# The structural basis of cis and trans protocadherin interactions

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2021 · $480,094

## Abstract

The overall objective of the proposed research is to determine the mechanism by which the
clustered protocadherins (Pcdhs) mediate neuronal self-avoidance, a critical property of all
nervous systems. The Pcdhs, encoded in three large gene clusters controlled by alternative
promoter choice, are expressed stochastically in neurons, diversifying each neuronal plasma
membranes with distinctive sets of Pcdh isoforms. This diversity is thought
to underlie a molecular “barcode” for individual neurons, which allows cells to distinguish
between self and non-self to mediate self-avoidance. In the prior funding period we defined the
functional architecture of Pcdhs, showing that they form promiscuous cis-dimer recognition units
in their membrane-proximal domains, and recognize other Pcdh recognition units in trans
through large interfaces encoded in domains EC1-EC4. We mapped these interfaces by x-ray
crystallography and mutagenesis. The requirement that Pcdhs encode sufficient diversity to
avoid inappropriate recognition of non-self neurons as self, has led us to propose two alternative
models, each in molecular detail, for Pcdh function. This proposal, is aimed at distinguishing
these models to arrive at the true mechanism of Pcdh function.

## Key facts

- **NIH application ID:** 10196925
- **Project number:** 5R01MH114817-09
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** THOMAS P MANIATIS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $480,094
- **Award type:** 5
- **Project period:** 2013-09-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10196925

## Citation

> US National Institutes of Health, RePORTER application 10196925, The structural basis of cis and trans protocadherin interactions (5R01MH114817-09). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10196925. Licensed CC0.

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