Protein structure prediction enables researchers to infer the three-dimensional arrangement of atoms in a protein directly from its amino acid sequence. By predicting how a protein folds, researchers can gain valuable insights into its function, interactions, and role in biological processes. Understanding protein structure is essential for a wide range of applications, including studying protein function, identifying potential binding sites, analyzing the effects of mutations, engineering novel proteins, and supporting drug discovery efforts. Structural information can help researchers generate hypotheses, prioritize experiments, and better interpret biological data. LiteFold provides a streamlined platform for generating protein structure predictions, helping researchers quickly move from sequence data to structural insights. Predicted structures can be used to support downstream analyses such as molecular docking, protein design, functional annotation, and other computational biology workflows.

Key Features

AI-powered protein structure prediction
Simple sequence-based prediction workflow
Interactive structure visualization
Downloadable structure files for downstream analysis
Integration with LiteFold molecular docking workflows
Integration with molecular dynamics simulations
Fast prediction turnaround for research applications

Available Models

Boltz-2

Supported Prediction Tasks

LiteFold supports both individual protein structure prediction and multi-component biological assemblies. Supported prediction workflows include:

Prediction Type	Description
Single Protein	Predict the structure of an individual protein chain
Protein-Protein Complex	Predict interactions between multiple protein chains
Protein-Ligand Complex	Predict protein structures together with bound ligands
Protein-DNA Complex	Model protein-DNA interactions
Protein-RNA Complex	Model protein-RNA interactions
Multi-Component Assemblies	Predict systems containing multiple biomolecular components

These prediction modes enable researchers to study binding interfaces, signaling complexes, regulatory proteins, enzyme-substrate interactions, and drug-target systems from a unified workflow.

Quick Start

This guide walks through the complete workflow for generating protein structures in LiteFold using Boltz-2.

Before You Begin

Before starting a prediction, ensure that:

A project has been created.
Your protein sequence has been uploaded as a FASTA file.
You have sufficient compute credits available.

Need help uploading files? See: Uploading Files to LiteFold

Step 1: Open the Structure Prediction Module

From the LiteFold Lab dashboard:

Navigate to Lab.
Select Structure Prediction from the sidebar.

The Structure Prediction dashboard displays all previous prediction jobs associated with your projects.

Step 2: Select a Project

Use the Project dropdown menu to choose the project where your prediction will be created. Projects help organize prediction jobs, input files, and generated structures.

Step 3: Create a New Structure Prediction

After selecting a project:

Open the project.
Create a new structure prediction job.

LiteFold will load all FASTA files associated with the selected project. LiteFold supports both single-chain and multi-chain prediction workflows. A prediction may contain one or more FASTA entries, allowing users to model biological complexes in addition to individual proteins.

Step 4: Review Input Sequences

Select one or more FASTA files that will be included in the prediction. LiteFold displays:

Protein sequences
Chain assignments
Residue counts
Sequence composition
Molecular component types

When multiple sequences are present, each sequence is assigned as a separate chain or biological component within the prediction workflow. This allows users to verify all submitted components before launching a prediction.

Multi-Sequence and Complex Prediction

LiteFold supports the submission of multiple FASTA entries within a single prediction job. This capability enables prediction of:

Protein-protein complexes
Protein-ligand systems
Protein-RNA assemblies
Protein-DNA assemblies
Multi-component biological complexes

Multiple molecular components can be combined into a single prediction, allowing LiteFold to predict both individual structures and their spatial arrangement within the final assembly.

Example

>Protein_A
MSEQNNTEMTFQIQRIYTKDISFEAPNAPHVFQKDWLDKKTGKEVVEFDNQKDVKAIANSLGKD

>Protein_B
MNKLLILTCLVAVALARPKTNAEAKKAAEAAKAAEAAKAAEAAKAAEAAKAAEA

LiteFold automatically identifies each component and predicts the complete complex structure.

Step 5: Review Boltz Configuration (Optional)

Before running the prediction, you may review the Boltz Configuration tab. The configuration panel provides information about:

Selected prediction engine
Sequence interpretation
Chain assignments
Model-specific settings

Most users can proceed with the default configuration. Structure Prediction Results

Step 6: Start a Prediction

Click Start Prediction in the upper-right corner. A prediction setup dialog will appear.

Step 7: Select the Prediction Model

Choose the model that will be used for structure generation. Available models include:

Boltz-2

Recommended for most prediction tasks. Provides:

High-quality structure prediction
Multi-chain support
Fast inference
Strong performance on protein complexes

Step 8: Select Input Files

Choose the FASTA file(s) to include in the prediction job. You may:

Select individual files
Select multiple files
Use Select All

Only validated FASTA files can be submitted for prediction.

