Protein Structure Similarity as Guiding Principle for Combinatorial Library Design

The polypharmacology of natural products

The once-popular approach of using natural products as a prime source for medicinal chemistry and drug discovery has waned considerably in the past two decades due to the advent of high-throughput screening of small molecule mega libraries. However, the growing appreciation of network pharmacology as the next drug-discovery paradigm suggests that natural products and their unique polypharmacology offer significant advantages for finding novel therapeutics particularly for the treatment of complex and multifactorial diseases. Drug discovery process is awaiting the revitalization of interest in natural products and their derivatives. The current challenge is how to decipher this natural chemical diversity.

Drug Development in Conformational Diseases: A Novel Family of Chemical Chaperones that Bind and Stabilise Several Polymorphic Amyloid Structures

The increasing prevalence of conformational diseases, including Alzheimer's disease, type 2 Diabetes Mellitus and Cancer, poses a global challenge at many different levels. It has devastating effects on the sufferers as well as a tremendous economic impact on families and the health system. In this work, we apply a cross-functional approach that combines ideas, concepts and technologies from several disciplines in order to study, in silico and in vitro, the role of a novel chemical chaperones family (NCHCHF) in processes of protein aggregation in conformational diseases. Given that Serum Albumin (SA) is the most abundant protein in the blood of mammals, and Bovine Serum Albumin (BSA) is an off-the-shelf protein available in most labs around the world, we compared the ligandability of BSA:NCHCHF with the interaction sites in the Human Islet Amyloid Polypeptide (hIAPP):NCHCHF, and in the amyloid pharmacophore fragments (Aβ17-42 and Aβ16-21):NCHCHF. We posit that the merging of this interaction sites is a meta-structure of pharmacophore which allows the development of chaperones that can prevent protein aggregation at various states from: stabilizing the native state to destabilizing oligomeric state and protofilament. Furthermore to stabilize fibrillar structures, thus decreasing the amount of toxic oligomers in solution, as is the case with the NCHCHF. The paper demonstrates how a set of NCHCHF can be used for studying and potentially treating the various physiopathological stages of a conformational disease. For instance, when dealing with an acute phase of cytotoxicity, what is needed is the recruitment of cytotoxic oligomers, thus chaperone F, which accelerates fiber formation, would be very useful; whereas in a chronic stage it is better to have chaperones A, B, C, and D, which stabilize the native and fibril structures halting self-catalysis and the creation of cytotoxic oligomers as a consequence of fiber formation. Furthermore, all the chaperones are able to protect and recondition the cerebellar granule cells (CGC) from the cytotoxicity produced by the hIAPP20-29 fragment or by a low potassium medium, regardless of their capacity for accelerating or inhibiting in vitro formation of fibers. In vivo animal experiments are required to study the impact of chemical chaperones in cognitive and metabolic syndromes.

Structure-based systems biology for analyzing off-target binding

Here off-target binding implies the binding of a small molecule of therapeutic interest to a protein target other than the primary target for which it was intended. Increasingly such off-targeting appears to be the norm rather than the exception, rational drug design notwithstanding, and can lead to detrimental side-effects, or opportunities to reposition a therapeutic agent to treat a different condition. Not surprisingly, there is significant interest in determining a priori what off-targets exist on a proteome-wide scale. Beyond determining putative off-targets is the need to understand the impact of such binding on the complete biological system, with the ultimate goal of being able to predict the phenotypic outcome. While a very ambitious goal, some progress is being made.

The protein meta-structure: a novel concept for chemical and molecular biology

The ultimate goal of bioinformatics or computational chemical biology is the sequence-based prediction of protein functionality. However, due to the degeneracy of the primary sequence code there is no unambiguous relationship. The degeneracy can be partly lifted by going to higher levels of abstraction and, for example, incorporating 3D structural information. However, sometimes even at this conceptual level functional ambiguities often remain. Here a novel conceptual framework is described (the protein meta-structure). At this level of abstraction, the protein structure is viewed as an intricate network of interacting residues. This novel conception offers unique possibilities for chemical (molecular) biology, structural genomics and drug discovery. In this review some prototypical applications will be presented that serve to illustrate the potential of the methodology.

Knowledge-based virtual screening: application to the MDM4/p53 protein-protein interaction

Chemogenomics knowledge-based drug discovery approaches aim to extract the knowledge gained from one target and to apply it for the discovery of ligands and hopefully drugs of a new target which is related to the parent target by homology or conserved molecular recognition. Herein, we demonstrate the potential of knowledge-based virtual screening by applying it to the MDM4-p53 protein-protein interaction where the MDM2-p53 protein-protein interaction constitutes the parent reference system; both systems are potentially relevant to cancer therapy. We show that a combination of virtual screening methods, including homology based similarity searching, QSAR (Quantitative Structure-Activity Relationship) methods, HTD (High Throughput Docking), and UNITY pharmacophore searching provide a successful approach to the discovery of inhibitors. The virtual screening hit list is of the magnitude of 50,000 compounds picked from the corporate compound library of approximately 1.2 million compounds. Emphasis is placed on the facts that such campaigns are only feasible because of the now existing HTCP (High throughput Cherry-Picking) automation systems in combination with robust MTS (Medium Throughput Screening) fluorescence-based assays. Given that the MDM2-p53 system constitutes the reference system, it is not surprising that significantly more and stronger hits are found for this interaction compared to the MDM4-p53 system. Novel, selective and dual hits are discovered for both systems. A hit rate analysis will be provided compared to the full HTS (High-throughput Screening).

