Molecular origins of folding rate differences in the thioredoxin family
TL;DR: An interesting take on the expected folding-stability-function constraint while arguing for additional factors that contribute to sequence evolution and hence impact folding efficiency are provided.
Abstract: Thioredoxins are a family of conserved oxidoreductases responsible for maintaining redox balance within cells. They have also served as excellent model systems for protein design and engineering studies particularly through ancestral sequence reconstruction methods. The recent work by Gamiz-Arco et al. [Biochem J (2019) 476, 3631-3647] answers fundamental questions on how specific sequence differences can contribute to differences in folding rates between modern and ancient thioredoxins but also among a selected subset of modern thioredoxins. They surprisingly find that rapid unassisted folding, a feature of ancient thioredoxins, is not conserved in the modern descendants suggestive of co-evolution of better folding machinery that likely enabled the accumulation of mutations that slow-down folding. The work thus provides an interesting take on the expected folding-stability-function constraint while arguing for additional factors that contribute to sequence evolution and hence impact folding efficiency.
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TL;DR: In this paper, the authors use the energy landscape approach to understand the structure of protein foldings and the mechanism of protein folding, and the success of energy landscape ideas in protein structure prediction.
Abstract: The understanding, and even the description of protein folding is impeded by the complexity of the process. Much of this complexity can be described and understood by taking a statistical approach to the energetics of protein conformation, that is, to the energy landscape. The statistical energy landscape approach explains when and why unique behaviors, such as specific folding pathways, occur in some proteins and more generally explains the distinction between folding processes common to all sequences and those peculiar to individual sequences. This approach also gives new, quantitative insights into the interpretation of experiments and simulations of protein folding thermodynamics and kinetics. Specifically, the picture provides simple explanations for folding as a two-state first-order phase transition, for the origin of metastable collapsed unfolded states and for the curved Arrhenius plots observed in both laboratory experiments and discrete lattice simulations. The relation of these quantitative ideas to folding pathways, to uni-exponential {\em vs.} multi-exponential behavior in protein folding experiments and to the effect of mutations on folding is also discussed. The success of energy landscape ideas in protein structure prediction is also described. The use of the energy landscape approach for analyzing data is illustrated with a quantitative analysis of some recent simulations, and a qualitative analysis of experiments on the folding of three proteins. The work unifies several previously proposed ideas concerning the mechanism protein folding and delimits the regions of validity of these ideas under different thermodynamic conditions.
206 citations
TL;DR: This work finds that Hha is less stable and folds an order of magnitude slower than Cnu despite similar packing and topological features, and highlights how electrostatic frustration contributes to the population of heterogeneous native ensembles in paralogs and the avenues through which evolutionary topological constraints could be overcome by modulating charge-charge interactions.
8 citations
TL;DR: This work challenges a lytic phage to propagate in a host in which a protein essential for the assembly of a functional viral replisome had been modified to hinder its recruitment, supporting that such virus-host interactions may be mediated by mutant proteins present at very low concentrations.
Abstract: Viruses repurpose the host molecular machinery for their own proliferation, block host antiviral factors and recruit host proteins for processes essential for virus propagation. Cross-species transmission requires that the virus can establish crucial interactions in the two different environments of the new and the old hosts. To explore the molecular mechanisms behind host promiscuity, we challenged a lytic phage to propagate in a host in which a protein essential for the assembly of a functional viral replisome had been modified to hinder its recruitment. The virus adapted to the engineered host without losing the capability to propagate in the original host, but no mutations that could directly explain the recruitment of the modified protein were fixed in the viral DNA genome. Adaptation, however, correlated with mutations in the gene for the viral RNA polymerase, supporting that transcription errors led to phenotypic mutations that contributed to promiscuous recruitment. Some key molecular interactions need only occur a few times per host cell to allow virus replication. Our results then support that such virus-host interactions may be mediated by mutant proteins present at very low concentrations. The possibility arises that phenotypic mutations facilitate cross-species transmission and contribute to evasion of antiviral strategies.
TL;DR: The WSME model successfully predicted the folding mechanisms of small single-domain proteins and the effects of amino-acid substitutions on protein stability and folding in a manner that was consistent with experimental results and is expected to be useful for predicting protein folding, stability, and dynamics in basic research and in industrial and medical applications.
Abstract: Despite the recent advances in the prediction of protein structures by deep neutral networks, the elucidation of protein-folding mechanisms remains challenging. A promising theory for describing protein folding is a coarse-grained statistical mechanical model called the Wako-Saitô-Muñoz-Eaton (WSME) model. The model can calculate the free-energy landscapes of proteins based on a three-dimensional structure with low computational complexity, thereby providing a comprehensive understanding of the folding pathways and the structure and stability of the intermediates and transition states involved in the folding reaction. In this review, we summarize previous and recent studies on protein folding and dynamics performed using the WSME model and discuss future challenges and prospects. The WSME model successfully predicted the folding mechanisms of small single-domain proteins and the effects of amino-acid substitutions on protein stability and folding in a manner that was consistent with experimental results. Furthermore, extended versions of the WSME model were applied to predict the folding mechanisms of multi-domain proteins and the conformational changes associated with protein function. Thus, the WSME model may contribute significantly to solving the protein-folding problem and is expected to be useful for predicting protein folding, stability, and dynamics in basic research and in industrial and medical applications.
