On network-based kernel methods for protein-protein interactions with applications in protein functions prediction

2010 ◽  
Vol 23 (5) ◽  
pp. 917-930 ◽  
Author(s):  
Limin Li ◽  
Waiki Ching ◽  
Yatming Chan ◽  
Hiroshi Mamitsuka
Keyword(s):  
Protein Interactions ◽  
Kernel Methods ◽  
Protein Protein Interactions ◽  
Protein Functions

scholarly journals Chemical toolbox for ‘live’ biochemistry to understand enzymatic functions in living systems

2019 ◽  
Author(s):  
Toru Komatsu ◽  
Yasuteru Urano
Keyword(s):  
Environmental Factors ◽  
Protein Interactions ◽  
Posttranslational Modifications ◽  
Live Cell ◽  
Living Systems ◽  
Protein Protein Interactions ◽  
Redox States ◽  
Recent Advances ◽  
Protein Functions ◽  
Research Tools

Abstract In this review, we present an overview of the recent advances in chemical toolboxes that are used to provide insights into ‘live’ protein functions in living systems. Protein functions are mediated by various factors inside of cells, such as protein−protein interactions, posttranslational modifications, and they are also subject to environmental factors such as pH, redox states and crowding conditions. Obtaining a true understanding of protein functions in living systems is therefore a considerably difficult task. Recent advances in research tools have allowed us to consider ‘live’ biochemistry as a valid approach to precisely understand how proteins function in a live cell context.

In Silico Recognition of Protein-Protein Interaction

2011 ◽  
pp. 248-268
Author(s):  
Byung-Hoon Park ◽  
Phuongan Dam ◽  
Chongle Pan ◽  
Ying Xu ◽  
Al Geist ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Regulatory Networks ◽  
Machine Learning Techniques ◽  
Translation Regulation ◽  
Future Research ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Protein Levels ◽  
Protein Functions ◽  
Model Protein

Protein-protein interactions are fundamental to cellular processes. They are responsible for phenomena like DNA replication, gene transcription, protein translation, regulation of metabolic pathways, immunologic recognition, signal transduction, etc. The identification of interacting proteins is therefore an important prerequisite step in understanding their physiological functions. Due to the invaluable importance to various biophysical activities, reliable computational methods to infer protein-protein interactions from either structural or genome sequences are in heavy demand lately. Successful predictions, for instance, will facilitate a drug design process and the reconstruction of metabolic or regulatory networks. In this chapter, we review: (a) high-throughput experimental methods for identification of protein-protein interactions, (b) existing databases of protein-protein interactions, (c) computational approaches to predicting protein-protein interactions at both residue and protein levels, (d) various statistical and machine learning techniques to model protein-protein interactions, and (e) applications of protein-protein interactions in predicting protein functions. We also discuss intrinsic drawbacks of the existing approaches and future research directions.

New advances in extracting and learning from protein–protein interactions within unstructured biomedical text data

2019 ◽  
Vol 3 (4) ◽  
pp. 357-369
Author(s):  
J. Harry Caufield ◽  
Peipei Ping
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Historical Context ◽  
Specific Protein ◽  
Basic Unit ◽  
Biomedical Text ◽  
Protein Protein Interactions ◽  
Text Data ◽  
Ppi Networks ◽  
Protein Functions

Abstract Protein–protein interactions, or PPIs, constitute a basic unit of our understanding of protein function. Though substantial effort has been made to organize PPI knowledge into structured databases, maintenance of these resources requires careful manual curation. Even then, many PPIs remain uncurated within unstructured text data. Extracting PPIs from experimental research supports assembly of PPI networks and highlights relationships crucial to elucidating protein functions. Isolating specific protein–protein relationships from numerous documents is technically demanding by both manual and automated means. Recent advances in the design of these methods have leveraged emerging computational developments and have demonstrated impressive results on test datasets. In this review, we discuss recent developments in PPI extraction from unstructured biomedical text. We explore the historical context of these developments, recent strategies for integrating and comparing PPI data, and their application to advancing the understanding of protein function. Finally, we describe the challenges facing the application of PPI mining to the text concerning protein families, using the multifunctional 14-3-3 protein family as an example.

