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The Methods Employed in Mass Spectrometric Analysis of Posttranslational Modifications (PTMs) and Protein-Protein Interactions (PPIs).
Yakubu, RR, Nieves, E, Weiss, LM
Advances in experimental medicine and biology. 2019;:169-198
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Abstract
Mass Spectrometry (MS) has revolutionized the way we study biomolecules, especially proteins, their interactions and posttranslational modifications (PTM). As such MS has established itself as the leading tool for the analysis of PTMs mainly because this approach is highly sensitive, amenable to high throughput and is capable of assigning PTMs to specific sites in the amino acid sequence of proteins and peptides. Along with the advances in MS methodology there have been improvements in biochemical, genetic and cell biological approaches to mapping the interactome which are discussed with consideration for both the practical and technical considerations of these techniques. The interactome of a species is generally understood to represent the sum of all potential protein-protein interactions. There are still a number of barriers to the elucidation of the human interactome or any other species as physical contact between protein pairs that occur by selective molecular docking in a particular spatiotemporal biological context are not easily captured and measured.PTMs massively increase the complexity of organismal proteomes and play a role in almost all aspects of cell biology, allowing for fine-tuning of protein structure, function and localization. There are an estimated 300 PTMS with a predicted 5% of the eukaryotic genome coding for enzymes involved in protein modification, however we have not yet been able to reliably map PTM proteomes due to limitations in sample preparation, analytical techniques, data analysis, and the substoichiometric and transient nature of some PTMs. Improvements in proteomic and mass spectrometry methods, as well as sample preparation, have been exploited in a large number of proteome-wide surveys of PTMs in many different organisms. Here we focus on previously published global PTM proteome studies in the Apicomplexan parasites T. gondii and P. falciparum which offer numerous insights into the abundance and function of each of the studied PTM in the Apicomplexa. Integration of these datasets provide a more complete picture of the relative importance of PTM and crosstalk between them and how together PTM globally change the cellular biology of the Apicomplexan protozoa. A multitude of techniques used to investigate PTMs, mostly techniques in MS-based proteomics, are discussed for their ability to uncover relevant biological function.
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Protein-protein interactions of the nicotinamide/nicotinate mononucleotide adenylyltransferase of Leishmania braziliensis.
Ortiz-Joya, L, Contreras-Rodríguez, LE, Ramírez-Hernández, MH
Memorias do Instituto Oswaldo Cruz. 2019;:e180506
Abstract
BACKGROUND Nicotinamide adenine dinucleotide (NAD) plays a central role in energy metabolism and integrates cellular metabolism with signalling and gene expression. NAD biosynthesis depends on the enzyme nicotinamide/nicotinate mononucleotide adenylyltransferase (NMNAT; EC: 2.7.7.1/18), in which converge the de novo and salvage pathways. OBJECTIVE The purpose of this study was to analyse the protein-protein interactions (PPI) of NMNAT of Leishmania braziliensis (LbNMNAT) in promastigotes. METHODS Transgenic lines of L. braziliensis promastigotes were established by transfection with the pSP72αneoαLbNMNAT-GFP vector. Soluble protein extracts were prepared, co-immunoprecipitation assays were performed, and the co-immunoprecipitates were analysed by mass spectrometry. Furthermore, bioinformatics tools such as network analysis were applied to generate a PPI network. FINDINGS Proteins involved in protein folding, redox homeostasis, and translation were found to interact with the LbNMNAT protein. The PPI network indicated enzymes of the nicotinate and nicotinamide metabolic routes, as well as RNA-binding proteins, the latter being the point of convergence between our experimental and computational results. MAIN CONCLUSION We constructed a model of PPI of LbNMNAT and showed its association with proteins involved in various functions such as protein folding, redox homeostasis, translation, and NAD synthesis.
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Global Vectors Representation of Protein Sequences and Its Application for Predicting Self-Interacting Proteins with Multi-Grained Cascade Forest Model.
