Enabling large-scale design, synthesis and validation of small molecule protein-protein antagonists

Although there is no shortage of potential drug targets, there are only a handful known low-molecular-weight inhibitors of protein-protein interactions (PPIs). One problem is that current efforts are dominated by low-yield high-throughput screening, whose rigid framework is not suitable for the diverse chemotypes present in PPIs. Here, we developed a novel pharmacophore-based interactive screening technology that builds on the role anchor residues, or deeply buried hot spots, have in PPIs, and redesigns these entry points with anchor-biased virtual multicomponent reactions, delivering tens of millions of readily synthesizable novel compounds. Application of this approach to the MDM2/p53 cancer target led to high hit rates, resulting in a large and diverse set of confirmed inhibitors, and co-crystal structures validate the designed compounds. Our unique open-access technology promises to expand chemical space and the exploration of the human interactome by leveraging in-house small-scale assays and user-friendly chemistry to rationally design ligands for PPIs with known structure. © 2012 Koes et al

Structure Based Ligand Design for Monoamine Transporters and Mitogen Activated Kinase 5

Depression is a major psychological disorder that affects a person\u27s mental and physical abilities. The National Institute of Mental Health (NIMH) classified it as a serious medical illness. It causes huge economic, as well as financial impact on the people, and it is also becoming a major public health issue. Antidepressant drugs are prescribed to mitigate the suffering caused by this disorder. Different generations of antidepressants have been developed with dissimilar mechanisms of action. According to the Center for Disease Control, the usage of antidepressants has skyrocketed by 400 percent increase over 2005- 2008 survey period. This dramatic rise in usage indicates that these are the most prescribed drugs in the US. Even with the FDA mandated black box warning of increased suicidal thoughts upon use of selected antidepressants, these drugs are still being used at a higher rate. All classes of antidepressants are plagued by side effects with mainly sexual dysfunction common among them. To avoid the adverse effects, an emphasis is to discover novel structural drug scaffolds that can be further developed as a new generation of antidepressants. The importance of this research is to discover structurally novel antidepressants by performing in silico virtual screening (VS) of chemical databases using the serotonin transporter (SERT). In the absence of a SERT crystal structure, a homology model was developed. The homology model was utilized to develop the first structure-based pharmacophore for the extracellular facing secondary ligand binding pocket. The pharmacophore captured the necessary drug-SERT interaction pattern for SERT inhibitory action. This pharmacophore was employed as one of the filters for VS of candidate ligands. The ten compounds identified were purchased and tested pharmacologically. Out of the ten hits, three structurally novel ligands were identified as lead compounds. Two of these compounds exhibited selectivity towards SERT; the remaining lead compound was selective towards the dopamine transporter and displayed cocaine inhibition. The two SERT selective compounds will provide new opportunities in the development of novel therapeutics to treat depression. For dopamine transporter (DAT), the study was based on recently developed structurally diverse photo probes. In an effort to better understand the binding profile similarities among these different scaffolds, the photo probes were docked into DAT. The finger print analysis of the interaction pattern of docked poses was performed to identify the inhibitor-binding sites. For mitogen activated protein kinase 5 (MEK5), given the lack of structural information, a homology model of MEK5 was developed to guide the rational design of inhibitors. Docking of known MEK5 inhibitors into the homology model was performed to understand the inhibitory interaction profile. Several series of analogues were designed utilizing the generated interaction profile

Knowledge-based design support and inductive learning

Designing and learning are closely related activities in that design as an ill-structure problem involves identifying the problem of the design as well as finding its solutions. A knowledge-based design support system should support learning by capturing and reusing design knowledge. This thesis addresses two fundamental problems in computational support to design activities: the development of an intelligent design support system architecture and the integration of inductive learning techniques in this architecture.This research is motivated by the belief that (1) the early stage of the design process can be modelled as an incremental learning process in which the structure of a design problem or the product data model of an artefact is developed using inductive learning techniques, and (2) the capability of a knowledge-based design support system can be enhanced by accumulating and storing reusable design product and process information.In order to incorporate inductive learning techniques into a knowledge-based design model and an integrated knowledge-based design support system architecture, the computational techniques for developing a knowledge-based design support system architecture and the role of inductive learning in Al-based design are investigated. This investigation gives a background to the development of an incremental learning model for design suitable for a class of design tasks whose structures are not well known initially.This incremental learning model for design is used as a basis to develop a knowledge-based design support system architecture that can be used as a kernel for knowledge-based design applications. This architecture integrates a number of computational techniques to support the representation and reasoning of design knowledge. In particular, it integrates a blackboard control system with an assumption-based truth maintenance system in an object-oriented environment to support the exploration of multiple design solutions by supporting the exploration and management of design contexts.As an integral part of this knowledge-based design support architecture, a design concept learning system utilising a number of unsupervised inductive learning techniques is developed. This design concept learning system combines concept formation techniques with design heuristics as background knowledge to build a design concept tree from raw data or past design examples. The design concept tree is used as a conceptual structure for the exploration of new designs.The effectiveness of this knowledge-based design support architecture and the design concept learning system is demonstrated through a realistic design domain, the design of small-molecule drugs one of the key tasks of which is to identify a pharmacophore description (the structure of a design problem) from known molecule examples.In this thesis, knowledge-based design and inductive learning techniques are first reviewed. Based on this review, an incremental learning model and an integrated architecture for intelligent design support are presented. The implementation of this architecture and a design concept learning system is then described. The application of the architecture and the design concept learning system in the domain of small-molecule drug design is then discussed. The evaluation of the architecture and the design concept learning system within and beyond this particular domain, and future research directions are finally discussed

