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Updated: Aug 6, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Electrostatics and disease: Large-scale assessment of human missense variants causing protein charge alterations and
Nirav Modha1, Shailesh Kumar Panday2, Emil Alexov3
1Department of Physics and Astronomy, Clemson University, 118 Kinard Laboratory, Clemson, SC 29634, USA; Medical Biophysics Graduate Program, Clemson University, 118 Kinard Laboratory, Clemson, SC 29634, USA.
Abstract:
How do changes in electrical charge drive protein dysfunction and result in pathogenicity? This study investigates the relationship between charge-altering missense mutations and pathogenicity across a large-scale dataset, the Monogenic Genetic Disorder (MOGEDO) database. Using electrostatic energy calculations, we quantified the shift in interaction energy between the rest of the protein and either the wild-type or mutant residues. Our analysis shows that pathogenic variants are characterized by substantially larger electrostatic perturbations than their benign counterparts. We found that the outcome of a mutation is highly transition specific: because protein interiors tend to maintain a negative electrostatic potential, the introduction of acidic residues creates a destabilizing "energy penalty." Conversely, basic residues often result in stabilizing shifts. Structural context further shaped these effects: buried residues showed larger electrostatic perturbation values overall, and pathogenic variants were enriched at deeply buried sites, consistent with reduced solvent screening amplifying charge perturbations. Because electrostatic interactions contribute substantially to protein binding and molecular recognition, the enrichment of pathogenic variants among binding-associated proteins suggests that electrostatic perturbations may be especially relevant in disease-associated contexts. Complementary analyses showed that pathogenic variants occur at more rigid local sites and are more often located in high-confidence modeled regions and that pathogenic-only genes show stronger gene constraint. This work therefore provides an electrostatics-focused framework for interpreting how charge-altering missense variants disrupt local protein electrostatic environments and how these disruptions are linked to pathogenicity.
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