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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+md5_to_str.fasta filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,62 @@
----
-license: apache-2.0
----
+# Dataset Card for Fold Prediction Dataset for RAGProtein
+
+### Dataset Summary
+
+Fold class prediction is a scientific classification task that assigns protein sequences to one of 1,195 known folds. The primary application of this task lies in the identification of novel remote homologs among proteins of interest, such as emerging antibiotic-resistant genes and industrial enzymes. The study of protein fold holds great significance in fields like proteomics and structural biology, as it facilitates the analysis of folding patterns, leading to the discovery of remote homologies and advancements in disease research. 
+
+## Dataset Structure
+
+### Data Instances
+For each instance, there is a string representing the protein sequence and an integer label indicating which know fold a protein sequence belongs to.  See the [fold prediction dataset viewer](https://huggingface.co/datasets/Bo1015/fold_prediction/viewer) to explore more examples.
+
+```
+{'seq':'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL'
+'label':6,
+'msa':     'MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL|MEHVIDNFDNIDKCLKCGKPIKVVKLKYIKKKIENIPNSHLINFKYCSKCKRENVIENL...',
+'str_emb': [seq_len, 384]
+}
+```
+
+The average  for the `seq` and the `label` are provided below:
+
+| Feature    | Mean Count |
+| ---------- | ---------------- |
+| seq    |    168   |
+
+### Data Fields
+
+- `seq`: a string containing the protein sequence.
+- `label`: an integer label indicating which know fold a protein sequence belongs to.
+- `msa`: "|" seperated MSA sequences
+- `str_emb`: AIDO.StructureTokenizer generated structure embedding from AF2 predicted structures
+
+### Data Splits
+
+The fold prediction dataset has 3 splits: _train_, _valid_ and _test_. Below are the statistics of the dataset.
+
+| Dataset Split | Number of Instances in Split                |
+| ------------- | ------------------------------------------- |
+| Train         | 12,312          |
+|Valid | 736|
+| Test          | 3,244                     |
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+The dataset employed for this task is based on [SCOP 1.75](https://scop.mrc-lmb.cam.ac.uk/), a release from 2009. 
+
+### Processed data collection 
+
+Single sequence data are collected from this paper:
+
+```
+@misc{chen2024xtrimopglm,
+  title={xTrimoPGLM: unified 100B-scale pre-trained transformer for deciphering the language of protein},
+  author={Chen, Bo and Cheng, Xingyi and Li, Pan and Geng, Yangli-ao and Gong, Jing and Li, Shen and Bei, Zhilei and Tan, Xu and Wang, Boyan and Zeng, Xin and others},
+  year={2024},
+  eprint={2401.06199},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  note={arXiv preprint arXiv:2401.06199}
+}
+```

codebook.pt

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+oid sha256:52139fb587368235751a0464fd3be7a6beb0fff2e96a0164012f858702a9bcf8
+size 787617

data/test-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4d2b7b16e1a1233b5731c8ff563cf22a56f9d75d8234bcb00bf9c1e66b2ccf49
+size 575690

data/train-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a4d00acfc8eeb08ca27fc617afa6cdd8e56aea2b196663d67d8744025772fc11
+size 2177604

data/valid-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4f29bfbd098f445e6ce5f62c0b3e525a1fef3535df3f781e88302c06b5099f80
+size 133240

