Akhil Reddy Peeketi
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Deep Learning-Guided Screening of Thermostable Enzyme Variants for Industrial Biocatalysis
Metadata
- Authors: J. Chen, R. Nakamura, S. Patel, K. Holmberg, M. Rivera
- Year: 2025
- Journal: ACS Catalysis
- DOI: 10.1021/acscatal.2025.XXXXX
- Author position: First author
- Status: Published (online Jan 2025)
- Citations: 12 (as of Mar 2026)
Methods & Tools
- Protein structure: AlphaFold2 for initial structure prediction, Rosetta for refinement
- ML framework: Fine-tuned protein language model (ESM-2, 650M parameters)
- Architecture: transformer encoder with task-specific regression head
- Training data: ~45,000 experimentally measured melting temperatures from ProTherm/FireProtDB
- Training/validation/test split: 70/15/15
- MD engine: GROMACS 2023 with CHARMM36m force field
- Enhanced sampling: Replica exchange MD (T-REMD) for conformational landscape mapping
- Docking: AutoDock Vina for substrate binding pose prediction
- Analysis: Python (BioPython, MDAnalysis, ProDy), PyMOL for visualization
- Plotting: matplotlib, seaborn for fitness landscapes and stability distributions
- Hardware: 320 GPU-hours on university HPC (NVIDIA A100)
- Workflow: Snakemake pipeline for automated screen-simulate-validate cycles
- Version control: Git, DVC for dataset versioning
Key Results (with numbers)
- Fine-tuned ESM-2 model achieving Spearman correlation of 0.82 on melting temperature prediction across 12 enzyme families
- Validation on held-out test set: MAE = 2.3 degrees C, R-squared = 0.79
- Screened 8,500 single- and double-mutant variants in silico in 48 hours (vs. estimated 14 months experimentally)
- Identified 7 thermostable variants of lipase B with predicted melting temperature 15+ degrees C above wild type
- Experimental collaborators confirmed stability improvement for 5 of 7 candidates (differential scanning calorimetry)
- 200-ns replica exchange MD simulations revealed stabilizing salt bridge networks absent in wild type
- Discovered sequence-dependent unfolding pathway divergence above 340 K across the variant library
- Achieved 3,000x throughput improvement over experimental screening for equivalent hit rate
- Transfer learning from ESM-2 reduced required training data by 60% compared to training from scratch
- Total compute: 320 GPU-hours (training) + 1,200 CPU-hours (MD validation) vs. estimated 18 months wet-lab
Collaboration & Scope
- PI / Senior author: K. Holmberg (Lakewood University, computational biology group lead)
- J. Chen's role: Designed ML pipeline, fine-tuned protein language model, ran all MD simulations, wrote manuscript draft
- R. Nakamura: Curated training data from ProTherm/FireProtDB databases
- S. Patel: Experimental validation of top-7 candidates (DSC and activity assays)
- M. Rivera: Snakemake workflow design (co-developed with J. Chen)
- Scope: Single-lab project with experimental validation collaboration
Provenance
- Publication status: Published, peer-reviewed
- Peer review notes: 3 reviewers, 1 revision cycle, accepted after minor revisions
- Claiming rules:
- FULL ownership: ML pipeline design, model fine-tuning, MD simulations, manuscript writing
- SHARED ownership: Snakemake workflow (co-developed with M. Rivera)
- NO ownership: Training data curation (R. Nakamura), experimental validation (S. Patel)
- Safe verbs for bullets: Developed, Designed, Built, Fine-tuned (for ML work); Co-developed (for workflow)
- Unsafe claims: Cannot claim experimental validation; cannot claim sole credit for workflow automation
- Data availability: Trained model weights deposited on Hugging Face (open access)
- Code availability: Screening pipeline on GitHub (public repo, MIT license)
Resume Bullet Seeds
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[STAR: Protein language model for stability prediction] Situation: Enzyme thermostability screening bottlenecked by experimental throughput. Task: Build ML model for rapid stability prediction across enzyme families. Action: Fine-tuned ESM-2 protein language model on 45K experimental melting temperatures. Result: 0.82 Spearman correlation, screened 8,500 variants in 48 hrs, 5/7 top hits confirmed.
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[STAR: Thermostable enzyme discovery] Situation: Industrial biocatalysis requires enzymes stable above 70 degrees C. Task: Identify lipase B variants with substantially improved thermostability. Action: Combined ML-accelerated screening with 200-ns replica exchange MD validation. Result: Identified 7 variants with 15+ degrees C stability gain, 5 experimentally confirmed.
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[STAR: Transfer learning pipeline] Situation: Limited labeled data for enzyme stability prediction. Task: Reduce training data requirements while maintaining accuracy. Action: Co-developed transfer learning pipeline from ESM-2 pretrained representations. Result: 60% reduction in required training data while maintaining sub-3 degrees C MAE.
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[STAR: Conformational dynamics] Situation: Static structure predictions cannot capture unfolding pathways. Task: Reveal stabilizing interactions in engineered enzyme variants. Action: Ran 200-ns T-REMD simulations of wild-type and 7 top variants at 300--400 K. Result: Discovered stabilizing salt bridge networks and sequence-dependent unfolding divergence at 340 K.