Thermostability Enhancement of GH 62 α-l-Arabinofuranosidase by Directed Evolution and Rational Design

GH 62 arabinofuranosidases are known for their excellent specificity for arabinoxylan of agroindustrial residues and their synergism with endoxylanases and other hemicellulases. However, the low thermostability of some GH enzymes hampers potential industrial applications. Protein engineering research highly desires mutations that can enhance thermostability. Therefore, we employed directed evolution using one round of error-prone PCR and site-saturation mutagenesis for thermostability enhancement of GH 62 arabinofuranosidase from Aspergillus fumigatus. Single mutants with enhanced thermostability showed significant ΔΔG changes (<-2.5 kcal/mol) and improvements in perplexity scores from evolutionary scale modeling inverse folding. The best mutant, G205K, increased the melting temperature by 5 °C and the energy of denaturation by 41.3%. We discussed the functional mechanisms for improved stability. Analyzing the adjustments in α-helices, β-sheets, and loops resulting from point mutations, we have obtained significant knowledge regarding the potential impacts on protein stability, folding, and overall structural integrity.

Structure-based neural network protein-carbohydrate interaction predictions at the residue level

Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate-binding sites on any given protein. Here, we present two deep learning (DL) models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predicts non-covalent carbohydrate-binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate-binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2-predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.

Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level

Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate binding sites on any given protein. Here, we present two deep learning models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predict carbohydrate binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2 predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.

Molecular Organization of a Raft-like Domain in a Polyunsaturated Phospholipid Bilayer: A Supervised Machine Learning Analysis of Molecular Dynamics Simulations

Numerous health benefits are associated with omega-3 polyunsaturated fatty acids (n-3 PUFA) consumed in fish oils. An understanding of the mechanism remains elusive. The plasma membrane as a site of action is the focus in this study. With large-scale all-atom MD simulations run on a model membrane (1050 lipid molecules), we observed the evolution over time (6 μs) of a circular (raft-like) domain composed of N-palmitoylsphingomyelin (PSM) and cholesterol embedded into a surrounding (non-raft) patch composed of polyunsaturated 1-palmitoyl-2-docosahexaenoylphosphatylcholine (PDPC) (1:1:1 mol). A supervised machine learning algorithm was developed to characterize the migration of each lipid based on molecular conformation and the local environment. PDPC molecules were seen to infiltrate the ordered raft-like domain in a small amount, while a small concentration of PSM and cholesterol molecules was seen to migrate into the disordered non-raft region. Enclosing the raft-like domain, a narrow (∼2 nm in width) interfacial zone composed of PDPC, PSM, and cholesterol that buffers the substantial difference in order (ΔS_CD ≈ 0.12) between raft-like and non-raft environments was seen to form. Our results suggest that n-3 PUFA regulate the architecture of lipid rafts enriched in sphingolipids and cholesterol with a minimal effect on order within their interior in membranes.

Docosahexaenoic acid regulates the formation of lipid rafts: A unified view from experiment and simulation

Docosahexaenoic acid (DHA, 22:6) is an n-3 polyunsaturated fatty acid (n-3 PUFA) that influences immunological, metabolic, and neurological responses through complex mechanisms. One structural mechanism by which DHA exerts its biological effects is through its ability to modify the physical organization of plasma membrane signaling assemblies known as sphingomyelin/cholesterol (SM/chol)-enriched lipid rafts. Here we studied how DHA acyl chains esterified in the sn-2 position of phosphatidylcholine (PC) regulate the formation of raft and non-raft domains in mixtures with SM and chol on differing size scales. Coarse grained molecular dynamics simulations showed that 1-palmitoyl-2-docosahexaenoylphosphatylcholine (PDPC) enhances segregation into domains more than the monounsaturated control, 1-palmitoyl-2-oleoyl-phosphatidylcholine (POPC). Solid state ²H NMR and neutron scattering experiments provided direct experimental evidence that substituting PDPC for POPC increases the size of raft-like domains on the nanoscale. Confocal imaging of giant unilamellar vesicles with a non-raft fluorescent probe revealed that POPC had no influence on phase separation in the presence of SM/chol whereas PDPC drove strong domain segregation. Finally, monolayer compression studies suggest that PDPC increases lipid-lipid immiscibility in the presence of SM/chol compared to POPC. Collectively, the data across model systems provide compelling support for the emerging model that DHA acyl chains of PC lipids tune the size of lipid rafts, which has potential implications for signaling networks that rely on the compartmentalization of proteins within and outside of rafts.

All n-3 PUFA are not the same: MD simulations reveal differences in membrane organization for EPA, DHA and DPA

Eicosapentaenoic (EPA, 20:5), docosahexaenoic (DHA, 22:6) and docosapentaenoic (DPA, 22:5) acids are omega-3 polyunsaturated fatty acids (n-3 PUFA) obtained from dietary consumption of fish oils that potentially alleviate the symptoms of a range of chronic diseases. We focus here on the plasma membrane as a site of action and investigate how they affect molecular organization when taken up into a phospholipid. All atom MD simulations were performed to compare 1-stearoyl-2-eicosapentaenoylphosphatylcholine (EPA-PC, 18:0-20:5PC), 1-stearoyl-2-docosahexaenoylphosphatylcholine (DHA-PC, 18:0-22:6PC), 1-stearoyl-2-docosapentaenoylphosphatylcholine (DPA-PC, 18:0-22:5PC) and, as a monounsaturated control, 1-stearoyl-2-oleoylphosphatidylcholine (OA-PC, 18:0-18:1PC) bilayers. They were run in the absence and presence of 20mol% cholesterol. Multiple double bonds confer high disorder on all three n-3 PUFA. The different number of double bonds and chain length for each n-3 PUFA moderates the reduction in membrane order exerted (compared to OA-PC, S¯CD=0.152). EPA-PC (S¯CD=0.131) is most disordered, while DPA-PC (S¯CD=0.140) is least disordered. DHA-PC (S¯CD=0.139) is, within uncertainty, the same as DPA-PC. Following the addition of cholesterol, order in EPA-PC (S¯CD=0.169), DHA-PC (S¯CD=0.178) and DPA-PC (S¯CD=0.182) is increased less than in OA-PC (S¯CD=0.214). The high disorder of n-3 PUFA is responsible, preventing the n-3 PUFA-containing phospholipids from packing as close to the rigid sterol as the monounsaturated control. Our findings establish that EPA, DHA and DPA are not equivalent in their interactions within membranes, which possibly contributes to differences in clinical efficacy.