dataset_info:
features:
- name: PDB_ID
dtype: string
- name: Sequence
dtype: string
- name: Secondary_structure
dtype: string
- name: AH
dtype: float64
- name: BS
dtype: float64
- name: T
dtype: float64
- name: UNSTRUCTURED
dtype: float64
- name: BETABRIDGE
dtype: float64
- name: 310HELIX
dtype: float64
- name: PIHELIX
dtype: float64
- name: BEND
dtype: float64
- name: Sequence_length
dtype: int64
- name: Sequence_spaced
dtype: string
- name: Primary_SS_Type
dtype: string
- name: Secondary_SS_Type
dtype: string
splits:
- name: train
num_bytes: 338419581
num_examples: 125957
download_size: 139433982
dataset_size: 338419581
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
This dataset contains 125,955 protein sequences, with protein PDB ID, length, the sequence (primary structure), as well as secondary structure as identified from experiment. The shortest protein is composed of only 11 amino acids, along with the longest one that features up to 19,350 amino acids. The standard deviation of the length is 855 amino acids.
The dataset further includes overall secondary sturctrure content, for all eight classes of secondary structure types.
The beta sheet content is less than 30% in most sequences and even features about 20,000 sequences under 10%.
The alpha helix ratio is typically higher than the beta sheet ratio.
Most of the sequences feature an alpha helix content between 30% and 50%, but a small part of the sequences has even more than 80% alpha helix content. Also, of note is the fact the data shows a high proportion of sequences with a low ratio under 5% of both alpha helix and beta sheet. Because there are many sequences in this database, each has a different length, structural organization, and secondary structure content, so we can quickly analyze the relationships between primary structure and the secondary structures of the different sequences.
Dataset statistics
The plots below show the length distribution and secondary structure content distribution.
Below is the distribution of the primary secondary structure type, and the secondary (second-largest) secondary structure type. This data is included as columns Primary_SS_Type and Secondary_SS_Type in the dataset.
Correlation matrix of secondary structure contents:
Protein secondary structure prediction results
As reported in Yu, Buehler, et al. (2022), this dataset was used to train a model to predict secondary structure contents of a protein based on the sequence.
Protein design example
The plot below shows a protein design example, showing the effect of systematic variation of point mutations on alpha-helix and beta-sheet contents. Panel A shows the original sequence of lysozyme (PDB ID: 194L), and an image of the molecular structure of the wildtype protein as found in nature. Panel B shows the effect of systematically substituting amino acids of a certain type in the entire sequence (left: AH content, right: BS content). In the plot, the substation numbers range from 0-19 and reflect the sequence of substitutions from AGILPVFWYDERHKSTCMNQ (i.e., A=0…Q=19).
As we go from top to bottom in each column, the plot indicates how the secondary structure content changes if all A are replaced with A, then G, then I, and so on. As we vary the columns the residue type that is replaced is varied. In the first column all A residue types are replaced, in the second column all G residue types are replaced, then I, and so on. As the plots show, while the protein remains largely alpha-helical for most changes, there are a few sequence mutations that lead to significant changes in the protein secondary structure content. These max/min results are extracted using a min/max algorithm and then folded using AlphaFold2, and depicted in panel C. The changes in secondary structure is clearly visible, confirming the predictions from our model and the optimization scheme used here.
Cite as:
@article{YuBuehler2022,
title={End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins},
author={Chi-Hua Yu and Wei Chen and Yu-Hsuan Chiang and Kai Guo and Zaira Martin Moldes and David L Kaplan and Markus J Buehler},
journal={ACS Biomaterials Science & Engineering},
volume={8},
number={3},
pages={1156-1165},
year={2022},
month={Mar},
doi={10.1021/acsbiomaterials.1c01343},
pmid={35129957},
pmcid={PMC9347213}
}