Overcoming drug resistance through in silico prediction.

Prediction tools are commonly used in pre-clinical research to assist target selection, to optimize drug potency or to predict the pharmacological profile of drug candidates. In silico prediction and overcoming drug resistance is a new opportunity that creates a high interest in pharmaceutical research. This review presents two main in silico strategies to meet this challenge: a structure-based approach to study the influence of mutations on the drug-target interaction and a system-biology approach to identify resistance pathways for a given drug. In silico screening of synergies between therapeutic and resistant pathways through biological network analysis is an example of technique to escape drug resistance. Structure-based drug design and in silico system biology are complementary approaches to reach few objectives at once: increase efficiency, reduce toxicity and overcoming drug resistance.

In Silico Target Druggability Assessment: From Structural to Systemic Approaches.

This chapter will focus on today's in silico direct and indirect approaches to assess therapeutic target druggability. The direct approach tries to infer from the 3D structure the capacity of the target protein to bind small molecule in order to modulate its biological function. Algorithms to recognize and characterize the quality of the ligand interaction sites whether within buried protein cavities or within large protein-protein interface will be reviewed in the first part of the paper. In the case a ligand-binding site is already identified, indirect aspects of target druggability can be assessed. These indirect approaches focus first on target promiscuity and the potential difficulties in developing specific drugs. It is based on large-scale comparison of protein-binding sites. The second aspect concerns the capacity of the target to induce resistant pathway once it is inhibited or activated by a drug. The emergence of drug-resistant pathways can be assessed through systemic analysis of biological networks implementing metabolism and/or cell regulation signaling.

In Silico Drug-Target Profiling.

Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.

Inhibition of protein-protein interactions: the discovery of druglike beta-catenin inhibitors by combining virtual and biophysical screening.

The interaction between beta-catenin and Tcf family members is crucial for the Wnt signal transduction pathway, which is commonly mutated in cancer. This interaction extends over a very large surface area (4800 A(2)), and inhibiting such interactions using low molecular weight inhibitors is a challenge. However, protein surfaces frequently contain "hot spots," small patches that are the main mediators of binding affinity. By making tight interactions with a hot spot, a small molecule can compete with a protein. The Tcf3/Tcf4-binding surface on beta-catenin contains a well-defined hot spot around residues K435 and R469. A 17,700 compounds subset of the Pharmacia corporate collection was docked to this hot spot with the QXP program; 22 of the best scoring compounds were put into a biophysical (NMR and ITC) screening funnel, where specific binding to beta-catenin, competition with Tcf4 and finally binding constants were determined. This process led to the discovery of three druglike, low molecular weight Tcf4-competitive compounds with the tightest binder having a K(D) of 450 nM. Our approach can be used in several situations (e.g., when selecting compounds from external collections, when no biochemical functional assay is available, or when no HTS is envisioned), and it may be generally applicable to the identification of inhibitors of protein-protein interactions.

Virtual screening to enrich a compound collection with CDK2 inhibitors using docking, scoring, and composite scoring models.

Docking programs can generate subsets of a compound collection with an increased percentage of actives against a target (enrichment) by predicting their binding mode (pose) and affinity (score), and retrieving those with the highest scores. Using the QXP and GOLD programs, we compared the ability of six single scoring functions (PLP, Ligscore, Ludi, Jain, ChemScore, PMF) and four composite scoring models (Mean Rank: MR, Rank-by-Vote: Vt, Bayesian Statistics: BS and PLS Discriminant Analysis: DA) to separate compounds that are active against CDK2 from inactives. We determined the enrichment for the entire set of actives (IC50 < 10 microM) and for three activity subsets. In all cases, the enrichment for each subset was lower than for the entire set of actives. QXP outperformed GOLD at pose prediction, but yielded only moderately better enrichments. Five to six scoring functions yielded good enrichments with GOLD poses, while typically only two worked well with QXP poses. For each program, two scoring functions generally performed better than the others (Ligscore2 and Ludi for GOLD; QXP and Jain for QXP). Composite scoring functions yielded better results than single scoring functions. The consensus approaches MR and Vt worked best when separating micromolar inhibitors from inactives. The statistical approaches BS and DA, which require training data, performed best when distinguishing between low and high nanomolar inhibitors. The key observation that all hit rate profiles for all four activity intervals for all scoring schemes for both programs are significantly better than random, is evidence that docking can be successfully applied to enrich compound collections.

