Project description:Paradigms in drug design and discovery are changing at a significant pace. Concomitant to the sequencing of over 180 several genomes, the high-throughput miniaturization of chemical synthesis and biological evaluation of a multiple compounds on gene/protein expression and function opens the way to global drug-discovery approaches, no more focused on a single target but on an entire family of related proteins or on a full metabolic pathway. Chemogenomics is this emerging research field aimed at systematically studying the biological effect of a wide array of small molecular-weight ligands on a wide array of macromolecular targets. Since the quantity of existing data (compounds, targets and assays) and of produced information (gene/protein expression levels and binding constants) are too large for manual manipulation, information technologies play a crucial role in planning, analysing and predicting chemogenomic data. The present review will focus on predictive in silico chemogenomic approaches to foster rational drug design and derive information from the simultaneous biological evaluation of multiple compounds on multiple targets. State-of-the-art methods for navigating in either ligand or target space will be presented and concrete drug design applications will be mentioned.
Project description:Aromatic aldehydes are important industrial raw materials mainly synthesized by anti-Markovnikov (AM) oxidation of corresponding aromatic olefins. The AM product selectivity remains a big challenge. P450 aMOx is the first reported enzyme that could catalyze AM oxidation of aromatic olefins. Here, we reported a rational design strategy based on the "butterfly" model of the active site of P450 aMOx. Constrained molecular dynamic simulations and a binding energy analysis of key residuals combined with an experimental alanine scan were applied. As a result, the mutant A275G showed high AM selectivity of >99%. The results also proved that the "butterfly" model is an effective design strategy for enzymes.
Project description:Cyclin-dependent kinases (CDKs) belong to the CMGC subfamily of protein kinases and play crucial roles in eukaryotic cell division cycle. At least seven different CDKs have been reported to be implicated in the cell cycle regulation in vertebrates. These CDKs are highly homologous and contain a conserved catalytic core. This makes the design of inhibitors specific for a particular CDK difficult. There is, however, growing need for CDK5 specific inhibitors to treat various neurodegenerative diseases. Recently, cis-substituted cyclobutyl-4-aminoimidazole inhibitors have been identified as potent CDK5 inhibitors that gave up to 30-fold selectivity over CDK2. Available IC50 values also indicate a higher potency of this class of inhibitors over commercially available drugs, such as roscovitine. To understand the molecular basis of higher potency and selectivity of these inhibitors, here, we present molecular dynamics simulation results of CDK5/p25 and CDK2/CyclinE complexed with a series of cyclobutyl-substituted imidazole inhibitors and roscovitine. The atomic details of the stereospecificity and selectivity of these inhibitors are obtained from energetics and binding characteristics to the CDK binding pocket. The study not only complements the experimental findings, but also provides a wealth of detailed information that could help the structure-based drug designing processes.
Project description:The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.
Project description:Due to the synergic relationship between medical chemistry, bioinformatics and molecular simulation, the development of new accurate computational tools for small molecules drug design has been rising over the last years. The main result is the increased number of publications where computational techniques such as molecular docking, de novo design as well as virtual screening have been used to estimate the binding mode, site and energy of novel small molecules. In this work I review some tools, which enable the study of biological systems at the atomistic level, providing relevant information and thereby, enhancing the process of rational drug design.
Project description:G protein-coupled receptors (GPCRs) comprise a large class of transmembrane proteins that play critical roles in both normal physiology and pathophysiology. These critical roles offer targets for therapeutic intervention, as exemplified by the substantial fraction of current pharmaceutical agents that target members of this family. Tremendous contributions to our understanding of GPCR structure and dynamics have come from both indirect and direct structural characterization techniques. Key features of GPCR conformations derived from both types of characterization techniques are reviewed.
Project description:Microtubules are highly dynamic polymers of α,β-tubulin dimers which play an essential role in numerous cellular processes such as cell proliferation and intracellular transport, making them an attractive target for cancer and neurodegeneration research. To date, a large number of known tubulin binders were derived from natural products, while only one was developed by rational structure-based drug design. Several of these tubulin binders show promising in vitro profiles while presenting unacceptable off-target effects when tested in patients. Therefore, there is a continuing demand for the discovery of safer and more efficient tubulin-targeting agents. Since tubulin structural data is readily available, the employment of computer-aided design techniques can be a key element to focus on the relevant chemical space and guide the design process. Due to the high diversity and quantity of structural data available, we compiled here a guide to the accessible tubulin-ligand structures. Furthermore, we review different ligand and structure-based methods recently used for the successful selection and design of new tubulin-targeting agents.
