# Mapping Fitness and Free Energy Landscapes of Proteins

> **NIH NIH R35** · TEMPLE UNIV OF THE COMMONWEALTH · 2021 · $371,303

## Abstract

Project Summary
Our long term goal is to integrate structure and sequence based approaches founded in statistical mechanics
to understand key features of molecular recognition by proteins, as well as protein fitness and function more
generally.
1. Mapping Complex Conformational and Fitness Landscapes of Proteins
Conformational dynamics plays a fundamental role in the regulation of molecular recognition and statistical
mechanics provides the framework to derive a comprehensive theory for the binding free energy of a ligand to
a protein. Our goal is to use advanced sampling methods based on molecular dynamics simulations to
construct conformational free energy landscapes of sufficient accuracy to be predictive for thermodynamic and
kinetic properties, but also as important, to generate qualitative insights about the molecular mechanisms for
binding and allosteric conformational transitions. Powerful inverse inference statistical approaches are being
developed to study the relationship between protein sequence co-variation and protein fitness. The co-
variation of pairs of mutations contained in multiple sequence alignments of protein families will be used to
build Potts Hamiltonian models of the sequence patterns that can be used to predict the change in fitness
resulting from drug selection pressure, as well as infer features of the conformational propensities of individual
proteins.
2. The Structural Basis for Kinase Selectivity and Regulation by Small Molecules
The human kinome encodes about 518 kinases (PKs) which constitute one of the largest class of genes.
Progress in kinase structural biology offers a conceptual framework for understanding many aspects of kinase
biology. With our collaborators at the Fox Chase Cancer Center and Columbia University we are working on
biophysical simulation and evolutionary sequence based approaches to rationalize biochemical profiling
studies of kinases and to devise a framework for understanding the molecular mechanisms of selectivity of
kinase inhibitors to their targets.
3. Inhibition of HIV-1 Proteins and Mechanisms of Drug Resistance
In collaboration with groups at the University of Colorado, Harvard and Scripps, I am working on the allosteric
basis for inhibition by small molecules of HIV-1 proteins, on mechanisms of drug resistance, and on
comparative studies of the fitness of HIV proteins in different HIV clades. Allosteric HIV-1 IN inhibitors called
ALLINIs are an important new class of anti-HIV-1 agents. ALLINIs bind at the IN catalytic core domain (CCD)
dimer interface occupying the principal binding pocket of LEDGF. Using our conformational free energy
simulation tools and the sequence based tools we are developing to understand correlated mutations, we are
working with our collaborators to ascertain the inhibitory mechansims of ALLINIs, and the basis for drug
resistance.

## Key facts

- **NIH application ID:** 10147114
- **Project number:** 5R35GM132090-03
- **Recipient organization:** TEMPLE UNIV OF THE COMMONWEALTH
- **Principal Investigator:** Ronald Levy
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $371,303
- **Award type:** 5
- **Project period:** 2019-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10147114

## Citation

> US National Institutes of Health, RePORTER application 10147114, Mapping Fitness and Free Energy Landscapes of Proteins (5R35GM132090-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10147114. Licensed CC0.

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