AlphaFold Structure Prediction Software
Welcome to the AlphaFold structure prediction Colabfold!!! This is designed to predict the 3D structure of proteins based on their amino acid sequence using MMseqs2. It is implemented in Google Colab and can be run in a web browser.
To get started, follow these steps: To access the AlphaFold structure prediction colabfold, start by signing into your Google account and visiting https://google.com. Then, download the AlphaFold.ipynb file onto your computer and upload it to your Google Drive. From there, simply open the file from your Google Drive to launch it in the Google Colab environment. After launching the AlphaFold software in the Google Colab environment, the next step is to provide the input for structure prediction. This input is a FASTA sequence containing the amino acid sequence of the protein that you want to predict the structure of. For example, I used the AlphaFold colab environment to predict the structure of triacylglycerol lipase, which is present in Staphylococcus xylosus (UniprotID: E7CRL0), I pasted the FASTA sequence of the protein into the designated input field and named the output file "ShakthiAlphafold.". There are several options available to customize the process, so I selected the parameters that are needed and set them accordingly. For Multiple Sequence Alignment, a software program called MMseqs2 is used to quickly and precisely align and search protein sequences. Proteomics and bioinformatics research frequently use this tool, which is made to be quick, sensitive, and accurate.
Once you have completed all the steps in the advanced settings, you can choose the modeling tool that you want to use. There are three options available:
-
AlphaFold2-ptm: It is intended to forecast protein's transmembrane topology, which describes how the transmembrane segments of a protein are arranged and oriented within the membrane.
-
AlphaFold-multimer-v [1,2] : The AlphaFold software suite includes tools called AlphaFold-multimer-v [1,2]. They are intended to forecast the multi-subunit proteins' quaternary structure, which describes how the protein's subunits are arranged within the larger protein complex. After that whether or not to add this file to your Drive. You can simply choose the "save_to_google_drive" option if you want to. To start the prediction process, go to the 'Runtime' menu in the Google Colab environment and select the 'Run All' option. While using the Google Colab environment, you will have access to cloud computing resources such as 12 GB of RAM and 75 GB of disk space, as well as a GPU. The specific resources available to you may vary depending on your account benefits or any current offers.
The running time of the AlphaFold software may be influenced by your internet connection speed, so it is important to have a good network connection before starting the prediction process. When you run the software in the Google Colab environment, it will begin executing each cell in sequence and will download any necessary tools or dependencies.
The process will also include ranking the predicted protein structures using pLDDT and pTM-score.
1)pLDDT (predicted local disulfide distance deviation) is used to evaluate the accuracy of the predicted disulfide bond distances in the protein structure. Lower values indicate a higher quality prediction, and it is calculated as the absolute deviation between the predicted distance and the experimental distance.
2)pTM-score (prediction of the transmembrane topology of proteins) is used to evaluate the quality of the predicted protein structure. Lower values indicate a higher quality prediction, and it is based on the root mean square deviation (RMSD) between the predicted structure and a reference structure. Once the prediction process is complete, the AlphaFold will display the predicted protein structure and indicate confidence in the prediction. In addition, a series of graphs will be generated to illustrate various aspects of the prediction, such as the sequence coverage and the accuracy of the predicted disulfide bond distances within the protein structure (predicted IDDT).
Upon completion, the colab will also produce a ZIP file that can be downloaded to your computer. This file contains all of the predicted protein structures and can be unzipped to access them. Simply download the ZIP file and unzip it on your computer to retrieve the predicted structures in the PDB file.
As previously mentioned, the predicted ZIP file for the job "ShakthiAlphafold" is included in this repository. You can access this file to view all of the predicted protein structures, which are in PDB format. Alternatively, you can download the ZIP file to your computer and unzip it to access the individual predicted structures.
#Acknowledgment Using AlphaFold has been a truly enlightening experience for me. The software's ability to accurately predict protein structures is truly impressive, and it has opened up new possibilities for understanding protein function and design.
I am grateful to AlphaFold and would like to express my appreciation to the developers and researchers behind the software. Their dedication and hard work have truly made a difference in the field of protein structure prediction, and I look forward to seeing what new insights and discoveries will be made possible with AlphaFold in the future. Thank you for your contributions to the scientific community!
