Paul Robustelli

Academic Appointments

Assistant Professor of Chemistry
Neukom Cluster of Computational Science

The Robustelli group develops and applies computational methods to obtain atomic-level descriptions of the functional motions of biomolecules, with a particular interest in intrinsically disordered proteins. 

[show more]

Intrinsically disordered proteins do not fold into well-defined tertiary structures under physiological conditions, but rather populate a heterogeneous conformational ensemble of rapidly interconverting structures.  These proteins are abundant in eukaryotic proteomes, play important functional roles in a large number cellular pathways and biomolecular assemblies, and have been implicated in a large number of human diseases.  Due to their highly dynamic nature and conformational variability, disordered proteins have proven difficult to experimentally characterize at an atomic level and are not suitable targets for conventional structure-based drug design methods, in which small molecules are designed to optimize interactions with well-defined binding pockets. 

Our group utilizes molecular simulations to obtain atomistic descriptions of the molecular recognition mechanisms of intrinsically disordered proteins.  We aim to use insights form these simulations to understand, predict and ultimately design dynamic and heterogeneous binding interactions of disordered proteins.  The computational methods of our group are tightly integrated with biophysical experiments, particularly from NMR spectroscopy.  We utilize experimental data to develop new underlying physical models for our simulations and we directly incorporate information from experimental data into our simulations when existing physical models are insufficiently accurate. 

A current focus of our laboratory is understanding the thermodynamic driving forces of small molecule ligands binding to disordered protein sequences.  We hope that our research will help contribute to a general understanding of the principles of molecular recognition in intrinsically disordered proteins, stimulate the development of new paradigms that account for conformational disorder in small molecule binding and ultimately provide new avenues to therapeutic interventions in diseases associated with disordered protein dysfunction through the rational design of biologic and small molecule inhibitors.

[show less]
B.A. Pomona College 2002-2006
Ph.D. University of Cambridge (with Michele Vendruscolo) 2006-2011
NSF Postdoctoral Fellow, Columbia University (with Arthur G. Palmer III) 2011-2013
Scientist, D.E. Shaw Research 2013-2019

Selected Publications

Robustell P, Ibanez-de-Opakua A, Campbell-Bezat C, Giordanetto F, Becker S, Zweckstette Mr, Pan AC, Shaw DE. "Molecular basis of small-molecule binding to α-synuclein", bioRxiv (preprint), (2021)

Robustelli P, Piana S, Shaw DE. "The mechanism of coupled folding-upon-binding of an intrinsically disordered protein" Journal of the American Chemical Society (2020)

Piana S*, Robustelli P*, Tan D,  Chen S, Shaw DE. "Development of a force field for the simulation of single-chain proteins and protein-protein complexes" Journal of Chemical Theory and Computation (2020)

Robustelli P, Piana S, Shaw DE "Developing a molecular dynamics force field for both folded and disordered protein states." Proceedings of the National Academy of Sciences (2018) 115(21):E4758-E4766

Piana S, Donchev AG, Robustelli P, Shaw DE. Water dispersion interactions strongly influence simulated structural properties of disordered protein states. The Journal of Physical Chemistry B. (2015) 119(16):5113-23

Robustelli P, Stafford KA, Palmer III AG. Interpreting protein structural dynamics from NMR chemical shifts. Journal of the American Chemical Society. (2012), 134(14):6365-74 

Neudecker P, Robustelli P, Cavalli A, Walsh P, Lundström P, Zarrine-Afsar A, Sharpe S, Vendruscolo M, Kay LE. "Structure of an intermediate state in protein folding and aggregation. Science. (2012) 336(6079):362-366

C Camilloni, P Robustelli, AD Simone, A Cavalli, M Vendruscolo "Characterization of the conformational equilibrium between the two major substates of RNase A using NMR chemical shifts"  Journal of the American Chemical Society (2012) 134 (9), 3968-3971

Robustelli P, Kohlhoff K, Cavalli A, Vendruscolo M. Using NMR chemical shifts as structural restraints in molecular dynamics simulations of proteins. Structure. (2010) 18(8):923-33 

Joining the Laboratory

The Robustelli laboratory is always eager to hear from prospective graduate students and postdoctoral scholars to discuss current opportunities to join us at Dartmouth. 

