N-Glycosylation Enhances Conformational Flexibility of Protein Disulfide Isomerase Revealed by Microsecond Molecular Dynamics and Markov State Modeling

Differential glycosylation in mutant vitamin D-binding protein decimates the binding stability of vitamin D

Vitamin D (VD) is produced by the skin upon exposure to sunlight or is obtained from dietary sources. Several risk factors are associated with VD deficiency including mutations and post-translational modifications in its transport protein known as vitamin D binding protein (VDBP) or GC-globulin. The two common single nucleotide polymorphisms rs7041 and rs4588 create three major isoforms of VDBP, including GC-1F also called wild type, GC1S, and GC-2. The 3D models for both GC-1F and GC-2 were constructed in their glycosylated states to decipher the effect of these mutations on the overall conformational changes and VD-binding affinity. The binding affinities were estimated using the Molecular Mechanics Poison-Boltzmann surface area (MM-PBSA) method and conformational changes were investigated after free energy landscapes estimations. Total free energies suggest that GC-1F exhibits stronger affinity (ΔE = -116.09 kJ/mol) than GC-2 (ΔE = -95 kJ/mol) variant with VD. The GC-1F isoforms had more streamlined motion compared to GC-2 isoforms, predicting a trade-off between cross-talk residues that significantly impacts protein structural stability. The data suggest that glycation at Thr418 plays a vital role in the overall VDBP-VD affinity by stabilizing the N-T loop that holds the domain I (VD-pocket) and domain III intact. The loss of glycation in GC-2 has a pivotal role in the inter-domain conformational stability of VDBP, which may ultimately affect VD transportation and maturation. These findings describe a novel mechanism in how mutations distant from the VD-active site change the overall conformational of the VDBP and abrogate the VDBP-VD interaction.Communicated by Ramaswamy H. Sarma.

Probability Density Reweighting of High-Temperature Molecular Dynamics

Molecular dynamics (MD) simulation is a popular method for elucidating the structures and functions of biomolecules. However, exploring the conformational space, especially for large systems with slow transitions, often requires enhanced sampling methods. Although conducting MD at high temperatures provides a straightforward approach, resulting conformational ensembles diverge significantly from those at low temperatures. To address this discrepancy, we propose a novel probability density-based reweighting (PDR) method. PDR exhibits robust performance across four distinct systems, including a miniprotein, a cyclic peptide, a protein loop, and a protein-peptide complex. It accurately restores the conformational distributions at high temperatures to those at low temperatures. Additionally, we apply PDR to reweight previously studied high-T MD simulations of 12 protein-peptide complexes, enabling a comprehensive investigation of the conformational space of protein-peptide complexes.

Glycosylation and Crowded Membrane Effects on Influenza Neuraminidase Stability and Dynamics

All protein simulations are conducted with varying degrees of simplification, oftentimes with unknown ramifications about how these simplifications affect the interpretability of the results. In this work, we investigated how protein glycosylation and lateral crowding effects modulate an array of properties characterizing the stability and dynamics of influenza neuraminidase. We constructed three systems: (1) glycosylated neuraminidase in a whole virion (i.e., crowded membrane) environment, (2) glycosylated neuraminidase in its own lipid bilayer, and (3) unglycosylated neuraminidase in its own lipid bilayer. We saw that glycans tend to stabilize the protein structure and reduce its conformational flexibility while restricting the solvent movement. Conversely, a crowded membrane environment encouraged exploration of the free energy landscape and a large-scale conformational change, while making the protein structure more compact. Understanding these effects informs what factors one must consider in attempting to recapture the desired level of physical accuracy.

Glycosylation and Crowded Membrane Effects on Influenza Neuraminidase Stability and Dynamics

All protein simulations are conducted with varying degrees of simplifications, oftentimes with unknown ramifications on how these simplifications affect the interpretability of the results. In this work we investigated how protein glycosylation and lateral crowding effects modulate an array of properties characterizing the stability and dynamics of influenza neuraminidase. We constructed three systems: 1) Glycosylated neuraminidase in a whole virion (i.e. crowded membrane) environment 2) Glycosylated neuraminidase in its own lipid bilayer 3) Unglycosylated neuraminidase in its own lipid bilayer. We saw that glycans tend to stabilize the protein structure and reduce its conformational flexibility while restricting solvent movement. Conversely, a crowded membrane environment encouraged exploration of the free energy landscape and a large scale conformational change while making the protein structure more compact. Understanding these effects informs what factors one must consider while attempting to recapture the desired level of physical accuracy.

