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

MDM2 inhibitors, nutlin-3a and navtemadelin, retain efficacy in human and mouse cancer cells cultured in hypoxia

Activation of p53 by small molecule MDM2 inhibitors can induce cell cycle arrest or death in p53 wildtype cancer cells. However, cancer cells exposed to hypoxia can develop resistance to other small molecules, such as chemotherapies, that activate p53. Here, we evaluated whether hypoxia could render cancer cells insensitive to two MDM2 inhibitors with different potencies, nutlin-3a and navtemadlin. Inhibitor efficacy and potency were evaluated under short-term hypoxic conditions in human and mouse cancer cells expressing different p53 genotypes (wild-type, mutant, or null). Treatment of wild-type p53 cancer cells with MDM2 inhibitors reduced cell growth by > 75% in hypoxia through activation of the p53-p21 signaling pathway; no inhibitor-induced growth reduction was observed in hypoxic mutant or null p53 cells except at very high concentrations. The concentration of inhibitors needed to induce the maximal p53 response was not significantly different in hypoxia compared to normoxia. However, inhibitor efficacy varied by species and by cell line, with stronger effects at lower concentrations observed in human cell lines than in mouse cell lines grown as 2D and 3D cultures. Together, these results indicate that MDM2 inhibitors retain efficacy in hypoxia, suggesting they could be useful for targeting acutely hypoxic cancer cells.

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

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.

Methods for Molecular Modelling of Protein Complexes

Biological processes are often mediated by complexes formed between proteins and various biomolecules. The 3D structures of such protein-biomolecule complexes provide insights into the molecular mechanism of their action. The structure of these complexes can be predicted by various computational methods. Choosing an appropriate method for modelling depends on the category of biomolecule that a protein interacts with and the availability of structural information about the protein and its interacting partner. We intend for the contents of this chapter to serve as a guide as to what software would be the most appropriate for the type of data at hand and the kind of 3D complex structure required. Particularly, we have dealt with protein-small molecule ligand, protein-peptide, protein-protein, and protein-nucleic acid interactions.Most, if not all, model building protocols perform some sampling and scoring. Typically, several alternate conformations and configurations of the interactors are sampled. Each such sample is then scored for optimization. To boost the confidence in these predicted models, their assessment using other independent scoring schemes besides the inbuilt/default ones would prove to be helpful. This chapter also lists such software and serves as a guide to gauge the fidelity of modelled structures of biomolecular complexes.

MDM2-Associated Clusterization-Triggered Emission and Apoptosis Induction Effectuated by a Theranostic Spiropolymer

Heteroatom-containing spiropolymers were constructed in a facile manner by a catalyst-free multicomponent spiropolymerization route. P1a2b as the most potent of these spiropolymers, demonstrates cluster-triggered emission resulting from strong interactions with the MDM2 protein. By preventing the anti-apoptotic p53/MDM2 interaction, P1a2b triggers apoptosis in cancerous cells, while demonstrating a good biocompatibility and non-toxicity in non-cancerous cells. The combined results from solution and cell-based cluster-triggered emission studies, docking, protein expression experiments and cytotoxicity data strongly support the MDM2-binding hypothesis and indicate a potential application as a fluorescent cancer marker as well as therapeutic for this spiropolymer.

Structural dynamics and allostery of Rab proteins: strategies for drug discovery and design

Rab proteins represent the largest family of the Rab superfamily guanosine triphosphatase (GTPase). Aberrant human Rab proteins are associated with multiple diseases, including cancers and neurological disorders. Rab subfamily members display subtle conformational variations that render specificity in their physiological functions and can be targeted for subfamily-specific drug design. However, drug discovery efforts have not focused much on targeting Rab allosteric non-nucleotide binding sites which are subjected to less evolutionary pressures to be conserved, hence are likely to offer subfamily specificity and may be less prone to undesirable off-target interactions and side effects. To discover druggable allosteric binding sites, Rab structural dynamics need to be first incorporated using multiple experimentally and computationally obtained structures. The high-dimensional structural data may necessitate feature extraction methods to identify manageable representative structures for subsequent analyses. We have detailed state-of-the-art computational methods to (i) identify binding sites using data on sequence, shape, energy, etc., (ii) determine the allosteric nature of these binding sites based on structural ensembles, residue networks and correlated motions and (iii) identify small molecule binders through structure- and ligand-based virtual screening. To benefit future studies for targeting Rab allosteric sites, we herein detail a refined workflow comprising multiple available computational methods, which have been successfully used alone or in combinations. This workflow is also applicable for drug discovery efforts targeting other medically important proteins. Depending on the structural dynamics of proteins of interest, researchers can select suitable strategies for allosteric drug discovery and design, from the resources of computational methods and tools enlisted in the workflow.

Predicting and designing therapeutics against the Nipah virus

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.

AppA: a web server for analysis, comparison, and visualization of contact residues and interfacial waters of antibody-antigen structures and models

The study of contact residues and interfacial waters of antibody–antigen (Ab-Ag) structures could help in understanding the principles of antibody–antigen interactions as well as provide guidance for designing antibodies with improved affinities. Given the rapid pace with which new antibody–antigen structures are deposited in the protein databank (PDB), it is crucial to have computational tools to analyze contact residues and interfacial waters, and investigate them at different levels. In this study, we have developed AppA, a web server that can be used to analyze and compare 3D structures of contact residues and interfacial waters of antibody–antigen complexes. To the best of our knowledge, this is the first web server for antibody–antigen structures equipped with the capability for dissecting the contributions of interfacial water molecules, hydrogen bonds, hydrophobic interactions, van der Waals interactions and ionic interactions at the antibody–antigen interface, and for comparing the structures and conformations of contact residues. Various examples showcase the utility of AppA for such analyses and comparisons that could help in the understanding of antibody–antigen interactions and suggest mutations of contact residues to improve affinities of antibodies. The AppA web server is freely accessible at http://mspc.bii.a-star.edu.sg/minhn/appa.html.