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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 +1,66 @@
----
-license: apache-2.0
----
+# Dataset Card for Contact Prediction Dataset for RAGProtein
+
+### Dataset Summary
+
+Contact map prediction aims to determine whether two residues, $i$ and $j$, are in contact or not, based on their distance with a certain threshold ($<$8 Angstrom). This task is an important part of the early Alphafold version for structural prediction. 
+
+## Dataset Structure
+
+### Data Instances
+
+For each instance, there is a string of the protein sequences, a sequence for the contact labels. Each of the sub-labels "[2, 3]" indicates the 3rd residue are in contact with the 4th residue (start from index 0). See the [Contact map prediction dataset viewer](https://huggingface.co/datasets/Bo1015/contact_prediction_binary/viewer/default/test) to explore more examples.
+
+```
+{'seq':'QNLLKNLAASLGRKPFVADKQGVYRLTIDKHLVMLAPHGSELVLRTPIDAPMLREGNNVNVTLLRSLMQQALAWAKRYPQTLVLDDCGQLVLEARLRLQELDTHGLQEVINKQLALLEHLIPQLTP'
+'label': [ [ 0, 0 ], [ 0, 1 ], [ 1, 1 ], [ 1, 2 ], [ 1, 3 ], [ 1, 101 ], [ 2, 2 ], [ 2, 3 ], [ 2, 4 ], [ 3, 3 ], [ 3, 4 ], [ 3, 5 ], [ 3, 99 ], [ 3, 100 ], [ 3, 101 ], [ 4, 4 ], [ 4, 5 ], [ 4, 53 ], ...], 'msa': 'QNLLKNLAASLGRKPFVADKQGVYRLTIDKHLVMLAPHGSELVLRTPIDAPMLREGNNVNVTLLRSLMQQALAWAKRYPQTLVLDDCGQLVLEARLRLQELDTHGLQEVINKQLALLEHLIPQLTP|QNLLKNLAASLGRKPFVADKQGVYRLTIDKHLVMLAPHGSELVLRTPIDAPMLREGNNVNVTLLRSLMQQALAWAKRYPQTLVLDDCGQLVLEARLRLQELDTHGLQEVINKQLALLEHLIPQLTP...', 'str_emb': [seq_len, 384]}
+```
+
+The average  for the `seq` and the `label` are provided below:
+
+| Feature    | Mean Count |
+| ---------- | ---------------- |
+| seq    | 249        |
+| label | 1,500             |
+
+### Data Fields
+
+- `seq`: a string containing the protein sequence
+- `label`: a string containing the contact label of each residue pair.
+- `msa`: "|" seperated MSA sequences
+- `str_emb`: AIDO.StructureTokenizer generated structure embedding from AF2 predicted structures
+
+### Data Splits
+
+The contact map prediction dataset has 3 splits: _train_, _validation_, and _test_. Below are the statistics of the dataset.
+
+| Dataset Split | Number of Instances in Split                |
+| ------------- | ------------------------------------------- |
+| Train         | 12,041                              |
+| Validation    | 1,505                                      |
+| Test          | 1,505                                      |
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The [trRosetta dataset](https://www.pnas.org/doi/10.1073/pnas.1914677117) is employed as the initilized dataset. 
+
+### Licensing Information
+
+The dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0). 
+
+### 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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+version https://git-lfs.github.com/spec/v1
+oid sha256:52139fb587368235751a0464fd3be7a6beb0fff2e96a0164012f858702a9bcf8
+size 787617

contact_prediction_binary-rag.py

@@ -0,0 +1,265 @@
+#-*- 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.Array2D(shape=(None, 2), dtype='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]:
+        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
+            }

data/test-00000-of-00001.parquet

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data/train-00000-of-00001.parquet

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data/valid-00000-of-00001.parquet

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md5_to_str.fasta

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msa.tar

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