The formation of an amide bond is an essential step in the synthesis of materials and drugs, and in the assembly of amino acids to form peptides. The mechanism of this reaction has been studied extensively, in particular to understand how it can be catalyzed, but a representation capable of explaining all the experimental data is still lacking. Numerical simulation should provide the necessary molecular description, but the solvent involvement poses a number of challenges. Here, we combine the efficiency and accuracy of neural network potential-based reactive molecular dynamics with the extensive and unbiased exploration of reaction pathways provided by transition path sampling. Using microsecond-scale simulations at the density functional theory level, we show that this method reveals the presence of two competing distinct mechanisms for peptide bond formation between alanine esters in aqueous solution. We describe how both reaction pathways, via a general base catalysis mechanism and via direct cleavage of the tetrahedral intermediate respectively, change with pH. This result contrasts with the conventional mechanism involving a single pathway in which only the barrier heights are affected by pH. We show that this new proposal involving two competing mechanisms is consistent with the experimental data, and we discuss the implications for peptide bond formation under prebiotic conditions and in the ribosome. Our work shows that integrating deep potential molecular dynamics with path sampling provides a powerful approach for exploring complex chemical mechanisms.