scholarly journals Selection for Protein Stability Enriches for Epistatic Interactions

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 423 ◽  
Author(s):  
Anna Posfai ◽  
Juannan Zhou ◽  
Joshua Plotkin ◽  
Justin Kinney ◽  
David McCandlish
Keyword(s):  
Free Energy ◽  
Protein Sequence ◽  
Native Structure ◽  
Evolutionary Time ◽  
Epistatic Interactions ◽  
Abundant Evidence ◽  
Additive Contribution ◽  
Selection For ◽  
Protein Sequence Space ◽  
Folding Free Energy

A now classical argument for the marginal thermodynamic stability of proteins explains the distribution of observed protein stabilities as a consequence of an entropic pull in protein sequence space. In particular, most sequences that are sufficiently stable to fold will have stabilities near the folding threshold. Here, we extend this argument to consider its predictions for epistatic interactions for the effects of mutations on the free energy of folding. Although there is abundant evidence to indicate that the effects of mutations on the free energy of folding are nearly additive and conserved over evolutionary time, we show that these observations are compatible with the hypothesis that a non-additive contribution to the folding free energy is essential for observed proteins to maintain their native structure. In particular, through both simulations and analytical results, we show that even very small departures from additivity are sufficient to drive this effect.

scholarly journals Selection for protein stability enriches for epistatic interactions

2018 ◽  
Author(s):  
Anna Posfai ◽  
Juannan Zhou ◽  
Joshua B. Plotkin ◽  
Justin B. Kinney ◽  
David M. McCandlish
Keyword(s):  
Free Energy ◽  
Protein Sequence ◽  
Native Structure ◽  
Evolutionary Time ◽  
Epistatic Interactions ◽  
Abundant Evidence ◽  
Additive Contribution ◽  
Selection For ◽  
Protein Sequence Space ◽  
Folding Free Energy

AbstractA now classical argument for the marginal thermodynamic stability of proteins explains the distribution of observed protein stabilities as a consequence of an entropic pull in protein sequence space. In particular, most sequences that are sufficiently stable to fold will have stabilities near the folding threshold. Here we extend this argument to consider its predictions for epistatic interactions for the effects of mutations on the free energy of folding. Although there is abundant evidence to indicate that the effects of mutations on the free energy of folding are nearly additive and conserved over evolutionary time, we show that these observations are compatible with the hypothesis that a non-additive contribution to the folding free energy is essential for observed proteins to maintain their native structure. In particular through both simulations and analytical results, we show that even very small departures from additivity are sufficient to drive this effect.

scholarly journals Selection for cooperativity causes epistasis predominately between native contacts and enables epistasis-based structure reconstruction

2021 ◽  
Vol 118 (16) ◽  
pp. e2010057118
Author(s):  
R. Charlotte Eccleston ◽  
David D. Pollock ◽  
Richard A. Goldstein
Keyword(s):  
Protein Structure ◽  
Native Structure ◽  
Guanine Nucleotide Binding Protein ◽  
Epistatic Interactions ◽  
Intermediate States ◽  
Predict Protein Structure ◽  
Selection For ◽  
Guanine Nucleotide Binding ◽  
Energetic Interactions ◽  
Protein Gb1

Epistasis and cooperativity of folding both result from networks of energetic interactions in proteins. Epistasis results from energetic interactions among mutants, whereas cooperativity results from energetic interactions during folding that reduce the presence of intermediate states. The two concepts seem intuitively related, but it is unknown how they are related, particularly in terms of selection. To investigate their relationship, we simulated protein evolution under selection for cooperativity and separately under selection for epistasis. Strong selection for cooperativity created strong epistasis between contacts in the native structure but weakened epistasis between nonnative contacts. In contrast, selection for epistasis increased epistasis in both native and nonnative contacts and reduced cooperativity. Because epistasis can be used to predict protein structure only if it preferentially occurs in native contacts, this result indicates that selection for cooperativity may be key for predicting structure using epistasis. To evaluate this inference, we simulated the evolution of guanine nucleotide-binding protein (GB1) with and without cooperativity. With cooperativity, strong epistatic interactions clearly map out the native GB1 structure, while allowing the presence of intermediate states (low cooperativity) obscured the structure. This indicates that using epistasis measurements to reconstruct protein structure may be inappropriate for proteins with stable intermediates.