Synthetic biology for pharmaceutical drug discovery.

Synthetic biology (SB) is an emerging discipline, which is slowly reorienting the field of drug discovery. For thousands of years, living organisms such as plants were the major source of human medicines. The difficulty in resynthesizing natural products, however, often turned pharmaceutical industries away from this rich source for human medicine. More recently, progress on transformation through genetic manipulation of biosynthetic units in microorganisms has opened the possibility of in-depth exploration of the large chemical space of natural products derivatives. Success of SB in drug synthesis culminated with the bioproduction of artemisinin by microorganisms, a tour de force in protein and metabolic engineering. Today, synthetic cells are not only used as biofactories but also used as cell-based screening platforms for both target-based and phenotypic-based approaches. Engineered genetic circuits in synthetic cells are also used to decipher disease mechanisms or drug mechanism of actions and to study cell-cell communication within bacteria consortia. This review presents latest developments of SB in the field of drug discovery, including some challenging issues such as drug resistance and drug toxicity.

Computational protein design methods for synthetic biology.

Computational protein design, a process that searches for mutants with desired improved properties, plays a central role in the conception of many synthetic biology devices including biosensors, bioproduction, or regulation circuits. To that end, a rational workflow for computational protein design is described here consisting of (a) searching in the sequence, structure or chemical spaces for the desired function and associated protein templates; (b) finding the list of potential hot regions to mutate in the parent proteins; and (c) performing in silico screening of mutants with predicted improved properties.

Catecholic flavonoids acting as telomerase inhibitors.

In recent years telomerase has been identified as a new promising target in oncology and consequently new telomerase inhibitors have been intensely explored as anticancer agents. Focused screening of several polyhydroxylated flavonoids has allowed us to identify 7,8,3',4'-tetrahydroxyflavone 1 as a new telomerase inhibitor with an interesting in vitro activity in a Flash-Plate assay (IC50 = 0.2 microM) that has been confirmed in the classical TRAP assay. Starting from this compound, we developed a medicinal chemistry program to optimize our lead, and in particular to replace one of the two catechols with potential bioisosteres. From this study, new structural analogues characterized by submicromolar potencies have been obtained. Their synthesis and biological activity are described.

Structure-based target druggability assessment.

The focus of this chapter is on the important concepts behind the in silico techniques that are used today to assess target druggability. The first step of the assessment consists of finding cavity space in the protein using 2D and/or 3D topological concepts. These concepts underlie the geometry and energy-based pocketfinder algorithms. Analysis pursues on the physico-chemical complementarity between the binding site and the drug like molecule. Geometrical and molecular flexibility aspect are also included in this assessment. The presence of hot interaction spots are shown to be particularly important for targeting protein-protein interactions. Finally, binding site promiscuity can be assessed by large scale structural comparison with other targets. Common chemical features amongst protein cavities can predict potential cross-reactivity with unwanted targets.

Synergistic Synthetic Biology: Units in Concert.

