Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC

Abstract

Protein subcellular localization is defined as predicting the functioning location of a given protein in the cell. It is considered an important step towards protein function prediction and drug design. Recent studies have shown that relying on Gene Ontology (GO) for feature extraction can improve protein subcellular localization prediction performance. However, relying solely on GO, this problem remains unsolved. At the same time, the impact of other sources of features especially evolutionary-based features has not been explored adequately for this task. In this study, we aim to extract discriminative evolutionary features to tackle this problem. To do this, we propose two segmentation based feature extraction methods to explore potential local evolutionary-based information for Gram-positive and Gram-negative subcellular localizations. We will show that by applying a Support Vector Machine (SVM) classifier to our extracted features, we are able to enhance Gram-positive and Gram-negative subcellular localization prediction accuracies by up to 6.4% better than previous studies including the studies that used GO for feature extraction.

Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC

“…In order to overcome this issue, the concept of pseudo amino acid composition (PseAAC) was proposed [38,39]. It has been adopted into many biomedicine and drug development areas [40], and nearly all the areas of computational proteomics [41][42][43][44][45][46][47]. Because it has been extensively applied, recently three powerful open access softwares, called 'PseAACBuilder' [38], 'propy' [39], and 'PseAAC-General ' [47], were developed: the former two are for generating various modes for Chou's special PseAAC: while the 3 rd one for those of Chou's general PseAAC [33], including not only " Functional domain" mode [12], "Gene Ontology" mode [12], and "Sequential Evolution" or "PSSM" mode [12].…”

TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition

“…28-30 in (Chou 2011). Accordingly, the jackknife test has been widely recognized and increasingly used by investigators to examine the quality of various predictors (Xu et al 2013;Chen et al 2014;Ding et al 2014;Lin et al 2014;Dehzangi et al For each feature and classifier, the average performance of each evaluation index is reported, followed by a standard deviation a The weight (w = 0.8) is optimized by varying its value from 0 to 1 with a step size of 0.1 on the training set over tenfold cross-validation For each feature and classifier, the average performance of each evaluation index is reported, followed by a standard deviation a The weight (w = 0.8) is optimized by varying its value from 0 to 1 with a step size of 0.1 on the training set over tenfold cross-validation X. He et al: TargetFreeze: Identifying Antifreeze Proteins… 2015; Khan et al 2015;Mandal et al 2015).…”

iLM-2L: A two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou׳s general PseAAC

“…To avoid completely losing the sequence-order information for proteins, the pseudo-amino acid composition or PseAAC was propose. Since the concept of pseudoamino acid composition or Chou's PseAAC (Du et al, 2012;Cao et al, 2013;Lin and Lapointe, 2013) was proposed, it has penetrated into many biomedicine and drug development areas (Zhong and Zhou, 2014) and nearly Q3 all the areas of computational proteomics (Lin et al, 2009;Khan et al, 2015;Dehzangi et al, 2015;Kumar et al, 2015;Mondal and Pai, 2014;Wang et al, 2015;Du et al, 2014;. Because it has been widely and increasingly used, recently three powerful open access softwares, called 'PseAAC-Builder' (Du et al, 2012), 'propy' (Lin and Lapointe, 2013), and 'PseAAC-General' (Du et al, 2014), were established: the former two are for generating various modes of Chou's special PseAAC; while the 3rd one for those of Chou's general PseAAC (Chou, 2011), including not only all the special modes of feature vectors for proteins but also the higher level feature vectors such as "Functional Domain" mode (see Eqs.…”