We are broadly interested in using atomistic molecular simulations to model the conformational dynamics and molecular recognition mechanisms of intrinsically disordered proteins.  We aim to use insights from simulations to understand, predict and ultimately design new dynamic and heterogeneous IDP binding interactions.  Current focuses of the laboratory include understanding the molecular mechanisms and diving forces of small molecules binding to IDPs, dissecting the intermolecular interactions that drive IDP phase separation and aggregation, and rationally designing small molecule and biologic IDP binders that modulate these processes.

Graduate students can join the group through the Chemistry ( and Molecular & Cell Biology ( PhD programs.  Feel free to reach out to discuss potential projects and rotation opportunities. 

At the postdoctoral level, we seek candidates with experience developing and applying advanced molecular simulation techniques which may include, but are not limited to, one or more of the following areas: enhanced sampling algorithms, maximum-entropy methods, adaptive sampling strategies, markov-state models, dimensionality reduction, clustering methods, protein or small molecule force field parameterization, alchemical free-energy calculations, de novo protein/biologics design, de novo small molecule drug design.  Ideal candidates have a fascination with intrinsically disordered protein biophysics, experience integrating molecular simulations and biophysical experiments, feel comfortable in high-dimensional spaces, and are interested in rational drug design.  Protein NMR experience (experimental or computational) is a plus. 

The computational methods of our group are tightly integrated with biophysical experiments, particularly from NMR spectroscopy, and opportunities exist to collaborate with experimental groups and/or conduct biophysical experiments in-house.  Our laboratory has access to excellent NMR facilities with high-throughput screening capabilities.

Based on the interdisciplinary nature of our research, there is substantial freedom to develop projects of mutual interest spanning a broad range of research areas related to IDP simulations, IDP molecular recognition, IDP NMR, and IDP drug design.  Potential projects might include any combination of the following:

Enhanced Sampling Method Development

MD Analysis Methods (MSMs, Clustering, Dimensionality Reduction, Machine Learning)

Small Molecule/Biologic Inhibitor Design

Ensemble Based De Novo Design Algorithms

NMR Spectroscopy

Protein aggregation inhibition

Drugging biological condensates

Interested candidates should send an inquiry to, including a CV, a brief description of your research experience, interests and aspirations, and a note about the potential project/area that you find most interesting.  Examples of code you have written or a link to a GitHub page are encouraged.   

About Dartmouth: Dartmouth College is private Ivy League Research University in Hanover, New Hampshire. The Dartmouth Department of Chemistry is a highly collaborative department which, in addition to traditional focus areas in biophysical chemistry, organic synthesis, materials chemistry, and inorganic chemistry, is currently establishing a growing community of computational chemistry scholars.  Dartmouth Chemistry is part of a broader biomedical research community at Dartmouth made up of labs across the Dartmouth Geisel School of Medicine, Institute for Biomolecular Targeting,  Molecular & Cellular Biology program and the Norris Cotton Cancer Center.

Hanover, New Hampshire is located along the Connecticut River on the border of New Hampshire and Vermont in the idyllic Upper Valley Region.  The Upper Valley is known for its scenic mountains, lakes, and rivers and its network of charming small towns.  The Upper Valley has a vibrant outdoor culture with extensive hiking, biking, and cross-country skiing trails, canoeing and kayaking, and proximity to a number of downhill skiing mountains.  Hanover is located 2-hours from Boston, MA, 1.5 hours from Burlington, VT, 2.5 hours from Portland Maine, and 3 hours from Montreal, Quebec.