DOI: 10.5281/zenodo.13910505
title: Evaluation of Machine Learning-Assisted Directed Evolution Across Diverse Combinatorial Landscapes

description: Refer to the SSMuLA Github repository for the associated code

See our paper titled "Evaluation of Machine Learning-Assisted Directed Evolution Across Diverse Combinatorial Landscapes"

 
description: The `data/` folder is organized by protein type. Each protein directory contains:



`.fasta`: FASTA file for the parent sequence

`.pdb`: PDB file for the parent structure

`.model`: EVmutation model file

`fitness_landscape/`: Folder containing CSV files for all fitness landscapes for this protein type, each listing amino acid substitutions and their corresponding fitness values from the original sources

`scale2max/`: the folder containing processed fitness csv files returned from the `process_all` function in the `SSMuLA.fitness_process_vis` module where the maximum fitness value is normalized to 1 for each landscape






The `results/` folder is organized such that each subfolder contains results from a specific type of simulation.



`alde/`: Contains ALDE and ftALDE results



`alde_all.csv`: Summarizes results across all landscapes

`results/`: Contains subfolders for each experimental condition



Example:



2eq_60`: Splits 120 samples into two equal rounds with the first round randomly sampled

`coves_2eq_60`: Same split, but the first round is sampled using coves zero-shot scores



Each subfolder contains an `all_results.csv` summarizing the results





`de/`: Directed evolution simulation results



`all_landscape_de_summary.csv`: Summary of DE results across all landscapes

Each landscape includes three CSV files:



`{landscape}-recomb_SSM.csv`: Recombination-based results

`{landscape}-single_step_DE.csv`: Single-step DE results

`{landscape}-top96_SSM.csv`: Top-96 variant recombination results





`finetune/`: Results from ESM-2 LoRA finetuning



Subfolders:



`none/`: Random sampling

`ev/`: Sampling guided by EVmutation zero-shot scores



Each subfolder contains folders for each landscape



Each CSV is named `{landscape}-{training_sample_number}-{replicate}.csv`





`hd/`: Hamming distance–based simulations



`hd_corr/`: Contains Spearman’s rho and ROC-AUC correlation between Hamming distance and fitness using a given active variant as the parent

`hd_fit/`: Contains the average fitness of each landscape using a given active variant as the parent



`landscape/`: Landscape metadata



`attributes.csv`: Attributes for each landscape

`lib_stats.csv`: Library statistics for each landscape



`local_optima/`: Local optima analysis



`local_optima_summary.csv`: Summary across all landscapes

`scale2max/`: Per-landscape results in `{landscape}_loc_opt_escape.csv`



`mlde/`: MLDE simulation results



`all_results.csv`: Summary of all MLDE results



`pairwise_epistasis/`: Pairwise epistasis calculations



`pairwise_epistasis_summary.csv`: Summary across all landscapes

`active_start/`: Contains `{landscape}.csv` with detailed results

`processed/`: Contains both raw `{landscape}.csv` and grouped `{landscape}_grouped.csv` files



`zs_comb/`: Raw zero-shot prediction results



`all/`: Full landscape

`double/`: Double-site libraries

`single/`: Single-site libraries



`zs_sum/`: Processed zero-shot results



`zs_stat_scale2max.csv`: Summary across all landscapes

`zs_stat_parsed.csv`: Further summarized and processed results