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Dar KB, Bhat AH, Amin S, Anjum S, Reshi BA, Zargar MA, Masood A, Ganie SA. Exploring Proteomic Drug Targets, Therapeutic Strategies and Protein - Protein Interactions in Cancer: Mechanistic View. Curr Cancer Drug Targets 2020; 19:430-448. [PMID: 30073927 DOI: 10.2174/1568009618666180803104631] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2018] [Revised: 07/17/2018] [Accepted: 07/19/2018] [Indexed: 12/31/2022]
Abstract
Protein-Protein Interactions (PPIs) drive major signalling cascades and play critical role in cell proliferation, apoptosis, angiogenesis and trafficking. Deregulated PPIs are implicated in multiple malignancies and represent the critical targets for treating cancer. Herein, we discuss the key protein-protein interacting domains implicated in cancer notably PDZ, SH2, SH3, LIM, PTB, SAM and PH. These domains are present in numerous enzymes/kinases, growth factors, transcription factors, adaptor proteins, receptors and scaffolding proteins and thus represent essential sites for targeting cancer. This review explores the candidature of various proteins involved in cellular trafficking (small GTPases, molecular motors, matrix-degrading enzymes, integrin), transcription (p53, cMyc), signalling (membrane receptor proteins), angiogenesis (VEGFs) and apoptosis (BCL-2family), which could possibly serve as targets for developing effective anti-cancer regimen. Interactions between Ras/Raf; X-linked inhibitor of apoptosis protein (XIAP)/second mitochondria-derived activator of caspases (Smac/DIABLO); Frizzled (FRZ)/Dishevelled (DVL) protein; beta-catenin/T Cell Factor (TCF) have also been studied as prospective anticancer targets. Efficacy of diverse molecules/ drugs targeting such PPIs although evaluated in various animal models/cell lines, there is an essential need for human-based clinical trials. Therapeutic strategies like the use of biologicals, high throughput screening (HTS) and fragment-based technology could play an imperative role in designing cancer therapeutics. Moreover, bioinformatic/computational strategies based on genome sequence, protein sequence/structure and domain data could serve as competent tools for predicting PPIs. Exploring hot spots in proteomic networks represents another approach for developing targetspecific therapeutics. Overall, this review lays emphasis on a productive amalgamation of proteomics, genomics, biochemistry, and molecular dynamics for successful treatment of cancer.
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Affiliation(s)
- Khalid Bashir Dar
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India.,Department of Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Aashiq Hussain Bhat
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India.,Department of Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Shajrul Amin
- Department of Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Syed Anjum
- Amity Institute of Biotechnology, Amity University, Rajasthan, India
| | - Bilal Ahmad Reshi
- Department of Biotechnology, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Mohammad Afzal Zargar
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Akbar Masood
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Showkat Ahmad Ganie
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
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Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites. Sci Rep 2015; 5:11940. [PMID: 26149854 PMCID: PMC4493637 DOI: 10.1038/srep11940] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Accepted: 06/04/2015] [Indexed: 12/30/2022] Open
Abstract
Experimentally-determined or computationally-predicted protein phosphorylation sites for distinctive species are becoming increasingly common. In this paper, we compare the predictive performance of a novel classification algorithm with different encoding schemes to develop a rice-specific protein phosphorylation site predictor. Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site. A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms. We have used SVM with AF-CKSAAP to construct a rice-specific protein phosphorylation sites predictor, Rice_Phospho 1.0 (http://bioinformatics.fafu.edu.cn/rice_phospho1.0). We measure the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) of Rice_Phospho 1.0 to be 82.0% and 0.64, significantly higher than those measures for other predictors such as Scansite, Musite, PlantPhos and PhosphoRice. Rice_Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC_Os03g51600.1, a protein sequence which did not appear in the training dataset. In summary, Rice_phospho 1.0 outputs reliable predictions of protein phosphorylation sites in rice, and will serve as a useful tool to the community.