FOLDING FREE ENERGY LANDSCAPE OF THE DECAPEPTIDE CHIGNOLIN

2008 ◽  
Vol 22 (31) ◽  
pp. 3087-3098 ◽  
Author(s):  
XIANGHUA DOU ◽  
JIHUA WANG
Keyword(s):  
Free Energy ◽  
Force Field ◽  
Force Fields ◽  
Energy Landscapes ◽  
Native Structure ◽  
Good Template ◽  
Free Energy Landscapes ◽  
Folded Structures ◽  
Dynamics Simulations ◽  
Folding Free Energy

Chignolin is an artificially designed ten-residue (GYDPETGTWG) folded peptide, which is the smallest protein and provides a good template for protein folding. In this work, we completed four explicit water molecular dynamics simulations of Chignolin folding using GROMOS and OPLS-AA force fields from extended initial states without any experiment informations. The four-folding free energy landscapes of the peptide has been drawn. The folded state of Chignolin has been successfully predicated based on the free energy landscapes. The four independent simulations gave similar results. (i) The four free energy landscapes have common characters. They are fairly smooth, barrierless, funnel-like and downhill without intermediate state, which consists with the experiment. (ii) The different extended initial structures converge at similar folded structures with the lowest free energy under GROMOS and OPLS-AA force fields. In the GROMOS force field, the backbone RMSD of the folded structures from the NMR native structure of Chignolin is only 0.114 nm, which is a stable structure in this force field. In the OPLS-AA force field, the similar results have been obtained. In addition, the smallest RMSD structure is in better agreement with the NMR native structure but unlikely stable in the force field.

COMPARING FOLDING MECHANISMS OF DIFFERENT PRION PROTEINS BY Gō MODEL

2013 ◽  
Vol 12 (08) ◽  
pp. 1341004
Author(s):  
XUE WU ◽  
TING FU ◽  
ZHI-LONG XIU ◽  
LIU YIN ◽  
JIN-GUANG WANG ◽  
...  
Keyword(s):  
Free Energy ◽  
Prion Protein ◽  
Energy Barrier ◽  
Prion Proteins ◽  
Coarse Grained ◽  
Transmissible Spongiform Encephalopathies ◽  
Free Energy Barrier ◽  
Spongiform Encephalopathies ◽  
Go Model ◽  
Folding Free Energy

Prions are associated with neurodegenerative diseases induced by transmissible spongiform encephalopathies. The infectious scrapie form is referred to as PrP Sc , which has conformational change from normal prion with predominant α-helical conformation to the abnormal PrP Sc that is rich in β-sheet content. Neurodegenerative diseases have been found from both human and bovine sources, but there are no reports about infected by transmissible spongiform encephalopathies from rabbit, canine and horse sources. Here we used coarse-grained Gō model to compare the difference among human, bovine, rabbit, canine, and horse normal (cellular) prion proteins. The denatured state of normal prion has relation with the conversion from normal to abnormal prion protein, so we used all-atom Gō model to investigate the folding pathway and energy landscape for human prion protein. Through using coarse-grained Gō model, the cooperativity of the five prion proteins was characterized in terms of calorimetric criterion, sigmoidal transition, and free-energy profile. The rabbit and horse prion proteins have higher folding free-energy barrier and cooperativity, and canine prion protein has slightly higher folding free-energy barrier comparing with human and bovine prion proteins. The results from all-atom Gō model confirmed the validity of C α-Gō model. The correlations of our results with previous experimental and theoretical researches were discussed.

scholarly journals Coupling Between Protein Level Selection and Codon Usage Optimization in the Evolution of Bacteria and Archaea

mBio ◽  
2014 ◽  
Vol 5 (2) ◽  
Author(s):  
Wenqi Ran ◽  
David M. Kristensen ◽  
Eugene V. Koonin
Keyword(s):  
Codon Usage ◽  
Protein Level ◽  
Codon Usage Bias ◽  
Protein Sequence ◽  
Gc Content ◽  
Protein Sequences ◽  
Microbial Evolution ◽  
Fine Tuning ◽  
Selection For ◽  
Genomic Gc Content

