Browsing by Author "Deznabi, Iman"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Open Access Deepkinzero: zero-shot learning for predicting kinase phosphorylation sites(Bilkent University, 2018-08) Deznabi, ImanProtein kinases are a large family of enzymes that catalyze the phosphorylation of other proteins. By acting as molecular switches for protein activity, the phosphorylation events regulate intracellular signal transduction, thereby assuming a central role in a broad range of cellular activities. On the other hand, aberrant kinase function is implicated in many diseases. Understanding the normal and malfunctioning signaling in the cell entails the identification of phosphorylation sites and the characterization of their interactions with kinases. Recent advances in mass spectrometry enable rapid identification of phosphosites at the proteome level. Alternatively, there are many computational models that predict phosphosites in a given input protein sequence. Once a phosphosite is identified, either experimentally or computationally, knowing which kinase would catalyze the phosphorylation on this particular site becomes the next question. Although a subset of available computational methods provides kinase-specific predictions for phosphorylation sites, due to the need for training data in such supervised methods, these tools can provide predictions only for kinases for which a substantial number of the phosphosites are already known. A particular problem that has not received any attention is the prediction of new sites for kinases with few or no a priori known sites. None of the current computational methods which rely on the classical supervised learning settings can predict additional sites for this kinases. We present DeepKinZero, the first zero-shot learning approach, that can predict phosphosites for kinases with no known phosphosite information. DeepKinZero takes a peptide sequence centered at the phosphorylation site and learns the embeddings of these phosphosite sequences via a bi-directional recurrent neural network, whereas kinase embeddings are based on protein sequence vector representations and the taxonomy of kinases based on their functional properties. Through a compatibility function that associates the representations of the site sequences and the kinases, DeepKinZero transfers knowledge from kinases with many known sites to those kinases with no known sites. Our computational experiments show that DeepKinZero achieves a 30-fold increase in accuracy compared to baseline models. DeepKinZero complements existing approaches by expanding the knowledge of kinases through mapping of the phosphorylation sites pertaining to understudied kinases with no prior information, which are increasingly investigated as novel drug targets.Item Open Access DeepKinZero: Zero-shot learning for predicting kinase–phosphosite associations involving understudied kinases(Oxford University Press, 2020-01) Deznabi, Iman; Arabacı, Büşra; Koyutürk, M.; Tastan, Ö.Motivation: Protein phosphorylation is a key regulator of protein function in signal transduction pathways. Kinases are the enzymes that catalyze the phosphorylation of other proteins in a target-specific manner. The dysregulation of phosphorylation is associated with many diseases including cancer. Although the advances in phosphoproteomics enable the identification of phosphosites at the proteome level, most of the phosphoproteome is still in the dark: more than 95% of the reported human phosphosites have no known kinases. Determining which kinase is responsible for phosphorylating a site remains an experimental challenge. Existing computational methods require several examples of known targets of a kinase to make accurate kinase-specific predictions, yet for a large body of kinases, only a few or no target sites are reported. Results: We present DeepKinZero, the first zero-shot learning approach to predict the kinase acting on a phosphosite for kinases with no known phosphosite information. DeepKinZero transfers knowledge from kinases with many known target phosphosites to those kinases with no known sites through a zero-shot learning model. The kinasespecific positional amino acid preferences are learned using a bidirectional recurrent neural network. We show that DeepKinZero achieves significant improvement in accuracy for kinases with no known phosphosites in comparison to the baseline model and other methods available. By expanding our knowledge on understudied kinases, DeepKinZero can help to chart the phosphoproteome atlas. Availability and implementation: The source codes are available at https://github.com/Tastanlab/DeepKinZero.Item Open Access An inference attack on genomic data using kinship, complex correlations, and phenotype information(IEEE, 2018) Deznabi, Iman; Mobayen, Mohammad; Jafari, Nazanin; Taştan, Öznur; Ayday, ErmanAbstract—Individuals (and their family members) share (partial) genomic data on public platforms. However, using special characteristics of genomic data, background knowledge that can be obtained from the Web, and family relationship between the individuals, it is possible to infer the hidden parts of shared (and unshared) genomes. Existing work in this field considers simple correlations in the genome (as well as Mendel’s law and partial genomes of a victim and his family members). In this paper, we improve the existing work on inference attacks on genomic privacy. We mainly consider complex correlations in the genome by using an observable Markov model and recombination model between the haplotypes. We also utilize the phenotype information about the victims. We propose an efficient message passing algorithm to consider all aforementioned background information for the inference. We show that the proposed framework improves inference with significantly less information compared to existing work.