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Competition-cooperation relationship networks characterize the competition and cooperation between proteins. Sci Rep 2015; 5:11619. [PMID: 26108281 PMCID: PMC4479874 DOI: 10.1038/srep11619] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2015] [Accepted: 06/01/2015] [Indexed: 01/04/2023] Open
Abstract
By analyzing protein-protein interaction (PPI) networks, one can find that a protein may have multiple binding partners. However, it is difficult to determine whether the interactions with these partners occur simultaneously from binary PPIs alone. Here, we construct the yeast and human competition-cooperation relationship networks (CCRNs) based on protein structural interactomes to clearly exhibit the relationship (competition or cooperation) between two partners of the same protein. If two partners compete for the same interaction interface, they would be connected by a competitive edge; otherwise, they would be connected by a cooperative edge. The properties of three kinds of hubs (i.e., competitive, modest, and cooperative hubs) are analyzed in the CCRNs. Our results show that competitive hubs have higher clustering coefficients and form clusters in the human CCRN, but these tendencies are not observed in the yeast CCRN. We find that the human-specific proteins contribute significantly to these differences. Subsequently, we conduct a series of computational experiments to investigate the regulatory mechanisms that avoid competition between proteins. Our comprehensive analyses reveal that for most yeast and human protein competitors, transcriptional regulation plays an important role. Moreover, the human-specific proteins have a particular preference for other regulatory mechanisms, such as alternative splicing.
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Aumentado-Armstrong TT, Istrate B, Murgita RA. Algorithmic approaches to protein-protein interaction site prediction. Algorithms Mol Biol 2015; 10:7. [PMID: 25713596 PMCID: PMC4338852 DOI: 10.1186/s13015-015-0033-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2014] [Accepted: 01/07/2015] [Indexed: 12/19/2022] Open
Abstract
Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.
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Paracha RZ, Ali A, Ahmad J, Hussain R, Niazi U, Muhammad SA. Structural evaluation of BTK and PKCδ mediated phosphorylation of MAL at positions Tyr86 and Tyr106. Comput Biol Chem 2014; 51:22-35. [PMID: 24840642 DOI: 10.1016/j.compbiolchem.2014.04.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Revised: 04/03/2014] [Accepted: 04/07/2014] [Indexed: 01/02/2023]
Abstract
A number of diseases including sepsis, rheumatoid arthritis, diabetes, cardiovascular diseases and hyperinflammatory immune disorders have been associated with Toll like receptor (TLR) 2 and TLR4. Endogenous adaptor protein known as MyD88 adapter-like protein (MAL) bind exclusively to the cytosolic portions of TLR2 and TLR4 to initiate downstream signalling. Brutons tyrosine kinase (BTK) and protein kinase C delta (PKCδ) have been implicated to phosphorylate MAL and activate it to initiate downstream signalling. BTK has been associated with phosphorylation at positions Tyr86 and Tyr106, necessary for the activation of MAL but definite residual target of PKCδ in MAL is still to be explored. To produce a better understanding of the functional domains involved in the formation of MAL-kinase complexes, computer-aided studies were used to characterize the protein-protein interactions (PPIs) of phosphorylated BTK and PKCδ with MAL. Docking and physicochemical studies indicated that BTK was involved in close contact with Tyr86 and Tyr106 of MAL whereas PKCδ may phosphorylate Tyr106 only. Moreover, the electrostatics charge distribution of binding interfaces of BTK and PKCδ were distinct but compatible with respective regions of MAL. Our results implicate that position of Tyr86 is specifically phosphorylated by BTK whereas Tyr106 can be phosphorylated by competitive action of both BTK and PKCδ. Additionally, the residues of MAL which are necessary for interaction with TLR2, TLR4, MyD88 and SOCS-1 also play their roles in maintaining interaction with kinases and can be targeted in future to reduce TLR2 and TLR4 induced pathological responses.
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Affiliation(s)
- Rehan Zafar Paracha
- Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Amjad Ali
- Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
| | - Jamil Ahmad
- Research Center for Modeling and Simulation (RCMS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.