Insights into the Development of Chemotherapeutics Targeting PFKFB Enzymes

The PFKFB enzymes control the primary checkpoint in the glycolytic pathway and are implicated in a multitude of diseases: from cancer, to schizophrenia, to diabetes, and heart disease. The inducible isoform, PFKFB3, is known to be associated with the upregulation of glycolysis in many cancers. The first study within this work investigates the potential for using tier-based approaches of virtual screening to target small molecule kinases, with PFKFB3 serving as a case study. For this investigation, bioactive compounds for PFKFB3 were identified from a compound library of 1364 compounds via high-throughput screening, with bioactive compounds being further characterized as either competitive or non-competitive for F6P. Using the F6P-competitive compounds, several structure based docking programs were assessed individually and in conjunction with a pharmacophore screening. The results showed that the tiered virtual screening approach, using pharmacophore screening in addition to structure-based docking, improved enrichments rates in 80% of cases, reduced CPU costs up to 7-fold, and lessened variability among different structure-based docking methods. The second study investigates the structural and kinetic characteristics of citrate inhibition on the heart PFKFB isoenzyme, PFKFB2. High levels of citrate, an intermediate of the TCA cycle, signify an abundance of biosynthetic precursors and that additional glucose need not be degraded for this purpose. Previous studies have noted that citrate acts as an important negative feed-back mechanism to limit glycolytic activity by inhibiting PFKFB enzymes, yet the structural and mechanistic details of citrate’s inhibition had not been determined. To study the molecular basis for citrate inhibition, the three-dimensional structures of the human and bovine PFKFB2 orthologues were solved, each in complex with citrate. For both cases, citrate primarily occupied the binding site of Fructose-6-phosphate (F6P), competitively blocking F6P from binding. Additionally, a carboxy arm of citrate extended into the γ-phosphate binding site of ATP, sterically and electrostatically blocking the catalytic binding mode for ATP. In the human orthologue, which utilized AMPPNP as an ATP analogue, conformational changes were observed in the 2-kinase domain as well as the binding mode for AMPPNP. This study gives new insights as to how the citrate-mediate negative feedback loop influences glycolytic flux through PFKFB enzymes

Recent Trends in In-silico Drug Discovery

A Drug designing is a process in which new leads (potential drugs) are discovered which have therapeutic benefits in diseased condition. With development of various computational tools and availability of databases (having information about 3D structure of various molecules) discovery of drugs became comparatively, a faster process. The two major drug development methods are structure based drug designing and ligand based drug designing. Structure based methods try to make predictions based on three dimensional structure of the target molecules. The major approach of structure based drug designing is Molecular docking, a method based on several sampling algorithms and scoring functions. Docking can be performed in several ways depending upon whether ligand and receptors are rigid or flexible. Hotspot grafting, is another method of drug designing. It is preferred when the structure of a native binding protein and target protein complex is available and the hotspots on the interface are known. In absence of information of three Dimensional structure of target molecule, Ligand based methods are used. Two common methods used in ligand based drug designing are Pharmacophore modelling and QSAR. Pharmacophore modelling explains only essential features of an active ligand whereas QSAR model determines effect of certain property on activity of ligand. Fragment based drug designing is a de novo approach of building new lead compounds using fragments within the active site of the protein. All the candidate leads obtained by various drug designing method need to satisfy ADMET properties for its development as a drug. In-silico ADMET prediction tools have made ADMET profiling an easier and faster process. In this review, various softwares available for drug designing and ADMET property predictions have also been listed

Computational Ligand-Based CNS Therapeutic Design: The Search for Novel-Scaffold Norepinephrine Transporter Inhibitors

