Binding free energy calculations of galectin-3–ligand interactions

Mandal, Tarun K.; Mukhopadhyay, Chaitali

doi:10.1093/protein/15.12.979

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…Galectin binds to beta-galactosides with very high specificity. The calculated binding free energies agree [237] quite well with the experimental data. Also the ranking of binding affinities is well reproduced.…”

Section: Galectinssupporting

confidence: 79%

A Binding Affinity Based Computational Pathway for Active-Site Directed Lead Molecule Design: Some Promises and Perspectives

Latha¹,

Jayaram

2005

DDRO

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Drug discovery in the 21 st century is expected to be different in at least two distinct ways: development of individualized medicine utilizing genomic information and emergence of an integrated in silico protocol for facilitating target identification, structure prediction and lead discovery. The expectations from computational methods for developing suggestions on potential leads reliably and expeditiously, are continuously on the increase. Several conceptual and methodological concerns remain before an automation of lead design in silico could be contemplated. The novelty of the candidates generated, their geometries, the partial atomic charges and other force field parameters for enabling energy evaluations is one concern. A proper account of the flexibility of the candidate molecule and the target, a consideration of solvent and salt effects in binding and a reliable methodology for developing quantitative estimates of binding affinities is another. Finally the drug-likeness of the candidates generated is yet another concern. Each of these issues warrants a careful consideration. In this review, we sketch a system independent, binding free energy based, comprehensive computational pathway from chemical templates to lead-like molecules, given the three dimensional structure of the target protein and a definition of its active site, focusing on some emerging in silico trends and techniques. We survey current methods for generation of candidate molecules and some popular protocols for docking candidates in the protein active site. We discuss the theory of protein-ligand binding in the rigorous framework of statistical mechanics and assess the current strategies for affinity based filtering of candidates. We address concerns related to flexibility of the target and the candidate, solvent and salt effects in lead design. We present a realization of the pathway proposed in a high performance computing environment for cyclooxygenase-2 target wherein the computational protocols could sort drugs from nondrugs, assuring the viability of the overall strategy. We highlight a few case studies indicating the current level of agreement between theory and experiment in eliciting binding affinities. Finally, we present a critical assessment of the computational steps involved in binding affinity based active site directed lead molecule design and further improvements envisioned for potential automation.

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Section: Galectinssupporting

confidence: 79%

A Binding Affinity Based Computational Pathway for Active-Site Directed Lead Molecule Design: Some Promises and Perspectives

Latha¹,

Jayaram

2005

DDRO

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“…(5) proposed by Åqvist18, 19 has two parameters. The electrostatic part α is usually fixed (0.5) for all systems and β is calibrated according to the system 39–41. Åqvist and coworkers18 used α = 0.5 and β = 0.16 to calculate the binding free energies for a number of proteins that were in good agreement with the experimental results.…”

Section: Resultsmentioning

confidence: 96%

“…Wang et al used α = 0.5 and β = 1 to get the binding free energies of substances binding to avidin in accordance with the experimental data 42. Mondal et al used α = 0.5 and β = 0.81 to calculate the binding free energy of a di/trisaccharide ligand to galectin and β = 1.15 for glucose binding 41. But Hou et al43 used this equation [Eq.…”

Section: Resultsmentioning

confidence: 99%

Central nervous system metastases in patients with high‐risk breast carcinoma after multimodality treatment

González-Angulo

Cristofanilli

Strom

et al. 2004

Cancer

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Ricin B is a galactose‐binding protein, which contains two binding sites. We have compared the binding properties of the two binding sites of ricin B chain toward different mono‐ and disaccharide ligands. The free energies of binding are calculated using the free energy perturbation simulation (thermodynamic integration method) and linear interaction energy approach using CHARMM force field. The second binding site of the protein was found to be weaker compared to the first. The details of the hydrogen‐bonding scheme suggested the origin of the epimeric specificity of the protein. The reason for the weaker binding capacity of the second binding site has been addressed. © 2006 Wiley Periodicals, Inc. Biopolymers 83: 83–94, 2006 This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com

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“…In more recent works the value of β has been a variable as well; values in the range 0.17 to 1.15 have been used. [40, 41] It has been shown that the values of α and β do only slightly depend on the applied force field; [40] for the Amber95 force field [42] (a similar force field to the one used in this work, CHARMM) the optimal agreement was found with α =0.30 and β =0.17. These values are very similar to those that were determined by Ganguly & Mukhopadhyay with the CHARMM force field for the protein ricin B lectin and sugar-type ligands ( α =0.36, β =0.16).…”

Section: Methodsmentioning

confidence: 87%

The HSP90 binding mode of a radicicol-like E-oxime determined by docking, binding free energy estimations, and NMR 15N chemical shifts

Spichty¹,

Taly²,

Hagn³

et al. 2009

Biophysical Chemistry

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We determine the binding mode of a macrocyclic radicicol-like oxime to yeast HSP90 by combining computer simulations and experimental measurements. We sample the macrocyclic scaffold of the unbound ligand by parallel tempering simulations and dock the most populated conformations to yeast HSP90. Docking poses are then evaluated by the use of binding free energy estimations with the linear interaction energy method. Comparison of QM/MM-calculated NMR chemical shifts with experimental shift data for a selective subset of back-bone 15 N provides an additional evaluation criteria. As a last test we check the binding modes against available structure-activity-relationships. We find that the most likely binding mode of the oxime to yeast HSP90 is very similar to the known structure of the radicicol-HSP90 complex.

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Binding free energy calculations of galectin-3–ligand interactions

Cited by 10 publications

References 29 publications

A Binding Affinity Based Computational Pathway for Active-Site Directed Lead Molecule Design: Some Promises and Perspectives

A Binding Affinity Based Computational Pathway for Active-Site Directed Lead Molecule Design: Some Promises and Perspectives

Central nervous system metastases in patients with high‐risk breast carcinoma after multimodality treatment

The HSP90 binding mode of a radicicol-like E-oxime determined by docking, binding free energy estimations, and NMR 15N chemical shifts

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