LITiCon: A Discrete Conformational Sampling Computational Method for Mapping Various Functionally Selective Conformational States of Transmembrane Helical Proteins

Bhattacharya, Supriyo; Vaidehi, Nagarajan

doi:10.1007/978-1-62703-023-6_10

Cited by 10 publications

(16 citation statements)

References 20 publications

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“…However, at the time we started this work we did not have a crystal structure for the blind test case of C5aR and hence we have not used the C5aR crystal structure that has been published since (Robertson et al, 2018). Starting from the respective crystal structure for each GPCR (see Table S1 for a list of crystal structures and their respective protein databank identities) we used the LiticonDesign method Bhattacharya and Vaidehi, 2012;Vaidehi et al, 2016) to generate a small ensemble of conformations for each GPCR. We have described the LiticonDesign method in detail in our previous work (Balaraman et al, 2010;Bhattacharya et al, 2014Bhattacharya et al, , 2008Bhattacharya and Vaidehi, 2012).…”

Section: Methods Used To Calculate Energy Related Featuresmentioning

confidence: 99%

“…Starting from the respective crystal structure for each GPCR (see Table S1 for a list of crystal structures and their respective protein databank identities) we used the LiticonDesign method Bhattacharya and Vaidehi, 2012;Vaidehi et al, 2016) to generate a small ensemble of conformations for each GPCR. We have described the LiticonDesign method in detail in our previous work (Balaraman et al, 2010;Bhattacharya et al, 2014Bhattacharya et al, , 2008Bhattacharya and Vaidehi, 2012). Briefly, the LiticonDesign method for predicting thermostable GPCR mutants involves two steps: (i) using a starting structural model of the GPCR, the method generates a small ensemble of conformations that allows for the perturbations in the GPCR conformation caused by mutations and, (ii) an all-atom energy function to calculate the stability of the conformations that takes into account the difference in the structural stability of the mutants and the wild type to score the positive thermostable mutants.…”

Section: Methods Used To Calculate Energy Related Featuresmentioning

confidence: 99%

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Machine Learning for Prioritization of Thermostabilizing Mutations for G-protein Coupled Receptors

Muk

Ghosh

Achuthan

et al. 2019

Preprint

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Although the three-dimensional structures of G-protein-coupled receptors (GPCRs), the largest superfamily of drug targets, have enabled structure-based drug design, there are no structures available for 87% of GPCRs. This is due to the stiff challenge in purifying the inherently flexible GPCRs. Identifying thermostabilized mutant GPCRs via systematic alanine scanning mutations has been a successful strategy in stabilizing GPCRs, but it remains a daunting task for each GPCR. We developed a computational method that combines sequence, structure and dynamics based molecular properties of GPCRs that recapitulate GPCR stability, with four different machine learning methods to predict thermostable mutations ahead of experiments. This method has been trained on thermostability data for 1231 mutants, the largest publicly available dataset. A blind prediction for thermostable mutations of the Complement factor C5a Receptor retrieved 36% of the thermostable mutants in the top 50 prioritized mutants compared to 3% in the first 50 attempts using systematic alanine scanning. Statement Of SignifiganceG-protein-coupled receptors (GPCRs), the largest superfamily of membrane proteins play a vital role in cellular physiology and are targets to blockbuster drugs. Hence it is imperative to solve the three dimensional structures of GPCRs in various conformational states with different types of ligands bound. To reduce the experimental burden in identifying thermostable GPCR mutants, we report a computational framework using machine learning algorithms trained on thermostability data for 1231 mutants and features calculated from analysis of GPCR sequences, structure and dynamics to predict thermostable mutations ahead of experiments. This work represents a significant advancement in the development, validation and testing of a computational framework that can be extended to other class A GPCRs and helical membrane proteins.

