Download Protein Coagulation Error Recovery Tool

July 17, 2020 by Corey McDonald



Over the past few weeks, some of our users have come across a well-known error code with a protein folding error rate. This problem arises due to a number of factors. We will discuss this below. Coagulated proteins are held together by various molecular interactions. During translation, each protein is synthesized as a linear chain of amino acids or as a random spiral that does not have a stable three-dimensional structure. Amino acids in the chain ultimately interact with each other, forming a well-defined coiled protein.


Empirical Evidence On The Protein Misfolding Hypothesis

January 2021 Update:




Our theoretical calculations and computer simulations have clearly shown that (i) error-free and error-induced folding occurs and contributes to the generation of anti-correlation ER, and (ii) choosing anti-protein folding reduces errors more efficiently - free folding than folding errors. It is important to note that when the hypothesis of translational stability was proposed, the authors mentioned two sources of incorrect protein folding, although attention was quickly focused on incorrect folding caused by error (Drummond et al., 2005). We suggest that the general assumption of avoiding abnormal protein folding, which takes into account both error-free and error-induced protein folding errors, is more complete and accurate than the translation stability assumption to explain ER correlation.

What is the folding code for denatured proteins?

The FaltcodeEdit
In the late 1980s, scientists discovered that there is a sequence of amino acid codes that add proteins in a certain way. Indeed, the starting point for protein folding is the primary structure (amino acid sequence), also known as the denatured state of the protein.

The protein malfold prevention hypothesis contains three important predictions. Firstly, high expression proteins are expectedIt is on average more stable than proteins with weak expression. Secondly, it is expected that codons that minimize abnormal folding of the protein will be used more often in highly expressed proteins than in weakly expressed ones. Thirdly, it is expected that within the same protein amino acid residues, where a non-synonymous random mutation increases the likelihood of protein folding, are more evolutionarily preserved. Next, we consider these three forecasts based on empirical data from baker's yeast Saccharomyces cerevisiae.

For the first forecast, the most direct support will be a positive correlation between the level of protein expression and its ΔG. ΔG was determined experimentally for only a few proteins of a certain type, and these ΔG values ​​of different proteins were often measured under different conditions, which made a meaningful comparison difficult. In addition, a computerized ΔG estimate is reliable only if the protein is very small and has an experimentally defined structure (Boas and Harbury, 2007; Dill et al., 2008). We searched the ProTherm database (Bava et al., 2004) and foundOnly five non-prion proteins of wild-type yeast. We extracted their ΔG values ​​from the nearest state of pH 7 and 25 ° C. According to our prediction, ΔG positively correlates with the level of mRNA expression (Holstege et al., 1998), although the correlation is insignificant due to the small sample size (ρ = 0, 80; P <0.13). We did not use protein expression data here, as the sample size will be further reduced. Another commonly used measure of protein stability is the protein melting point (Tmm). ProTherm experimentally measured 11 wild-type yeast proteins. After extracting their Tm values ​​from the nearest pH 7 state, we found that Tm also positively correlates with the level of mRNA expression, but the correlation was not significant (ρ = 0 32; P <0.44)

Protein instability can also be measured by protein aggregation, which is a common form of mal-folding and negatively correlates with the level of gene expression in bacteria (de Groot and Ventura, 2010) and humans (Tartaglia et al., 2007). We tried to test this anticorrelation in yeast using two different computer predictions of agronomic tendencyGenerations based on the TANGO and AGGRESCAN protein sequences (Fernandez-Escamilla et al., 2004; Conchillo-Sole et al., 2007). Significant anticorrelation between the level of mRNA expression and the tendency to protein aggregation was observed using TANGO (P 10-16, Mann-Whitney test; additional S5A figure), while no significant correlation with AGGRESCAN was observed (P = 0.182, Mann-Whitney test; additional figure S5B). However, 5% of the most expressed genes on average have a significantly weaker tendency to aggregate than 5% of the least expressed genes, regardless of the prognostic method used (TANGO: P 10-6-6, additional S5C figure); AGGRESCAN: P = 0.027, additional digit S5D). Combined with G and Tmm comparisons, these results support our first assumption that high expression proteins tend to be more stable than low expression proteins.

