Search for: protein-function
PFP-WGAN: Protein function prediction by discovering gene ontology term correlations with generative adversarial networks, Article PLoS ONE ; Volume 16, Issue 2 , 2021 ; 19326203 (ISSN) ; Soleymani, M ; Rabiee, H. R ; Kaazempur Mofrad, M. R ; Sharif University of Technology
Public Library of Science 2021
Understanding the functionality of proteins has emerged as a critical problem in recent years due to significant roles of these macro-molecules in biological mechanisms. However, in-laboratory techniques for protein function prediction are not as efficient as methods developed and processed for protein sequencing. While more than 70 million protein sequences are available today, only the functionality of around one percent of them are known. These facts have encouraged researchers to develop computational methods to infer protein functionalities from their sequences. Gene Ontology is the most well-known database for protein functions which has a hierarchical structure, where deeper terms are...
Expression and function of c1orf132 long-noncoding rna in breast cancer cell lines and tissues, Article International Journal of Molecular Sciences ; Volume 22, Issue 13 , 2021 ; 16616596 (ISSN) ; Sharifi Zarchi, A ; Rahmani, S ; Nafissi, N ; Mowla, S. J ; Lauria, A ; Oliviero, S ; Matin, M. M ; Sharif University of Technology
miR-29b2 and miR-29c play a suppressive role in breast cancer progression. C1orf132 (also named MIR29B2CHG) is the host gene for generating both microRNAs. However, the region also expresses longer transcripts with unknown functions. We employed bioinformatics and experimental approaches to decipher C1orf132 expression and function in breast cancer tissues. We also used the CRISPR/Cas9 technique to excise a predicted C1orf132 distal promoter and followed the behavior of the edited cells by real-time PCR, flow cytometry, migration assay, and RNA-seq techniques. We observed that C1orf132 long transcript is significantly downregulated in triple-negative breast cancer. We also identified a...
PyGTED: Python application for computing graph traversal edit distance, Article Journal of Computational Biology ; Volume 27, Issue 3 , 2020 , Pages 436-439 ; Shrestha, A ; Sharifi Zarchi, A ; Gallagher, S. R ; Sahinalp, S. C ; Chitsaz, H ; Sharif University of Technology
Mary Ann Liebert Inc 2020
Graph Traversal Edit Distance (GTED) is a measure of distance (or dissimilarity) between two graphs introduced. This measure is based on the minimum edit distance between two strings formed by the edge labels of respective Eulerian traversals of the two graphs. GTED was motivated by and provides the first mathematical formalism for sequence coassembly and de novo variation detection in bioinformatics. Many problems in applied machine learning deal with graphs (also called networks), including social networks, security, web data mining, protein function prediction, and genome informatics. The kernel paradigm beautifully decouples the learning algorithm from the underlying geometric space,...
Type V collagen in scar tissue regulates the size of scar after heart injury, Article Cell ; Volume 182, Issue 3 , 2020 , Pages 545-562.e23 ; McCourt, J ; Ma, F ; Ren, S ; Li, S ; Kim, T. H ; Kurmangaliyev, Y. Z ; Nasiri, R ; Ahadian, S ; Nguyen, T ; Tan, X. H. M ; Zhou, Y ; Wu, R ; Rodriguez, A ; Cohn, W ; Wang, Y ; Whitelegge, J ; Ryazantsev, S ; Khademhosseini, A ; Teitell, M. A ; Chiou, P. Y ; Birk, D. E ; Rowat, A. C ; Crosbie, R. H ; Pellegrini, M ; Seldin, M ; Lusis, A. J ; Deb, A ; Sharif University of Technology
Cell Press 2020
Scar tissue size following myocardial infarction is an independent predictor of cardiovascular outcomes, yet little is known about factors regulating scar size. We demonstrate that collagen V, a minor constituent of heart scars, regulates the size of heart scars after ischemic injury. Depletion of collagen V led to a paradoxical increase in post-infarction scar size with worsening of heart function. A systems genetics approach across 100 in-bred strains of mice demonstrated that collagen V is a critical driver of postinjury heart function. We show that collagen V deficiency alters the mechanical properties of scar tissue, and altered reciprocal feedback between matrix and cells induces...
GTED: Graph traversal edit distance, Article 22nd International Conference on Research in Computational Molecular Biology, RECOMB 2018, 21 April 2018 through 24 April 2018 ; Volume 10812 LNBI , 2018 , Pages 37-53 ; 03029743 (ISSN); 9783319899282 (ISBN) ; Shrestha, A ; Sharifi Zarchi, A ; Gallagher, S. R ; Sahinalp, S. C ; Chitsaz, H ; Sharif University of Technology
Springer Verlag 2018
Many problems in applied machine learning deal with graphs (also called networks), including social networks, security, web data mining, protein function prediction, and genome informatics. The kernel paradigm beautifully decouples the learning algorithm from the underlying geometric space, which renders graph kernels important for the aforementioned applications. In this paper, we give a new graph kernel which we call graph traversal edit distance (GTED). We introduce the GTED problem and give the first polynomial time algorithm for it. Informally, the graph traversal edit distance is the minimum edit distance between two strings formed by the edge labels of respective Eulerian traversals...
