News

"Determining accurate conformational ensembles of intrinsically disordered proteins at atomic resolution" published In Nature Communications

Our manuscript describing a method to calculate atomic resolution ensembles of intrinsically disordered proteins from NMR and SAXS data by graduate students Kaushik Borthakur and Tommy Sisk, in collaboration with Francesco Panei and Max Bonomi from the Institut Pasteur, is now published at Nature Communications

https://www.nature.com/articles/s41467-025-64098-3

You can read a summary of the work here:

https://x.com/PaulRobustelli/status/1843277802243834147

All code, experimental data used for reweighting and reweighted ensembles can be downloaded from our github repository:

https://github.com/paulrobustelli/Borthakur_MaxEnt_IDPs_2024

15 atomic resolution ensembles of IDPs calculated in this work have been deposited in the protein ensemble database (https://proteinensemble.org/)

See an example here:
https://proteinensemble.org/entries/PED00542

Congrats Kaushik and Tommy!

"Characterizing structural and kinetic ensembles of intrinsically disordered proteins using writhe" accepted in Journal of Chemical Theory and Computation

Our manuscript "Characterizing structural and kinetic ensembles of intrinsically disordered proteins using writhe" by graduate student Tommy Sisk, in collaboration with Simon Olsson the from University of Chalmers, has been accepted for publication in JCTC .

It is currently in press, but you can read the manuscript on biorxiv:

https://www.biorxiv.org/content/10.1101/2025.04.26.650781v1

You can read a summary of the work here:

https://bsky.app/profile/paulrobustelli.bsky.social/post/3lo2dzuxu322y

All code can be downloaded from our github repository:

https://github.com/paulrobustelli/Sisk_IDP_Writhe_2025

and we have released a python package to calculate writhe from protein ensembles

https://pypi.org/project/writhe-tools/

Congrats Tommy!

"Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields" published in Living Journal of Computational Molecular Science

Paul contributed several sections to the review article "Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields" now published in Living Journal of Computational Science (LiveCoMS)

You can read the review here:

https://livecomsjournal.org/index.php/livecoms/article/view/v6i1e3871

You can find the Robustelli et. al. NMR benchmark dataset described in the review, and code to calculate experimental data from ensemble, on the github page for our Nature Communications article "Determining accurate conformational ensembles of intrinsically disordered proteins at atomic resolution"

https://github.com/paulrobustelli/Borthakur_MaxEnt_IDPs_2024

"Monomer binding modes of small molecules that modulate the aggregation kinetics of hIAPP" on bioRxiv

Our manuscript "Monomer binding modes of small molecules that modulate the aggregation kinetics of hIAPP" by graduate student Michelle Garcia and postdoctoral scholar Korey Reid is available on bioRxiv:

https://www.biorxiv.org/content/10.1101/2025.09.22.677832v1

You can read a summary of the work here:

https://bsky.app/profile/paulrobustelli.bsky.social/post/3lzt4icnrkk2k

All code and ensembles can be downloaded from our github repository:

https://github.com/paulrobustelli/Garcia_hIAPP_monomer_binders_2025/

Congrats Michelle and Korey!

"Ensemble Docking for Intrinsically Disordered Proteins" published in Journal of Chemical Information and Modeling

Our manuscript "Ensemble Docking for Intrinsically Disordered Proteins" - by Dartmouth undergraduate Anjali Dhar 24' and graduate student Tommy Sisk is now out in JCIM

https://pubs.acs.org/doi/10.1021/acs.jcim.5c00370

You can read a summary of the work here:

https://bsky.app/profile/paulrobustelli.bsky.social/post/3lh2rvhvd4s26

All code and docked ensembles can be downloaded from our github repository:

https://github.com/paulrobustelli/Dhar_IDP_ensemble_docking_25

Congrats to Anjali (who is now working on a PhD in the computational biophysics Ding Lab at Tufts) and Tommy!

"Covalent Adducts Formed by the Androgen Receptor Transactivation Domain and Small Molecule Drugs Remain Disordered" published in Journal of Chemical Information and Modeling

Our manuscript "Covalent Adducts Formed by the Androgen Receptor Transactivation Domain and Small Molecule Drugs Remain Disordered" - by graduate student Jiaqi Zhu is now out in JCIM

https://pubs.acs.org/doi/full/10.1021/acs.jcim.5c00833

You can read a summary of the work here:

https://bsky.app/profile/paulrobustelli.bsky.social/post/3lbpjqzoqik25

All code and ensembles can be downloaded from our github repository:

https://github.com/paulrobustelli/Zhu_Robustelli_AR_Covalent_Adducts_24

We have deposited an NMR-refined ensemble of the disordered activation domain of androgen receptor covalently bound to the small molecule drug EPI-001 in the protein ensemble database:

https://proteinensemble.org/entries/PED00530

"Performing all-atom molecular dynamics simulations of intrinsically disordered proteins with replica exchange solute tempering" on arXiv

Postdoctoral Scholars Jaya Krishna Koneru and Korey Reid and Paul have written a practical guide to performing and analyzing replica exchange with solute tempering (REST) enhanced sampling simulations for an upcoming edition of "Methods in Molecular Biology: Biomolecular Simulations".

