Add description about flags

README.md

@@ -39,27 +39,28 @@ Built on Mon_Oct_12_20:09:46_PDT_2020
 Cuda compilation tools, release 11.1, V11.1.105
 Build cuda_11.1.TC455_06.29190527_0
 </pre>DO NOT use `nvidia-smi` to check the version.<br>See [NVIDIA CUDA Installation Guide for Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) if you haven't installed it.
-1. Make sure your GNU compiler version is **4.9 or later** because `GLIBCXX_3.4.20` is required:<pre>$ gcc --version
+3. Make sure your GNU compiler version is **4.9 or later** because `GLIBCXX_3.4.20` is required:<pre>$ gcc --version
 gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
 Copyright (C) 2019 Free Software Foundation, Inc.
 This is free software; see the source for copying conditions.  There is NO
 warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 </pre>If the version is 4.8.5 or older (e.g. CentOS 7), install a new one and add `PATH` to it.
-1. Download `install_colabbatch_linux.sh` from this repository:<pre>$ wget https://raw.githubusercontent.com/YoshitakaMo/localcolabfold/main/install_colabbatch_linux.sh</pre> and run it in the directory where you want to install:<pre>$ bash install_colabbatch_linux.sh</pre>About 5 minutes later, `colabfold_batch` directory will be created. Do not move this directory after the installation.
+4. Download `install_colabbatch_linux.sh` from this repository:<pre>$ wget https://raw.githubusercontent.com/YoshitakaMo/localcolabfold/main/install_colabbatch_linux.sh</pre> and run it in the directory where you want to install:<pre>$ bash install_colabbatch_linux.sh</pre>About 5 minutes later, `colabfold_batch` directory will be created. Do not move this directory after the installation.
 
     Keep the network unblocked. And **check the log** output to see if there are any errors.
 
     If you find errors in the output log, the easiest way is to check the network and delete the colabfold_batch directory, then re-run the installation script.
 
-2. Add environment variable PATH:<pre># For bash or zsh<br># e.g. export PATH="/home/moriwaki/Desktop/colabfold_batch/colabfold-conda/bin:\$PATH"<br>export PATH="<COLABFOLDBATCH_DIR>/bin:\$PATH"</pre>
-It is recommended to add this export command to \~/.bashrc and restart bash (\~/.bashrc will be executed every time bash is started)
+5. Add environment variable PATH:<pre># For bash or zsh<br># e.g. export PATH="/home/moriwaki/Desktop/colabfold_batch/colabfold-conda/bin:\$PATH"<br>export PATH="<COLABFOLDBATCH_DIR>/bin:\$PATH"</pre>
+It is recommended to add this export command to `~/.bashrc` and restart bash (`~/.bashrc` will be executed every time bash is started)
 
-3. To run the prediction, type <pre>colabfold_batch --amber --templates --num-recycle 3 --use-gpu-relax inputfile outputdir/ </pre>The result files will be created in the `outputdir`.
-Just use cpu to run the prediction, type <pre>colabfold_batch --amber --templates --num-recycle 3 --use-gpu-relax inputfile outputdir/ --cpu</pre>
-To run the AlphaFold2-multimer with the versioned AF2-multimer weights, add `--model-type alphafold2_multimer_v3` in the arguments. e.g. <pre>colabfold_batch --amber --templates --num-recycle 3 --use-gpu-relax --model-type alphafold2_multimer_v3 inputfile outputdir/</pre>`alphafold2_multimer_v1, alphafold2_multimer_v2` are also available. Default is `auto` (use `alphafold2_ptm` for monomers and
-`alphafold2_multimer_v3` for complexes.)
+6. To run the prediction, type <pre>colabfold_batch input outputdir/</pre>The result files will be created in the `outputdir`. This command will execute the prediction without templates and relaxation (energy minimization). If you want to use templates and relaxation, add `--templates` and `--amber` flags, respectively. For example,
 
-For more details, see `colabfold_batch --help`.
+    <pre>colabfold_batch --templates --amber input outputdir/</pre>
+
+    To run the AlphaFold2-multimer with the versioned AF2-multimer weights, add `--model-type alphafold2_multimer_v3` in the arguments. e.g. <pre>colabfold_batch --templates --amber --model-type alphafold2_multimer_v3 input outputdir/</pre>`alphafold2_multimer_v1, alphafold2_multimer_v2` are also available. Default is `auto` (use `alphafold2_ptm` for monomers and `alphafold2_multimer_v3` for complexes.)
+
+For more details, see [Flags](#flags) and `colabfold_batch --help`.
 
 #### For WSL2 (in windows)
 
@@ -173,6 +174,22 @@ id,sequence
 
 You can input your a3m format MSA file. For multimer predictions, the a3m file should be compatible with colabfold format.
 
+### Flags
+
+These flags are useful for the predictions.
+
+- **`--amber`** : Use amber for structure refinement (relaxation / energy minimization). To control number of top ranked structures are relaxed set `--num-relax`.
+- **`--templates`** : Use templates from pdb.
+- **`--use-gpu-relax`** : Run amber on NVidia GPU instead of CPU. This feature is only available on a machine with Nvidia GPUs.
+- **`--num-recycle <int>`** : Number of prediction recycles. Increasing recycles can improve the quality but slows down the prediction. Default is `3`. (e.g. `--num-recycle 10`)
+- `--custom-template-path <directory>` : Restrict template files used for `--template` to only those contained in the specified directory. This flag enables us to use non-public pdb files for the prediction. See also https://github.com/sokrypton/ColabFold/issues/177 .
+- `--random-seed <int>` **Changing the seed for the random number generator can result in different structure predictions.** (e.g. `--random-seed 42`)
+- `--num-seeds <int>` Number of seeds to try. Will iterate from range(random_seed, random_seed+num_seeds). (e.g. `--num-seed 5`)
+- `--max-msa` : Defines: `max-seq:max-extra-seq` number of sequences to use (e.g. `--max-msa 512:1024`). `--max-seq` and `--max-extra-seq` arguments are also available if you want to specify separately. This is a reimplementation of the paper of [Sampling alternative conformational states of transporters and receptors with AlphaFold2](https://elifesciences.org/articles/75751) demonstrated by del Alamo *et al*.
+- `--use-dropout` : activate dropouts during inference to sample from uncertainity of the models.
+- `--overwrite-existing-results` : Overwrite the result files.
+- For more information, `colabfold_batch --help`.
+
 ## How to update
 
 Since [ColabFold](https://github.com/sokrypton/ColabFold) is still a work in progress, your localcolabfold should be also updated frequently to use the latest features. An easy-to-use update script is provided for this purpose.

README_ja.md

@@ -4,6 +4,7 @@
 
 ## アップデート情報
 
+- 2023年2月5日, version 1.5.0-preリリース。
 - 2022年6月18日, version 1.4.0 リリース。[Release v1.4.0](https://github.com/YoshitakaMo/localcolabfold/releases/tag/v1.4.0)
 - 2021年12月9日, β版。簡単に使えるアップデートスクリプトを追加。[アップデートのやり方](#アップデートのやり方)を参照。
 - 2021年12月4日, LocalColabFoldは最新版の[pipでインストール可能なColabFold](https://github.com/sokrypton/ColabFold#running-locally)に対応しました。このリポジトリではrelax（構造最適化）処理を行うために必要な他のパラメータファイルとともにColabFoldをインストールするためのスクリプトを提供しています。AlphaFoldとAlphaFold-Multimerの重みパラメータは初回の実行時に自動的にダウンロードされます。