Step 9: Validate Files

Click Validate Files. LiteFold performs automatic validation checks, including:

FASTA format verification
Sequence integrity checks
Input compatibility checks
Model readiness verification

Once validation succeeds, the prediction can be submitted.

Step 10: Launch the Prediction

Click Start Prediction to submit the job. LiteFold will:

Queue the prediction task.
Allocate computational resources.
Begin structure generation.
Track job progress automatically.

A confirmation message will indicate that the job has been submitted successfully.

Monitoring Prediction Progress

After submission, return to the Structure Prediction Home Page. The dashboard displays:

Running predictions
Queued jobs
Completed predictions
Failed jobs (if any)
Historical prediction records

From this page you can monitor progress, review completed structures, and access previously generated prediction results.

Understanding Results

Once a prediction has completed, LiteFold automatically generates a detailed results page where you can inspect the predicted structure, evaluate confidence metrics, save results to your project, and download structure files for downstream analysis. The results workspace combines interactive visualization with quantitative quality metrics, allowing researchers to quickly assess whether a predicted structure is suitable for further study. Structure Prediction Analysis

Overview

The results page provides:

Interactive 3D structure visualization
Confidence metrics
Structure quality assessment
Prediction model information
File export options
Project organization tools

The left panel displays completed prediction jobs, while the main workspace provides detailed information about the selected structure.

Structure Visualization

The Structure tab displays an interactive three-dimensional representation of the predicted protein. The viewer allows you to:

Rotate the structure
Zoom in and out
Inspect individual regions
Examine chain organization
Analyze folded domains
Review confidence coloring

This provides a quick visual assessment of structural quality and folding patterns.

Confidence Coloring (pLDDT)

LiteFold automatically colors the predicted structure according to pLDDT confidence scores. For residue i:

pLDDTi∈[0,100]

Based on the Local Distance Difference Test:

LDDT_i= \frac{1}{N_i} \sum_{j} I\left( |d_{ij}^{pred}-d_{ij}^{true}|<\tau \right)

Where:

$d_{ij}^{pred}$ denotes the predicted distance.
$d_{ij}^{true}$ denotes the experimental distance.
$\tau$ represents the distance threshold.
$N_i$ represents the neighboring residues.
$I$ is the indicator function.

The color legend is displayed above the structure viewer.

Color	pLDDT Range	Interpretation
Dark Blue	90+	Very high confidence
Light Blue	70–90	Reliable prediction
Yellow	50–70	Moderate confidence
Orange	<50	Low confidence

Regions displayed in dark blue are generally the most reliable portions of the prediction.

Prediction Metrics

Below the structure viewer, LiteFold provides several quality metrics to help evaluate prediction reliability.

Mean pLDDT

Mean pLDDT represents the average confidence score across all residues in the structure.

\text{Mean pLDDT} = \frac{1}{N}\sum_{i=1}^{N} pLDDT_i

Where: N = total residues Example: Mean pLDDT: 93.3% Interpretation:

Above 90 → Very high confidence
70–90 → Reliable prediction
50–70 → Moderate confidence
Below 50 → Low confidence

Higher values generally indicate more reliable structural predictions.

Confidence Score

The overall confidence score summarizes the model’s confidence in the generated structure.

Confidence=w1(Mean pLDDT)+w2(pTM)+w3(ipTM)

Where: w1+w2+w3=1 w1+w2+w3=1 Example: Confidence: 90.2% Structures with higher confidence scores are typically better candidates for downstream applications such as docking and molecular dynamics.

Clash Score

The Clash Score measures steric overlaps between atoms. Example: Clash Score: 3.42 Lower clash scores generally indicate better structural geometry and fewer unrealistic atomic contacts.

pTM Score

The Predicted Template Modeling (pTM) score evaluates overall structural accuracy. Example: pTM: 0.802 pTM values range from 0 to 1.

Score	Interpretation
> 0.8	High structural confidence
0.6–0.8	Moderate confidence
< 0.6	Lower confidence

ipTM Score

For multi-chain complexes, LiteFold reports the Interface Predicted Template Modeling (ipTM) score. Example: ipTM: 0.779 This metric estimates confidence in the predicted interactions between biological components within a complex. Depending on the prediction setup, ipTM may be used to assess:

Protein-protein interfaces
Protein-ligand interactions
Protein-RNA interfaces
Protein-DNA interfaces

Higher ipTM values generally indicate greater confidence in the predicted intermolecular contacts. Higher ipTM values generally indicate more reliable protein-protein interfaces. For multi-component predictions, LiteFold evaluates both individual chain quality and interface quality. Additional metrics may include:

Metric	Purpose
pTM	Overall structural confidence
ipTM	Interface confidence between chains
Mean pLDDT	Average residue confidence
Clash Score	Structural geometry quality

For protein-protein, protein-ligand, protein-RNA, and protein-DNA predictions, interface-focused metrics are particularly important when assessing biological relevance.

