Publications

Peptomers are oligomeric molecules composed of both α-amino acids and N-substituted glycine monomers, thus creating a hybrid of peptide and peptoid units. Peptomers have been used in several applications such as antimicrobials, protease inhibitors, and antibody mimics. Despite the considerable promise of peptomers as chemically diverse molecular scaffolds, we know little about their conformational tendencies. This lack of knowledge limits the ability to implement computational approaches for peptomer design. Here we computationally evaluate the local structural propensities of the peptide–peptoid linkage. We find some general similarities between the peptide residue conformational preferences and the Ramachandran distribution of residues that precede proline in folded protein structures. However, there are notable differences. For example, several β-turn motifs are disallowed when the i+2 residue is also a peptoid monomer. Significantly, the lowest energy geometry, when dispersion forces are accounted for, corresponds to a “cis-Pro touch-turn” conformation, an unusual turn motif that has been observed at protein catalytic centers and binding sites. The peptomer touch-turn thus represents a useful design element for the construction of folded oligomers capable of molecular recognition and as modules in the assembly of structurally complex peptoid–protein hybrid macromolecules.

Are legislators responsive to their constituents in their public communication? To what extent are they able to shape the agenda that the mass public cares about, as expressed by the issues they discuss? We address this twofold question with an analysis of all tweets sent by Members of the U.S. Congress and a random sample of their followers from January  to March . Using a Latent Dirichlet Allocation model, we extract topics that represent the diversity of issues that legislators and ordinary citizens discuss on this social networking site. en, we exploit variation in the distribution of topics over time to test whether Members of Congress lead or follow their constituents in their selection of issues to discuss, employing a Granger-causality framework. We find that legislators are responsive in their public statements to their constituents, but also that they have limited influence on their followers’ public agenda. To further understand the mechanisms that explain political responsiveness, we also examine whether Members of Congress are more responsive to specific constituents groups, showing that they are more influenced by co-partisans, politically interested citizens, and social media users located within their constituency.

With the notable exception of B-cell malignancies, the efficacy of chimeric antigen receptor (CAR) T cells has been limited, and CAR T cells have not been shown to expand and persist in patients with nonlymphoid tumors. Here we demonstrate that redirection of primary human T cells with a CAR containing the inducible costimulator (ICOS) intracellular domain generates tumor-specific IL-17-producing effector cells that show enhanced persistence. Compared with CARs containing the CD3ζ chain alone, or in tandem with the CD28 or the 4-1BB intracellular domains, ICOS signaling increased IL-17A, IL-17F, and IL-22 following antigen recognition. In addition, T cells redirected with an ICOS-based CAR maintained a core molecular signature characteristic of TH17 cells and expressed higher levels of RORC, CD161, IL1R-1, and NCS1. Of note, ICOS signaling also induced the expression of IFN-γ and T-bet, consistent with a TH17/TH1 bipolarization. When transferred into mice with established tumors, TH17 cells that were redirected with ICOS-based CARs mediated efficient antitumor responses and showed enhanced persistence compared with CD28- or 4-1BB-based CAR T cells. Thus, redirection of TH17 cells with a CAR encoding the ICOS intracellular domain is a promising approach to augment the function and persistence of CAR T cells in hematologic malignancies.

Cells respond to environmental stimuli with expression changes at both the mRNA and protein level, and a plethora of known and unknown regulators affect synthesis and degradation rates of the resulting proteins. To investigate the major principles of gene expression regulation in dynamic systems, we estimated protein synthesis and degradation rates from parallel time series data of mRNA and protein expression and tested the degree to which expression changes can be modeled by a simple linear differential equation. Examining three published datasets for yeast responding to diamide, rapamycin, and sodium chloride treatment, we find that almost one-third of genes can be well-modeled, and the estimated rates assume realistic values. Prediction quality is linked to low measurement noise and the shape of the expression profile. Synthesis and degradation rates do not correlate within one treatment, consistent with their independent regulation. When performing robustness analyses of the rate estimates, we observed that most genes adhere to one of two major modes of regulation, which we term synthesis- and degradation-independent regulation. These two modes, in which only one of the rates has to be tightly set, while the other one can assume various values, offer an efficient way for the cell to respond to stimuli and re-establish proteostasis. We experimentally validate degradation-independent regulation under oxidative stress for the heatshock protein Ssa4.

