Automatic and accurate ligand structure determination guided by cryo-electron microscopy maps.

Advances in cryo-electron microscopy (cryoEM) and deep-learning guided protein structure prediction have expedited structural studies of protein complexes. However, methods for accurately determining ligand conformations are lacking. In this manuscript, we develop EMERALD, a tool for automatically determining ligand structures guided by medium-resolution cryoEM density. We show this method is robust at predicting ligands along with surrounding side chains in maps as low as 4.5 Å local resolution. Combining this with a measure of placement confidence and running on all protein/ligand structures in the EMDB, we show that 57% of ligands replicate the deposited model, 16% confidently find alternate conformations, 22% have ambiguous density where multiple conformations might be present, and 5% are incorrectly placed. For five cases where our approach finds an alternate conformation with high confidence, high-resolution crystal structures validate our placement. EMERALD and the resulting analysis should prove critical in using cryoEM to solve protein-ligand complexes.

Scaffolding protein functional sites using deep learning.

The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, "constrained hallucination," optimizes sequences such that their predicted structures contain the desired functional site. The second approach, "inpainting," starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network. We use these two methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests.

Structure and lipid dynamics in the maintenance of lipid asymmetry inner membrane complex of A. baumannii.

Multi-resistant bacteria are a major threat in modern medicine. The gram-negative coccobacillus Acinetobacter baumannii currently leads the WHO list of pathogens in critical need for new therapeutic development. The maintenance of lipid asymmetry (MLA) protein complex is one of the core machineries that transport lipids from/to the outer membrane in gram-negative bacteria. It also contributes to broad-range antibiotic resistance in several pathogens, most prominently in A. baumannii. Nonetheless, the molecular details of its role in lipid transport has remained largely elusive. Here, we report the cryo-EM maps of the core MLA complex, MlaBDEF, from the pathogen A. baumannii, in the apo-, ATP- and ADP-bound states, revealing multiple lipid binding sites in the cytosolic and periplasmic side of the complex. Molecular dynamics simulations suggest their potential trajectory across the membrane. Collectively with the recently-reported structures of the E. coli orthologue, this data also allows us to propose a molecular mechanism of lipid transport by the MLA system.

Biology, Mechanism, and Structure of Enzymes in the α-d-Phosphohexomutase Superfamily.

Enzymes in the α-d-phosphohexomutases superfamily catalyze the reversible conversion of phosphosugars, such as glucose 1-phosphate and glucose 6-phosphate. These reactions are fundamental to primary metabolism across the kingdoms of life and are required for a myriad of cellular processes, ranging from exopolysaccharide production to protein glycosylation. The subject of extensive mechanistic characterization during the latter half of the 20th century, these enzymes have recently benefitted from biophysical characterization, including X-ray crystallography, NMR, and hydrogen-deuterium exchange studies. This work has provided new insights into the unique catalytic mechanism of the superfamily, shed light on the molecular determinants of ligand recognition, and revealed the evolutionary conservation of conformational flexibility. Novel associations with inherited metabolic disease and the pathogenesis of bacterial infections have emerged, spurring renewed interest in the long-appreciated functional roles of these enzymes.

Sequence-structure relationships, expression profiles, and disease-associated mutations in the paralogs of phosphoglucomutase 1.

The key metabolic enzyme phosphoglucomutase 1 (PGM1) controls glucose homeostasis in most human cells. Four proteins related to PGM1, known as PGM2, PGM2L1, PGM3 and PGM5, and referred to herein as paralogs, are encoded in the human genome. Although all members of the same enzyme superfamily, these proteins have distinct substrate preferences and different functional roles. The recent association of PGM1 and PGM3 with inherited enzyme deficiencies prompts us to revisit sequence-structure and other relationships among the PGM1 paralogs, which are understudied despite their importance in human biology. Using currently available sequence, structure, and expression data, we investigated evolutionary relationships, tissue-specific expression profiles, and the amino acid preferences of key active site motifs. Phylogenetic analyses indicate both ancient and more recent divergence between the different enzyme sub-groups comprising the human paralogs. Tissue-specific protein and RNA expression profiles show widely varying patterns for each paralog, providing insight into function and disease pathology. Multiple sequence alignments confirm high conservation of key active site regions, but also reveal differences related to substrate specificity. In addition, we find that sequence variants of PGM2, PGM2L1, and PGM5 verified in the human population affect residues associated with disease-related mutants in PGM1 or PGM3. This suggests that inherited diseases related to dysfunction of these paralogs will likely occur in humans.