Molecular dynamics simulations elucidate oligosaccharide recognition pathways by galectin-3 at atomic resolution

Scrutinising the Conformational Ensemble of the Intrinsically Mixed-Folded Protein Galectin-3

Galectin-3 is a protein involved in many intra- and extra-cellular processes. It has been identified as a diagnostic or prognostic biomarker for certain types of heart disease, kidney disease and cancer. Galectin-3 comprises a carbohydrate recognition domain (CRD) and an N-terminal domain (NTD), which is unstructured and contains eight collagen-like Pro-Gly-rich tandem repeats. While the structure of the CRD has been solved using protein crystallography, current knowledge about conformations of full-length galectin-3 is limited. To fill in this knowledge gap, we performed molecular dynamics (MD) simulations of full-length galectin-3. We systematically re-scaled the solute-solvent interactions in the Martini 3 force field to obtain the best possible agreement between available data from SAXS experiments and the ensemble of conformations generated in the MD simulations. The simulation conformations were found to be very diverse, as reflected, e.g., by (i) large fluctuations in the radius of gyration, ranging from about 2 to 5 nm, and (ii) multiple transient contacts made by amino acid residues in the NTD. Consistent with evidence from NMR experiments, contacts between the CRD and NTD were observed to not involve the carbohydrate-binding site on the CRD surface. Contacts within the NTD were found to be made most frequently by aromatic residues. Formation of fuzzy complexes with unspecific stoichiometry was observed to be mediated mostly by the NTD. Taken together, we offer a detailed picture of the conformational ensemble of full-length galectin-3, which will be important for explaining the biological functions of this protein at the molecular level.

Crystal structure of mango α1,3/α1,4-fucosyltransferase elucidates unique elements that regulate Lewis A-dominant oligosaccharide assembly

In various organisms, α1,3/α1,4-fucosyltransferases (CAZy GT10 family enzymes) mediate the assembly of type I (Galβ1,3GlcNAc) and/or type II (Galβ1,4GlcNAc)-based Lewis structures that are widely distributed in glycoconjugates. Unlike enzymes of other species, plant orthologues show little fucosyltransferase activity for type II-based glycans and predominantly catalyze the assembly of the Lewis A structure [Galβ1,3(Fucα1,4)GlcNAc] on the type I disaccharide unit of their substrates. However, the structural basis underlying this unique substrate selectivity remains elusive. In this study, we investigated the structure-function relationship of MiFUT13A, a mango α1,3/α1,4-fucosyltransferase. The prepared MiFUT13A displayed distinct α1,4-fucosyltransferase activity. Consistent with the enzymatic properties of this molecule, X-ray crystallography revealed that this enzyme has a typical GT-B fold-type structure containing a set of residues that are responsible for its SN2-like catalysis. Site-directed mutagenesis and molecular docking analyses proposed a rational binding mechanism for type I oligosaccharides. Within the catalytic cleft, the pocket surrounding Trp121 serves as a binding site, anchoring the non-reducing terminal β1,3-galactose that belongs to the type I disaccharide unit. Furthermore, Glu177 was postulated to function as a general base catalyst through its interaction with the 4-hydroxy group of the acceptor N-acetylglucosamine residue. Adjacent residues, specifically Thr120, Thr157 and Asp175 were speculated to assist in binding of the reducing terminal residues. Intriguingly, these structural elements were not fully conserved in mammalian orthologue which also shows predominant α1,4-fucosyltransferase activity. In conclusion, we have proposed that MiFUT13A generates the Lewis A structure on type I glycans through a distinct mechanism, divergent from that of mammalian enzymes.

Long-time-step molecular dynamics can retard simulation of protein-ligand recognition process

Molecular dynamics (MD) simulation of biologically relevant processes at realistic time scale and atomistic precision is generally limited by prohibitively large computational cost, due to its restriction of using an ultrashort integration time step (1-2 fs). A popular numerical recipe to reduce the associated computational burden is adopting schemes that would allow relatively longer-time-step for MD propagation. Here, we explore the perceived potential of one of the most frequently used long-time-step protocols, namely the hydrogen mass repartitioning (HMR) approach, in alleviating the computational overhead associated with simulation of the kinetic process of protein-ligand recognition events. By repartitioning the mass of heavier atoms to their linked hydrogen atoms, HMR leverages around twofold longer time step than regular simulation, holding promise of significant performance boost. However, our probe into direct simulation of the protein-ligand recognition event, one of the computationally most challenging processes, shows that long-time-step HMR MD simulations do not necessarily translate to a computationally affordable solution. Our investigations spanning cumulative 176 μs in three independent proteins (T4 lysozyme, sensor domain of MopR, and galectin-3) show that long-time-step HMR-based MD simulations can catch the ligand in its act of recognizing the native cavity. But, as a major caveat, the ligand is found to require significantly longer time to identify buried native protein cavity in an HMR MD simulation than regular simulation, thereby defeating the purpose of its usage for performance upgrade. A molecular analysis shows that the longer time required by a ligand to recognize the protein in HMR is rooted in faster diffusion of the ligand, which reduces the survival probability of decisive on-pathway metastable intermediates, thereby slowing down the eventual recognition process at the native cavity. Together, the investigation stresses careful assessment of pitfalls of long-time-step algorithms before attempting to utilize them for higher performance for biomolecular recognition simulations.