Detecting Selection on Protein Stability through Statistical Mechanical Models of Folding and Evolution

Bastolla, Ugo

doi:10.3390/biom4010291

Cited by 14 publications

(11 citation statements)

References 88 publications

(131 reference statements)

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“…We predicted the folding free energy (Equation ) for every simulated and inferred ancestral sequence with the program DeltaGREM (Bastolla, ; Minning et al, ), which considers the free energy of the native, unfolded and misfolded states. Note that, even if these predictions are not completely reliable, they are correlated with experimental measures of folding free energy (Bastolla, ). Indeed, we use the same approach to evaluate the stability of simulated and reconstructed proteins so that their comparison should be little affected by inaccuracies in the predicted free energy.…”

Section: Resultsmentioning

confidence: 99%

ProtASR2: Ancestral reconstruction of protein sequences accounting for folding stability

Arenas

Bastolla

2020

Methods Ecol Evol

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The ancestral sequence reconstruction (ASR) is a molecular evolution techniquethat provides applications to a variety of fields such as biotechnology and biomedicine. To infer ancestral sequences with realistic biological properties, the accuracy of ASR methods is crucial. We previously developed an ASR framework for proteins, called ProtASR, which is based on our site-specific stability-constrained substitution (SCS) model with selection on protein folding stability against both unfolding and misfolding. This model improved the empirical substitution models traditionally applied in ASR without increasing the computational complexity.However, it adopted a global exchangeability matrix, an approximation that we overcome here by considering site-specific exchangeability matrices based on the Halpern-Bruno approach.2. Here we present ProtASR2, a new version of our ASR framework that implements novel SCS models of protein evolution, namely mean-field (MF) and wild-type (WT). ProtASR2 under MF and WT SCS models outperforms empirical models andprevious SCS models in terms of goodness of fit and site-specific distributions of amino acids. Importantly, the framework infers ancestral sequences with more realistic predicted folding stability with respect to simulated sequences, while empirical, CAT and other SCS models tend to overestimate the folding stability. We applied ProtASR2 to explore the evolution of two protein families present in diverse Prokaryota and found fluctuations of protein stability over time in both families. ProtASR2 is available from https ://github.com/migue laren as/protasr and the new SCS models are also available from https ://github.com/ ugoba s/protevol. 4. Use of ProtASR2 will allow more realistic inferences of ancestral proteins in terms of folding stability with respect to those based on traditional empirical and CAT substitution models of protein evolution. K E Y W O R D Sancestral protein reconstruction, ancestral sequence reconstruction, molecular adaptation, molecular evolution, phylogenetics, protein evolution, protein folding stability, protein sequences | 249Methods in Ecology and Evoluঞon ARENAS ANd BASTOLLA

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Section: Resultsmentioning

confidence: 99%

ProtASR2: Ancestral reconstruction of protein sequences accounting for folding stability

Arenas

Bastolla

2020

Methods Ecol Evol

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“…The structural stability of each protein structural model was inferred using DeltaGREM 2009, which estimates the change in free-energy/sequence-length of a given protein structure, compared with a statistical model of misfolded or unfolded protein ensembles, using a contact-based energy function ( Minning et al. 2013 ; Bastolla 2014 ). We calculated structural stability errors as the absolute value of the difference in estimated stabilities between the correct ancestral sequence’s structural model and that of the inferred ancestral sequence.…”

Section: Methodsmentioning

confidence: 99%

Alignment-Integrated Reconstruction of Ancestral Sequences Improves Accuracy

Aadland

Kolaczkowski

2020

Genome Biology and Evolution

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Ancestral sequence reconstruction (ASR) uses an alignment of extant protein sequences, a phylogeny describing the history of the protein family and a model of the molecular-evolutionary process to infer the sequences of ancient proteins, allowing researchers to directly investigate the impact of sequence evolution on protein structure and function. Like all statistical inferences, ASR can be sensitive to violations of its underlying assumptions. Previous studies have shown that, whereas phylogenetic uncertainty has only a very weak impact on ASR accuracy, uncertainty in the protein sequence alignment can more strongly affect inferred ancestral sequences. Here, we show that errors in sequence alignment can produce errors in ASR across a range of realistic and simplified evolutionary scenarios. Importantly, sequence reconstruction errors can lead to errors in estimates of structural and functional properties of ancestral proteins, potentially undermining the reliability of analyses relying on ASR. We introduce an alignment-integrated ASR approach that combines information from many different sequence alignments. We show that integrating alignment uncertainty improves ASR accuracy and the accuracy of downstream structural and functional inferences, often performing as well as highly accurate structure-guided alignment. Given the growing evidence that sequence alignment errors can impact the reliability of ASR studies, we recommend that future studies incorporate approaches to mitigate the impact of alignment uncertainty. Probabilistic modeling of insertion and deletion events has the potential to radically improve ASR accuracy when the model reflects the true underlying evolutionary history, but further studies are required to thoroughly evaluate the reliability of these approaches under realistic conditions.

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“…The structural stability of each protein structural model was inferred using DeltaGREM 2009, which estimates the change in free-energy/sequence-length of a given protein structure, compared to a statistical model of misfolded or unfolded protein ensembles, using a contactbased energy function (Minning et al 2013;Bastolla 2014). We calculated structural stability errors as the absolute value of the difference in estimated stabilities between the correct ancestral sequence's structural model and that of the inferred ancestral sequence.…”

Section: Structural Modeling and Rna-affinity Estimationmentioning

confidence: 99%

Alignment-Integrated Reconstruction of Ancestral Sequences Improves Accuracy

Aadland

Kolaczkowski

2020

Preprint

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Ancestral sequence reconstruction (ASR) uses an alignment of extant protein sequences, a phylogeny describing the history of the protein family and a model of the molecular-evolutionary process to infer the sequences of ancient proteins, allowing researchers to directly investigate the impact of sequence evolution on protein structure and function. Like all statistical inferences, ASR can be sensitive to violations of its underlying assumptions. Previous studies have shown that, while phylogenetic uncertainty has only a very weak impact on ASR accuracy, uncertainty in the protein sequence alignment can more strongly affect inferred ancestral sequences. Here we show that errors in sequence alignment can produce errors in ASR across a range of realistic and simplified evolutionary scenarios. Importantly, sequence reconstruction errors can lead to errors in estimates of structural and functional properties of ancestral proteins, potentially undermining the reliability of analyses relying on ASR. We introduce an alignmentintegrated ASR approach that combines information from many different sequence alignments. We show that integrating over alignment uncertainty improves ASR accuracy and the accuracy of downstream structural and functional inferences, often performing as well as highly-accurate structure-guided alignment. Given the growing evidence that sequence alignment errors can impact the reliability of ASR studies, we recommend that future studies incorporate approaches to mitigate the impact of alignment uncertainty.

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Detecting Selection on Protein Stability through Statistical Mechanical Models of Folding and Evolution

Cited by 14 publications

References 88 publications

ProtASR2: Ancestral reconstruction of protein sequences accounting for folding stability

ProtASR2: Ancestral reconstruction of protein sequences accounting for folding stability

Alignment-Integrated Reconstruction of Ancestral Sequences Improves Accuracy

Alignment-Integrated Reconstruction of Ancestral Sequences Improves Accuracy

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