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Computational Prediction of Protein-Protein Interactions in Leishmania Predicted Proteomes  [PDF]
Antonio M. Rezende, Edson L. Folador, Daniela de M. Resende, Jeronimo C. Ruiz
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0051304
Abstract: The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI) study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping) and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks received some degree of functional annotation which represents an important contribution since approximately 60% of Leishmania predicted proteomes has no predicted function.
From Experimental Approaches to Computational Techniques: A Review on the Prediction of Protein-Protein Interactions  [PDF]
Fiona Browne,Huiru Zheng,Haiying Wang,Francisco Azuaje
Advances in Artificial Intelligence , 2010, DOI: 10.1155/2010/924529
Abstract: A crucial step towards understanding the properties of cellular systems in organisms is to map their network of protein-protein interactions (PPIs) on a proteomic-wide scale completely and as accurately as possible. Uncovering the diverse function of proteins and their interactions within the cell may improve our understanding of disease and provide a basis for the development of novel therapeutic approaches. The development of large-scale high-throughput experiments has resulted in the production of a large volume of data which has aided in the uncovering of PPIs. However, these data are often erroneous and limited in interactome coverage. Therefore, additional experimental and computational methods are required to accelerate the discovery of PPIs. This paper provides a review on the prediction of PPIs addressing key prediction principles and highlighting the common experimental and computational techniques currently employed to infer PPI networks along with relevant studies in the area. 1. Introduction Proteins are involved in many essential processes within the cell such as metabolism, cell structure, immune response and cell signaling [1]. Although advances have been made within the realm of genome biology and bioinformatics, the function of a large proportion of sequenced proteins remains uncharacterised [2]. Uncovering the function of proteins is a complex process as one protein may perform more than one function and many proteins may have undiscovered functionality [3]. Research in [4] has suggested that the functionality of unknown proteins could be identified from studying the interaction of unknown proteins with a known protein target with a known function. Thus, the determination of protein-protein interactions (PPIs) is an important challenge currently faced in computational biology [5]. Interaction patterns among proteins can suggest novel drug targets aiding in the design of new drugs by providing a clearer picture of the biological pathways in the neighbourhoods of the potential drugs targets [6]. Large-scale high-throughput experiments have assisted in defining PPIs within the interactome (all possible PPIs in a cell). However, data generated by these experiments often contain false positives, false negatives, missing values with little overlap observed between experimentally generated datasets [3]. This may suggest that the data are erroneous, incomplete or both [3]. Previous studies have estimated that 50% of the yeast PPI map and only 10% of the human PPI network have been characterised [7]. Due to the limitations of experimental data
Computational Prediction of Host-Parasite Protein Interactions between P. falciparum and H. sapiens  [PDF]
Stefan Wuchty
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0026960
Abstract: To obtain candidates of interactions between proteins of the malaria parasite Plasmodium falciparum and the human host, homologous and conserved interactions were inferred from various sources of interaction data. Such candidate interactions were assessed by applying a machine learning approach and further filtered according to expression and molecular characteristics, enabling involved proteins to indeed interact. The analysis of predicted interactions indicated that parasite proteins predominantly target central proteins to take control of a human host cell. Furthermore, parasite proteins utilized their protein repertoire in a combinatorial manner, providing a broad connection to host cellular processes. In particular, several prominent pathways of signaling and regulation proteins were predicted to interact with parasite chaperones. Such a result suggests an important role of remodeling proteins in the interaction interface between the human host and the parasite. Identification of such molecular strategies that allow the parasite to take control of the host has the potential to deepen our understanding of the parasite specific remodeling processes of the host cell and illuminate new avenues of disease intervention.
Challenges and open problems in computational prediction of protein complexes: the case of membrane complexes  [PDF]
Sriganesh Srihari
Quantitative Biology , 2015,
Abstract: Identifying the entire set of complexes is essential not only to understand complex formations, but also to map the high level organisation of the cell. Computational prediction of protein complexes faces several challenges including the lack of sufficient protein interactions, presence of noise in protein interaction datasets and difficulty in predicting small and sparse complexes. These challenges are covered in most reviews of complex prediction methods. However, an important challenge that needs to be addressed is the prediction of membrane complexes. These are often ignored because existing protein interaction detection techniques do not detect interactions between membrane proteins. But, recently there have been several new experimental techniques including MY2H that are capable of detecting membrane protein interactions. In the light of this new data, we discuss here new challenges and the kind of open problems that need to be solved to effectively detect membrane complexes.
