Research Institutions without Walls

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PMID: 24834307 (PubMed) - PMCID: PMC4009096 - View online: PubReader
Volume 6, Issue 2, April-June , Page 63 to 63
Saturday, March 22, 2014 :Received , Saturday, March 22, 2014 :Accepted

Editorial: A network can be loosely defined as a structure linking together individual and organizational actors with shared goals or values, though often not a shared geography. A large body of literature highlights the important interaction between knowledge and networks. Interest in the impact of networking on knowledge translation and exchange, diffusion of innovations, knowledge management, and organizational outcomes is also increasing 1. There has been a growing interest in research networks and its implications on the creation of new knowledge. For example, there seems to be a consensus that those "scientists who collaborate with each other are more productive, oftentimes producing 'better' science, than are individual investigators. An open science platform can empowers researchers in their daily work and where everybody has equal opportunity to seek, share and generate knowledge. A value network can be defined as a network of relationships, which creates both tangible and intangible value through a complicated dynamic exchange between individuals, groups and organisations 2. The partnership for research and innovation in the health system funding opportunity recognizes the need to create networks of health researchers and clinical practitioners that can generate solutions to improve sustainable quality and value for money in the health system. The partnership for health research and innovation in the health system will support research and innovation. It seems that the concept of research networking in developing countries with several limitations such as research budgets should be engraved in the minds rather than papers.



Discussion :
Three dimensional structures of proteins are essential for computational interaction research. As only parts of FHIT, MDM2 and p53, had been determined as three dimensional structures in protein databank, we performed modeling for their structures.
Docking is considered as an in silico method for investigating the best interaction between two molecules which can be used in the rational drug design 22. HEX program actually accelerates the process compared with the classical FFT docking algorithms 23. Earlier studies have proved that HEX method has no limitation for protein size 24.
We tested docking of MDM2, p53 optimized parts as the receptor to compare the interaction tendencies of a special motif or a group of them within FHIT. The results of our previous docking study indicate that interaction of full FHIT with p53 (E-total: -568.66) and MDM2 (E-total: -459.53) is associated with lower total energy compared to the interaction of the complete MDM2 with p53 (E-total: -399.25). The abovementioned interaction occurred with higher total energy in comparison with the optimized p53 and MDM2 (E-total: -407.20). Moreover, subsequent to MDM2 and P53 optimization, it appeared that their relative tendency was augmented compared to their corresponding complete models.
Given the interaction of full FHIT with optimized models of p53 and MDM2, it is evident that FHIT truncates have higher affinity to interact with MDM2 optimized part than p53 optimized model. According to the interaction values, FHIT truncates interact with optimized MDM2 at lower E-total than optimized part of p53 and the total energies of docking interactions are directly related to shape energies of the mentioned interactions. Our results revealed that the tendency of β4-7, α1 segment of FHIT to p53 optimized model is more than other parts. Likewise, the β5-7, α1 structure of FHIT has more affinity to MDM2 optimized part than other forms. Thus, one can suggest the β5-7, α1 segment of FHIT as an interacting domain for both p53 and MDM2.
Having studied the above mentioned interactions, we found that FHIT remarkably has better affinity to bind MDM2 optimized part in the presence of p53 and MDM2 optimized models in most of the cases. Even though it can bind to p53 optimized model with low energy, when MDM2 optimized part is added to the model, the interaction with p53 optimized part is further attenuated (Table 4).
Interestingly, complex of FHIT truncates interacting with MDM2 optimized part at lower total energy usually interact with p53 optimized model at higher total energy. Thus, these findings indicate a sequence/ conformation specificity of FHIT truncates for interacting with MDM2 or p53.
E-totals of interaction between MDM2 optimized model (for interaction with p53) and FHIT truncates reveal that α1 is important in these interactions. E-totals of interaction between p53 optimized model (for interaction with MDM2) and FHIT truncates show that β5-7 and α1 are important parts in these interactions.
Experimental reports using yeast two-hybrid 25 and immunoprecipitation indicate that p53 at amino acids 1-41 25 or 1-52 26 interacts with MDM2. On MDM2, the interaction at amino acids 1-118 25 or 19-102 26 is the binding site to p53. Site-directed mutagenesis confirms the Leu14, Phe19, Leu22, and Trp23 of p53 are essential amino acids for interaction 27. The co-crystal structure of MDM2-p53 complex shows that Phe19, Trp23, and Leu26 are three main interacting residues in p53 28.
MDM2 directly binds to p53 18 and regulates p53 function and degradation 29-33. Moreover, a number of studies reported p53 and FHIT interaction 14,19 and their possible association 34. As shown in table 3, in p53-MDM2 interaction, the significant part of p53 is amino acids 18-26, and the important part of MDM2 is amino acids 23-119 35. Based on our results, the interaction sites of FHIT with MDM2 and p53 have overlapping parts. The best interaction site for MDM2-FHIT is residues 34-106 containing β5-7, α1. Conversely, for FHIT-p53 interaction, amino acids 21-104 containing β4-7, α1 are involved. However, residues 34-104 are involved in both interactions (Table 2). Interestingly, when the interaction of FHIT with MDM2 optimized part is challenged with p53 optimized part, the interaction site is different from interaction with MDM2 optimized part alone. Based upon these results, the interaction sites of FHIT with MDM2 and p53 are different with overlapping parts. FHIT binds to MDM2 with lower energy in the presence of p53 and the binding site shifts toward FHIT-MDM2 interaction. These data provide information involving competition of FHIT with p53 in binding to MDM2. Then, in the presence of FHIT, p53 is released from MDM2 and can increase apoptosis or cell cycle arrest. Figures 3 and 4 confirm the results indicated in the tables.


References :
  1. Abbott S, Petchey R, Kessel A, Killoran A. What sort of networks are public health networks? Public Health 2006;120(6):551-556.
  2. Hara N, Hew KF. Knowledge sharing in an online community of health care professionals. Inform Technol People 2007;20(3):235-261.