For more information read this article AlphaFold Structure Prediction Software
Welcome to the AlphaFold structure prediction Colabfold!!! This is designed to predict the 3D structure of proteins based on their amino acid sequence using MMseqs2. It is implemented in Google Colab and can be run in a web browser.
Getting Started To get started, follow these steps: To access the AlphaFold structure prediction colabfold, start by signing into your Google account and visiting https://google.com. Then, download the AlphaFold.ipynb file onto your computer and upload it to your Google Drive. From there, simply open the file from your Google Drive to launch it in the Google Colab environment. After launching the AlphaFold software in the Google Colab environment, the next step is to provide the input for structure prediction. This input is a FASTA sequence containing the amino acid sequence of the protein that you want to predict the structure of. For example, I used the AlphaFold colab environment to predict the structure of triacylglycerol lipase, which is present in Staphylococcus xylosus (UniprotID: E7CRL0), I pasted the FASTA sequence of the protein into the designated input field and named the output file "ShakthiAlphafold.". There are several options available to customize the process, so I selected the parameters that are needed and set them accordingly. For Multiple Sequence Alignment, a software program called MMseqs2 is used to quickly and precisely align and search protein sequences. Proteomics and bioinformatics research frequently use this tool, which is made to be quick, sensitive, and accurate.
Once you have completed all the steps in the advanced settings, you can choose the modeling tool that you want to use. There are three options available:
-
AlphaFold2-ptm: It is intended to forecast protein's transmembrane topology, which describes how the transmembrane segments of a protein are arranged and oriented within the membrane.
-
AlphaFold-multimer-v [1,2] : The AlphaFold software suite includes tools called AlphaFold-multimer-v [1,2]. They are intended to forecast the multi-subunit proteins' quaternary structure, which describes how the protein's subunits are arranged within the larger protein complex. After that whether or not to add this file to your Drive. You can simply choose the "save_to_google_drive" option if you want to. To start the prediction process, go to the 'Runtime' menu in the Google Colab environment and select the 'Run All' option. While using the Google Colab environment, you will have access to cloud computing resources such as 12 GB of RAM and 75 GB of disk space, as well as a GPU. The specific resources available to you may vary depending on your account benefits or any current offers.
The running time of the AlphaFold software may be influenced by your internet connection speed, so it is important to have a good network connection before starting the prediction process. When you run the software in the Google Colab environment, it will begin executing each cell in sequence and will download any necessary tools or dependencies.
The process will also include ranking the predicted protein structures using pLDDT and pTM-score.
1)pLDDT (predicted local disulfide distance deviation) is used to evaluate the accuracy of the predicted disulfide bond distances in the protein structure. Lower values indicate a higher quality prediction, and it is calculated as the absolute deviation between the predicted distance and the experimental distance.
2)pTM-score (prediction of the transmembrane topology of proteins) is used to evaluate the quality of the predicted protein structure. Lower values indicate a higher quality prediction, and it is based on the root mean square deviation (RMSD) between the predicted structure and a reference structure. Once the prediction process is complete, the AlphaFold will display the predicted protein structure and indicate confidence in the prediction. In addition, a series of graphs will be generated to illustrate various aspects of the prediction, such as the sequence coverage and the accuracy of the predicted disulfide bond distances within the protein structure (predicted IDDT).
Upon completion, the colab will also produce a ZIP file that can be downloaded to your computer. This file contains all of the predicted protein structures and can be unzipped to access them. Simply download the ZIP file and unzip it on your computer to retrieve the predicted structures in the PDB file.
As previously mentioned, the predicted ZIP file for the job "ShakthiAlphafold" is included in this repository. You can access this file to view all of the predicted protein structures, which are in PDB format. Alternatively, you can download the ZIP file to your computer and unzip it to access the individual predicted structures.
Acknowledgment Using AlphaFold has been a truly enlightening experience for me. The software's ability to accurately predict protein structures is truly impressive, and it has opened up new possibilities for understanding protein function and design.
I am grateful to AlphaFold and would like to express my appreciation to the developers and researchers behind the software. Their dedication and hard work have truly made a difference in the field of protein structure prediction, and I look forward to seeing what new insights and discoveries will be made possible with AlphaFold in the future. Thank you for your contributions to the scientific community!