Rigidity and flexibility in the tetrasaccharide linker of proteoglycans from atomic‐resolution molecular simulation

Ng, Cathy; Premnath, Padmavathy Nandha; Guvench, Olgun

doi:10.1002/jcc.24738

Cited by 13 publications

(20 citation statements)

References 66 publications

(97 reference statements)

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“…Additionally, some studies have used results from LC-MS [45], X-ray crystallography [46], and NMR [46][47][48][49][50][51] to compare and validate conformational data from molecular dynamics (MD) simulations. This suggests that MD simulations can produce results complementary to experimental analysis methods by providing realistic three-dimensional atomic-resolution molecular models of GAG conformational ensembles [52][53][54][55][56]. Compact non-sulfated chondroitin 20-mer conformation arising from flexible glycosidic linkages (red) between monosaccharide rings (GalNAc in blue and GlcA in cyan).…”

Section: Introductionmentioning

confidence: 99%

Efficient Construction of Atomic-Resolution Models of Non-Sulfated Chondroitin Glycosaminoglycan Using Molecular Dynamics Data

et al. 2020

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Glycosaminoglycans (GAGs) are linear, structurally diverse, conformationally complex carbohydrate polymers that may contain up to 200 monosaccharides. These characteristics present a challenge for studying GAG conformational thermodynamics at atomic resolution using existing experimental methods. Molecular dynamics (MD) simulations can overcome this challenge but are only feasible for short GAG polymers. To address this problem, we developed an algorithm that applies all conformational parameters contributing to GAG backbone flexibility (i.e., bond lengths, bond angles, and dihedral angles) from unbiased all-atom explicit-solvent MD simulations of short GAG polymers to rapidly construct models of GAGs of arbitrary length. The algorithm was used to generate non-sulfated chondroitin 10- and 20-mer ensembles which were compared to MD-generated ensembles for internal validation. End-to-end distance distributions in constructed and MD-generated ensembles have minimal differences, suggesting that our algorithm produces conformational ensembles that mimic the backbone flexibility seen in simulation. Non-sulfated chondroitin 100- and 200-mer ensembles were constructed within a day, demonstrating the efficiency of the algorithm and reduction in time and computational cost compared to simulation.

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Section: Introductionmentioning

confidence: 99%

Efficient Construction of Atomic-Resolution Models of Non-Sulfated Chondroitin Glycosaminoglycan Using Molecular Dynamics Data

et al. 2020

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“…Compared with many other enhanced sampling methods, ABF is physically intuitive and depends little on specific information about the free energy profiles. As a consequence, ABF is widely applied in protein-ligand binding [1,123], the host-guest system [124], the conformational transition of peptides and proteins [4,125,126], and the membrane permeability of small molecules [127,128].…”

Section: Adaptive Biasing Force Methodsmentioning

confidence: 99%

Advances in enhanced sampling molecular dynamics simulations for biomolecules

Wang

Zhang

2019

Chinese Journal of Chemical Physics

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Molecular dynamics simulation has emerged as a powerful computational tool for studying biomolecules as it can provide atomic insights into the conformational transitions involved in biological functions. However, when applied to complex biological macromolecules, the conformational sampling ability of conventional molecular dynamics is limited by the rugged free energy landscapes, leading to inherent timescale gaps between molecular dynamics simulations and real biological processes. To address this issue, several advanced enhanced sampling methods have been proposed to improve the sampling efficiency in molecular dynamics. In this review, the theoretical basis, practical applications, and recent improvements of both constraint and unconstrained enhanced sampling methods are summarized. Furthermore, the combined utilizations of different enhanced sampling methods that take advantage of both approaches are also briefly discussed.

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“…95 In fact, a recent application attempts to understand the flexibility of the core tetrasaccharide linker sequence connecting the GAG to the protein of the proteoglycan using CHARMM C36 force field in CHARMM. 125 1.11 | GLYCAM force field/parameter set using AMBER More computational studies on sulfated GAGs have been carried out using the GLYCAM parameters in AMBER than any other force field. One of the reasons for this is the earlier widespread use of AMBER in MD simulations of peptides, proteins and small molecules.…”

Section: Simulations Involving the Charmm Force Field In Charmmmentioning

confidence: 99%

“…Since the earlier CHARMM force fields did not carry N ‐sulfamate parameters, this enhancement is expected to enable more effective applications of the recent versions of CHARMM . In fact, a recent application attempts to understand the flexibility of the core tetrasaccharide linker sequence connecting the GAG to the protein of the proteoglycan using CHARMM C36 force field in CHARMM …”

Section: Introductionmentioning

confidence: 99%

Perspective on computational simulations of glycosaminoglycans

Nagarajan

Sankaranarayanan

Desai

2018

WIREs Comput Mol Sci

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Glycosaminoglycans (GAGs) represent a formidable frontier for chemists, biochemists, biologists, medicinal chemists, and drug delivery specialists because of massive structural complexity. GAGs are arguably the most complex, natural linear biopolymers with theoretical diversity orders of magnitude higher than proteins and nucleic acids. Yet, this diversity remains generally untapped. Computational approaches offer major routes to understand GAG structure and dynamics so as to enable novel applications of these biopolymers. In fact, computational algorithms, softwares, online tools, and techniques have reached a level of sophistication that help understand atomistic details of conformational variation and protein recognition of individual GAG sequences. This review describes current approaches and challenges in computational study of GAGs. It presents a history of major findings since the earliest mention of GAGs (the 1960s), the development of parameters and force fields specific for GAGs, and the application of these tools in understanding GAG structure–function relationship. This review also presents a section on how to perform simulation of GAGs, which is directed toward researchers interested in entering this promising field with potential to impact therapy. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Molecular Structures

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Rigidity and flexibility in the tetrasaccharide linker of proteoglycans from atomic‐resolution molecular simulation

Cited by 13 publications

References 66 publications

Efficient Construction of Atomic-Resolution Models of Non-Sulfated Chondroitin Glycosaminoglycan Using Molecular Dynamics Data

Efficient Construction of Atomic-Resolution Models of Non-Sulfated Chondroitin Glycosaminoglycan Using Molecular Dynamics Data

Advances in enhanced sampling molecular dynamics simulations for biomolecules

Perspective on computational simulations of glycosaminoglycans

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