protein surface, 2) high occurrence frequency of residues in the transmembrane regions of the protein, 3) large hydrophobicity scale from the native protein structure, and 4) high correlation coefficient (R = 0.921) between the amino acid compositions of BLPs and integral membrane proteins. Further analyzing BLPs reveals that luciferases have a larger value of R (0.937) than FPs (0.635), suggesting that luciferases tend to locate near the cell membrane location rather than FPs for convenient receipt of extracellular ions. Importantly, the propensity scores of dipeptides and amino acids and the identified properties facilitate efforts to predict, characterize, and apply BLPs, including luciferases, photoproteins, and FPs. The web server is available at http://iclab.life.nctu.edu.tw/SCMBLP/index.html.

Dataset Information

AlphaFold2-based prediction of the co-condensation propensity of proteins.

Publications

AlphaFold2-based prediction of the co-condensation propensity of proteins.

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