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Summary We present Icolos, a workflow manager written in Python as a tool for automating complex structure-based workflows for drug design. Icolos can be used as a standalone tool, for example in virtual screening campaigns, or can be used in conjunction with deep learning-based molecular generation facilitated for example by REINVENT, a previously published molecular de novo design package. In this publication, we focus on the internal structure and general capabilities of Icolos, using molecular docking experiments as an illustrative example. Availability and implementation The source code is freely available at https://github.com/MolecularAI/Icolos under the Apache 2.0 license. Tutorial notebooks containing minimal working examples can be found at https://github.com/MolecularAI/IcolosCommunity. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Many cancers have the tumor suppressor p53 inactivated by mutation, making reactivation of mutant p53 with small molecules a promising strategy for the development of novel anticancer therapeutics. The oncogenic p53 mutation Y220C, which accounts for approximately 100,000 cancer cases per year, creates an extended surface crevice in the DNA-binding domain, which destabilizes p53 and causes denaturation and aggregation. Here, we describe the structure-guided design of a novel class of small-molecule Y220C stabilizers and the challenging synthetic routes developed in the process. The synthesized chemical probe MB710 , an aminobenzothiazole derivative, binds tightly to the Y220C pocket and stabilizes p53-Y220C in vitro . MB725 , an ethylamide analogue of MB710 , induced selective viability reduction in several p53-Y220C cancer cell lines while being well tolerated in control cell lines. Reduction of viability correlated with increased and selective transcription of p53 target genes such as BTG2 , p21 , PUMA , FAS , TNF , and TNFRSF10B, which promote apoptosis and cell cycle arrest, suggesting compound-mediated transcriptional activation of the Y220C mutant. Our data provide a framework for the development of a class of potent, non-toxic compounds for reactivating the Y220C mutant in anticancer therapy. [ABSTRACT FROM AUTHOR]

The identification of a targeted therapy for patients with triple-negative breast cancer (TNBC) is one of the most urgent needs in breast cancer therapeutics. The p53 gene is mutated in approximately 80% of patients with TNBC, and is a potential therapeutic target for patients with this form of breast cancer. The 2-sulfonylpyrimidine compound, PK11007, preferentially decreases viability in p53-compromised cancer cell lines. We investigated PK11007 as a potential new treatment for TNBC. IC50 values for inhibition of proliferation in a panel of 17 breast cell lines by PK11007 ranged from 2.3 to 42.2 μM. There were significantly lower IC50 values for TNBC than for non-TNBC cell lines (p = 0.03) and for p53-mutated cell lines compared with p53 WT cells (p = 0.003). Response to PK11007 however, was independent of the estrogen receptor (ER) or HER2 status of the cell lines. In addition to inhibiting cell proliferation, PK11007 induced apoptosis in p53 mutant cell lines. Using RNAseq and gene ontology analysis, we found that PK11007 altered the expression of genes enriched in pathways involved in regulated cell death, regulation of apoptosis, signal transduction, protein refolding and locomotion. The observations that PK11007 inhibited cell proliferation, induced apoptosis and altered genes involved in cell death are all consistent with the ability of PK11007 to reactivate mutant p53. Based on our data, we conclude that targeting mutant p53 with PK11007 is a potential approach for treating p53-mutated breast cancer, including the subgroup with TN disease. [ABSTRACT FROM AUTHOR]

The tumor suppressor p53 has the most frequently mutated gene in human cancers. Many of p53's oncogenic mutants are just destabilized and rapidly aggregate, and are targets for stabilization by drugs. We found certain 2-sulfonylpyrimidines, including one named PK11007, to be mild thiol alkylators with anticancer activity in several cell lines, especially those with mutationally compromised p53. PK11007 acted by two routes: p53 dependent and p53 independent. PK11007 stabilized p53 in vitro via selective alkylation of two surface-exposed cysteines without compromising its DNA binding activity. Unstable p53 was reactivated by PK11007 in some cancer cell lines, leading to up-regulation of p53 target genes such as p21 and PUMA. More generally, there was cell death that was independent of p53 but dependent on glutathione depletion and associated with highly elevated levels of reactive oxygen species and induction of endoplasmic reticulum (ER) stress, as also found for the anticancer agent PRIMA-1MET(APR-246). PK11007 may be a lead for anticancer drugs that target cells with nonfunctional p53 or impaired reactive oxygen species (ROS) detoxification in a wide variety of mutant p53 cells. [ABSTRACT FROM AUTHOR]

