T7 RNA polymerase (T7 RNAP) catalyzes in vitro transcription (IVT) for mRNA vaccines and therapeutics, yet the molecular basis of its pH-dependent activity remains unclear. Here we employ constant pH replica exchange molecular dynamics (pH-REMD) spanning pH 4.75-9.15 (2.88 μs total sampling) to characterize pH-dependent conformational dynamics of the T7 RNAP initiation complex. Our simulations reveal that His811, adjacent to catalytic Asp812, functions as the primary active site pH sensor (pK a 5.9). Protonation of His811 at acidic pH creates an electrostatic trap that restricts conformational flexibility required for catalytic turnover, while its neutral state at optimal pH (∼8.0) permits dynamic motions essential for nucleotide incorporation. Two critical structural parameters, C-terminal Ala883 to active site distance and specificity loop to β-hairpin spacing, exhibit opposing pH-dependencies that converge at optimal pH to establish productive catalytic geometry. The Y639-RNA 3'-end distance analysis shows that optimal pH maintains the functionally open conformation required for binding by the incoming nucleotide. In contrast, acidic and basic pH conditions have shorter Y639-RNA 3'-end distances, promoting collapsed geometries that sterically occlude binding of the incoming nucleotide. Energy decomposition analysis reveals that C-terminal repositioning is driven by a pH-dependent switch in the electrostatic coupling hierarchy from protonated His545 and His790 at acidic pH to catalytic metal ions at optimal pH. Glu148 (pK a 5.3) emerges as a structural pH sensor stabilizing the specificity loop-subdomain H interaction critical for elongation complex formation. Aggregate charge analysis reveals that excessive positive charge at acidic pH (+6 at pH 4) impedes promoter escape, while reduced charge at optimal pH (+2 at pH 8) balances binding and release. These insights establish important electrostatic and dynamic features that promote effective catalysis and can guide optimization of IVT conditions for the manufacturing of mRNA therapeutics and inform engineering of pH-stable T7 RNAP variants.