protein folding error rate

To check the second prognosis, we need to calculate the relative probability of incorrect protein folding (p bad refolding , including incorrect error-free folding and error-folding) when each of 61 is sensitive The codon is possibly used at each position of the gene codon. The difference in & Dgr; G between homologous proteins with one difference in amino acids (i.e., ΔΔG) can be calculated with fairly high accuracy, with or without information on the structure of proteins (Capriotti and al., 2005). Based on this computer estimate and the hypotheses of the diagrams and the rate of erroneous translation of each of the 61 sensitive codons, we calculated a p-fold for each of the 61 possible semantic codons at each position of the gene codon (4A); see materials and methods). Note that the p-coagulation described above refers to the overall probability of protein folding for the wild-type gene, and not to the absolute probability that cannot be calculated without knowing ΔG. We identify a codon that minimizes p misfolding for each codon position. If the wild-type codon matches this codon, we call the wild-type codon the appropriate codon. The protein folding prevention hypothesis predicts that the proportion of corresponding codons in a gene (codon corresponding to f) is greater for genes with high expRussia than for weakly expressed genes. Indeed, we found that the f-correction codon positively correlates with the level of gene expression (ρ = 0.43; P 10 -166; 4B). Here, we used protein expression levels measured by immunodetection of labeled proteins (Ghaemmaghami et al., 2003). The use of mRNA expression levels based on DNA chips (Holstege et al., 1998) gave similar results (ρ = 0.36; P 10 -153 ). Although the above analyzes used an estimate based on the ΔΔG sequence, we repeated it using an estimate based on the ΔΔG protein structure for a subset of yeast proteins whose structures (or in most cases homologous structures were determined experimentally (see Materials and methods) Although the sample size is reduced, the results are similar (additional figure S6)

Why is protein folding thermodynamically favored?

Confirmation of native protein is that it has minimal free energy and is therefore very stable. Protein folding is determined by thermodynamic principles. This is extremely necessary because if there were separate machines for folding all the proteins, it would be too complicated.

When calculating p misfolding , we assumed that the error translation coefficients of the preferred synonymous codons were one fifth of those of the non-preferred synonymous codons (4A). Because preferred codons are more common in high expressGenes than in weakly expressed genes (Hershberg and Petrov, 2008), the corresponding f codon may be greater in genes with higher expression even without selection against poor protein folding. To find out whether factors other than bias in the use of synonymous codons influence the correlation between the corresponding codon and the level of gene expression, we define the amino acid residues in the wild-type proteins as the corresponding residues, when they correspond to the amino acids of the codons, are encoded with the smallest p misfolding . We calculated the proportion of these corresponding amino acid residues (f aa) in each wild-type protein. Since different synonymous versions of a gene have the same f corresponding to aa , this does not affect the use of synonymous codons. We found a significant correlation between f Matching aa and the level of gene expression, measured either on protein (ρ = 0.074; P 10 -5 5; 4C) or on mRNA (ρ = 0.044; P <0.002 ) Level. Compared to weakly expressed proteins, highly expressed proteins not only use more preferred codons to reduce poor ttranslations, but also with greater refolding, minimize amino acid residues. Since the level of gene expression correlates much better with the codon corresponding to f than with aa corresponding to f , most of the covariance between the level of expression and the codon corresponding to f is associated with the use of codons. Refers to bias. Although the biased use of codons is the result of at least partial selection against abnormal folding of the protein, it can also have other reasons (see Discussion). Thus, part of the covariance between the expression and the may be due to factors that are not related to avoid incorrect folding. Therefore, our results do not imply that the prevention of incorrect folding leads mainly to the use of preferred synonymous codons instead of preferred amino acids.

Amino acids that are more expensive to synthesize have been reported to be used less frequently in very common proteins than in poorly expressed proteins (Akashi and Gojobori, 2002). This biased use of amino acids can affect f Matching aa and therefore needs to be controlled. We calculate using previously published data on the cost of amino acid synthesis (Wagner, 2005)









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