Matteo Dal Peraro graduated in Physics at the University of Padua in 2000. He obtained his Ph.D. in Biophysics at the International School for Advanced Studies (SISSA, Trieste) in 2004. He then received postdoctoral training at the University of Pennsylvania (Philadelphia) under the guidance of Prof. M. L. Klein. He was nominated Tenure Track Assistant Professor at the EPFL School of Life Sciences in late 2007.
His research at the Laboratory for Biomolecular Modeling (LBM), within the Interfaculty Institute of Bioengineering (IBI), focuses on the multi-scale modelling of large macromolecular systems.
The main goal of his laboratory (LBM) is to understand the physical and chemical properties of complex biological systems, in particular their function emerging from structure. To address these questions, they use and develop a broad spectrum of computational tools fully integrated with experimental data, including power, a framework designed to tackle the assembly of large molecular systems by treating flexibility of its individual subunits with a multi-step approach including a fitness functions derived from 1 energetic (Lennard-Jones) and n experiment-based geometric contributions.
1) Can you introduce your software in a few words?
power stands for parallel optimisation workbench to enhance resolution of biological complexes. It is an integrative modelling (IM) framework designed to solve structural biology problems specifically related with the assembly of large molecular systems. Importantly, power features some novel state-of-the-art optimisation algorithms and is designed to treat flexibility upon assembly by sampling individual subunits dynamics with molecular simulations.
2) Regarding the development model of your software, who is in charge of the development/maintenance/support?
power is a relative young project, which started in the Laboratory for Biomolecular Modeling (LBM) at the EPFL. It was originally designed and implemented by Matteo Dal Peraro, head of the LBM, and Matteo Degiacomi, at that time a Ph.D. student in the laboratory. Currently, the main development and maintenance of power is done by Giorgio Tamò, a Ph.D. student at the LBM. Sylvain Traeger, another Ph.D. student in the laboratory, is also involved in the development of new methods and algorithms.
3) In which regards does your software fall within the field of integrative modelling?
power has been designed to be natively an integrative modelling framework. It specifically aims at solving the complex optimisation problem one has to deal with when integrating a large and heterogeneous amount of experimental data that can be converted in spatial constraints defining a given molecular assembly. It has been designed to be flexible enough to include data from sources as diverse as X-ray crystallography, cryo-EM, SAXS, proteomics and co-evolution analyses. One distinctive feature is its ability to treat the native flexibility of individual subunits constituting an assembly by using molecular dynamics simulations as a means to exhaustively explore their conformational space. That’s the reason why we rather like to call it an integrative dynamic modelling framework.
In its first incarnation power was using an ad hoc tailored version of Particle Swarm Optimisation (PSO) to minimise the fitness function associated with the assembly problem (Structure 2013). More recently, we have introduced a new powerful evolutionary-based algorithm called memetic viability evolution (mViE), which has the benefit to naturally disentangle the diverse and heterogeneous components of the canonical IM fitness function, treating these multiple inputs one at the time in an independent and flexible way (Scientific Report 2017)
4) Can you share with us an example in which the use of your software was key to answer a scientific question?
The most striking example so far where the integrative dynamic modeling approach implemented in power was key to advance in our mechanistic understanding of a biological system concerns the pore-forming mechanism of the aerolysin toxin from Aeromonas sp. (Nature Chemical Biology 2013). In this case, we showed that only integrating the native dynamics of the individual subunits as extracted from molecular dynamics simulations we could produce near-atomistic models that recapitulated a large array of experimental observations, including low-resolution cryo-EM density maps, mutagenesis, and membrane interactions. From these models describing multiple states en route to the formation of the mature oligomeric pore structure we learnt new mechanistic aspects of the pore formation mechanism that can be translated to the whole family of pore-forming toxins represented by aerolysin, and are currently key to explore its potential as novel nano-biosensor device.
Degiacomi, Matteo T., and Matteo Dal Peraro. “Macromolecular symmetric assembly prediction using swarm intelligence dynamic modeling.” Structure 21.7 (2013): 1097-1106.
Tamò, Giorgio, Andrea Maesani, Sylvain Träger, Matteo T. Degiacomi, Dario Floreano, and Matteo Dal Peraro. “Disentangling constraints using viability evolution principles in integrative modeling of macromolecular assemblies.” Scientific reports 7, no. 1 (2017): 235.
Degiacomi, Matteo T., Ioan Iacovache, Lucile Pernot, Mohamed Chami, Misha Kudryashev, Henning Stahlberg, F. Gisou Van Der Goot, and Matteo Dal Peraro. “Molecular assembly of the aerolysin pore reveals a swirling membrane-insertion mechanism.” Nature chemical biology 9, no. 10 (2013): 623-629.
