Discovering Putative Protein Targets of Small Molecules: A Study of the p53 Activator Nutlin

Nguyen, Minh N. H.; Sen, Neeladri; Lin, Meiyin; Joseph, Thomas L.; Vaz, Candida; Tanavde, Vivek; Way, Luke; Hupp, Ted R.; Verma, Chandra; Madhusudhan, M.S.

doi:10.1021/acs.jcim.8b00762

Cited by 13 publications

(11 citation statements)

References 39 publications

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“…The Amber99SB-ILDN force field has been used for the MD simulations of protein-peptide and protein-ligand complexes extensively [57–61]. In an earlier study, we used the same force field to study various protein-ligand interactions and validated one such purported complex experimentally [62]. In the cases where the small molecule ligand dissociated from the binding site, we re-simulated the system using the CHARMM27 force field [63], another popularly used molecular mechanics package.…”

Section: Methodsmentioning

confidence: 99%

Predicting and designing therapeutics against the Nipah virus

et al. 2019

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Despite Nipah virus outbreaks having high mortality rates (>70% in Southeast Asia), there are no licensed drugs against it. In this study, we have considered all 9 Nipah proteins as potential therapeutic targets and computationally identified 4 putative peptide inhibitors (against G, F and M proteins) and 146 small molecule inhibitors (against F, G, M, N, and P proteins). The computations include extensive homology/ab initio modeling, peptide design and small molecule docking. An important contribution of this study is the increased structural characterization of Nipah proteins by approximately 90% of what is deposited in the PDB. In addition, we have carried out molecular dynamics simulations on all the designed protein-peptide complexes and on 13 of the top shortlisted small molecule ligands to check for stability and to estimate binding strengths. Details, including atomic coordinates of all the proteins and their ligand bound complexes, can be accessed at http://cospi.iiserpune.ac.in/Nipah. Our strategy was to tackle the development of therapeutics on a proteome wide scale and the lead compounds identified could be attractive starting points for drug development. To counter the threat of drug resistance, we have analysed the sequences of the viral strains from different outbreaks, to check whether they would be sensitive to the binding of the proposed inhibitors.

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Section: Methodsmentioning

confidence: 99%

Predicting and designing therapeutics against the Nipah virus

et al. 2019

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“…However, optimizing model quality cutoffs for such consensus estimators will involve benchmarking and is out of the scope of the current study. The high-confidence models (where both the techniques provide consensus structures) could be used to predict and design inhibitors and drugs against them [ 91 ], predict off-target effects of putative drugs [ 92 ], explain the functioning of proteins, the impact of variations [ 93 , 94 ] or model protein complexes [ 95 ].…”

Section: Discussionmentioning

confidence: 99%

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs

Sen

Anishchenko

Bordin

et al. 2022

Briefings in Bioinformatics

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Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein–protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.

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“…In some others, the predicted structure of the local regions are similar, however, changes in loop orientations increase the RMSD. These high confidence models could be used to predict/design inhibitors/drugs against them (Sen et al, 2019), predict off-target effects of putative drugs (Nguyen et al, 2019), explain the functioning of proteins, the impact of variations (Waman et al, 2021) or model protein complexes (Kanitkar et al, 2021). RMSDs between the models built using AlphaFold and RoseTTAFold had an inverse correlation with model quality.…”

Section: Discussionmentioning

confidence: 99%

“…However, optimising model quality cut-offs for such consensus estimators will involve benchmarking and is out of the scope of the current study. The high confidence models (where both the techniques provide consensus structures) could be used to predict/design inhibitors/drugs against them [91], predict off-target effects of putative drugs [92], explain the functioning of proteins, the impact of variations [93,94] or model protein complexes [95].…”

Section: Discussionmentioning

confidence: 99%

Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues

Sen

Anishchenko

Bordin

et al. 2021

Preprint

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Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques such as RoseTTAFold and AlphaFold, we can predict the structure of these proteins even in the absence of structural homologues. We modeled and extracted the domains from 553 disease-associated human proteins. We noticed that the model quality was higher and the RMSD lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein-protein interfaces, conserved residues and destabilising effects caused by residue mutations in these predicted structures. We then explored whether the disease-associated mutations were in the proximity of these predicted functional sites or if they destabilized the protein structure based on ddG calculations. We could explain 80% of these disease-associated mutations based on proximity to functional sites or structural destabilization. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.

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Discovering Putative Protein Targets of Small Molecules: A Study of the p53 Activator Nutlin

Cited by 13 publications

References 39 publications

Predicting and designing therapeutics against the Nipah virus

Predicting and designing therapeutics against the Nipah virus

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs

Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues

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