Somasays: High-Throughput ESM3 Fine-Tuning & Inference Optimization Engine

Somasays is an end-to-end computational biology framework for de novo plant-like peptide design, 3D structural folding, and inference-optimization tooling using the EvolutionaryScale ESM3 (1.4B Parameter) Multimodal Protein Foundation Model.

This repository provides a complete pipeline from raw sequence preprocessing to distributed multi-GPU training, real-time performance profiling, structural FlashAttention optimizations, and downstream biophysical evaluation.

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Core Platform Architecture

graph TD
    subgraph Data Pipeline
        A[UniProt Metadata Extraction] -->|fetch_uniprot_data.py| B[Sequence Preprocessing]
        B -->|preprocess_sequences.py| C[AlphaFold 3D Cache]
    end
    
    subgraph Distributed Training
        C -->|esm3_lora_finetune.py| D[Distributed Data Parallel DDP]
        D -->|NCCL, Static Graph, BF16 AMP| E[Fine-Tuned Adapter Weights]
    end
    
    subgraph Optimization & Generation
        E -->|generate_candidates.py| F[Optimized ESM3 Generator]
        F -->|ProteinMPNN sequence rescue| G[Candidate Sequences]
        G -->|evolutionary_optimizer.py| H[In Silico Directed Evolution]
        H -->|Simulated Annealing + pI / GRAVY / MHC Filters| I[Evolved Cysteine-Free Candidates]
    end
    
    subgraph Downstream Validation & Serving
        I -->|evaluate_cysteine_free_complexes.py| K[Joint WLSS Evaluation Pipeline]
        K -->|Calculates WLSS score| L[Wet-Lab Success Leaderboard]
        I -->|api_service/server.py| J[FastAPI Model Serving API]
    end

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Key Technical Features

1. High-Throughput Distributed Fine-Tuning:

   • Multi-GPU distributed training using PyTorch Distributed Data Parallel (DDP) with NCCL communication backends.
   • Leverages transformer gradient checkpointing and compiles a static computation graph topology (_set_static_graph()) to eliminate graph re-entrancy overhead.
   • Integrates pre-cached data-loading entirely in host memory (100GB Host RAM pre-caching) to eliminate disk I/O bottlenecks.

2. Inference Optimization Tooling:

   • System-level Scaled Dot Product Attention (SDPA) backend configuration forcing FlashAttention-2 execution, reducing matrix complexity from quadratic $O(N^2)$ to linear $O(N)$.
   • Automatic Mixed Precision (AMP) utilizing hardware bfloat16 Tensor Cores for memory-efficient and accelerated execution.
   • Low-precision casting configuration interfaces (bfloat16/float16).

3. High-Resolution Performance Profiling:

   • Low-overhead GPU profiler tracking token-by-token latency, VRAM footprint allocation, and residue generation throughput curves.
   • Automated benchmark suites sweeping protein sequence contexts up to 2,048 residues.

4. In Silico Directed Evolution Engine:

   • Simulated annealing sequence search loop to optimize monomer folding stability ($\Delta G$) using a 3-model SaProtΔG ensemble.
   • Implements strict biophysical constraint checks in the active search loop:
     • pI Filter: Automatically rejects sequences with an isoelectric point inside the physiological pH precipitation zone ($6.8 \le pI \le 8.0$).
     • Solubility Filter: GRAVY hydropathy index cannot exceed a baseline limit (0.45).
     • MHC-II Immunogenicity Filter: Ensures no new HLA-DRB1 binding core epitopes are introduced.
     • Thiol-free Constraint: Enforces 100% cysteine-free designs to prevent uncontrolled disulfide aggregation in wet-lab assays.

5. Joint Wet-Lab Success Scoring (WLSS):

   • Consolidates multi-parameter metrics into a single Wet-Lab Success Score (WLSS): 50% target binding kinetics, 30% folding energy, and 20% manufacturability.
   • Scans for post-translational modification (PTM) risk hotspots (glycosylation, deamidation, acid cleavage, methionine oxidation), hydrophobic patches, and estimates cyclization feasibility.

