"The application of machine learning tools to simulations of biomolecules and materials self-assembly allows us to uncover the fundamental dynamical motions and assembly pathways driving these phenomena."

Andrew Ferguson

Andrew Ferguson
Assistant Professor of Materials Science and Engineering

Office 204 Materials Science and Engineering Building

Telephone 217-300-2354 Fax 217-333-2736

Mail Address Department of Materials Science and Engineering
1304 W. Green St., Urbana, IL 61801

alf@illinois.edu     Ferguson research group page

  • Profile
  • Research
  • Publications
  • Awards


Professor Andrew Ferguson received an M.Eng. with first class honors in Chemical Engineering from Imperial College London in 2005, and a Ph.D. in Chemical and Biological Engineering from Princeton University in 2010. His doctoral work focused on the application and development of techniques for the nonlinear embedding of biomolecular simulation trajectories to systematically identify the fundamental dynamical motions, folding pathways, and role of solvent interaction. In 2010 he assumed a post-doctoral research position at MIT as a Ragon Fellow, where he applied statistical mechanical tools to develop data-driven models of HIV viral fitness landscapes. Andrew joined the faculty of the University of Illinois at Urbana-Champaign as Assistant Professor of Materials Science and Engineering in 2012.


The Ferguson Lab is an interdisciplinary computational and theoretical research group whose interests lie at the intersection of materials science, biomolecular simulation and bioinformatics. We leverage tools from statistical physics, high performance computing and machine learning to support our three primary research directions.

I. Self-Assembly of Biomaterials. Self-assembly of complex aggregates is the driving force for the synthesis of complex cellular structures such as lipid membranes and viral capsids. Modern advances in the fabrication of micro and nanoscale “building blocks” have rendered self-assembly a potential synthesis route for novel materials such as 3D photonic crystals and drug delivery vessels, but the fundamental mechanisms and rules for robust synthesis of a desired structure remain poorly understood. We have developed a novel adaptation of a nonlinear machine learning technique rendering it extensible to the automated identification of self-assembly pathways from molecular simulations. As part of this research program, we are interested in the following goals:

  • Development of the collective diffusion map
  • Data mining of viral capsid assembly pathways
  • Determination of patchy colloid design rules
  • Simulation and data mining of the assembly paths of antimicrobial peptide nanostructures
  • “Inverse design” of tailored nanomaterials

II. Accelerated Sampling in Biophysical Simulation. A generic problem in molecular simulation is the presence of high free energy barriers that prevent the simulation trajectory from adequately exploring the thermally accessible phase space. Biasing techniques seek to enhance sampling by driving simulations in a small number of order parameters to artificially enhance barrier crossing. A persistent difficulty in such approaches, however, is the availability of “good” biasing variables – typically associated with the important dynamical modes – in which to perform sampling. We have reformulated linear and nonlinear dimensionality reduction techniques to recover unbiased order parameters from biased simulation trajectories, facilitating an iterative algorithm to simultaneously recover good biasing variables and efficiently accelerate phase space exploration. We shall deploy this powerful methodology in pursuit of the following goals:

  • Initial validation of the approach for solvated polyalanine
  • Extension to proteins of therapeutic import (e.g. HIV env)
  • Adaptation of the technique to many-body phenomena
  • Plugin development to integrate the approach with simulation packages

III. In Silico Viral Fitness Landscapes. Our third research focus is centered on the application and development of statistical mechanical tools to study viral fitness landscapes and evolutionary dynamics. Maximum entropy models parameterized from viral sequence databases permit the synthesis of effective Hamiltonians quantifying viral fitness in multidimensional sequence space. Such models for HIV have exhibited remarkable agreement with experimental fitness assays, escape mutations and compensatory mutational patterns, and have allowed us to quantitatively design Pareto optimal vaccine candidates. We are extending this modeling paradigm to other viral systems (influenza, dengue fever, hepatitis C), and are interested the following projects:

  • Development of Potts fitness landscapes
  • Application to deep sequencing data
  • Viral “phase transitions” in fitness space
  • Coupling of landscapes to evolutionary dynamics
  • Machine learning of viral escape pathways

