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# How to Contribute
We welcome small patches related to bug fixes and documentation, but we do not
plan to make any major changes to this repository.
## Contributor License Agreement
Contributions to this project must be accompanied by a Contributor License
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FROM image.sourcefind.cn:5000/dcu/admin/base/jax:0.4.23-ubuntu20.04-dtk24.04.1-py3.10
# RUN apt update
# WORKDIR /app
# WORKDIR /app/softwares
# RUN git clone https://github.com/soedinglab/hh-suite.git
# RUN mkdir -p hh-suite/build && cd hh-suite/build && cmake -DCMAKE_INSTALL_PREFIX=. .. && make -j 4 && make install
# ENV PATH=/app/softwares/hh-suite/build/bin:/app/softwares/hh-suite/build/scripts:$PATH
# WORKDIR /app/softwares
# RUN wget https://github.com/TimoLassmann/kalign/archive/refs/tags/v3.4.0.zip && unzip v3.4.0.zip && cd kalign-3.4.0 && mkdir build && cd build && cmake .. && make && make install
# WORKDIR /app/softwares
# RUN sudo apt install doxygen -y
# RUN wget https://github.com/openmm/openmm/archive/refs/tags/8.0.0.zip && unzip 8.0.0.zip && cd openmm-8.0.0 && mkdir build && cd build && cmake .. && make && sudo make install && sudo make PythonInstall
# WORKDIR /app/softwares
# RUN wget https://github.com/openmm/pdbfixer/archive/refs/tags/1.9.zip && unzip 1.9.zip && cd pdbfixer-1.9 && python setup.py install
# RUN sudo apt install hmmer -y
# WORKDIR /app
# COPY . /app/alphafold2
# RUN ls
# RUN pip install --no-cache-dir -r /app/alphafold2/requirements_dcu.txt -i https://mirrors.ustc.edu.cn/pypi/web/simple
# RUN pip install dm-haiku==0.0.11 flax==0.7.1 jmp==0.0.2 tabulate==0.8.9 --no-deps jax -i https://mirrors.ustc.edu.cn/pypi/web/simple
# RUN pip install orbax==0.1.6 orbax-checkpoint==0.1.6 optax==0.2.2 -i https://mirrors.ustc.edu.cn/pypi/web/simple
# WORKDIR /app/alphafold2
# RUN python setup.py install
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# AF2
## 论文
- [https://www.nature.com/articles/s41586-021-03819-2](https://www.nature.com/articles/s41586-021-03819-2)
## 模型结构
模型核心是一个基于Transformer架构的神经网络，包括两个主要组件：Sequence to Sequence Model和Structure Model，这两个组件通过迭代训练进行优化，以提高其预测准确性。
![img](./docs/alphafold2.png)
## 算法原理
AlphaFold2通过从蛋白质序列和结构数据中提取信息，使用神经网络模型来预测蛋白质三维结构。
![img](./docs/alphafold2_1.png)
## 环境配置
### Docker（方法一）
# 使用该方法不需要下载本仓库，镜像中已包含可运行代码，但需要挂载相应的数据文件
docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:alphafold2-dtk24.04.1-py310
docker run --shm-size 100g --network=host --name=alphafold2 --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 本地数据地址:镜像数据地址 -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
### Docker（方法二）
docker pull image.sourcefind.cn:5000/dcu/admin/base/jax:0.4.23-ubuntu20.04-dtk24.04.1-py3.10
docker run --shm-size 50g --network=host --name=alphafold2 --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home/ -v /opt/hyhal:/opt/hyhal:ro -it <your IMAGE ID> bash
# 1. 一般依赖项安装
pip install -r requirements_dcu.txt
pip install dm-haiku==0.0.11 flax==0.7.1 jmp==0.0.2 tabulate==0.8.9 --no-deps jax
pip install orbax==0.1.6 orbax-checkpoint==0.1.6 optax==0.2.2
python setup.py install
# 2、hh-suite
git clone https://github.com/soedinglab/hh-suite.git
mkdir -p hh-suite/build && cd hh-suite/build
cmake -DCMAKE_INSTALL_PREFIX=. ..
make -j 4 && make install
export PATH="$(pwd)/bin:$(pwd)/scripts:$PATH"
wget https://github.com/TimoLassmann/kalign/archive/refs/tags/v3.4.0.zip
unzip 3.4.0.zip && cd kalign-3.4.0
mkdir build
cd build
cmake ..
make
make test
make install
# 3. openmm + pdbfixer
sudo apt install doxygen
wget https://github.com/openmm/openmm/archive/refs/tags/8.0.0.zip
unzip 8.0.0.zip && cd openmm-8.0.0 && mkdir build && cd build
cmake .. && make && sudo make install && sudo make PythonInstall
wget https://github.com/openmm/pdbfixer/archive/refs/tags/1.9.zip
unzip 1.9.zip && cd pdbfixer-1.9 && python setup.py install
## 数据集
推荐使用AlphaFold2中的开源数据集，包括BFD、MGnify、PDB70、Uniclust、Uniref90等,数据集大小约2.62TB。数据集格式如下：
```
$DOWNLOAD_DIR/
bfd/
bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt_hhm.ffindex
bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt_hhm.ffdata
bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt_cs219.ffindex
...
mgnify/
mgy_clusters_2022_05.fa
params/
params_model_1.npz
params_model_2.npz
params_model_3.npz
...
pdb70/
pdb_filter.dat
pdb70_hhm.ffindex
pdb70_hhm.ffdata
...
pdb_mmcif/
mmcif_files/
100d.cif
101d.cif
101m.cif
...
obsolete.dat
pdb_seqres/
pdb_seqres.txt
small_bfd/
bfd-first_non_consensus_sequences.fasta
uniref30/
UniRef30_2021_03_hhm.ffindex
UniRef30_2021_03_hhm.ffdata
UniRef30_2021_03_cs219.ffindex
...
uniprot/
uniprot.fasta
uniref90/
uniref90.fasta
```
此处提供了一个脚本download_all_data.sh用于下载使用的数据集和模型文件：
./scripts/download_all_data.sh 数据集下载目录
数据集快速下载中心：[SCNet AIDatasets](http://113.200.138.88:18080/aidatasets) ，项目中数据集可从快速下载通道下载：[alphafold](http://113.200.138.88:18080/aidatasets/project-dependency/alphafold) 。
## 推理
分别提供了基于Jax的单体和多体的推理脚本.
```bash
# 进入工程目录
cd alphafold2_jax
```
### 单体
```bash
./run_monomer.sh
```
单体推理参数说明：download_dir为数据集下载目录，monomer.fasta为推理的单体序列；`--output_dir`为输出目录；`model_names`为推理的模型名称，`--model_preset=monomer`为单体模型配置；`--run_relax=true`为进行relax操作；`--use_gpu_relax=true`为使用gpu进行relax操作（速度更快，但可能不太稳定），`--use_gpu_relax=false`为使用CPU进行relax操作（速度慢，但稳定）；若添加--use_precomputed_msas=true则可以加载已有的MSAs，否则默认运行MSA工具。
### 多体
```bash
./run_multimer.sh
```
多体推理参数说明：multimer.fasta为推理的多体序列，`--model_preset=multimer`为多体模型配置；`--num_multimer_predictions_per_model`为每个模型预测数量，其他参数同单体推理参数说明一致。
## result
`--output_dir`目录结构如下：
```
<target_name>/
features.pkl
ranked_{0,1,2,3,4}.pdb
ranking_debug.json
relaxed_model_{1,2,3,4,5}.pdb
result_model_{1,2,3,4,5}.pkl
timings.json
unrelaxed_model_{1,2,3,4,5}.pdb
msas/
bfd_uniclust_hits.a3m
mgnify_hits.sto
uniref90_hits.sto
...
```
[查看蛋白质3D结构](https://www.pdbus.org/3d-view)
ID: 8U23
蓝色的为预测结构，黄色为真实结构
![alt text](image.png)
### 精度
无
## 应用场景
### 算法类别
蛋白质预测
### 热点应用行业
医疗,科研,教育
## 预训练权重
预训练权重快速下载中心：[SCNet AIModels](http://113.200.138.88:18080/aimodels) ，项目中的预训练权重可从快速下载通道下载：[alphafold](http://113.200.138.88:18080/aimodels/findsource-dependency/alphafold-params) 。
## 源码仓库及问题反馈
* [https://developer.hpccube.com/codes/modelzoo/alphafold2_jax](https://developer.hpccube.com/codes/modelzoo/alphafold2_jax)
## 参考
* [https://github.com/deepmind/alphafold](https://github.com/deepmind/alphafold)
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# AlphaFold Protein Structure Database
## Introduction
The AlphaFold UniProt release (214M predictions) is hosted on
[Google Cloud Public Datasets](https://console.cloud.google.com/marketplace/product/bigquery-public-data/deepmind-alphafold),
and is available to download at no cost under a
[CC-BY-4.0 licence](http://creativecommons.org/licenses/by/4.0/legalcode). The
dataset is in a Cloud Storage bucket, and metadata is available on BigQuery. A
Google Cloud account is required for the download, but the data can be freely
used under the terms of the
[CC-BY 4.0 Licence](http://creativecommons.org/licenses/by/4.0/legalcode).