The p53 tumor suppressor protein performs a critical role in stimulating apoptosis and cell cycle arrest in response to oncogenic stress. The function of p53 can be compromised by mutation, leading to increased risk of cancer; approximately 50% of cancers are associated with mutations in the p53 gene, the majority of which are in the core DNA-binding domain. The Y220C mutation of p53, for example, destabilizes the core domain by 4 kcal/mol, leading to rapid denaturation and aggregation. The associated loss of tumor suppressor functionality is associated with approximately 75 000 new cancer cases every year. Destabilized p53 mutants can be 'rescued' and their function restored; binding of a small molecule into a pocket on the surface of mutant p53 can stabilize its wild-type structure and restore its function. Here, we describe an in silico algorithm for identifying potential rescue pockets, including the algorithm's integration with the Dynameomics molecular dynamics data warehouse and the DIVE visual analytics engine. We discuss the results of the application of the method to the Y220C p53 mutant, entailing finding a putative rescue pocket through MD simulations followed by an in silico search for stabilizing ligands that dock into the putative rescue pocket. The top three compounds from this search were tested experimentally and one of them bound in the pocket, as shown by nuclear magnetic resonance, and weakly stabilized the mutant. [ABSTRACT FROM AUTHOR]

Summary The destabilizing p53 cancer mutation Y220C creates an extended crevice on the surface of the protein that can be targeted by small-molecule stabilizers. Here, we identify different classes of small molecules that bind to this crevice and determine their binding modes by X-ray crystallography. These structures reveal two major conformational states of the pocket and a cryptic, transiently open hydrophobic subpocket that is modulated by Cys220. In one instance, specifically targeting this transient protein state by a pyrrole moiety resulted in a 40-fold increase in binding affinity. Molecular dynamics simulations showed that both open and closed states of this subsite were populated at comparable frequencies along the trajectories. Our data extend the framework for the design of high-affinity Y220C mutant binders for use in personalized anticancer therapy and, more generally, highlight the importance of implementing protein dynamics and hydration patterns in the drug-discovery process. [ABSTRACT FROM AUTHOR]

Background: Structure-based virtual screening techniques can help to identify new lead structures and complement other screening approaches in drug discovery. Prior to docking, the data (protein crystal structures and ligands) should be prepared with great attention to molecular and chemical details. Results: Using a subset of 18 diverse targets from the recently introduced DEKOIS 2.0 benchmark set library, we found differences in the virtual screening performance of two popular docking tools (GOLD and Glide) when employing two different commercial packages (e.g. MOE and Maestro) for preparing input data. We systematically investigated the possible factors that can be responsible for the found differences in selected sets. For the Angiotensin-I-converting enzyme dataset, preparation of the bioactive molecules clearly exerted the highest influence on VS performance compared to preparation of the decoys or the target structure. The major contributing factors were different protonation states, molecular flexibility, and differences in the input conformation (particularly for cyclic moieties) of bioactives. In addition, score normalization strategies eliminated the biased docking scores shown by GOLD (ChemPLP) for the larger bioactives and produced a better performance. Generalizing these normalization strategies on the 18 DEKOIS 2.0 sets, improved the performances for the majority of GOLD (ChemPLP) docking, while it showed detrimental performances for the majority of Glide (SP) docking. Conclusions: In conclusion, we exemplify herein possible issues particularly during the preparation stage of molecular data and demonstrate to which extent these issues can cause perturbations in the virtual screening performance. We provide insights into what problems can occur and should be avoided, when generating benchmarks to characterize the virtual screening performance. Particularly, careful selection of an appropriate molecular preparation setup for the bioactive set and the use of score normalization for docking with GOLD (ChemPLP) appear to have a great importance for the screening performance. For virtual screening campaigns, we recommend to invest time and effort into including alternative preparation workflows into the generation of the master library, even at the cost of including multiple representations of each molecule. [ABSTRACT FROM AUTHOR]

Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures. [ABSTRACT FROM AUTHOR]

The proteins MDM2 and MDM4 are key negative regulators of the tumor suppressor protein p53, which are frequently upregulated in cancer cells. They inhibit the transactivation activity of p53 by binding separately or in concert to its transactivation domain. MDM2 is also a ubiquitin ligase that leads to the degradation of p53. Accordingly, MDM2 and MDM4 are important targets for drugs to inhibit their binding to p53. We found from in silico screening and confirmed by experiment that lithocholic acid (LCA) binds to the p53 binding sites of both MDM2 and MDM4 with a fivefold preference for MDM4. LCA is an endogenous steroidal bile acid, variously reported to have both carcinogenic and apoptotic activities. The comparison of LCA effects on apoptosis in HCT116 p53-/+ vs. p53 -/- cells shows a predominantly p53-mediated induction of caspase-3/7. The dissociation constants are in the |iM region, but only modest inhibition of binding of MDM2 and MDM4 is required to negate their upregulation because they have to compete with transcriptional coactivator p300 for binding to p53. Binding was weakened by structural changes in LCA, and so it may be a natural ligand of MDM2 and MDM4, raising the possibility that MDM proteins may be sensors for specific steroids. [ABSTRACT FROM AUTHOR]

In this issue of Structure, Bista and colleagues report that inhibitors of the MDM2/p53 interaction can be designed to interact with a transiently folded α-helical segment of the MDM2 lid region. This suggests that targeting transient protein states in PPI inhibitor design could be a promising strategy to improve affinity and/or selectivity profiles. [ABSTRACT FROM AUTHOR]

