Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

We describe a computational protocol to aid the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. To achieve this goal, we explore multiple strategies, including finding binding hot spots, incorporating chemical similarity and bioactivity data, and sampling similar binding sites from homologous protein complexes. We demonstrate how to combine existing interdisciplinary resources with examples of semi-automated workflows. Finally, we discuss several major problems, including the occurrence of drug-resistant mutations, drug promiscuity, and the design of dual-effect inhibitors.Fil: Goncearenco, Alexander. National Institutes of Health; Estados UnidosFil: Li, Minghui. Soochow University; China. National Institutes of Health; Estados UnidosFil: Simonetti, Franco Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Shoemaker, Benjamin A. National Institutes of Health; Estados UnidosFil: Panchenko, Anna R. National Institutes of Health; Estados Unido

Protein-protein interactions: network analysis and applications in drug discovery

Physical interactions among proteins constitute the backbone of cellular function, making them an attractive source of therapeutic targets. Although the challenges associated with targeting protein-protein interactions (PPIs) -in particular with small molecules are considerable, a growing number of functional PPI modulators is being reported and clinically evaluated. An essential starting point for PPI inhibitor screening or design projects is the generation of a detailed map of the human interactome and the interactions between human and pathogen proteins. Different routes to produce these biological networks are being combined, including literature curation and computational methods. Experimental approaches to map PPIs mainly rely on the yeast two-hybrid (Y2H) technology, which have recently shown to produce reliable protein networks. However, other genetic and biochemical methods will be essential to increase both coverage and resolution of current protein networks in order to increase their utility towards the identification of novel disease-related proteins and PPIs, and their potential use as therapeutic targets

Structural analysis of MDM2 RING separates degradation from regulation of p53 transcription activity

MDM2–MDMX complexes bind the p53 tumor-suppressor protein, inhibiting p53's transcriptional activity and targeting p53 for proteasomal degradation. Inhibitors that disrupt binding between p53 and MDM2 efficiently activate a p53 response, but their use in the treatment of cancers that retain wild-type p53 may be limited by on-target toxicities due to p53 activation in normal tissue. Guided by a novel crystal structure of the MDM2–MDMX–E2(UbcH5B)–ubiquitin complex, we designed MDM2 mutants that prevent E2–ubiquitin binding without altering the RING-domain structure. These mutants lack MDM2's E3 activity but retain the ability to limit p53′s transcriptional activity and allow cell proliferation. Cells expressing these mutants respond more quickly to cellular stress than cells expressing wild-type MDM2, but basal p53 control is maintained. Targeting the MDM2 E3-ligase activity could therefore widen the therapeutic window of p53 activation in tumors

Simulating Molecular Mechanisms Of the Mdm2-Mediated Regulatory Interactions: a Conformational Selection Model Of the Mdm2 Lid Dynamics

Diversity and complexity of MDM2 mechanisms govern its principal function as the cellular antagonist of the p53 tumor suppressor. Structural and biophysical studies have demonstrated that MDM2 binding could be regulated by the dynamics of a pseudo-substrate lid motif. However, these experiments and subsequent computational studies have produced conflicting mechanistic models of MDM2 function and dynamics. We propose a unifying conformational selection model that can reconcile experimental findings and reveal a fundamental role of the lid as a dynamic regulator of MDM2-mediated binding. In this work, structure, dynamics and energetics of apo-MDM2 are studied as a function of posttranslational modifications and length of the lid. We found that the dynamic equilibrium between closed and semi-closed lid forms may be a fundamental characteristic of MDM2 regulatory interactions, which can be modulated by phosphorylation, phosphomimetic mutation as well as by the lid size. Our results revealed that these factors may regulate p53-MDM2 binding by fine-tuning the thermodynamic equilibrium between preexisting conformational states of apo-MDM2. In agreement with NMR studies, the effect of phosphorylation on MDM2 interactions was more pronounced with the truncated lid variant that favored the thermodynamically dominant closed form. The phosphomimetic mutation S17D may alter the lid dynamics by shifting the thermodynamic equilibrium towards the ensemble of semi-closed conformations. The dominant semi-closed lid form and weakened dependence on the phosphorylation seen in simulations with the complete lid can provide a rationale for binding of small p53-based mimetics and inhibitors without a direct competition with the lid dynamics. The results suggested that a conformational selection model of preexisting MDM2 states may provide a robust theoretical framework for understanding MDM2 dynamics. Probing biological functions and mechanisms of MDM2 regulation would require further integration of computational and experimental studies and may help to guide drug design of novel anti-cancer therapeutics