This document provides an overview of how to access and download the dataset for
different use cases. Please refer to the [AlphaFold database FAQ](https://www.alphafold.com/faq)
for further information on what proteins are in the database and a changelog of
releases.
:ledger: **Note: The full dataset is difficult to manipulate without significant
computational resources (the size of the dataset is 23 TiB, 3 * 214M files).**
There are also alternatives to downloading the full dataset:
1. Download a premade subset (covering important species / Swiss-Prot) via our
[download page](https://alphafold.ebi.ac.uk/download).
2. Download a custom subset of the data. See below.
If you need to download the full dataset then please see the "Bulk download"
section. See "Creating a Google Cloud Account" below for more information on how
to avoid any surprise costs when using Google Cloud Public Datasets.
## Licence
Data is available for academic and commercial use, under a
[CC-BY-4.0 licence](http://creativecommons.org/licenses/by/4.0/legalcode).
EMBL-EBI expects attribution (e.g. in publications, services or products) for
any of its online services, databases or software in accordance with good
scientific practice.
If you make use of an AlphaFold prediction, please cite the following papers:
* [Jumper, J et al. Highly accurate protein structure prediction with
AlphaFold. Nature
(2021).](https://www.nature.com/articles/s41586-021-03819-2)
* [Varadi, M et al. AlphaFold Protein Structure Database: massively expanding
the structural coverage of protein-sequence space with high-accuracy models.
Nucleic Acids Research
(2021).](https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkab1061/6430488)
AlphaFold Data Copyright (2022) DeepMind Technologies Limited.
## Disclaimer
The AlphaFold Data and other information provided on this site is for
theoretical modelling only, caution should be exercised in its use. It is
provided 'as-is' without any warranty of any kind, whether expressed or implied.
For clarity, no warranty is given that use of the information shall not infringe
the rights of any third party. The information is not intended to be a
substitute for professional medical advice, diagnosis, or treatment, and does
not constitute medical or other professional advice.
## Format
Dataset file names start with a protein identifier of the form `AF-[a UniProt
accession]-F[a fragment number]`.
Three files are provided for each entry:
* **model_v4.cif** – contains the atomic coordinates for the predicted protein
structure, along with some metadata. Useful references for this file format
are the [ModelCIF](https://github.com/ihmwg/ModelCIF) and
[PDBx/mmCIF](https://mmcif.wwpdb.org) project sites.
* **confidence_v4.json** – contains a confidence metric output by AlphaFold
called pLDDT. This provides a number for each residue, indicating how
confident AlphaFold is in the *local* surrounding structure. pLDDT ranges
from 0 to 100, where 100 is most confident. This is also contained in the
CIF file.
* **predicted_aligned_error_v4.json** – contains a confidence metric output by
AlphaFold called PAE. This provides a number for every pair of residues,
which is lower when AlphaFold is more confident in the relative position of
the two residues. PAE is more suitable than pLDDT for judging confidence in
relative domain placements.
[See here](https://alphafold.ebi.ac.uk/faq#faq-7) for a description of the
format.
Predictions grouped by NCBI taxonomy ID are available as
`proteomes/proteome-tax_id-[TAX ID]-[SHARD ID]_v4.tar` within the same
bucket.
There are also two extra files stored in the bucket:
* `accession_ids.csv` – This file contains a list of all the UniProt
accessions that have predictions in AlphaFold DB. The file is in CSV format
and includes the following columns, separated by a comma:
* UniProt accession, e.g. A8H2R3
* First residue index (UniProt numbering), e.g. 1
* Last residue index (UniProt numbering), e.g. 199
* AlphaFold DB identifier, e.g. AF-A8H2R3-F1
* Latest version, e.g. 4
* `sequences.fasta` – This file contains sequences for all proteins in the
current database version in FASTA format. The identifier rows start with
">AFDB", followed by the AlphaFold DB identifier and the name of the
protein. The sequence rows contain the corresponding amino acid sequences.
Each sequence is on a single line, i.e. there is no wrapping.
## Creating a Google Cloud Account
Downloading from the Google Cloud Public Datasets (rather than from AFDB or 3D
Beacons) requires a Google Cloud account. See the
[Google Cloud get started](https://cloud.google.com/docs/get-started) page, and
explore the [free tier account usage limits](https://cloud.google.com/free).
**IMPORTANT: After the trial period has finished (90 days), to continue access,
you are required to upgrade to a billing account. While your free tier access
(including access to the Public Datasets storage bucket) continues, usage beyond
the free tier will incur costs – please familiarise yourself with the pricing
for the services that you use to avoid any surprises.**
1. Go to
[https://cloud.google.com/datasets](https://cloud.google.com/datasets).
2. Create an account:
1. Click "get started for free" in the top right corner.
2. Agree to all terms of service.
3. Follow the setup instructions. Note that a payment method is required,
but this will not be used unless you enable billing.
4. Access to the Google Cloud Public Datasets storage bucket is always at
no cost and you will have access to the
[free tier.](https://cloud.google.com/free/docs/gcp-free-tier#free-tier-usage-limits)
3. Set up a project:
1. In the top left corner, click the navigation menu (three horizontal bars
icon).
2. Select: "Cloud overview" -> "Dashboard".
3. In the top left corner there is a project menu bar (likely says "My
First Project"). Select this and a "Select a Project" box will appear.
4. To keep using this project, click "Cancel" at the bottom of the box.
5. To create a new project, click "New Project" at the top of the box:
1. Select a project name.
2. For location, if your organization has a Cloud account then select
this, otherwise leave as is.
4. Install `gsutil`:
1. Follow these
[instructions](https://cloud.google.com/storage/docs/gsutil_install).
## Accessing the dataset
The data is available from:
* GCS data bucket:
[gs://public-datasets-deepmind-alphafold-v4](https://console.cloud.google.com/storage/browser/public-datasets-deepmind-alphafold-v4)
## Bulk download
We don't recommend downloading the full dataset unless required for processing
with local computational resources, for example in an academic high performance
computing centre.
We estimate that a 1 Gbps internet connection will allow download of the full
database in roughly 2.5 days.
While we don’t know the exact nature of your computational infrastructure, below
are some suggested approaches for downloading the dataset. Please reach out to
[alphafold@deepmind.com](mailto:alphafold@deepmind.com) if you have any
questions.
The recommended way of downloading the whole database is by downloading
1,015,797 sharded proteome tar files using the command below. This is
significantly faster than downloading all of the individual files because of
large constant per-file latency.
```bash
gsutil -m cp -r gs://public-datasets-deepmind-alphafold-v4/proteomes/ .
```
You will then have to un-tar all of the proteomes and un-gzip all of the
individual files. Note that after un-taring, there will be about 644M files, so
make sure your filesystem can handle this.
### Storage Transfer Service
Some users might find the
[Storage Transfer Service](https://cloud.google.com/storage-transfer-service) a
convenient way to set up the transfer between this bucket and another bucket, or
another cloud service. *Using this service may incur costs*. Please check the
[pricing page](https://cloud.google.com/storage-transfer/pricing) for more
detail, particularly for transfers to other cloud services.
## Downloading subsets of the data
### AlphaFold Database search
For simple queries, for example by protein name, gene name or UniProt accession
you can use the main search bar on
[alphafold.ebi.ac.uk](https://alphafold.ebi.ac.uk).
### 3D Beacons
[3D-Beacons](https://3d-beacons.org) is an international collaboration of
protein structure data providers to create a federated network with unified data
access mechanisms. The 3D-Beacons platform allows users to retrieve coordinate
files and metadata of experimentally determined and theoretical protein models
from data providers such as AlphaFold DB.
More information about how to access AlphaFold predictions using 3D-Beacons is
available at
[3D-Beacons documentation](https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/docs).
### Other premade species subsets
Downloads for some model organism proteomes, global health proteomes and
Swiss-Prot are available on the
[AFDB website](https://alphafold.ebi.ac.uk/download). These are generated from
[reference proteomes](https://www.uniprot.org/help/reference_proteome). If you
want other species, or *all* proteins for a particular species, please continue
reading.
We provide 1,015,797 sharded tar files for all species in
[gs://public-datasets-deepmind-alphafold-v4/proteomes/](https://console.cloud.google.com/storage/browser/public-datasets-deepmind-alphafold-v4/proteomes/).
We shard each proteome so that each shard contains at most 10,000 proteins
(which corresponds to 30,000 files per shard, since there are 3 files per
protein). To download a proteome of your choice, you have to do the following
steps:
1. Find the [NCBI taxonomy ID](https://www.ncbi.nlm.nih.gov/taxonomy)
(`[TAX_ID]`) of the species in question.