Synthetic biology aims at translating the methods and strategies from engineering into biology in order to streamline the design and construction of biological devices through standardized parts. Modular synthetic biology devices are designed by means of an adequate elimination of cross-talk that makes circuits orthogonal and specific. To that end, synthetic constructs need to be adequately optimized through in silico modeling by choosing the right complement of genetic parts and by experimental tuning through directed evolution and craftsmanship. In this review, we consider an additional and complementary tool available to the synthetic biologist for innovative design and successful construction of desired circuit functionalities: biological synergies. Synergy is a prevalent emergent property in biological systems that arises from the concerted action of multiple factors producing an amplification or cancelation effect compared with individual actions alone. Synergies appear in domains as diverse as those involved in chemical and protein activity, polypharmacology, and metabolic pathway complementarity. In conventional synthetic biology designs, synergistic cross-talk between parts and modules is generally attenuated in order to verify their orthogonality. Synergistic interactions, however, can induce emergent behavior that might prove useful for synthetic biology applications, like in functional circuit design, multi-drug treatment, or in sensing and delivery devices. Synergistic design principles are therefore complementary to those coming from orthogonal design and may provide added value to synthetic biology applications. The appropriate modeling, characterization, and design of synergies between biological parts and units will allow the discovery of yet unforeseeable, novel synthetic biology applications.

Hot spots in Tcf4 for the interaction with beta-catenin.

The interaction of beta-catenin with T-cell factor (Tcf) 4 plays a central role in the Wnt signaling pathway and has been discussed as a possible site of intervention for the development of anti-cancer drugs. In this study, we performed Ala-scanning mutagenesis of all Tcf4 residues in the Tcf-beta-catenin interface and studied the binding energetics of these mutants using isothermal titration calorimetry. Binding of Tcf4 was found to be highly cooperative. Single site mutations of most Tcf4 residues resulted in a significant reduction in binding enthalpies but in similar binding constants as compared with wild type Tcf4. Interestingly, this was also true for residues that are disordered in the reported crystal structures. The mutation D16A caused the largest reduction in binding constant (50-fold) accompanied by a large unfavorable enthalpy change (DeltaDeltaHobs) of +8 kcal/mol at 25 degrees C. In contrast, the mutation of the Tcf residues Glu24 and Glu28, which have been proposed as an interaction hot spot due to their location in a field of strong positive electrostatic potential on the beta-catenin surface (charge button), resulted only in a significant reduction of binding enthalpies, which were largely compensated for by unfavorable entropic contributions to the binding. Other mutations that significantly reduced Tcf binding constants were D11A and alanine mutations of the hydrophobic residues Leu41, Val44, and Leu48. The measured thermodynamic data are discussed with the available structural information of Tcf-beta-catenin crystal structures and allow us to propose possible sites for development of Tcf antagonists.

Novel scoring functions comprising QXP, SASA, and protein side-chain entropy terms.

Novel scoring functions that predict the affinity of a ligand for its receptor have been developed. They were built with several statistical tools (partial least squares, genetic algorithms, neural networks) and trained on a data set of 100 crystal structures of receptor-ligand complexes, with affinities spanning 10 log units. The new scoring functions contain both descriptors generated by the QXP docking program and new descriptors that were developed in-house. These new descriptors are based on solvent accessible surface areas and account for conformational entropy changes and desolvation effects of both ligand and receptor upon binding. The predictive r(2) values for a test set of 24 complexes are in the 0.712-0.741 range and RMS prediction errors in the 1.09-1.16 log K(d) range. Inclusion of the new descriptors led to significant improvements in affinity prediction, compared to scoring functions based on QXP descriptors alone. However, the QXP descriptors by themselves perform better in binding mode prediction. The performance of the linear models is comparable to that of the neural networks. The new functions perform very well, but they still need to be validated as universal tools for the prediction of binding affinity.

Assessment of docking poses: interactions-based accuracy classification (IBAC) versus crystal structure deviations.

Six docking programs (FlexX, GOLD, ICM, LigandFit, the Northwestern University version of DOCK, and QXP) were evaluated in terms of their ability to reproduce experimentally observed binding modes (poses) of small-molecule ligands to macromolecular targets. The accuracy of a pose was assessed in two ways: First, the RMS deviation of the predicted pose from the crystal structure was calculated. Second, the predicted pose was compared to the experimentally observed one regarding the presence of key interactions with the protein. The latter assessment is referred to as interactions-based accuracy classification (IBAC). In a number of cases significant discrepancies were found between IBAC and RMSD-based classifications. Despite being more subjective, the IBAC proved to be a more meaningful measure of docking accuracy in all these cases.