AlphaFold2 Protein Structure Prediction Platform

AlphaFold2 Protein Structure Prediction

AlphaFold2 is a deep learning-based tool that predicts the 3D structures of proteins from their amino acid sequences with high accuracy. This platform provides an intuitive interface for running AlphaFold2 predictions and analyzing the results.

Predict protein structures with state-of-the-art accuracy.

Protein Sequence Input

Provide the amino acid sequence for which you want to predict the 3D structure.

Input Method

Paste Sequence Upload File UniProt ID

Amino Acid Sequence

Example: MAAHKGAEHHHKAAEHHEQAAKHHHAAAEHHEKGEHEQAAHHADTAYAHHKHAEEHAAQAAKHDAEHHAPKPH

Sequence Information

Length

Molecular Weight

Isoelectric Point

Prediction Parameters

Adjust parameters to optimize the protein structure prediction process.

Basic Parameters

Number of Models

Generate multiple models for comparison (1-5)

Random Seed

For reproducibility of results

Max Prediction Time (minutes)

Template Date Cutoff

Use only templates released before this date

Advanced Parameters

MSA Method

Method for generating Multiple Sequence Alignments

Homooligomer Prediction

Pairing Mode (for multimers)

Recycling Iterations

More iterations may improve accuracy but increase runtime

Template Parameters

Use structural templates from PDB

Max Template Hits

Minimum Template Identity (%)

Model Selection

Choose AlphaFold2 model parameters and training configurations.

AlphaFold Version

Model Version

Model Type

Model Parameters

Confidence Threshold

pLDDT score threshold for high confidence regions

Attention Head

Embedding Dimension

Use Dropout

For model regularization

Custom Model (Optional)

Upload custom trained AlphaFold model checkpoint

Select Model File

Structure Prediction

Initiate and monitor the protein structure prediction process.

Prediction Status

Prediction not started

Prediction Log

Prediction log will appear here once started...

Structure Evaluation

Evaluate the quality and confidence of predicted protein structures.

Model Quality Metrics

Average pLDDT

Predicted Local Distance Difference Test

pTM Score

Predicted TM-score for multimers

RMSD (to template)

Root Mean Square Deviation (Å)

Select Model to Evaluate

pLDDT Confidence Plot

Per-residue confidence scores (0-100)

Predicted Aligned Error

Expected positional error between residues

Structure Validation

Ramachandran Plot Quality

Fraction of residues in favored regions

97.3%

Passed

Clash Score

Number of severe steric clashes per 1000 atoms

2.1

Passed

MolProbity Score

Composite quality score

1.7

Passed

Structure Refinement

Optimize and refine the predicted protein structures.

Refinement Options

Refinement Method

Model to Refine

Amber Refinement Parameters

Force Field

Solvent Model

Simulation Time (ns)

Temperature (K)

Structure Visualization

Visualize and analyze the predicted protein structures.

Visualization Controls

Select Structure

Coloring Scheme

Representation

3D Structure View

Drag to rotate

View Controls

Orientation Presets

Display Options

Show residue labels

Show water molecules

Show ligands/ions

Secondary Structure

Alpha helices and beta sheets

Active Site Prediction

Identify potential binding sites

Pocket Detection

Find ligand binding pockets

Prediction Results

Download the predicted protein structures and associated data.

No results available yet. Please run a prediction first.

Models Generated

Avg. pLDDT Score

87.3

Prediction Time

00:42:18

Residues

342

Model Quality Metrics

Model	pLDDT Score	pTM Score	RMSD (Å)	Status
Model 1	91.2	0.92	1.2	Best
Model 2	89.7	0.90	1.4	Good
Model 3	87.5	0.88	1.7	Good
Model 4	85.3	0.85	2.1	Medium
Model 5	83.8	0.83	2.3	Medium

Refined Model

Amber-refined Model (from Model 1)

Refinement completed: 2023-06-15 14:32:18

Final pLDDT

92.1

RMSD to original

0.87 Å

MolProbity Score

1.2