AutoClickChem: click chemistry in silico

Academic researchers and many in industry often lack the financial resources available to scientists working in "big pharma." High costs include those associated with high-throughput screening and chemical synthesis. In order to address these challenges, many researchers have in part turned to alternate methodologies. Virtual screening, for example, often substitutes for high-throughput screening, and click chemistry ensures that chemical synthesis is fast, cheap, and comparatively easy. Though both in silico screening and click chemistry seek to make drug discovery more feasible, it is not yet routine to couple these two methodologies. We here present a novel computer algorithm, called AutoClickChem, capable of performing many click-chemistry reactions in silico. AutoClickChem can be used to produce large combinatorial libraries of compound models for use in virtual screens. As the compounds of these libraries are constructed according to the reactions of click chemistry, they can be easily synthesized for subsequent testing in biochemical assays. Additionally, in silico modeling of click-chemistry products may prove useful in rational drug design and drug optimization. AutoClickChem is based on the pymolecule toolbox, a framework that may facilitate the development of future python-based programs that require the manipulation of molecular models. Both the pymolecule toolbox and AutoClickChem are released under the GNU General Public License version 3 and are available for download from http://autoclickchem.ucsd.edu.     

Virtual high throughput screening (vHTS) - A perspective

With the exponential rise in the number of viable novel drug targets, computational methods are being increasingly applied to accelerate the drug discovery process. Virtual High Throughput Screening (vHTS) is one such established methodology to identify drug candidates from large collection of compound libraries. Although it complements the expensive and time consuming High Throughput Screening (HTS) of compound libraries, vHTS possess inherent challenges. The successful vHTS requires the careful implementation of each phase of computational screening experiment right from target preparation to hit identification and lead optimization. This article discusses some of the important considerations that are imperative for designing a successful vHTS experiment.     

Lead compounds discovered from libraries: part 2

Many lead compounds with the potential to progress to viable drug candidates have been identified from libraries during the past two years. There are two key strategies most often employed to find leads from libraries: first, high-throughput biological screening of corporate compound collections; and second, synthesis and screening of project-directed libraries (i.e. target-based libraries). Numerous success stories, including the discovery of several clinical candidates, testify to the utility of chemical library collections as proven sources of new leads for drug development.     

Selectide Technology: Bead-Binding Screening

The Selectide process is a random synthetic chemical library method based on the one-bead one-peptide (structure) concept. A "split-synthesis" method is used to generate huge random libraries (10⁶-10⁸). At the end of the synthesis, each bead expresses only one chemical entity (e.g., peptide). The whole library is then tested simultaneously for binding to a specific acceptor molecule of biologic interest. The ligand bead that interacts specifically with the acceptor molecule is then isolated for structure determination. Once a binding motif is identified, a secondary library (based on the motif of the primary screen) is generated and screened under a more stringent condition to identify leads of higher affinity. This process can be applied to both peptide and nonpeptide (small organic) libraries. In the case of nonsequencable structure libraries, the coding principle has to be applied for structure elucidation of positively reacting beads. Coding peptide is synthesized in parallel to the screening structure, and classical Edman degradation (one or multiple-step) is used for structural analysis. To exclude the possibility of interaction of the macromolecular target (e.g., receptor, enzyme, antibody) with the coding structure, a synthetic technique for segregation of the surface (screening structure) and the interior (coding structure) of the beads was developed. The one-bead one-structure process is invaluable in drug discovery for lead identification as well as further optimization of the initial leads. It also serves as an important research tool for molecular recognition.     

Ligand scaffold hopping combining 3D maximal substructure search and molecular similarity

Background  

Virtual screening methods are now well established as effective to identify hit and lead candidates and are fully integrated in most drug discovery programs. Ligand-based approaches make use of physico-chemical, structural and energetics properties of known active compounds to search large chemical libraries for related and novel chemotypes. While 2D-similarity search tools are known to be fast and efficient, the use of 3D-similarity search methods can be very valuable to many research projects as integration of "3D knowledge" can facilitate the identification of not only related molecules but also of chemicals possessing distant scaffolds as compared to the query and therefore be more inclined to scaffolds hopping. To date, very few methods performing this task are easily available to the scientific community.     