2. Run `gsutil -m cp
gs://public-datasets-deepmind-alphafold-v4/proteomes/proteome-tax_id-[TAX
ID]-*_v4.tar .` to download all shards for this proteome.
3. Un-tar all of the downloaded files and un-gzip all of the individual files.
### File manifests
Pre-made lists of files (manifests) are available at
[gs://public-datasets-deepmind-alphafold-v4/manifests](https://console.cloud.google.com/storage/browser/public-datasets-deepmind-alphafold-v4/manifests/).
Note that these filenames do not include the bucket prefix, but this can be
added once the files have been downloaded to your filesystem.
One can also define their own list of files, for example created by BigQuery
(see below). `gsutil` can be used to download these files with
```bash
cat [manifest file] | gsutil -m cp -I .
```
This will be much slower than downloading the tar files (grouped by species)
because each file has an associated overhead.
### BigQuery
**IMPORTANT: The
[free tier](https://cloud.google.com/bigquery/pricing#free-tier) of Google Cloud
comes with [BigQuery Sandbox](https://cloud.google.com/bigquery/docs/sandbox)
with 1 TB of free processed query data each month. Repeated queries within a
month could exceed this limit and if you have
[upgraded to a paid Cloud Billing account](https://cloud.google.com/free/docs/gcp-free-tier#how-to-upgrade)
you may be charged.**
**This should be sufficient for running a number of queries on the metadata
table, though the usage depends on the size of the columns queried and selected.
Please look at the
[BigQuery pricing page](https://cloud.google.com/bigquery/pricing) for more
information.**
**This is the user's responsibility so please ensure you keep track of your
billing settings and resource usage in the console.**
BigQuery provides a serverless and highly scalable analytics tool enabling SQL
queries over large datasets. The metadata for the UniProt dataset takes up ​​113
GiB and so can be challenging to process and analyse locally. The table name is:
* BigQuery metadata table:
[bigquery-public-data.deepmind_alphafold.metadata](https://console.cloud.google.com/bigquery?project=bigquery-public-data&ws=!1m5!1m4!4m3!1sbigquery-public-data!2sdeepmind_alphafold!3smetadata)
With BigQuery SQL you can do complex queries, e.g. find all high accuracy
predictions for a particular species, or even join on to other datasets, e.g. to
an experimental dataset by the `uniprotSequence`, or to the NCBI taxonomy by
`taxId`.
If you would find additional information in the metadata useful please file a
GitHub issue.
#### Setup
Follow the
[BigQuery Sandbox set up guide](https://cloud.google.com/bigquery/docs/sandbox).
#### Exploring the metadata
The column names and associated data types available can be found using the
following query.
```sql
SELECT column_name, data_type FROM bigquery-public-data.deepmind_alphafold.INFORMATION_SCHEMA.COLUMNS
WHERE table_name = 'metadata'
```
**Column name** | **Data type** | **Description**
---------------------- | --------------- | ---------------
allVersions | `ARRAY<INT64>` | An array of AFDB versions this prediction has had
entryId | `STRING` | The AFDB entry ID, e.g. "AF-Q1HGU3-F1"
fractionPlddtConfident | `FLOAT64` | Fraction of the residues in the prediction with pLDDT between 70 and 90
fractionPlddtLow | `FLOAT64` | Fraction of the residues in the prediction with pLDDT between 50 and 70
fractionPlddtVeryHigh | `FLOAT64` | Fraction of the residues in the prediction with pLDDT greater than 90
fractionPlddtVeryLow | `FLOAT64` | Fraction of the residues in the prediction with pLDDT less than 50
gene | `STRING` | The name of the gene if known, e.g. "COII"
geneSynonyms | `ARRAY<STRING>` | Additional synonyms for the gene
globalMetricValue | `FLOAT64` | The mean pLDDT of this prediction
isReferenceProteome | `BOOL` | Is this protein part of the reference proteome?
isReviewed | `BOOL` | Has this protein been reviewed, i.e. is it part of SwissProt?
latestVersion | `INT64` | The latest AFDB version for this prediction
modelCreatedDate | `DATE` | The date of creation for this entry, e.g. "2022-06-01"
organismCommonNames | `ARRAY<STRING>` | List of common organism names
organismScientificName | `STRING` | The scientific name of the organism
organismSynonyms | `ARRAY<STRING>` | List of synonyms for the organism
proteinFullNames | `ARRAY<STRING>` | Full names of the protein
proteinShortNames | `ARRAY<STRING>` | Short names of the protein
sequenceChecksum | `STRING` | [CRC64 hash](https://www.uniprot.org/help/checksum) of the sequence. Can be used for cheaper lookups.
sequenceVersionDate | `DATE` | Date when the sequence data was last modified in UniProt
taxId | `INT64` | NCBI taxonomy id of the originating species
uniprotAccession | `STRING` | Uniprot accession ID
uniprotDescription | `STRING` | The name recommended by the UniProt consortium
uniprotEnd | `INT64` | Number of the last residue in the entry relative to the UniProt entry. This is equal to the length of the protein unless we are dealing with protein fragments.
uniprotId | `STRING` | The Uniprot EntryName field
uniprotSequence | `STRING` | Amino acid sequence for this prediction
uniprotStart | `INT64` | Number of the first residue in the entry relative to the UniProt entry. This is 1 unless we are dealing with protein fragments.
#### Producing summary statistics
The following query gives the mean of the prediction confidence fractions per
species.
```sql
SELECT
organismScientificName AS name,
SUM(fractionPlddtVeryLow) / COUNT(fractionPlddtVeryLow) AS mean_plddt_very_low,
SUM(fractionPlddtLow) / COUNT(fractionPlddtLow) AS mean_plddt_low,
SUM(fractionPlddtConfident) / COUNT(fractionPlddtConfident) AS mean_plddt_confident,
SUM(fractionPlddtVeryHigh) / COUNT(fractionPlddtVeryHigh) AS mean_plddt_very_high,
COUNT(organismScientificName) AS num_predictions
FROM bigquery-public-data.deepmind_alphafold.metadata
GROUP by name
ORDER BY num_predictions DESC;
```
#### Producing lists of files
We expect that the most important use for the metadata will be to create subsets
of proteins according to various criteria, so that users can choose to only copy
a subset of the 214M proteins that exist in the dataset. An example query is
given below:
```sql
with file_rows AS (
with file_cols AS (
SELECT
CONCAT(entryID, '-model_v4.cif') as m,
CONCAT(entryID, '-predicted_aligned_error_v4.json') as p
FROM bigquery-public-data.deepmind_alphafold.metadata
WHERE organismScientificName = "Homo sapiens"
AND (fractionPlddtVeryHigh + fractionPlddtConfident) > 0.5
)
SELECT * FROM file_cols UNPIVOT (files for filetype in (m, p))
)
SELECT CONCAT('gs://public-datasets-deepmind-alphafold-v4/', files) as files
from file_rows
```
In this case, the list has been filtered to only include proteins from *Homo
sapiens* for which over half the residues are confident or better (>70 pLDDT).
This creates a table with one column "files", where each row is the cloud
location of one of the two file types that has been provided for each protein.
There is an additional `confidence_v4.json` file which contains the
per-residue pLDDT. This information is already in the CIF file but may be
preferred if only this information is required.
This allows users to bulk download the exact proteins they need, without having
to download the entire dataset. Other columns may also be used to select subsets
of proteins, and we point the user to the
[BigQuery documentation](https://cloud.google.com/bigquery/docs) to understand
other ways to filter for their desired protein lists. Likewise, the
documentation should be followed to download these file subsets locally, as the
most appropriate approach will depend on the filesize. Note that it may be
easier to download large files using [Colab](https://colab.research.google.com/)
(e.g. pandas to_csv).
#### Previous versions
Previous versions of AFDB will remain available at
[gs://public-datasets-deepmind-alphafold](https://console.cloud.google.com/storage/browser/public-datasets-deepmind-alphafold)
to enable reproducible research. We recommend using the latest version (v4).
\ No newline at end of file
alphafold/__init__.py 0 → 100644
View file @ eb93322b
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""An implementation of the inference pipeline of AlphaFold v2.0."""
alphafold/common/__init__.py 0 → 100644
View file @ eb93322b
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Common data types and constants used within Alphafold."""
alphafold/common/confidence.py 0 → 100644
View file @ eb93322b
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Functions for processing confidence metrics."""
import json
from typing import Dict, Optional, Tuple
import numpy as np
import scipy.special
def compute_plddt(logits: np.ndarray) -> np.ndarray:
"""Computes per-residue pLDDT from logits.
Args:
logits: [num_res, num_bins] output from the PredictedLDDTHead.
Returns:
plddt: [num_res] per-residue pLDDT.
