Protein folding in the cell requires the assistance of enzymes collectively called chaperones. Among these, the chaperonins are 1 MDa ring-shaped oligomeric complexes that bind unfolded polypeptides and promote their folding within an isolated chamber in an ATP-dependent manner. Group II chaperonins, found in archaea and eukaryotes, contain a built-in lid that opens and closes over the central chamber. In eukaryotes, the chaperonin TRiC/CCT is hetero-oligomeric, consisting of two stacked rings of eight paralogous subunits each. TRiC facilitates folding of approximately 10% of the eukaryotic proteome, including many cytoskeletal components and cell cycle regulators. Folding of many cellular substrates of TRiC cannot be assisted by any other chaperone. A complete structural and mechanistic understanding of this highly conserved and essential chaperonin remains elusive. However, recent work is beginning to shed light on key aspects of chaperonin function, and how their unique properties underlie their contribution to maintaining cellular proteostasis.

Toxin-antitoxin (TA) systems form a ubiquitous class of prokaryotic proteins with functional roles in plasmid inheritance, environmental stress response, and cell development. ParDE-family TA systems are broadly conserved on plasmids and bacterial chromosomes, and have been well characterized as genetic elements that promote stable plasmid inheritance. We present a crystal structure of a chromosomally-encoded ParD-ParE complex from Caulobacter crescentus at 2.6 Å resolution. This TA system forms an α 2 β 2 heterotetramer in the crystal and in solution. The toxinantitoxin binding interface reveals extensive polar and hydrophobic contacts of ParD antitoxin helices with a conserved recognition and binding groove on the ParE toxin. A cross-species comparison of this complex structure with related toxin structures identified an antitoxin recognition and binding sub-domain that is conserved between distantly-related members of the RelE/ParE toxin superfamily despite low overall primary sequence identity. We further demonstrate that ParD antitoxin is dimeric, stably folded, and largely helical when not bound to ParE toxin. Thus, the paradigmatic model in which antitoxin undergoes a disorder-to-order transition upon toxin binding does not apply to this chromosomal ParD-ParE TA system. Two-gene operons encoding toxin-antitoxin (TA) systems are broadly-distributed components of plasmids, bacterial and archaeal chromosomes, and bacteriophage genomes (1-3). TA toxins kill or arrest growth of bacterial cells by inhibiting essential processes including DNA replication and translation (4-7). TA antitoxins bind and neutralize their cognate, cotranscribed toxin proteins, but are generally less structured than toxins and thus more susceptible to proteolysis (8-10). The differential protein stability between toxins and antitoxins underlies the function of TA systems as genetic stability elements. Specifically, plasmid and prophage TA systems increase stability of their encoding genes via postsegregational killing of daughter cells that fail to inherit the TA genes (11)(12)(13)(14)(15). The functions of chromosomally-encoded TA systems appear to be more diverse: these genes have been implicated in processes as varied as stress adaptation (4,5,16), persistence (17), maintenance of genome stability (18), and multicellular development (19). For some chromosomal TA systems, the association between toxin and antitoxin genes and a cellular phenotype remains under debate (20). † K.M.D. is supported by an NIH Roadmap Physical and Chemical Biology training program grant (T90-DK070076). S.C. acknowledges support for this project from the Arnold and Mabel Beckman Foundation (BYI), the Mallinckrodt Foundation, and the NIH-NIAID Regional Center of Excellence for Bio-defense and Emerging Infectious Diseases Research (RCE) Program (Region V 'Great Lakes' RCE; NIH award 1-U54-AI-057153). Advanced Photon Source is supported by the DOE Office of Basic Energy Sciences (Contract No. A current model of recognition and binding in TA systems invo...

Novel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a molecule, the intensity of each reflection must be estimated, and redundant observations reduced to consensus intensities. Systematic effects, however, lead to the measurement of equivalent reflections on different scales, corrupting observation of changes in electron density. Here, we present a modern Bayesian solution to this problem, which uses deep learning and variational inference to simultaneously rescale and merge reflection observations. We successfully apply this method to monochromatic and polychromatic single-crystal diffraction data, as well as serial femtosecond crystallography data. We find that this approach is applicable to the analysis of many types of diffraction experiments, while accurately and sensitively detecting subtle dynamics and anomalous scattering.

Crystallography uses the diffraction of X-rays, electrons or neutrons by crystals to provide invaluable data on the atomic structure of matter, from single atoms to ribosomes. Much of crystallography's success is due to the software packages developed to enable automated processing of diffraction data. However, the analysis of unconventional diffraction experiments can still pose significant challenges – many existing programs are closed source, sparsely documented, or challenging to integrate with modern libraries for scientific computing and machine learning. Described here is reciprocalspaceship, a Python library for exploring reciprocal space. It provides a tabular representation for reflection data from diffraction experiments that extends the widely used pandas library with built-in methods for handling space groups, unit cells and symmetry-based operations. As is illustrated, this library facilitates new modes of exploratory data analysis while supporting the prototyping, development and release of new methods.

Enzymes catalyze biochemical reactions through precise positioning of substrates, cofactors, and amino acids to modulate the transition-state free energy. However, the role of conformational dynamics remains poorly understood due to lack of experimental access. This shortcoming is evident with E. coli dihydrofolate reductase (DHFR), a model system for the role of protein dynamics in catalysis, for which it is unknown how the enzyme regulates the different active site environments required to facilitate proton and hydride transfer. Here, we present ligand-, temperature-, and electric-field-based perturbations during X-ray diffraction experiments that enable identification of coupled conformational changes in DHFR. We identify a global hinge motion and local networks of structural rearrangements that are engaged by substrate protonation to regulate solvent access and promote efficient catalysis. The resulting mechanism shows that DHFR's two-step catalytic mechanism is guided by a dynamic free energy landscape responsive to the state of the substrate.