ABSTRACT The relationship between the selection affecting codon usage and selection on protein sequences of orthologous genes in diverse groups of bacteria and archaea was examined by using the Alignable Tight Genome Clusters database of prokaryote genomes. The codon usage bias is generally low, with 57.5% of the gene-specific optimal codon frequencies (F opt ) being below 0.55. This apparent weak selection on codon usage contrasts with the strong purifying selection on amino acid sequences, with 65.8% of the gene-specific dN/dS ratios being below 0.1. For most of the genomes compared, a limited but statistically significant negative correlation between F opt and dN/dS was observed, which is indicative of a link between selection on protein sequence and selection on codon usage. The strength of the coupling between the protein level selection and codon usage bias showed a strong positive correlation with the genomic GC content. Combined with previous observations on the selection for GC-rich codons in bacteria and archaea with GC-rich genomes, these findings suggest that selection for translational fine-tuning could be an important factor in microbial evolution that drives the evolution of genome GC content away from mutational equilibrium. This type of selection is particularly pronounced in slowly evolving, “high-status” genes. A significantly stronger link between the two aspects of selection is observed in free-living bacteria than in parasitic bacteria and in genes encoding metabolic enzymes and transporters than in informational genes. These differences might reflect the special importance of translational fine-tuning for the adaptability of gene expression to environmental changes. The results of this work establish the coupling between protein level selection and selection for translational optimization as a distinct and potentially important factor in microbial evolution. IMPORTANCE Selection affects the evolution of microbial genomes at many levels, including both the structure of proteins and the regulation of their production. Here we demonstrate the coupling between the selection on protein sequences and the optimization of codon usage in a broad range of bacteria and archaea. The strength of this coupling varies over a wide range and strongly and positively correlates with the genomic GC content. The cause(s) of the evolution of high GC content is a long-standing open question, given the universal mutational bias toward AT. We propose that optimization of codon usage could be one of the key factors that determine the evolution of GC-rich genomes. This work establishes the coupling between selection at the level of protein sequence and at the level of codon choice optimization as a distinct aspect of genome evolution.

scholarly journals Deep learning enables therapeutic antibody optimization in mammalian cells by deciphering high-dimensional protein sequence space

2019 ◽  
Author(s):  
Derek M Mason ◽  
Simon Friedensohn ◽  
Cédric R Weber ◽  
Christian Jordi ◽  
Bastian Wagner ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sequence Space ◽  
Protein Sequence ◽  
In Silico ◽  
Mammalian Cells ◽  
Therapeutic Antibody ◽  
Quality Data ◽  
High Dimensional ◽  
Antigen Specificity ◽  
Protein Sequence Space

ABSTRACTTherapeutic antibody optimization is time and resource intensive, largely because it requires low-throughput screening (103 variants) of full-length IgG in mammalian cells, typically resulting in only a few optimized leads. Here, we use deep learning to interrogate and predict antigen-specificity from a massively diverse sequence space to identify globally optimized antibody variants. Using a mammalian display platform and the therapeutic antibody trastuzumab, rationally designed site-directed mutagenesis libraries are introduced by CRISPR/Cas9-mediated homology-directed repair (HDR). Screening and deep sequencing of relatively small libraries (104) produced high quality data capable of training deep neural networks that accurately predict antigen-binding based on antibody sequence. Deep learning is then used to predict millions of antigen binders from an in silico library of ~108 variants, where experimental testing of 30 randomly selected variants showed all 30 retained antigen specificity. The full set of in silico predicted binders is then subjected to multiple developability filters, resulting in thousands of highly-optimized lead candidates. With its scalability and capacity to interrogate high-dimensional protein sequence space, deep learning offers great potential for antibody engineering and optimization.

The effect of redox state on the folding free energy of azurin

1997 ◽  
Vol 2 (3) ◽  
pp. 368-371 ◽  
Author(s):  
Johan Leckner ◽  
Pernilla Wittung ◽  
Nicklas Bonander ◽  
B. G. Karlsson ◽  
Bo G. Malmström
Keyword(s):  
Free Energy ◽  
Redox State ◽  
Folding Free Energy

scholarly journals Systematic mapping of free energy landscapes of a growing filamin domain during biosynthesis

2018 ◽  
Vol 115 (39) ◽  
pp. 9744-9749 ◽  
Author(s):  
Christopher A. Waudby ◽  
Tomasz Wlodarski ◽  
Maria-Evangelia Karyadi ◽  
Anaïs M. E. Cassaignau ◽  
Sammy H. S. Chan ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Biosynthesis ◽  
Energy Landscapes ◽  
Native Structure ◽  
Folding Intermediates ◽  
Cotranslational Folding ◽  
Partially Folded Intermediate ◽  
Free Energy Landscapes ◽  
Peptidyl Prolyl Isomerases ◽  
Structural Characterizations

Cotranslational folding (CTF) is a fundamental molecular process that ensures efficient protein biosynthesis and minimizes the formation of misfolded states. However, the complexity of this process makes it extremely challenging to obtain structural characterizations of CTF pathways. Here, we correlate observations of translationally arrested nascent chains with those of a systematic C-terminal truncation strategy. We create a detailed description of chain length-dependent free energy landscapes associated with folding of the FLN5 filamin domain, in isolation and on the ribosome, and thus, quantify a substantial destabilization of the native structure on the ribosome. We identify and characterize two folding intermediates formed in isolation, including a partially folded intermediate associated with the isomerization of a conserved cis proline residue. The slow folding associated with this process raises the prospect that neighboring unfolded domains might accumulate and misfold during biosynthesis. We develop a simple model to quantify the risk of misfolding in this situation and show that catalysis of folding by peptidyl-prolyl isomerases is sufficient to eliminate this hazard.

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