Applications of Mass Spectrometry in Proteomics

2013 ◽  
Vol 66 (7) ◽  
pp. 721 ◽  
Author(s):  
Izabela Sokolowska ◽  
Armand G. Ngounou Wetie ◽  
Alisa G. Woods ◽  
Costel C. Darie
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Protein Identification ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Cellular Processes ◽  
Physiological Processes ◽  
Protein Functions ◽  
Instruments And Techniques ◽  
Accurate Mass Spectrometry

Characterisation of proteins and whole proteomes can provide a foundation to our understanding of physiological and pathological states and biological diseases or disorders. Constant development of more reliable and accurate mass spectrometry (MS) instruments and techniques has allowed for better identification and quantification of the thousands of proteins involved in basic physiological processes. Therefore, MS-based proteomics has been widely applied to the analysis of biological samples and has greatly contributed to our understanding of protein functions, interactions, and dynamics, advancing our knowledge of cellular processes as well as the physiology and pathology of the human body. This review will discuss current proteomic approaches for protein identification and characterisation, including post-translational modification (PTM) analysis and quantitative proteomics as well as investigation of protein–protein interactions (PPIs).

scholarly journals IL-12 and IL-23—Close Relatives with Structural Homologies but Distinct Immunological Functions

Cells ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 2184
Author(s):  
Doreen M. Floss ◽  
Jens M. Moll ◽  
Jürgen Scheller
Keyword(s):  
Protein Interactions ◽  
Intracellular Signaling ◽  
Signaling Cascades ◽  
Structural Knowledge ◽  
Nucleotide Polymorphisms ◽  
Protein Protein Interactions ◽  
Single Nucleotide ◽  
Mediated Effects ◽  
Protein Functions ◽  
Close Relatives

Cytokines of the IL-12 family show structural similarities but have distinct functions in the immune system. Prominent members of this cytokine family are the pro-inflammatory cytokines IL-12 and IL-23. These two cytokines share cytokine subunits and receptor chains but have different functions in autoimmune diseases, cancer and infections. Accordingly, structural knowledge about receptor complex formation is essential for the development of new therapeutic strategies preventing and/or inhibiting cytokine:receptor interaction. In addition, intracellular signaling cascades can be targeted to inhibit cytokine-mediated effects. Single nucleotide polymorphisms can lead to alteration in the amino acid sequence and thereby influencing protein functions or protein–protein interactions. To understand the biology of IL-12 and IL-23 and to establish efficient targeting strategies structural knowledge about cytokines and respective receptors is crucial. A highly efficient therapy might be a combination of different drugs targeting extracellular cytokine:receptor assembly and intracellular signaling pathways.

scholarly journals Prediction of Protein-protein Interactions on the Basis of Evolutionary Conservation of Protein Functions

2007 ◽  
Vol 3 ◽  
pp. 117693430700300 ◽  
Author(s):  
Ekaterina Kotelnikova ◽  
Andrey Kalinin ◽  
Anton Yuryev ◽  
Sergei Maslov
Keyword(s):  
Protein Interactions ◽  
Evolutionary Conservation ◽  
Protein Protein Interactions ◽  
Protein Functions

scholarly journals PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables

2011 ◽  
Vol 16 (2) ◽  
Author(s):  
Piyali Chatterjee ◽  
Subhadip Basu ◽  
Mahantapas Kundu ◽  
Mita Nasipuri ◽  
Dariusz Plewczynski
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Three Dimensional ◽  
Prediction Method ◽  
Protein Sequences ◽  
Dimensional Structure ◽  
Support Vector ◽  
Interacting Proteins ◽  
Protein Protein Interactions ◽  
Protein Functions

AbstractProtein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: http://code.google.com/p/cmater-bioinfo/.

scholarly journals Synthetic protein interactions reveal a functional map of the cell