The Effect of D-(?)-arabinose on Tyrosinase: An Integrated Study Using Computational Simulation and Inhibition Kinetics  [PDF]
Hong-Jian Liu,Sunyoung Ji,Yong-Qiang Fan,Li Yan,Jun-Mo Yang,Hai-Meng Zhou,Jinhyuk Lee,Yu-Long Wang
Enzyme Research , 2012, DOI: 10.1155/2012/731427
Abstract: Tyrosinase is a ubiquitous enzyme with diverse physiologic roles related to pigment production. Tyrosinase inhibition has been well studied for cosmetic, medicinal, and agricultural purposes. We simulated the docking of tyrosinase and D-(?)-arabinose and found a binding energy of ?4.5?kcal/mol for theup-formof D-(?)-arabinose and ?4.4?kcal/mol for thedown-form of D-(?)-arabinose. The results of molecular dynamics simulation suggested that D-(?)-arabinose interacts mostly with HIS85, HIS259, and HIS263, which are believed to be in the active site. Our kinetic study showed that D-(?)-arabinose is a reversible, mixed-type inhibitor of tyrosinase ( -value? , ?M). Measurements of intrinsic fluorescence showed that D-(?)-arabinose induced obvious tertiary changes to tyrosinase (binding constant ?M?1, binding number ). This strategy of predicting tyrosinase inhibition based on specific interactions of aldehyde and hydroxyl groups with the enzyme may prove useful for screening potential tyrosinase inhibitors. 1. Introduction Tyrosinase (EC is a ubiquitous enzyme with diverse physiologic roles related to pigment production. It plays a central role in melanin synthesis in skin [1, 2], the browning of vegetables [3, 4], wound healing [5], and cuticle formation in insects [6, 7]. Structurally, tyrosinase belongs to the type 3 copper protein family [8, 9], which consists of two copper ions individually coordinated with three histidine residues at the active site. Tyrosinases are directly involved in several reactions and carry out catalytic steps such as the hydroxylation of tyrosine to 3,4-dihydroxyphenylalanine (DOPA), the oxidation of DOPA to DOPA quinone, and the oxidation of 5,6-dihydroxyindole to 5,6-dihydroxuquinone [10, 11]. In addition to its catalytic features, tyrosinase is distinctive from other enzymes because it displays various inhibition patterns. Tyrosinase inhibition has been extensively studied for cosmetic, medicinal, and agricultural purposes [12]. The tyrosinase mechanism is complex, and this enzyme can catalyze multiple reactions. Despite several reported crystallographic structures of tyrosinase, the 3D structure and architecture of the active site are not well understood [22, 23]. Mechanistic studies must involve a variety of computational methods and kinetic analysis to derive the structure-function relationship between substrates and ligands. The inhibitory effect of compounds with sugar backbones on tyrosinase are of great interest [20, 24, 25]. D-(–)-arabinose, a potential tyrosinase inhibitor, is an aldopentose with one
Computer applications for prediction of protein–protein interactions and rational drug design
Solène Grosdidier, Max Totrov, Juan Fernández-Recio
Advances and Applications in Bioinformatics and Chemistry , 2009, DOI: http://dx.doi.org/10.2147/AABC.S6272
Abstract: mputer applications for prediction of protein–protein interactions and rational drug design Review (8802) Total Article Views Authors: Solène Grosdidier, Max Totrov, Juan Fernández-Recio Published Date November 2009 Volume 2009:2 Pages 101 - 123 DOI: http://dx.doi.org/10.2147/AABC.S6272 Solène Grosdidier1, Max Totrov2, Juan Fernández-Recio1 1Life Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain; 2Molsoft LLC, La Jolla, CA, USA Abstract: In recent years, protein–protein interactions are becoming the object of increasing attention in many different fields, such as structural biology, molecular biology, systems biology, and drug discovery. From a structural biology perspective, it would be desirable to integrate current efforts into the structural proteomics programs. Given that experimental determination of many protein–protein complex structures is highly challenging, and in the context of current high-performance computational capabilities, different computer tools are being developed to help in this task. Among them, computational docking aims to predict the structure of a protein–protein complex starting from the atomic coordinates of its individual components, and in recent years, a growing number of docking approaches are being reported with increased predictive capabilities. The improvement of speed and accuracy of these docking methods, together with the modeling of the interaction networks that regulate the most critical processes in a living organism, will be essential for computational proteomics. The ultimate goal is the rational design of drugs capable of specifically inhibiting or modifying protein–protein interactions of therapeutic significance. While rational design of protein–protein interaction inhibitors is at its very early stage, the first results are promising.
