Publications

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.

Soil-transmitted helminths colonize more than 1.5 billion people worldwide, yet little is known about how they interact with bacterial communities in the gut microbiota. Differences in the gut microbiota between individuals living in developed and developing countries may be partly due to the presence of helminths, since they predominantly infect individuals from developing countries, such as the indigenous communities in Malaysia we examine in this work. We compared the composition and diversity of bacterial communities from the fecal microbiota of 51 people from two villages in Malaysia, of which 36 (70.6%) were infected by helminths. The 16S rRNA V4 region was sequenced at an average of nineteen thousand sequences per samples. Helminth-colonized individuals had greater species richness and number of observed OTUs with enrichment of Paraprevotellaceae, especially with Trichuris infection. We developed a new approach of combining centered log-ratio (clr) transformation for OTU relative abundances with sparse Partial Least Squares Discriminant Analysis (sPLS-DA) to enable more robust predictions of OTU interrelationships. These results suggest that helminths may have an impact on the diversity, bacterial community structure and function of the gut microbiota.

Recently developed chemical ligation protocols were elaborated for rapid N-terminal protein PEGylation. We introduce a PEG-salicylaldehyde ester and demonstrate its site-specific ligation to the N-terminus of the RNase S protein and to the therapeutic polypeptide PTH (1–34).

Peptoids are a family of synthetic oligomers composed of N-substituted glycine units. Along with other “foldamer” systems, peptoid oligomer sequences can be predictably designed to form a variety of stable secondary structures. It is not yet evident if foldamer design can be extended to reliably create tertiary structure features that mimic more complex biomolecular folds and functions. Computational modeling and prediction of peptoid conformations will likely play a critical role in enabling complex biomimetic designs. We introduce a computational approach to provide accurate conformational and energetic parameters for peptoid side chains needed for successful modeling and design. We find that peptoids can be described by a “rotamer” treatment, similar to that established for proteins, in which the peptoid side chains display rotational isomerism to populate discrete regions of the conformational landscape. Because of the insufficient number of solved peptoid structures, we have calculated the relative energies of side-chain conformational states to provide a backbone-dependent (BBD) rotamer library for a set of 54 different peptoid side chains. We evaluated two rotamer library development methods that employ quantum mechanics (QM) and/or molecular mechanics (MM) energy calculations to identify side-chain rotamers. We show by comparison to experimental peptoid structures that both methods provide an accurate prediction of peptoid side chain placements in folded peptoid oligomers and at protein interfaces. We have incorporated our peptoid rotamer libraries into ROSETTA, a molecular design package previously validated in the context of protein design and structure prediction.

In vertebrates, pluripotent pharyngeal mesoderm progenitors produce the cardiac precursors of the second heart field as well as the branchiomeric head muscles and associated stem cells. However, the mechanisms underlying the transition from multipotent progenitors to distinct muscle precursors remain obscured by the complexity of vertebrate embryos. Using Ciona intestinalis as a simple chordate model, we show that bipotent cardiopharyngeal progenitors are primed to activate both heart and pharyngeal muscle transcriptional programs, which progressively become restricted to corresponding precursors. The transcription factor COE (Collier/OLF/EBF) orchestrates the transition to pharyngeal muscle fate both by promoting an MRF-associated myogenic program in myoblasts and by maintaining an undifferentiated state in their sister cells through Notch-mediated lateral inhibition. The latter are stem cell-like muscle precursors that form most of the juvenile pharyngeal muscles. We discuss the implications of our findings for the development and evolution of the chordate cardiopharyngeal mesoderm.