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Molecular dynamics simulations to understand glycosaminoglycan interactions in the free- and protein-bound states.

ABSTRACT: Natural glycosaminoglycans (GAGs) are informational molecules with astounding structural diversity. Understanding the behavior of GAGs in the free and protein-bound states is critical for harnessing this diversity. Molecular dynamics (MD) offers atomistic insight into principles governing GAG recognition by proteins. Here, we discuss how MD can be used to understand local and global properties of GAGs in free solution, including torsions, puckering, hydrogen bonding, flexibility, and energetics. We discuss MD studies on GAG-protein complexes, which help elucidate the strength of interacting residues, role of water, energetics, and so on. The MD results accumulated so far suggest that GAG recognition of proteins is a continuum from the highly selective on one end to the fully non-selective on the other with intermediate levels of selectivity, including moderately selective and plastic. The advancements in MD technology, such as coarse-grained MD, coupled with really long simulations will help understand macroscale molecular movements in the future.

SUBMITTER: Nagarajan B

PROVIDER: S-EPMC9189024 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Molecular dynamics simulations to understand glycosaminoglycan interactions in the free- and protein-bound states.

Nagarajan Balaji B Holmes Samuel G SG Sankaranarayanan Nehru Viji NV Desai Umesh R UR

Current opinion in structural biology 20220317

Natural glycosaminoglycans (GAGs) are informational molecules with astounding structural diversity. Understanding the behavior of GAGs in the free and protein-bound states is critical for harnessing this diversity. Molecular dynamics (MD) offers atomistic insight into principles governing GAG recognition by proteins. Here, we discuss how MD can be used to understand local and global properties of GAGs in free solution, including torsions, puckering, hydrogen bonding, flexibility, and energetics. ...[more]

PMID: 35306321

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Project description:We have performed microsecond molecular dynamics (MD) simulations to characterize the structural dynamics of cation-bound E1 intermediate states of the calcium pump (sarcoendoplasmic reticulum Ca²⁺-ATPase, SERCA) in atomic detail, including a lipid bilayer with aqueous solution on both sides. X-ray crystallography with 40 mM Mg²⁺ in the absence of Ca²⁺ has shown that SERCA adopts an E1 structure with transmembrane Ca²⁺-binding sites I and II exposed to the cytosol, stabilized by a single Mg²⁺ bound to a hybrid binding site I'. This Mg²⁺-bound E1 intermediate state, designated E1•Mg²⁺, is proposed to constitute a functional SERCA intermediate that catalyzes the transition from E2 to E1•2Ca²⁺ by facilitating H⁺/Ca²⁺ exchange. To test this hypothesis, we performed two independent MD simulations based on the E1•Mg²⁺ crystal structure, starting in the presence or absence of initially-bound Mg²⁺. Both simulations were performed for 1 µs in a solution containing 100 mM K⁺ and 5 mM Mg²⁺ in the absence of Ca²⁺, mimicking muscle cytosol during relaxation. In the presence of initially-bound Mg²⁺, SERCA site I' maintained Mg²⁺ binding during the entire MD trajectory, and the cytosolic headpiece maintained a semi-open structure. In the absence of initially-bound Mg²⁺, two K⁺ ions rapidly bound to sites I and I' and stayed loosely bound during most of the simulation, while the cytosolic headpiece shifted gradually to a more open structure. Thus MD simulations predict that both E1•Mg²⁺ and E•2K+ intermediate states of SERCA are populated in solution in the absence of Ca²⁺, with the more open 2K+-bound state being more abundant at physiological ion concentrations. We propose that the E1•2K⁺ state acts as a functional intermediate that facilitates the E2 to E1•2Ca²⁺ transition through two mechanisms: by pre-organizing transport sites for Ca²⁺ binding, and by partially opening the cytosolic headpiece prior to Ca²⁺ activation of nucleotide binding.

Project description:End-point free energy calculations using MM-GBSA and MM-PBSA provide a detailed understanding of molecular recognition in protein-ligand interactions. The binding free energy can be used to rank-order protein-ligand structures in virtual screening for compound or target identification. Here, we carry out free energy calculations for a diverse set of 11 proteins bound to 14 small molecules using extensive explicit-solvent MD simulations. The structure of these complexes was previously solved by crystallography and their binding studied with isothermal titration calorimetry (ITC) data enabling direct comparison to the MM-GBSA and MM-PBSA calculations. Four MM-GBSA and three MM-PBSA calculations reproduced the ITC free energy within 1 kcal·mol(-1) highlighting the challenges in reproducing the absolute free energy from end-point free energy calculations. MM-GBSA exhibited better rank-ordering with a Spearman ? of 0.68 compared to 0.40 for MM-PBSA with dielectric constant (? = 1). An increase in ? resulted in significantly better rank-ordering for MM-PBSA (? = 0.91 for ? = 10), but larger ? significantly reduced the contributions of electrostatics, suggesting that the improvement is due to the nonpolar and entropy components, rather than a better representation of the electrostatics. The SVRKB scoring function applied to MD snapshots resulted in excellent rank-ordering (? = 0.81). Calculations of the configurational entropy using normal-mode analysis led to free energies that correlated significantly better to the ITC free energy than the MD-based quasi-harmonic approach, but the computed entropies showed no correlation with the ITC entropy. When the adaptation energy is taken into consideration by running separate simulations for complex, apo, and ligand (MM-PBSAADAPT), there is less agreement with the ITC data for the individual free energies, but remarkably good rank-ordering is observed (? = 0.89). Interestingly, filtering MD snapshots by prescoring protein-ligand complexes with a machine learning-based approach (SVMSP) resulted in a significant improvement in the MM-PBSA results (? = 1) from ? = 0.40 to ? = 0.81. Finally, the nonpolar components of MM-GBSA and MM-PBSA, but not the electrostatic components, showed strong correlation to the ITC free energy; the computed entropies did not correlate with the ITC entropy.

Dataset Information

Molecular dynamics simulations to understand glycosaminoglycan interactions in the free- and protein-bound states.

Publications

Molecular dynamics simulations to understand glycosaminoglycan interactions in the free- and protein-bound states.

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