Methods for Computer-aided Chemical Biology. Part 1: Design of a Benchmark System for the Evaluation of Compound Selectivity

Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors

Microtubules are receiving enormous interest in drug discovery due to the important roles they play in cellular functions. Targeting tubulin polymerization presents an excellent opportunity for the development of anti-tubulin drugs. Drug resistance and high toxicity of currently used tubulin-binding agents have necessitated the pursuit of novel drug candidates with increased therapeutic potency. The design of novel drug candidates can be achieved using efficient computational techniques to support existing efforts. Proteochemometric (PCM) modeling is a computational technique that can be employed to elucidate the bioactivity relations between related targets and multiple ligands. We have developed a PCM-based Support Vector Machine (SVM) approach for predicting the bioactivity between tubulin receptors and small, drug-like molecules. The bioactivity datasets used for training the SVM algorithm were obtained from the Binding DB database. The SVM-based PCM model yielded a good overall predictive performance with an area under the curve (AUC) of 87%, Matthews correlation coefficient (MCC) of 72%, overall accuracy of 93%, and a classification error of 7%. The algorithm allows the prediction of the likelihood of new interactions based on confidence scores between the query datasets, comprising ligands in SMILES format and protein sequences of tubulin targets. The algorithm has been implemented as a web server known as TubPred, accessible via http://35.167.90.225:5000/ .

Predicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D)

Adenosine receptors (ARs) are potential therapeutic targets for Parkinson’s disease, diabetes, pain, stroke and cancers. Prediction of subtype selectivity is therefore important from both therapeutic and mechanistic perspectives. In this paper, we introduced a shape similarity profile as molecular descriptor, namely three-dimensional biologically relevant spectrum (BRS-3D), for AR selectivity prediction. Pairwise regression and discrimination models were built with the support vector machine methods. The average determination coefficient (r²) of the regression models was 0.664 (for test sets). The 2B-3 (A_2Bvs A₃) model performed best with q² = 0.769 for training sets (10-fold cross-validation), and r² = 0.766, RMSE = 0.828 for test sets. The models’ robustness and stability were validated with 100 times resampling and 500 times Y-randomization. We compared the performance of BRS-3D with 3D descriptors calculated by MOE. BRS-3D performed as good as, or better than, MOE 3D descriptors. The performances of the discrimination models were also encouraging, with average accuracy (ACC) 0.912 and MCC 0.792 (test set). The 2A-3 (A_2Avs A₃) selectivity discrimination model (ACC = 0.882 and MCC = 0.715 for test set) outperformed an earlier reported one (ACC = 0.784). These results demonstrated that, through multiple conformation encoding, BRS-3D can be used as an effective molecular descriptor for AR subtype selectivity prediction.

Follow up: Compound data sets and software tools for chemoinformatics and medicinal chemistry applications: update and data transfer

In 2012, we reported 30 compound data sets and/or programs developed in our laboratory in a data article and made them freely available to the scientific community to support chemoinformatics and computational medicinal chemistry applications. These data sets and computational tools were provided for download from our website. Since publication of this data article, we have generated 13 new data sets with which we further extend our collection of publicly available data and tools. Due to changes in web servers and website architectures, data accessibility has recently been limited at times. Therefore, we have also transferred our data sets and tools to a public repository to ensure full and stable accessibility. To aid in data selection, we have classified the data sets according to scientific subject areas. Herein, we describe new data sets, introduce the data organization scheme, summarize the database content and provide detailed access information in ZENODO (doi: 10.5281/zenodo.8451 and doi:10.5281/zenodo.8455).

Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications

We have generated a number of  compound data sets and programs for different types of applications in pharmaceutical research. These data sets and programs were originally designed for our research projects and are made publicly available. Without consulting original literature sources, it is difficult to understand specific features of data sets and software tools, basic ideas underlying their design, and applicability domains. Currently, 30 different entries are available for download from our website. In this data article, we provide an overview of the data and tools we make available and designate the areas of research for which they should be useful. For selected data sets and methods/programs, detailed descriptions are given. This article should help interested readers to select data and tools for specific computational investigations.

Molecular test systems for computational selectivity studies and systematic analysis of compound selectivity profiles

For chemical genetics and chemical biology, an important task is the identification of small molecules that are selective against individual targets and can be used as molecular probes for specific biological functions. To aid in the development of computational methods for selectivity analysis, molecular benchmark systems have been developed that capture compound selectivity data for pairs of targets. These molecular test systems are utilized for "selectivity searching" and the analysis of structure-selectivity relationships. Going beyond binary selectivity sets focusing on target pairs, a methodological framework, Molecular Formal Concept Analysis (MolFCA), is described for the definition and systematic mining of compound selectivity profiles.

Iterative Shannon Entropy - a Methodology to Quantify the Information Content of Value Range Dependent Data Distributions. Application to Descriptor and Compound Selectivity Profiling

We introduce an entropy-based methodology, Iterative Shannon entropy (ISE), to quantify the information contained in molecular descriptors and compound selectivity data sets taking data spread directly into account. The method is applicable to determine the information content of any value range dependent data distribution. An analysis of descriptor information content has been carried out to explore alternative binning schemes for entropy calculation. Using this entropic measure we have profiled 153 compound selectivity data sets for combinations of 68 target proteins belonging to 10 target families. With the ISE measure, we aim to assign high information content to compound data sets that span a wide range of selectivity values and different selectivity relationships and hence correspond to more than one biological phenotype. Target families with high average entropy scores are identified. For members of these families, active compounds display highly differentiated selectivity profiles.

