William M. Jacobs

I use statistical mechanics, coarse-grained models, and computer simulations to tackle problems in biophysics and nanoscience, with a focus on complex self-assembly.

I am currently a Postdoctoral Fellow working with Eugene Shakhnovich at Harvard. My research is aimed at developing non-equilibrium strategies for optimizing nanoscale and colloidal self-assembly. I am also interested in understanding self-assembly in a biological context, from the factors that govern translation efficiency to co-translational protein folding. Before beginning my postdoc, I studied multicomponent phase separation with Daan Frenkel at the University of Cambridge.


Self-assembly of addressable structures

The ability to program specific interactions between biomolecules and nanoparticles is opening up new possibilities for engineering intricately patterned, self-assembled materials. While current examples primarily include DNA ‘tiles’ and ‘bricks’, a similar level of control may soon be achieved using functionalized colloids as well. My research is focused on understanding how these complex, multicomponent structures self-assemble. I have found that the nucleation behavior of an ‘addressable’ structure is a primary determinant of its ability to self-assemble and that this behavior can be significantly different from that of a single-component crystal. Furthermore, a non-equilibrium experimental protocol, in which the assembly conditions change with time, is often necessary to reach the target structure. By identifying universal design principles that govern addressable self-assembly, it may be possible to access more complex structures and functionalities while extending this approach to building blocks beyond DNA.

Co-translational protein folding and transition-path theory

The pathway that a protein takes as it folds to its native state may depend on whether folding commences while the protein is being synthesized. In particular, modulating the speed at which an amino-acid sequence is translated can affect the chance that a protein will misfold during synthesis. To what extent has evolution selected for translation rates that optimize the kinetics of co-translational protein folding? By looking for evolutionarily conserved patterns of unusual codon usage and comparing these results with a model of co-translational protein folding, I have found strong support for the hypothesis that slowly translated codons have been systematically selected to reduce the translation rate following the formation of on-pathway folding intermediates in the E. coli proteome (see below). In addition, I have used a similar model to provide a physical rationale for the widespread existence of kinetically distinct, high-free-energy protein-folding intermediates and a relatively small number of parallel, sequential folding pathways in globular proteins. This model also predicts dynamical properties of folding transition paths that agree well with all-atom simulations and experimental single-molecule measurements.

Mechanistic basis of unusual codon usage

Most protein-coding genes use synonymous codons (i.e., codons that code for the same amino acid) in a highly biased fashion. In general, the more commonly used codons tend to be translated more rapidly and accurately than the rare alternative codons. However, mutating rare codons at specific loci within genes can have strikingly deleterious effects, resulting in reduced mRNA levels and protein abundances, increased chances of protein misfolding, and severe fitness defects. I have investigated two general mechanisms that account for such unusual codon usage. First, at the mRNA-transcript level, codons are selected to avoid the formation of both transient and equilibrium RNA secondary structures that inhibit key interactions between transcripts and ribosomes. Second, at the protein-folding level, codons are chosen to adjust the local translation rates in order to optimize a protein's co-translational folding kinetics (see above).

Phase behavior of multicomponent mixtures

The phase behavior of mixtures with many distinct components is, unsurprisingly, richer than that of a single-component system. In addition to studying the conditions under which phase separation can occur, in a multicomponent system we can also ask which of the interacting components will demix first and how many phases will form. These questions are relevant for our understanding of self-organization inside living cells, where many subsets of proteins and nucleic acids are now known to segregate reversibly into phase-separated droplets. I have used simulations and mean-field theory to study the phase behavior of a simple model of a multicomponent cytosol with random interactions. This model predicts the basic ingredients that are necessary to observe multiphase coexistence, and provides a starting point for thinking about how to tune the intracellular phase behavior. I have also studied the phase behavior of ‘patchy’ particle mixtures, which serve as a minimal model of protein solutions with directional interactions.

Prediction of high-dimensional free-energy landscapes

Free-energy landscapes help to rationalize the behavior and dynamics of a complex system by describing its phase space in terms of a small number of collective variables. However, such an approach is only useful if the most important collective variables can be identified. Furthermore, this challenge is magnified when attempting to describe systems with many distinct components, often leading to free-energy landscapes that are themselves multidimensional. For example, calculating an informative free-energy landscape to describe multicomponent phase separation (see above) requires prior knowledge of the compositions of the coexisting phases. I have developed a number of Monte Carlo approaches to tackle this problem in various contexts, including self-assembling addressable structures (as shown in the figure on the left) and multistate globular proteins.


  1. W.M. Jacobs and E.I. Shakhnovich, “Accurate protein-folding transition-path statistics from a simple free-energy landscape,” J. Phys. Chem. B (2018).
  2. M. Sajfutdinow, W.M. Jacobs, A. Reinhardt, C. Schneider, and David M. Smith, “Direct observation and rational design of nucleation behavior in addressable self-assembly,” Proc. Natl. Acad. Sci. U.S.A. 115, E5877–E5886 (2018).
  3. S. Bhattacharyya*, W.M. Jacobs*, B.V. Adkar, J. Yan, W. Zhang and E.I. Shakhnovich, “Accessibility of the Shine–Dalgarno sequence dictates N-terminal codon bias in E. coli,” Mol. Cell 114, 894–905 (2018). [*Equal contribution]
  4. W.M. Jacobs and E.I. Shakhnovich, “Evidence of evolutionary selection for co-translational folding,” Proc. Natl. Acad. Sci. U.S.A. 114, 11434–11439 (2017).
  5. W.M. Jacobs and D. Frenkel, “Phase transitions in biological systems with many components,” Biophys. J. 112, 683–691 (2017).
    [See also commentary.]
  6. W.M. Jacobs and E.I. Shakhnovich, “Structure-based prediction of protein-folding transition paths,” Biophys. J. 111, 925–936 (2016).
    [See also commentary.]
  7. W.M. Jacobs and D. Frenkel, “Self-assembly of structures with addressable complexity,” J. Am. Chem. Soc. 138, 2457–2467 (2016).
  8. W.M. Jacobs, T.P.J. Knowles, and D. Frenkel, “Oligomers of heat-shock proteins: Structures that don't imply function,” PLoS Comp. Biol. 12, e1004756 (2016).
  9. W.M. Jacobs and D. Frenkel, “Self-assembly protocol design for periodic multicomponent structures,” Soft Matter 11, 8930–8938 (2015).
  10. W.M. Jacobs, A. Reinhardt, and D. Frenkel, “Rational design of self-assembly pathways for complex multicomponent structures,” Proc. Natl. Acad. Sci. U.S.A. 112, 6313–6318 (2015).
  11. W.M. Jacobs, A. Reinhardt, and D. Frenkel, “Theoretical prediction of free-energy landscapes for complex self-assembly,” J. Chem. Phys. 142, 021101 (2015).
  12. W.M. Jacobs, D.W. Oxtoby, and D. Frenkel, “Phase separation in solutions with specific and nonspecific interactions,” J. Chem. Phys. 140, 024108 (2014).
  13. W.M. Jacobs and D. Frenkel, “Predicting phase behavior in multicomponent mixtures,” J. Chem. Phys. 139, 024108 (2013).
  14. W.M. Jacobs, D.A. Nicholson, H. Zemmer, A.N. Volkov, and L.V. Zhigilei, “Acoustic energy dissipation and thermalization in carbon nanotubes: Atomistic modeling and mesoscopic description,” Phys. Rev. B 86, 165414 (2012).



Shakhnovich Lab Web Site