You can find it an arXiv:

https://arxiv.org/abs/2505.01860

We've also prepared an accompanying GROMACS tutorial to accompany this book chapter for those looking to get started with these sampling methods for IDPs:

https://github.com/paulrobustelli/IDP_REST_tutorial

"Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model" published in Proceedings of National Academy of Sciences

Tommy Sisk's first manuscript in the group is out now in PNAS

https://www.pnas.org/doi/abs/10.1073/pnas.2313360121

Tommy and Paul have utilized a deep learning approach to construct Markov state models of disordered protein folding-upon-binding from molecular dynamics simulations.

You can read a summary of the work here:

https://twitter.com/PaulRobustelli/status/1683984994623008768

All code for this work can be found here:

https://github.com/paulrobustelli/Sisk_NTAIL_DeepMSM_2023

"Rational Optimization of a Transcription Factor Activation Domain Inhibitor" published In Nature Structural and Molecular Biology

Paul and Jiaqi have contributed simulation results to an exciting manuscript spearheaded by the laboratory of our collaborator Xavier Salvatella at the IRB Barcelona:

https://www.nature.com/articles/s41594-023-01159-5

This manuscript provides compelling evidence that the transcriptional activity of the androgen receptor is mediated through biomolecular condensate formation, and that prospective castration-resistant prostate cancer therapeutics bind to the same regions of the disordered transactivation that mediate condensate formation.

Jiaqi has performed simulations of a promising new androgen receptor inhibitor binding to the disordered activation domain of androgen receptor in this work

You can read a summary of the work here:

https://twitter.com/PaulRobustelli/status/1560735215647178754

All code and bound ensembles can be downloaded from our github repository:

https://github.com/paulrobustelli/Basu_Rational_Optimization_AR_inhibitors_NSMB_2023

"Clustering Heterogeneous Conformational Ensembles of Intrinsically Disordered Proteins with t-Distributed Stochastic Neighbor Embedding" published in JCTC!

Jaya Krishna and Paul's collarboration with Rajeswari Appadurai and Anand Srivastava from IIS Bangalore and Max Bonomi from the Institut Pasteur is no published in The Journal of Chemical Theory and Computation.

https://pubs.acs.org/doi/full/10.1021/acs.jctc.3c00224

We demonstrated that t-distributed stochastic neighbor embedding is a promising approach for clustering the conformations of IDPs, and we use t-SNE based clustering to analyze how the binding mechanisms of small molecules to disordered proteins differs among conformational substates.

You can read more about the work here:

https://twitter.com/PaulRobustelli/status/1603431841838530561

Clustering Code for the paper is here:

https://github.com/codesrivastavalab/tSNE-IDPClustering

Code for Jaya Krishna's analyses of ligand binding modes observed in a-synuclein conformational states is here:

https://github.com/vilaxara/TSNE_analysis/

And an illustrative walk-through applying the approach to analyze a simulation of the Androgen Receptor can be found here:

https://github.com/paulrobustelli/tSNE/blob/main/Apo_Tau5R2_R3_Cluster.ipynb

"Structure-Based Experimental Datasets for Benchmarking of Protein Simulation Force Fields" out on arxiv!

Paul has contributed to a community effort to provide practical guidance for benchmarking protein force fields against data from NMR spectroscopy and room temperature crystallography.

https://arxiv.org/abs/2303.11056

Paul has written sections about using NMR chemical shifts and J-couplings to benchmark simulations.

You can read more about this work here:

https://twitter.com/PaulRobustelli/status/1638531491578748930

"Demultiplexing the heterogeneous conformational ensembles of intrinsically disordered proteins into structurally similar clusters" on biorxiv!

Jaya Krishna and Paul are excited to have contributed to a collaboration with Rajeswari Appadurai and Anand Srivastava from IIS Bangalore and Max Bonomi from the Institut Pasteur.

"Demultiplexing the heterogeneous conformational ensembles of intrinsically disordered proteins into structurally similar clusters"

https://www.biorxiv.org/content/10.1101/2022.11.11.516231v2.abstract

We demonstrate that t-distributed stochastic neighbor embedding is a promising approach for clustering the conformations of IDPs, and we use t-SNE based clustering to analyze how the binding mechanisms of small molecules to disordered proteins differs among conformational substates.