"""
num_bins = logits.shape[-1]
bin_width = 1.0 / num_bins
bin_centers = np.arange(start=0.5 * bin_width, stop=1.0, step=bin_width)
probs = scipy.special.softmax(logits, axis=-1)
predicted_lddt_ca = np.sum(probs * bin_centers[None, :], axis=-1)
return predicted_lddt_ca * 100
def _confidence_category(score: float) -> str:
"""Categorizes pLDDT into: disordered (D), low (L), medium (M), high (H)."""
if 0 <= score < 50:
return 'D'
if 50 <= score < 70:
return 'L'
elif 70 <= score < 90:
return 'M'
elif 90 <= score <= 100:
return 'H'
else:
raise ValueError(f'Invalid pLDDT score {score}')
def confidence_json(plddt: np.ndarray) -> str:
"""Returns JSON with confidence score and category for every residue.
Args:
plddt: Per-residue confidence metric data.
Returns:
String with a formatted JSON.
Raises:
ValueError: If `plddt` has a rank different than 1.
"""
if plddt.ndim != 1:
raise ValueError(f'The plddt array must be rank 1, got: {plddt.shape}.')
confidence = {
'residueNumber': list(range(1, len(plddt) + 1)),
'confidenceScore': [round(float(s), 2) for s in plddt],
'confidenceCategory': [_confidence_category(s) for s in plddt],
}
return json.dumps(confidence, indent=None, separators=(',', ':'))
def _calculate_bin_centers(breaks: np.ndarray):
"""Gets the bin centers from the bin edges.
Args:
breaks: [num_bins - 1] the error bin edges.
Returns:
bin_centers: [num_bins] the error bin centers.
"""
step = (breaks[1] - breaks[0])
# Add half-step to get the center
bin_centers = breaks + step / 2
# Add a catch-all bin at the end.
bin_centers = np.concatenate([bin_centers, [bin_centers[-1] + step]],
axis=0)
return bin_centers
def _calculate_expected_aligned_error(
alignment_confidence_breaks: np.ndarray,
aligned_distance_error_probs: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
"""Calculates expected aligned distance errors for every pair of residues.
Args:
alignment_confidence_breaks: [num_bins - 1] the error bin edges.
aligned_distance_error_probs: [num_res, num_res, num_bins] the predicted
probs for each error bin, for each pair of residues.
Returns:
predicted_aligned_error: [num_res, num_res] the expected aligned distance
error for each pair of residues.
max_predicted_aligned_error: The maximum predicted error possible.
"""
bin_centers = _calculate_bin_centers(alignment_confidence_breaks)
# Tuple of expected aligned distance error and max possible error.
return (np.sum(aligned_distance_error_probs * bin_centers, axis=-1),
np.asarray(bin_centers[-1]))
def compute_predicted_aligned_error(
logits: np.ndarray,
breaks: np.ndarray) -> Dict[str, np.ndarray]:
"""Computes aligned confidence metrics from logits.
Args:
logits: [num_res, num_res, num_bins] the logits output from
PredictedAlignedErrorHead.
breaks: [num_bins - 1] the error bin edges.
Returns:
aligned_confidence_probs: [num_res, num_res, num_bins] the predicted
aligned error probabilities over bins for each residue pair.
predicted_aligned_error: [num_res, num_res] the expected aligned distance
error for each pair of residues.
max_predicted_aligned_error: The maximum predicted error possible.
"""
aligned_confidence_probs = scipy.special.softmax(
logits,
axis=-1)
predicted_aligned_error, max_predicted_aligned_error = (
_calculate_expected_aligned_error(
alignment_confidence_breaks=breaks,
aligned_distance_error_probs=aligned_confidence_probs))
return {
'aligned_confidence_probs': aligned_confidence_probs,
'predicted_aligned_error': predicted_aligned_error,
'max_predicted_aligned_error': max_predicted_aligned_error,
}
def pae_json(pae: np.ndarray, max_pae: float) -> str:
"""Returns the PAE in the same format as is used in the AFDB.
Note that the values are presented as floats to 1 decimal place, whereas AFDB
returns integer values.
Args:
pae: The n_res x n_res PAE array.
max_pae: The maximum possible PAE value.
Returns:
PAE output format as a JSON string.
"""
# Check the PAE array is the correct shape.
if pae.ndim != 2 or pae.shape[0] != pae.shape[1]:
raise ValueError(f'PAE must be a square matrix, got {pae.shape}')
# Round the predicted aligned errors to 1 decimal place.
rounded_errors = np.round(pae.astype(np.float64), decimals=1)
formatted_output = [{
'predicted_aligned_error': rounded_errors.tolist(),
'max_predicted_aligned_error': max_pae,
}]
return json.dumps(formatted_output, indent=None, separators=(',', ':'))
def predicted_tm_score(
logits: np.ndarray,
breaks: np.ndarray,
residue_weights: Optional[np.ndarray] = None,
asym_id: Optional[np.ndarray] = None,
interface: bool = False) -> np.ndarray:
"""Computes predicted TM alignment or predicted interface TM alignment score.
Args:
logits: [num_res, num_res, num_bins] the logits output from
PredictedAlignedErrorHead.
breaks: [num_bins] the error bins.
residue_weights: [num_res] the per residue weights to use for the
expectation.
asym_id: [num_res] the asymmetric unit ID - the chain ID. Only needed for
ipTM calculation, i.e. when interface=True.
interface: If True, interface predicted TM score is computed.
Returns:
ptm_score: The predicted TM alignment or the predicted iTM score.
"""
# residue_weights has to be in [0, 1], but can be floating-point, i.e. the
# exp. resolved head's probability.
if residue_weights is None:
residue_weights = np.ones(logits.shape[0])
bin_centers = _calculate_bin_centers(breaks)
num_res = int(np.sum(residue_weights))
# Clip num_res to avoid negative/undefined d0.
clipped_num_res = max(num_res, 19)
# Compute d_0(num_res) as defined by TM-score, eqn. (5) in Yang & Skolnick
# "Scoring function for automated assessment of protein structure template
# quality", 2004: http://zhanglab.ccmb.med.umich.edu/papers/2004_3.pdf
d0 = 1.24 * (clipped_num_res - 15) ** (1./3) - 1.8
# Convert logits to probs.
probs = scipy.special.softmax(logits, axis=-1)
# TM-Score term for every bin.
tm_per_bin = 1. / (1 + np.square(bin_centers) / np.square(d0))
# E_distances tm(distance).
predicted_tm_term = np.sum(probs * tm_per_bin, axis=-1)
pair_mask = np.ones(shape=(num_res, num_res), dtype=bool)
if interface:
pair_mask *= asym_id[:, None] != asym_id[None, :]
predicted_tm_term *= pair_mask
pair_residue_weights = pair_mask * (
residue_weights[None, :] * residue_weights[:, None])
normed_residue_mask = pair_residue_weights / (1e-8 + np.sum(
pair_residue_weights, axis=-1, keepdims=True))
per_alignment = np.sum(predicted_tm_term * normed_residue_mask, axis=-1)
return np.asarray(per_alignment[(per_alignment * residue_weights).argmax()])
alphafold/common/confidence_test.py 0 → 100644
View file @ eb93322b
# Copyright 2023 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Test confidence metrics."""
from absl.testing import absltest
from alphafold.common import confidence
import numpy as np
class ConfidenceTest(absltest.TestCase):
def test_pae_json(self):
pae = np.array([[0.01, 13.12345], [20.0987, 0.0]])
pae_json = confidence.pae_json(pae=pae, max_pae=31.75)
self.assertEqual(
pae_json, '[{"predicted_aligned_error":[[0.0,13.1],[20.1,0.0]],'
'"max_predicted_aligned_error":31.75}]')
def test_confidence_json(self):
plddt = np.array([42, 42.42])
confidence_json = confidence.confidence_json(plddt=plddt)
print(confidence_json)
self.assertEqual(
confidence_json,
('{"residueNumber":[1,2],'
'"confidenceScore":[42.0,42.42],'
'"confidenceCategory":["D","D"]}'),
)
if __name__ == '__main__':
absltest.main()
alphafold/common/mmcif_metadata.py 0 → 100644
View file @ eb93322b
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""mmCIF metadata."""
from typing import Mapping, Sequence
from alphafold import version
import numpy as np
_DISCLAIMER = """ALPHAFOLD DATA, COPYRIGHT (2021) DEEPMIND TECHNOLOGIES LIMITED.
THE INFORMATION PROVIDED IS THEORETICAL MODELLING ONLY AND CAUTION SHOULD BE
EXERCISED IN ITS USE. IT IS PROVIDED "AS-IS" WITHOUT ANY WARRANTY OF ANY KIND,
WHETHER EXPRESSED OR IMPLIED. NO WARRANTY IS GIVEN THAT USE OF THE INFORMATION
SHALL NOT INFRINGE THE RIGHTS OF ANY THIRD PARTY. DISCLAIMER: THE INFORMATION IS
NOT INTENDED TO BE A SUBSTITUTE FOR PROFESSIONAL MEDICAL ADVICE, DIAGNOSIS, OR
TREATMENT, AND DOES NOT CONSTITUTE MEDICAL OR OTHER PROFESSIONAL ADVICE. IT IS
AVAILABLE FOR ACADEMIC AND COMMERCIAL PURPOSES, UNDER CC-BY 4.0 LICENCE."""
