by jay krishnan
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By Jay Krishnan. Introduction. Information gathered from Proteomic techniques + neuroscientific research = Information on protein composition and function of mammalian neurons ( neuroproteomic data) - PowerPoint PPT PresentationTRANSCRIPT
By Jay Krishnan
Introduction Information gathered from Proteomic techniques +
neuroscientific research = Information on protein composition and function of mammalian neurons (neuroproteomic data)
Mass spectrometric (MS) analyses/identifies proteins associated with various synaptic preparations
Synaptosomes Synaptic Membranes Postsynaptic Density (PSD) Synaptic Vesicles Presynapse (PRE)
AIM: This study has a goal to combine proteomics with graph theory analysis to characterize protein composition of the PRE nerve terminal
Proteomics Procedures
Proteomics In-gel digestion In-solution digestion Mass spectrometry Database search and protein
identification
Getting the Proteins Background Literature based PPI network
of 6,442 proteins were created 17,879 interactions extracted from 12,462
publicationsObtained from BioGrid, HPRD, PPID, and a
CA1 neuronal regulatory network 306 Proteins were obtained from
proteomic studies
Database search and protein identification
MS data and NCBI (RefSeq) allows same data to be searched that was obtained from the literature using the Sonar programThe data was now cross checked to identify
the false positive rate or alpha errors(False Positive Rate) = RP/ (NP +RP)
(RP + NP) = the matches observed between the random and normal databases
Protein and peptide scores were changed in order to eliminate the false positives
Literature-based PRE PPI network
Interactions (306) are abstracted into a mixed graph where proteins are nodes and interactions are links
UniProt accession numbers; Entrez Gene IDs were used to for standard protein identification so that data from different sources can be effectively combined
SNAVI was used to analyze and visualize the network
Interactions between the Merged Data
In Silico network PRE interactions created by extracting PPI data from biochemical and physiological literature
• Calcium plays a central role inneurotransmitter release from the PRE nerve terminal
Review of Basoc Statistics Z Score = how many
standard deviations are you away from the mean
z = (x – u)/ sigma Within two SD lies
68.2% of the data Within 4 SD lies
95.4% of the data Within 6 SD lies
99.7% of the data
Normal Curve
Statistical Analysis
N1 = number of proteins in the merged list (306) N2 = number of proteins in background data (6,442)P1 = number of direct interactions in merged listP2 = number of interactions in background list – law of large numbers
* This binomial proportion test was used to determine how, “good,” the 306 proteins obtained from studies in proteomics compared to the Backaround genes obtained from BioGrid , HPRD ,PPID, and a CA1 neuronal regulatory network *
Statistical Analysis P (difference in proportion) = (p1-p2) / (N1 + N2) H0 = (p1/N1) – (p2/N2) = 0 Ha = (p1/N1) – (p2/N2) > 0 P value – the probability of obtaining a statistic
as extreme as the null hypothesis
If P value is lower that .05 we can reject the null hypothesis and verify that the merged list has a greater percentage of direct interactions
Comparison of Proteins based on z-score
After statistical analysis proteins with a z-score > 3 were compared to proteins with a z-score < -1 these proteins were than categorized based on Biological Process, Cellular Component and Molecular Function
Confirming Genuity of Data
(Western Blot) PRE fractions were separated by SDSPAGE and probed with selected antibodies to confirm the presence of the predicted proteins
Validation of the predicted presynaptic protein complex by co-immunoprecipitation
For further confirmation immunofluorescence studies were performed using cultured primary cortical neurons
Predict a PRE complexProteins from merged list were analyzed
for the presence of overlapping interactions
21 pairs were observedPercent SN = SN / (SN + ON1 + ON2)
SN = shared neighbors ON1: other neighbors of a chosen proteinON2: other neighbors of another chosen
protein
Interactions between Background proteins and Proteins from Merged List
Protein interactions (17 proteins) between background proteins and merged proteins when combined
Identification of Proteins using LC-MS/MS followed by In-Gel and In-Solution
Digestion
Sonar helps identifies the proteins based on based on statistical analysis and stored algorithms
Output that helped identify what are the proteins and what they interacted with
Core List – Confirmed Interactions; Contains101 proteins
Core PRE list is a compiled lists of proteins gathered from…1) proteomic studies of PRE
fractions 2) Literature based PRE
network (converted to list of components), and
3) Two published proteomic studies of PRE fractions
Generating the final corepresynaptic list
With Proteomics and literature-based networks lists of proteins were created.
Core list = PRE Proteins identified twice in independent experiments
Schematic illustrating the data compilation process creates a core presynaptic list of 117 PRE proteins.
Protein lists from proteomic studies, two other published studies, and a literature-based presynaptic network were combined to form a merged list containing 306 proteins.
16 intermediates identified from the merged list that interact directly with proteins from the core list.
These proteins were added to the core list
ConclusionBiological Relevant predictions deduced
from the literature can be tested experimentally
A complex of PPI has been created successfully and proper constraints have been made to reduce the FPR
ConclusionA described approach to characterize the
composition of the PRE nerve terminal was found
Testing (as indicated from p value and z score) proved that the merged list was a good list of proteins with interactions
Future ResearchScientists can use the knowledge of PPI
present in this paper in order to expand their knowledge over a designed/chosen protein
The network created can be always expanded and added to in the future as long as the same experimental procedures are used
References 1) Ma’ayan, A., Jenkins, S. L., Neves, S., Hasseldine, A. et al.,
Formation of regulatory patterns during signal propagation in a Mammalian cellular network. Science 2005, 309, 1078–1083.
2) Krycer, James R., Chi NI Pang, and Mark R. Wilkins. "High throughput protein-protein interaction data: clues for the architecture of protein complexes." Proteome Science (2008). Print.
3) Ling, Lee. Normal Curve. Digital image. Web.