Virtual Screening of Histone Lysine Demethylase(JMJD2) identifies new inhibitors

The JmjC domain-containing proteins are hydroxylases that confer posttranslational modifications on histone tails, by removing methylation marks on methylated lysine residues. This serves to either promote or repress gene transcription. The JMJD2A-D family members include the enzyme Jumonji domain 2C (JMJD2C), which specifically demethylates di- and trimethylated histone H3 at Lys 9 or Lys 36.[1] Dysregulation of JMJD2C has been implicated in prostate, colonic, and breast cancer as the demethylase can modify the expression levels of oncogenes.[2] The goal of the present study was to identify potent and selective small-molecule inhibitors of JMJD2C, to be used as chemical biology tools to further investigate the role of JMJD2C in cell proliferation and survival. Using high-resolution crystal structures of the JMJD2 subfamily members as templates, we have performed a small molecule virtual docking screen. From the ~3 million molecules that were docked, this experiment identified 21 compounds as possible leads. These compounds were tested against JMJD2C in enzymatic assays and here we report an overall hit rate of 76%, with 8 compounds demonstrating an IC50 of 176μM to 1.18μM. A molecule containing a salicylate core was selected as a candidate for optimization and thus far we have completed several rounds of iterative target-specific compound docking, hybrid molecule design, compound synthesis and in vitro characterization. Notably, our method demonstrated a substantial increase in potency when we linked two docked fragments together and further derivatized this new scaffold, through which we have successfully derived a 65nM inhibitor of JMJD2C. A compound representing the inhibitor scaffold has been co-crystallized with JMJD2A to a resolution of 2.4 Å. In the crystal structure each asymmetric unit contains two JMJD2A monomers, each bound to a single inhibitor molecule. This complex-structure superposes well with the docked pose for the hybrid series of compounds. We are now focusing our efforts on identifying an inhibitor that is selective for the JMJD2 family over other JmjC domain-containing proteins

Comparing the Chemical Structure and Protein Content of ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database

ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database are resources of curated chemistry-to-protein relationships widely used in the chemogenomic arena. In this work we have extended an earlier analysis (PMID 22821596) by comparing chemistry and protein target content between 2010 and 2013. For the former, details are presented for overlaps and differences, statistics of stereochemistry as well as stereo representation and MW profiles between the four databases. For 2013 our results indicate quality improvements, major expansion, increased achiral structures and changes in MW distributions. An orthogonal comparison of chemical content with different sources inside PubChem highlights further interpretable differences. Expansion of protein content by UniProt IDs is also recorded for 2013 and Gene Ontology comparisons for human-only sets indicate differences. These emphasise the expanding complementarity of chemistry-to-protein relationships between sources, although different criteria are used for their capture

A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma

Neuroblastoma is a complex and aggressive type of cancer that affects children. Current treatments involve a combination of surgery, chemotherapy, radiotherapy, and stem cell transplantation. However, treatment outcomes vary due to the heterogeneous nature of the disease. Computational models have been used to analyse data, simulate biological processes, and predict disease progression and treatment outcomes. While continuum cancer models capture the overall behaviour of tumours, and agent-based models represent the complex behaviour of individual cells, multiscale models represent interactions at different organisational levels, providing a more comprehensive understanding of the system. In 2018, the PRIMAGE consortium was formed to build a cloud-based decision support system for neuroblastoma, including a multi-scale model for patient-specific simulations of disease progression. In this work we have developed this multi-scale model that includes data such as patient's tumour geometry, cellularity, vascularization, genetics and type of chemotherapy treatment, and integrated it into an online platform that runs the simulations on a high-performance computation cluster using Onedata and Kubernetes technologies. This infrastructure will allow clinicians to optimise treatment regimens and reduce the number of costly and time-consuming clinical trials. This manuscript outlines the challenging framework's model architecture, data workflow, hypothesis, and resources employed in its development

Plate-based diversity subset screening generation 2: An improved paradigm for high throughput screening of large compound files

High throughput screening (HTS) is an effective method for lead and probe discovery that is widely used in industry and academia to identify novel chemical matter and to initiate the drug discovery process. However, HTS can be time-consuming and costly and the use of subsets as an efficient alternative to screening these large collections has been investigated. Subsets may be selected on the basis of chemical diversity, molecular properties, biological activity diversity, or biological target focus. Previously we described a novel form of subset screening: plate-based diversity subset (PBDS) screening, in which the screening subset is constructed by plate selection (rather than individual compound cherry-picking), using algorithms that select for compound quality and chemical diversity on a plate basis. In this paper, we describe a second generation approach to the construction of an updated subset: PBDS2, using both plate and individual compound selection, that has an improved coverage of the chemical space of the screening file, whilst only selecting the same number of plates for screening. We describe the validation of PBDS2 and its successful use in hit and lead discovery. PBDS2 screening became the default mode of singleton (one compound per well) HTS for lead discovery in Pfizer

A Chemocentric Approach to the Identification of Cancer Targets

A novel chemocentric approach to identifying cancer-relevant targets is introduced. Starting with a large chemical collection, the strategy uses the list of small molecule hits arising from a differential cytotoxicity screening on tumor HCT116 and normal MRC-5 cell lines to identify proteins associated with cancer emerging from a differential virtual target profiling of the most selective compounds detected in both cell lines. It is shown that this smart combination of differential in vitro and in silico screenings (DIVISS) is capable of detecting a list of proteins that are already well accepted cancer drug targets, while complementing it with additional proteins that, targeted selectively or in combination with others, could lead to synergistic benefits for cancer therapeutics. The complete list of 115 proteins identified as being hit uniquely by compounds showing selective antiproliferative effects for tumor cell lines is provided

