Computational study of ligand binding to protein receptors

Wembridge, Paul; Robinson, Heather; Novak, Igor

doi:10.1016/j.bioorg.2008.08.001

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…Interestingly, in a quantum-chemical study of PDB crystal structures of 12 ligand molecules bound to multiple sites within human serum albumin, 58 the authors reported enthalpic contributions of ligand reorganization for the same molecule in the different binding sites of up to 27 kcal/mol.…”

Section: Journal Of Chemical Information and Modelingmentioning

confidence: 99%

PDB Ligand Conformational Energies Calculated Quantum-Mechanically

Sitzmann

Weidlich²,

Filippov³

et al. 2012

J. Chem. Inf. Model.

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We present here a greatly updated version of an earlier study on the conformational energies of protein-ligand complexes in the Protein Data Bank (PDB) [Nicklaus et al. Bioorg. Med. Chem. 1995, 3, 411-428], with the goal of improving on all possible aspects such as number and selection of ligand instances, energy calculations performed, and additional analyses conducted. Starting from about 357,000 ligand instances deposited in the 2008 version of the Ligand Expo database of the experimental 3D coordinates of all small-molecule instances in the PDB, we created a "high-quality" subset of ligand instances by various filtering steps including application of crystallographic quality criteria and structural unambiguousness. Submission of 640 Gaussian 03 jobs yielded a set of about 415 successfully concluded runs. We used a stepwise optimization of internal degrees of freedom at the DFT level of theory with the B3LYP/6-31G(d) basis set and a single-point energy calculation at B3LYP/6-311++G(3df,2p) after each round of (partial) optimization to separate energy changes due to bond length stretches vs bond angle changes vs torsion changes. Even for the most "conservative" choice of all the possible conformational energies-the energy difference between the conformation in which all internal degrees of freedom except torsions have been optimized and the fully optimized conformer-significant energy values were found. The range of 0 to ~25 kcal/mol was populated quite evenly and independently of the crystallographic resolution. A smaller number of "outliers" of yet higher energies were seen only at resolutions above 1.3 Å. The energies showed some correlation with molecular size and flexibility but not with crystallographic quality metrics such as the Cruickshank diffraction-component precision index (DPI) and R(free)-R, or with the ligand instance-specific metrics such as occupancy-weighted B-factor (OWAB), real-space R factor (RSR), and real-space correlation coefficient (RSCC). We repeated these calculations with the solvent model IEFPCM, which yielded energy differences that were generally somewhat lower than the corresponding vacuum results but did not produce a qualitatively different picture. Torsional sampling around the crystal conformation at the molecular mechanics level using the MMFF94s force field typically led to an increase in energy.

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Section: Journal Of Chemical Information and Modelingmentioning

confidence: 99%

PDB Ligand Conformational Energies Calculated Quantum-Mechanically

Sitzmann

Weidlich²,

Filippov³

et al. 2012

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…Numerous studies have been performed to understand the conformational energy cost when a ligand binds to its target. Some argue that the acceptable conformational energy penalty is relatively low (below 3 kcal mol −1 [74,75], mostly below 5 kcal mol −1 [76], between 4 and 6 kcal mol −1 [77], and mostly below 6 kcal mol −1 [78]) However, energies, above 9 kcal mol −1 [76], around 15.9 ± 11.5 kcal mol −1 [79], between 0 -25 kcal mol −1 [80], and even up to 27 kcal mol −1 [81], have been suggested as feasible for protein-bound ligands. For conformational sampling of macrocycles, Chen and Foloppe noticed an improved reproduction of the X-ray conformation for MTLMOD using an increased energy window of up to 15 kcal mol −1 [44].…”

Section: Energy Window For Sampling Macrocyclesmentioning

confidence: 98%

Conformational analysis of macrocycles: comparing general and specialized methods

Olanders

Alogheli

Brandt

et al. 2020

J Comput Aided Mol Des

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Macrocycles represent an important class of medicinally relevant small molecules due to their interesting biological properties. Therefore, a firm understanding of their conformational preferences is important for drug design. Given the importance of macrocycle-protein modelling in drug discovery, we envisaged that a systematic study of both classical and recent specialized methods would provide guidance for other practitioners within the field. In this study we compare the performance of the general, well established conformational analysis methods Monte Carlo Multiple Minimum (MCMM) and Mixed Torsional/Low-Mode sampling (MTLMOD) with two more recent and specialized macrocycle sampling techniques: MacroModel macrocycle Baseline Search (MD/LLMOD) and Prime macrocycle conformational sampling (PRIME-MCS). Using macrocycles extracted from 44 macrocycle-protein X-ray crystallography complexes, we evaluated each method based on their ability to (i) generate unique conformers, (ii) generate unique macrocycle ring conformations, (iii) identify the global energy minimum, (iv) identify conformers similar to the X-ray ligand conformation after Protein Preparation Wizard treatment (X-rayppw), and (v) to the X-rayppw ring conformation. Computational speed was also considered. In addition, conformational coverage, as defined by the number of conformations identified, was studied. In order to study the relative energies of the bioactive conformations, the energy differences between the global energy minima and the energy minimized X-rayppw structures and, the global energy minima and the MCMM-Exhaustive (1,000,000 search steps) generated conformers closest to the X-rayppw structure, were calculated and analysed. All searches were performed using relatively short run times (10,000 steps for MCMM, MTLMOD and MD/LLMOD). To assess the performance of the methods, they were compared to an exhaustive MCMM search using 1,000,000 search steps for each of the 44 macrocycles (requiring ca 200 times more CPU time). Prior to our analysis, we also investigated if the general search methods MCMM and MTLMOD could also be optimized for macrocycle conformational sampling. Taken together, our work concludes that the more general methods can be optimized for macrocycle modelling by slightly adjusting the settings around the ring closure bond. In most cases, MCMM and MTLMOD with either standard or enhanced settings performed well in comparison to the more specialized macrocycle sampling methods MD/LLMOD and PRIME-MCS. When using enhanced settings for MCMM and MTLMOD, the X-rayppw conformation was regenerated with the greatest accuracy. The, MD/LLMOD emerged as the most efficient method for generating the global energy minima. Graphic abstract

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Towards understanding the unbound state of drug compounds: Implications for the intramolecular reorganization energy upon binding

Foloppe

Chen

2016

Bioorganic & Medicinal Chemistry

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Computational study of ligand binding to protein receptors

Cited by 5 publications

References 20 publications

PDB Ligand Conformational Energies Calculated Quantum-Mechanically

PDB Ligand Conformational Energies Calculated Quantum-Mechanically

Conformational analysis of macrocycles: comparing general and specialized methods

Towards understanding the unbound state of drug compounds: Implications for the intramolecular reorganization energy upon binding

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