SYSTEM AND METHOD FOR UNIFIED ENZYME ENGINEERING FRAMEWORK

1 . A computer-implemented method to prepare a framework for enzyme optimization, the method comprising:

providing a starting enzyme structure comprising an enzyme scaffold library;

performing a similarity search against one or more known enzyme databases to characterize each enzyme scaffold with respect to one or more features selected from the group consisting of enzyme domains, active-site pockets, coordination spheres, immunogenicity profiles, stability, and kinetic parameters;

executing a plurality of optimization modules to obtain engineered enzyme variants;

evaluating the engineered enzyme variants generated by the optimization modules according to predefined goals to obtain a multi-property optimization (MPO) score;

generating a feedback based on the MPO score to iteratively refine the optimization modules and improve enzyme variant properties; and

producing optimized enzyme sequences in accordance with the predefined optimization goals.

2 . The computer-implemented method of claim 1 , wherein the plurality of optimization modules comprising:

a. cofactor binding optimization;

b. substrate binding optimization;

c. metal ion coordination optimization;

d. hinge interaction optimization;

e. surface and electrostatics optimization; and

f. allosteric network optimization.

3 . The computer-implemented method of claim 2 , wherein the cofactor-binding optimization module is configured to improve cofactor affinity, enable cofactor switching, or enhance catalytic turnover by optimizing cofactor positioning and binding dynamics, the method comprising:

identifying amino acid residues interacting with one or more cofactors selected from the group consisting of NAD 30 , NADP + , FAD, FMN, heme, or metal-sulfur clusters;

mutating one or more of said residues to alter steric or electrostatic interactions within the cofactor-binding pocket, thereby modulating binding strength, redox potential, or orientation of the cofactor relative to the catalytic center; and

introducing amino acid substitutions or charge modifications that enable switching between cofactors having distinct charge or size characteristics, including switching between NAD + and NADP + utilization.

4 . The computer-implemented method of claim 3 , further comprising engineering distal or

allosteric residues to enhance cofactor binding affinity or influence catalytic turnover rate (Kcat) through conformational coupling

5 . The computer-implemented method of claim 2 , wherein the substrate-binding optimization module is configured to improve substrate specificity, selectivity, or catalytic efficiency by altering substrate recognition and positioning, the method comprising:

identifying residues lining a substrate-binding pocket of the enzyme and characterizing their spatial orientation, hydrophobicity, and electrostatic properties;

mutating one or more residues within the binding pocket to adjust steric constraints, thereby enabling accommodation of bulkier substrates or exclusion of off-target molecules;

optimizing the three-dimensional geometry and electrostatic potential of the pocket to achieve enhanced shape complementarity and charge compatibility with a target substrate; and

employing saturation mutagenesis, molecular docking, or computational screening to identify and select pocket variants exhibiting improved substrate binding affinity or catalytic turnover relative to the parent enzyme.

6 . The computer-implemented method of claim 2 , wherein the metal-ion coordination optimization module is configured to modulate catalytic function by adjusting the metal coordination environment, the method comprising:

identifying a metal coordination site within the enzyme scaffold and characterizing its geometry, electronic structure, and coordination number;

mutating one or more second-shell or distal residues surrounding the metal-binding site to modify the geometry or electronic environment of the coordination sphere, thereby tuning the Lewis acidity, substrate orientation, redox potential, or reaction specificity; and

maintaining the primary coordinating residues responsible for metal chelation while modifying the electrostatic microenvironment or solvent-access channels to influence metal-ligand interactions.

7 . The computer-implemented method of claim 6 , further comprising introducing additional coordinating residues or substituting existing residues to enable binding of alternative metal cofactors, thereby allowing replacement of a native metal ion with a different catalytic metal species selected from the group consisting of Fe, Mn, Co, Zn, Cu, and Ni.

8 . The computer-implemented method of claim 2 , wherein the hinge interaction optimization module is configured to enhance catalytic turnover (Kcat) by optimizing conformational dynamics involved in substrate binding, catalysis, or product release, the method comprising:

identifying flexible loop or hinge regions contributing to active-site occlusion, substrate channeling, or product egress through molecular dynamics simulation or normal mode analysis;

mutating amino acid residues within said loop or hinge regions to modulate conformational flexibility, domain motion, or closure kinetics associated with catalytic turnover; and

adjusting hinge angles or inter-domain linkers to optimize the range or frequency of catalytic conformational transitions.

9 . The computer-implemented method of claim 8 , further comprising introducing stabilizing or destabilizing substitutions that fine-tune dynamic equilibrium between open and closed enzyme states to reduce rate-limiting conformational barriers.