A pharmacological organization of G protein–coupled receptors

Protein classification typically uses structural, sequence, or functional similarity. Here we introduce an orthogonal method that organizes proteins by ligand similarity, focusing here on the class A G protein-coupled receptor (GPCR) protein family. Comparing a ligand-based dendogram to a sequence-based one, we sought examples of GPCRs that were distantly linked by sequence but neighbors by ligand similarity. Experimental testing of compounds predicted to link three of these new pairs confirmed the predicted association, with potencies ranging from the low-nanomolar to low-micromolar. We then identified hundreds of non-GPCRs closely related to GPCRs by ligand similarity, including the CXCR2 chemokine receptor to Casein kinase I, the cannabinoid receptors to epoxide hydrolase 2, and the α2 adrenergic receptor to phospholipase D. These, too, were confirmed experimentally. Ligand similarities among these targets may reflect a chemical integration in the time domain of molecular signaling

A new Ligand-Based approach to virtual screening, and prolifing or large chemical libraries

La representació de les molècules per mitjà de descriptors moleculars és la base de moltes de les eines computacionals pel disseny de fàrmacs. Aquests mètodes computacionals es basen en l'abstracció de l'estructura química per resumir aquelles característiques rellevants sent al mateix temps eficients en la comparació de grans llibreries de molècules. Una característica molt important d'aquests descriptors és la seva habilitat de capturar la informació rellevant per la interacció amb qualsevol proteïna independentment de l'esquelet del compost. Això permet detectar com a similars qualsevol parella de compostos amb les mateixes característiques ordenades de la mateixa manera al voltant d'esquelets essencialment diferents, una propietat a la qual hom es refereix com a "scaffold hopping". Tenint en compte això, un nou conjunt de descriptors basat en la distribució de parelles de característiques farmacofòriques centrades en els àtoms per mitjà del concepte de teoria de la informació de l'entropia de Shannon [1], anomenats SHED, s'han desenvolupat.Aquests descriptors han sigut usats amb èxit en nombroses aplicacions importants en el procés de descoberta de fàrmacs. Després de la implementació de noves tecnologies in vitro com ara el "high-throughput screening" i la química combinatòria, la capacitat de sintetitzar i assajar compostos va augmentar exponencialment però alhora la necessitat d'una selecció racional dels compostos va fer-se patent. La priorització dels compostos en termes de la predicció de la seva probabilitat de mostrar la activitat desitjada és per tant una de les primeres aplicacions del perfilat virtual basat en lligands usant els descriptors SHED.En realitat, aquesta metodologia es pot estendre al punt de vista quimiogenòmic del procés de descoberta de fàrmacs, usant els descriptors per generar models basats en ligands de totes les proteïnes amb informació de lligands. Aquesta aproximació més ampla, el perfilat virtual de proteïnes, és un pas més per completar la matriu d'activitat entre tots els possibles compostos químics i totes les proteïnes rellevants. A més, una anàlisi més aprofundida d'aquesta matriu completa generada per mitjà del perfilat virtual de proteïnes pot dur-nos a una perspectiva de farmacologia en xarxa del procés de descoberta de fàrmacs. Aquesta direcció pot ser seguida afegint a aquesta informació de lligands i proteïnes la informació relativa a rutes de reaccions i anàlisi de sistemes, donant lloc a l'anomenada biologia química de sistemes que pot ajudar a entendre els processos biològics com un conjunt i a identificar de manera més racional noves i prometedores dianes terapèutiques.The representation of molecules by means of molecular descriptors is the basis of most of the computational tools for drug design. These computational methods are based on the abstraction from the chemical structure to summarize its relevant features while being efficient in the comparison of large molecule libraries. A very important feature of these descriptors is their ability to capture the information relevant for the interaction with any target independently from the scaffold of the compound. This will allow detecting as similar any two compounds with the same features arranged in the same way around essentially different scaffolds, a property referred to as scaffold hopping. With this in mind, a new set of descriptors based on the distribution of atom-centred pharmacophoric feature pairs by means of the information theory concept of Shannon entropy [1], called SHED, have been developed.These descriptors have been successfully used in a number of applications important in the drug discovery process. After the implementation of novel in vitro technologies like high-throughput screening and combinatorial chemistry, the capacity of synthesizing and testing compounds increased exponentially but the need for a rational selection of the compounds arose as well. The prioritisation of compounds in terms of their predicted chances of displaying the targeted activity is thus one of the first applications of the ligand-based virtual ligand screening based on SHED descriptors. This application has shown very good results, both in terms of enrichment of actives in the hit list and in terms of scaffold hopping ability, i.e. the novelty of the scaffolds of the found actives in the top ranked compounds.Actually, this methodology can be extended to a chemogenomics view of the drug discovery process, using the descriptors to build ligand-based models of all the proteins with any ligand information. This broader approach, the virtual target profiling, is a step towards completing the activity matrix between all possible chemical compounds and all relevant targets. Moreover, a deeper analysis of this complete matrix generated by virtual target profiling can lead us to a network pharmacology perspective of the drug discovery process. This direction can be further followed by adding to ligand-target information the information about pathways and systems approaches, leading to a systems chemical biology approach that could help understanding biological processes as a whole and identifying more rationally novel and promising drug targets