Prediction Model

The metrics table also records the model used to generate the structure. Example: Model: Boltz This allows researchers to track which prediction engine produced a particular result.

Saving Results

LiteFold allows completed predictions to be assigned a custom alias and stored within your project for easier organization. Click Save As to open the save dialog.

Creating an Alias

An alias provides a more meaningful name for the prediction. Examples:

KRAS_G12D_Prediction
EGFR_T790M_Model
B2AR_Active_State

Aliases must be unique within the selected project.

Adding Results to Libraries

Predicted structures can optionally be added to a LiteFold Library for later reuse across projects and workflows. This is useful for:

Frequently used targets
Reference structures
Shared project resources

Converting to Additional Formats

During the save process, LiteFold can optionally convert the prediction into additional structure formats. Supported formats may include:

CIF
PDB
PDBQT

This simplifies preparation for docking and simulation workflows.

Downloading Structure Files

Predicted structures can be downloaded directly from the results page. Click Download .cif to export the structure.

CIF Format

The Crystallographic Information File (CIF) format contains:

Atomic coordinates
Structural annotations
Chain information
Residue information

The CIF format is widely supported by structural biology software.

Best Practices

Before proceeding to downstream analysis:

Review the Mean pLDDT score.
Check confidence coloring in critical regions.
Examine clash scores for structural quality.
Verify interface quality using ipTM for complexes.
Save important predictions using descriptive aliases.
Download structures for archival and external analysis.

After reviewing the prediction results, you can:

Save the structure to your project.
Add it to a LiteFold Library.
Download the CIF file.
Use the structure in Molecular Docking workflows.
Run Molecular Dynamics simulations.
Continue with De Novo Design and drug discovery pipelines.

These results provide the foundation for structure-guided biological research and computational drug discovery within LiteFold.

Next Steps

Molecular Docking

Dock ligands to your predicted structure

Molecular Dynamics

Validate and refine with MD simulations

De Novo Design

Design molecules for your structure

Workflows

End-to-end drug discovery workflows

​Key Features

​Available Models

​Supported Prediction Tasks

​Quick Start

​Before You Begin

​Step 1: Open the Structure Prediction Module

​Step 2: Select a Project

​Step 3: Create a New Structure Prediction

​Step 4: Review Input Sequences

​Multi-Sequence and Complex Prediction

​Example

​Step 5: Review Boltz Configuration (Optional)

​Step 6: Start a Prediction

​Step 7: Select the Prediction Model

​Boltz-2

​Step 8: Select Input Files

​Step 9: Validate Files

​Step 10: Launch the Prediction

​Monitoring Prediction Progress

​Understanding Results

​Overview

​Structure Visualization

​Confidence Coloring (pLDDT)

​Prediction Metrics

​Mean pLDDT

​Confidence Score

​Clash Score

​pTM Score

​ipTM Score

​Prediction Model

​Saving Results

​Creating an Alias

​Adding Results to Libraries

​Converting to Additional Formats

​Downloading Structure Files

​CIF Format

​Best Practices

​Next Steps

Molecular Docking

Molecular Dynamics

De Novo Design

Workflows

Key Features

Available Models

Supported Prediction Tasks

Quick Start

Before You Begin

Step 1: Open the Structure Prediction Module

Step 2: Select a Project

Step 3: Create a New Structure Prediction

Step 4: Review Input Sequences

Multi-Sequence and Complex Prediction

Example

Step 5: Review Boltz Configuration (Optional)

Step 6: Start a Prediction

Step 7: Select the Prediction Model

Boltz-2

Step 8: Select Input Files

Step 9: Validate Files

Step 10: Launch the Prediction

Monitoring Prediction Progress

Understanding Results

Overview

Structure Visualization

Confidence Coloring (pLDDT)

Prediction Metrics

Mean pLDDT

Confidence Score

Clash Score

pTM Score

ipTM Score

Prediction Model

Saving Results

Creating an Alias

Adding Results to Libraries

Converting to Additional Formats

Downloading Structure Files

CIF Format

Best Practices

Next Steps