We are a computational lab and our research is within the areas of Computational Biophysics,
Bioinformatics, and modeling molecular effects associated with disease-causing mutations. We
develop methods, software, and webservers to enable modeling of various processes in Molecular Biology.
In the past, our primary interest was in Structural Biology, and thus the focus was on using structural
information to investigate proteins, RNAs, DNAs and their assemblages with regard with their stability,
interactions, dynamics, conformational and protonation changes, and effects of amino acid mutations (our lab
won CAGI-6 MAPK-3 challenge in 2022, to predict folding free energy changes caused by mutations). We
carry such investigations in collaborations with experimental labs. Recently we became very interested in
human genetic disorders caused by missense mutations and the molecular mechanisms which cause
pathogenicity. Our efforts were recognized by the community, and we were given the privilege to establish
and chair the first Gordon Research Conference (2014) on “Human SNPs and disease”, which is now
permanent event in GRC schedule.
With this proposal we are seeking support to continue maintain and develop DelPhi (popular method for
modeling electrostatics which currently has 8,000+ registered users), along with other methods and software
for predicting the effect of missense mutations on folding and binding free energy changes of the
corresponding macromolecules and their assemblages. Regarding DelPhi, we will focus on further
development of Gaussian-based approach of treating biological macromolecules as inhomogeneous objects,
which was shown to result in ensemble averaged folding and binding free energies. Regarding methods for
modeling effects of missense mutations on folding and binding free energies, maintenance and further
development will be done by combined first-principle and machine learning (ML) approaches. First-principle
approaches have the advantage to be applicable to any case, but are not as accurate as the ML. The ML
approaches are more accurate than the first-principle methods, however, they fail on cases not seen in the
training database. We anticipate that combining them into a consensus algorithm for predicting the change of
folding and binding free energy change will have the advantage to amplify their strengths while reducing their
deficiencies. First-principle methods further development will include the Gaussian-based entropy estimation
and novel method for calculation of electrostatic energy of inhomogeneous macromolecules; while for ML
methods it will include new features as the Gaussian-based density and entropy estimation. We will use the
above-mentioned developments along with third party methods to predict the dominant molecular effect of
missense mutations associated with diseases, in collaboration with experimental labs.