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Accurate prediction of cellular co-translational folding indicates proteins can switch from post- to co-translational folding.

Nissley DA, Sharma AK, Ahmed N, Friedrich UA, Kramer G, Bukau B, O'Brien EP - Nat Commun (2016)

Bottom Line: We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules.We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally--a result consistent with previous experimental studies.Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Pennsylvania State University, University Park, Pennsylvania 16802, USA.

ABSTRACT
The rates at which domains fold and codons are translated are important factors in determining whether a nascent protein will co-translationally fold and function or misfold and malfunction. Here we develop a chemical kinetic model that calculates a protein domain's co-translational folding curve during synthesis using only the domain's bulk folding and unfolding rates and codon translation rates. We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules. We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally--a result consistent with previous experimental studies. Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process.

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Comparison between the predicted and experimentally-measured FRB and HA1 co-translational folding curves.(a) The co-translational folding probability calculated with Supplementary equation (1) (black line) and the experimentally-measured fraction folded using FactSeq25 (blue circles) for (a) FRB, HA1 using antibody binding epitope (b) H28-E23 and (c) Y8-10C2 are shown. Regions I, II and III, as described in the main text, are indicated, respectively, by the shaded regions in green, blue and red. (d) The median values of the FactSeq-measured PF,B(i) in Regions I, II and III are shown with bootstrapped error bars for FRB, H28-E23 and Y8-10C2.The statistical significance of the PF,B(i) values was determined using the Mann–Whitney U-Test. Region I versus Region II: FRB: P=0.078, H28-E23: P=0.1933 and Y8-10C2: P=0.4471. Region III versus Region I: FRB: P=5.04 × 10−11, H28-E23: P=2.56 × 10−11 and Y8-10C2: P=9.11 × 10−8. Region III versus Region II FRB: P=3.2 × 10−9, H28-E23: P=2.75 × 10−15 and Y8-10C2: P=8.98 × 10−11. Hence, the experimental data from FactSeq are consistent with the predicted co-translational folding curves in panels a, b, and c of this figure.
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f4: Comparison between the predicted and experimentally-measured FRB and HA1 co-translational folding curves.(a) The co-translational folding probability calculated with Supplementary equation (1) (black line) and the experimentally-measured fraction folded using FactSeq25 (blue circles) for (a) FRB, HA1 using antibody binding epitope (b) H28-E23 and (c) Y8-10C2 are shown. Regions I, II and III, as described in the main text, are indicated, respectively, by the shaded regions in green, blue and red. (d) The median values of the FactSeq-measured PF,B(i) in Regions I, II and III are shown with bootstrapped error bars for FRB, H28-E23 and Y8-10C2.The statistical significance of the PF,B(i) values was determined using the Mann–Whitney U-Test. Region I versus Region II: FRB: P=0.078, H28-E23: P=0.1933 and Y8-10C2: P=0.4471. Region III versus Region I: FRB: P=5.04 × 10−11, H28-E23: P=2.56 × 10−11 and Y8-10C2: P=9.11 × 10−8. Region III versus Region II FRB: P=3.2 × 10−9, H28-E23: P=2.75 × 10−15 and Y8-10C2: P=8.98 × 10−11. Hence, the experimental data from FactSeq are consistent with the predicted co-translational folding curves in panels a, b, and c of this figure.

Mentions: As a further test of our approach, we also modelled in vivo co-translational folding curves for the 99-amino acid FKBP12-rapamycin-binding domain of a Flag-FRB-GFP construct (Fig. 1b) and the 290 structured residues of the viral protein HA1 from influenza A/PR8 (Fig. 1b). These co-translational folding curves have been measured using the experimental technique known as folding-associated co-translational sequencing (FactSeq)25. FactSeq is a Next-Gen sequencing technique that uses substrate or antibody binding to monitor the co-translational folding status of a protein segment as a function of the nascent chain length rather than as a function of time as in pulse-chase measurements. Thus, Supplementary equation (1) (described in Supplementary Note 1) and not equation (2) is appropriate for predicting these co-translational folding curves. For FRB and HA1, we used the kF and kU values reported in Table 1. The typical range of translation rates in eukaryotic cells is 3.2–5.6 AA per second714. Using this range of kA values we find Supplementary equation (1) predicts very similar in vivo co-translational folding trends as are observed experimentally for FRB and HA1; the results when a kA of 3.9 AA per second is used are displayed here in Fig. 4.


