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Computational Platform

The Impossible Made Possible

What makes us unique is the power of our computational platform, which enables us to reconstruct the series of conformational rearrangements underlying the process of protein folding at an atomistic level of detail.

The folding pathway of biologically-relevant proteins is characterized by the presence of intermediate states separated by high energy barriers. Due to the exponential dependency between the transition rate and the height of the barrier, the conformational changes between metastable states only rarely occur. For this reason, protein folding can last milliseconds to minutes. Standard molecular dynamics techniques are unable to reach such timescales for biologically-relevant proteins, even by employing the most powerful existing special-purpose supercomputer.

In contrast, our algorithms are capable of driving the polypeptide chain through the conformational landscape, overcoming the process of trial and error responsible for the timescale-inaccessibility typical of protein folding. Our scheme enables us to accurately characterize the folding pathways of proteins with size up to 400 amino acids in less than a month.

Figure 1:

During protein folding the polypeptide spends significant time in visiting off-pathway states and for crossing the encountered energy barriers (A). Our scheme enables us to enhance the sampling rate by focusing only on the relevant conformational transitions (B).

A Powerful Idea Calls for Powerful Algorithms

Our sole requirement to fully characterize the folding mechanism of a target protein is the structure of its native state. This is used by our approach to generate a collective variable that defines the reaction progress. Our algorithm relies on such information to drive the dynamics by employing a custom implementation of adiabatic-biased molecular dynamics.

We then ensure the reliability of the sampled pathways by using filtering schemes derived from theoretical physics. Density-based clustering algorithms are then employed to deliver a network of states and transitions accurately representing the mechanism, from which relevant folding intermediates can be identified. Once the candidate structures are selected, we scout their surfaces to identify druggable pockets which are absent in the native state. These sites can be used as the starting point of an in-house virtual screening campaign, eventually delivering promising hit compounds for the target of interest.

Our platform is powered by a large GPU cluster, able to run molecular dynamics simulations with a computing performance of hundreds of TFLOPS.

Figure 2:

Schematic representation of our computational pipeline to identify druggable states on protein folding intermediates.

Always on the edge

We are continuously improving our algorithms and developing new techniques to push innovation forward. The reconstruction of the protein folding mechanism can be improved by taking into account the complexity of the cellular environment in which proteins acquire their native fold. One of the key elements of folding in vivo is that it occurs sequentially during protein synthesis. Indeed, vectoriality can have a significant influence on the folding mechanism of proteins and should be taken into consideration for a more accurate characterization of folding intermediates. Here at Sibylla we are developing dedicated algorithms that for the first time are capable of simulating the folding process during synthesis by the ribosome and translocation across the endoplasmic reticulum membrane. This will allow us to improve the accuracy and reliability of our predictions.

At the same time, we are investing our research efforts in expanding the range of druggable targets developing algorithms for the identification of so-called “cryptic pockets”. These sites represent potentially valid ligandable candidates for therapeutic purposes, but have the characteristics of not being immediately apparent in unbound protein native states. We are able to detect the presence of these sites by means of enhanced conformational exploration techniques.

Figure 3:

Snapshots of the lysozyme protein co-translocational folding simulated with our methods.


F. Dingfelder, I. Macocco, S. Benke, D. Nettels, P. Faccioli and B. Schuler. Slow escape from a helical misfolded state of the pore-forming toxin Cytolysin A (to appear on J. A.C.S. Au, 2021).

Ianeselli, A. et al. Atomic detail of protein folding revealed by an ab initio reappraisal of circular dichroism. 23 Feb 2018. J.A.C.S. 140 (10), 3674-3682. doi: 10.1021/jacs.7b12399.

F. Wang et al. All-Atom Simulations Reveal How Single-Point Mutations Promote Serpin Misfolding. 8 May 2018. Biophys. J. 114 2083-2094. doi:10.1016/j.bpj.2018.03.027

F. Wang, G. Cazzolli, P. Wintrode, and P. Faccioli “Folding Mechanism of Proteins Im7 and Im9: Insight from All-Atom Simulations in Implicit and Explicit Solvent “ 17 Aug 2016. J. Phys. Chem. B 120 (35), 9297-9307. doi: 10.1021/acs.jpcb.6b05819

G Cazzolli, F. Wang, S. a Beccara, A. Gershenson, P. Faccioli, and P. L. Wintrode, Serpin latency transition at atomic resolution. 28 Oct 2014. P.N.A.S 111 (43) 15414-15419; doi: 10.1073/pnas.1407528111

L. Terruzzi, G. Spagnolli, A. Boldrini, J. Raquena, E. Biasini, and P. Faccioli. All-atom simulation of the HET-s prion replication 18 Sept 2020. PLOS Computational Biology 16(9): e1007922. doi:10.1371/journal.pcbi.1007922

S. Orioli, S. a Beccara, and P. Faccioli, “Self-consistent calculation of protein folding pathways”, 10 Aug 2017. J. Chem. Phys. 147, 064108 (2017); dot: 10.1063/1.4997197

S. a Beccara, L. Fant, and P. Faccioli “Variational Scheme to Compute Protein Reaction Pathways Using Atomistic Force Fields with Explicit Solvent, 4 Mar 2015 Phys. Rev. Lett. 114, 098103. doi:10.1103/PhysRevLett.114.098103

S. A Beccara, T. Skrbic, R. Covino and P. Faccioli, S a Beccara, T Škrbić, R Covino, P Faccioli. Dominant Folding of a WW domain. 14 Feb 2012. P.N.A.S. 109 (7), 2330-2335. doi: 10.1073/pnas.1111796109