Is it time to upgrade your protein search tool?

You might use search engines every day to precisely find a piece of obscure knowledge, but in the field of life sciences, trying to retrieve a perfect enzyme from the vast array of proteins in nature, or an antibody that can precisely bind to a target, always feels like searching for a planet with no light in an endless sea of stars. Traditional tools are like giving you only a rough sketch, making you rely on the outline for comparison. Today, AI gives protein retrieval the ability to "understand semantics" for the first time. The MatwingsVenus™ (Xiaowu™) platform developed by Shanghai Matwings Technology is a benchmark in this transformation, upgrading protein retrieval from the tedious "spelling correction" to intelligent "semantic understanding."

Upgrade to the protein search tool.

01 Protein Retrieval: The "Super Search Engine" of Life Sciences

Proteins are the direct executors of life's functions. Whether it's high-temperature enzymes in industrial catalysis, smart switches in synthetic biology, or precision-guided missiles in antibody drugs, the core boils down to a fundamental question: how can we retrieve the unique answer with a specific function from billions of protein sequences?

This is the essence of protein retrieval. It is far more complex than typing keywords into a database. What you often need to retrieve is not an exact string, but an abstract property—for example, "a transaminase that is heat-resistant, solvent-tolerant, and has broad substrate specificity." Traditional methods have long been limited by a basic assumption: if sequences are similar, then functions are similar. Therefore, sequence retrieval based on local alignment (such as BLAST) has dominated bioinformatics for decades.

However, the world of proteins is far from so straightforward.

02 The Dilemma of Traditional Sequence Alignment

Retrieval based on sequence similarity is essentially a comparison of characters. It is very good at finding proteins with "similar spelling," but it easily misses distant relatives that are "spelled differently but have the same meaning."

A classic fact in the protein universe is that proteins with less than 25% sequence similarity can still fold into nearly identical three-dimensional structures and perform the same function. This is the well-known phenomenon of "remote homology." If you rely solely on sequence alignment to retrieve "enzymes that can degrade PET plastic," you are likely to find only a small handful of known relatives and will fail to cross the sequence gap to discover another protein family that evolved to perform the same function.

Even more challenging, modern protein engineering often requires "non-conventional" retrieval: finding a stable protein without cysteine, locating a variant with dramatically increased activity at low pH, or discovering a scaffold with hidden catalytic activity for non-natural substrates… These multidimensional, highly semantic retrieval demands can hardly be satisfied by a single sequence similarity score. What we need is a new model that can understand the language of proteins, rather than just their spelling.

03 Technological Innovations in Protein Retrieval — AI Semantic Search

Protein language model

Scientists have found that if billions of natural protein sequences are regarded as a "book written in an unknown language," then training a protein language model with a GPT-like Transformer architecture can naturally learn the syntax, context, and even "semantics" in this book. After training, each protein sequence can be compressed into a high-dimensional vector—this vector is the protein's "semantic embedding," which contains structural information, evolutionary constraints, and potential functional tags far beyond sequence alignment.

In this vector space, something magical happens: proteins with similar functions, regardless of their sequence similarity, automatically cluster together. Some heat-tolerant esterases are closely adjacent to other enzymes that are unrelated to other sequences but are equally heat-tolerant. This means you can directly use a functional description, a protein of interest, or even an imaginary attribute combination to "search" for the most compatible candidate protein. This is like searching for images with text or searching for songs by melody—protein retrieval has truly entered the era of multimodal semantics.

04 One-stop intelligent protein retrieval and analysis platform

Making this cutting-edge technology a productivity tool accessible to every researcher requires top-tier engineering skills and deep industry insight. This is precisely the original intention behind Shanghai Matwings Technology's independently developed MatwingsVenus™ platform ™.

MatwingsVenus™ ™ is not just another sequence matching tool, but a "protein semantic operating system." It is built on Matwings Technology's self-developed protein large model, which deeply trains on trillions of protein data, especially in key areas such as industrial enzymes and pharmaceutical proteins, enabling the model to truly understand the functional language of proteins.

On MatwingsVenus™ ™, protein retrieval says goodbye to tedious single sequence frames. You can experience three high-dimensional search modes:

Search by description – Using intelligent agents to break down functional descriptions into keywords, linking multiple databases for approximate search, helping you quickly narrow down the range of candidates.

Protein Search by Protein – Supports homologous search for mature sequences to quickly find similar proteins.

Multidimensional attribute evaluation — After searching, key protein attributes such as solubility, stability, optimal temperature, and pH can be analyzed online via the VenusG module, providing rapid judgment for subsequent drug or industrial adaptation.

Behind MatwingsVenus™ ™ is a protein semantic vector index library with millisecond-level response, pre-organizing billions of proteins' "semantic fingerprints" to make rapid intelligent retrieval feasible. It connects retrieval, evaluation, and design, becoming an indispensable intelligent partner for engineers in fields such as synthetic biology, enzyme engineering, and antibody discovery.

05 Application Blueprint

Intelligent Semantic Search Engine for Proteins

As protein search evolves into the semantic era, the inherent bottlenecks in a series of industrial scenarios will be broken:

Enzyme directed evolution: No longer relying on a needle-in-a-haystack approach with random mutation libraries, first use semantic search to lock in an optimal starting scaffold, then combine with prediction modules to focus on key sites for thermal stability or activity, multiplying evolutionary efficiency.

Synthetic biology component mining: Quickly retrieve transcription factors or biosensors that respond to specific inducers or withstand harsh industrial conditions, building better gene circuits.

Novel biopharmaceutical discovery: Using target antigen epitopes as semantic search terms, traverse both natural antibody libraries and camelid single-domain antibody spaces, discovering entirely new binding clusters that traditional sequence alignment struggles to capture.

These are precisely the paths that MatwingsVenus™ (Xiaowu™) is currently pursuing with numerous partners. We firmly believe that every researcher rooted at the forefront of biotechnology deserves an intelligent engine that connects the universe of proteins.