Computational methods to examine conformational changes and ligand-binding properties: Examples in neurobiology

Dämgen, Marc A.; Biggin, Philip C.

doi:10.1016/j.neulet.2018.03.004

Cited by 11 publications

(19 citation statements)

References 47 publications

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“…( a ) Time evolution of (from top to bottom): the RMSD of all the Cα atoms of the receptor (black line) and of the transmembrane domain only (grey lines); the distance between TM3 and TM6, measured as the distance between the Cα atoms of residues R134 3 . 50 and K234 6 . 30 ; the RMSD of the sodium ion; the number of water molecules within 6 Å of the Oδ1 or Oδ2 atoms of D84 2 .…”

Section: Resultsmentioning

confidence: 99%

“… 35 and H203 5 . 42 (with 6 common contacts out of a total of 9 and 7 contacts, respectively), but also a secondary network around the chi1 angle of W252 6 . 48 .…”

Section: Resultsmentioning

confidence: 99%

“…5 ). The CIRCULAR second component highlighted angles that rotamerized with W252 6 . 48 at the end of the simulation and the third component highlighted a few angles with a double rotamerization.…”

Section: Resultsmentioning

confidence: 99%

“…In particular, L127 3 . 43 was tightly involved in van der Waals interactions with I245 6 . 41 and F278 6 .…”

Section: Resultsmentioning

confidence: 99%

“…To bridge the gap between computationally accessible and biologically relevant timescales, numerous molecular dynamics techniques aimed at accelerating simulations have been developed. These simulations include coarse grained simulations, normal mode analysis and a variety of techniques aimed at reducing the energy barriers between interconverting conformations 4 , 6 . Among them, accelerated molecular dynamics (aMD) provides boosts of dihedral and potential energies, helping the system to overcome energy barriers 13 , 14 .…”

Section: Introductionmentioning

confidence: 99%

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Deciphering collaborative sidechain motions in proteins during molecular dynamics simulations

Taddese

Garnier

Abdi

et al. 2020

Sci Rep

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The dynamic structure of proteins is essential for their functions and may include large conformational transitions which can be studied by molecular dynamics (MD) simulations. However, details of these transitions are difficult to automatically track. To facilitate their analysis, we developed two scores of correlation between sidechain dihedral angles. The CIRCULAR and OMES scores are computed from, respectively, dihedral angle values and rotamer distributions. As a case study, we applied our methods to an activation-like transition of the chemokine receptor CXCR4, observed during accelerated MD simulations. The principal component analysis of the correlation matrices was consistent with the networking structure of the top ranking pairs. Both scores identify a set of residues whose “collaborative” sidechain rotamerization immediately preceded or accompanied the conformational transition of CXCR4. Detailed analysis of the sequential order of these rotamerizations suggests that an allosteric mechanism, involving the outward motion of an asparagine residue in transmembrane helix 3, might be a prerequisite to the large scale conformational transition of CXCR4. This case study provides the proof-of-concept that the correlation methods developed here are valuable exploratory techniques to help decipher complex reactional pathways.

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Section: Resultsmentioning

confidence: 99%

“… 35 and H203 5 . 42 (with 6 common contacts out of a total of 9 and 7 contacts, respectively), but also a secondary network around the chi1 angle of W252 6 . 48 .…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…In particular, L127 3 . 43 was tightly involved in van der Waals interactions with I245 6 . 41 and F278 6 .…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deciphering collaborative sidechain motions in proteins during molecular dynamics simulations

Taddese

Garnier

Abdi

et al. 2020

Sci Rep

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Heavy metal ion separation from industrial wastewater using stacked graphene Membranes: A molecular dynamics simulation study

Giri

Cordeiro

2021

Journal of Molecular Liquids

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Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Tu,

Fu,

Zheng

et al. 2024

J. Chem. Inf. Model.

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Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning-and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

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Computational methods to examine conformational changes and ligand-binding properties: Examples in neurobiology

Cited by 11 publications

References 47 publications

Deciphering collaborative sidechain motions in proteins during molecular dynamics simulations

Deciphering collaborative sidechain motions in proteins during molecular dynamics simulations

Heavy metal ion separation from industrial wastewater using stacked graphene Membranes: A molecular dynamics simulation study

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

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