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Section: Methods Used To Calculate Energy Related Featuresmentioning

confidence: 99%

Section: Methods Used To Calculate Energy Related Featuresmentioning

confidence: 99%

Machine Learning for Prioritization of Thermostabilizing Mutations for G-protein Coupled Receptors

Muk

Ghosh

Achuthan

et al. 2019

Preprint

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“…The details of the LITiCon method have been published elsewhere. 17,18 We describe the method briefly as applied here. Starting from an initial receptor structure, all the seven TM helices were simultaneously rotated about the helical axis between ±5° in 10° increment, thus generating 2 7 = 128 conformations.…”

Section: Methodsmentioning

confidence: 99%

“…We have developed a rapid screening computational method, LITiConDesign (by extending the computational method LITiCon for GPCR conformational sampling 17 ), for predicting single point alanine mutations to thermostabilize GPCRs. Systematic computational alanine scanning on the TM residues was performed on an ensemble of receptor conformations generated using a structural model of the receptor.…”

Section: Introductionmentioning

confidence: 99%

Rapid Computational Prediction of Thermostabilizing Mutations for G Protein-Coupled Receptors

Bhattacharya

Lee

Grisshammer

et al. 2014

J. Chem. Theory Comput.

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G protein-coupled receptors (GPCRs) are highly dynamic and often denature when extracted in detergents. Deriving thermostable mutants has been a successful strategy to stabilize GPCRs in detergents, but this process is experimentally tedious. We have developed a computational method to predict the position of the thermostabilizing mutations for a given GPCR sequence. We have validated the method against experimentally measured thermostability data for single mutants of the β1-adrenergic receptor (β1AR), adenosine A2A receptor (A2AR) and neurotensin receptor 1 (NTSR1). To make these predictions we started from homology models of these receptors of varying accuracies and generated an ensemble of conformations by sampling the rigid body degrees of freedom of transmembrane helices. Then, an all-atom force field function was used to calculate the enthalpy gain, known as the “stability score” upon mutation of every residue, in these receptor structures, to alanine. For all three receptors, β1AR, A2AR, and NTSR1, we observed that mutations of hydrophobic residues in the transmembrane domain to alanine that have high stability scores correlate with high experimental thermostability. The prediction using the stability score improves when using an ensemble of receptor conformations compared to a single structure, showing that receptor flexibility is important. We also find that our previously developed LITiCon method for generating conformation ensembles is similar in performance to predictions using ensembles obtained from microseconds of molecular dynamics simulations (which is computationally hundred times slower than LITiCon). We improved the thermostability prediction by including other properties such as residue-based stress and the extent of allosteric communication by each residue in the stability score. Our method is the first step toward a computational method for rapid prediction of thermostable mutants of GPCRs.

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“…The latter plays a key role 90 for the interaction between the ligand and the protein matrix. 91 Some of the classical docking algorithms take into account the 92 flexibility of both ligand and receptor's binding sites, increasing 93 in this way the chance to generate reliable hypotheses on the pre- 94 dominant conformations of receptor-ligand complexes [25][26][27][28][29]. 95 Nevertheless, experimental mutagenesis data is still the only tool 96 for the selection of the most probable binding mode [30].…”

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confidence: 99%

Structural predictions of neurobiologically relevant G-protein coupled receptors and intrinsically disordered proteins

Rossetti

Dibenedetto

Calandrini

et al. 2015

Archives of Biochemistry and Biophysics

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LITiCon: A Discrete Conformational Sampling Computational Method for Mapping Various Functionally Selective Conformational States of Transmembrane Helical Proteins

Cited by 10 publications

References 20 publications

Machine Learning for Prioritization of Thermostabilizing Mutations for G-protein Coupled Receptors

Machine Learning for Prioritization of Thermostabilizing Mutations for G-protein Coupled Receptors

Rapid Computational Prediction of Thermostabilizing Mutations for G Protein-Coupled Receptors

Structural predictions of neurobiologically relevant G-protein coupled receptors and intrinsically disordered proteins

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