IV. Self-assembly of organic electronics. In collaboration with the experimental labs of J.D. Tovar (Chem, Johns-Hopkins), Howard Katz (MSE, Johns-Hopkins), Bill Wilson (MRL, UIUC), JJ Cheng (MatSE, UIUC) and Charles Schroeder (ChBE, UIUC), we are investigating self-assembling organic electronics for energy transport at biotic/abiotic interface. Synthetic monomers comprising functionalized aromatic linkers flanked by short peptide sequences have demonstrated the capacity to self-assemble into high aspect ratio ribbons. Stacking of the aromatic linkers along the ribbon backbone leads to delocalization of the pi orbitals enabling conduction of electrons along the ribbon, presenting a biocompatible “wire” for bioelectronic applications. Using biomolecular simulations, we are probing the thermodynamics and kinetics of ribbon assembly at various resolutions, exploring the hierarchical structure of these ribbons, and developing rules for the rational engineering of more stable and robust bioorganic conductive ribbons. We are pursuing the following projects in this theme:

  • Simulation of the potential of mean force between peptide monomers in explicit solvent
  • Implicit solvent modeling of atomistically detailed peptide monomers
  • Simulations of assembly and evolution at long length and time scales with coarse grained models
  • Development of rational design rules to control backbone morphology with peptide sequence

V. Lasso peptide structure, dynamics and stability. In collaboration with the experimental lab of A. Jamie Link (CBE, Princeton) we are studying the structure, stability, and maturation of so-called “lasso” peptides. Comprising approximately 20 amino acid residues, these proteins undergo post-translational modification to install an isopeptide bond between the N-terminal amino group and an acidic side chain that “captures” the C-terminal tail into a threaded lasso configuration. Possessing antimicrobial activity, structural rigidity, thermal stability, and denaturant resistance, the lasso motif is of extreme interest in protein engineering. We are conducting molecular modeling to understand the structure, dynamics, maturation, and thermal stability of these peptides. We are pursuing the following projects in this theme:

  • Biased sampling exploration of the folding landscape of the Capistruin linear precursor
  • Atomic structure, dynamics, and thermal stability of Astexin-2 and -3
  • Evaluation of the reversible work of unthreading the Astexin-3 lasso as a function of sequence

VI. Recovery of protein folding landscapes from low-dimensional time series. A beautiful result from dynamical systems theory is that the topography of the multidimensional free energy landscape governing the evolution of a high-dimensional system may be ascertained by appropriate processing of time series in a single generic system variable. By combing these techniques with advanced nonlinear manifold learning algorithms, we have developed a means to infer protein folding landscapes from univariate time series recordings of, for example, the protein radius of gyration. We are developing and testing these new tools in biomolecular simulations of protein folding to establish conditions on the time series and acceptable levels of sampling noise to enable their application to experimental single molecule FRET data. We are pursuing the following projects in this theme:

  • Comparison of landscapes from full-dimensional and univariate simulation trajectories
  • Inference of Jacobians of the diffeomorphism linking the two landscapes
  • Investigation of the effects of sampling noise and uncertainty on the landscape
  • Modeling of system evolution over these landscapes using low-dimensional Langevin equations
  • Partnering with single molecule biophysicists to recover experimental folding landscapes

PublicationsJ. Wang and A.L. Ferguson "Nonlinear inference of single-molecule free energy surfaces from univariate time series" (submitted 2015)

R.A. Mansbach and A.L. Ferguson "Machine learning of single molecule free energy surfaces and the impact of chemistry and environment upon structure and dynamics" (submitted 2014)

G.R. Hart and A.L. Ferguson "Empirical fitness models for hepatitis C virus immunogen design" (submitted 2014)

G.R. Hart and A.L. Ferguson "Error catastrophe and phase transition in the empirical fitness landscape of HIV" (submitted 2014)

J. Hu and A.L. Ferguson "Global graph matching by nonlinear manifold learning" (submitted 2014)

L. Tang, X. Yang, I. Chaudhury, C. Yao, Q. Yin, Q. Zhou, M. Kwon, L.W. Dobrucki, L.B. Borst, S. Lezmi, W.G. Helferich, A.L. Ferguson, T.M. Fan and J. Cheng "Investigating the optimal size of anticancer nanomedicine" PNAS 111 43 15344-15349 (2014)

