In Silico Enzyme Candidate Selection Service

In Silico Enzyme Candidate Selection uses advanced computational methods to evaluate and prioritize potential enzyme variants or novel enzymes identified from discovery pipelines (e.g., metagenomics, directed evolution). This process leverages molecular modeling, dynamics simulations, and virtual screening to accurately predict key performance indicators like catalytic efficiency, substrate specificity, and thermal stability before expensive wet-lab synthesis and testing are initiated. By computationally filtering vast libraries down to the most promising few, we drastically reduce costs and accelerate the timeline for biocatalyst development.

CD Biosynsis offers specialized In Silico Enzyme Selection services, combining the power of physics-based simulations with machine learning scoring functions. We take your library of sequences or existing enzyme models and provide quantitative predictions of fitness for your desired reaction conditions. Our services include substrate docking, transition state modeling, and free energy calculations (e.g., FEP, MM/GBSA) to precisely rank candidates. This rational approach ensures that your experimental resources are focused only on the variants with the highest probability of success, turning large datasets into optimized enzyme leads.

Highlights

We provide a quantitative, predictive layer to enzyme discovery, ensuring your experimental resources are optimally allocated.

• Quantitative Performance Ranking: Predict the relative catalytic turnover (kcat) and binding affinity (Km) of variants, allowing direct comparison before synthesis.
• High-Throughput Virtual Screening: Quickly evaluate thousands of enzyme sequences or variant libraries against a target substrate using rapid docking protocols.
• Accuracy via Free Energy Methods: Utilize advanced FEP and MM/GBSA calculations to accurately estimate the energetics of binding and catalysis.
• Rational Mutagenesis Guidance: Identify and confirm the effect of specific mutations on active site geometry, stability, and substrate channeling.

Applications

In silico selection is crucial for optimizing enzymes across various industrial and therapeutic applications:

Biocatalysis Optimization

Identifying variants with increased turnover rates and enhanced tolerance to non-aqueous solvents or high temperatures for industrial scale-up.

Substrate Specificity Engineering

Predicting mutations that alter the active site to accommodate novel substrates or exclude unwanted side-reaction substrates (promiscuity reduction).

Lead Candidate Ranking

Prioritizing the best performing enzyme sequences generated from AI design or large-scale metagenomic screening efforts.

Disease Mechanism Study

Modeling how pathogenic mutations alter enzyme function (e.g., kcat, allostery) to understand molecular disease mechanisms.

Platform

Our In Silico platform integrates quantum mechanics, molecular mechanics, and data science for maximum predictive power.

Homology Modeling and Refinement

Building high-quality 3D models for novel enzyme sequences, followed by energy minimization and dynamics to prepare for selection.

Molecular Dynamics (MD) Simulation

Simulation of enzyme behavior in solution (with substrate/solvent) to capture flexibility and conformational changes critical for function.

High-Precision Substrate Docking

Accurate placement and orientation of the target substrate and transition state analogs within the active site of candidate enzymes.

Free Energy Perturbation (FEP) Analysis

Highly accurate calculation of relative binding free energies (delta G) for substrate/inhibitor binding to rank candidates quantitatively.

Thermal Stability Prediction

Computational tools to predict the change in unfolding temperature (delta Tm) induced by specific mutations, guiding thermostability improvements.

Workflow

Our In Silico Enzyme Candidate Selection follows a systematic computational funnel to identify the optimal enzyme variants:

• Candidate Data Intake and Structural Modeling: Receive the list of enzyme sequences (from a library or design effort). Build and refine 3D models for all target candidates.
• Initial High-Throughput Filtering: Perform fast docking and physics-based scoring of all candidates against the substrate to eliminate poor performers (Funnel Stage 1).
• Molecular Dynamics Simulation: Select the top-ranked candidates for MD simulations to assess structural stability and active site accessibility in a dynamic environment.
• Precision Free Energy Calculation: Apply high-accuracy FEP or MM/GBSA methods to the reduced candidate set to calculate quantitative binding and catalytic activation energies (Funnel Stage 2).
• Final Prioritization and Reporting: Rank the final candidates based on predicted performance metrics (e.g., predicted Km, delta G). Deliver the ranked list and detailed computational reports, guiding wet-lab synthesis.

CD Biosynsis ensures the computational results are reliable and directly translatable to your experimental pipeline, maximizing the value of your synthesis budget. Every project includes:

• Quantitative Prediction Data: Ranked lists of candidates based on predicted binding energy (e.g., kcal/mol) and stability (e.g., delta Tm).
• Optimized 3D Models: Delivery of the refined, simulated 3D coordinates for all prioritized enzyme variants, ready for further analysis.
• Detailed Mutational Analysis: Clear visual and quantitative analysis showing how specific residues in the active site influence catalysis and specificity.
• Methodological Report: Full documentation of all software, parameters, force fields, and simulation times used for total reproducibility.