Pre-docking filter for protein and ligand 3D structures

Virtual drug screening using protein-ligand docking techniques is a time-consuming process, which requires high computational power for binding affinity calculation. There are millions of chemical compounds available for docking. Eliminating compounds that are unlikely to exhibit high binding affinity from the screening set should speed-up the virtual drug screening procedure. We performed docking of 6353 ligands against twenty-one protein X-ray crystal structures. The docked ligands were ranked according to their calculated binding affinities, from which the top five hundred and the bottom five hundred were selected. We found that the volume and number of rotatable bonds of the top five hundred docked ligands are similar to those found in the crystal structures and corresponded with the volume of the binding sites. In contrast, the bottom five hundred set contains ligands that are either too large to enter the binding site, or too small to bind with high specificity and affinity to the binding site. A pre-docking filter that takes into account shapes and volumes of the binding sites as well as ligand volumes and flexibilities can filter out low binding affinity ligands from the screening sets. Thus, the virtual drug screening procedure speed is increased.     

Neural networks as robust tools in drug lead discovery and development

Empirical methods for building predictive models of the relationships between molecular structure and useful properties are becoming increasingly important. This has arisen because drug discovery and development have become more complex. A large amount of biological target information is becoming available though molecular biology. Automation of chemical synthesis and pharmacological screening has also provided a vast amount of experimental data. Tools for designing libraries and extracting information from molecular databases and high-throughput screening (HTS) experiments robustly and quickly enable leads to be discovered more effectively. As drug leads progress down the development pipeline, the ability to predict physicochemical, pharmacokinetic, and toxicological properties of these leads is becoming increasingly important in reducing the number of expensive, late-development failures. Neural network methods have much to offer in these areas. This review introduces the concepts behind neural networks applied to quantitative structure-activity relationships (QSARs), points out problems that may be encountered, suggests ways of avoiding the pitfalls, and introduces several exciting new neural network methods discovered during the last decade.     

Scaffold composition and biological relevance of screening libraries

The chemical scaffolds from which screening libraries are built have strong influence on the libraries' utility for screening campaigns. Here we present analysis of the scaffold composition of several types of commercially available screening collections and compare those compositions to those of drugs and drug candidates.     

The role of quantitative structure--activity relationships (QSAR) in biomolecular discovery

Empirical methods for building predictive models of the relationships between molecular structure and useful properties are becoming increasingly important. This has arisen because drug discovery and development have become more complex. A large amount of biological target information is becoming available through molecular biology. Automation of chemical synthesis and pharmacological screening has also provided a vast amount of experimental data. Tools for designing libraries and extracting information from molecular databases and high-throughput screening experiments robustly and quickly enable leads to be discovered more effectively. As drug leads progress down the development pipeline, the ability to predict physicochemical, pharmacokinetic and toxicological properties of these leads is becoming increasingly important in reducing the number of expensive, late development failures. Quantitative structure-activity relationship (QSAR) methods have much to offer in these areas. However, QSAR analysis has many traps for unwary practitioners. This review introduces the concepts behind QSAR, points out problems that may be encountered, suggests ways of avoiding the pitfalls and introduces several exciting, new QSAR methods discovered during the last decade.     

Molecular complexity and fragment-based drug discovery: ten years on

We review the concept of molecular complexity in the context of the very simple model of molecular interactions that we introduced over ten years ago. A summary is presented of efforts to validate this simple model using screening data. The relationship between the complexity model and the problem of sampling chemical space is discussed, together with the relevance of these theoretical concepts to fragment-based drug discovery.     

Quantitative codon optimisation of DNA libraries encoding sub-random peptides: design and characterisation of a novel library encoding transmembrane domain peptides

Codons for amino acids sharing similar chemical properties seem to cluster on the genetic codon table. Such a geographical distribution of the codons was exploited to create chemically synthesised DNA that encodes peptide libraries containing only a subset of the 20 natural amino acids. The frequency of each amino acid in the subset was further optimised by quantitatively manipulating the ratio of the four phosphoamidites during chemical synthesis of the libraries. Peptides encoded by such libraries show a reduced complexity and could be enriched in peptides of a desired property, which are thus more suitable when screening for functional peptides. Proof of concept for the codon-biased design of peptide libraries was shown by design, synthesis, and characterisation of a transmembrane peptide library that contains >80% transmembrane peptides, representing a 160-fold enrichment compared with a fully randomised library.     