fold_prediction-rag.py

@@ -0,0 +1,266 @@
+#-*- coding:utf-8 -*-
+
+# import sys, os, shutil, re, logging, subprocess, string, io, argparse, bisect, concurrent, gzip, zipfile, tarfile, json, pickle, time, datetime, random, math, copy, itertools, functools, collections, multiprocessing, threading, queue, signal, inspect, warnings, distutils.spawn
+import sys
+import os
+import pickle
+import re
+import torch
+import random
+import gzip
+from os.path import exists, join, getsize, isfile, isdir, abspath, basename
+from typing import Dict, Union, Optional, List, Tuple, Mapping
+import numpy as np
+import pandas as pd
+from tqdm.auto import trange, tqdm
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import Dict, Union, Optional, List, Tuple, Mapping
+import datasets
+
+def get_md5(aa_str):
+    """
+    Calculate MD5 values for protein sequence
+    """
+    import hashlib
+    assert isinstance(aa_str, str), aa_str
+
+    aa_str = aa_str.upper()
+    return hashlib.md5(aa_str.encode('utf-8')).hexdigest()
+
+def load_fasta(seqFn, rem_tVersion=False, load_annotation=False, full_line_as_id=False):
+    """
+    seqFn               -- Fasta file or input handle (with readline implementation)
+    rem_tVersion        -- Remove version information. ENST000000022311.2 => ENST000000022311
+    load_annotation     -- Load sequence annotation
+    full_line_as_id     -- Use the full head line (starts with >) as sequence ID. Can not be specified simutanouly with load_annotation
+
+    Return:
+        {tid1: seq1, ...} if load_annotation==False
+        {tid1: seq1, ...},{tid1: annot1, ...} if load_annotation==True
+    """
+    if load_annotation and full_line_as_id:
+        raise RuntimeError("Error: load_annotation and full_line_as_id can not be specified simutanouly")
+    if rem_tVersion and full_line_as_id:
+        raise RuntimeError("Error: rem_tVersion and full_line_as_id can not be specified simutanouly")
+
+    fasta = {}
+    annotation = {}
+    cur_tid = ''
+    cur_seq = ''
+    
+    if isinstance(seqFn, str):
+        IN = open(seqFn)
+    elif hasattr(seqFn, 'readline'):
+        IN = seqFn
+    else:
+        raise RuntimeError(f"Expected seqFn: {type(seqFn)}")
+    for line in IN:
+        if line[0] == '>':
+            if cur_tid != '':
+                fasta[cur_tid] = re.sub(r"\s", "", cur_seq)
+                cur_seq = ''
+            data = line[1:-1].split(None, 1)
+            cur_tid = line[1:-1] if full_line_as_id else data[0]
+            annotation[cur_tid] = data[1] if len(data)==2 else ""
+            if rem_tVersion and '.' in cur_tid: 
+                cur_tid = ".".join(cur_tid.split(".")[:-1])
+        elif cur_tid != '':
+            cur_seq += line.rstrip()
+    
+    if isinstance(seqFn, str):
+        IN.close()
+
+    if cur_seq != '':
+        fasta[cur_tid] = re.sub(r"\s", "", cur_seq)
+    
+    if load_annotation:
+        return fasta, annotation
+    else:
+        return fasta
+
+def load_msa_txt(file_or_stream, load_id=False, load_annot=False, sort=False):
+    """
+    Read msa txt file
+    
+    Parmeters
+    --------------
+    file_or_stream: file or stream to read (with read method)
+    load_id: read identity and return
+    
+    Return
+    --------------
+    msa: list of msa sequences, the first sequence in msa is the query sequence
+    id_arr: Identity of msa sequences
+    annotations: Annotations of msa sequences
+    """
+    msa = []
+    id_arr = []
+    annotations = []
+    
+    if hasattr(file_or_stream, 'read'):
+        lines = file_or_stream.read().strip().split('\n')
+    elif file_or_stream.endswith('.gz'):
+        with gzip.open(file_or_stream) as IN:
+            lines = IN.read().decode().strip().split('\n')
+    else:
+        with open(file_or_stream) as IN:
+            lines = IN.read().strip().split('\n')
+        # lines = open(file_or_stream).read().strip().split('\n')
+    
+    for idx,line in enumerate(lines):
+        data = line.strip().split()
+        if idx == 0:
+            assert len(data) == 1, f"Expect 1 element for the 1st line, but got {data} in {file_or_stream}"
+            q_seq = data[0]
+        else:
+            if len(data) >= 2:
+                id_arr.append( float(data[1]) )
+            else:
+                assert len(q_seq) == len(data[0])
+                id_ = round(np.mean([ r1==r2 for r1,r2 in zip(q_seq, data[0]) ]), 3)
+                id_arr.append(id_)
+            msa.append( data[0] )
+            if len(data) >= 3:
+                annot = " ".join(data[2:])
+                annotations.append( annot )
+            else:
+                annotations.append(None)
+    