From Nonspecific DNA–Protein Encounter Complexes to the Prediction of DNA–Protein Interactions  [PDF]
Mu Gao,Jeffrey Skolnick
PLOS Computational Biology , 2009, DOI: 10.1371/journal.pcbi.1000341
Abstract: DNA–protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA–protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA–protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA–protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA–protein interaction modes exhibit some similarity to specific DNA–protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Cα deviation from native is up to 5 ? from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA–protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein.
Large-scale prediction of protein-protein interactions from structures
Martial Hue, Michael Riffle, Jean-Philippe Vert, William S Noble
BMC Bioinformatics , 2010, DOI: 10.1186/1471-2105-11-144
Abstract: Here, we describe a computational method to predict efficiently in silico whether two protein structures interact. This yes/no question is presumably easier to answer than the standard protein docking question, "How do these two protein structures interact?" Our approach is to discriminate between interacting and non-interacting protein pairs using a statistical pattern recognition method known as a support vector machine (SVM). We demonstrate that our structure-based method performs well on this task and scales well to the size of an interactome.The use of structure information for the prediction of protein interaction yields significantly better performance than other sequence-based methods. Among structure-based classifiers, the SVM algorithm, combined with the metric learning pairwise kernel and the MAMMOTH kernel, performs best in our experiments.The knowledge of the interactions among proteins is essential to understanding the molecular mechanisms inside the cell. However, the experimental measurement of protein-protein interactions by two main procedures--the yeast two-hybrid system and mass spectrometry combined with tandem affinity purification--suffers from a high false positive rate, as evidenced by the small intersection between several independently generated interaction data sets [1]. Recent years have seen much progress in understanding of the false positive predictions [2]. The limitations of current experimental methods therefore highlight the need for in silico interaction predictions.The elucidation of an increasing number of protein 3D structures is likely to continue at a fast pace as a result of several large-scale initiatives. These structures provide both an opportunity and a challenge for in silico prediction methods. The opportunity is that if in silico methods are able to predict whether two given 3D structures interact, then these methods may be applied to predict interactions among the large amount of proteins with known or inferred 3D s
Improving the prediction of yeast protein function using weighted protein-protein interactions
Khaled S Ahmed, Nahed H Saloma, Yasser M Kadah
Theoretical Biology and Medical Modelling , 2011, DOI: 10.1186/1742-4682-8-11
Abstract: A new technique to weight interactions in the yeast proteome is presented. The weights are related to the network topology (local and global) and the number of identified methods, and the results revealed improvement in the sensitivity and specificity of prediction in terms of cellular role and cellular locations. This method (new weights) was compared with a method that utilises interactions with the same weight and it was shown to be superior.A new method for weighting the interactions in protein-protein interaction networks is presented. Experimental results concerning yeast proteins demonstrated that weighting interactions integrated with the neighbor counting method improved the sensitivity and specificity of prediction in terms of two functional categories: cellular role and cell locations.Determining protein functions is an important challenge in the post-genomic era and Automated Function Prediction is currently one of the most active research fields. Previously, researchers have attempted to determine protein functions using the structure of the protein and comparing it with similar proteins. Similarities between the protein and homologues from other organisms have been investigated to predict functions. However, the diversity of homologues meant that these time-consuming methods were inaccurate. Other techniques to predict protein functions including analyzing gene expression patterns [1,2], phylogenetic profiles [3-5], protein sequences [6,7] and protein domains [8,9] have been utilised, but these technologies have high error rates, leading to the use of integrated multi-sources [10,11]. The computational approach was designed to resolve the inaccuracy of protein prediction, using information gained from physical and genetic interaction maps to predict protein functions. Recently, researchers have introduced various techniques to determine the probability of protein function prediction using information extracted from PPI. Results from these trials have b
Computational prediction of protein-protein complexes
Seema Mishra
BMC Research Notes , 2012, DOI: 10.1186/1756-0500-5-495
Abstract: FindingsA plausible protein-protein hetero-complex was fished out from 10 docked complexes which are a representative set of complexes obtained after clustering of 2000 generated complexes using protein-protein docking softwares. The interfacial area for this complex was predicted by two "hotspot" prediction programs employing different algorithms. Further, this complex had the lowest energy and most buried surface area of all the complexes with the same interfacial residues.For the generation of a plausible protein heterocomplex, various software tools were employed. Prominent are the protein-protein docking methods, prediction of 'hotspots' which are the amino acid residues likely to be in an interface and measurement of buried surface area of the complexes. Consensus generated in their predictions lends credence to the use of the various softwares used.
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