Akhil Reddy Peeketi
c51b49882f
Complete AI-assisted resume/CV generation framework: - 6 Claude Code skills (setup-extract, setup-build-kb, make-resume, make-cl, edit-resume, critique) - LaTeX templates (resume, CV, cover letter) with .cls class files - 6 reference docs (shared_ops, resume_reference, cl_reference, critical_rules, session_file_template, critique_framework) - Fictional Dr. Jordan Chen examples (extraction, experience, bundle, config, session, JD) - Knowledge base scaffolding and config template - README with setup guide and workflow documentation
89 lines
5.3 KiB
Markdown
89 lines
5.3 KiB
Markdown
# Deep Learning-Guided Screening of Thermostable Enzyme Variants for Industrial Biocatalysis
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## Metadata
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- **Authors:** J. Chen, R. Nakamura, S. Patel, K. Holmberg, M. Rivera
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- **Year:** 2025
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- **Journal:** ACS Catalysis
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- **DOI:** 10.1021/acscatal.2025.XXXXX
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- **Author position:** First author
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- **Status:** Published (online Jan 2025)
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- **Citations:** 12 (as of Mar 2026)
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## Methods & Tools
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- **Protein structure:** AlphaFold2 for initial structure prediction, Rosetta for refinement
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- **ML framework:** Fine-tuned protein language model (ESM-2, 650M parameters)
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- Architecture: transformer encoder with task-specific regression head
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- Training data: ~45,000 experimentally measured melting temperatures from ProTherm/FireProtDB
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- Training/validation/test split: 70/15/15
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- **MD engine:** GROMACS 2023 with CHARMM36m force field
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- **Enhanced sampling:** Replica exchange MD (T-REMD) for conformational landscape mapping
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- **Docking:** AutoDock Vina for substrate binding pose prediction
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- **Analysis:** Python (BioPython, MDAnalysis, ProDy), PyMOL for visualization
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- **Plotting:** matplotlib, seaborn for fitness landscapes and stability distributions
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- **Hardware:** 320 GPU-hours on university HPC (NVIDIA A100)
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- **Workflow:** Snakemake pipeline for automated screen-simulate-validate cycles
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- **Version control:** Git, DVC for dataset versioning
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## Key Results (with numbers)
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- Fine-tuned ESM-2 model achieving Spearman correlation of 0.82 on melting temperature prediction across 12 enzyme families
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- Validation on held-out test set: MAE = 2.3 degrees C, R-squared = 0.79
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- Screened 8,500 single- and double-mutant variants in silico in 48 hours (vs. estimated 14 months experimentally)
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- Identified 7 thermostable variants of lipase B with predicted melting temperature 15+ degrees C above wild type
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- Experimental collaborators confirmed stability improvement for 5 of 7 candidates (differential scanning calorimetry)
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- 200-ns replica exchange MD simulations revealed stabilizing salt bridge networks absent in wild type
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- Discovered sequence-dependent unfolding pathway divergence above 340 K across the variant library
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- Achieved 3,000x throughput improvement over experimental screening for equivalent hit rate
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- Transfer learning from ESM-2 reduced required training data by 60% compared to training from scratch
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- Total compute: 320 GPU-hours (training) + 1,200 CPU-hours (MD validation) vs. estimated 18 months wet-lab
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## Collaboration & Scope
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- **PI / Senior author:** K. Holmberg (Lakewood University, computational biology group lead)
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- **J. Chen's role:** Designed ML pipeline, fine-tuned protein language model, ran all MD simulations, wrote manuscript draft
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- **R. Nakamura:** Curated training data from ProTherm/FireProtDB databases
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- **S. Patel:** Experimental validation of top-7 candidates (DSC and activity assays)
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- **M. Rivera:** Snakemake workflow design (co-developed with J. Chen)
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- **Scope:** Single-lab project with experimental validation collaboration
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## Provenance
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- **Publication status:** Published, peer-reviewed
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- **Peer review notes:** 3 reviewers, 1 revision cycle, accepted after minor revisions
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- **Claiming rules:**
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- FULL ownership: ML pipeline design, model fine-tuning, MD simulations, manuscript writing
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- SHARED ownership: Snakemake workflow (co-developed with M. Rivera)
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- NO ownership: Training data curation (R. Nakamura), experimental validation (S. Patel)
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- **Safe verbs for bullets:** Developed, Designed, Built, Fine-tuned (for ML work); Co-developed (for workflow)
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- **Unsafe claims:** Cannot claim experimental validation; cannot claim sole credit for workflow automation
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- **Data availability:** Trained model weights deposited on Hugging Face (open access)
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- **Code availability:** Screening pipeline on GitHub (public repo, MIT license)
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## Resume Bullet Seeds
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1. **[STAR: Protein language model for stability prediction]**
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Situation: Enzyme thermostability screening bottlenecked by experimental throughput.
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Task: Build ML model for rapid stability prediction across enzyme families.
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Action: Fine-tuned ESM-2 protein language model on 45K experimental melting temperatures.
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Result: 0.82 Spearman correlation, screened 8,500 variants in 48 hrs, 5/7 top hits confirmed.
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2. **[STAR: Thermostable enzyme discovery]**
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Situation: Industrial biocatalysis requires enzymes stable above 70 degrees C.
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Task: Identify lipase B variants with substantially improved thermostability.
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Action: Combined ML-accelerated screening with 200-ns replica exchange MD validation.
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Result: Identified 7 variants with 15+ degrees C stability gain, 5 experimentally confirmed.
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3. **[STAR: Transfer learning pipeline]**
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Situation: Limited labeled data for enzyme stability prediction.
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Task: Reduce training data requirements while maintaining accuracy.
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Action: Co-developed transfer learning pipeline from ESM-2 pretrained representations.
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Result: 60% reduction in required training data while maintaining sub-3 degrees C MAE.
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4. **[STAR: Conformational dynamics]**
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Situation: Static structure predictions cannot capture unfolding pathways.
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Task: Reveal stabilizing interactions in engineered enzyme variants.
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Action: Ran 200-ns T-REMD simulations of wild-type and 7 top variants at 300--400 K.
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Result: Discovered stabilizing salt bridge networks and sequence-dependent unfolding divergence at 340 K.
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