# Computational Core

> **NIH NIH U54** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $315,539

## Abstract

Computational Core
The computational core will support the three PCHPI projects by providing cutting-edge computational tools and
expertise to PCHPI researchers for determining structural and dynamic bases underlying HIV-1 replication
processes across multiple scales, with atomic resolution. Specific aims are designed to: i) derive full-scale
dynamic models of HIV-1 and host protein complexes related to HIV-1 cytosolic transport and nuclear pore
trafficking, and ii) develop techniques to derive molecular determinants of protein-lipid, and lipid-lipid interactions
in the context of full-scale virion dynamics and realistic lipidomic compositions. The computational core will
design methodologies using canonical molecular dynamics simulations, enhanced sampling calculations and
free energy calculations to determine the effects of small molecules on the dynamics of capsid protein complexes
and assemblies. In-situ dynamic models will address large-scale systems, up to a billion atoms. These innovative
approaches, in conjunction with experimental validations, will yield a complete atomic-level model of intact HIV-
1 virions and provide transformative information about structures and dynamics of key processes in the viral
replication cycle, to guide the development of new therapeutic interventions.

## Key facts

- **NIH application ID:** 10506948
- **Project number:** 1U54AI170791-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Juan Roberto Perilla Jimenez
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $315,539
- **Award type:** 1
- **Project period:** 2022-07-01 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10506948

## Citation

> US National Institutes of Health, RePORTER application 10506948, Computational Core (1U54AI170791-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10506948. Licensed CC0.

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