Project description:Proteins are the most multifaceted macromolecules in living systems and have various important functions, including structural, catalytic, sensory, and regulatory functions. Rational design of enzymes is a great challenge to our understanding of protein structure and physical chemistry and has numerous potential applications. Protein design algorithms have been applied to design or engineer proteins that fold, fold faster, catalyze, catalyze faster, signal, and adopt preferred conformational states. The field of de novo protein design, although only a few decades old, is beginning to produce exciting results. Developments in this field are already having a significant impact on biotechnology and chemical biology. The application of powerful computational methods for functional protein designing has recently succeeded at engineering target activities. Here, we review recently reported de novo functional proteins that were developed using various protein design approaches, including rational design, computational optimization, and selection from combinatorial libraries, highlighting recent advances and successes.
Project description:BackgroundCaM-KIIN has evolved to inhibit stimulated and autonomous activity of the Ca(2+)/calmodulin (CaM)-dependent protein kinase II (CaMKII) efficiently, selectively, and potently (IC50 ∼100 nM). The CN class of peptides, derived from the inhibitory region of CaM-KIIN, provides powerful new tools to study CaMKII functions. The goal of this study was to identify the residues required for CaMKII inhibition, and to assess if artificial mutations could further improve the potency achieved during evolution.Methodology/principal findingsFirst, the minimal region with full inhibitory potency was identified (CN19) by determining the effect of truncated peptides on CaMKII activity in biochemical assays. Then, individual residues of CN19 were mutated. Most individual Ala substitutions decreased potency of CaMKII inhibition, however, P3A, K13A, and R14A increased potency. Importantly, this initial Ala scan suggested a specific interaction of the region around R11 with the CaMKII substrate binding site, which was exploited for further rational mutagenesis to generate an optimized pseudo-substrate sequence. Indeed, the potency of the optimized peptide CN19o was >250fold improved (IC50 <0.4 nM), and CN19o has characteristics of a tight-binding inhibitor. The selectivity for CaMKII versus CaMKI was similarly improved (to almost 100,000fold for CN19o). A phospho-mimetic S12D mutation decreased potency, indicating potential for regulation by cellular signaling. Consistent with importance of this residue in inhibition, most other S12 mutations also significantly decreased potency, however, mutation to V or Q did not.Conlusions/significanceThese results provide improved research tools for studying CaMKII function, and indicate that evolution fine-tuned CaM-KIIN not for maximal potency of CaMKII inhibition, but for lower potency that may be optimal for dynamic regulation of signal transduction.
Project description:Chromatographic characterization and parameterization studies targeting many solutes require the judicious choice of operating conditions to minimize analysis time without compromising the accuracy of the results. To minimize analysis time, solutes are often grouped into a small number of mixtures; however, this increases the risk of peak overlap. While multivariate curve resolution methods are often able to resolve analyte signals based on their spectral qualities, these methods require that the chromatographically overlapped compounds have dissimilar spectra. In this work, a strategy for grouping compounds into sample mixtures containing solutes with distinct spectral and, optionally, with distinct chromatographic properties, in order to ensure successful solute resolution either chromatographically or with curve resolution methods is proposed. We name this strategy rational design of mixtures (RDM). RDM utilizes multivariate selectivity as a metric for making decisions regarding group membership (i.e., whether to add a particular solute to a particular sample). A group of 97 solutes was used to demonstrate this strategy. Utilizing both estimated chromatographic properties and measured spectra to group these 97 analytes, only 12 groups were required to avoid a situation where two or more solutes in the same group could not be resolved either chromatographically (i.e., they have significantly different retention times) or spectrally (i.e., spectra are different enough to enable resolution by curve resolution methods). When only spectral properties were utilized (i.e., the chromatographic properties are unknown ahead of time) the number of groups required to avoid unresolvable overlaps increased to 20. The grouping strategy developed here will improve the time and instrument efficiency of studies that aim to obtain retention data for solutes as a function of operating conditions, whether for method development or determination of the chromatographic parameters of solutes of interest (e.g., k w ).