10 . The computer-implemented method of claim 2 , wherein the surface and electrostatics optimization module is configured to enhance enzyme stability, solubility, or modulate long-range electrostatic effects on the active site, the method comprising:

identifying surface-exposed residues contributing to hydrophobic patches, charge imbalance, or electrostatic interactions affecting catalytic performance;

introducing one or more charged or polar residues to form stabilizing salt bridges or hydrogen-bond networks that improve overall protein solubility or thermostability; removing surface-exposed hydrophobic residues to reduce aggregation propensity and improve folding efficiency; and

modifying surface charge distribution to adjust the pKa values of active-site residues through long-range electrostatic coupling.

11 . The computer-implemented method of claim 10 , further comprising introducing mutations that increase surface entropy to enhance thermal stability without perturbing the enzyme's active conformation

12 . The computer-implemented method of claim 2 , wherein the allosteric network optimization module is configured to modulate enzyme activity or substrate specificity through reprogramming of remote control points, the method comprising:

identifying allosteric nodes or communication pathways within the enzyme using one or more analyses selected from evolutionary coupling analysis, nuclear magnetic resonance (NMR) spectroscopy, molecular dynamics simulation, or correlated motion mapping;

determining residue-residue interaction networks that transmit conformational or energetic signals between distal sites and the catalytic center; and

introducing amino acid substitutions, deletions, or insertions at allosteric nodes to rewire communication pathways and enhance cooperative or inhibitory coupling between functional domains.

13 . The computer-implemented method of claim 12 , further comprising attenuating inter-domain or intra-domain connectivity to achieve desired modulation of catalytic efficiency, substrate selectivity, or regulatory response.

14 . A computer-implemented system for preparing a framework for enzyme optimization, the system comprising:

a data input module configured to provide a starting enzyme structure comprising an enzyme scaffold library;

a similarity-search module configured to perform a similarity search against one or more known enzyme databases to characterize each enzyme scaffold with respect to one or more features selected from the group consisting of enzyme domains, active-site pockets, coordination spheres, immunogenicity profiles, stability, and kinetic parameters;

an optimization module suite comprising a plurality of optimization modules configured to generate engineered enzyme variants based on the characterized enzyme scaffolds;

an evaluation module configured to evaluate the engineered enzyme variants generated by the optimization modules according to predefined optimization goals and to compute a multi-property optimization (MPO) score;

a feedback-generation module configured to generate a feedback signal based on the MPO score and to iteratively refine parameters of the optimization modules to improve enzyme variant properties; and

an output module configured to produce optimized enzyme sequences in accordance with the predefined optimization goals.

15 . The computer-implemented system of claim 14 , wherein the optimization module suite comprising a plurality of optimization further comprises:

a cofactor binding optimization module configured to analyze and modify cofactor interaction regions of the enzyme scaffold;

a substrate binding optimization module configured to evaluate and optimize substrate-binding residues, pockets, and access tunnels;

a metal-ion coordination optimization module configured to assess and adjust parameters of the metal coordination environment to modulate catalytic function;

a hinge interaction optimization module configured to characterize and optimize hinge-region interactions to influence conformational transitions;

a surface and electrostatics optimization module configured to evaluate and refine surface charge distribution, solvent exposure, and electrostatic interactions; and

an allosteric network optimization module configured to identify and optimize long-range residue interaction networks that influence allosteric regulation.

16 . The computer-implemented system of claim 14 , further comprising a storage database configured to store the enzyme scaffold library, engineered variants, MPO evaluation results, and produced optimized enzyme sequences.

17 . A non-transitory computer-readable medium storing instructions that, when executed by a computing device, cause the computing device to perform a method for preparing a framework for enzyme optimization, the method comprising:

providing a starting enzyme structure comprising an enzyme scaffold library;

performing a similarity search against one or more known enzyme databases to characterize each enzyme scaffold with respect to one or more features selected from the group consisting of enzyme domains, active-site pockets, coordination spheres, immunogenicity profiles, stability, and kinetic parameters;

executing a plurality of optimization modules to generate engineered enzyme variants;

evaluating the engineered enzyme variants generated by the optimization modules according to predefined optimization goals to compute a multi-property optimization (MPO) score;

generating a feedback signal based on the MPO score to iteratively refine the optimization modules and improve enzyme variant properties; and

producing optimized enzyme sequences in accordance with the predefined optimization goals.

18 . The non-transitory computer-readable medium of claim 17 , wherein the instructions further cause the computing device to perform sequence-or structure-based similarity searches using alignment algorithms.

19 . The non-transitory computer-readable medium of claim 17 , wherein the plurality of optimization modules comprises at least one of: cofactor binding optimization, substrate binding optimization, metal-ion coordination optimization, structural stability optimization, kinetic optimization, and immunogenicity reduction.

20 . The non-transitory computer-readable medium of claim 17 , wherein evaluating the engineered enzyme variants comprises computing the MPO score as a weighted combination of catalytic, structural, and immunological performance metrics.