Abstract
Background: Identification of interaction between drugs and target proteins plays an important role in discovering new drug candidates. However, through the experimental method to identify the drug-target interactions remain to be extremely time-consuming, expensive and challenging even nowadays. Therefore, it is urgent to develop new computational methods to predict potential drugtarget interactions (DTI).
Methods: In this article, a novel computational model is developed for predicting potential drug-target interactions under the theory that each drug-target interaction pair can be represented by the structural properties from drugs and evolutionary information derived from proteins. Specifically, the protein sequences are encoded as Position-Specific Scoring Matrix (PSSM) descriptor which contains information of biological evolutionary and the drug molecules are encoded as fingerprint feature vector which represents the existence of certain functional groups or fragments.
Results: Four benchmark datasets involving enzymes, ion channels, GPCRs and nuclear receptors, are independently used for establishing predictive models with Rotation Forest (RF) model. The proposed method achieved the prediction accuracy of 91.3%, 89.1%, 84.1% and 71.1% for four datasets respectively. In order to make our method more persuasive, we compared our classifier with the state-of-theart Support Vector Machine (SVM) classifier. We also compared the proposed method with other excellent methods.
Conclusions: Experimental results demonstrate that the proposed method is effective in the prediction of DTI, and can provide assistance for new drug research and development.
Keywords: Target interactions, position-specific scoring matrix, auto covariance, rotation forest, support vector machine, drug substructure fingerprint.
Graphical Abstract
Call for Papers in Thematic Issues
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The scope of this thematic issue centers on the integration of proteomic and peptidomic technologies into the field of cancer immunotherapy, with a particular emphasis on exploring the tumor immune microenvironment. This issue aims to gather contributions that illustrate the application of these advanced methodologies in unveiling the complex interplay ...read more
Nutrition and Metabolism in Musculoskeletal Diseases
The musculoskeletal system consists mainly of cartilage, bone, muscles, tendons, connective tissue and ligaments. Balanced metabolism is of vital importance for the homeostasis of the musculoskeletal system. A series of musculoskeletal diseases (for example, sarcopenia, osteoporosis) are resulted from the dysregulated metabolism of the musculoskeletal system. Furthermore, metabolic diseases (such ...read more
Protein Folding, Aggregation and Liquid-Liquid Phase Separation
Protein folding, misfolding and aggregation remain one of the main problems of interdisciplinary science not only because many questions are still open, but also because they are important from the point of view of practical application. Protein aggregation and formation of fibrillar structures, for example, is a hallmark of a ...read more
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