Insights into the Dynamic Fluctuations of the Protein HPV16 E1 and Identification of Motifs by Using Elastic Network Modeling

2021 ◽  
Vol 28 ◽  
Author(s):  
Rabbiah Malik ◽  
Sahar Fazal
Keyword(s):  
Drug Targets ◽  
Network Models ◽  
Hpv Infection ◽  
Elastic Network ◽  
Conserved Sequence ◽  
Linear Peptide ◽  
Peptide Motifs ◽  
E1 Protein ◽  
Human Proteins ◽  
Viral Helicase

Background: Cancers of cervix, head and neck regions have been found to be associated with Human Papilloma Virus (HPV) infection. E1 protein makes an important papillomavirus replication factor. Among the ORFs of papillomaviruses, the most conserved sequence is that of the E1 ORF. It is the viral helicase with being a member of class of ATPases associated with diverse cellular activities (AAA+) helicases. The interactions of E1 with human DNA and proteins occurs in the presence of short linear peptide motifs on E1 identical to those on human proteins. Methods: Different Motifs were identified on HPV16 E1 by using ELMs. Elastic network models were generated by using 3D structures of E1. Their dynamic fluctuations were analyzed on the basis of B factors, correlation analysis and deformation energies. Results: 3 motifs were identified on E1 which can interact with Cdk and Cyclin domains of human proteins. 11 motifs identified on E1 have their CDs of Pkinase on human proteins. LIG_MYND_2 has been identified as involved in stabilizing interaction of E1 with Hsp40 and Hsp70. These motifs and amino acids comprising these motifs play a major role in maintaining interactions with human proteins, ultimately causing infections leading to cancers. Conclusion: Our study identified various motifs on E1 which interact with specific counter domains found in human proteins, already reported having the interactions with E1. We also validated the involvement of these specific motifs containing regions of E1 by modeling elastic networks of E1. These motif involving interactions could be used as drug targets.

Computational analysis of domains vulnerable To HPV-16 E6 Oncoprotein and corresponding hot spot residues.

2020 ◽  
Vol 27 ◽  
Author(s):  
Rabbiah Manzoor Malik ◽  
Sahar Fazal ◽  
Mohammad Amjad Kamal
Keyword(s):  
Cervical Cancer ◽  
Hot Spot ◽  
Network Models ◽  
Host Protein ◽  
Hpv 16 ◽  
Protein Protein Interaction ◽  
Linear Peptide ◽  
Peptide Motifs ◽  
E6 Oncoprotein ◽  
Human Proteins

Background: Human Papilloma Virus (HPV) is the primary cause of cancers in cervix, head and neck regions. Oncoprotein E6 of HPV-16, after infecting human body, alters host protein-protein interaction networks. E6 interacts with several proteins, causing the infection to progress into cervical cancer. The molecular basis for these interactions is the presence of short linear peptide motifs on E6 identical to those on human proteins. Methods: Motifs of LXXLL and E/DLLL/V-G after identification on E6, were analyzed for their dynamic fluctuations by use of elastic network models. Correlation analysis of amino acid residues of E6 was also performed in specific regions of motifs. Results: Arginine, Leucine, Glutamine, Threonine and Glutamic acid have been identified as hot spot residues of E6 which can subsequently provide a platform for drug designing and understanding of pathogenesis of cervical cancer. These amino acids play a significant role in stabilizing interactions with host proteins, ultimately causing infections and cancers. Conclusion: Our study validates the role of linear binding motifs of E6 of HPV in interacting with these proteins as an important event in the propagation of HPV in human cells and its transformation into cervical cancer. The study further predicts the domains of protein kinase and armadillo as part of the regions involved in the interaction of E6AP, Paxillin and TNF R1, with viral E6.

scholarly journals Prediction of Human proteins having interactions with HPV16 proteins using virus and host sequence motifs.