Chembench: a cheminformatics workbench

MOTIVATION: Advances in the field of cheminformatics have been hindered by a lack of freely available tools. We have created Chembench, a publicly available cheminformatics portal for analyzing experimental chemical structure-activity data. Chembench provides a broad range of tools for data visualization and embeds a rigorous workflow for creating and validating predictive Quantitative Structure-Activity Relationship models and using them for virtual screening of chemical libraries to prioritize the compound selection for drug discovery and/or chemical safety assessment. AVAILABILITY: Freely accessible at: http://chembench.mml.unc.edu CONTACT: alex_tropsha@unc.edu     

The interdependence between screening methods and screening libraries

The most common methods for discovery of chemical compounds capable of manipulating biological function involves some form of screening. The success of such screens is highly dependent on the chemical materials - commonly referred to as libraries - that are assayed. Classic methods for the design of screening libraries have depended on knowledge of target structure and relevant pharmacophores for target focus, and on simple count-based measures to assess other properties. The recent proliferation of two novel screening paradigms, structure-based screening and high-content screening, prompts a profound rethink about the ideal composition of small-molecule screening libraries. We suggest that currently utilized libraries are not optimal for addressing new targets by high-throughput screening, or complex phenotypes by high-content screening.     

A 3D-QSAR model based screen for dihydropyridine-like compound library to identify inhibitors of amyloid beta (Aβ) production

Abnormal accumulation of amyloid beta peptide (Aβ) is one of the hallmarks of Alzheimer's disease progression. Practical limitations such as cost , poor hit rates and a lack of well characterized targets are a major bottle neck in the in vitro screening of a large number of chemical libraries and profiling them to identify Aβ inhibitors. We used a limited set of 1,4 dihydropyridine (DHP)-like compounds from our model set (MS) of 24 compounds which inhibit Aβ as a training set and built 3D-QSAR (Three-dimensional Quantitative Structure-Activity Relationship) models using the Phase program (Schr?dinger, USA). We developed a 3D-QSAR model that showed the best prediction for Aβ inhibition in the test set of compounds and used this model to screen a 1,043 DHP-like small library set of (LS) compounds. We found that our model can effectively predict potent hits at a very high rate and result in significant cost savings when screening larger libraries. We describe here our in silico model building strategy, model selection parameters and the chemical features that are useful for successful screening of DHP and DHP-like chemical libraries for Aβ inhibitors.     

Structure-based virtual ligand screening with LigandFit: pose prediction and enrichment of compound collections

Virtual ligand screening methods based on the structure of the receptor are extensively used to facilitate the discovery of lead compounds. In the present study, we investigated the LigandFit package on four different proteins (coagulation factor VIIa, estrogen receptor, thymidine kinase, and neuraminidase), a relatively large compound collection of 65,560 unique "drug-like" molecules and four focused libraries (1950 molecules each). We performed virtual screening experiments with the large database and evaluated six scoring functions available in the package (DockScore, LigScore1, LigScore2, PLP1, PLP2, and PMF). The results showed that LigandFit is an efficient program, especially when used with LigScore1. Similar computations were carried out using focused libraries. In this situation the LigScore1 scoring function outperformed the other ones on three out of the four proteins tested. Even for the difficult neuraminidase case, the LigandFit/LigScore1 combination was still reasonably successful. Assessment of docking accuracy was also performed and again, we found that LigandFit (with DockScore and the CFF parameters) was performing well. On the basis of these results and observed increased enrichments after LigandFit/Ligscore1 screening on focused libraries, we suggest that using this program as a final step of a hierarchical protocol can be very beneficial to assist lead finding.     

Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets

Virtual screening is one of the major tools used in computer-aided drug discovery. In structure-based virtual screening, the scoring function is critical to identifying the correct docking pose and accurately predicting the binding affinities of compounds. However, the performance of existing scoring functions has been shown to be uneven for different targets, and some important drug targets have proven especially challenging. In these targets, scoring functions cannot accurately identify the native or near-native binding pose of the ligand from among decoy poses, which affects both the accuracy of the binding affinity prediction and the ability of virtual screening to identify true binders in chemical libraries. Here, we present an approach to discriminating native poses from decoys in difficult targets for which several scoring functions failed to correctly identify the native pose. Our approach employs Discrete Molecular Dynamics simulations to incorporate protein-ligand dynamics and the entropic effects of binding. We analyze a collection of poses generated by docking and find that the residence time of the ligand in the native and nativelike binding poses is distinctly longer than that in decoy poses. This finding suggests that molecular simulations offer a unique approach to distinguishing the native (or nativelike) binding pose from decoy poses that cannot be distinguished using scoring functions that evaluate static structures. The success of our method emphasizes the importance of protein-ligand dynamics in the accurate determination of the binding pose, an aspect that is not addressed in typical docking and scoring protocols.     

