Next Gen Protein Engineering

MatwingsVenus™A Shared Lab for AI

Conversational Protein Design

Automated Experiment Delivery

Expert Online Collaboration

Start Use

Protein Data

16B+

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

Core High-Resolution Structure List

Structural Features of Different Functional Domains

Full-Length Receptors and Supramolecular Arrays (Cryo-EM Progress)

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:

Structure Folding Analysis: Check predicted structure quality

Key Site Analysis: Analyze potential key sites [active sites, conserved sites]

Activity Mutation Site Prediction: Invoke directed evolution toolkit for prediction

Analyze Prediction Results: Further consult user requirements

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:

Information Aggregation: Query popular cellulases and select templates

Enzyme Discovery Pipeline: Employ enzyme discovery toolkit to find new cellulases

Enzyme Property Analysis: Apply enzyme series filters to analyze enzyme properties

Analyze Prediction Results: Further consult user requirements

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

Type	Accession	Protein Name	Organism
Protein	VP_001234	Protein X variant (Fragment)	Homo sapiens
Protein	VP_001235	Protein Y isoform 2	Mus musculus
Protein	VP_001236	Protein Z mutant R5	Danio rerio
Protein	VP_001237	Protein A Domain 3	E. coli
Protein	VP_001238	Synthetic Construct 9	Synthetic
Protein	VP_001239	Fusion Protein 22	S. cerevisiae
Protein	VP_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

Highlights

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

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

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

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.

News

Protein Sequence Search: When 'Search' Becomes 'Design'

Protein sequence searches are evolving from simple information retrieval into AI-driven computational design tools. Mastering efficient search methods not only lets you quickly get protein identities, families, and functional information, but also opens the door to a revolutionary leap from data to design—enter a sequence, and AI will generate optimization plans for you, turning the end of a search into the start of innovation.

Jun 23, 2026

Protein Stability: How the Code of Life Stands Up to Environmental Challenges?

Proteins are the core players in life, and their stability determines how well biological functions work. From understanding disease mechanisms to biotech innovations, cracking the code of protein stability could lead to more durable drugs, more efficient industrial enzymes, and even life materials tailored for space exploration.

Jun 22, 2026

Can one-stop protein development turn fragmented trial-and-error into a ‘conversation equals closed loop’?

Currently, protein research faces pain points like fragmented tools and a disconnect between wet and dry experiments. The MatwingsVenus platform uses a conversational AI to automate the entire workflow, running design, validation, and iteration in a closed loop, freeing researchers from tedious processes so they can focus on core innovation.

Jun 22, 2026

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Questionnaire

MatwingsVenus™A Shared Lab for AI

Overview

Agent-Core R&D Pipeline

Intelligent Assistant

Protein Engineering

Protein Discovery

Massive Protein Database Retrieval

Core Retrieval Technologies

Highlights

Advanced Model Capabilities

Massive Database Support

Verifiable Prediction Results

Ultimate Lightweight Experience

News

Protein Sequence Search: When 'Search' Becomes 'Design'

Protein Stability: How the Code of Life Stands Up to Environmental Challenges?

Can one-stop protein development turn fragmented trial-and-error into a ‘conversation equals closed loop’?

Academic Publications

VenusX: Unlocking Fine-Grained Functional Understanding of Proteins

Fast and Interpretable Protein Substructure Alignment via Optimal Transport

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

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

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

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

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

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

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

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

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

Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection

PROTSOLM: Protein Solubility Prediction with Multi-modal Features

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

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