Recently, we have released the de novo design platform REINVENT in version 2.0. This improved and extended iteration supports far more features and scoring function components, which allows bespoke and tailor-made protocols to maximize impact in small molecule drug discovery projects. A major obstacle of generative models is producing active compounds, in which predictive (QSAR) models have been applied to enrich target activity. However, QSAR models are inherently limited by their applicability domains. To overcome these limitations, we introduce a structure-based scoring component for REINVENT. DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends. Using the benchmarking and analysis workflow provided in DockStream, execution and subsequent analysis of a variety of docking configurations can be automated. Docking algorithms vary greatly in performance depending on the target and the benchmarking and analysis workflow provides a streamlined solution to identifying productive docking configurations. We show that an informative docking configuration can inform the REINVENT agent to optimize towards improving docking scores using public data. With docking activated, REINVENT is able to retain key interactions in the binding site, discard molecules which do not fit the binding cavity, harness unused (sub-)pockets, and improve overall performance in the scaffold-hopping scenario. The code is freely available at https://github.com/MolecularAI/DockStream. [ABSTRACT FROM AUTHOR]

p53 is an important tumor-suppressor protein that is mutated in more than 50% of cancers. Strategies for restoring normal p53 function are complicated by the oncogenic properties of mutant p53 and have not met with clinical success. To counteract mutant p53 activity, a variety of drugs with the potential to reconvert mutant p53 to an active wildtype form have been developed. However, these drugs are associated with various negative effects such as cellular toxicity, nonspecific binding to other proteins, and inability to induce a wildtype p53 response in cancer tissue. Here, we report on the effects of a curcumin analog, HO-3867, on p53 activity in cancer cells from different origins. We found that HO-3867 covalently binds to mutant p53, initiates a wildtype p53-like anticancer genetic response, is exclusively cytotoxic toward cancer cells, and exhibits high anticancer efficacy intumormodels. In conclusion, HO-3867 is a p53 mutant-reactivating drug with high clinical anticancer potential. [ABSTRACT FROM AUTHOR]

Background: A plethora of studies indicate that the development of multi-target drugs is beneficial for complex diseases like cancer. Accurate QSAR models for each of the desired targets assist the optimization of a lead candidate by the prediction of affinity profiles. Often, the targets of a multi-target drug are sufficiently similar such that, in principle, knowledge can be transferred between the QSAR models to improve the model accuracy. In this study, we present two different multi-task algorithms from the field of transfer learning that can exploit the similarity between several targets to transfer knowledge between the target specific QSAR models. Results: We evaluated the two methods on simulated data and a data set of 112 human kinases assembled from the public database ChEMBL. The relatedness between the kinase targets was derived from the taxonomy of the humane kinome. The experiments show that multi-task learning increases the performance compared to training separate models on both types of data given a sufficient similarity between the tasks. On the kinase data, the best multi-task approach improved the mean squared error of the QSAR models of 58 kinase targets. Conclusions: Multi-task learning is a valuable approach for inferring multi-target QSAR models for lead optimization. The application of multi-task learning is most beneficial if knowledge can be transferred from a similar task with a lot of in-domain knowledge to a task with little in-domain knowledge. Furthermore, the benefit increases with a decreasing overlap between the chemical space spanned by the tasks. [ABSTRACT FROM AUTHOR]

Half of human cancers harbor TP53 mutations that render p53 inactive as a tumor suppressor. In these cancers, reactivation of mutant p53 (mutp53) through restoration of wild-type-like function constitutes a valuable anticancer therapeutic strategy. In order to search for mutp53 reactivators, a small library of tryptophanol-derived oxazoloisoindolinones was synthesized and the potential of these compounds as mutp53 reactivators and anticancer agents was investigated in human tumor cells and xenograft mouse models. By analysis of their anti-proliferative effect on a panel of p53-null NCI-H1299 tumor cells ectopically expressing highly prevalent mutp53, the compound SLMP53-2 was selected based on its potential reactivation of multiple structural mutp53. In mutp53-Y220C-expressing hepatocellular carcinoma (HCC) cells, SLMP53-2-induced growth inhibition was mediated by cell cycle arrest, apoptosis, and endoplasmic reticulum stress response. In these cells, SLMP53-2 restored wild-type-like conformation and DNA-binding ability of mutp53-Y220C by enhancing its interaction with the heat shock protein 70 (Hsp70), leading to the reestablishment of p53 transcriptional activity. Additionally, SLMP53-2 displayed synergistic effect with sorafenib, the only approved therapy for advanced HCC. Notably, it exhibited potent antitumor activity in human HCC xenograft mouse models with a favorable toxicological profile. Collectively, SLMP53-2 is a new mutp53-targeting agent with promising antitumor activity, particularly against HCC. [ABSTRACT FROM AUTHOR]