Paul Robustelli

Also, take note of our 15 (!) exp. refined all-atom IDP ensembles deposited in the protein ensemble database:

proteinensemble.org/entries/PED0...

If you think you have good method / force field for generating IDP ensembles, you can benchmark your agreement with exp. against these ensembles.

GitHub - paulrobustelli/Borthakur_MaxEnt_IDPs_2024: Code Accompanying "Towards accurate, force field independent conformational ensembles of intrinsically disordered proteins" by Borkthakur et al. Code Accompanying "Towards accurate, force field independent conformational ensembles of intrinsically disordered proteins" by Borkthakur et al. - GitHub - paulrobustelli/Borthakur_MaxE...

Code is here: github.com/paulrobustel...

Determining accurate conformational ensembles of intrinsically disordered proteins at atomic resolution Nature Communications - This study demonstrates how to combine molecular dynamics computer simulations with experimental biophysical data to determine accurate atomic-resolution ensembles of...

Our work developing a maximum entropy reweighting method to refine all-atom ensembles of IDPs with extensive NMR and SAXS datasets is now out in @natcomms.nature.com:

rdcu.be/eKlK7

Led by @dartmouthchem.bsky.social graduate student Kaushilk Borthakur in collaboration with @bonomimax.bsky.social

Circuit Topology Approach for the Comparative Analysis of Intrinsically Disordered Proteins Intrinsically disordered proteins (IDPs) lack a stable native conformation, making it challenging to characterize their structure and dynamics. Key topological motifs with fundamental biological relevance are often hidden in the conformational noise, eluding detection. Here, we develop a circuit topology toolbox to extract conformational patterns, critical contacts, and timescales from simulated dynamics of intrinsically disordered proteins. We follow the dynamics of IDPs by providing a smart low-dimensionality representation of their three-dimensional (3D) configuration in the topology space. Such an approach allows us to quantify topological similarity in dynamic systems, therefore providing a pipeline for structural comparison of IDPs.

pubs.acs.org/doi/10.1021/...

Monomer binding modes of small molecules that modulate the kinetics of hIAPP amyloid formation Human islet amyloid polypeptide (hIAPP) forms amyloid fibrils that accumulate in pancreatic β-cells of Type II Diabetes (T2D) patients. Recently discovered small molecules that modulate the kinetics o...

Congrats to Michelle and Korey on their beautiful work!

Paper:
www.biorxiv.org/content/10.1...

Ensembles, Simulation Inputs + Code: github.com/paulrobustel...

...we're super excited to use MD simulations to study how these ligands affect the process of oligomerization of hIAPP and start trying to design more potent aggregation inhibitors. The GPUs are already churning!

Kinetic Steering of Amyloid Formation and Polymorphism by Canagliflozin, a Type-2 Diabetes Drug Amyloid formation is involved in widespread health conditions such as Alzheimer’s disease, Parkinson’s disease, and type-2 diabetes. Amyloid fibrils have a similar cross-β architecture, but fibrils fo...

These simulations gives us a mechanistic framework to begin to understand the structure-activity-relationship of ligands that inhibit or accelerate the aggregation of hIAPP. New @radford-lab.bsky.social work (pubs.acs.org/doi/full/10....) gives us new ligands to look at and...

While they're only rarely populated at the same time - these multisite binding mode give us a better understanding of how a network of hydrogen bond donors and acceptors confer pronounced affinity to residues 7_CATQRLANFLV_17.

To get more insight into the diversity of binding modes and search for more structured modes - we looked at binding poses where at least 15 residues of hIAPP were in contact with each ligand. This represented 12.9% of bound frames for YX-I-1 but only 3.9% of YX-A-1.

Interestingly, the exposed regions of YX-A-1 are quite hydrophobic (cylcohexane and benzene) - and we think this could be part of how it accelerates aggregation into higher order oligomers and protofilaments

We see that each ligand has moieties that are consistently buried and others that are consistently exposed across binding modes. We think that buried moieties might confer monomer affinity - while exposed moieties could affect rates of oligomerization into higher order species.

Comparing populations of intermolecular interactions we found something unique about this pair: the largest difference is elevated populations of hydrogen bonds with YX-I-1, not increased populations of aromatic stacking interactions -which we usually see in tighter IDP ligands

We have a detailed comparison of contact profiles and helicity changes with NMR CSPs from in the SI. We don't see perfect agreement, but observe that the average magnitude of CSPs correlate pretty well with average contact populations and changes in helicity upon and binding.