# Authors of the Nature methods paper we reference in the mmCIF.
_MMCIF_PAPER_AUTHORS = (
'Jumper, John',
'Evans, Richard',
'Pritzel, Alexander',
'Green, Tim',
'Figurnov, Michael',
'Ronneberger, Olaf',
'Tunyasuvunakool, Kathryn',
'Bates, Russ',
'Zidek, Augustin',
'Potapenko, Anna',
'Bridgland, Alex',
'Meyer, Clemens',
'Kohl, Simon A. A.',
'Ballard, Andrew J.',
'Cowie, Andrew',
'Romera-Paredes, Bernardino',
'Nikolov, Stanislav',
'Jain, Rishub',
'Adler, Jonas',
'Back, Trevor',
'Petersen, Stig',
'Reiman, David',
'Clancy, Ellen',
'Zielinski, Michal',
'Steinegger, Martin',
'Pacholska, Michalina',
'Berghammer, Tamas',
'Silver, David',
'Vinyals, Oriol',
'Senior, Andrew W.',
'Kavukcuoglu, Koray',
'Kohli, Pushmeet',
'Hassabis, Demis',
)
# Authors of the mmCIF - we set them to be equal to the authors of the paper.
_MMCIF_AUTHORS = _MMCIF_PAPER_AUTHORS
def add_metadata_to_mmcif(
old_cif: Mapping[str, Sequence[str]], model_type: str
) -> Mapping[str, Sequence[str]]:
"""Adds AlphaFold metadata in the given mmCIF."""
cif = {}
# ModelCIF conformation dictionary.
cif['_audit_conform.dict_name'] = ['mmcif_ma.dic']
cif['_audit_conform.dict_version'] = ['1.3.9']
cif['_audit_conform.dict_location'] = [
'https://raw.githubusercontent.com/ihmwg/ModelCIF/master/dist/'
'mmcif_ma.dic'
]
# License and disclaimer.
cif['_pdbx_data_usage.id'] = ['1', '2']
cif['_pdbx_data_usage.type'] = ['license', 'disclaimer']
cif['_pdbx_data_usage.details'] = [
'Data in this file is available under a CC-BY-4.0 license.',
_DISCLAIMER,
]
cif['_pdbx_data_usage.url'] = [
'https://creativecommons.org/licenses/by/4.0/',
'?',
]
cif['_pdbx_data_usage.name'] = ['CC-BY-4.0', '?']
# Structure author details.
cif['_audit_author.name'] = []
cif['_audit_author.pdbx_ordinal'] = []
for author_index, author_name in enumerate(_MMCIF_AUTHORS, start=1):
cif['_audit_author.name'].append(author_name)
cif['_audit_author.pdbx_ordinal'].append(str(author_index))
# Paper author details.
cif['_citation_author.citation_id'] = []
cif['_citation_author.name'] = []
cif['_citation_author.ordinal'] = []
for author_index, author_name in enumerate(_MMCIF_PAPER_AUTHORS, start=1):
cif['_citation_author.citation_id'].append('primary')
cif['_citation_author.name'].append(author_name)
cif['_citation_author.ordinal'].append(str(author_index))
# Paper citation details.
cif['_citation.id'] = ['primary']
cif['_citation.title'] = [
'Highly accurate protein structure prediction with AlphaFold'
]
cif['_citation.journal_full'] = ['Nature']
cif['_citation.journal_volume'] = ['596']
cif['_citation.page_first'] = ['583']
cif['_citation.page_last'] = ['589']
cif['_citation.year'] = ['2021']
cif['_citation.journal_id_ASTM'] = ['NATUAS']
cif['_citation.country'] = ['UK']
cif['_citation.journal_id_ISSN'] = ['0028-0836']
cif['_citation.journal_id_CSD'] = ['0006']
cif['_citation.book_publisher'] = ['?']
cif['_citation.pdbx_database_id_PubMed'] = ['34265844']
cif['_citation.pdbx_database_id_DOI'] = ['10.1038/s41586-021-03819-2']
# Type of data in the dataset including data used in the model generation.
cif['_ma_data.id'] = ['1']
cif['_ma_data.name'] = ['Model']
cif['_ma_data.content_type'] = ['model coordinates']
# Description of number of instances for each entity.
cif['_ma_target_entity_instance.asym_id'] = old_cif['_struct_asym.id']
cif['_ma_target_entity_instance.entity_id'] = old_cif[
'_struct_asym.entity_id'
]
cif['_ma_target_entity_instance.details'] = ['.'] * len(
cif['_ma_target_entity_instance.entity_id']
)
# Details about the target entities.
cif['_ma_target_entity.entity_id'] = cif[
'_ma_target_entity_instance.entity_id'
]
cif['_ma_target_entity.data_id'] = ['1'] * len(
cif['_ma_target_entity.entity_id']
)
cif['_ma_target_entity.origin'] = ['.'] * len(
cif['_ma_target_entity.entity_id']
)
# Details of the models being deposited.
cif['_ma_model_list.ordinal_id'] = ['1']
cif['_ma_model_list.model_id'] = ['1']
cif['_ma_model_list.model_group_id'] = ['1']
cif['_ma_model_list.model_name'] = ['Top ranked model']
cif['_ma_model_list.model_group_name'] = [
f'AlphaFold {model_type} v{version.__version__} model'
]
cif['_ma_model_list.data_id'] = ['1']
cif['_ma_model_list.model_type'] = ['Ab initio model']
# Software used.
cif['_software.pdbx_ordinal'] = ['1']
cif['_software.name'] = ['AlphaFold']
cif['_software.version'] = [f'v{version.__version__}']
cif['_software.type'] = ['package']
cif['_software.description'] = ['Structure prediction']
cif['_software.classification'] = ['other']
cif['_software.date'] = ['?']
# Collection of software into groups.
cif['_ma_software_group.ordinal_id'] = ['1']
cif['_ma_software_group.group_id'] = ['1']
cif['_ma_software_group.software_id'] = ['1']
# Method description to conform with ModelCIF.
cif['_ma_protocol_step.ordinal_id'] = ['1', '2', '3']
cif['_ma_protocol_step.protocol_id'] = ['1', '1', '1']
cif['_ma_protocol_step.step_id'] = ['1', '2', '3']
cif['_ma_protocol_step.method_type'] = [
'coevolution MSA',
'template search',
'modeling',
]
# Details of the metrics use to assess model confidence.
cif['_ma_qa_metric.id'] = ['1', '2']
cif['_ma_qa_metric.name'] = ['pLDDT', 'pLDDT']
# Accepted values are distance, energy, normalised score, other, zscore.
cif['_ma_qa_metric.type'] = ['pLDDT', 'pLDDT']
cif['_ma_qa_metric.mode'] = ['global', 'local']
cif['_ma_qa_metric.software_group_id'] = ['1', '1']
# Global model confidence metric value.
cif['_ma_qa_metric_global.ordinal_id'] = ['1']
cif['_ma_qa_metric_global.model_id'] = ['1']
cif['_ma_qa_metric_global.metric_id'] = ['1']
global_plddt = np.mean(
[float(v) for v in old_cif['_atom_site.B_iso_or_equiv']]
)
cif['_ma_qa_metric_global.metric_value'] = [f'{global_plddt:.2f}']
cif['_atom_type.symbol'] = sorted(set(old_cif['_atom_site.type_symbol']))
return cif
alphafold/common/protein.py 0 → 100644
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alphafold/common/protein_test.py 0 → 100644
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# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for protein."""
import os
from absl.testing import absltest
from absl.testing import parameterized
from alphafold.common import protein
from alphafold.common import residue_constants
import numpy as np
# Internal import (7716).
TEST_DATA_DIR = 'alphafold/common/testdata/'
class ProteinTest(parameterized.TestCase):
def _check_shapes(self, prot, num_res):
"""Check that the processed shapes are correct."""
