Plegamiento y evolución de proteínas

DeltaGREM

Computes the folding free energy of proteins with known structure and sequence. Sequences can be input either through a Multiple Sequence Alignment including the sequence of at least one of the input PDB or through a list of mutations.

The folding free energy takes into account the native state as represented in one of the input PDB files, the unfolded state, and the misfolded state (compact conformations dissimilar from the native), modelled as a Random Energy Model (REM) up to the second (REM2) or third (REM3) moment of the contact energy.

1. Instalación: Documentación incluida en el paquete.
2. Licencia: Uso gratuito para fines educacionales y de investigación.
3. Contactar con: ubastolla@cbm.csic.es
4. Descargar : Es necesario registrarse primero.
5. References:
   1. Model of the misfolded state: Minning J, Porto M, Bastolla U. Detecting selection for negative design in proteins through an improved model of the misfolded state. Proteins. 2013 81:1102-12. [Pubmed]
   2. Numerical computation of the REM free energy: Arenas M, Sánchez-Cobos A, Bastolla U. Maximum-Likelihood Phylogenetic Inference with Selection on Protein Folding Stability. Mol Biol Evol. 2015 32:2195-207. [Pubmed]
   3. Complete model: Bastolla U. Detecting selection on protein stability through statistical mechanical models of folding and evolution. Biomolecules. 2014 4:291-314. [Pubmed]

ProtEvol

For a protein with known structure, the program ProtEvol performs two kinds of computation:

1. It computes the mean-field model of protein evolution, which consists in the stationary amino acid distribution with independent sites that has minimum Kullback-Leibler divergence with respect to a background distribution and that constraints the average stability of the native state of the protein against both unfolding and misfolding. The background distribution and the selection parameter that enforces the desidered stability are optimized by maximum likelihood.
   For each site of the protein with known structure, it outputs the site-specific stationary amino acid distribution and the exchangeability matrix related to it through the Halpern-Bruno formula, with which phylogenetic inference can be performed. The global model needed for computing the branch lengths is obtained by averaging the site-specific models.
   Reference: Arenas M, Sánchez-Cobos A, Bastolla U. Maximum-Likelihood Phylogenetic Inference with Selection on Protein Folding Stability. Mol Biol Evol. 2015 32:2195-207. [Pubmed]


2. It simulates protein evolution subject to the constraint of selection on the folding stability of the native state of the protein against both unfolding and misfolding. It implements three selection models:
   1. Neutral: Mutations are accepted if DeltaG smaller X*DeltaG(wild type), otherwise they are rejected.
   2. From the DeltaG of the mutant, the fitness is computed as f=1/(1+exp(DeltaG/kT)) and the acceptance probability is computed with Kimura's formula.
   3. Based on the mean-field evolutionary model computed at the previous point.
   Reference: R. Méndez, M. Fritsche, M. Porto and U.Bastolla. Mutation bias favors protein folding stability in the evolution of small populations. PLoS Comput Biol 6: e1000767 2010 [Pubmed]

1. Instalación : Documentación incluida en el paquete.
2. Licencia: Uso gratuito para fines educacionales y de investigación.
3. Contactar con: ub@cbm.uam.es
4. Descargar:  Es necesario registrarse primero.

ProteinEvolver

ProteinEvolver generates samples of protein-coding genes and protein sequences evolved along phylogenies under structure-based substitution models. These models consider the protein structure to evaluate candidate mutations, which can be accepted (substitutions) or rejected depending on the energy of the protein structure of the mutated sequence. The simulation of molecular evolution occurs along phylogenetic histories, which can be either user-specified or simulated by the coalescent modified withrecombination (including recombination hotspots), migration, demographics and longitudinal sampling.

1. Instalación : Documentación incluida en el paquete.
2. Licencia: Uso gratuito para fines educacionales y de investigación.
3. Contactar con: miguelmmmab@gmail.com
4. Descargar: Google Code.
5. Reference: Arenas, M.; Dos Santos, H.G.; Posada, D. and Bastolla, U. (2013) Bioinformatics, 29:3020-3028.