+    id_arr = np.array(id_arr, dtype=np.float64)
+    if sort:
+        id_order = np.argsort(id_arr)[::-1]
+        msa      = [ msa[i] for i in id_order ]
+        id_arr   = id_arr[id_order]
+        annotations = [ annotations[i] for i in id_order ]
+    msa = [q_seq] + msa
+    
+    outputs = [ msa ]
+    if load_id:
+        outputs.append( id_arr )
+    if load_annot:
+        outputs.append( annotations )
+    if len(outputs) == 1:
+        return outputs[0]
+    return outputs
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """
+"""
+
+# You can copy an official description
+_DESCRIPTION = """
+"""
+
+_HOMEPAGE = "xxxxx"
+
+_LICENSE = "xxxxx"
+
+class DownStreamConfig(datasets.BuilderConfig):
+    """BuilderConfig for downstream taks dataset."""
+
+    def __init__(self, *args, **kwargs):
+        """BuilderConfig downstream tasks dataset.
+        Args:
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(*args, name=f"downstream", **kwargs)
+
+class DownStreamTasks(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.1.0")
+    BUILDER_CONFIG_CLASS = DownStreamConfig
+    BUILDER_CONFIGS = [ DownStreamConfig() ]
+    DEFAULT_CONFIG_NAME = None
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "seq":         datasets.Value("string"),
+                "label":       datasets.Value("int32"),
+                "msa":         datasets.Value("string"),
+                "str_emb":     datasets.Array2D(shape=(None, 384), dtype='float32'),
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+        # breakpoint()
+        train_parquet_file  = dl_manager.download(f"data/train-00000-of-00001.parquet")
+        valid_parquet_file  = dl_manager.download(f"data/valid-00000-of-00001.parquet")
+        test_parquet_file   = dl_manager.download(f"data/test-00000-of-00001.parquet")
+        msa_path            = dl_manager.download_and_extract(f"msa.tar")
+        str_file            = dl_manager.download(f"md5_to_str.fasta")
+        codebook_file       = dl_manager.download(f"codebook.pt")
+
+        assert os.path.exists(join(msa_path, 'msa'))
+        msa_path = join(msa_path, 'msa')
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, 
+                gen_kwargs={
+                            "parquet_file": train_parquet_file,
+                            "msa_path": msa_path, 
+                            "str_file": str_file, 
+                            "codebook_file": codebook_file
+                            }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, 
+                gen_kwargs={
+                            "parquet_file": valid_parquet_file,
+                            "msa_path": msa_path, 
+                            "str_file": str_file, 
+                            "codebook_file": codebook_file
+                            }
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, 
+                gen_kwargs={
+                            "parquet_file": test_parquet_file,
+                            "msa_path": msa_path, 
+                            "str_file": str_file, 
+                            "codebook_file": codebook_file
+                            }
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, parquet_file, msa_path, str_file, codebook_file):
+        
+        dataset = datasets.Dataset.from_parquet(parquet_file)
+        md5_to_str = load_fasta(str_file)
+        codebook = torch.load(codebook_file, 'cpu', weights_only=True).numpy()
+        
+        for key, item in enumerate(dataset):
+            seq   = item['seq']
+            label = item['label']
+            md5_val = get_md5(seq)
+            if md5_val not in md5_to_str or md5_to_str[md5_val] == "":
+                str_emb  = np.zeros([len(seq), 384], dtype=np.float32)
+            else:
+                str_toks = np.array([ int(x) for x in md5_to_str[md5_val].split('-')])
+                str_emb  = codebook[str_toks]
+            
+            msa = load_msa_txt(join(msa_path, md5_val+'.txt.gz'))
+            assert len(msa[0]) == len(seq), f"Error: {len(msa[0])} != {len(seq)}"
+            assert len(msa[0]) == str_emb.shape[0], f"Error: {len(msa[0])} != {str_emb.shape[0]}"
+            # breakpoint()
+            yield key, {
+                "seq":     seq,
+                "label":   label,
+                "msa":     "|".join(msa),
+                "str_emb": str_emb
+            }

md5_to_str.fasta

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+oid sha256:2bd6843a0c18415f270860f37394e25aa158ef78f20d61642c21fccaf20f4970
+size 10808271

msa.tar

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+oid sha256:340f8472a51949226429af393bb4990b1c3567ec79b4539b1bb9ac94ffdecec8
+size 3123640320