num_atoms = residue_constants.atom_type_num
self.assertEqual((num_res, num_atoms, 3), prot.atom_positions.shape)
self.assertEqual((num_res,), prot.aatype.shape)
self.assertEqual((num_res, num_atoms), prot.atom_mask.shape)
self.assertEqual((num_res,), prot.residue_index.shape)
self.assertEqual((num_res,), prot.chain_index.shape)
self.assertEqual((num_res, num_atoms), prot.b_factors.shape)
@parameterized.named_parameters(
dict(testcase_name='chain_A',
pdb_file='2rbg.pdb', chain_id='A', num_res=282, num_chains=1),
dict(testcase_name='chain_B',
pdb_file='2rbg.pdb', chain_id='B', num_res=282, num_chains=1),
dict(testcase_name='multichain',
pdb_file='2rbg.pdb', chain_id=None, num_res=564, num_chains=2))
def test_from_pdb_str(self, pdb_file, chain_id, num_res, num_chains):
pdb_file = os.path.join(absltest.get_default_test_srcdir(), TEST_DATA_DIR,
pdb_file)
with open(pdb_file) as f:
pdb_string = f.read()
prot = protein.from_pdb_string(pdb_string, chain_id)
self._check_shapes(prot, num_res)
self.assertGreaterEqual(prot.aatype.min(), 0)
# Allow equal since unknown restypes have index equal to restype_num.
self.assertLessEqual(prot.aatype.max(), residue_constants.restype_num)
self.assertLen(np.unique(prot.chain_index), num_chains)
def test_to_pdb(self):
with open(
os.path.join(absltest.get_default_test_srcdir(), TEST_DATA_DIR,
'2rbg.pdb')) as f:
pdb_string = f.read()
prot = protein.from_pdb_string(pdb_string)
pdb_string_reconstr = protein.to_pdb(prot)
for line in pdb_string_reconstr.splitlines():
self.assertLen(line, 80)
prot_reconstr = protein.from_pdb_string(pdb_string_reconstr)
np.testing.assert_array_equal(prot_reconstr.aatype, prot.aatype)
np.testing.assert_array_almost_equal(
prot_reconstr.atom_positions, prot.atom_positions)
np.testing.assert_array_almost_equal(
prot_reconstr.atom_mask, prot.atom_mask)
np.testing.assert_array_equal(
prot_reconstr.residue_index, prot.residue_index)
np.testing.assert_array_equal(
prot_reconstr.chain_index, prot.chain_index)
np.testing.assert_array_almost_equal(
prot_reconstr.b_factors, prot.b_factors)
@parameterized.named_parameters(
dict(
testcase_name='glucagon',
pdb_file='glucagon.pdb',
model_type='Monomer',
),
dict(testcase_name='7bui', pdb_file='5nmu.pdb', model_type='Multimer'),
)
def test_to_mmcif(self, pdb_file, model_type):
with open(
os.path.join(
absltest.get_default_test_srcdir(), TEST_DATA_DIR, pdb_file
)
) as f:
pdb_string = f.read()
prot = protein.from_pdb_string(pdb_string)
file_id = 'test'
mmcif_string = protein.to_mmcif(prot, file_id, model_type)
prot_reconstr = protein.from_mmcif_string(mmcif_string)
np.testing.assert_array_equal(prot_reconstr.aatype, prot.aatype)
np.testing.assert_array_almost_equal(
prot_reconstr.atom_positions, prot.atom_positions
)
np.testing.assert_array_almost_equal(
prot_reconstr.atom_mask, prot.atom_mask
)
np.testing.assert_array_equal(
prot_reconstr.residue_index, prot.residue_index
)
np.testing.assert_array_equal(prot_reconstr.chain_index, prot.chain_index)
np.testing.assert_array_almost_equal(
prot_reconstr.b_factors, prot.b_factors
)
def test_ideal_atom_mask(self):
with open(
os.path.join(
absltest.get_default_test_srcdir(), TEST_DATA_DIR, '2rbg.pdb'
)
) as f:
pdb_string = f.read()
prot = protein.from_pdb_string(pdb_string)
ideal_mask = protein.ideal_atom_mask(prot)
non_ideal_residues = set([102] + list(range(127, 286)))
for i, (res, atom_mask) in enumerate(
zip(prot.residue_index, prot.atom_mask)
):
if res in non_ideal_residues:
self.assertFalse(np.all(atom_mask == ideal_mask[i]), msg=f'{res}')
else:
self.assertTrue(np.all(atom_mask == ideal_mask[i]), msg=f'{res}')
def test_too_many_chains(self):
num_res = protein.PDB_MAX_CHAINS + 1
num_atom_type = residue_constants.atom_type_num
with self.assertRaises(ValueError):
_ = protein.Protein(
atom_positions=np.random.random([num_res, num_atom_type, 3]),
aatype=np.random.randint(0, 21, [num_res]),
atom_mask=np.random.randint(0, 2, [num_res]).astype(np.float32),
residue_index=np.arange(1, num_res+1),
chain_index=np.arange(num_res),
b_factors=np.random.uniform(1, 100, [num_res]))
if __name__ == '__main__':
absltest.main()
alphafold/common/residue_constants.py 0 → 100644
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alphafold/common/residue_constants_test.py 0 → 100644
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# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Test that residue_constants generates correct values."""
from absl.testing import absltest
from absl.testing import parameterized
from alphafold.common import residue_constants
import numpy as np
class ResidueConstantsTest(parameterized.TestCase):
@parameterized.parameters(
('ALA', 0),
('CYS', 1),
('HIS', 2),
('MET', 3),
('LYS', 4),
('ARG', 4),
)
def testChiAnglesAtoms(self, residue_name, chi_num):
chi_angles_atoms = residue_constants.chi_angles_atoms[residue_name]
self.assertLen(chi_angles_atoms, chi_num)
for chi_angle_atoms in chi_angles_atoms:
self.assertLen(chi_angle_atoms, 4)
def testChiGroupsForAtom(self):
for k, chi_groups in residue_constants.chi_groups_for_atom.items():
res_name, atom_name = k
for chi_group_i, atom_i in chi_groups:
self.assertEqual(
atom_name,
residue_constants.chi_angles_atoms[res_name][chi_group_i][atom_i])
@parameterized.parameters(
('ALA', 5), ('ARG', 11), ('ASN', 8), ('ASP', 8), ('CYS', 6), ('GLN', 9),
('GLU', 9), ('GLY', 4), ('HIS', 10), ('ILE', 8), ('LEU', 8), ('LYS', 9),
('MET', 8), ('PHE', 11), ('PRO', 7), ('SER', 6), ('THR', 7), ('TRP', 14),
('TYR', 12), ('VAL', 7)
)
def testResidueAtoms(self, atom_name, num_residue_atoms):
residue_atoms = residue_constants.residue_atoms[atom_name]
self.assertLen(residue_atoms, num_residue_atoms)
def testStandardAtomMask(self):
with self.subTest('Check shape'):
self.assertEqual(residue_constants.STANDARD_ATOM_MASK.shape, (21, 37,))
with self.subTest('Check values'):
str_to_row = lambda s: [c == '1' for c in s] # More clear/concise.
np.testing.assert_array_equal(
residue_constants.STANDARD_ATOM_MASK,
np.array([
# NB This was defined by c+p but looks sane.
str_to_row('11111 '), # ALA
str_to_row('111111 1 1 11 1 '), # ARG
str_to_row('111111 11 '), # ASP
str_to_row('111111 11 '), # ASN
str_to_row('11111 1 '), # CYS
str_to_row('111111 1 11 '), # GLU
str_to_row('111111 1 11 '), # GLN
str_to_row('111 1 '), # GLY
str_to_row('111111 11 1 1 '), # HIS
str_to_row('11111 11 1 '), # ILE
str_to_row('111111 11 '), # LEU
str_to_row('111111 1 1 1 '), # LYS
str_to_row('111111 11 '), # MET
str_to_row('111111 11 11 1 '), # PHE
str_to_row('111111 1 '), # PRO
str_to_row('11111 1 '), # SER
str_to_row('11111 1 1 '), # THR
str_to_row('111111 11 11 1 1 11 '), # TRP
str_to_row('111111 11 11 11 '), # TYR
str_to_row('11111 11 '), # VAL
str_to_row(' '), # UNK
]))
with self.subTest('Check row totals'):
# Check each row has the right number of atoms.
for row, restype in enumerate(residue_constants.restypes): # A, R, ...
long_restype = residue_constants.restype_1to3[restype] # ALA, ARG, ...
atoms_names = residue_constants.residue_atoms[
long_restype] # ['C', 'CA', 'CB', 'N', 'O'], ...
