1. Semantic Similarity Inference:
  - This benchmark evaluates how well protein representation models can infer functional similarities between proteins. Ground truth functional similarities are derived from Gene Ontology (GO) annotations.
  - Different distance metrics (Cosine, Manhattan, Euclidean) are used to compute protein vector similarities, which are then correlated with the functional similarities.
  - The benchmark uses three different datasets:
    • Sparse: A sparse uniform dataset with broader protein coverage
    • 200: A set of well-annotated 200 proteins
    • 500: A set of well-annotated 500 proteins
  
  - Metrics (sim_ prefix):
    • sim_sparse_MF_correlation/sim_200_MF_correlation/sim_500_MF_correlation: Correlation between protein embeddings and Molecular Function (MF) similarity scores
    • sim_sparse_BP_correlation/sim_200_BP_correlation/sim_500_BP_correlation: Correlation between protein embeddings and Biological Process (BP) similarity scores
    • sim_sparse_CC_correlation/sim_200_CC_correlation/sim_500_CC_correlation: Correlation between protein embeddings and Cellular Component (CC) similarity scores
    • sim_sparse_Ave_correlation/sim_200_Ave_correlation/sim_500_Ave_correlation: Average correlation across MF, BP, and CC aspects
    • sim_sparse_*_pvalue/sim_200_*_pvalue/sim_500_*_pvalue: Statistical significance (p-value) of the respective correlations

2. Ontology-Based Protein Function Prediction (PFP):
  - This benchmark assesses the ability of representation models to predict ontology-based functional annotations (GO terms). The models are tested on how well they classify proteins based on molecular functions, biological processes, and cellular components.
  - A linear classifier is used to ensure that the models themselves are responsible for good performance, rather than the complexity of the classifier.
  - The evaluation uses 5-fold cross-validation to ensure robust assessment.
  
  - Metrics (func_ prefix):
    • func_BP_accuracy/func_CC_accuracy/func_MF_accuracy: Accuracy of predicting protein function in respective GO aspects
    • func_BP_F1/func_CC_F1/func_MF_F1: F1 score for protein function prediction
    • func_BP_precision/func_CC_precision/func_MF_precision: Precision of function prediction
    • func_BP_recall/func_CC_recall/func_MF_recall: Recall of function prediction
    • func_Ave_accuracy/func_Ave_F1/func_Ave_precision/func_Ave_recall: Average metrics across BP, CC, and MF aspects

3. Drug Target Protein Family Classification:
  - This benchmark focuses on predicting the family of drug target proteins (enzymes, receptors, ion channels, etc.). This task tests the ability of models to learn structural features critical to these classifications.
  - The benchmark uses datasets with varying sequence similarity thresholds to test generalization:
    • nc: Non-clustered/Random split
    • uc50: UniClust50 (50% sequence similarity-based split)
    • uc30: UniClust30 (30% sequence similarity-based split)
    • mm15: Maximum 15% sequence identity between train and test sets (MMseqs2-based split)
  
  - Metrics (fam_ prefix):
    • fam_nc_*_ave: Metrics for family classification on the non-clustered (nc) dataset
    • fam_uc50_*_ave: Metrics for family classification on the UniRef50 clustered (uc50) dataset
    • fam_uc30_*_ave: Metrics for family classification on the UniRef30 clustered (uc30) dataset
    • fam_mm15_*_ave: Metrics for family classification on the maximum 15% sequence identity (mm15) dataset

4. Protein–Protein Binding Affinity Estimation:
  - This benchmark evaluates models' ability to predict the change in binding affinities between proteins due to mutations. The dataset used is the **SKEMPI** dataset, which contains experimentally determined binding affinities.
  - The task measures how well models can extract critical structural features important for protein-protein interactions.
  - Evaluation is performed through 10-fold cross-validation.
  
  - Metrics (aff_ prefix):
    • aff_mse_ave: Mean Squared Error for binding affinity prediction
    • aff_mae_ave: Mean Absolute Error for binding affinity prediction
    • aff_corr_ave: Correlation between predicted and actual binding affinity values

- For benchmarks 1, 2, and 3 (similarity, function, and family), you will need to generate representation vectors for all human proteins. The amino acid sequences for canonical isoforms of human proteins can be found [here](https://drive.google.com/file/d/1wXF2lmj4ZTahMrl66QpYM2TvHmbcIL6b/view?usp=sharing).
- For benchmark 4 (affinity), representation vectors will need to be generated for the samples in the SKEMPI dataset, which can be accessed [here](https://drive.google.com/file/d/1m5jssC0RMsiFT_w-Ykh629Pw_An3PInI/view?usp=sharing).

1. Each row corresponds to the representation vector of a particular protein.
2. Rows & columns: first column's header one should be "Entry", and the rest of the column headers should contain the index number that correspond to dimensions of the vector. After the column header, the rows of the first column should contain the UniProt protein accessions of respective proteins (i.e., each row in this file corresponds to a different protein), rows of other columns should contain representation vector values for the corresponding proteins (i.e. each column in this file corresponds to a dimension of representation vectors).
3. All representation vectors in a file should have the same size (i.e., fixed sized vectors).
4. Representation vectors of the whole dataset should be saved in a comma separated (csv) text file.