Protein Structure Prediction Overview

False (B)

Substantial improvement in protein-protein docking.

<p>AlphaFold</p> Signup and view all the answers

<p>AlphaFold = AI model for protein structure prediction CASP = Community-wide experiment on protein structure prediction Deep Learning = Method utilizing neural networks to improve predictions Correlated Mutations = Signals indicating functional interactions in protein complexes</p> Signup and view all the answers

<p>To learn to reverse the noise addition process (D)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Shape complementarity and electrostatic complementarity</p> Signup and view all the answers

<p>unbound</p> Signup and view all the answers

<p>It has numerous known hetero-dimers. (D)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Ab initio docking = No template-based approach with rigid body assumptions Homology modeling = Based on comparison to known structures Rigid body docking = Evaluates shape and electrostatic complementarity Template-based docking = Utilizes existing structures for predictions</p> Signup and view all the answers

<p>Finding a good overlap of protein surfaces</p> Signup and view all the answers

<p>Predicted Distance Difference Test (pLDDT) (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>predicted local distance difference test</p> Signup and view all the answers

<p>Predicted Alignment Error (PAE)</p> Signup and view all the answers

<p>Evoformer (C)</p> Signup and view all the answers

<p>pLDDT &gt; 90 = High accuracy pLDDT 70-80 = Generally good backbone prediction pLDDT 50-70 = Low confidence pLDDT &lt; 50 = Often disordered with ribbon-like appearance</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Amber</p> Signup and view all the answers

A method for predicting protein-protein interactions by analyzing evolutionary relationships between amino acids in different proteins.

A powerful tool for predicting protein structures, including protein complexes, using deep learning.

A version of AlphaFold2 designed to predict the structures of protein complexes.

A technique for predicting protein-protein interactions using scoring functions that evaluate the interactions between individual amino acids.

A method for predicting protein-protein interactions by analyzing the evolutionary relationships between proteins.

A process that progressively adds noise to a set of 3D coordinates, disrupting their structure and eventually leading to a random distribution.

A deep neural network that learns to reverse the diffusion process, predicting the original 3D coordinates of a protein from a noisy representation.

The initial stage of diffusion that adds noise to the protein's 3D coordinates, disrupting its structure.

The stage of diffusion where the model learns to predict the original structure from the noisy representation.

A technique used in protein structure prediction involves predicting the structure of a protein complex, starting with its unbound components.

The first approach to protein docking, which uses a trial-and-error method to search for the best fitting arrangement between two unbound proteins

A crucial aspect of protein docking where the proteins are treated as rigid bodies, allowing for a simpler and more efficient way to determine their interaction.

A step in ab initio docking where the algorithm looks for a good overlap of the surfaces of two proteins, indicating potential for interaction.

AlphaFold2 uses a deep learning approach to predict the probable distance between residues, which can be visualized in a distogram.

AlphaFold2 employs a two-track learning process: Evoformer and Structure networks. Evoformer focuses on calculating residue-residue contacts at different distances, while the Structure network predicts residue positions independently and optimizes for structural accuracy.

AlphaFold2 uses a multiple sequence alignment of the query sequence and known PDB structures as input, which serves as templates for structural insights.

pLDDT (predicted local distance difference test) assesses the accuracy of individual residues in a protein structure predicted by AlphaFold2. A higher pLDDT score indicates more confidence in the prediction.

PAE (predicted alignment error) measures the accuracy of predicted distances between residues in a protein structure. It is used to assess the confidence in domain packing, with lower values indicating more reliable predictions.

AlphaFold2 uses Amber molecular dynamics to refine the predicted structure, improving local stereochemistry while not necessarily enhancing overall RMSD.

Despite achieving high accuracy for 98.5% of human proteins, AlphaFold2 predictions are only highly confident (pLDDT > 70) for about 65% of the human proteome.

While a low PAE generally indicates reliable domain packing, there are instances where very low PAE regions in AlphaFold2 predictions could be incorrectly positioned, leading to biologically unrealistic structures.