background basebackground lines
starNext Gen Protein Engineering

MatwingsVenus™A Shared Lab for AI

featureConversational Protein Design
featureAutomated Experiment Delivery
featureExpert Online Collaboration
Protein Data
Protein Data
16B+
Agent Tools
Agent Tools
200+
Automated Equipment
7.2x Efficiency
Expert Count
50+

Overview

MatwingsVenus™ is a lightweight R&D closed-loop platform for university researchers, corporate R&D personnel, healthcare workers, and biology enthusiasts — integrating AI biological design, wet-lab validation, and expert collaboration.

Data Accumulation: Integrates billions of annotated sequence datasets from major databases including NCBI, UniProt, MGnify, and more.

Agent Capabilities: Integrates intelligent dialogue, protein sequence analysis, directed mutation design, enzyme mining, de novo design, structure prediction, and database retrieval, enabling complex bioinformatics tasks to be completed conversationally.

Wet-Lab Validation: The platform supports one-click ordering for gene synthesis, protein expression validation, protein purification, and more.

Expert Collaboration: Throughout the entire protein design workflow, you can initiate expert consultations at any time. We coordinate domain experts to promptly answer your questions, evaluate AI model design results, and provide professional judgment, optimization suggestions, and in-depth insights.

Core Advantages
Highlighting conversational completion of complex bioinformatics tasks, focusing on protein design and scientific research search to empower individual research.
Target Users
University Researchers
Corporate R&D
Biology Enthusiasts
Healthcare Workers
Product Positioning
Focusing on an efficient and practical toolchain
Core Functions
Full-process AI automation, Agent collaborative R&D, Ultra-large-scale search engine
Underlying Infrastructure
Protein Large Models | Multi-source Biological Databases

Agent-Core R&D Pipeline

Simulates the thought process of human experts to automatically execute complex bioinformatics tasks.

Help me find MCP protein (bacterial chemotaxis) structures, the higher resolution the better
MCP (Methyl-accepting Chemotaxis Proteins) are core receptors in bacterial chemotaxis signal transduction systems...
Here is the high-resolution structural information for MCP proteins, covering full-length models, periplasmic binding domains (LBD), and intracellular signaling domains
1
Core High-Resolution Structure List
2
Structural Features of Different Functional Domains
3
Full-Length Receptors and Supramolecular Arrays (Cryo-EM Progress)
Find

Intelligent Assistant

Proactive AI assistant that helps users solve research problems through natural language, accelerating scientific discovery. It completes tasks such as structure prediction, patent retrieval, and literature search, supporting direct connection to multiple databases like PubMed, PDB, and UniProt.

I want to improve the activity of Cas12i3 [upload sequence]
Understanding user requirements, automatically screening enzyme activity engineering approaches:
1
Structure Folding Analysis: Check predicted structure quality
2
Key Site Analysis: Analyze potential key sites [active sites, conserved sites]
3
Activity Mutation Site Prediction: Invoke directed evolution toolkit for prediction
4
Analyze Prediction Results: Further consult user requirements
Find

Protein Engineering

The world's leading technology in AI directed evolution: supports multiple rounds of activity enhancement, validated in over 30 industrial projects. The platform can perform single-point saturation mutagenesis prediction, or combine user wet-lab data for multi-point mutation prediction, identify functional sites for risk analysis and physicochemical property prediction.

I want to find some new cellulases
Understanding user requirements, automatically screening cellulase discovery approaches:
1
Information Aggregation: Query popular cellulases and select templates
2
Enzyme Discovery Pipeline: Employ enzyme discovery toolkit to find new cellulases
3
Enzyme Property Analysis: Apply enzyme series filters to analyze enzyme properties
4
Analyze Prediction Results: Further consult user requirements
Find

Protein Discovery

Quickly locate target proteins from hundreds of millions of sequences, relying on zero-shot prediction capabilities and crossing sequence limitations to search for structurally similar proteins.

Massive Protein Database Retrieval

Connected to tens of billions of protein sequences and structure databases

Powered by VenusPod Database | National Competition Second Prize Technical Support
TypeAccessionProtein NameOrganism
ProteinVP_001234
Protein X variant (Fragment)
Homo sapiens
ProteinVP_001235
Protein Y isoform 2
Mus musculus
ProteinVP_001236
Protein Z mutant R5
Danio rerio
ProteinVP_001237
Protein A Domain 3
E. coli
ProteinVP_001238
Synthetic Construct 9
Synthetic
ProteinVP_001239
Fusion Protein 22
S. cerevisiae
ProteinVP_001240
Signal Peptide Var
B. subtilis

Core Retrieval Technologies

MMseqs2 Acceleration
Ultra-Large-Scale Sequence Clustering and Search
BLAST Deep Alignment
High-Precision Local Sequence Alignment Standard Algorithm
Vector Semantic Retrieval
Semantic Matching Based on VenusPLM Model
Database Relations
features background

Highlights

Advanced Model Capabilities

Advanced Model Capabilities

Tens of billions of parameters Venus-series models, with world-leading zero-shot prediction, multi-round evolution, and functional prediction capabilities.