self.assertLen(atoms_names,
residue_constants.STANDARD_ATOM_MASK[row, :].sum(),
long_restype)
def testAtomTypes(self):
self.assertEqual(residue_constants.atom_type_num, 37)
self.assertEqual(residue_constants.atom_types[0], 'N')
self.assertEqual(residue_constants.atom_types[1], 'CA')
self.assertEqual(residue_constants.atom_types[2], 'C')
self.assertEqual(residue_constants.atom_types[3], 'CB')
self.assertEqual(residue_constants.atom_types[4], 'O')
self.assertEqual(residue_constants.atom_order['N'], 0)
self.assertEqual(residue_constants.atom_order['CA'], 1)
self.assertEqual(residue_constants.atom_order['C'], 2)
self.assertEqual(residue_constants.atom_order['CB'], 3)
self.assertEqual(residue_constants.atom_order['O'], 4)
self.assertEqual(residue_constants.atom_type_num, 37)
def testRestypes(self):
three_letter_restypes = [
residue_constants.restype_1to3[r] for r in residue_constants.restypes]
for restype, exp_restype in zip(
three_letter_restypes, sorted(residue_constants.restype_1to3.values())):
self.assertEqual(restype, exp_restype)
self.assertEqual(residue_constants.restype_num, 20)
def testSequenceToOneHotHHBlits(self):
one_hot = residue_constants.sequence_to_onehot(
'ABCDEFGHIJKLMNOPQRSTUVWXYZ-', residue_constants.HHBLITS_AA_TO_ID)
exp_one_hot = np.array(
[[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]])
np.testing.assert_array_equal(one_hot, exp_one_hot)
def testSequenceToOneHotStandard(self):
one_hot = residue_constants.sequence_to_onehot(
'ARNDCQEGHILKMFPSTWYV', residue_constants.restype_order)
np.testing.assert_array_equal(one_hot, np.eye(20))
def testSequenceToOneHotUnknownMapping(self):
seq = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
expected_out = np.zeros([26, 21])
for row, position in enumerate(
[0, 20, 4, 3, 6, 13, 7, 8, 9, 20, 11, 10, 12, 2, 20, 14, 5, 1, 15, 16,
20, 19, 17, 20, 18, 20]):
expected_out[row, position] = 1
aa_types = residue_constants.sequence_to_onehot(
sequence=seq,
mapping=residue_constants.restype_order_with_x,
map_unknown_to_x=True)
self.assertTrue((aa_types == expected_out).all())
@parameterized.named_parameters(
('lowercase', 'aaa'), # Insertions in A3M.
('gaps', '---'), # Gaps in A3M.
('dots', '...'), # Gaps in A3M.
('metadata', '>TEST'), # FASTA metadata line.
)
def testSequenceToOneHotUnknownMappingError(self, seq):
with self.assertRaises(ValueError):
residue_constants.sequence_to_onehot(
sequence=seq,
mapping=residue_constants.restype_order_with_x,
map_unknown_to_x=True)
if __name__ == '__main__':
absltest.main()
alphafold/common/stereo_chemical_props.txt 0 → 100644
View file @ eb93322b
Bond Residue Mean StdDev
CA-CB ALA 1.520 0.021
N-CA ALA 1.459 0.020
CA-C ALA 1.525 0.026
C-O ALA 1.229 0.019
CA-CB ARG 1.535 0.022
CB-CG ARG 1.521 0.027
CG-CD ARG 1.515 0.025
CD-NE ARG 1.460 0.017
NE-CZ ARG 1.326 0.013
CZ-NH1 ARG 1.326 0.013
CZ-NH2 ARG 1.326 0.013
N-CA ARG 1.459 0.020
CA-C ARG 1.525 0.026
C-O ARG 1.229 0.019
CA-CB ASN 1.527 0.026
CB-CG ASN 1.506 0.023
CG-OD1 ASN 1.235 0.022
CG-ND2 ASN 1.324 0.025
N-CA ASN 1.459 0.020
CA-C ASN 1.525 0.026
C-O ASN 1.229 0.019
CA-CB ASP 1.535 0.022
CB-CG ASP 1.513 0.021
CG-OD1 ASP 1.249 0.023
CG-OD2 ASP 1.249 0.023
N-CA ASP 1.459 0.020
CA-C ASP 1.525 0.026
C-O ASP 1.229 0.019
CA-CB CYS 1.526 0.013
CB-SG CYS 1.812 0.016
N-CA CYS 1.459 0.020
CA-C CYS 1.525 0.026
C-O CYS 1.229 0.019
CA-CB GLU 1.535 0.022
CB-CG GLU 1.517 0.019
CG-CD GLU 1.515 0.015
CD-OE1 GLU 1.252 0.011
CD-OE2 GLU 1.252 0.011
N-CA GLU 1.459 0.020
CA-C GLU 1.525 0.026
C-O GLU 1.229 0.019
CA-CB GLN 1.535 0.022
CB-CG GLN 1.521 0.027
CG-CD GLN 1.506 0.023
CD-OE1 GLN 1.235 0.022
CD-NE2 GLN 1.324 0.025
N-CA GLN 1.459 0.020
CA-C GLN 1.525 0.026
C-O GLN 1.229 0.019
N-CA GLY 1.456 0.015
CA-C GLY 1.514 0.016
C-O GLY 1.232 0.016
CA-CB HIS 1.535 0.022
CB-CG HIS 1.492 0.016
CG-ND1 HIS 1.369 0.015
CG-CD2 HIS 1.353 0.017
ND1-CE1 HIS 1.343 0.025
CD2-NE2 HIS 1.415 0.021
CE1-NE2 HIS 1.322 0.023
N-CA HIS 1.459 0.020
CA-C HIS 1.525 0.026
C-O HIS 1.229 0.019
CA-CB ILE 1.544 0.023
CB-CG1 ILE 1.536 0.028
CB-CG2 ILE 1.524 0.031
CG1-CD1 ILE 1.500 0.069
N-CA ILE 1.459 0.020
CA-C ILE 1.525 0.026
C-O ILE 1.229 0.019
CA-CB LEU 1.533 0.023
CB-CG LEU 1.521 0.029
CG-CD1 LEU 1.514 0.037
CG-CD2 LEU 1.514 0.037
N-CA LEU 1.459 0.020
CA-C LEU 1.525 0.026
C-O LEU 1.229 0.019
CA-CB LYS 1.535 0.022
CB-CG LYS 1.521 0.027
CG-CD LYS 1.520 0.034
CD-CE LYS 1.508 0.025
CE-NZ LYS 1.486 0.025
N-CA LYS 1.459 0.020
CA-C LYS 1.525 0.026
C-O LYS 1.229 0.019
CA-CB MET 1.535 0.022
CB-CG MET 1.509 0.032
CG-SD MET 1.807 0.026
SD-CE MET 1.774 0.056
N-CA MET 1.459 0.020
CA-C MET 1.525 0.026
C-O MET 1.229 0.019
CA-CB PHE 1.535 0.022
CB-CG PHE 1.509 0.017
CG-CD1 PHE 1.383 0.015
CG-CD2 PHE 1.383 0.015
CD1-CE1 PHE 1.388 0.020
CD2-CE2 PHE 1.388 0.020
CE1-CZ PHE 1.369 0.019
CE2-CZ PHE 1.369 0.019
N-CA PHE 1.459 0.020
CA-C PHE 1.525 0.026
C-O PHE 1.229 0.019
CA-CB PRO 1.531 0.020
CB-CG PRO 1.495 0.050
CG-CD PRO 1.502 0.033
CD-N PRO 1.474 0.014
N-CA PRO 1.468 0.017
CA-C PRO 1.524 0.020
C-O PRO 1.228 0.020
CA-CB SER 1.525 0.015
CB-OG SER 1.418 0.013
N-CA SER 1.459 0.020
CA-C SER 1.525 0.026
C-O SER 1.229 0.019
CA-CB THR 1.529 0.026
CB-OG1 THR 1.428 0.020
CB-CG2 THR 1.519 0.033
N-CA THR 1.459 0.020
CA-C THR 1.525 0.026
