Protein sequences: From decoding to design, how is AI reshaping the code of life?

Proteins are the main executors of life activities. What defines a protein's identity and function is precisely the linear chain behind it, composed of 20 amino acids connected in a specific sequence — the protein sequence. If DNA is likened to the blueprint of life, then the protein sequence is the 'source code' that enables countless life functions such as catalysis, transport, signal transduction, and immune defense.

What a protein molecule can do is ultimately determined by its sequence. Properties of the amino acids in the sequence, such as hydrophobicity, charge, and size, guide the polypeptide chain to fold orderly in three-dimensional space, forming precise structural units like α-helices and β-sheets, and assembling into molecular machines with specific functions. This is one of the most fundamental principles of molecular biology: sequence determines structure, and structure determines function.

For this reason, protein sequences have always been the core research focus in the fields of life sciences and biotechnology. Whether it is exploring the molecular mechanisms of genetic diseases, modifying industrial enzymes to enhance high-temperature tolerance, or designing antibody drugs that can accurately target cancer cells, researchers must face the challenges and opportunities brought by protein sequences.

01 The Vastness and Complexity of the Sequence: A High-Dimensional Treasure Hunt

The vastness and complexity of protein sequences

However, the difficulty of interpreting and rewriting this set of 'source code' far exceeds imagination.

Firstly, the protein sequence space is immensely vast. A small protein composed of only 100 amino acids has a theoretical sequence diversity of up to 20¹⁰⁰—this number even surpasses the total number of atoms in the observable universe. The protein sequences that nature has explored through evolution up to now are merely a tiny corner of this endless possibility. Efficiently pinpointing the one with specific functions from this nearly infinite sequence space has long been a bottleneck for traditional 'rational design' and 'directed evolution.'

Secondly, the mapping relationship between sequence, structure, and function is extremely complex. A mutation at a single amino acid site may have no effect, may completely disrupt protein folding, or may remotely allosterically regulate the active center. Accurately predicting the effects of mutations, especially when multiple sites change synergistically, has always been a holy grail-level challenge in computational biology.

02 AI Enters: Mastering the 'Language' of Proteins

The breakthroughs in artificial intelligence are bringing a paradigm shift to protein sequence research.

By performing self-supervised learning on massive amounts of natural protein sequences, AI models can capture the 'grammar' and 'semantics' of amino acid arrangements as if they were understanding human language. Frequently co-occurring functional motifs, conserved sites, and co-evolutionary signals among residues together form the vocabulary and context of this 'protein language.' Protein language models based on cutting-edge deep learning architectures can transform any protein sequence into an information-rich high-dimensional representation, from which key properties such as folding propensity, thermal stability, and binding affinity can be interpreted.

More importantly, this type of AI not only can 'read' sequences but can also actively 'write' entirely new sequences. Leveraging generative algorithms, researchers can start from vague functional requirements and directly generate novel protein sequences that can fold into the desired structures and carry the required biochemical properties—truly accomplishing the leap from 'discovering nature' to 'designing nature.'

03 MatwingsVenus™: Making Sequence Design Accessible

In this wave of technology, Shanghai Matwings Technology has developed an all-in-one AI protein sequence design and analysis platform—MatwingsVenus™ (Matwings™), which is transforming these cutting-edge capabilities into tangible research and development productivity.

The MatwingsVenus™ (Matwings™) platform deeply integrates self-developed protein sequence large models with structure-aware algorithms to address practical needs in the fields of biomedicine, chemicals, agriculture, and more. It provides an intelligent solution that covers the full process of protein sequence "analysis—prediction—optimization—generation." The platform focuses on the following core capabilities:

Accurate Prediction of Sequence Properties

For a given sequence, it quickly predicts a series of drug-like and process-critical properties, including thermal stability, soluble expression levels, aggregation propensity, and affinity, providing high-precision virtual screening for experimental prioritization.

Intelligent Mutation Scanning and Combinatorial Optimization

It can systematically perform virtual evaluations on hundreds to thousands of mutation sites, precisely identify beneficial mutations, and recommend synergistic mutation combinations to avoid antagonistic effects, greatly reducing experimental throughput.

De Novo Sequence Generation

Without needing a natural template, by simply setting target functions or structural constraints, MatwingsVenus™ (Matwings™) can generate protein sequences that do not exist in nature, useful for innovative enzyme preparations, functional protein materials, novel antibody frameworks, and other cutting-edge R&D.

Multi-Objective Collaborative Design

The platform’s unique multi-objective optimization engine supports the simultaneous optimization of multiple mutually antagonistic indicators, such as activity, stability, and immunogenicity, producing "best-in-class" molecules that meet the stringent requirements of industrial applications.

Case Studies in Industrial Enzyme Engineering

Taking industrial enzyme modification as an example. A biocatalysis company hopes to enhance the activity and thermal stability of transaminase in organic solvents. Traditional methods require constructing thousands to tens of thousands of mutants for repeated screening, which takes a long time. With the help of the MatwingsVenus™ (Xiaowu™) platform, researchers only need to upload the wild-type sequence and set goals such as 'improve organic solvent tolerance' and 'maintain high catalytic activity.' Within a few hours, the platform outputs a batch of high-potential sequence recommendations. Experimental validation showed that over 30% of the variants performed significantly better than the wild type in the target properties, and the top sequence improved both key indicators by more than fivefold, while screening costs and time were reduced by over 80%.

The outstanding performance of MatwingsVenus™ (Xiaowu™) is due to the strong expertise of the Matwings Technology team in the intersection of protein science and AI. At the same time, MatwingsVenus™ (Xiaowu™) offers low-threshold visual interactions and standardized API interfaces, allowing cutting-edge AI protein sequence design capabilities to be seamlessly integrated into internal R&D pipelines.

04 Infinite Sequences, Infinite Possibilities

Today, protein sequence design is rapidly evolving from a craft highly dependent on intuition and luck into a predictable engineering process driven by data and models. By starting from sequences to reshape protein functions and empower biomanufacturing, it serves as a key technological engine for humanity's move towards a green and sustainable future.

Shanghai Matwings Technology, starting from MatwingsVenus™ (Xiaowu™), is committed to fully unlocking the potential of every protein sequence. In the future, we will continue to delve into intelligent sequence design and work together with industry partners to jointly write the story of infinite possibilities extended from sequences.