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Repository Directory Layout

Somasays/
├── api_service/                 # Atlassian-inspired Model Serving Edge Service
│   ├── server.py                    # FastAPI web server, routing & polling schema
│   └── tasks.db                     # Decoupled task queue state tracking
├── data_pipeline/               # Data ingestion & caching
│   ├── fetch_uniprot_data.py        # Extracts raw metadata and sequence strings
│   ├── preprocess_sequences.py      # Standardizes sequences for tokenization
│   └── fetch_alphafold_structures.py # Pulls PDB coordinates from AlphaFold DB
├── model_training/              # Heavy GPU training scripts
│   ├── training_config.yaml         # Core hyperparameters configuration
│   ├── esm3_lora_finetune.py        # PEFT Masked Language Modeling
│   └── esm3_multimodal_trainer.py   # Distributed Multimodal DDP training
├── generation_engine/           # Synthesis and performance optimization
│   ├── generate_candidates.py       # MLM peptide sampling
│   ├── esm3_multimodal_generator.py # Dual-track sequence & coordinate generator
│   ├── optimized_inference.py       # FlashAttention SDPA / AMP optimization wrapper
│   └── profile_inference.py         # Latency and peak VRAM profiling engine
├── evaluation_and_rescue/       # Downstream verification
│   ├── evolutionary_optimizer.py    # Simulated annealing directed evolution
│   ├── evaluate_cysteine_free_complexes.py # Joint WLSS validation pipeline
│   ├── proteinmpnn_rescue.py        # Backbone sequence co-design
│   ├── mpnn_stability_rescue.py     # Stability optimization scripts
│   ├── candidate_rescuer.py         # Rescues wildtypes using ProteinMPNN
│   ├── cysteine_free_rescuer.py     # Mutates cysteines and evaluates stability
│   ├── manufacturability_profiler.py # Biophysical risk assessment profiler
│   ├── binding_interface_analyzer.py # Parses complexes for contact maps, ipTM & pLDDT
│   ├── codon_optimizer_carbon.py    # Codon optimization (E. coli & Human expression)
│   ├── structural_qc.py             # MHC-II epitope prediction using absolute stability methods
│   └── umap_embedding_analysis.py   # Synthesized space embedding projection
├── analysis/                    # Benchmarking suite & visualizers
│   ├── benchmark_suite.py           # Auto-sweeps lengths, batch sizes & configs
│   ├── plot_convergence_curves.py   # Visualizes training loss curves
│   └── outputs/                     # Latency, throughput, and memory charts
├── README.md                    # Platform overview & execution guide
└── optimizations_case_study.md  # Professional ESM3 performance report

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Execution & Quick Start Guide

1. Environment Activation & Dependencies

Ensure your environment contains CUDA 12+ and PyTorch 2.0+ with matching drivers:

# Activate virtual environment
source venv_somasays/bin/activate

# Install core packages
pip install torch torchvision torchaudio esm biopython matplotlib pandas --quiet

2. High-Resolution Model Profiling

Profile the latency and VRAM footprint of sequence autoregression and coordinate folding under baseline configurations:

python generation_engine/profile_inference.py --prompt "MKA___________________VLA" --steps 8

3. Running the Optimization Benchmark Sweep

Sweep sequence lengths up to 2,048 residues to generate comparative line charts mapping latency, throughput, and VRAM efficiency:

python analysis/benchmark_suite.py --outdir analysis/outputs

4. Deploying the Model Serving API Service (Edge Architecture)

Spin up the systems-optimized model serving API. This utilizes a decoupled, asynchronous queue architecture to isolate heavy GPU inference tasks:

# Install server dependencies
pip install fastapi uvicorn pydantic --quiet

# Launch the FastAPI web engine
uvicorn api_service.server:app --host 0.0.0.0 --port 8000 --reload

Interaction Flow:

• Submit a folding/generation task:

  curl -X POST "http://localhost:8000/v1/tasks" \
       -H "Content-Type: application/json" \
       -d "{\"prompt_sequence\": \"MKA___________________VLA\", \"num_steps\": 8, \"temperature\": 0.7}"

  Response: {"task_id": "a90f117c-...", "status": "PENDING", ...}

• Poll for completion and coordinate outputs:

  curl "http://localhost:8000/v1/tasks/<task_id>"

5. Downstream Validation & Analysis

A. Run In Silico Directed Evolution

Execute the simulated annealing directed evolution search starting from a baseline sequence to maximize folding stability under biophysical constraints (no cysteines, safe pI, solubility, non-immunogenic):

python evaluation_and_rescue/evolutionary_optimizer.py --steps 50 --output outputs/evolution_history.csv

B. Run Joint Validation & WLSS Scoring

Compile structural and biophysical characteristics for all generated candidates into a ranked leaderboard scoring Wet-Lab Success Score (WLSS):

python evaluation_and_rescue/evaluate_cysteine_free_complexes.py \
    --in_dir outputs/combined_runs \
    --out_dir outputs

This generates outputs/joint_evaluation_report.md (leaderboard) and outputs/joint_evaluation_report.csv.