Cooperation within von Willebrand factors enhances adsorption mechanism, Article Journal of the Royal Society Interface ; Volume 12, Issue 109 , 2015 ; 17425689 (ISSN) ; Mehrbod, M ; Ejtehadi, M. R ; Mofrad, M. R ; Sharif University of Technology
Royal Society of London 2015
von Willebrand factor (VWF) is a naturally collapsed protein that participates in primary haemostasis and coagulation events. The clotting process is triggered by the adsorption and conformational changes of the plasma VWFs localized to the collagen fibres found near the site of injury. We develop coarse-grained models to simulate the adsorption dynamics of VWF flowing near the adhesive collagen fibres at different shear rates and investigate the effect of factors such as interaction and cooperativity of VWFs on the success of adsorption events. The adsorption probability of a flowing VWF confined to the receptor field is enhanced when it encounters an adhered VWF in proximity to the...
Gating and conduction of nano-channel forming proteins: A computational approach, Article Journal of Biomolecular Structure and Dynamics ; Volume 31, Issue 8 , 2013 , Pages 818-828 ; 07391102 (ISSN) ; Mobasheri, H ; Ejtehadi, M. R ; Sharif University of Technology
Monitoring conformational changes in ion channels is essential to understand their gating mechanism. Here, we explore the structural dynamics of four outer membrane proteins with different structures and functions in the slowest nonzero modes of vibration. Normal mode analysis was performed on the modified elastic network model of channel in the membrane. According to our results, when membrane proteins were analyzed in the dominant mode, the composed pores, TolC and α-hemolysin showed large motions at the intramembrane β-barrel region while, in other porins, OmpA and OmpF, largest motions observed in the region of external flexible loops. A criterion based on equipartition theorem was used...
Nonparametric simulation of signal transduction networks with semi-synchronized update, Article PLoS ONE ; Volume 7, Issue 6 , 2012 ; 19326203 (ISSN) ; Masoudi Nejad, A ; Jalili, M ; Moeini, A ; Sharif University of Technology
Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational...
A tale of two symmetrical tails: Structural and functional characteristics of palindromes in proteins, Article BMC Bioinformatics ; Volume 9 , 2008 ; 14712105 (ISSN) ; Kargar, M ; Katanforoush, A ; Arab, S ; Sadeghi, M ; Pezeshk, H ; Eslahchi, C ; Marashi, S. A ; Sharif University of Technology
Background: It has been previously shown that palindromic sequences are frequently observed in proteins. However, our knowledge about their evolutionary origin and their possible importance is incomplete. Results: In this work, we tried to revisit this relatively neglected phenomenon. Several questions are addressed in this work. (1) It is known that there is a large chance of finding a palindrome in low complexity sequences (i.e. sequences with extreme amino acid usage bias). What is the role of sequence complexity in the evolution of palindromic sequences in proteins? (2) Do palindromes coincide with conserved protein sequences? If yes, what are the functions of these conserved segments?...
Evolution of 'ligand-deffusion chreodes' on protein-surface models: A genetic-algorithm study, Article Chemistry and Biodiversity ; Volume 4, Issue 12 , 2007 , Pages 2766-2771 ; 16121872 (ISSN) ; Kargar, M ; Katanforoush, A ; Abolhassani, H ; Sadeghi, M ; Sharif University of Technology
Lattice models have been previously used to model ligand diffusion on protein surfaces. Using such models, it has been shown that the presence of pathways (or 'chreodes') of consecutive residues with certain properties can decrease the number of steps required for the arrival of a ligand at the active site. In this work, we show that, based on a genetic algorithm, ligand-diffusion pathways can evolve on a protein surface, when this surface is selected for shortening the travel length toward the active site. Biological implications of these results are discussed. © 2007 Verlag Helvetica Chimica Acta AG, Zürich
Deep Learning in a Structured Output Space, Ph.D. Dissertation Sharif University of Technology ; Rabiee, Hamid Reza ; Soleymani, Mahdieh
A large number of machine learning problems are considered as structured output problems in which the goal is to find the mapping function between an input vector to a number of variables in the output side which are statistically correlated. Motivated by the advantages of simultaneous learning of these variables compared to learning them separately, many structured output models have been introduced. Decreasing the sample complexity, increasing the generalization ability and overcoming to noisy data are some of these benefits. So in the first step of this research we concentrate on one of classical but important problems in bioinformatics which is automatic protein function prediction....
Protein Function Prediction Using Protein Structure and Computational Methods, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emad ; Arab, Shahriar
Predicting the Amino Acids that have a catalytic effect in the enzymes, is a big step in appointing the activity of the enzymes and classifying them. This is a very challenging job, because an Amino Acid can appear in a variety of active sites.The biological activity of a protein usually depends on the existence of a small number of Amino Acids. Detecting these Amino Acids from the sequence of Amino Acids has many applications. Usually, the Amino Acids that are preserved are known as the Amino Acids that build up the active site, but the algorithms for finding the preserved Amino Acids are much more complex. There are a lot of algorithms for predicting the active sites of Amino Acids, but...
Protein Function Prediction using Protein Interaction Networks, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emadoddin
Predicting protein function accurately is an important issue in the post genomic era. To achieve this goal, several approaches have been proposed deduce the function of unclassified proteins through sequence similarity, co expression profiles, and other information. Among these methods, the Global Optimization Method is an interesting and powerful tool that assigns functions to unclassified proteins based on their positions in a physical interaction network. To boost both the accuracy and speed of global optimization method, a new prediction method, Accurate Global Optimization Method (AGOM), is presented in this thesis, which employs optimal repetition method enhanced with frequency of...