You can read more about the work here:

https://twitter.com/PaulRobustelli/status/1603431841838530561

Clustering Code for the paper is here:

https://github.com/codesrivastavalab/tSNE-IDPClustering

"Rational Optimization of a Transcription Factor Activation Domain Inhibitor" on biorxiv!

Jiaqi and Paul have contributed results to an exciting manuscript spearheaded by the laboratory of Xavier Salvatella at the IRB Barcelona, now out on bioRxiv:

"Rational Optimization of a Transcription Factor Activation Domain Inhibitor"

https://www.biorxiv.org/content/10.1101/2022.08.18.504385v2.abstract

You can read a summary of the work here

Robustelli Laboratory Awarded NIH R35 Award

Our laboratory was awarded a 5-year NIH R35 MIRA for our project "Characterizing the binding mechanisms of castration-resistant prostate cancer therapeutics to the intrinsically disordered N-terminal domain of the androgen receptor". Jiaqi and Paul have been hard at work on this system since the lab opened its doors, and we are thrilled to receive this support!

"Small Molecules Targeting the Disordered Transactivation Domain of the Androgen Receptor" published in Nature Communications

Jiaqi's paper

"Small Molecules Targeting the Disordered Transactivation Domain of the Androgen Receptor Induce the Formation of Collapsed Helical States"

is now published in Nature Communications!

https://www.nature.com/articles/s41467-022-34077-z

All analysis code and trajectories from this manuscript can be found here:

https://github.com/paulrobustelli/AR_ligand_binding

You can read a summary of the work here

Congrats Jiaqi!

CHEM 101.6: "Computational Methods in Chemistry & Biophysics" Course Materials available

Paul has posted all of his lecture notes, jupyter notebook assignments & solutions, and lecture videos for his graduate course in computational chemistry CHEM 101.6.

https://github.com/paulrobustelli/CHEM101.6

This course covers topics in statistical thermodynamics, molecular simulations, proteins dynamics, protein folding, disordered proteins, and enhanced sampling methods.

The course was designed to give students a practical introduction to a modern computational research infrastructure. Students set up a python package library with anaconda, and all assignments are completed in python jupyter notebooks and were submitted through github uploads.

Instructors at other universities are welcome to utilize these materials, and I request that if you incorporate material from these slides or adapt coding exercises from the materials I have shared, that you freely share your materials in turn.

Robustelli Laboratory Awarded Funding From the Dartmouth Innovations Accelerator for Cancer (DIAC)

This year, Paul and Jiaqi participated in the Dartmouth Innovations Accelerator for Cancer (DIAC), a program sponsored by the Dartmouth Magnuson Center for Entrepreneurship.

They developed a venture capital pitch titled "A Drug Discovery Platform for Intrinsically Disordered Proteins" and competed in two pitch contests judged by an external review panel of biotech entrepreneurs and investors. This pitch was awarded the $25,000 research funding Stu Trembly Award this winter, and in the spring they split the "Venture Development Award", the top prize in the final contest, and were awarded an additional $150,000 in research funding

The Robustelli Laboratory will be utilizing this funding to develop new methods to design small molecule inhibitors for IDPs.

Paul and Jiaqi had a great experience in the accelerator, and learned a great deal about biotech entrepreneurship!

"Molecular Basis of Small-Molecule Binding to α-synulcein" published in JACS

Our paper "Molecular Basis of Small-Molecule Binding to α-synulcein" is now out in JACS:

https://pubs.acs.org/doi/abs/10.1021/jacs.1c07591

Code for the analyses can be found at:

https://github.com/paulrobustelli/asyn_calcs

And all trajectories (>6 milliseconds of explicit solvent MD of IDPs with over 50 small-molecule ligands) are available upon request from:

Trajectories@DEShawResearch.com

We hope others can use this code and these trajectories to help the field obtain more mechanistic insight into IDP ligand binding modes!

Conformational Ensemble of a Disordered Region of the Transactivation Domain of the Androgen Receptor now released on the Protein Ensemble Database

We're very happy to have our first entry in the protein ensemble database released:

https://proteinensemble.org/PED00206

This is an NMR-reweighted MD ensemble of the Tau-5_R2R3 region of the Androgen Receptor from our paper

"Small Molecules Targeting the Disordered Transactivation Domain of the Androgen Receptor Induce the Formation of Collapsed Helical States"

https://www.biorxiv.org/content/10.1101/2021.12.23.474012v1.abstract

The full trajectory, and the code to perform the reweighting, are available:

https://github.com/paulrobustelli/AR_ligand_binding

The protein ensemble database is great resource for the molecular simulation/ensemble calculation community and we're thrilled to contribute to it!