Massive Database Support

Massive Database Support

A massive private dataset of extreme-environment protein sequences, covering more than ten professional databases with comprehensive information.

Verifiable Prediction Results

Verifiable Prediction Results

Protein evolution has been successfully delivered in 30+ industrial projects, and protein discovery can break through the limitation of low sequence similarity to find enzymes with similar functions.

Ultimate Lightweight Experience

Ultimate Lightweight Experience

All professional capabilities can be triggered by a single sentence, requiring no bioinformatics background or complex operations, allowing researchers to focus on scientific problems themselves.

Academic Publications

VenusX: Unlocking Fine-Grained Functional Understanding of Proteins

ICLR, 2026.

2026-01-26

Fast and Interpretable Protein Substructure Alignment via Optimal Transport

ICLR, 2026.

2026-01-26

Venus-MAXWELL: Efficient Learning of Protein-Mutation Stability Landscapes using Protein Language Models

NeurIPS, 2025

2025-09-19

STAGE: A compact and versatile TnpB-based genome editing toolkit for Streptomyces

Proceedings of the National Academy of Sciences, 2025

2025-08-26

From high-throughput evaluation to wet-lab studies: advancing mutation effect prediction with a retrieval-enhanced model

ISMB/ECCB, 2025.

22025-07-15

Harnessing protein language model for structure-based discovery of highly efficient and robust PET hydrolases

Nature Communications, 2025

2025-07-05

VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning

ACL Demo, 2025.

2025-07-01

A Deep Retrieval-Enhanced Meta-Learning Framework for Enzyme Optimum pH Prediction

J. Chem. Inf. Model, 2025,

2025-03-24

VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data

Acta Pharmaceutica Sinica B, 2025

2025-03-14

Entropy-driven zero-shot deep learning model selection for viral proteins

Physical Review Research, 2025,

2025-02-28

AI-enabled Alkaline-resistant Evolution of Protein to Apply in Mass Production

Elife, 2025,

2025-02-19

Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection

ICLR, 2025.

2025-01-23

PROTSOLM: Protein Solubility Prediction with Multi-modal Features

IEEE BIBM, 2024.

2025-01-10

Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion

IEEE BIBM, 2024.

2025-01-10

Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance?

IEEE BIBM, 2024.

2025-01-10

VenusX: Unlocking Fine-Grained Functional Understanding of Proteins

ICLR, 2026.

2026-01-26

Fast and Interpretable Protein Substructure Alignment via Optimal Transport

ICLR, 2026.

2026-01-26

Venus-MAXWELL: Efficient Learning of Protein-Mutation Stability Landscapes using Protein Language Models

NeurIPS, 2025

2025-09-19

STAGE: A compact and versatile TnpB-based genome editing toolkit for Streptomyces

Proceedings of the National Academy of Sciences, 2025

2025-08-26

From high-throughput evaluation to wet-lab studies: advancing mutation effect prediction with a retrieval-enhanced model

ISMB/ECCB, 2025.

22025-07-15

Harnessing protein language model for structure-based discovery of highly efficient and robust PET hydrolases

Nature Communications, 2025

2025-07-05

VenusFactory: An Integrated System for Protein Engineering with Data Retrieval and Language Model Fine-Tuning

ACL Demo, 2025.

2025-07-01

A Deep Retrieval-Enhanced Meta-Learning Framework for Enzyme Optimum pH Prediction

J. Chem. Inf. Model, 2025,

2025-03-24

VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data

Acta Pharmaceutica Sinica B, 2025

2025-03-14

Entropy-driven zero-shot deep learning model selection for viral proteins

Physical Review Research, 2025,

2025-02-28

AI-enabled Alkaline-resistant Evolution of Protein to Apply in Mass Production

Elife, 2025,

2025-02-19

Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection

ICLR, 2025.

2025-01-23

PROTSOLM: Protein Solubility Prediction with Multi-modal Features

IEEE BIBM, 2024.

2025-01-10

Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion

IEEE BIBM, 2024.

2025-01-10

Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance?

IEEE BIBM, 2024.

2025-01-10

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