Common recognition principles across diverse sequence and structural families of sialic acid binding proteins

Many locks to one key: N-acetylneuraminic acid binding to proteins

Sialic acids play crucial roles in cell surface glycans of both eukaryotic and prokaryotic organisms, mediating various biological processes, including cell-cell interactions, development, immune response, oncogenesis and host-pathogen interactions. This review focuses on the β-anomeric form of N-acetylneuraminic acid (Neu5Ac), particularly its binding affinity towards various proteins, as elucidated by solved protein structures. Specifically, we delve into the binding mechanisms of Neu5Ac to proteins involved in sequestering and transporting Neu5Ac in Gram-negative bacteria, with implications for drug design targeting these proteins as antimicrobial agents. Unlike the initial assumptions, structural analyses revealed significant variability in the Neu5Ac binding pockets among proteins, indicating diverse evolutionary origins and binding modes. By comparing these findings with existing structures from other systems, we can effectively highlight the intricate relationship between protein structure and Neu5Ac recognition, emphasizing the need for tailored drug design strategies to inhibit Neu5Ac-binding proteins across bacterial species.

Metabolomic changes in Mycobacterium avium subsp. paratuberculosis (MAP) challenged Holstein-Friesian cattle highlight the role of serum amino acids as indicators of immune system activation

INTRODUCTION

Paratuberculosis, commonly known as Johne's disease, is a chronic granulomatous infection of ruminants caused by Mycobacterium avium subspecies paratuberculosis (MAP). Clinical signs, including reduced milk yields, weight loss and diarrhoea, are typically absent until 2 to 6 years post exposure.

OBJECTIVES

To identify metabolomic changes profiles of MAP challenged Holstein-Friesian (HF) cattle and correlate identified metabolites to haematological and immunological parameters.

METHODS

At approximately 6 weeks of age, calves (n = 9) were challenged with 3.8 × 10⁹ cells of MAP (clinical isolate CIT003) on 2 consecutive days. Additional unchallenged calves (n = 9) formed the control group. The study used biobanked serum from cattle sampled periodically from 3- to 33-months post challenge. The assessment of sera using flow infusion electrospray high resolution mass spectrometry (FIE-HRMS) for high throughput, sensitive, non-targeted metabolite fingerprinting highlighted differences in metabolite levels between the two groups.

RESULTS

In total, 25 metabolites which were differentially accumulated in MAP challenged cattle were identified, including 20 which displayed correlation to haematology parameters, particularly monocyte levels.

CONCLUSION

The targeted metabolites suggest shifts in amino acid metabolism that could reflect immune system activation linked to MAP and as well as differences in phosphocholine levels which could reflect activation of the Th1 (tending towards pro-inflammatory) immune response. If verified by future work, selected metabolites could be used as biomarkers to diagnose and manage MAP infected cattle.

An Augmented Pocketome: Detection and Analysis of Small-Molecule Binding Pockets in Proteins of Known 3D Structure

Protein-ligand interactions form the basis of most cellular events. Identifying ligand binding pockets in proteins will greatly facilitate rationalizing and predicting protein function. Ligand binding sites are unknown for many proteins of known three-dimensional (3D) structure, creating a gap in our understanding of protein structure-function relationships. To bridge this gap, we detect pockets in proteins of known 3D structures, using computational techniques. This augmented pocketome (PocketDB) consists of 249,096 pockets, which is about seven times larger than what is currently known. We deduce possible ligand associations for about 46% of the newly identified pockets. The augmented pocketome, when subjected to clustering based on similarities among pockets, yielded 2,161 site types, which are associated with 1,037 ligand types, together providing fold-site-type-ligand-type associations. The PocketDB resource facilitates a structure-based function annotation, delineation of the structural basis of ligand recognition, and provides functional clues for domains of unknown functions, allosteric proteins, and druggable pockets.

A genome-wide structure-based survey of nucleotide binding proteins in M. tuberculosis

Nucleoside tri-phosphates (NTP) form an important class of small molecule ligands that participate in, and are essential to a large number of biological processes. Here, we seek to identify the NTP binding proteome (NTPome) in M. tuberculosis (M.tb), a deadly pathogen. Identifying the NTPome is useful not only for gaining functional insights of the individual proteins but also for identifying useful drug targets. From an earlier study, we had structural models of M.tb at a proteome scale from which a set of 13,858 small molecule binding pockets were identified. We use a set of NTP binding sub-structural motifs derived from a previous study and scan the M.tb pocketome, and find that 1,768 proteins or 43% of the proteome can theoretically bind NTP ligands. Using an experimental proteomics approach involving dye-ligand affinity chromatography, we confirm NTP binding to 47 different proteins, of which 4 are hypothetical proteins. Our analysis also provides the precise list of binding site residues in each case, and the probable ligand binding pose. As the list includes a number of known and potential drug targets, the identification of NTP binding can directly facilitate structure-based drug design of these targets.

Deciphering common recognition principles of nucleoside mono/di and tri-phosphates binding in diverse proteins via structural matching of their binding sites

Nucleoside triphosphate (NTP) ligands are of high biological importance and are essential for all life forms. A pre-requisite for them to participate in diverse biochemical processes is their recognition by diverse proteins. It is thus of great interest to understand the basis for such recognition in different proteins. Towards this, we have used a structural bioinformatics approach and analyze structures of 4677 NTP complexes available in Protein Data Bank (PDB). Binding sites were extracted and compared exhaustively using PocketMatch, a sensitive in-house site comparison algorithm, which resulted in grouping the entire dataset into 27 site-types. Each of these site-types represent a structural motif comprised of two or more residue conservations, derived using another in-house tool for superposing binding sites, PocketAlign. The 27 site-types could be grouped further into 9 super-types by considering partial similarities in the sites, which indicated that the individual site-types comprise different combinations of one or more site features. A scan across PDB using the 27 structural motifs determined the motifs to be specific to NTP binding sites, and a computational alanine mutagenesis indicated that residues identified to be highly conserved in the motifs are also most contributing to binding. Alternate orientations of the ligand in several site-types were observed and rationalized, indicating the possibility of some residues serving as anchors for NTP recognition. The presence of multiple site-types and the grouping of multiple folds into each site-type is strongly suggestive of convergent evolution. Knowledge of determinants obtained from this study will be useful for detecting function in unknown proteins. Proteins 2017; 85:1699-1712. © 2017 Wiley Periodicals, Inc.