Computational Biophysics and Bioinformatics

Professor Emil Alexov Group


Atomic-style Clemson Robot

Robot holding barnase-barstar complex.

About the Lab

The research in the lab focuses on computational modeling of biological macromolecules and their assemblages as well as predicting biophysical quantities associated with them. One of the primary roles of the lab is to develop and maintain the popular software package DelPhi, which calculates electrostatic potentials and energies of systems comprised of biological macromolecules. We are also interested in modeling disease-causing missense mutations, pKa's of amino acids and nucleic groups, and pH-dependence of stability and binding. Visit our commercial site for details about services for human genetic differences and their interpretation.

Software & Servers See repositories at github-logo 

DelPhi Webserver

Delphi webserver is an online interface for the popular Poisson-Boltzmann solver (Delphi) for calculating electrostatic energies and potential in biological macromolecules.

Delphi pKa

Delphi-PKA is a DelPhi-based C++ program which allows to predict pKa's for ionizable groups at given pH in proteins, RNA and DNA.


DelphiForce is a DelPhi-based script allowing to calculate electrostatic force between two objects, such as proteins, DNAs, lipids, small molecules, etc.


SAMPDI Web Server provides fast and accurate predictions for the effects of single amino acid substitution on the binding free energy of protein-DNA complex.


A group of structure and sequece based methods for predicting protein-protein Binding Energy change due to Single Amino Acid Mutation (SAAMBE).


An online application for calculating folding free energy changes in proteins caused by missense mutations for provided protein sequence.


BION-2 predicts the positions of non-specifically surface-bound ions utilizing the Gaussian-based treatment of ions within the framework of the modified Poisson–Boltzmann equation.

pKa Database

pKa Database contains experimentally measured pKa values for >1500 ionizable residues in wild type as well as mutant proteins.


SAMPDI-3D uses a machine learning algorithm with features as physico-chemical properties, structure of mutation site and protein-DNA interactions to predict the change of binding free energy.


Head of Lab

Emil Alexov

Professor Emil Alexov, PhD

Postdoctoral Associate

Shailesh Kumar Panday

Shailesh K Panday, PhD

Postdoctoral Associate

Bohua Wu

Bohua Wu (Angela), PhD

Ph.D. Student

Mihiri Bosthanthirige

Mihiri Bosthanthirige

Ph.D. Student

Mahesh Koirala

Mahesh Koirala

Graduate Student

Shannon Bonomi

Shannon Bonomi

Undergraduate Student

Jacob Jeffries

Jacob Jeffries

Copyright © Computational Biology and Bioinformatics - Emil Alexov Group.