In ligand binding simulations, we see heterogenous ensembles of binding modes. We see YX-I-1 is a tighter binder than YX-A-1, and produces larger conformational changes upon binding, consistent with larger NMR chemical shift perturbations (CSPs) and other assays from @radford-lab.bsky.social

Michelle had an awesome idea to use matrices of circuit topology assignments from work by @alirezamashaghi.bsky.social (pubs.acs.org/doi/full/10....) for dimensionality reduction to project all our apo and holo ensembles onto latent space reflecting the topological similarity of conformations.

Our WT hIAPP ensemble is in good agreement with NMR chemical shifts, showing us we have a good force field (a99SB-disp) for this system. S20G introduces a central hinge that increases populations of intramolecular contact and beta-sheets bewteen residues in hIAPP fibril cores

We used 400us of enhanced sampling (REST2) all-atom MD to characterize the conformational ensembles of wild-type (WT) hIAPP, and the S20G variant (which accelerates aggregation and is associated with early-onset T2D) and characterize their interactions with these ligands.

They found molecules that inhibit (YX-I-1) and accelerate (YX-A-1) hIAPP aggregation.

YX-I-1, which was found to bind monomer by NMR, SPR, and mass-spec could be a lead for developing T2D therapies. Kinetics assays show YX-A-1 mainly interacts with higher order oligomers.

Aggregation and amyloid formation of the disordered protein human islet amyloid polypeptide (hIAPP) is associated with type-2-diabetes (T2D).

Recently, @radford-lab.bsky.social ran a screen of 1500 small molecules to find hIAPP binders.

nature.com/articles/s41...

Excited to share a new preprint:

"Monomer binding modes of small molecules that modulate the kinetics of hIAPP amyloid formation"

by graduate student Michelle Garcia together with post-doc Korey Reid.

Paper:
www.biorxiv.org/content/10.1...

Code + Ensembles: github.com/paulrobustel...

Performing all-atom molecular dynamics simulations of intrinsically disordered proteins with replica exchange solute tempering All-atom molecular dynamics (MD) computer simulations are a valuable tool for characterizing the conformational ensembles of intrinsically disordered proteins (IDPs). IDP conformational ensembles are ...

Brought to you by post-doctoral scholars Korey Reid and Jaya Krishna Koneru.

Paper: arxiv.org/abs/2505.01860

Tutorial + Code: github.com/paulrobustel...

We hope you find it helpful!

Performing all-atom molecular dynamics simulations of intrinsically disordered proteins with replica exchange solute tempering All-atom molecular dynamics (MD) computer simulations are a valuable tool for characterizing the conformational ensembles of intrinsically disordered proteins (IDPs). IDP conformational ensembles are ...

Want to run and analyze MD simulations of a disordered protein?

Check out our step-by-step guide (+ tutorial with code) for preparing, running and analyzing replica exchange solute tempering (REST2) simulations of IDPs with @gromacs.bsky.social and @plumed.org on arxiv!

arxiv.org/abs/2505.01860

Thanks Grant!

Characterizing structural and kinetic ensembles of intrinsically disordered proteins using writhe The biological functions of intrinsically disordered proteins (IDPs) are governed by the conformational states they adopt in solution and the kinetics of transitions between these states. We apply wri...

Thanks for coming on this writhe journey and congrats to Tommy on this beautiful work! Links again:

Paper: www.biorxiv.org/content/10.1...

Code:
github.com/paulrobustel...

We think this means that Writhe could be a useful feature for training generative models of IDP conformations and assessing their topological complexity, to ultimately produce models that are in closer agreement with all-atom MD.

As a proof-of-principle, we showed that if you train DDPMs with Writhe-PaiNN on a single long timescale MD trajectory, you can accurately described the populations of chiral chain crossings seen in that simulations, whereas a DDPM trained with PaiNN can't distinguish their populations.

...generative models you can switch from D- to L-amino acids. It also means that if you model IDPs with chiral chain crossings in popular 1-bead per residue coarse grain (CG) models, you won't capture differences in populations of chain crossings with different writhe.

...and incorporated this into the E(3)-
equivariant, polarizable atom interaction network (PaiNN), to develop Writhe-PaiNN, augmenting its symmetry from E(3) to SE(3).

Why do this? A DDPM trained with an E(3)-equivariant model can invert the chirality of generated structures. In all-atom...

..that could be used to sample IDP conformations in a score-based denoising diffusion probabilistic model (DDPM). He packed his bags and headed off to Sweden to work with @smnlssn.bsky.social on this. To construct message passing NN layers between atoms, Tommy derived a writhe-graph Laplacian..

...inter-residue distances of reflected conformation are the same (invariant to parity) while the writhe of the mirror image is distinguished exactly by a change in sign (odd parity). He thought it would be cool to show you could leverage this symmetry to train an SE(3)-equivariant NN...