C. Run Biophysical Manufacturability Profiler

Evaluate designed candidates for glycosylation traps, deamidation, acid cleavage susceptibility, and GRAVY hydropathy:

python evaluation_and_rescue/manufacturability_profiler.py \
    --in_dir outputs/mpnn_best_sequences \
    --out_dir outputs

D. Run Binding Interface & Contact Analyzer

Parse PDB structural complexes and confidence JSONs to compute contact maps, hydrogen bonds, salt bridges, pLDDT scores, and ipTM rankings:

python evaluation_and_rescue/binding_interface_analyzer.py \
    --in_dir outputs/af3_results \
    --out_dir outputs \
    --binder_chain A

E. Run Carbon-Conscious Codon Optimizer

Convert the designed peptide amino acid sequence into expression-optimized DNA sequences for target host expression systems (E. coli or Human) to maximize protein yield:

python evaluation_and_rescue/codon_optimizer_carbon.py \
    --in_seq MKARRLAAGLLAAAEEAKKAAPVLA \
    --host ecoli \
    --out_file outputs/top_lead_codon_optimized.csv

F. Regenerate Academic Visualizations

Run the Seaborn-based plotting suite to compile and refresh the design space, optimization trajectory, and biophysical heatmap figures:

python evaluation_and_rescue/plot_scientific_insights.py

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Projected Performance Projections

Our structural modeling sweeps indicate that combining bfloat16 AMP with FlashAttention (SDPA) significantly scales execution limits on NVIDIA L4/A100 hardware profiles:

• 3.4x Projected Speedup: Projected execution latency during structural folding loops scales linearly rather than quadratically.
• 58% Memory Savings: Projected peak VRAM footprint at 1,024 residues falls from 14.6 GB to 5.9 GB.
• Increased Sequence Context: Designed to mitigate memory-related Out-of-Memory (OOM) failures for sequences up to 2,048 residues.

For a complete breakdown of optimization methodologies, theoretical scaling profiles, and benchmark simulation scripts, read our full ESM3 Optimization and Performance Case Study.

Scientific Validation & Design Space Charts

Design Space Landscape	Directed Evolution Trajectory	Biophysical Heatmap Leaderboard

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What is Left to be Done (Roadmap)

To elevate Somasays into a fully automated, web-scale biological factory, the following roadmap features are planned for future development:

1. Dynamic Tensor Parallelism (TP):

   • Integrate DeepSpeed or Megatron-LM to shard the 1.4B parameters and attention matrices across multiple GPU nodes. This will enable structural folding of large multi-domain complexes exceeding 4,000 residues.

2. Hopper Native FP8 & FlashAttention-3:

   • Migrate SDPA backends to native FlashAttention-3 kernels on Hopper GPU architectures (H100/H200). This will utilize low-precision FP8 Tensor Cores to speed up sequential autoregressive decoding.

3. 8-Bit and 4-Bit Weight Quantization:

   • Integrate 8-bit and 4-bit NormalFloat (NF4) loading configurations using bitsandbytes. This will compress the active model footprint to enable foundation model inference on consumer-grade GPUs.

4. Asynchronous AlphaFold 3 API Loop:

   • Build a background daemon to automatically submit generated protein coordinates to the AlphaFold 3 server, parse confidence metrics (pLDDT, iPAE), and store results in a PostgreSQL database for real-time downstream validation.

5. Speculative Speculative Decoding (SSD) / Saguaro Integration:

   • Incorporate asynchronous draft-target token co-speculation. Deploy a lightweight sequence draft model (e.g., ESM-2 35M) in parallel with the target ESM3 model using non-blocking PyTorch CUDA streams and Saguaro sampling (top-logit downweighting) to eliminate sequential drafting bottlenecks and accelerate sequence generation by $2\times$ to $3\times$ without quality degradation.