2021 ◽  
Vol 2 (2) ◽  
pp. 107-126
Author(s):  
Rabbiah Manzoor Malik ◽  
Sahar Fazal ◽  
Syed Touqeer Abbas ◽  
Aamer Bhatti ◽  
Mukhtar Ullah ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Hpv Infection ◽  
Cervical Region ◽  
Sequence Motifs ◽  
Host Proteins ◽  
Human Host ◽  
Linear Peptide ◽  
Peptide Motifs ◽  
Human Proteins ◽  
E6 And E7

Background: Human Papillomavirus (HPV) infection has been found to be the major cause of cancer of cervical region, in females.  Genome of HPV codes for 6 functional proteins E1, E2, E4, E5, E6 and E7. These proteins play different roles in development of HPV infection and its progression towards cervical cancer. The interactions of HPV proteins with human DNA and proteins occurs in the presence of short linear peptide motifs on these proteins, have similar sequence to those found on proteins in human cells. Methods: After identification of human motifs in HPV proteins, by use of ELM resource, their counter domains were found from PROSITE. The proteins of human proteome containing these counter domains were predicted as the proteins having possibility of interactions with HPV proteins.    Results: we predicted 9468 human proteins for having interactions with HPV proteins. Our predicted proteins were enriched with the host proteins having possibility of being interacted by HPV proteins. 10% of our predicted proteins were already reported to be affected by one or more HPV proteins. The list of predicated proteins can be utilized to find out the connectivity between the virus HPV and human host. It can also be used to determine the pathways involved in pathogenesis of HPV leading towards the cervical cancer Conclusion: The list of predicated proteins can be utilized to find out the connectivity between the virus HPV and human host. It can also be used to determine the pathways involved in pathogenesis of HPV leading towards the cervical cancer.

scholarly journals Cyclophilin A Binds to Linear Peptide Motifs Containing a Consensus That Is Present in Many Human Proteins

2005 ◽  
Vol 280 (25) ◽  
pp. 23668-23674 ◽  
Author(s):  
Kirill Piotukh ◽  
Wei Gu ◽  
Michael Kofler ◽  
Dirk Labudde ◽  
Volkhard Helms ◽  
...  
Keyword(s):  
Cyclophilin A ◽  
Linear Peptide ◽  
Peptide Motifs ◽  
Human Proteins

scholarly journals New Mechanistic Insights on Carbon Nanotubes’ Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches

Biology ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 171
Author(s):  
Michael González-Durruthy ◽  
Riccardo Concu ◽  
Juan M. Ruso ◽  
M. Natália D. S. Cordeiro
Keyword(s):  
Carbon Nanotubes ◽  
Network Models ◽  
Single Walled Carbon Nanotubes ◽  
Docking Studies ◽  
Fractal Surface ◽  
Submitochondrial Particles ◽  
Elastic Network ◽  
Elastic Network Models ◽  
Walled Carbon Nanotubes

Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and computational study, since the use of only one or few techniques may not fully describe the phenomena. To this end, the in vitro inhibition responses in submitochondrial particles (SMP) was combined with docking, elastic network models, fractal surface analysis, and Nano-QSTR models. In vitro studies suggest that inhibition responses in SMP of F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 µg/mL) for both pristine and COOH single-walled carbon nanotubes types (SWCNT). Besides, both SWCNTs show an interaction inhibition pattern mimicking the oligomycin A (the specific mitochondria F0F1-ATPase inhibitor blocking the c-ring F0 subunit). Performed docking studies denote the best crystallography binding pose obtained for the docking complexes based on the free energy of binding (FEB) fit well with the in vitro evidence from the thermodynamics point of view, following an affinity order such as: FEB (oligomycin A/F0-ATPase complex) = −9.8 kcal/mol > FEB (SWCNT-COOH/F0-ATPase complex) = −6.8 kcal/mol ~ FEB (SWCNT-pristine complex) = −5.9 kcal/mol, with predominance of van der Waals hydrophobic nano-interactions with key F0-ATPase binding site residues (Phe 55 and Phe 64). Elastic network models and fractal surface analysis were performed to study conformational perturbations induced by SWCNT. Our results suggest that interaction may be triggering abnormal allosteric responses and signals propagation in the inter-residue network, which could affect the substrate recognition ligand geometrical specificity of the F0F1-ATPase enzyme in order (SWCNT-pristine > SWCNT-COOH). In addition, Nano-QSTR models have been developed to predict toxicity induced by both SWCNTs, using results of in vitro and docking studies. Results show that this method may be used for the fast prediction of the nanotoxicity induced by SWCNT, avoiding time- and money-consuming techniques. Overall, the obtained results may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms, rational drug design-based nanotechnology, and potential biomedical application in precision nanomedicine.

