MOTIVATION: Bacterial resistance to antibiotics, particularly plasmid-encoded resistance to beta lactam drugs, poses an increasing threat to human health. Point mutations to beta-lactamase enzymes can greatly alter the level of resistance conferred, but predicting the effects of such mutations has been challenging due to the large combinatorial space involved and the subtle relationships of distant residues to catalytic function. Therefore we desire an information-theoretic metric to sensitively and robustly detect both local and distant residues that affect substrate conformation and catalytic activity. RESULTS: Here, we report the use of positional mutual information in multiple microsecond-length molecular dynamics (MD) simulations to predict residues linked to catalytic activity of the CTX-M9 beta lactamase. We find that motions of the bound drug are relatively isolated from motions of the protein as a whole, which we interpret in the context of prior theories of catalysis. In order to robustly identify residues that are weakly coupled to drug motions but nonetheless affect catalysis, we utilize an excess mutual information metric. We predict 31 such residues for the cephalosporin antibiotic cefotaxime. Nine of these have previously been tested experimentally, and all decrease both enzyme rate constants and empirical drug resistance. We prospectively validate our method by testing eight high-scoring mutations and eight low-scoring controls in bacteria. Six of eight predicted mutations decrease cefotaxime resistance greater than 2-fold, while only one control shows such an effect. The ability to prospectively predict new variants affecting bacterial drug resistance is of great interest to clinical and epidemiological surveillance. AVAILABILITY AND IMPLEMENTATION: Excess mutual information code is available at https://github.com/kassonlab/positionalmi CONTACT: [email protected].