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TL;DR: An efficient means for generating mutation data matrices from large numbers of protein sequences is presented, by means of an approximate peptide-based sequence comparison algorithm, which is fast enough to process the entire SWISS-PROT databank in 20 h on a Sun SPARCstation 1, and is fastenough to generate a matrix from a specific family or class of proteins in minutes.
Abstract: An efficient means for generating mutation data matrices from large numbers of protein sequences is presented here. By means of an approximate peptide-based sequence comparison algorithm, the set sequences are clustered at the 85% identity level. The closest relating pairs of sequences are aligned, and observed amino acid exchanges tallied in a matrix. The raw mutation frequency matrix is processed in a similar way to that described by Dayhoff et al. (1978), and so the resulting matrices may be easily used in current sequence analysis applications, in place of the standard mutation data matrices, which have not been updated for 13 years. The method is fast enough to process the entire SWISS-PROT databank in 20 h on a Sun SPARCstation 1, and is fast enough to generate a matrix from a specific family or class of proteins in minutes. Differences observed between our 250 PAM mutation data matrix and the matrix calculated by Dayhoff et al. are briefly discussed.
6,355 citations
TL;DR: The work unifies several previously proposed ideas concerning the mechanism protein folding and delimits the regions of validity of these ideas under different thermodynamic conditions.
Abstract: The understanding, and even the description of protein folding is impeded by the complexity of the process. Much of this complexity can be described and understood by taking a statistical approach to the energetics of protein conformation, that is, to the energy landscape. The statistical energy landscape approach explains when and why unique behaviors, such as specific folding pathways, occur in some proteins and more generally explains the distinction between folding processes common to all sequences and those peculiar to individual sequences. This approach also gives new, quantitative insights into the interpretation of experiments and simulations of protein folding thermodynamics and kinetics. Specifically, the picture provides simple explanations for folding as a two-state first-order phase transition, for the origin of metastable collapsed unfolded states and for the curved Arrhenius plots observed in both laboratory experiments and discrete lattice simulations. The relation of these quantitative ideas to folding pathways, to uniexponential vs. multiexponential behavior in protein folding experiments and to the effect of mutations on folding is also discussed. The success of energy landscape ideas in protein structure prediction is also described. The use of the energy landscape approach for analyzing data is illustrated with a quantitative analysis of some recent simulations, and a qualitative analysis of experiments on the folding of three proteins. The work unifies several previously proposed ideas concerning the mechanism protein folding and delimits the regions of validity of these ideas under different thermodynamic conditions. © 1995 Wiley-Liss, Inc.
2,437 citations
TL;DR: Way of predicting and analyzing stability effects of mutations, and mechanisms that buffer or compensate for these destabilizing effects and thereby promote protein evolvabilty, in nature and in the laboratory are described.
874 citations
TL;DR: A statistical method was developed for reconstructing the nucleotide or amino acid sequences of extinct ancestors, given the phylogeny and sequences of the extant species, and the new likelihood-based method was found to be superior to the parsimony method.
Abstract: A statistical method was developed for reconstructing the nucleotide or amino acid sequences of extinct ancestors, given the phylogeny and sequences of the extant species. A model of nucleotide or amino acid substitution was employed to analyze data of the present-day sequences, and maximum likelihood estimates of parameters such as branch lengths were used to compare the posterior probabilities of assignments of character states (nucleotides or amino acids) to interior nodes of the tree; the assignment having the highest probability was the best reconstruction at the site. The lysozyme c sequences of six mammals were analyzed by using the likelihood and parsimony methods. The new likelihood-based method was found to be superior to the parsimony method. The probability that the amino acids for all interior nodes at a site reconstructed by the new method are correct was calculated to be 0.91, 0.86, and 0.73 for all, variable, and parsimony-informative sites, respectively, whereas the corresponding probabilities for the parsimony method were 0.84, 0.76, and 0.51, respectively. The probability that an amino acid in an ancestral sequence is correctly reconstructed by the likelihood analysis ranged from 91.3 to 98.7% for the four ancestral sequences.
710 citations
TL;DR: The success of these calculations suggests that folding speed is largely determined by the distribution and strength of contacts in the native structure, and the effect of mutations on the folding kinetics of chymotrypsin inhibitor 2, the most intensively studied two-state protein, with some success.
Abstract: An elementary statistical mechanical model was used to calculate the folding rates for 22 proteins from their known three-dimensional structures. In this model, residues come into contact only after all of the intervening chain is in the native conformation. An additional simplifying assumption is that native structure grows from localized regions that then fuse to form the complete native molecule. The free energy function for this model contains just two contributions—conformational entropy of the backbone and the energy of the inter-residue contacts. The matrix of inter-residue interactions is obtained from the atomic coordinates of the three-dimensional structure. For the 18 proteins that exhibit two-state equilibrium and kinetic behavior, profiles of the free energy versus the number of native peptide bonds show two deep minima, corresponding to the native and denatured states. For four proteins known to exhibit intermediates in folding, the free energy profiles show additional deep minima. The calculated rates of folding the two-state proteins, obtained by solving a diffusion equation for motion on the free energy profiles, reproduce the experimentally determined values surprisingly well. The success of these calculations suggests that folding speed is largely determined by the distribution and strength of contacts in the native structure. We also calculated the effect of mutations on the folding kinetics of chymotrypsin inhibitor 2, the most intensively studied two-state protein, with some success.
627 citations