scholarly journals Edge-based formulation of elastic network models

2019 ◽  
Vol 1 (3) ◽  
Author(s):  
Maxwell Hodges ◽  
Sophia N. Yaliraki ◽  
Mauricio Barahona
Keyword(s):  
Network Models ◽  
Elastic Network ◽  
Elastic Network Models ◽  
Edge Based

scholarly journals Functional Stratification of Cancer Drugs through Integrated Network Similarity

2022 ◽  
Author(s):  
Nurcan Tuncbag ◽  
Seyma Unsal Beyge
Keyword(s):  
Cell Lines ◽  
Drug Targets ◽  
Hdac Inhibitors ◽  
Treatment Strategies ◽  
Network Models ◽  
Drug Combinations ◽  
Multiple Cancer ◽  
Integrated Network ◽  
Drug Molecules ◽  
The Difference

Abstract Heterogeneity across tumors is the main obstacle in developing treatment strategies. Drug molecules not only perturb their immediate protein targets but also modulate multiple signaling pathways. In this study, we explored the networks modulated by several drug molecules across multiple cancer cell lines by integrating the drug targets with transcriptomic and phosphoproteomic data. As a result, we obtained 236 reconstructed networks covering five cell lines and 70 drugs. A rigorous topological and pathway analysis showed that chemically and functionally different drugs may modulate overlapping networks. Additionally, we revealed a set of tumor-specific hidden pathways with the help of drug network models that are not detectable from the initial data. The difference in the target selectivity of the drugs leads to disjoint networks despite sharing the exact mechanism of action, e.g., HDAC inhibitors. We also used the reconstructed network models to study potential drug combinations based on the topological separation, found literature evidence for a set of drug pairs. Overall, the network-level exploration of the drug perturbations may potentially help optimize treatment strategies and suggest new drug combinations.

scholarly journals Coarse-Grained Models Reveal Functional Dynamics - I. Elastic Network Models – Theories, Comparisons and Perspectives

2008 ◽  
Vol 2 ◽  
pp. BBI.S460 ◽  
Author(s):  
Lee-Wei Yang ◽  
Choon-Peng Chng
Keyword(s):  
Conformational Changes ◽  
Conformational Dynamics ◽  
Computational Cost ◽  
Network Models ◽  
Coarse Graining ◽  
Coarse Grained ◽  
Elastic Network ◽  
Elastic Network Models ◽  
Time Space ◽  
Functional Dynamics

In this review, we summarize the progress on coarse-grained elastic network models (CG-ENMs) in the past decade. Theories were formulated to allow study of conformational dynamics in time/space frames of biological interest. Several highlighted models and their underlined hypotheses are introduced in physical depth. Important ENM offshoots, motivated to reproduce experimental data as well as to address the slow-mode-encoded configurational transitions, are also introduced. With the theoretical developments, computational cost is significantly reduced due to simplified potentials and coarse-grained schemes. Accumulating wealth of data suggest that ENMs agree equally well with experiment in describing equilibrium dynamics despite their distinct potentials and levels of coarse-graining. They however do differ in the slowest motional components that are essential to address large conformational changes of functional significance. The difference stems from the dissimilar curvatures of the harmonic energy wells described for each model. We also provide our views on the predictability of ‘open to close’ (open→close) transitions of biomolecules on the basis of conformational selection theory. Lastly, we address the limitations of the ENM formalism which are partially alleviated by the complementary CG-MD approach, to be introduced in the second paper of this two-part series.

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