Chen, ZH, You, ZH, Zhang, WB, Wang, YB, Cheng, L, Alghazzawi, D
Genes. 2019;(11)
Abstract
Self-interacting proteins (SIPs) is of paramount importance in current molecular biology. There have been developed a number of traditional biological experiment methods for predicting SIPs in the past few years. However, these methods are costly, time-consuming and inefficient, and often limit their usage for predicting SIPs. Therefore, the development of computational method emerges at the times require. In this paper, we for the first time proposed a novel deep learning model which combined natural language processing (NLP) method for potential SIPs prediction from the protein sequence information. More specifically, the protein sequence is de novo assembled by k-mers. Then, we obtained the global vectors representation for each protein sequences by using natural language processing (NLP) technique. Finally, based on the knowledge of known self-interacting and non-interacting proteins, a multi-grained cascade forest model is trained to predict SIPs. Comprehensive experiments were performed on yeast and human datasets, which obtained an accuracy rate of 91.45% and 93.12%, respectively. From our evaluations, the experimental results show that the use of amino acid semantics information is very helpful for addressing the problem of sequences containing both self-interacting and non-interacting pairs of proteins. This work would have potential applications for various biological classification problems.
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Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach.
Tian, B, Wu, X, Chen, C, Qiu, W, Ma, Q, Yu, B
Journal of theoretical biology. 2019;:329-346
Abstract
Research on protein-protein interactions (PPIs) not only helps to reveal the nature of life activities but also plays a driving role in understanding the mechanisms of disease activity and the development of effective drugs. The rapid development of machine learning provides new opportunities and challenges for understanding the mechanism of PPIs. It plays an important role in the field of proteomics research. In recent years, an increasing number of computational methods for predicting PPIs have been developed. This paper proposes a new method for predicting PPIs based on multi-information fusion. First, the pseudo-amino acid composition (PseAAC), auto-covariance (AC) and encoding based on grouped weight (EBGW) methods are used to extract the features of protein sequences, and the extracted three groups of feature vectors were fused. Secondly, the fused feature vectors are denoised by two-dimensional (2-D) wavelet denoising. Finally, the denoised feature vectors are input to the support vector machine (SVM) classifier to predict the PPIs. The ACC of PPIs of Helicobacter pylori (H. pylori) and Saccharomyces cerevisiae (S. cerevisiae) datasets were 95.97% and 95.55% by 5-fold cross-validation test and compared with other prediction methods. The experimental results show that the proposed multi-information fusion prediction method can effectively improve the prediction performance of PPIs. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/PPIs-WDSVM/.
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Bioinformatics Approach to Identify Novel AMPK Targets.
Gongol, B, Marin, T, Johnson, DA, Shyy, JY
Methods in molecular biology (Clifton, N.J.). 2018;:99-109
Abstract
In silico analysis of Big Data is a useful tool to identify putative kinase targets as well as nodes of signaling cascades that are difficult to discover by traditional single molecule experimentation. System approaches that use a multi-tiered investigational methodology have been instrumental in advancing our understanding of cellular mechanisms that result in phenotypic changes. Here, we present a bioinformatics approach to identify AMP-activated protein kinase (AMPK) target proteins on a proteome-wide scale and an in vitro method for preliminary validation of these targets. This approach offers an initial screening for the identification of AMPK targets that can be further validated using mutagenesis and molecular biology techniques.
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PSPEL: In Silico Prediction of Self-Interacting Proteins from Amino Acids Sequences Using Ensemble Learning.
Li, JQ, You, ZH, Li, X, Ming, Z, Chen, X
IEEE/ACM transactions on computational biology and bioinformatics. 2017;(5):1165-1172
Abstract
Self interacting proteins (SIPs) play an important role in various aspects of the structural and functional organization of the cell. Detecting SIPs is one of the most important issues in current molecular biology. Although a large number of SIPs data has been generated by experimental methods, wet laboratory approaches are both time-consuming and costly. In addition, they yield high false negative and positive rates. Thus, there is a great need for in silico methods to predict SIPs accurately and efficiently. In this study, a new sequence-based method is proposed to predict SIPs. The evolutionary information contained in Position-Specific Scoring Matrix (PSSM) is extracted from of protein with known sequence. Then, features are fed to an ensemble classifier to distinguish the self-interacting and non-self-interacting proteins. When performed on Saccharomyces cerevisiae and Human SIPs data sets, the proposed method can achieve high accuracies of 86.86 and 91.30 percent, respectively. Our method also shows a good performance when compared with the SVM classifier and previous methods. Consequently, the proposed method can be considered to be a novel promising tool to predict SIPs.
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Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM.