Accurate prediction of cellular co-translational folding indicates proteins can switch from post- to co-translational folding.

Nissley DA, Sharma AK, Ahmed N, Friedrich UA, Kramer G, Bukau B, O'Brien EP - Nat Commun (2016)

Comparison between the predicted and experimentally-measured FRB and HA1 co-translational folding curves.(a) The co-translational folding probability calculated with Supplementary equation (1) (black line) and the experimentally-measured fraction folded using FactSeq25 (blue circles) for (a) FRB, HA1 using antibody binding epitope (b) H28-E23 and (c) Y8-10C2 are shown. Regions I, II and III, as described in the main text, are indicated, respectively, by the shaded regions in green, blue and red. (d) The median values of the FactSeq-measured PF,B(i) in Regions I, II and III are shown with bootstrapped error bars for FRB, H28-E23 and Y8-10C2.The statistical significance of the PF,B(i) values was determined using the Mann–Whitney U-Test. Region I versus Region II: FRB: P=0.078, H28-E23: P=0.1933 and Y8-10C2: P=0.4471. Region III versus Region I: FRB: P=5.04 × 10−11, H28-E23: P=2.56 × 10−11 and Y8-10C2: P=9.11 × 10−8. Region III versus Region II FRB: P=3.2 × 10−9, H28-E23: P=2.75 × 10−15 and Y8-10C2: P=8.98 × 10−11. Hence, the experimental data from FactSeq are consistent with the predicted co-translational folding curves in panels a, b, and c of this figure.
© Copyright Policy - open-access
Related In: Results  -  Collection

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f4: Comparison between the predicted and experimentally-measured FRB and HA1 co-translational folding curves.(a) The co-translational folding probability calculated with Supplementary equation (1) (black line) and the experimentally-measured fraction folded using FactSeq25 (blue circles) for (a) FRB, HA1 using antibody binding epitope (b) H28-E23 and (c) Y8-10C2 are shown. Regions I, II and III, as described in the main text, are indicated, respectively, by the shaded regions in green, blue and red. (d) The median values of the FactSeq-measured PF,B(i) in Regions I, II and III are shown with bootstrapped error bars for FRB, H28-E23 and Y8-10C2.The statistical significance of the PF,B(i) values was determined using the Mann–Whitney U-Test. Region I versus Region II: FRB: P=0.078, H28-E23: P=0.1933 and Y8-10C2: P=0.4471. Region III versus Region I: FRB: P=5.04 × 10−11, H28-E23: P=2.56 × 10−11 and Y8-10C2: P=9.11 × 10−8. Region III versus Region II FRB: P=3.2 × 10−9, H28-E23: P=2.75 × 10−15 and Y8-10C2: P=8.98 × 10−11. Hence, the experimental data from FactSeq are consistent with the predicted co-translational folding curves in panels a, b, and c of this figure.
Mentions: As a further test of our approach, we also modelled in vivo co-translational folding curves for the 99-amino acid FKBP12-rapamycin-binding domain of a Flag-FRB-GFP construct (Fig. 1b) and the 290 structured residues of the viral protein HA1 from influenza A/PR8 (Fig. 1b). These co-translational folding curves have been measured using the experimental technique known as folding-associated co-translational sequencing (FactSeq)25. FactSeq is a Next-Gen sequencing technique that uses substrate or antibody binding to monitor the co-translational folding status of a protein segment as a function of the nascent chain length rather than as a function of time as in pulse-chase measurements. Thus, Supplementary equation (1) (described in Supplementary Note 1) and not equation (2) is appropriate for predicting these co-translational folding curves. For FRB and HA1, we used the kF and kU values reported in Table 1. The typical range of translation rates in eukaryotic cells is 3.2–5.6 AA per second714. Using this range of kA values we find Supplementary equation (1) predicts very similar in vivo co-translational folding trends as are observed experimentally for FRB and HA1; the results when a kA of 3.9 AA per second is used are displayed here in Fig. 4.

Bottom Line: We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules.We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally--a result consistent with previous experimental studies.Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Pennsylvania State University, University Park, Pennsylvania 16802, USA.

ABSTRACT
The rates at which domains fold and codons are translated are important factors in determining whether a nascent protein will co-translationally fold and function or misfold and malfunction. Here we develop a chemical kinetic model that calculates a protein domain's co-translational folding curve during synthesis using only the domain's bulk folding and unfolding rates and codon translation rates. We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules. We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally--a result consistent with previous experimental studies. Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process.

Show MeSH