Bayesian Markov Random Field Analysis for Protein Function Prediction Based on Network Data

Inference of protein functions is one of the most important aims of modern biology. To fully exploit the large volumes of genomic data typically produced in modern-day genomic experiments, automated computational methods for protein function prediction are urgently needed. Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act. Current high-throughput biological experiments produce large amounts of data on the interactions between proteins. Such data can be used to infer interaction networks and to predict the biological process that the protein is involved in. Here, we develop a probabilistic approach for protein function prediction using network data, such as protein-protein interaction measurements. We take a Bayesian approach to an existing Markov Random Field method by performing simultaneous estimation of the model parameters and prediction of protein functions. We use an adaptive Markov Chain Monte Carlo algorithm that leads to more accurate parameter estimates and consequently to improved prediction performance compared to the standard Markov Random Fields method. We tested our method using a high quality S.cereviciae validation network with 1622 proteins against 90 Gene Ontology terms of different levels of abstraction. Compared to three other protein function prediction methods, our approach shows very good prediction performance. Our method can be directly applied to protein-protein interaction or coexpression networks, but also can be extended to use multiple data sources. We apply our method to physical protein interaction data from S. cerevisiae and provide novel predictions, using 340 Gene Ontology terms, for 1170 unannotated proteins and we evaluate the predictions using the available literature

Improving protein secondary structure prediction based on short subsequences with local structure similarity

<p>Abstract</p> <p>Background</p> <p>When characterizing the structural topology of proteins, protein secondary structure (PSS) plays an important role in analyzing and modeling protein structures because it represents the local conformation of amino acids into regular structures. Although PSS prediction has been studied for decades, the prediction accuracy reaches a bottleneck at around 80%, and further improvement is very difficult.</p> <p>Results</p> <p>In this paper, we present an improved dictionary-based PSS prediction method called SymPred, and a meta-predictor called SymPsiPred. We adopt the concept behind natural language processing techniques and propose synonymous words to capture local sequence similarities in a group of similar proteins. A synonymous word is an <it>n-</it>gram pattern of amino acids that reflects the sequence variation in a protein’s evolution. We generate a protein-dependent synonymous dictionary from a set of protein sequences for PSS prediction.</p> <p>On a large non-redundant dataset of 8,297 protein chains (<it>DsspNr-25</it>), the average <it>Q</it>₃ of SymPred and SymPsiPred are 81.0% and 83.9% respectively. On the two latest independent test sets (<it>EVA Set_1</it> and <it>EVA_Set2</it>), the average <it>Q</it>₃ of SymPred is 78.8% and 79.2% respectively. SymPred outperforms other existing methods by 1.4% to 5.4%. We study two factors that may affect the performance of SymPred and find that it is very sensitive to the number of proteins of both known and unknown structures. This finding implies that SymPred and SymPsiPred have the potential to achieve higher accuracy as the number of protein sequences in the NCBInr and PDB databases increases.</p> <p>Conclusions</p> <p>Our experiment results show that local similarities in protein sequences typically exhibit conserved structures, which can be used to improve the accuracy of secondary structure prediction. For the application of synonymous words, we demonstrate an example of a sequence alignment which is generated by the distribution of shared synonymous words of a pair of protein sequences. We can align the two sequences nearly perfectly which are very dissimilar at the sequence level but very similar at the structural level. The SymPred and SymPsiPred prediction servers are available at <url>http://bio-cluster.iis.sinica.edu.tw/SymPred/</url>.</p

From E-MAPs to module maps: dissecting quantitative genetic interactions using physical interactions

Recent technological breakthroughs allow the quantification of hundreds of thousands of genetic interactions (GIs) in Saccharomyces cerevisiae. The interpretation of these data is often difficult, but it can be improved by the joint analysis of GIs along with complementary data types. Here, we describe a novel methodology that integrates genetic and physical interaction data. We use our method to identify a collection of functional modules related to chromosomal biology and to investigate the relations among them. We show how the resulting map of modules provides clues for the elucidation of function both at the level of individual genes and at the level of functional modules