Asymmetric multicomponent reactions (AMCRs): the new frontier

Asymmetric multicomponent reactions involve the preparation of chiral compounds by the reaction of three or more reagents added simultaneously. This kind of addition and reaction has some advantages over classic divergent reaction strategies, such as lower costs, time, and energy, as well as environmentally friendlier aspects. All these advantages, together with the high level of stereoselectivity attained in some of these reactions, will force chemists in industry as in academia to adopt this new strategy of synthesis, or at least to consider it as a viable option. The positive aspects as well as the drawbacks of this strategy are discussed in this Review.

Structure-based development of target-specific compound libraries

The success or failure of a small-molecule drug discovery project ultimately lies in the choice of the scaffolds to be screened -- chosen from among the many millions of available compounds. Therefore, the methods used to design compound screening libraries are key for the development of new drugs that target a wide range of diseases. Currently, there is a trend towards the construction of receptor-structure-based focused libraries. Recent advances in high-throughput computational docking, NMR and crystallography have facilitated the development of these libraries. A structure-based target-specific library can save time and money by reducing the number of compounds to be experimentally tested, also improving the drug discovery success rate by identifying more-potent and specific binders.

Solid-Phase Synthesis and Biological Activity of a Combinatorial Cross-Conjugated Dienone Library

The solid-phase synthesis of a combinatorial cross-conjugated dienone library based on the structure of clavulones and their biological activity are reported. Clavulones are a family of marine prostanoids, and are composed of a cross-conjugated dienone system bearing two alkyl side-chains. The cross-conjugated dienone system irreversibly reacted with two nucleophiles. Our strategy for the solid-phase synthesis of the cross-conjugated dienones involves the Sonogashira-coupling reaction of a solid-supported cyclopentenone 10 bearing an acetylene group, followed by aldol condensation with aldehydes. The diphenyl derivative 7 aA was prepared from the solid-supported cyclopentenone 10 in 56% total yield. Combinatorial synthesis of a small library using twelve halides and eight aldehydes resulted in the production of 74 desired compounds from 98 candidates, and were detected by their mass spectra. Antiproliferative effects of the crude compounds against HeLaS3 cells showed that eleven samples showed strong antitumor activity (IC50<0.05 microM). Further biological examination of four purified compounds by using five tumor cell lines (A549, HeLaS3, MCF7, TMF1, and P388) revealed strong cytotoxicity comparable to that of adriamycin.

Chemogenomics: drug discovery's panacea?

Chemogenomics aims towards the systematic identification of small molecules that interact with the products of the genome and modulate their biological function. This Opinion article summarizes the different knowledge-based chemogenomics strategies that are followed and outlines the challenges and opportunities that will impact drug discovery. Chemogenomics aims towards the systematic identification of small molecules that interact with the products of the genome and modulate their biological function. While historically the approach is based on efforts that systematically explore target gene families like kinases, today additional knowledge-based systematization principles are followed within early drug discovery projects which aim to biologically validate the targets and to identify starting points for chemical lead optimization. While the expectations of chemogenomics are very high, the reality of drug discovery is quite sobering with very high project attrition rates. This article summarizes the different knowledge-based chemogenomics strategies that are followed and outlines the challenges and potential opportunities that will impact drug discovery.

Protein structure similarity clustering and natural product structure as guiding principles for chemical genomics

The majority of all proteins are modularly built from a limited set of approximately 1,000 structural domains. The knowledge of a common protein fold topology in the ligand-sensing cores of protein domains can be exploited for the design of small-molecule libraries in the development of inhibitors and ligands. Thus, a novel strategy of clustering protein domain cores based exclusively on structure similarity considerations (protein structure similarity clustering, PSSC) has been successfully applied to the development of small-molecule inhibitors of acetylcholinesterase and the 11beta-hydroxysteroid dehydrogenases based on the structure of a naturally occurring Cdc25 inhibitor. The efficiency of making use of the scaffolds of natural products as biologically prevalidated starting points for the design of compound libraries is further highlighted by the development of benzopyran-based FXR ligands.