Wang, YB, You, ZH, Li, LP, Huang, YA, Yi, HC
Molecules (Basel, Switzerland). 2017;(8)
Abstract
Protein-protein interactions (PPIs) play a very large part in most cellular processes. Although a great deal of research has been devoted to detecting PPIs through high-throughput technologies, these methods are clearly expensive and cumbersome. Compared with the traditional experimental methods, computational methods have attracted much attention because of their good performance in detecting PPIs. In our work, a novel computational method named as PCVM-LM is proposed which combines the probabilistic classification vector machine (PCVM) model and Legendre moments (LMs) to predict PPIs from amino acid sequences. The improvement mainly comes from using the LMs to extract discriminatory information embedded in the position-specific scoring matrix (PSSM) combined with the PCVM classifier to implement prediction. The proposed method was evaluated on Yeast and Helicobacter pylori datasets with five-fold cross-validation experiments. The experimental results show that the proposed method achieves high average accuracies of 96.37% and 93.48%, respectively, which are much better than other well-known methods. To further evaluate the proposed method, we also compared the proposed method with the state-of-the-art support vector machine (SVM) classifier and other existing methods on the same datasets. The comparison results clearly show that our method is better than the SVM-based method and other existing methods. The promising experimental results show the reliability and effectiveness of the proposed method, which can be a useful decision support tool for protein research.
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Protein-protein interactions: scoring schemes and binding affinity.
Gromiha, MM, Yugandhar, K, Jemimah, S
Current opinion in structural biology. 2017;:31-38
Abstract
Protein-protein interactions mediate several cellular functions, which can be understood from the information obtained using the three-dimensional structures of protein-protein complexes and binding affinity data. This review focuses on computational aspects of predicting the best native-like complex structure and binding affinities. The first part covers the prediction of protein-protein complex structures and the advantages of conformational searching and scoring functions in protein-protein docking. The second part is devoted to various aspects of protein-protein interaction thermodynamics, such as databases for binding affinities and other thermodynamic parameters, computational methods to predict the binding affinity using either the three-dimensional structures of complexes or amino acid sequences, and change in binding affinities of the complexes upon mutations. We provide the latest developments on protein-protein docking and binding affinity studies along with a list of available computational resources for understanding protein-protein interactions.
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PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences.
Wang, Y, You, Z, Li, X, Chen, X, Jiang, T, Zhang, J
International journal of molecular sciences. 2017;(5)
Abstract
Protein-protein interactions (PPIs) are essential for most living organisms' process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori, the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.
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Genetically encoded releasable photo-cross-linking strategies for studying protein-protein interactions in living cells.
Yang, Y, Song, H, He, D, Zhang, S, Dai, S, Xie, X, Lin, S, Hao, Z, Zheng, H, Chen, PR
Nature protocols. 2017;(10):2147-2168
Abstract
Although protein-protein interactions (PPIs) have crucial roles in virtually all cellular processes, the identification of more transient interactions in their biological context remains challenging. Conventional photo-cross-linking strategies can be used to identify transient interactions, but these approaches often suffer from high background due to the cross-linked bait proteins. To solve the problem, we have developed membrane-permeable releasable photo-cross-linkers that allow for prey-bait separation after protein complex isolation and can be installed in proteins of interest (POIs) as unnatural amino acids. Here we describe the procedures for using two releasable photo-cross-linkers, DiZSeK and DiZHSeC, in both living Escherichia coli and mammalian cells. A cleavage after protein photo-cross-linking (CAPP ) strategy based on the photo-cross-linker DiZSeK is described, in which the prey protein pool is released from a POI after affinity purification. Prey proteins are analyzed using mass spectrometry or 2D gel electrophoresis for global comparison of interactomes from different experimental conditions. An in situ cleavage and mass spectrometry (MS)-label transfer after protein photo-cross-linking (IMAPP) strategy based on the photo-cross-linker DiZHSeC is also described. This strategy can be used for the identification of cross-linking sites to allow detailed characterization of PPI interfaces. The procedures for photo-cross-linker incorporation, photo-cross-linking of interaction partners and affinity purification of cross-linked complexes are similar for the two photo-cross-linkers. The final section of the protocol describes prey-bait separation (for CAPP) and MS-label transfer and identification (for IMAPP). After plasmid construction, the CAPP and IMAPP strategies can be completed within 6 and 7 d, respectively.