The role of vacuolar membrane proteins in acetic acid-induced cell death

Dissertação de mestrado em Bioquímica Aplicada (área de especialização em Biomedicina)Saccharomyces cerevisiae has been one of the most widely used model organism for understanding the molecular mechanisms underlying apoptosis. Apoptosis is a form of regulated cell death that can be triggered by a wide variety of external or internal stimuli, such as acetic acid (AA). This acid triggers, in the yeast, a cascade of intracellular apoptotic-like events, both at mitochondria and vacuole level. A similar process occurs during acetate-induced apoptosis in colorectal cancer (CRC) cells. Although not much is known about the role of vacuole/lysosome, their membrane permeabilization (VMP/LMP) appears to be crucial in the regulated cell death process triggered by AA/acetate. Herein, we aimed to assess the involvement of different vacuolar membrane proteins in AA-induced apoptosis, as well as to evaluate their putative role in VMP and translocation of the protease Pep4p from the vacuole to the cytosol. To this end, a functional genetic approach based on a set of mutants lacking the vacuolar membrane proteins Csc1p, Pep3p, Vma4p, Vma16p, Vtc4p and Zrt3p, together with biochemical and analytical techniques, were used. We found that absence of Zrt3p, Vtc4p, Csc1p and Vma4p render cells more resistant to AA. The resistant phenotype of csc1Δ and vtc4Δ mutants was associated with a delayed VMP and release of Pep4p to the cytosol, as previously shown for the zrt3Δ mutant. Altogether, these results indicate that these three proteins or their associated cellular functions, such as their contribution to the intracellular levels of zinc, calcium and polyphosphate, determine cell survival in response to AA. Particularly, and corroborating the involvement of zinc, we found that zinc availability influences survival of cells undergoing AA-induced cell death, reducing or enhancing cell survival under zinc limitation or supplementation, respectively. The levels of intracellular calcium also appear to play a role in AA-induced cell death, as suggested by Ca2+ lower levels in csc1Δ cells after AA treatment. While deletion of VMA16 does not affect cell survival in response to AA, PEP3 and VMA4 deletions do affect. However, further studies are required to characterize the phenotypes of these deletion mutants. In summary, this study allowed to unveil molecular components/cellular processes involved in AA-induced cell death, contributing to the elucidation of the underlying mechanisms and their modulation towards the improvement of yeast industrial strains and the design of a non-conventional therapy for CRC.A levedura Saccharomyces cerevisiae tem sido um dos organismos modelo mais utilizados para elucidar os mecanismos subjacentes à apoptose. A apoptose é uma forma de morte celular regulada, que pode ser desencadeada por vários estímulos, como por exemplo o ácido acético (AA). Este ácido desencadeia, na levedura, uma cascata de eventos intracelulares a nível mitocondrial e vacuolar. Um processo semelhante ocorre na apoptose induzida por acetato em células do cancro colorretal (CCR). Embora pouco se saiba sobre o papel da permeabilização seletiva da membrana do vacúolo/lisossoma (PMV/PML), esta parece ser crucial na morte celular induzida por AA/acetato. Neste trabalho, pretendemos avaliar o envolvimento de diferentes proteínas da membrana vacuolar na apoptose induzida por AA, bem como avaliar o seu papel na PMV e na translocação da protease Pep4p do vacúolo para o citosol. Para tal, foi utilizada uma abordagem de genética funcional baseada na utilização de mutantes deficientes nas proteínas Zrt3p, Pep3p, Csc1p, Vtc4p, Vma4p e Vma16p, assim como métodos bioquímicos e analíticos. A ausência das proteínas Zrt3p, Vtc4p, Csc1p e Vma4p torna as células mais resistentes ao AA. O fenótipo de resistência dos mutantes csc1Δ e vtc4Δ foi também associado a um atraso na PMV e na libertação da Pep4p para o citosol, tal como demonstrado anteriormente para o mutante zrt3Δ. Estes resultados indicam que estas proteínas, ou as suas funções celulares, como a regulação dos níveis intracelulares de zinco, cálcio e polifosfato, determinam a sobrevivência celular em resposta ao AA. Relativamente ao zinco, descobrimos que a sua disponibilidade influencia a sobrevivência celular em resposta ao AA, reduzindo-a/aumentando-a em situações de limitação/suplementação de zinco, respetivamente. Os níveis de Ca2+ intracelulares parecem também desempenhar um papel na morte celular, uma vez que se observam baixos níveis de Ca2+ no mutante csc1Δ após tratamento com AA. A deleção do VMA16 não afeta a sobrevivência celular em resposta ao AA, no entanto a deleção do PEP3 e VMA4 afeta. Contudo, são necessários mais estudos para caracterizar os fenótipos destes mutantes. Em suma, identificamos proteínas/processos celulares envolvidos na morte celular induzida por AA, contribuindo para a elucidação dos mecanismos subjacentes e para a sua modulação com vista ao melhoramento de estirpes industriais de leveduras e ao desenvolvimento de terapias não convencionais para o CCR

Graph - Based Methods for Protein Function Prediction

Ph.DDOCTOR OF PHILOSOPH