G protein-coupled receptors: from ligand identification to drug targets

Multi-algorithm and multi-model based drug target prediction and web server

AIM

To develop a reliable computational approach for predicting potential drug targets based merely on protein sequence.

METHODS

With drug target and non-target datasets prepared and 3 classification algorithms (Support Vector Machine, Neural Network and Decision Tree), a multi-algorithm and multi-model based strategy was employed for constructing models to predict potential drug targets.

RESULTS

Twenty one prediction models for each of the 3 algorithms were successfully developed. Our evaluation results showed that ∼30% of human proteins were potential drug targets, and ∼40% of putative targets for the drugs undergoing phase II clinical trials were probably non-targets. A public web server named D3TPredictor (http://www.d3pharma.com/d3tpredictor) was constructed to provide easy access.

CONCLUSION

Reliable and robust drug target prediction based on protein sequences is achieved using the multi-algorithm and multi-model strategy.

Topography-biased compound library design: the shape of things to come?

The design and synthesis of quality compound libraries is of critical importance to any pharmaceutical company that relies on high throughput screening efforts for the identification of lead compounds. In this perspective, we use a moment of inertia derived shape analysis to interrogate potential libraries for chemical synthesis. An analysis of known 'Rule of Five' compliant drug shapes using this methodology clearly highlights compound libraries that may be reasonably expected, shape wise, to interact with biologically relevant protein active site topography and those that, although being structurally diverse in shape, have little chance of being pharmacologically productive. The use of multicomponent reactions as a means of producing structurally novel, bioactive compounds in a synthetically expeditious manner is also highlighted.

Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness

Identification and validation of viable targets is an important first step in drug discovery and new methods, and integrated approaches are continuously explored to improve the discovery rate and exploration of new drug targets. An in silico machine learning method, support vector machines, has been explored as a new method for predicting druggable proteins from amino acid sequence independent of sequence similarity, thereby facilitating the prediction of druggable proteins that exhibit no or low homology to known targets.

Therapeutic targets: progress of their exploration and investigation of their characteristics

Modern drug discovery is primarily based on the search and subsequent testing of drug candidates acting on a preselected therapeutic target. Progress in genomics, protein structure, proteomics, and disease mechanisms has led to a growing interest in and effort for finding new targets and more effective exploration of existing targets. The number of reported targets of marketed and investigational drugs has significantly increased in the past 8 years. There are 1535 targets collected in the therapeutic target database compared with approximately 500 targets reported in a 1996 review. Knowledge of these targets is helpful for molecular dissection of the mechanism of action of drugs and for predicting features that guide new drug design and the search for new targets. This article summarizes the progress of target exploration and investigates the characteristics of the currently explored targets to analyze their sequence, structure, family representation, pathway association, tissue distribution, and genome location features for finding clues useful for searching for new targets. Possible "rules" to guide the search for druggable proteins and the feasibility of using a statistical learning method for predicting druggable proteins directly from their sequences are discussed.