Virtual screening for inhibitors of the human TSLP:TSLPR interaction

The pro-inflammatory cytokine thymic stromal lymphopoietin (TSLP) plays a pivotal role in the pathophysiology of various allergy disorders that are mediated by type 2 helper T cell (Th2) responses, such as asthma and atopic dermatitis. TSLP forms a ternary complex with the TSLP receptor (TSLPR) and the interleukin-7-receptor subunit alpha (IL-7Ra), thereby activating a signaling cascade that culminates in the release of pro-inflammatory mediators. In this study, we conducted an in silico characterization of the TSLP: TSLPR complex to investigate the drugability of this complex. Two commercially available fragment libraries were screened computationally for possible inhibitors and a selection of fragments was subsequently tested in vitro. The screening setup consisted of two orthogonal assays measuring TSLP binding to TSLPR: a BLI-based assay and a biochemical assay based on a TSLP: alkaline phosphatase fusion protein. Four fragments pertaining to diverse chemical classes were identified to reduce TSLP: TSLPR complex formation to less than 75% in millimolar concentrations. We have used unbiased molecular dynamics simulations to develop a Markov state model that characterized the binding pathway of the most interesting compound. This work provides a proof-ofprinciple for use of fragments in the inhibition of TSLP: TSLPR complexation

Computational Studies of Difference in Binding Modes of Peptide and Non-Peptide Inhibitors to MDM2/MDMX Based on Molecular Dynamics Simulations

Inhibition of p53-MDM2/MDMX interaction is considered to be a promising strategy for anticancer drug design to activate wild-type p53 in tumors. We carry out molecular dynamics (MD) simulations to study the binding mechanisms of peptide and non-peptide inhibitors to MDM2/MDMX. The rank of binding free energies calculated by molecular mechanics generalized Born surface area (MM-GBSA) method agrees with one of the experimental values. The results suggest that van der Waals energy drives two kinds of inhibitors to MDM2/MDMX. We also find that the peptide inhibitors can produce more interaction contacts with MDM2/MDMX than the non-peptide inhibitors. Binding mode predictions based on the inhibitor-residue interactions show that the π–π, CH–π and CH–CH interactions dominated by shape complimentarity, govern the binding of the inhibitors in the hydrophobic cleft of MDM2/MDMX. Our studies confirm the residue Tyr99 in MDMX can generate a steric clash with the inhibitors due to energy and structure. This finding may theoretically provide help to develop potent dual-specific or MDMX inhibitors

Molecular Dynamics studies on Mdm2 complexes: an analysis of the inhibitor influence

p53 is a powerful anti-tumoral molecule frequently inactivated by mutations or deletions in cancer. However, half of all human tumors expresses wild-type p53, and its activation, by antagonizing its negative regulator Mdm2, might offer a new strategy for therapeutic protocol. In this work, we present a molecular dynamics study on Mdm2 structure bound to two different known inhibitors with the aim to investigate the structural transitions between apo-Mdm2 and Mdm2-inhibitor complexes. We tried to gain information about conformational changes binding a benzodiazepine derivative inhibitor with respect the known nutlin and the apo form. The conformational changes alter the size of the cleft and were mainly in the linker regions, suggesting that the overall dynamic nature of Mdm2 is related to dynamic movements in these regions

Predicting and Testing Helix-Mimetic Inhibitors of the p53-Mdm2 Interaction

Aberrant protein-protein interactions (PPIs) are found in many disease states. Consequently, there is a need for PPI inhibitors for use as research tools and pharmaceutical lead compounds. Computational methods could greatly assist with the search for new PPIs. Oligobenzamides are novel PPI inhibitors which can theoretically be produced to display any sequence of side chains. Understanding the nature of oligobenzamide binding is important for identification of the most efficient strategy of predicting oligobenzamide inhibitors. The prediction of oligobenzamide affinities using thermodynamic integration and implicit solvent methods is described. Affinities of oligobenzamides for Mdm2 predicted using implicit solvent methods bore a moderate correlation with measured affinities. Examination of MM-PBSA results using analysis of variance revealed that it is not necessary to run simulations with every member of a large combinatorial library in order to predict their relative affinities because within a particular binding site, the degree of interaction between the side chains is small. However, it could be useful to separate molecules based on their predicted binding pose because oligobenzamides can bind to Mdm2 in many different ways, depending on the choice of side chains. This insight will be valuable for future attempts to predict oligobenzamide affinities. The 1H-15N HSQC NMR spectrum peaks of 15N-labelled Mdm2 L33E were assigned to facilitate the future validation of binding poses. An oligoamide was shown using NMR to bind in the correct place. However, NMR testing revealed that oligobenzamides can aggregate in aqueous solution despite being soluble. A novel FRET-based method was also developed which can be used to test potential inhibitors with a low solubility and high absorbance during their development. It was adapted for a microwell plate to facilitate future high throughput screening and an assay involving Cherry-labelled Mdm2 was tested which could be developed into an in vivo assay in the future

Designing Focused Chemical Libraries Enriched in Protein-Protein Interaction Inhibitors using Machine-Learning Methods

Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is freely available on request from our CDithem platform website, www.CDithem.com