CommonNNClustering─A Python Package for Generic Common-Nearest-Neighbor Clustering

Density-based clustering procedures are widely used in a variety of data science applications. Their advantage lies in the capability to find arbitrarily shaped and sized clusters and robustness against outliers. In particular, they proved effective in the analysis of molecular dynamics simulations, where they serve to identify relevant, low-energetic molecular conformations. As such, they can provide a convenient basis for the construction of kinetic (core-set) Markov-state models. Here we present the open-source Python project CommonNNClustering, which provides an easy-to-use and efficient reimplementation of the common-nearest-neighbor (CommonNN) method. The package provides functionalities for hierarchical clustering and an evaluation of the results. We put our emphasis on a generic API design to keep the implementation flexible and open for customization.

Sequential in vitro enzymatic N-glycoprotein modification reveals site-specific rates of glycoenzyme processing

N-glycosylation is an essential eukaryotic posttranslational modification that affects various glycoprotein properties, including folding, solubility, protein–protein interactions, and half-life. N-glycans are processed in the secretory pathway to form varied ensembles of structures, and diversity at a single site on a glycoprotein is termed ‘microheterogeneity’. To understand the factors that influence glycan microheterogeneity, we hypothesized that local steric and electrostatic factors surrounding each site influence glycan availability for enzymatic modification. We tested this hypothesis via expression of reporter N-linked glycoproteins in N-acetylglucosaminyltransferase MGAT1-null HEK293 cells to produce immature Man₅GlcNAc₂ glycoforms (38 glycan sites total). These glycoproteins were then sequentially modified in vitro from high mannose to hybrid and on to biantennary, core-fucosylated, complex structures by a panel of N-glycosylation enzymes, and each reaction time course was quantified by LC-MS/MS. Substantial differences in rates of in vitro enzymatic modification were observed between glycan sites on the same protein, and differences in modification rates varied depending on the glycoenzyme being evaluated. In comparison, proteolytic digestion of the reporters prior to N-glycan processing eliminated differences in in vitro enzymatic modification. Furthermore, comparison of in vitro rates of enzymatic modification with the glycan structures found on the mature reporters expressed in WT cells correlated well with the enzymatic bottlenecks observed in vivo. These data suggest higher order local structures surrounding each glycosylation site contribute to the efficiency of modification both in vitro and in vivo to establish the spectrum of microheterogeneity in N-linked glycoproteins.

Pathway engineering facilitates efficient protein expression in Pichia pastoris

Pichia pastoris has been recognized as an important platform for the production of various heterologous proteins in recent years. The strong promoter AOX1, induced by methanol, with the help of the α-pre-pro signal sequence, can lead to a high expression level of extracellular protein. However, this combination was not always efficient, as protein secretion in P. pastoris involves numerous procedures mediated by several cellular proteins, including folding assisted by endoplasmic reticulum (ER) molecular chaperones, degradation through ubiquitination, and an efficient vesicular transport system. Efficient protein expression requires the cooperation of various intracellular pathways. This article summarizes the process of protein secretion, modification, and transportation in P. pastoris. In addition, the roles played by the key proteins in these processes and the corresponding co-expression effects are also listed. It is expected to lay the foundation for the industrial protein production of P. pastoris. KEY POINTS: • Mechanisms of chaperones in protein folding and their co-expression effects are summarized. • Protein glycosylation modifications are comprehensively reviewed. • Current dilemmas in the overall protein secretion pathway of Pichia pastoris and corresponding solutions are demonstrated.

