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In past decades, the experimental determination of protein functions was expensive and time-consuming, so numerous computational methods were developed to speed up and guide the process. However, most of these methods predict protein functions at the gene level and do not consider the fact that protein isoforms (translated from alternatively spliced transcripts), not genes, are the actual function...
Proteins serve various functions in living cells. When they exert their functions, physical contact with other molecules occurs. A close connection therefore exists between their functions and structures. Therefore, comparison and classification about known and predicted protein structures provides important insight into the structural features of proteins, elucidating their functions and structures...
Protein–peptide interactions play essential functional roles in living organisms and their structural characterization is a hot subject of current experimental and theoretical research. Computational modeling of the structure of protein–peptide interactions is usually divided into two stages: prediction of the binding site at a protein receptor surface, and then docking (and modeling) the peptide...
Protein–protein interactions orchestrate virtually all cellular processes, therefore, their exhaustive exploration is essential for the comprehensive understanding of cellular networks. A reliable identification of interfacial residues is vital not only to infer the function of individual proteins and their assembly into biological complexes, but also to elucidate the molecular and physicochemical...
In this study, we developed a method named GoFDR for predicting Gene Ontology (GO)-based protein functions. The input for GoFDR is simply a query sequence-based multiple sequence alignment (MSA) produced by PSI-BLAST. For each GO term annotated to the sequences in the MSA, GoFDR identifies a number of functionally discriminating residues (FDRs) specific to the GO term, and scores the query sequence...
Protein function prediction is an important and challenging problem in bioinformatics and computational biology. Functionally relevant biological information such as protein sequences, gene expression, and protein–protein interactions has been used mostly separately for protein function prediction. One of the major challenges is how to effectively integrate multiple sources of both traditional and...
Protein function prediction is an active area of research in computational biology. Function prediction can help biologists make hypotheses for characterization of genes and help interpret biological assays, and thus is a productive area for collaboration between experimental and computational biologists. Among various function prediction methods, predicting binding ligand molecules for a target protein...
As a result of the genome sequencing and structural genomics initiatives, we have a wealth of protein sequence and structural data. However, only about 1% of these proteins have experimental functional annotations. As a result, computational approaches that can predict protein functions are essential in bridging this widening annotation gap. This article reviews the current approaches of protein function...
Protein sorting is an important mechanism for transporting proteins to their target subcellular locations after their synthesis. Mutations on genes may disrupt the well regulated protein sorting process, leading to a variety of mislocation related diseases. This paper proposes a methodology to discover such disease genes based on gene expression data and computational protein localization prediction...
Argot2.5 (Annotation Retrieval of Gene Ontology Terms) is a web server designed to predict protein function. It is an updated version of the previous Argot2 enriched with new features in order to enhance its usability and its overall performance. The algorithmic strategy exploits the grouping of Gene Ontology terms by means of semantic similarity to infer protein function. The tool has been challenged...
Thousands of protein structures of unknown or uncertain function have been reported as a result of high-throughput structure determination techniques developed by Structural Genomics (SG) projects. However, many of the putative functional assignments of these SG proteins in the Protein Data Bank (PDB) are incorrect. While high-throughput biochemical screening techniques have provided valuable functional...
Quantitative proteomics allows the characterization of molecular changes between healthy and disease states. To interpret such datasets, their integration to the protein–protein interaction network provides a more comprehensive understanding of cellular function dysregulation in diseases than just considering lists of dysregulated proteins. Here, we propose a novel computational method, which combines...
Prediction problems in biomedical sciences, including protein function prediction (PFP), are generally quite difficult. This is due in part to incomplete knowledge of the cellular phenomenon of interest, the appropriateness and data quality of the variables and measurements used for prediction, as well as a lack of consensus regarding the ideal predictor for specific problems. In such scenarios, a...
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