First insight into the prediction of protein folding rate change upon point mutation

Huang, Liang-Tsung; Gromiha, M. Michael

doi:10.1093/bioinformatics/btq350

Cited by 22 publications

(18 citation statements)

References 57 publications

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“…However, the folding energy perturbations are likely to be quite modest, since both the N-and the C-terminus tend to be solvent exposed and it is well known that mutations/insertion/deletions at the protein surface do not affect considerably the protein folding thermodynamics, especially if they do not occur in helices and strands. 5,6 It is also possible that subtle changes at the sequence termini might influence the overall solubility, with consequences in the crystallization process. 7 And it is also possible, under the hypothesis of a cotranslational folding mechanism at the ribosome level, that the length of the N-terminal segment, which would be sequestered, directly or indirectly, by the ribosome, might have an importance in the expression of well-folded proteins.…”

Section: Introductionmentioning

confidence: 99%

Participation of protein sequence termini in crystal contacts

Carugo

2011

Protein Science

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The analysis of the crystal packing interactions, in a nonredundant set of high resolution and monomeric globular protein crystal structures, shows that the residues located at the N-and C-termini of the sequence tend to participate in packing interaction more often than expected and that often they interact with each other. Since the sequence termini are, in general, conformationally very flexible and since they host electrical charges of opposite sign, it can be hypothesized that they play a crucial role in the early formation of the nonphysiological contacts that bring to protein crystallization. It is thus not surprising that modest lengthening/shortening of the sequence termini have often a dramatic effect on protein crystallogenesis.

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Section: Introductionmentioning

confidence: 99%

Participation of protein sequence termini in crystal contacts

Carugo

2011

Protein Science

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“…Finally, the method is mainly for predicting the change in real value folding rates of proteins. Hence, the accuracy of discriminating the accelerating and decelerating mutants is less that that obtained with the model specifically developed for discrimination [11].…”

Section: Limitation Of Our Methodsmentioning

confidence: 88%

“…In addition, we have collected the data for several new mutants and set up a dataset with 467 unique mutants, which can be used for understanding/predicting the folding rate change upon amino acid substitution [11].…”

Section: Introductionmentioning

confidence: 99%

“…These methods include the relationship with amino acid properties [17][18][19][20], predicted secondary structures [21], predicted inter-residue contacts [22], amino acid composition [23,24], secondary structure length [25], hybrid sequence representation [26], and flexibility and solvent accessibility [27]. The details of computational methods for predicting protein folding rates have been reviewed recently [11,28].…”

Section: Introductionmentioning

confidence: 99%

“…In our earlier work, we have set up a dataset of 467 mutants and developed a method to discriminate the mutants that accelerate or decelerate the folding process [11]. In this work, we have focused on the real value prediction of protein folding rate change upon amino acid substitution.…”

Section: Introductionmentioning

confidence: 99%

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Real value prediction of protein folding rate change upon point mutation

Huang

Gromiha

2012

J Comput Aided Mol Des

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Prediction of protein folding rate change upon amino acid substitution is an important and challenging problem in protein folding kinetics and design. In this work, we have analyzed the relationship between amino acid properties and folding rate change upon mutation. Our analysis showed that the correlation is not significant with any of the studied properties in a dataset of 476 mutants. Further, we have classified the mutants based on their locations in different secondary structures and solvent accessibility. For each category, we have selected a specific combination of amino acid properties using genetic algorithm and developed a prediction scheme based on quadratic regression models for predicting the folding rate change upon mutation. Our results showed a 10-fold cross validation correlation of 0.72 between experimental and predicted change in protein folding rates. The correlation is 0.73, 0.65 and 0.79, respectively in strand, helix and coil segments. The method has been further tested with an extended dataset of 621 mutants and a blind dataset of 62 mutants, and we observed a good agreement with experiments. We have developed a web server for predicting the folding rate change upon mutation and it is available at http://bioinformatics.myweb.hinet.net/fora.htm.

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Predicting protein folding rate change upon point mutation using residue-level coevolutionary information

Mallik

Kundu

2015

Proteins

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Change in folding kinetics of globular proteins upon point mutation is crucial to a wide spectrum of biological research, such as protein misfolding, toxicity, and aggregations. Here we seek to address whether residue-level coevolutionary information of globular proteins can be informative to folding rate changes upon point mutations. Generating residue-level coevolutionary networks of globular proteins, we analyze three parameters: relative coevolution order (rCEO), network density (ND), and characteristic path length (CPL). A point mutation is considered to be equivalent to a node deletion of this network and respective percentage changes in rCEO, ND, CPL are found linearly correlated (0.84, 0.73, and -0.61, respectively) with experimental folding rate changes. The three parameters predict the folding rate change upon a point mutation with 0.031, 0.045, and 0.059 standard errors, respectively.

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First insight into the prediction of protein folding rate change upon point mutation

Abstract: http://bioinformatics.myweb.hinet.net/freedom.htm.

Cited by 22 publications

References 57 publications

Participation of protein sequence termini in crystal contacts

Participation of protein sequence termini in crystal contacts

Real value prediction of protein folding rate change upon point mutation

Predicting protein folding rate change upon point mutation using residue-level coevolutionary information

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