Dataset Information

RFCM-PALM: In-Silico Prediction of S-Palmitoylation Sites in the Synaptic Proteins for Male/Female Mouse Data.

ABSTRACT: S-palmitoylation is a reversible covalent post-translational modification of cysteine thiol side chain by palmitic acid. S-palmitoylation plays a critical role in a variety of biological processes and is engaged in several human diseases. Therefore, identifying specific sites of this modification is crucial for understanding their functional consequences in physiology and pathology. We present a random forest (RF) classifier-based consensus strategy (RFCM-PALM) for predicting the palmitoylated cysteine sites on synaptic proteins from male/female mouse data. To design the prediction model, we have introduced a heuristic strategy for selection of the optimum set of physicochemical features from the AAIndex dataset using (a) K-Best (KB) features, (b) genetic algorithm (GA), and (c) a union (UN) of KB and GA based features. Furthermore, decisions from best-trained models of the KB, GA, and UN-based classifiers are combined by designing a three-star quality consensus strategy to further refine and enhance the scores of the individual models. The experiment is carried out on three categorized synaptic protein datasets of a male mouse, female mouse, and combined (male + female), whereas in each group, weighted data is used as training, and knock-out is used as the hold-out set for performance evaluation and comparison. RFCM-PALM shows ~80% area under curve (AUC) score in all three categories of datasets and achieve 10% average accuracy (male-15%, female-15%, and combined-7%) improvements on the hold-out set compared to the state-of-the-art approaches. To summarize, our method with efficient feature selection and novel consensus strategy shows significant performance gains in the prediction of S-palmitoylation sites in mouse datasets.

SUBMITTER: Bandyopadhyay SS

PROVIDER: S-EPMC8467992 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:S-palmitoylation, the covalent attachment of 16-carbon palmitic acids to a cysteine residue via a thioester linkage, is an important reversible lipid modification that plays a regulatory role in a variety of physiological and biological processes. As the number of experimentally identified S-palmitoylated peptides increases, it is imperative to investigate substrate motifs to facilitate the study of protein S-palmitoylation. Based on 710 non-homologous S-palmitoylation sites obtained from published databases and the literature, we carried out a bioinformatics investigation of S-palmitoylation sites based on amino acid composition. Two Sample Logo indicates that positively charged and polar amino acids surrounding S-palmitoylated sites may be associated with the substrate site specificity of protein S-palmitoylation. Additionally, maximal dependence decomposition (MDD) was applied to explore the motif signatures of S-palmitoylation sites by categorizing a large-scale dataset into subgroups with statistically significant conservation of amino acids. Single features such as amino acid composition (AAC), amino acid pair composition (AAPC), position specific scoring matrix (PSSM), position weight matrix (PWM), amino acid substitution matrix (BLOSUM62), and accessible surface area (ASA) were considered, along with the effectiveness of incorporating MDD-identified substrate motifs into a two-layered prediction model. Evaluation by five-fold cross-validation showed that a hybrid of AAC and PSSM performs best at discriminating between S-palmitoylation and non-S-palmitoylation sites, according to the support vector machine (SVM). The two-layered SVM model integrating MDD-identified substrate motifs performed well, with a sensitivity of 0.79, specificity of 0.80, accuracy of 0.80, and Matthews Correlation Coefficient (MCC) value of 0.45. Using an independent testing dataset (613 S-palmitoylated and 5412 non-S-palmitoylated sites) obtained from the literature, we demonstrated that the two-layered SVM model could outperform other prediction tools, yielding a balanced sensitivity and specificity of 0.690 and 0.694, respectively. This two-layered SVM model has been implemented as a web-based system (MDD-Palm), which is now freely available at http://csb.cse.yzu.edu.tw/MDDPalm/.

Project description:The precise regulation of synaptic integrity is critical for neuronal network connectivity and proper brain function. Essential aspects of the activity and localization of synaptic proteins are regulated by posttranslational modifications. S-palmitoylation is a reversible covalent modification of the cysteine with palmitate. It modulates affinity of the protein for cell membranes and membranous compartments. Intracellular palmitoylation dynamics are regulated by crosstalk with other posttranslational modifications, such as S-nitrosylation. S-nitrosylation is a covalent modification of cysteine thiol by nitric oxide and can modulate protein functions. Therefore, simultaneous identification of endogenous site-specific proteomes of both cysteine modifications under certain biological conditions offers new insights into the regulation of functional pathways. Still unclear, however, are the ways in which this crosstalk is affected in brain pathology, such as stress-related disorders. Using a newly developed mass spectrometry-based approach Palmitoylation And Nitrosylation Interplay Monitoring (PANIMoni), we analyzed the endogenous S-palmitoylation and S-nitrosylation of postsynaptic density proteins at the level of specific single cysteine in a mouse model of chronic stress. Among a total of 813 S-PALM and 620 S-NO cysteine sites that were characterized on 465 and 360 proteins, respectively, we sought to identify those that were differentially affected by stress. Our data show involvement of S-palmitoylation and S-nitrosylation crosstalk in the regulation of 122 proteins including receptors, scaffolding proteins, regulatory proteins and cytoskeletal components. Our results suggest that atypical crosstalk between the S-palmitoylation and S-nitrosylation interplay of proteins involved in synaptic transmission, protein localization and regulation of synaptic plasticity might be one of the main events associated with chronic stress disorder, leading to destabilization in synaptic networks.

Dataset Information

RFCM-PALM: In-Silico Prediction of S-Palmitoylation Sites in the Synaptic Proteins for Male/Female Mouse Data.

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