Charting biologically relevant chemical space: a structural classification of natural products (SCONP)

The identification of small molecules that fall within the biologically relevant subfraction of vast chemical space is of utmost importance to chemical biology and medicinal chemistry research. The prerequirement of biological relevance to be met by such molecules is fulfilled by natural product-derived compound collections. We report a structural classification of natural products (SCONP) as organizing principle for charting the known chemical space explored by nature. SCONP arranges the scaffolds of the natural products in a tree-like fashion and provides a viable analysis- and hypothesis-generating tool for the design of natural product-derived compound collections. The validity of the approach is demonstrated in the development of a previously undescribed class of selective and potent inhibitors of 11beta-hydroxysteroid dehydrogenase type 1 with activity in cells guided by SCONP and protein structure similarity clustering. 11beta-hydroxysteroid dehydrogenase type 1 is a target in the development of new therapies for the treatment of diabetes, the metabolic syndrome, and obesity.

Metabolomics: from pattern recognition to biological interpretation

Metabolomics is a technology that aims to identify and quantify the metabolome -- the dynamic set of all small molecules present in an organism or a biological sample. In this sense, the technique is distinct from metabolic profiling, which looks for target compounds and their biochemical transformation. The combination of both approaches is an emerging technique for the characterization of biological samples and for drug treatment. Metabolomics has proven to be very rapid and superior to any other post-genomics technology for pattern-recognition analyses of biological samples. Changing steady state concentrations and fluctuations of metabolites that occur within milliseconds are a result of biochemical processes such as signalling cascades: metabolomic techniques are instrumental in measuring these changes rapidly and sensitively. Metabolite data can be complemented by protein, transcript and external (environmental) data, thereby leading to the identification of multiple physiological biomarkers embedded in correlative molecular networks that are not approachable with targeted studies.

Protein structure similarity clustering (PSSC) and natural product structure as inspiration sources for drug development and chemical genomics

Finding small molecules that modulate protein function is of primary importance in drug development and in the emerging field of chemical genomics. To facilitate the identification of such molecules, we developed a novel strategy making use of structural conservatism found in protein domain architecture and natural product inspired compound library design. Domains and proteins identified as being structurally similar in their ligand-sensing cores are grouped in a protein structure similarity cluster (PSSC). Natural products can be considered as evolutionary pre-validated ligands for multiple proteins and therefore natural products that are known to interact with one of the PSSC member proteins are selected as guiding structures for compound library synthesis. Application of this novel strategy for compound library design provided enhanced hit rates in small compound libraries for structurally similar proteins.

Design of compound libraries based on natural product scaffolds and protein structure similarity clustering (PSSC)

Recent advances in structural biology, bioinformatics and combinatorial chemistry have significantly impacted the discovery of small molecules that modulate protein functions. Natural products which have evolved to bind to proteins may serve as biologically validated starting points for the design of focused libraries that might provide protein ligands with enhanced quality and probability. The combined application of natural product derived scaffolds with a new approach that clusters proteins according to structural similarity of their ligand sensing cores provides a new principle for the design and synthesis of such libraries. This article discusses recent advances in the synthesis of natural product inspired compound collections and the application of protein structure similarity clustering for the development of such libraries.

Exploring the chemogenomic knowledge space with annotated chemical libraries

The recent human genome initiatives have led to the discovery of a multitude of genes that are potentially associated with various pathologic conditions and, thus, have opened new horizons in drug discovery. Simultaneously, annotated chemical libraries have emerged as information-rich databases to integrate biological and chemical data. They can be useful for the discovery of new pharmaceutical leads, the validation of new biotargets and the determination of the structural basis of ligand selectivity within target families. Annotated libraries provide a strong information basis for computational design of target-directed combinatorial libraries, which are a key component of modern drug discovery. Today, the rational design of chemical libraries enhanced with chemogenomics data is a new area of progressive research.

Compound library development guided by protein structure similarity clustering and natural product structure

To identify biologically relevant and drug-like protein ligands for medicinal chemistry and chemical biology research the grouping of proteins according to evolutionary relationships and conservation of molecular recognition is an established method. We propose to employ structure similarity clustering of the ligand-sensing cores of protein domains (PSSC) in conjunction with natural product guided compound library development as a synergistic approach for the identification of biologically prevalidated ligands with high fidelity. This is supported by the concepts that (i) in nature spatial structure is more conserved than amino acid sequence, (ii) the number of fold types characteristic for all protein domains is limited, and (iii) the underlying frameworks of natural product classes with multiple biological activities provide evolutionarily selected starting points in structural space. On the basis of domain core similarity considerations and irrespective of sequence similarity, Cdc25A phosphatase, acetylcholinesterase, and 11beta-hydroxysteroid dehydrogenases type 1 and type 2 were grouped into a similarity cluster. A 147-member compound collection derived from the naturally occurring Cdc25A inhibitor dysidiolide yielded potent and selective inhibitors of the other members of the similarity cluster with a hit rate of 2-3%. Protein structure similarity clustering may provide an experimental opportunity to identify supersites in proteins.