Protein model discrimination attempts using mutational sensitivity, predicted secondary structure, and model quality information

Khare, Shruti; Bhasin, Munmun; Sahoo, Anusmita; Varadarajan, Raghavan

doi:10.1002/prot.25654

Cited by 7 publications

(8 citation statements)

References 59 publications

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“…Deep mutational scanning data could contribute to the investigation of Aβ fibril structure beyond the analysis of existing models we present. For example, others have used site-saturation mutagenesis and deep mutational scanning data to evaluate proposed structural models (Bajaj et al 2008; Khare et al 2019). Additionally, deep mutational scanning data have now been used to generate distance constraints for the prediction of tertiary protein structure (Schmiedel and Lehner 2018; Rollins et al 2018).…”

Section: Discussionmentioning

confidence: 99%

Elucidating the Molecular Determinants of Aβ Aggregation with Deep Mutational Scanning

Gray¹,

Sitko²,

Kameni³

et al. 2019

G3 Genes|Genomes|Genetics

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Despite the importance of Aβ aggregation in Alzheimer’s disease etiology, our understanding of the sequence determinants of aggregation is sparse and largely derived from in vitro studies. For example, in vitro proline and alanine scanning mutagenesis of Aβ40 proposed core regions important for aggregation. However, we lack even this limited mutagenesis data for the more disease-relevant Aβ42. Thus, to better understand the molecular determinants of Aβ42 aggregation in a cell-based system, we combined a yeast DHFR aggregation assay with deep mutational scanning. We measured the effect of 791 of the 798 possible single amino acid substitutions on the aggregation propensity of Aβ42. We found that ∼75% of substitutions, largely to hydrophobic residues, maintained or increased aggregation. We identified 11 positions at which substitutions, particularly to hydrophilic and charged amino acids, disrupted Aβ aggregation. These critical positions were similar but not identical to critical positions identified in previous Aβ mutagenesis studies. Finally, we analyzed our large-scale mutagenesis data in the context of different Aβ aggregate structural models, finding that the mutagenesis data agreed best with models derived from fibrils seeded using brain-derived Aβ aggregates.

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Section: Discussionmentioning

confidence: 99%

Elucidating the Molecular Determinants of Aβ Aggregation with Deep Mutational Scanning

Gray¹,

Sitko²,

Kameni³

et al. 2019

G3 Genes|Genomes|Genetics

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“…During this iterative process of model building, all the models build are compared and aligned by the program MODELLER and later these models were assessed by a number of criteria depending upon GA341 score and DOPE score. Therefore, for a target sequence to be the best model, the GA341 score equal or near to 1 and the lowest DOPE score is considered [41]. Now this model is regarded as the target for in silico docking studies (Figure 1).…”

Section: Results and Discussion 31 In Silico Validation By Targeting Hbcatc In The Neuropathic Pains 311 Homology Modelingmentioning

confidence: 99%

In-Silico Validation and Fabrication of Matrix Diffusion-Based Polymeric Transdermal Patches for Repurposing Gabapentin Hydrochloride in Neuropathic Pain

Singh

Agarwal

Pancham

et al. 2021

CNSNDDT

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Background: Gabapentin (GBP) is an FDA approved drug for the treatment of partial and secondary generalized seizures, apart from being used for diabetic neuropathic pain. GBP displays highly intricate mechanism of action and its inhibitory response in elevated antagonism of NMDA (N-methyl-D-aspartate receptor) receptor and thus, can be repurposed for controlling neuropathic pain. Objective: Therefore, in the present study, we have selected hBCATc (human Pyridoxal 5’-phosphate dependent branched-chain aminotransferase cytosolic) gene that is highly expressed in neuropathic stressed conditions. Thereafter, have analyzed the GBP as its competitive inhibitor by homology modeling, molecular docking, also predicting its structural alerts and pharmacokinetic suitability through ADMET. However, GBP was found to be a potential drug in controlling neuropathic pain, still it has certain critical pharmacokinetics limitations therefore, the need for its targeted delivery was required and the same was attained by designing a GBP loaded trandermal patch (TDP). Methods: A suitable and equally efficacious GBP – TDP was developed by solvent evaporation method using PVP and HPMC in ratio of 2:1 as a polymer base for reservoir type of TDP. Also, PEG 400 was used as a plasticizer and PVA (4%) was taken for backing membrane preparation and then the optimized GBP-TDP was subjected for physical characterization, optimization and ex vivo release kinetics. Methods: A suitable and equally efficacious GBP – TDP was developed by solvent evaporation method using PVP and HPMC in ratio of 2:1 as a polymer base for reservoir type of TDP. Also, PEG 400 was used as a plasticizer and PVA (4%) was taken for backing membrane preparation and then the optimized GBP-TDP was subjected for physical characterization, optimization and ex vivo release kinetics. Results and conclusion: The results showed desired specifications with uneven and flaky surface appearance giving an avenue for controlled release of the drugs with 92.34 ± 1.43% of drug release in 10 h, further suggesting that GBP-TDP can be used as an effective tool against diabetic neuropathy pain.

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“…The present analysis distinguishes between the above possibilities, and is therefore able to distinguish buried from exposed, active-site positions. This is useful for applications that attempt to use saturation mutagenesis data for protein model discrimination and structure prediction [63,64] as well as interpreting clinical data on disease causing mutations [65,66] MFIseq (bind) was also used to predict the Tm of CcdB mutants. We found a good correlation between predicted and measured ΔTm for a subset of CcdB mutants.…”

Section: Discussionmentioning

confidence: 99%

Prediction of residue-specific contributions to binding and thermal stability using yeast surface display

Ahmed

Manjunath

Varadarajan

2021

Preprint

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Quantitative prediction of residue-specific contributions to protein stability and activity is challenging, especially in the absence of experimental structural information. This is important for prediction and understanding of disease causing mutations, and for protein stabilization and design. Using yeast surface display of a saturation mutagenesis library of the bacterial toxin CcdB, we probe the relationship between ligand binding and expression level of displayed protein, with in vivo solubility in E.coli and in vitro thermal stability. We find that both the stability and solubility correlate well with the total amount of active protein on the yeast cell surface but not with total amount of expressed protein. We coupled FACS and deep sequencing to reconstruct the binding and expression mean fluorescent intensity of each mutant. The reconstructed mean fluorescence intensity (MFIseq) was used to differentiate between buried site, exposed non active-site and exposed active-site positions with high accuracy. The MFIseq was also used as a criterion to identify destabilized as well as stabilized mutants in the library, and to predict the melting temperatures of destabilized mutants. These predictions were experimentally validated and were more accurate than those of various computational predictors. The approach was extended to successfully identify buried and active-site residues in the receptor binding domain of the spike protein of SARS-CoV-2, suggesting it has general applicability.

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Protein model discrimination attempts using mutational sensitivity, predicted secondary structure, and model quality information

Cited by 7 publications

References 59 publications

Elucidating the Molecular Determinants of Aβ Aggregation with Deep Mutational Scanning

Elucidating the Molecular Determinants of Aβ Aggregation with Deep Mutational Scanning

In-Silico Validation and Fabrication of Matrix Diffusion-Based Polymeric Transdermal Patches for Repurposing Gabapentin Hydrochloride in Neuropathic Pain

Prediction of residue-specific contributions to binding and thermal stability using yeast surface display

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