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Lisa K Berry ◽  
Guðjón Ólafsson ◽  
Elena Ledesma-Fernández ◽  
Peter H Thorpe
Keyword(s):  
Protein Interactions ◽  
Protein Localization ◽  
Eukaryotic Cells ◽  
Protein Protein Interactions ◽  
Yeast Saccharomyces Cerevisiae ◽  
Synthetic Protein ◽  
Protein Functions ◽  
Functional Map ◽  
Cellular Compartments ◽  
New Protein

To understand the function of eukaryotic cells, it is critical to understand the role of protein-protein interactions and protein localization. Currently, we do not know the importance of global protein localization nor do we understand to what extent the cell is permissive for new protein associations – a key requirement for the evolution of new protein functions. To answer this question, we fused every protein in the yeast Saccharomyces cerevisiae with a partner from each of the major cellular compartments and quantitatively assessed the effects upon growth. This analysis reveals that cells have a remarkable and unanticipated tolerance for forced protein associations, even if these associations lead to a proportion of the protein moving compartments within the cell. Furthermore, the interactions that do perturb growth provide a functional map of spatial protein regulation, identifying key regulatory complexes for the normal homeostasis of eukaryotic cells.

scholarly journals Global Landscape of Native Protein Complexes in Synechocystis sp. PCC 6803

2020 ◽  
Author(s):  
Chen Xu ◽  
Bing Wang ◽  
Lin Yang ◽  
Lucas Zhongming Hu ◽  
Lanxing Yi ◽  
...  
Keyword(s):  
Cell Motility ◽  
Protein Interactions ◽  
Higher Plants ◽  
Protein Complexes ◽  
Model Organism ◽  
Protein Interaction Data ◽  
Protein Protein Interactions ◽  
Protein Functions ◽  
Additional Level ◽  
Pcc 6803

AbstractSynechocystis sp. PCC 6803 (hereafter: Synechocystis) is a model organism for studying photosynthesis, energy metabolism, and environmental stress. Though known as the first fully sequenced phototrophic organism, Synechocystis still has almost half of its proteome without functional annotations. In this study, we obtained 291 protein complexes, including 24,092 protein-protein interactions (PPIs) among 2062 proteins by using co–fractionation and LC/MS/MS. The additional level of PPIs information not only revealed the roles of photosynthesis in metabolism, cell motility, DNA repair, cell division, and other physiological processes, but also showed how protein functions vary from bacteria to higher plants due to the changed interaction partner. It also allows us to uncover functions of hypothetical proteins, such as Sll0445, Sll0446, S110447 participating in photosynthesis and cell motility, and Sll1334 regulating the expression of fatty acid. Here we presented the most extensive protein interaction data in Synechocystis so far, which might provide critical insights into the fundamental molecular mechanism in Cyanobacterium.

scholarly journals ProCbA: Protein Function Prediction based on Clique Analysis

2020 ◽  
Author(s):  
A. Khanteymoori ◽  
M. B. Ghajehlo ◽  
S. Behrouzinia ◽  
M. H. Olyaee
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Experimental Result ◽  
Protein Protein Interactions ◽  
Ppi Networks ◽  
Protein Functions ◽  
Important Challenge ◽  
Result Analysis

AbstractProtein function prediction based on protein-protein interactions (PPI) is one of the most important challenges of the Post-Genomic era. Due to the fact that determining protein function by experimental techniques can be costly, function prediction has become an important challenge for computational biology and bioinformatics. Some researchers utilize graph- (or network-) based methods using PPI networks for un-annotated proteins. The aim of this study is to increase the accuracy of the protein function prediction using two proposed methods.To predict protein functions, we propose a Protein Function Prediction based on Clique Analysis (ProCbA) and Protein Function Prediction on Neighborhood Counting using functional aggregation (ProNC-FA). Both ProCbA and ProNC-FA can predict the functions of unknown proteins. In addition, in ProNC-FA which is not including new algorithm; we try to address the essence of incomplete and noisy data of PPI era in order to achieving a network with complete functional aggregation. The experimental results on MIPS data and the 17 different explained datasets validate the encouraging performance and the strength of both ProCbA and ProNC-FA on function prediction. Experimental result analysis as can be seen in Section IV, the both ProCbA and ProNC-FA are generally able to outperform all the other methods.

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