Create README.md

README.md

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+---
+# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
+{}
+---
+
+
+# Dataset Description
+The benchmark consists of five proteins tasks (4 regression and one amino-acid level classification) frequent in the literature
+which have the associated true CDS seqeunces for each protein. The motivation for this benchmark
+is to compile a set of protein tasks on which genomic models can be evaluated with the highest reliability.
+
+# Tasks Overview
+Each of the true CDS protein tasks can be loaded by passing the corresponding `name` into huggingface `load_dataset` function.
+
+|        Task         |        `name`        |      Sample Output          | # Train Seqs | # Validation Seqs| # Test Seqs |
+|        ---         |         ---        |      ---          | --- | --- |-------------|
+| avGFP Fluorescence Prediction | `fluorescence`|  {sequence, labels}  |     21464        |  5366  | 27217       |
+| Secondary Structure Prediction (SSP) |   `ssp`    |  {sequence, labels}  |   7780  |   NA     | 334         |
+| Melting Point Prediction (MPP) |   `mpp`    |  {sequence, labels} |  9432     |   1064 | 1648        |
+| Stability Prediction |   `stability`    |  {sequence, labels} |    53700 |   2512   | 12851       |
+| Beta-Lactamase Activity (Complete Split) |   `beta_lactamase_complete`    |  {sequence, labels} |   11252    | 2814 | 1080        |
+| Beta-Lactamase Activity (Unique Split) |   `beta_lactamase_unique`    |  {sequence, labels} | 3417 |    865   | 1080        |
+
+### Splits
+Each task has one validation, train and test set, except for SSP. SSP has one training set, and 3 independent test sets. 
+The validation set can simply be randomly split from the training set.
+
+# Loading a Dataset Example
+```python
+from datasets import load_dataset
+
+task_name = "ssp"
+
+dataset = load_dataset(
+    "InstaDeepAI/true-cds-protein-tasks",
+    name=name,
+)
+```
+
+# Dataset Tasks
+## Secondary Structure Prediction (SSP)
+The task is a multi-label classification task where each input amino-acid is associated with one of 8 labels, denoting which secondary structure that residue is a part of. All secondary structures were empirically derived using crystallography or NMR.
+The structural data for the training and validation sets were collected by [Klausen *et al*](https://pubmed.ncbi.nlm.nih.gov/30785653/). Crystal structures were retrieved from Protein Data Bank and filtered with a 25\% sequence similarity threshold, a resolution at least as fine as 2.5 angstrom and a length of at least 20 amino acids. Following the work of Klausen we used splits filtered at 25% sequence identity to ensure generalization, and evaluated on 3 independent test sets: CASP12, CB513, TS115.
+
+---
+## Melting Point Prediction
+Melting point prediction (MPP) is a sequence-level regression task that evaluates a model’s ability to predict a measure of melting temperature. 
+
+The data orginates from the [thermostability atlas](https://www.nature.com/articles/s41592-020-0801-4), which was originally measured and compiled using a mass spectrometry-based proteomic approach. We follow the same “mixed” splits described in [FLIP](https://www.biorxiv.org/content/10.1101/2021.11.09.467890v1) which seek to avoid over-emphasis of large clusters. Sequences are clustered at 20\% identity with 80% of clusters assigned to the train dataset and 20% of clusters assigned to the test dataset.
+
+---
+
+## Beta-Lactamase Activity Prediction
+Beta-Lactamase is a regression task exploring the fitness landscape of all single codon substitutions in the TEM-1 gene. Labels indicate the ability of mutant genes to confer ampicillin resistance. 
+The data for this task is from [Firnberg *et al*](https://pubmed.ncbi.nlm.nih.gov/24567513/) which systematically examined fitness landscape of all single codon mutations in the TEM-1 Beta-lacatamase gene synthesized in native host E. coli. The TEM-1 gene is known to confer antibiotic resistance, and fitness is taken to be a function of this resistance. In particular, gene fitness was measured by splitting the library of mutants onto thirteen sub-libraries exposed to increasing levels of ampicillin concentration. Fitness was calculated as a weighted average of allele counts over plates normalized by WT fitness.
+
+Since beta-lactamase task consists of all single codon mutations, the dataset contains many degenerate coding sequences. In [PEER](https://arxiv.org/abs/2206.02096) labels were averaged over degenerate coding sequences in the original dataset, however this process removes much data and does not allow us to study gLMs on degenerate sequences.
+Consequently, we propose two training datasets, sharing a single test set. 
+
+### Complete Dataset
+The __Complete__ set contains all CDS samples except those that are degenerate with respect to any CDS in the test set. Fitness values are the raw CDS fitness values.
+
+### Unique Dataset
+The __Unique__ set contains a random, maximal, subset of the non-degenerate coding sequences.
+Comparing the Unique to the Complete allows the study of impact of degenerate CDS on gLM performance. 
+Notably, we use the raw CDS fitness values of the CDS, rather than those averaged over degenerate sequences.
+
+---
+
+## Fluorescence Prediction
+This task evaluates a model’s ability to predict log-fluorescence of higher-order mutant green fluorescent protein (avGFP) sequences. 
+
+Original data is from an [Sarkisyan _et al_](https://www.nature.com/articles/nature17995), an experimental study of the avGFP fitness landscape. The library was generated via random mutagensis of the wildtype sequence and synthesized in E. coli. Inspired from the [TAPE](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7774645/) and [PEER](https://arxiv.org/abs/2206.02096) benchmarks, we restrict the training set to amino-acid sequences with three or fewer mutations from parent GFP sequences, while the test set is all sequences with four or more mutations. 
+
+Like the Beta-lactamase task, random mutagenesis of the avGFP gene led to CDS which were degenerate. However, since this process less systematic, this was true of a much smaller fraction of the sequences. In the training set and validation sets there were 54,025 uniquely translating CDS of the 58,417 total sequences. Since most sequences were non-degenerate we selected a random maximal subset and did not study the degenerate sequences. 
+Notably, since the test set was higher order mutants, there was only one amino acid sequence (SS26C:SN168H:SD188V:SS200G) of the 27,217 which did not have a unique coding sequence. We removed randomly one of the two corresponding CDS. Notably, this means the test set is near identical to that used in  [TAPE](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7774645/) and [PEER](https://arxiv.org/abs/2206.02096) benchmarks.
+
+---
+
+## Stability Prediction
+This regression task evaluates how well models predict stability around a small region of high-fitness sequences. 
+The train and validation originate from a multi-round experiment and consist of a broad selection of de novo computationally designed proteins composing a small number of topologies. 
+The test set consists of the neighborhoods of single codon mutations around a few of the most stable candidates. 
+
+The data for this task originates from [Rocklin _et al_](https://www.science.org/doi/10.1126/science.aan0693)  in which stability is measured as a function of the resistance to increasing levels of protease. 
+In particular, the designed libraries were synthesized in yeast and exposed to different concentrations of protease. 
+At each level of protease the fraction of proteins remaining folded was measured, and these values were used to infer the _EC<sub>50</sub>_: the value at which half of cells express proteins that pass a defined stability threshold. 
+The stability of a protein is then defined as the difference between the _EC<sub>50</sub>_ value of the protein and that of the predicted _EC<sub>50</sub>_ in the unfolded state, calculated in a _log<sub>10</sub>_ scale.