Research
We develop multiscale computational tools for biomolecular systems aiming to
1. Understand & control the condensation mechanisms of polypeptides and their response to the biological environments;
2. Develop & design modulating bioinspired agents with targeting abilities, e.g. peptide-based therapeutics tailored for neurodegenerative and cancerous diseases;
3. Develop smart (bio)materials with tunable and responsive properties.
In terms of tools, we use atomistic simulations to gain insight into the biochemical mechanisms of protein folding, which we then modulate by rationally designing and evolutionary optimizing bio-inspired agents (e.g. peptides, proteins) to control their response. We also develop coarse-grained models for proteins and biomaterials uniquely capturing their intrinsic flexibility to understand the interaction mechanisms with the biological environment on the nanoscopic scale, which we then exploit to design novel materials. Importantly, we parametrize the coarse-grained models relying on our atomistic simulations and experimental input. Hence, we learn across scales, linking and incorporating the relevant properties at different spatio-temporal resolutions.
Understanding protein folding, condensation and aggregation
A plethora of diseases are associated with the misfolding and aggregation of polypeptides. The mechanisms associated with these processes are poorly understood and we lack the physical and structural understanding that would enable the development of novel effective preventive therapies.
To gain insight into the structural details of protein folding and aggregation, we use (enhanced sampling) molecular dynamics simulations at full atomistic resolution. To access spatiotemporal resolutions exceeding conventional techniques, we develop coarse-grained models for polypeptides. Our models uniquely capture the conformational changes of the proteins by endowing the molecules with responsiveness to the environment, i.e. they can shape-shift and adapt their interaction preferences according to the surrounding.
References:
I.M. Ilie*, W.K. den Otter and W.J. Briels, A coarse grained protein model with internal degrees of freedom. Application to α-synuclein aggregation, J. Chem. Phys., 144, 085103 (2016)
I.M. Ilie* and A. Caflisch, Simulation Studies of Amyloidogenic Peptides and their Aggregates. Chem. Rev., 119, 6956-6993 (2019)
Computational peptide design
Protein misfolding and aggregation are linked to several diseases, such as cancer and neurodegenerative disorders (e.g. Alzheimer’s and Parkinson’s disease).
We computationally design and experimentally validate peptides that inhibit the toxic propagation of the responsible species.
Our focus is on
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Developing cyclic peptides to inhibit prion induced toxicity
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Developing peptides to inhibit the aggregation of alpha-synuclein (the protein associated with Parkinson’s disease)
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Develop peptides to control cellular fate, i.e. inhibit cell death and onset of cancer.
We computationally design and validate peptides that interfere in the interaction between the responsible proteins. Importantly, based on the effects at atomistic resolution, we develop coarse-grained models aiming to understand the effects on larger time and length scales.
In all our projects collaborate with experimental colleagues to validate the in vitro and in vivo efficiency of the designed peptides.
References:
D. de Raffele and I.M. Ilie*, Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning, Chem. Commun., 60, 632 - 645 (2024)
C. Durukan, F. Arbore, R. Klintrot, C. Bigiotti, I.M. Ilie, J. Vreede, T.N. Grossmann, S. Hennig Binding dynamics of a stapled peptide targeting the transcription factor NF‐Y, Chem. Bio. Chem. (2024)
Smart biomaterials with tunable and responsive properties for drug delivery
Nature has been developing smart materials for billions of years. Drawing inspiration from the ultimate creator, we develop de novo biomaterials with emergent shape, sensing abilities and adaptable to the environment. These smart materials are shape-shifters, have responsive properties and are tunable.
Specifically, we develop coarse grained models able to capture the intrinsic flexibility of nanoparticles, arising from soft core platforms with anisotropic interactiona. Our “MetaParticles” find applications in drug delivery and development of novel metamaterials.
References:
M. Paesani and I.M. Ilie*, Metaparticles: Computationally engineered nano-materials with unable and responsive properties, arXiv (2024)
