### Clone Repository and Install LocalColabFold

Source: https://github.com/yoshitakamo/localcolabfold/blob/main/README.md

Clones the LocalColabFold repository and installs the necessary packages using pixi. This is a common step for both Linux and macOS installations.

```bash
git clone https://github.com/yoshitakamo/localcolabfold.git
cd localcolabfold
pixi install && pixi run setup
```

--------------------------------

### Install Pixi Package Manager

Source: https://github.com/yoshitakamo/localcolabfold/blob/main/README.md

Installs the pixi package manager using a curl command. This is a prerequisite for installing LocalColabFold.

```bash
curl -fsSL https://pixi.sh/install.sh | sh
```

--------------------------------

### FASTA format example for a single protein

Source: https://github.com/yoshitakamo/localcolabfold/blob/main/README.md

Input a single protein sequence in FASTA format. The header line starting with '>' will be used as the prefix for output files. Line breaks are acceptable within the amino acid sequence.

```fasta
>sp|P61823
MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAASSSNYCNQMMKSRN
LTKDRCKPVNTFVHESLADVQAVCSQKNVACKNGQTNCYQSYSTMSITDCRETGSSKYPN
CAYKTTQANKHIIVACEGNPYVPVHFDASV
```

--------------------------------

### Install Pixi with Homebrew on macOS

Source: https://github.com/yoshitakamo/localcolabfold/blob/main/README.md

Installs the pixi package manager on macOS using Homebrew. This is a prerequisite for installing LocalColabFold on Apple Silicon.

```bash
brew install pixi
```

--------------------------------

### Run ColabFold Batch Sample Script

Source: https://github.com/yoshitakamo/localcolabfold/blob/main/README.md

Executes the sample script to start structure prediction using colabfold_batch. Ensure the PATH is correctly set.

```bash
bash run_colabfoldbatch_sample.sh
```

--------------------------------

### a3m format example for MSA input

Source: https://github.com/yoshitakamo/localcolabfold/blob/main/README.md

Input multiple sequence alignment (MSA) data in a3m format. Ensure the file is compatible with ColabFold for multimer predictions.

```a3m
positional arguments:
  input                 Can be one of the following: Directory with fasta/a3m
                        files, a csv/tsv file, a fasta file or an a3m file
  results               Directory to write the results to
```

--------------------------------

### Update LocalColabFold Script

Source: https://github.com/yoshitakamo/localcolabfold/blob/main/README.md

Execute this script to update your localcolabfold installation to the latest version. Ensure you set the correct OS variable and navigate to your installation directory before running.

```bash
# set your OS. Select one of the following variables {linux,intelmac,M1mac}
$ OS=linux # if Linux
# navigate to the directory where you installed localcolabfold, e.g.
$ cd /home/moriwaki/Desktop/localcolabfold/
# get the latest updater
$ wget https://raw.githubusercontent.com/YoshitakaMo/localcolabfold/main/update_${OS}.sh -O update_${OS}.sh
$ chmod +x update_${OS}.sh
# execute it.
$ ./update_${OS}.sh .
```

--------------------------------

### CSV format example for protein sequences

Source: https://github.com/yoshitakamo/localcolabfold/blob/main/README.md

Input protein sequences in CSV format, with 'id' and 'sequence' columns separated by commas. This format is suitable for batch predictions.

```csv
id,sequence
5AWL_1,YYDPETGTWY
3G5O_A_3G5O_B,MRILPISTIKGKLNEFVDAVSSTQDQITITKNGAPAAVLVGADEWESLQETLYWLAQPGIRESIAEADADIASGRTYGEDEIRAEFGVPRRPH:MPYTVRFTTTARRDLHKLPPRILAAVVEFAFGDLSREPLRVGKPLRRELAGTFSARRGTYRLLYRIDDEHTTVVILRVDHRADIYRR
```

--------------------------------

### FASTA format example for protein multimers

Source: https://github.com/yoshitakamo/localcolabfold/blob/main/README.md

For predicting multimers, separate protein sequences within the FASTA file using a colon ':'. Each sequence will be predicted as part of a multimer complex.

```fasta
>1BJP_homohexamer
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR
```

```fasta
>3KUD_RasRaf_complex
MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQ
YMRTGEGFLCVFAINNTKSFEDIHQYREQIKRVKDSDDVPMVLVGNKCDLAARTVESRQAQDLARSYGIP
YIETSAKTRQGVEDAFYTLVREIRQH:PSKT SNTIRVFLPNKQRTVVNVRNGMSLHDCLMKALKVRGLQPECCAVFRLLHEHKGKKARLDWNTDAAS
LIGEELQVDFL
```

--------------------------------

### Set Environment Variables for WSL2 GPU Usage

Source: https://github.com/yoshitakamo/localcolabfold/blob/main/README.md

Sets environment variables to optimize GPU memory usage and allocation for TensorFlow on WSL2. These variables are crucial for efficient structure prediction with GPUs.

```bash
export TF_FORCE_UNIFIED_MEMORY="1"
export XLA_PYTHON_CLIENT_MEM_FRACTION="4.0"
export XLA_PYTHON_CLIENT_ALLOCATOR="platform"
export TF_FORCE_GPU_ALLOW_GROWTH="true"
```

--------------------------------

### Check CUDA Compiler Version

Source: https://github.com/yoshitakamo/localcolabfold/blob/main/README.md

Verify that your CUDA compiler driver is version 11.8 or later. This command is used on Linux systems.

```shell
$ nvcc --version
  nvcc: NVIDIA (R) Cuda compiler driver
  Copyright (c) 2005-2022 NVIDIA Corporation
  Built on Wed_Sep_21_10:33:58_PDT_2022
  Cuda compilation tools, release 11.8, V11.8.89
  Build cuda_11.8.r11.8/compiler.31833905_0
```

--------------------------------

### Enable Case Sensitivity for WSL2

Source: https://github.com/yoshitakamo/localcolabfold/blob/main/README.md

Enables case sensitivity for file system operations in WSL2 to resolve potential symbolic link issues. This command is run on Windows PowerShell.

```powershell
fsutil file SetCaseSensitiveInfo path\to\localcolabfold\installation enable
```

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