Yu Wan1, Zhuo Wang2, Tzong-Yi Lee3,4. 1. School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, Guangdong, People's Republic of China. 2. Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, Guangdong, People's Republic of China. 3. School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, Guangdong, People's Republic of China. francislee0215@gmail.com. 4. Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Shenzhen, 518172, Guangdong, People's Republic of China. francislee0215@gmail.com.
Abstract
BACKGROUND: Cancer is one of the major causes of death worldwide. To treat cancer, the use of anticancer peptides (ACPs) has attracted increased attention in recent years. ACPs are a unique group of small molecules that can target and kill cancer cells fast and directly. However, identifying ACPs by wet-lab experiments is time-consuming and labor-intensive. Therefore, it is significant to develop computational tools for ACPs prediction. Though some ACP prediction tools have been developed recently, their performances are not well enough and most of them do not offer a function to distinguish ACPs from antimicrobial peptides (AMPs). Considering the fact that a growing number of studies have shown that some AMPs exhibit anticancer function, this work tries to build a model for distinguishing AMPs from ACPs in addition to a model that predicts ACPs from whole peptides. RESULTS: This study chooses amino acid composition, N5C5, k-space, position-specific scoring matrix (PSSM) as features, and analyzes them by machine learning methods, including support vector machine (SVM) and sequential minimal optimization (SMO) to build a model (model 2) for distinguishing ACPs from whole peptides. Another model (model 1) that distinguishes ACPs from AMPs is also developed. Comparing to previous models, models developed in this research show better performance (accuracy: 85.5% for model 1 and 95.2% for model 2). CONCLUSIONS: This work utilizes a new feature, PSSM, which contributes to better performance than other features. In addition to SVM, SMO is used in this research for optimizing SVM and the SMO-optimized models show better performance than non-optimized models. Last but not least, this work provides two different functions, including distinguishing ACPs from AMPs and distinguishing ACPs from all peptides. The second SMO-optimized model, which utilizes PSSM as a feature, performs better than all other existing tools.
BACKGROUND:Cancer is one of the major causes of death worldwide. To treat cancer, the use of anticancer peptides (ACPs) has attracted increased attention in recent years. ACPs are a unique group of small molecules that can target and kill cancer cells fast and directly. However, identifying ACPs by wet-lab experiments is time-consuming and labor-intensive. Therefore, it is significant to develop computational tools for ACPs prediction. Though some ACP prediction tools have been developed recently, their performances are not well enough and most of them do not offer a function to distinguish ACPs from antimicrobial peptides (AMPs). Considering the fact that a growing number of studies have shown that some AMPs exhibit anticancer function, this work tries to build a model for distinguishing AMPs from ACPs in addition to a model that predicts ACPs from whole peptides. RESULTS: This study chooses amino acid composition, N5C5, k-space, position-specific scoring matrix (PSSM) as features, and analyzes them by machine learning methods, including support vector machine (SVM) and sequential minimal optimization (SMO) to build a model (model 2) for distinguishing ACPs from whole peptides. Another model (model 1) that distinguishes ACPs from AMPs is also developed. Comparing to previous models, models developed in this research show better performance (accuracy: 85.5% for model 1 and 95.2% for model 2). CONCLUSIONS: This work utilizes a new feature, PSSM, which contributes to better performance than other features. In addition to SVM, SMO is used in this research for optimizing SVM and the SMO-optimized models show better performance than non-optimized models. Last but not least, this work provides two different functions, including distinguishing ACPs from AMPs and distinguishing ACPs from all peptides. The second SMO-optimized model, which utilizes PSSM as a feature, performs better than all other existing tools.
Authors: Hanneke van Zoggel; Gilles Carpentier; Célia Dos Santos; Yamina Hamma-Kourbali; José Courty; Mohamed Amiche; Jean Delbé Journal: PLoS One Date: 2012-09-20 Impact factor: 3.240
Authors: Michael O Palumbo; Petr Kavan; Wilson H Miller; Lawrence Panasci; Sarit Assouline; Nathalie Johnson; Victor Cohen; Francois Patenaude; Michael Pollak; R Thomas Jagoe; Gerald Batist Journal: Front Pharmacol Date: 2013-05-07 Impact factor: 5.810