Monoamine transporter (MAT) proteins are responsible for regulating cellular signal transduction through control of neurotransmitter reuptake in the synapse, and are therefore relevant to diseases including addiction, psychosis, anxiety and depression. MATs, specifically the serotonin transporter (SERT or 5-HTT), norepinephrine transporter (NET), and dopamine transporter (DAT), serve as the principal targets for antidepressant drugs, such as SSRIs (selective serotonin reuptake inhibitors), NRIs (norepinephrine reuptake inhibitors) and TCAs (tricyclic antidepressants), as well as psychostimulant drugs of abuse such as cocaine and the amphetamines. Due to a lack of crystallographic MAT data, it is unclear as to which of two MAT protein ligand binding sites these drugs bind, hindering knowledge of the specific binding modes of MAT ligands. In this study an in silico pharmacophore model was created using a ligand-based method aimed at drug screening for the ability to specifically inhibit NET, using Molecular Operating Environment software. A group of four structurally-diverse compounds with high NET binding affinities comprised the training set used to generate the model. A test set, which included ten compounds with a range of known NET affinities, served in the validation of the model. The constructed pharmacophore model selected all high affinity NET inhibitors and one relatively inactive compound from the test set. Following model validation, the ZINC small molecule structural database was virtually screened to identify novel MAT inhibitor candidates. Hit compounds were ranked by an overlay score, which calculated how well novel compounds aligned to the original training set alignment. Six top-ranking compounds were purchased and evaluated via in vitro pharmacology to determine the binding affinity at the MATs. Although no significant inhibition was observed at the MATs, compound AC-1 showed a 15% inhibition at the DAT in radioligand binding assays. This result suggests that with further refinement of key pharmacophore features or alteration of the AC-1 structure, more potent MAT inhibitors could be discovered. Pharmacophore-based drug design has become one of the most important tools in drug discovery. Using the molecular modeling approaches described in this study, it is possible to rationally design novel and more selective central nervous system drugs

Rational Design of Small-Molecule Inhibitors of Protein-Protein Interactions: Application to the Oncogenic c-Myc/Max Interaction

Protein-protein interactions (PPIs) constitute an emerging class of targets for pharmaceutical intervention pursued by both industry and academia. Despite their fundamental role in many biological processes and diseases such as cancer, PPIs are still largely underrepresented in today's drug discovery. This dissertation describes novel computational approaches developed to facilitate the discovery/design of small-molecule inhibitors of PPIs, using the oncogenic c-Myc/Max interaction as a case study.First, we critically review current approaches and limitations to the discovery of small-molecule inhibitors of PPIs and we provide examples from the literature.Second, we examine the role of protein flexibility in molecular recognition and binding, and we review recent advances in the application of Elastic Network Models (ENMs) to modeling the global conformational changes of proteins observed upon ligand binding. The agreement between predicted soft modes of motions and structural changes experimentally observed upon ligand binding supports the view that ligand binding is facilitated, if not enabled, by the intrinsic (pre-existing) motions thermally accessible to the protein in the unliganded form.Third, we develop a new method for generating models of the bioactive conformations of molecules in the absence of protein structure, by identifying a set of conformations (from different molecules) that are most mutually similar in terms of both their shape and chemical features. We show how to solve the problem using an Integer Linear Programming formulation of the maximum-edge weight clique problem. In addition, we present the application of the method to known c-Myc/Max inhibitors.Fourth, we propose an innovative methodology for molecular mimicry design. We show how the structure of the c-Myc/Max complex was exploited to designing compounds that mimic the binding interactions that Max makes with the leucine zipper domain of c-Myc.In summary, the approaches described in this dissertation constitute important contributions to the fields of computational biology and computer-aided drug discovery, which combine biophysical insights and computational methods to expedite the discovery of novel inhibitors of PPIs

First-principles molecular structure search with a genetic algorithm

The identification of low-energy conformers for a given molecule is a fundamental problem in computational chemistry and cheminformatics. We assess here a conformer search that employs a genetic algorithm for sampling the low-energy segment of the conformation space of molecules. The algorithm is designed to work with first-principles methods, facilitated by the incorporation of local optimization and blacklisting conformers to prevent repeated evaluations of very similar solutions. The aim of the search is not only to find the global minimum, but to predict all conformers within an energy window above the global minimum. The performance of the search strategy is: (i) evaluated for a reference data set extracted from a database with amino acid dipeptide conformers obtained by an extensive combined force field and first-principles search and (ii) compared to the performance of a systematic search and a random conformer generator for the example of a drug-like ligand with 43 atoms, 8 rotatable bonds and 1 cis/trans bond