Recently we demonstrated that incorporating p-fluorophenylalanine (pFF) into phosphotriesterase dramatically improved folding, thereby leading to enhanced stability and function at elevated temperatures. To further improve the stability of the fluorinated enzyme, Rosetta was used to identify multiple potential stabilizing mutations. One such variant, pFF-F104A, exhibited enhanced activity at elevated temperature and maintained activity over many days in solution at room temperature.

Interleukin (IL)-22–producing group 3 innate lymphoid cells (ILC3) promote mucosal healing and maintain barrier integrity, but how microbial signals are integrated to regulate mucosal protection offered by these cells remains unclear. Here, we show that in vivo depletion of CX3CR1+ mononuclear phagocytes (MNPs) resulted in more severe colitis and death after infection with Citrobacter rodentium. This phenotype was rescued by exogenous IL-22, which was endogenously produced by ILC3 in close spatial proximity to CX3CR1+ MNPs that were dependent on MyD88 signaling. CX3CR1+ MNPs from both mouse and human tissue produced more IL-23 and IL-1β than conventional CD103+ dendritic cells (cDCs) and were more efficient than cDCs in supporting IL-22 production in ILC3 in vitro and in vivo. Further, colonic ILC3 from patients with mild to moderate ulcerative colitis or Crohn’s disease had increased IL-22 production. IBD-associated SNP gene set analysis revealed enrichment for genes selectively expressed in human intestinal MNPs. The product of one of these, TL1A, potently enhanced IL-23– and IL-1β-induced production of IL-22 and GM-CSF by ILC3. Collectively, these results reveal a critical role for CX3CR1+ mononuclear phagocytes in integrating microbial signals to regulate colonic ILC3 function in IBD.

The fabrication of de novo proteins able to self-assemble on the nano- to meso-length scales is critical in the development of protein-based biomaterials in nanotechnology and medicine. Here we report the design and characterization of a protein engineered coiled-coil that not only assembles into microfibers, but also can bind hydrophobic small molecules. Under ambient conditions, the protein forms fibers with nanoscale structure possessing large aspect ratios formed by bundles of α-helical homopentameric assemblies, which further assemble into mesoscale fibers in the presence of curcumin through aggregation. Surprisingly, these biosynthesized fibers are able to form in conditions of remarkably low concentrations. Unlike previously designed coiled-coil fibers, these engineered protein microfibers can bind the small molecule curcumin throughout the assembly, serving as a depot for encapsulation and delivery of other chemical agents within protein-based 3D microenvironments.

Negative examples – genes that are known not to carry out a given protein function – are rarely recorded in genome and proteome annotation databases, such as the Gene Ontology database. Negative examples are required, however, for several of the most powerful machine learning methods for integrative protein function prediction. Most protein function prediction efforts have relied on a variety of heuristics for the choice of negative examples. Determining the accuracy of methods for negative example prediction is itself a non-trivial task, given that the Open World Assumption as applied to gene annotations rules out many traditional validation metrics. We present a rigorous comparison of these heuristics, utilizing a temporal holdout, and a novel evaluation strategy for negative examples. We add to this comparison several algorithms adapted from Positive-Unlabeled learning scenarios in text-classification, which are the current state of the art methods for generating negative examples in low-density annotation contexts. Lastly, we present two novel algorithms of our own construction, one based on empirical conditional probability, and the other using topic modeling applied to genes and annotations. We demonstrate that our algorithms achieve significantly fewer incorrect negative example predictions than the current state of the art, using multiple benchmarks covering multiple organisms. Our methods may be applied to generate negative examples for any type of method that deals with protein function, and to this end we provide a database of negative examples in several well-studied organisms, for general use (The NoGO database, available at: bonneaulab.bio.nyu.edu/nogo.html).

Protein–protein interactions encompass large surface areas, but often a handful of key residues dominate the binding energy landscape. Rationally designed small molecule scaffolds that reproduce the relative positioning and disposition of important binding residues, termed “hotspot residues”, have been shown to successfully inhibit specific protein complexes. Although this strategy has led to development of novel synthetic inhibitors of protein complexes, often direct mimicry of natural amino acid residues does not lead to potent inhibitors. Experimental screening of focused compound libraries is used to further optimize inhibitors but the number of possible designs that can be efficiently synthesized and experimentally tested in academic settings is limited. We have applied the principles of computational protein design to optimization of nonpeptidic helix mimics as ligands for protein complexes. We describe the development of computational tools to design helix mimetics from canonical and noncanonical residue libraries and their application to two therapeutically important protein–protein interactions: p53-MDM2 and p300-HIF1α. The overall study provides a streamlined approach for discovering potent peptidomimetic inhibitors of protein–protein interactions.