Crystal structure of LGR ligand α2/β5 from Caenorhabditis elegans with implications for the evolution of glycoprotein hormones

Evolutionary conserved peptide and glycoprotein hormone-like neuroendocrine systems in C. elegans

Peptides and protein hormones form the largest group of secreted signals that mediate intercellular communication and are central regulators of physiology and behavior in all animals. Phylogenetic analyses and biochemical identifications of peptide-receptor systems reveal a broad evolutionary conservation of these signaling systems at the molecular level. Substantial progress has been made in recent years on characterizing the physiological and putative ancestral roles of many peptide systems through comparative studies in invertebrate models. Several peptides and protein hormones are not only molecularly conserved but also have conserved roles across animal phyla. Here, we focus on functional insights gained in the nematode Caenorhabditis elegans that, with its compact and well-described nervous system, provides a powerful model to dissect neuroendocrine signaling networks involved in the control of physiology and behavior. We summarize recent discoveries on the evolutionary conservation and knowledge on the functions of peptide and protein hormone systems in C. elegans.

Facing the phase problem

The marvel of X-ray crystallography is the beauty and precision of the atomic structures deduced from diffraction patterns. Since these patterns record only amplitudes, phases for the diffracted waves must also be evaluated for systematic structure determination. Thus, we have the phase problem as a central complication, both intellectually for the field and practically so for many analyses. Here, I discuss how we - myself, my laboratory and the diffraction community - have faced the phase problem, considering the evolution of methods for phase evaluation as structural biology developed to the present day. During the explosive growth of macromolecular crystallography, practice in diffraction analysis evolved from a universal reliance on isomorphous replacement to the eventual domination of anomalous diffraction for de novo structure determination. As the Protein Data Bank (PDB) grew and familial relationships among proteins became clear, molecular replacement overtook all other phasing methods; however, experimental phasing remained essential for molecules without obvious precedents, with multi- and single-wavelength anomalous diffraction (MAD and SAD) predominating. While the mathematics-based direct methods had proved to be inadequate for typical macromolecules, they returned to crack substantial selenium substructures in SAD analyses of selenomethionyl proteins. Native SAD, exploiting the intrinsic S and P atoms of biomolecules, has become routine. Selenomethionyl SAD and MAD were the mainstays of structural genomics efforts to populate the PDB with novel proteins. A recent dividend has been paid in the success of PDB-trained artificial intelligence approaches for protein structure prediction. Currently, molecular replacement with AlphaFold models often obviates the need for experimental phase evaluation. For multiple reasons, we are now unfazed by the phase problem. Cryo-EM analysis is an attractive alternative to crystallography for many applications faced by today's structural biologists. It simply finesses the phase problem; however, the principles and procedures of diffraction analysis remain pertinent and are adopted in single-particle cryo-EM studies of biomolecules.

Ancestral glycoprotein hormone-receptor pathway controls growth in C. elegans

In vertebrates, thyrostimulin is a highly conserved glycoprotein hormone that, besides thyroid stimulating hormone (TSH), is a potent ligand of the TSH receptor. Thyrostimulin is considered the most ancestral glycoprotein hormone and orthologs of its subunits, GPA2 and GPB5, are widely conserved across vertebrate and invertebrate animals. Unlike TSH, however, the functions of the thyrostimulin neuroendocrine system remain largely unexplored. Here, we identify a functional thyrostimulin-like signaling system in Caenorhabditis elegans. We show that orthologs of GPA2 and GPB5, together with thyrotropin-releasing hormone (TRH) related neuropeptides, constitute a neuroendocrine pathway that promotes growth in C. elegans. GPA2/GPB5 signaling is required for normal body size and acts through activation of the glycoprotein hormone receptor ortholog FSHR-1. C. elegans GPA2 and GPB5 increase cAMP signaling by FSHR-1 in vitro. Both subunits are expressed in enteric neurons and promote growth by signaling to their receptor in glial cells and the intestine. Impaired GPA2/GPB5 signaling causes bloating of the intestinal lumen. In addition, mutants lacking thyrostimulin-like signaling show an increased defecation cycle period. Our study suggests that the thyrostimulin GPA2/GPB5 pathway is an ancient enteric neuroendocrine system that regulates intestinal function in ecdysozoans, and may ancestrally have been involved in the control of organismal growth.

Accelerating crystal structure determination with iterative AlphaFold prediction

Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of C^α atoms matching those in the deposited models within 2 Å. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested.