Simulations of Intrinsically Disordered Proteins integrating NMR and SAXS data

Main Research line: Protein dynamics and enzyme catalysis
Main Researcher: Ramon Crehuet

Intrinsically Disordered Proteins (IDPs) are a new family of proteins characterized by functioning while being unfolded. They challenge the traditional sequence-structure-function paradigm, as they function without a structure. These proteins are involved in cell signaling, transcription and cell cycle control.
Experimental determination of their biophysical properties is complex because of the large number of structures present in a sample. Every measured data reflects an average among all these structures and one needs computational models to describe individual properties of molecules.
Computational modelling of IDPs is challenging because IDPs are so flexible that one needs to account for a vast conformational space. Subtle errors in modelling the energy of different conformations can lead to substantial different populations of these conformations.
Combining computational models with experimental data, such as NMR or SAXS, results in more realistic models, because the experimental data constrain the computationally generated ensemble, and at the same time, the ensemble members can be analyzed individually.

What we do
We develop methods based on Bayesian statistics and Maximum Entropy to bias or prune computed ensembles based on experimental data.[1,2] At the same time, we explore the information content and redundancy of data from different experimental methods.
We also test how different force fields work, especially some coarse-grained force fields, as they represent a good compromise between accuracy and computational cost.
In collaboration with experimental groups, we model IDPs and their interactions with folded proteins. We are also studying the behavior of these proteins under pressure and pH changes.

We code new methods with the Python programming language and using many of their scientific libraries such as Scipy, Numpy, Matplotlib, Biopython, and MDTraj .

We have experimented with the Profasi, Campari software and the Probabilistic generative models in Phaistos.

Related publications

Iglesias, J.; Sanchez-Martínez, M.; Crehuet, R, Visualizing Cooperative Secondary Structure Elements in Protein Ensembles SS-Map, Intrinsically Disord. Proteins, 2013, 1 (June), e25323

Sanchez-Martinez, M.; Crehuet, R, Application of the Maximum Entropy Principle to Determine Ensembles of Intrinsically Disordered Proteins from Residual Dipolar Couplings, Phys. Chem. Chem. Phys, 2014, 16 (47), 26030–26039