B.D. Wall, Y. Zhou, S. Mei, H.A.M. Ardona, A.L. Ferguson and J.D. Tovar "Variation of formal hydrogen bonding networks within electronically delocalized pi-conjugated oligopeptide nanostructures" Langmuir 30 38 11375-11385 (2014)

J.K. Mann, J.P. Barton, A.L. Ferguson, S. Omarjee, B.D. Walker, A.K. Chakraborty and T. Ndung’u "The fitness landscape of HIV-1 gag: Advanced modeling approaches and validation of model predictions by in vitro testing" PLOS Computational Biology 10 8 e1003776 (2014)

B.D. Wall, A.E. Zacca, A.M. Sanders, W.L. Wilson, A.L. Ferguson and J.D. Tovar "Supramolecular polymorphism: Tunable electronic interactions within pi-conjugated peptide nanostructures dictated by primary amino acid sequence" Langmuir 30 20 5946-5956 (2014)

A.W. Long and A.L. Ferguson “Nonlinear machine learning of patchy colloid self-assembly mechanisms and pathways” J. Phys. Chem. B 118 15 4228-4244 (2014)

K. Shekhar, C.F. Ruberman, A.L. Ferguson, J.P. Barton, M. Kardar, A.K. Chakraborty "Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes" Phys. Rev. E 88 062705 (2013)

A.L. Ferguson, E. Falkowska, L.M. Walker, M.S. Seaman, D.R. Burton and A.K. Chakraborty “Computational prediction of broadly neutralizing HIV-1 antibody epitopes from neutralization activity data” PLOS ONE 8 12 e80562 (2013)

A.L. Ferguson, J.K. Mann, S. Omarjee, T. Ndung’u, B.D. Walker and A.K. Chakraborty “Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design” Immunity 38 606-617 (2013)

Immunity Preview: N. Goonetilleke and A.J. McMichael "HIV-1 Vaccines: Let’s Get Physical” Immunity 38 410-413 (2013)

Cell Leading Edge Select: L. Gay and N. Neuman "Antiviral Strategies: Building a Better Defense” Cell 153 727-728 (2013)

A.L. Ferguson, N. Giovambattista, P.J. Rossky, A.Z. Panagiotopoulos and P.G. Debenedetti “A computational investigation of the phase behavior and capillary sublimation of water confined between nanoscale hydrophobic plates” J. Chem. Phys. 137 144501 (2012)
- Featured on the cover of JCP 137 14 (2012)
- Most read regular JCP article in October 2012
- Selected as a 2012 Editor’s Choice Article

A.L. Ferguson, A.Z. Panagiotopoulos, I.G. Kevrekidis and P.G. Debenedetti “Nonlinear dimensionality reduction in molecular simulation: The diffusion map approach” Chem. Phys. Lett. Frontiers 509 1 1-11 (2011)
◦ Featured on the cover of Chem. Phys. Lett. 509 1 (2011)

A.L. Ferguson, A.Z. Panagiotopoulos, P.G. Debenedetti and I.G. Kevrekidis “Integrating diffusion maps with umbrella sampling: Application to alanine dipeptide” J. Chem. Phys. 134 135103 (2011)

A.L. Ferguson, S. Zhang, I. Dikiy, A.Z. Panagiotopoulos, P.G. Debenedetti and A.J. Link “An experimental and computational investigation of lasso formation in microcin J25” Biophys. J. 99 9 3056-3065 (2010)

A.L. Ferguson, A.Z. Panagiotopoulos, P.G. Debenedetti and I.G. Kevrekidis “Systematic determination of order parameters for chain dynamics using diffusion maps” PNAS 107 31 13597-13602 (2010)

A.L. Ferguson, P.G. Debenedetti and A.Z. Panagiotopoulos “Solubility and molecular conformations of n-alkane chains in water” J. Phys. Chem. B 113 18 6405-6414 (2009)


  • NSF CAREER Award (2014)
  • ACS PRF Doctoral New Investigator Award (2014)
  • IChemE North America “Young Chemical Engineer of the Year” (2013)
  • Ragon Institute of MGH, MIT and Harvard Research Fellowship (2010-2012)
  • Princeton University School of Engineering Wu Fellowship (2005-2009)