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Abstract

The Estimation of Model Accuracy problem is a cornerstone problem in the field of bioinformatics. Due to the resources required for X-ray crystallography and Nuclear Magnetic Resonance, computational methods for predicting a protein's tertiary structure are becoming more and more common. However, when predictions are made for proteins of which we do not know the crystal structure, we run into an issue; How do we tell how good a tertiary structure prediction is? This is the goal of the estimation of model accuracy problem. Here we introduce ZoomQA, a novel, single-model method for assessing the accuracy of a tertiary structure prediction at a residue level. ZoomQA differs from other quality assessment tools, even those that consider the 3D structure of a protein by taking this three-dimensional data one step further. ZoomQA considers the change in chemical and physical features of a fragment structure (a portion of a protein within a radius r of the target amino acid) as the radius of contact increases. ZoomQA uses fourteen physical and chemical properties of amino acids to build a comprehensive representation of every residue within a protein and grades their placement within the protein as a whole.

Setup

Note, this software only works on linux environments for the time being

1. Create python virtual environment
   1. Virtualenv
      1. pip install virtualenv pip3 if you still have python2
      2. python3 -m venv virtual-env-name This creates a new virtual environment
      3. source virtual-env-name/bin/activate This activates your new virtual environment
   2. Conda
      1. Download Anaconda download the linux version to your linux machine
      2. Install Anaconda and follow the isntallation instructions, select yes for the init question at very end
      3. conda activate base to get into your 'base' environment, do not install packages to 'base'
      4. conda create -n virtual-env-name python=3.7
      5. conda activate virtual-env-name this activates your new environment, this is where you install packages
2. pip install -r requirements.txt
3. Navigate to the ZoomQA/ folder
4. Run python install.py to complete setup

Execution

1. Navigate to ZoomQA folder (You can now run this script from anywhere!)
2. python prediction.py ./QA_examples/Input/T1096 ./TEST_OUT/

• This command runs the prediction and places a text file in TEST_OUT/ folder
• An example output is provided in Example_Out/

Notes for execution

• Currently, the input data must be in a folder even if you are only running one pdb. Please put pdbs in a folder named as the target name.

QA_examples
└───Input
      └───target_name
          │   input_file_1.pdb
          │   input_file_1.pdb
          │   ...

• Currently only works on one target_name as shown above, will be updated soon

Ideas

• Distance map
  • Calculate the differnece betwen the features as another feature vector. (i.e value 1 is diff between featuer 0 and 1, value 2 is diff between feature 1 and 2, so on)
  • We could even make this a matrix by calculating the difference between feature 0 and all other features (this is a vector) then calculating the difference of feature 1 and all other features making a sort of distance map