Reduction of protein disulfide isomerase results in open conformations and stimulates dynamic exchange between structural ensembles

Human protein disulfide isomerase (PDI) is an essential redox-regulated enzyme required for oxidative protein folding. It comprises four thioredoxin domains, two catalytically active (a, a’) and two inactive (b, b’), organized to form a flexible abb’a’ U-shape. Snapshots of unbound oxidized and reduced PDI have been obtained by X-ray crystallography. Yet, how PDI’s structure changes in response to the redox environment and inhibitor binding remains controversial. Here, we used multiparameter confocal single-molecule FRET to track the movements of the two catalytic domains with high temporal resolution. We found that at equilibrium, PDI visits three structurally distinct conformational ensembles, two “open” (O₁ and O₂) and one “closed” (C). We show that the redox environment dictates the time spent in each ensemble and the rate at which they exchange. While oxidized PDI samples O₁, O₂, and C more evenly and in a slower fashion, reduced PDI predominantly populates O₁ and O₂ and exchanges between them more rapidly, on the submillisecond timescale. These findings were not expected based on crystallographic data. Using mutational analyses, we further demonstrate that the R300-W396 cation-π interaction and active site cysteines dictate, in unexpected ways, how the catalytic domains relocate. Finally, we show that irreversible inhibitors targeting the active sites of reduced PDI did not abolish these protein dynamics but rather shifted the equilibrium toward the closed ensemble. This work introduces a new structural framework that challenges current views of PDI dynamics, helps rationalize its multifaceted role in biology, and should be considered when designing PDI-targeted therapeutics.

Molecular simulations of complex carbohydrates and glycoconjugates

Complex carbohydrates (glycans) are the most abundant and versatile biopolymers in nature. The broad diversity of biochemical functions that carbohydrates cover is a direct consequence of the variety of 3D architectures they can adopt, displaying branched or linear arrangements, widely ranging in sizes, and with the highest diversity of building blocks of any other natural biopolymer. Despite this unparalleled complexity, a common denominator can be found in the glycans' inherent flexibility, which hinders experimental characterization, but that can be addressed by high-performance computing (HPC)-based molecular simulations. In this short review, I present and discuss the state-of-the-art of molecular simulations of complex carbohydrates and glycoconjugates, highlighting methodological strengths and weaknesses, important insights through emblematic case studies, and suggesting perspectives for future developments.

Glycosylation is key for enhancing drug recognition into spike glycoprotein of SARS-CoV-2

The emergence of COVID-19 caused by SARS-CoV-2 and its spread since 2019 represents the major public health problem worldwide nowadays, which has generated a high number of infections and deaths. The spike protein (S protein) is the most studied protein of SARS-CoV-2, and key to host-cell entry through ACE2 receptor. This protein presents a large pattern of glycosylations with important roles in immunity and infection mechanisms. Therefore, understanding key aspects of the molecular mechanisms of these structures, during drug recognition in SARS-CoV-2, may contribute to therapeutic alternatives. In this work, we explored the impact of glycosylations on the drug recognition on two domains of the S protein, the receptor-binding domain (RBD) and the N-terminal domain (NTD) through molecular dynamics simulations and computational biophysics analysis. Our results show that glycosylations in the S protein induce structural stability and changes in rigidity/flexibility related to the number of glycosylations in the structure. These structural changes are important for its biological activity as well as the correct interaction of ligands in the RBD and NTD regions. Additionally, we evidenced a roto-translation phenomenon in the interaction of the ligand with RBD in the absence of glycosylation, which disappears due to the influence of glycosylation and the convergence of metastable states in RBM. Similarly, glycosylations in NTD promote an induced fit phenomenon, which is not observed in the absence of glycosylations; this process is decisive for the activity of the ligand at the cryptic site. Altogether, these results provide an explanation of glycosylation relevance in biophysical properties and drug recognition to S protein of SARS-CoV-2, which must be considered in the rational drug development and virtual screening targeting S protein.

Enhanced sampling without borders: on global biasing functions and how to reweight them

Molecular dynamics (MD) simulations are a powerful tool to follow the time evolution of biomolecular motions in atomistic resolution. However, the high computational demand of these simulations limits the timescales of motions that can be observed. To resolve this issue, so called enhanced sampling techniques are developed, which extend conventional MD algorithms to speed up the simulation process. Here, we focus on techniques that apply global biasing functions. We provide a broad overview of established enhanced sampling methods and promising new advances. As the ultimate goal is to retrieve unbiased information from biased ensembles, we also discuss benefits and limitations of common reweighting schemes. In addition to concisely summarizing critical assumptions and implications, we highlight the general application opportunities as well as uncertainties of global enhanced sampling.