# MELD: accelerating MD modeling of proteins using Bayesian inference

> **NIH NIH R01** · STATE UNIVERSITY NEW YORK STONY BROOK · 2020 · $317,007

## Abstract

PROJECT SUMMARY
This proposal is to develop MELD, a computational Bayesian accelerator that “melds” together
molecular dynamics simulations with external knowledge. It is novel in harnessing information
that has not been usable before – because it is too sparse, noisy, ambiguous, combinatoric, or
too corrupted for traditional approaches. In contrast to the high-certainty restraints traditionally
used in MD simulations, MELD leverages a much broader range of real-world high-uncertainty
restraints. The first specific aim is to incorporate such information in protein structure
determination, in several collaboration projects with experimentalists who perform solution x-ray
scattering, ESR, and high-throughput alanine scanning structures of peptide protein complexes.
The second aim is to also harness information about processes, trajectories, and dynamic routes
to speed the identification of protein states. MELD promises to extend physics-based simulations
for determining larger protein structures, for folding larger proteins, for binding more flexible
ligands, and for exploring larger mechanistic actions, than current MD simulation methods can
handle.

## Key facts

- **NIH application ID:** 9848587
- **Project number:** 5R01GM125813-03
- **Recipient organization:** STATE UNIVERSITY NEW YORK STONY BROOK
- **Principal Investigator:** Ken A Dill
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $317,007
- **Award type:** 5
- **Project period:** 2018-01-01 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9848587

## Citation

> US National Institutes of Health, RePORTER application 9848587, MELD: accelerating MD modeling of proteins using Bayesian inference (5R01GM125813-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9848587. Licensed CC0.

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