C-O THR 1.229 0.019
CA-CB TRP 1.535 0.022
CB-CG TRP 1.498 0.018
CG-CD1 TRP 1.363 0.014
CG-CD2 TRP 1.432 0.017
CD1-NE1 TRP 1.375 0.017
NE1-CE2 TRP 1.371 0.013
CD2-CE2 TRP 1.409 0.012
CD2-CE3 TRP 1.399 0.015
CE2-CZ2 TRP 1.393 0.017
CE3-CZ3 TRP 1.380 0.017
CZ2-CH2 TRP 1.369 0.019
CZ3-CH2 TRP 1.396 0.016
N-CA TRP 1.459 0.020
CA-C TRP 1.525 0.026
C-O TRP 1.229 0.019
CA-CB TYR 1.535 0.022
CB-CG TYR 1.512 0.015
CG-CD1 TYR 1.387 0.013
CG-CD2 TYR 1.387 0.013
CD1-CE1 TYR 1.389 0.015
CD2-CE2 TYR 1.389 0.015
CE1-CZ TYR 1.381 0.013
CE2-CZ TYR 1.381 0.013
CZ-OH TYR 1.374 0.017
N-CA TYR 1.459 0.020
CA-C TYR 1.525 0.026
C-O TYR 1.229 0.019
CA-CB VAL 1.543 0.021
CB-CG1 VAL 1.524 0.021
CB-CG2 VAL 1.524 0.021
N-CA VAL 1.459 0.020
CA-C VAL 1.525 0.026
C-O VAL 1.229 0.019
-
Angle Residue Mean StdDev
N-CA-CB ALA 110.1 1.4
CB-CA-C ALA 110.1 1.5
N-CA-C ALA 111.0 2.7
CA-C-O ALA 120.1 2.1
N-CA-CB ARG 110.6 1.8
CB-CA-C ARG 110.4 2.0
CA-CB-CG ARG 113.4 2.2
CB-CG-CD ARG 111.6 2.6
CG-CD-NE ARG 111.8 2.1
CD-NE-CZ ARG 123.6 1.4
NE-CZ-NH1 ARG 120.3 0.5
NE-CZ-NH2 ARG 120.3 0.5
NH1-CZ-NH2 ARG 119.4 1.1
N-CA-C ARG 111.0 2.7
CA-C-O ARG 120.1 2.1
N-CA-CB ASN 110.6 1.8
CB-CA-C ASN 110.4 2.0
CA-CB-CG ASN 113.4 2.2
CB-CG-ND2 ASN 116.7 2.4
CB-CG-OD1 ASN 121.6 2.0
ND2-CG-OD1 ASN 121.9 2.3
N-CA-C ASN 111.0 2.7
CA-C-O ASN 120.1 2.1
N-CA-CB ASP 110.6 1.8
CB-CA-C ASP 110.4 2.0
CA-CB-CG ASP 113.4 2.2
CB-CG-OD1 ASP 118.3 0.9
CB-CG-OD2 ASP 118.3 0.9
OD1-CG-OD2 ASP 123.3 1.9
N-CA-C ASP 111.0 2.7
CA-C-O ASP 120.1 2.1
N-CA-CB CYS 110.8 1.5
CB-CA-C CYS 111.5 1.2
CA-CB-SG CYS 114.2 1.1
N-CA-C CYS 111.0 2.7
CA-C-O CYS 120.1 2.1
N-CA-CB GLU 110.6 1.8
CB-CA-C GLU 110.4 2.0
CA-CB-CG GLU 113.4 2.2
CB-CG-CD GLU 114.2 2.7
CG-CD-OE1 GLU 118.3 2.0
CG-CD-OE2 GLU 118.3 2.0
OE1-CD-OE2 GLU 123.3 1.2
N-CA-C GLU 111.0 2.7
CA-C-O GLU 120.1 2.1
N-CA-CB GLN 110.6 1.8
CB-CA-C GLN 110.4 2.0
CA-CB-CG GLN 113.4 2.2
CB-CG-CD GLN 111.6 2.6
CG-CD-OE1 GLN 121.6 2.0
CG-CD-NE2 GLN 116.7 2.4
OE1-CD-NE2 GLN 121.9 2.3
N-CA-C GLN 111.0 2.7
CA-C-O GLN 120.1 2.1
N-CA-C GLY 113.1 2.5
CA-C-O GLY 120.6 1.8
N-CA-CB HIS 110.6 1.8
CB-CA-C HIS 110.4 2.0
CA-CB-CG HIS 113.6 1.7
CB-CG-ND1 HIS 123.2 2.5
CB-CG-CD2 HIS 130.8 3.1
CG-ND1-CE1 HIS 108.2 1.4
ND1-CE1-NE2 HIS 109.9 2.2
CE1-NE2-CD2 HIS 106.6 2.5
NE2-CD2-CG HIS 109.2 1.9
CD2-CG-ND1 HIS 106.0 1.4
N-CA-C HIS 111.0 2.7
CA-C-O HIS 120.1 2.1
N-CA-CB ILE 110.8 2.3
CB-CA-C ILE 111.6 2.0
CA-CB-CG1 ILE 111.0 1.9
CB-CG1-CD1 ILE 113.9 2.8
CA-CB-CG2 ILE 110.9 2.0
CG1-CB-CG2 ILE 111.4 2.2
N-CA-C ILE 111.0 2.7
CA-C-O ILE 120.1 2.1
N-CA-CB LEU 110.4 2.0
CB-CA-C LEU 110.2 1.9
CA-CB-CG LEU 115.3 2.3
CB-CG-CD1 LEU 111.0 1.7
CB-CG-CD2 LEU 111.0 1.7
CD1-CG-CD2 LEU 110.5 3.0
N-CA-C LEU 111.0 2.7
CA-C-O LEU 120.1 2.1
N-CA-CB LYS 110.6 1.8
CB-CA-C LYS 110.4 2.0
CA-CB-CG LYS 113.4 2.2
CB-CG-CD LYS 111.6 2.6
CG-CD-CE LYS 111.9 3.0
CD-CE-NZ LYS 111.7 2.3
N-CA-C LYS 111.0 2.7
CA-C-O LYS 120.1 2.1
N-CA-CB MET 110.6 1.8
CB-CA-C MET 110.4 2.0
CA-CB-CG MET 113.3 1.7
CB-CG-SD MET 112.4 3.0
CG-SD-CE MET 100.2 1.6
N-CA-C MET 111.0 2.7
CA-C-O MET 120.1 2.1
N-CA-CB PHE 110.6 1.8
CB-CA-C PHE 110.4 2.0
CA-CB-CG PHE 113.9 2.4
CB-CG-CD1 PHE 120.8 0.7
CB-CG-CD2 PHE 120.8 0.7
CD1-CG-CD2 PHE 118.3 1.3
CG-CD1-CE1 PHE 120.8 1.1
CG-CD2-CE2 PHE 120.8 1.1
CD1-CE1-CZ PHE 120.1 1.2
CD2-CE2-CZ PHE 120.1 1.2
CE1-CZ-CE2 PHE 120.0 1.8
N-CA-C PHE 111.0 2.7
CA-C-O PHE 120.1 2.1
N-CA-CB PRO 103.3 1.2
CB-CA-C PRO 111.7 2.1
CA-CB-CG PRO 104.8 1.9
CB-CG-CD PRO 106.5 3.9
CG-CD-N PRO 103.2 1.5
CA-N-CD PRO 111.7 1.4
N-CA-C PRO 112.1 2.6
CA-C-O PRO 120.2 2.4
N-CA-CB SER 110.5 1.5
CB-CA-C SER 110.1 1.9
CA-CB-OG SER 111.2 2.7
N-CA-C SER 111.0 2.7
CA-C-O SER 120.1 2.1
N-CA-CB THR 110.3 1.9
CB-CA-C THR 111.6 2.7
CA-CB-OG1 THR 109.0 2.1
CA-CB-CG2 THR 112.4 1.4
OG1-CB-CG2 THR 110.0 2.3
N-CA-C THR 111.0 2.7
CA-C-O THR 120.1 2.1
N-CA-CB TRP 110.6 1.8
CB-CA-C TRP 110.4 2.0
CA-CB-CG TRP 113.7 1.9
CB-CG-CD1 TRP 127.0 1.3
CB-CG-CD2 TRP 126.6 1.3
CD1-CG-CD2 TRP 106.3 0.8
CG-CD1-NE1 TRP 110.1 1.0
CD1-NE1-CE2 TRP 109.0 0.9
NE1-CE2-CD2 TRP 107.3 1.0
CE2-CD2-CG TRP 107.3 0.8
CG-CD2-CE3 TRP 133.9 0.9
NE1-CE2-CZ2 TRP 130.4 1.1
CE3-CD2-CE2 TRP 118.7 1.2
CD2-CE2-CZ2 TRP 122.3 1.2
CE2-CZ2-CH2 TRP 117.4 1.0
CZ2-CH2-CZ3 TRP 121.6 1.2
CH2-CZ3-CE3 TRP 121.2 1.1
CZ3-CE3-CD2 TRP 118.8 1.3
N-CA-C TRP 111.0 2.7
CA-C-O TRP 120.1 2.1
N-CA-CB TYR 110.6 1.8
CB-CA-C TYR 110.4 2.0
CA-CB-CG TYR 113.4 1.9
CB-CG-CD1 TYR 121.0 0.6
CB-CG-CD2 TYR 121.0 0.6
CD1-CG-CD2 TYR 117.9 1.1
CG-CD1-CE1 TYR 121.3 0.8
CG-CD2-CE2 TYR 121.3 0.8
CD1-CE1-CZ TYR 119.8 0.9
CD2-CE2-CZ TYR 119.8 0.9
CE1-CZ-CE2 TYR 119.8 1.6
CE1-CZ-OH TYR 120.1 2.7
CE2-CZ-OH TYR 120.1 2.7
N-CA-C TYR 111.0 2.7
CA-C-O TYR 120.1 2.1
N-CA-CB VAL 111.5 2.2
CB-CA-C VAL 111.4 1.9
CA-CB-CG1 VAL 110.9 1.5
CA-CB-CG2 VAL 110.9 1.5
CG1-CB-CG2 VAL 110.9 1.6
N-CA-C VAL 111.0 2.7
CA-C-O VAL 120.1 2.1
-
Non-bonded distance Minimum Dist Tolerance
C-C 3.4 1.5
C-N 3.25 1.5
C-S 3.5 1.5
C-O 3.22 1.5
N-N 3.1 1.5
N-S 3.35 1.5
N-O 3.07 1.5
O-S 3.32 1.5
O-O 3.04 1.5
S-S 2.03 1.0
-
alphafold/common/testdata/2rbg.pdb 0 → 100755
View file @ eb93322b
This diff is collapsed.
alphafold/common/testdata/5nmu.pdb 0 → 100644
View file @ eb93322b
This diff is collapsed.
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