| | - Riaz Hussain
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad 44000, Pakistan
| | - Umar Niazi
- IBERS, Aberystwyth University, Edward Llwyd Building, Penglais Campus, Aberystwyth, Ceredigion, Wales SY23 3FG, UK
| | - Syed Aun Muhammad
- Department of Pharmacy, COMSATS Institute of Information Technology Abbottabad, 22060, Pakistan
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Ahmed MH, Habtemariam M, Safo MK, Scarsdale JN, Spyrakis F, Cozzini P, Mozzarelli A, Kellogg GE. Unintended consequences? Water molecules at biological and crystallographic protein–protein interfaces. Comput Biol Chem 2013; 47:126-41. [DOI: 10.1016/j.compbiolchem.2013.08.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2013] [Revised: 08/27/2013] [Accepted: 08/27/2013] [Indexed: 01/31/2023]
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Maleki M, Vasudev G, Rueda L. The role of electrostatic energy in prediction of obligate protein-protein interactions. Proteome Sci 2013; 11:S11. [PMID: 24564955 PMCID: PMC3907787 DOI: 10.1186/1477-5956-11-s1-s11] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Prediction and analysis of protein-protein interactions (PPI) and specifically types of PPIs is an important problem in life science research because of the fundamental roles of PPIs in many biological processes in living cells. In addition, electrostatic interactions are important in understanding inter-molecular interactions, since they are long-range, and because of their influence in charged molecules. This is the main motivation for using electrostatic energy for prediction of PPI types. RESULTS We propose a prediction model to analyze protein interaction types, namely obligate and non-obligate, using electrostatic energy values as properties. The prediction approach uses electrostatic energy values for pairs of atoms and amino acids present in interfaces where the interaction occurs. The main features of the complexes are found and then the prediction is performed via several state-of-the-art classification techniques, including linear dimensionality reduction (LDR), support vector machine (SVM), naive Bayes (NB) and k-nearest neighbor (k-NN). For an in-depth analysis of classification results, some other experiments were performed by varying the distance cutoffs between atom pairs of interacting chains, ranging from 5Å to 13Å. Moreover, several feature selection algorithms including gain ratio (GR), information gain (IG), chi-square (Chi2) and minimum redundancy maximum relevance (mRMR) are applied on the available datasets to obtain more discriminative pairs of atom types and amino acid types as features for prediction. CONCLUSIONS Our results on two well-known datasets of obligate and non-obligate complexes confirm that electrostatic energy is an important property to predict obligate and non-obligate protein interaction types on the basis of all the experimental results, achieving accuracies of over 98%. Furthermore, a comparison performed by changing the distance cutoff demonstrates that the best values for prediction of PPI types using electrostatic energy range from 9Å to 12Å, which show that electrostatic interactions are long-range and cover a broader area in the interface. In addition, the results on using feature selection before prediction confirm that (a) a few pairs of atoms and amino acids are appropriate for prediction, and (b) prediction performance can be improved by eliminating irrelevant and noisy features and selecting the most discriminative ones.
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Affiliation(s)
- Mina Maleki
- School of Computer Science, University of Windsor, 401 Sunset Avenue, Windsor, Ontario, N9B 3P4, Canada
| | - Gokul Vasudev
- School of Computer Science, University of Windsor, 401 Sunset Avenue, Windsor, Ontario, N9B 3P4, Canada
| | - Luis Rueda
- School of Computer Science, University of Windsor, 401 Sunset Avenue, Windsor, Ontario, N9B 3P4, Canada
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Russo LC, Asega AF, Castro LM, Negraes PD, Cruz L, Gozzo FC, Ulrich H, Camargo ACM, Rioli V, Ferro ES. Natural intracellular peptides can modulate the interactions of mouse brain proteins and thimet oligopeptidase with 14-3-3ε and calmodulin. Proteomics 2012; 12:2641-55. [DOI: 10.1002/pmic.201200032] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2012] [Revised: 05/31/2012] [Accepted: 06/03/2012] [Indexed: 02/03/2023]
Affiliation(s)
- Lilian C. Russo
- Department of Cell Biology and Development; Biomedical Sciences Institute; University of São Paulo; São Paulo Brazil
| | - Amanda F. Asega
- Laboratory of Applied Toxinology (LETA); Butantan Institute; SP Brazil
| | - Leandro M. Castro
- Department of Cell Biology and Development; Biomedical Sciences Institute; University of São Paulo; São Paulo Brazil
| | - Priscilla D. Negraes
- Biochemistry Department; Chemistry Institute; University of São Paulo; São Paulo Brazil
| | - Lilian Cruz
- Department of Cell Biology and Development; Biomedical Sciences Institute; University of São Paulo; São Paulo Brazil
| | - Fabio C. Gozzo
- Chemistry Institute; Campinas State University; Campinas SP Brazil
| | - Henning Ulrich
- Biochemistry Department; Chemistry Institute; University of São Paulo; São Paulo Brazil
| | | | - Vanessa Rioli
- Laboratory of Applied Toxinology (LETA); Butantan Institute; SP Brazil
| | - Emer S. Ferro
- Department of Cell Biology and Development; Biomedical Sciences Institute; University of São Paulo; São Paulo Brazil
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