Natural product-like synthetic libraries

There is a paucity of chemical matter suitably poised for effective drug development. Improving the quality and efficiency of research early on in the drug discovery process has been a long standing objective for the drug industry and improvements to the accessibility and quality of compound screening decks might have a significant and positive impact. In the absence of specific molecular information that can be modeled and used predicatively we are far from identifying which small molecules are most relevant to emerging biological targets such as protein-protein interactions. Natural products have been historically successful as an entry point for drug discovery and recently screening libraries are being synthesized to emulate natural product like features.     

FACTA: a text search engine for finding associated biomedical concepts

FACTA is a text search engine for MEDLINE abstracts, which is designed particularly to help users browse biomedical concepts (e.g. genes/proteins, diseases, enzymes and chemical compounds) appearing in the documents retrieved by the query. The concepts are presented to the user in a tabular format and ranked based on the co-occurrence statistics. Unlike existing systems that provide similar functionality, FACTA pre-indexes not only the words but also the concepts mentioned in the documents, which enables the user to issue a flexible query (e.g. free keywords or Boolean combinations of keywords/concepts) and receive the results immediately even when the number of the documents that match the query is very large. The user can also view snippets from MEDLINE to get textual evidence of associations between the query terms and the concepts. The concept IDs and their names/synonyms for building the indexes were collected from several biomedical databases and thesauri, such as UniProt, BioThesaurus, UMLS, KEGG and DrugBank. AVAILABILITY: The system is available at http://www.nactem.ac.uk/software/facta/     

Robust production of a peptide library using methodological synchronization

Peptide libraries have proven to be useful in applications such as substrate profiling, drug candidate screening and identifying protein–protein interaction partners. However, issues of fidelity, peptide length, and purity have been encountered when peptide libraries are chemically synthesized. Biochemically produced libraries, on the other hand, circumvent many of these issues due to the fidelity of the protein synthesis machinery. Using thioredoxin as an expression partner, a stably folded peptide scaffold (avian pancreatic polypeptide) and a compatible cleavage site for human rhinovirus 3C protease, we report a method that allows robust expression of a genetically encoded peptide library, which yields peptides of high purity. In addition, we report the use of methodological synchronization, an experimental design created for the production of a library, from initial cloning to peptide characterization, within a 5-week period of time. Total peptide yields ranged from 0.8% to 16%, which corresponds to 2–70 mg of pure peptide. Additionally, no correlation was observed between the ability to be expressed or overall yield of peptide-fusions and the intrinsic chemical characteristics of the peptides, indicating that this system can be used for a wide variety of peptide sequences with a range of chemical characteristics.     

Searching for Synergies: Matrix Algebraic Approaches for Efficient Pair Screening

Functionally interacting perturbations, such as synergistic drugs pairs or synthetic lethal gene pairs, are of key interest in both pharmacology and functional genomics. However, to find such pairs by traditional screening methods is both time consuming and costly. We present a novel computational-experimental framework for efficient identification of synergistic target pairs, applicable for screening of systems with sizes on the order of current drug, small RNA or SGA (Synthetic Genetic Array) libraries (>1000 targets). This framework exploits the fact that the response of a drug pair in a given system, or a pair of genes'' propensity to interact functionally, can be partly predicted by computational means from (i) a small set of experimentally determined target pairs, and (ii) pre-existing data (e.g. gene ontology, PPI) on the similarities between targets. Predictions are obtained by a novel matrix algebraic technique, based on cyclical projections onto convex sets. We demonstrate the efficiency of the proposed method using drug-drug interaction data from seven cancer cell lines and gene-gene interaction data from yeast SGA screens. Our protocol increases the rate of synergism discovery significantly over traditional screening, by up to 7-fold. Our method is easy to implement and could be applied to accelerate pair screening for both animal and microbial systems.