Evolutionary footprint of coevolving positions in genes

Dib, Linda; Silvestro, Daniele; Salamin, Nicolas

doi:10.1093/bioinformatics/btu012

Cited by 27 publications

(61 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This should diminish the bias and allow to exponentiate a smaller number of Q matrices than for the aggregation per site, while still computing on smaller Q matrices than in non-aggregated mode. It is also possible that aggregation of the Q matrix could be more useful for other types of models, especially those with large instantaneous rate matrices, such as coevolution models (Dib et al , 2014). Finally, a second round of aggregation might be performed after the exponentiation in order to speedup the tree pruning stage (Supplementary Fig.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

State aggregation for fast likelihood computations in molecular evolution

2016

Self Cite

View full text Add to dashboard Cite

MotivationCodon models are widely used to identify the signature of selection at the molecular level and to test for changes in selective pressure during the evolution of genes encoding proteins. The large size of the state space of the Markov processes used to model codon evolution makes it difficult to use these models with large biological datasets. We propose here to use state aggregation to reduce the state space of codon models and, thus, improve the computational performance of likelihood estimation on these models.ResultsWe show that this heuristic speeds up the computations of the M0 and branch-site models up to 6.8 times. We also show through simulations that state aggregation does not introduce a detectable bias. We analyzed a real dataset and show that aggregation provides highly correlated predictions compared to the full likelihood computations. Finally, state aggregation is a very general approach and can be applied to any continuous-time Markov process-based model with large state space, such as amino acid and coevolution models. We therefore discuss different ways to apply state aggregation to Markov models used in phylogenetics.Availability and ImplementationThe heuristic is implemented in the godon package (https://bitbucket.org/Davydov/godon) and in a version of FastCodeML (https://gitlab.isb-sib.ch/phylo/fastcodeml).Supplementary information Supplementary data are available at Bioinformatics online.

show abstract

Section: Discussionmentioning

confidence: 99%

“…But even for amino acids models we can expect some degree of speedup. In contrast, we expect state aggregation to provide a significant performance improvement for the models with a large number of states, such as amino acid coevolution models that can include up to 400 states (Dib et al , 2014; Yeang and Haussler, 2007). …”

Section: Discussionmentioning

confidence: 99%

State aggregation for fast likelihood computations in molecular evolution

2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…We analyzed the Ascomycota and bacterial data sets discussed above ( Fig. 5b), applying the probabilistic Coev model 32 of sequence evolution that allowed us to discriminate between coevolving and independently evolving positions, while incorporating the underlying phylogenetic relationships ( Supplementary Fig. S13-14).…”

Section: Hsc20 Binding To Ssq1 Is Driven By Long-range Electrostatic mentioning

confidence: 99%

Two-step mechanism of J-domain action in driving Hsp70 function

Tomiczek

Delewski

Nierzwicki

et al. 2020

Preprint

View full text Add to dashboard Cite

J-domain proteins (JDPs), obligatory Hsp70 cochaperones, play critical roles in protein homeostasis. They promote key allosteric transitions that stabilize Hsp70 interaction with substrate polypeptides upon hydrolysis of its bound ATP. Although a recent crystal structure revealed the physical mode of interaction between a J-domain and an Hsp70, the structural and dynamic consequences of J-domain action once bound and how Hsp70s discriminate among its multiple JDP partners remain enigmatic. We combined free energy simulations, biochemical assays and evolutionary analyses to address these issues. Our results indicate that the invariant aspartate of the J-domain perturbs a conserved intramolecular Hsp70 network of contacts that crosses domains. This perturbation leads to destabilization of the domain-domain interface -thereby promoting the allosteric transition that triggers ATP hydrolysis. While this mechanistic step is driven by conserved residues, evolutionarily variable residues are key to initial JDP/Hsp70 recognition -via electrostatic interactions between oppositely charged surfaces. We speculate that these variable residues allow an Hsp70 to discriminate amongst JDP partners, as many of them have coevolved. Together, our data points to a two-step mode of J-domain action, a recognition stage followed by a mechanistic stage. are able to discriminate amongst their multiple JDP partners, thus enabling evolution of complex JDP/Hsp70 interaction networks. ResultsStructural model of the Hsc20-Ssq1 complex. To obtain a structural model of the JDP-Hsp70 complex formed by Hsc20 and Ssq1, we combined protein-protein docking with all-atom molecular dynamics (MD) simulations ( Supplementary Fig. S1). This approach revealed a dominant bound state that accounted for 56% of the bound population ( Fig. 1, Supplementary Fig. S2a). No other bound state accounted for more than 6% of the ensemble; these minor states rapidly interconverted and had a clear tendency to converge to the most populated bound state ( Supplementary Fig. S2b). The binding mode in this dominant state closely resembles the recently published X-ray structure of DnaJ's J-domain in complex with DnaK (DnaJ JD -DnaK) 22 . Helices II and III are oriented very similarly in the two complexes -with a backbone root mean square deviation (RMSD) of 4.17 Å ( Supplementary Fig. S3). More specifically, the J-domains bind at the NBD/SBDb,linker interface such that helix II predominantly interacts with the b-sheet region of NBD subdomain IIa and helix III with SBDb (Fig. 1). The HPD residues are positioned nearly identically in the two complexes, towards R167/R207 of NBD subdomain Ia in DnaK/Ssq1 (referred to as R NBD throughout), the arginine that previous analyses of DnaK established as important for allosteric transitions 14,20,21 . D HPD interferes in Hsp70 interdomain interactions by perturbing critical R NBD contacts.To address the question of the mechanism of J-domain action, we employed MD simulations that, unlike most other methods, allow studying of transient r...

show abstract

“…Indeed, MCMC made possible the numerical approximation or sampling of high-dimensional posterior distribution, thus broadening the inference of more complex statistical models. Evolutionary biology particularly benefited from this progress and a large number of applications, ranging from phylogenetic reconstructions (Lartillot et al , 2013; Ronquist et al , 2012), divergence time analyses (Drummond et al , 2006), molecular evolution (Dib et al , 2014), comparative methods (Beaulieu et al , 2012; FitzJohn, 2012) or population genetics (Kuhner, 2006; Fischer et al , 2011), makes extensive use of these MCMC approaches.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Bayesian inference for evolutionary biology models

2016

Self Cite

View full text Add to dashboard Cite

MotivationBayesian inference is widely used nowadays and relies largely on Markov chain Monte Carlo (MCMC) methods. Evolutionary biology has greatly benefited from the developments of MCMC methods, but the design of more complex and realistic models and the ever growing availability of novel data is pushing the limits of the current use of these methods.ResultsWe present a parallel Metropolis-Hastings (M-H) framework built with a novel combination of enhancements aimed towards parameter-rich and complex models. We show on a parameter-rich macroevolutionary model increases of the sampling speed up to 35 times with 32 processors when compared to a sequential M-H process. More importantly, our framework achieves up to a twentyfold faster convergence to estimate the posterior probability of phylogenetic trees using 32 processors when compared to the well-known software for Bayesian inference of phylogenetic trees.Availability and Implementation https://bitbucket.org/XavMeyer/hogan Supplementary information Supplementary data are available at Bioinformatics online.

show abstract

Evolutionary footprint of coevolving positions in genes

Cited by 27 publications

References 41 publications

State aggregation for fast likelihood computations in molecular evolution

State aggregation for fast likelihood computations in molecular evolution

Two-step mechanism of J-domain action in driving Hsp70 function

Accelerating Bayesian inference for evolutionary biology models

Contact Info

Product

Resources

About