Ligand prediction from protein sequence and small molecule information using support vector machines and fingerprint descriptors

Support vector machine (SVM) database search strategies are presented that aim at the identification of small molecule ligands for targets for which no ligand information is currently available. In pharmaceutical research and chemical biology, this situation is faced, for example, when studying orphan targets or newly identified members of protein families. To investigate methods for de novo ligand identification in the absence of known three-dimensional target structures or active molecules, we have focused on combining sequence and ligand information for closely and distantly related proteins. To provide a basis for these investigations, a set of 11 protease targets from different families was assembled together with more than 2000 inhibitors directed against individual proteases. We have compared SVM approaches that combine protein sequence and ligand information in different ways and utilize 2D fingerprints as ligand descriptors. These methodologies were applied to search for inhibitors of individual proteases not taken into account during learning. A target sequence-ligand kernel and, in particular, a linear combination of multiple target-directed SVMs consistently identified inhibitors with high accuracy including test cases where homology-based similarity searching using data fusion and conventional SVM ranking nearly or completely failed. The SVM linear combination and target-ligand kernel methods described herein are intuitive and straightforward to adopt for ligand prediction against other targets.

Searching for target-selective compounds using different combinations of multiclass support vector machine ranking methods, kernel functions, and fingerprint descriptors

The identification of small chemical compounds that are selective for a target protein over one or more closely related members of the same family is of high relevance for applications in chemical biology. Conventional 2D similarity searching using known selective molecules as templates has recently been found to preferentially detect selective over non-selective and inactive database compounds. To improve the initially observed search performance, we have attempted to use 2D fingerprints as descriptors for support vector machine (SVM)-based selectivity searching. Different from typically applied binary SVM compound classification, SVM analysis has been adapted here for multiclass predictions and compound ranking to distinguish between selective, active but non-selective, and inactive compounds. In systematic database search calculations, we tested combinations of four alternative SVM ranking schemes, four different kernel functions, and four fingerprints and were able to further improve selectivity search performance by effectively removing non-selective molecules from high ranking positions while retaining high recall of selective compounds.

Powerful partners: Arabidopsis and chemical genomics

Chemical genomics (i.e. genomics scale chemical genetics) approaches capitalize on the ability of low molecular mass molecules to modify biological processes. Such molecules are used to modify the activity of a protein or a pathway in a manner that it is tunable and reversible. Bioactive chemicals resulting from forward or reverse chemical screens can be useful in understanding and dissecting complex biological processes due to the essentially limitless variation in structure and activities inherent in chemical space. A major advantage of this approach as a powerful addition to conventional plant genetics is the fact that chemical genomics can address loss-of-function lethality and redundancy. Furthermore, the ability of chemicals to be added at will and to act quickly can permit the study of processes that are highly dynamic such as endomembrane trafficking. An important aspect of utilizing small molecules effectively is to characterize bioactive chemicals in detail including an understanding of structure-activity relationships and the identification of active and inactive analogs. Bioactive chemicals can be useful as reagents to probe biological pathways directly. However, the identification of cognate targets and their pathways is also informative and can be achieved by screens for genetic resistance or hypersensitivity in Arabidopsis thaliana or other organisms from which the results can be translated to plants. In addition, there are approaches utilizing "tagged" chemical libraries that possess reactive moieties permitting the immobilization of active compounds. This opens the possibility for biochemical purification of putative cognate targets. We will review approaches to screen for bioactive chemicals that affect biological processes in Arabidopsis and provide several examples of the power and challenges inherent in this new approach in plant biology.

Formal concept analysis for the identification of molecular fragment combinations specific for active and highly potent compounds

We introduce fragment formal concept analysis (FragFCA) to study complex relationships between fragments in active compounds taking potency information into account. Fragment combinations that are unique to active or highly potent compounds or that are shared by molecules having different or overlapping activity profiles are systematically identified using chemically intuitive queries of varying complexity. The methodology is applied to analyze fragment distributions in antagonists of seven G protein coupled receptor targets and identify signature fragments. Pairs or triplets of molecular fragments are found to be most specific for different activity profiles and compound potency levels. In addition, we demonstrate the ability of FragFCA to identify selective hits in high-throughput screening data sets.

Computational analysis of ligand relationships within target families

Computational tools for the large-scale analysis and prediction of ligand-target interactions and the identification of small molecules having different selectivity profiles within target protein families complement research in chemical genetics and chemogenomics. For computational analysis and design, such tasks require a departure from the traditional focus on single targets, hit identification, and lead optimization. Recently, studies have been reported that profile compounds in silico against arrays of targets or systematically map ligand-target space. In order to identify small molecular probes that are suitable for chemical genetics applications, molecular diversity needs to be viewed in a way that partly differs from principles guiding conventional library design.

Exploring structure-selectivity relationships of biogenic amine GPCR antagonists using similarity searching and dynamic compound mapping

We design and analyze compound selectivity sets of antagonists with differential selectivity against seven biogenic amine G-protein coupled receptors. The selectivity sets consist of a total of 267 antagonists and contain a spectrum of in part closely related molecular scaffolds. Each set represents a different selectivity profile. Using these com- pound sets, a systematic computational analysis of structure-selectivity relationships is carried out with different 2D similarity methods including fingerprints, recursive partitioning, clustering, and dynamic compound mapping. Screening calculations are performed in a background database containing nearly four million molecules. Fingerprint searching and compound mapping are found to enrich target-selective antagonists over family-selective ones. Dynamic compound mapping effectively discriminates database compounds from GPCR antagonists and consistently retains target-selective antagonists during the final dimension extension levels. Furthermore, the widely used MACCS key fingerprint displays a strong tendency to distinguish between target- and family-selective GPCR antagonists. Taken together, the results indicate that different types of 2D similarity methods are capable of distinguishing closely related molecules having different selectivity. The reported compound benchmark system is made freely available in order to enable selectivity-oriented analyses using other computational approaches.