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.