# Maintenance and development of DelPhi and associated resources

> **NIH NIH R01** · CLEMSON UNIVERSITY · 2022 · $375,603

## Abstract

Modeling macromolecular thermodynamic properties, such as stability, dynamics and interactions,
is essential for revealing details of biochemical processes occurring in the cell and further for
figuring out what molecular effects are causing diseases. Among the forces and energies
manifested at atomic level of details, the electrostatics is one of the most prominent, because all
atoms carry partial charge and the electrostatic force is a long-range force dominating all
other forces at long distances. Particularly, the electrostatics is the driving force for pH-
dependence of macromolecular stability and activity. However, modeling electrostatic forces
and energies of biological macromolecules is highly nontrivial because of their irregular shape, the
conformational changes occurring during the corresponding process and the presence of water
phase. An efficient way to overcome such a complexity is to consider water phase and
macromolecule(s) on the same footage as continuum media with inhomogeneous polarizability.
This is the approach currently available ONLY in DelPhi package, where internal cavities, low
density macromolecular regions and surface waters are modeled via inhomogeneous
Gaussian and super-Gaussian dielectric functions. In a series of works, it has been shown that
this approach outperforms the traditional two-dielectric model and delivers ensemble-averaged
quantities. With this proposal we are seeking support to continue maintaining and developing
DelPhi suite and associated resources. In parallel with continuous support that we provide to our
users (more than 7,000 registered users), we plan to develop many new features in DelPhi as: (1)
enabling DelPhi to handle molecular dynamics (MD) generated trajectories by the most
frequently used MD packages as NAMD, CHARMM, GROMACS and AMBER; (2) upgrading DelPhi
to model geometrical properties as volume and molecular surface, which combined with (1) will
allow DelPhi to carry MM/PBSA calculations without third party software in fast and efficient
manner; (3) estimation of entropy, (4) novel energy partitioning and (5) residue-specific
Gaussian-based dielectric function. These new developments will be used to improve and
completely re-design DelPhi associated resources as SAAFEC, SAAMBE, and SAMPDI, which
are methods for predicting the change of the protein folding, protein binding, and protein-RNA/DNA
binding free energies due to mutations, respectively. In parallel, machine learning (ML)
approaches will be utilized to improve SAAFEC, SAAMBE and SAMPDI performance and a new
feature “Gaussian total density” will be implemented in the ML protocols.

## Key facts

- **NIH application ID:** 10360977
- **Project number:** 2R01GM093937-10A1
- **Recipient organization:** CLEMSON UNIVERSITY
- **Principal Investigator:** Emil Georgiev Alexov
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $375,603
- **Award type:** 2
- **Project period:** 2010-08-10 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10360977

## Citation

> US National Institutes of Health, RePORTER application 10360977, Maintenance and development of DelPhi and associated resources (2R01GM093937-10A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10360977. Licensed CC0.

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