Prot42: a Novel Family of Protein Language Models for Target-aware Protein Binder Generation

• URL: http://arxiv.org/abs/2504.04453v1
• Date: Sun, 06 Apr 2025 11:43:12 GMT
• Title: Prot42: a Novel Family of Protein Language Models for Target-aware Protein Binder Generation
• Authors: Mohammad Amaan Sayeed, Engin Tekin, Maryam Nadeem, Nancy A. ElNaker, Aahan Singh, Natalia Vassilieva, Boulbaba Ben Amor,
• Abstract summary: We introduce Prot42, a novel family of Protein Language Models (pLMs) pretrained on vast amounts of unlabeled protein sequences.<n>Remarkably, our models handle sequences up to 8,192 amino acids, significantly surpassing standard limitations.<n>Prot42 excels in generating high-affinity protein binders and sequence-specific DNA-binding proteins.
• Score: 3.2039076408339353
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unlocking the next generation of biotechnology and therapeutic innovation demands overcoming the inherent complexity and resource-intensity of conventional protein engineering methods. Recent GenAI-powered computational techniques often rely on the availability of the target protein's 3D structures and specific binding sites to generate high-affinity binders, constraints exhibited by models such as AlphaProteo and RFdiffusion. In this work, we explore the use of Protein Language Models (pLMs) for high-affinity binder generation. We introduce Prot42, a novel family of Protein Language Models (pLMs) pretrained on vast amounts of unlabeled protein sequences. By capturing deep evolutionary, structural, and functional insights through an advanced auto-regressive, decoder-only architecture inspired by breakthroughs in natural language processing, Prot42 dramatically expands the capabilities of computational protein design based on language only. Remarkably, our models handle sequences up to 8,192 amino acids, significantly surpassing standard limitations and enabling precise modeling of large proteins and complex multi-domain sequences. Demonstrating powerful practical applications, Prot42 excels in generating high-affinity protein binders and sequence-specific DNA-binding proteins. Our innovative models are publicly available, offering the scientific community an efficient and precise computational toolkit for rapid protein engineering.

Related papers

• An All-Atom Generative Model for Designing Protein Complexes [49.09672038729524]
  APM (All-Atom Protein Generative Model) is a model specifically designed for modeling multi-chain proteins. By integrating atom-level information and leveraging data on multi-chain proteins, APM is capable of precisely modeling inter-chain interactions and designing protein complexes with binding capabilities from scratch.
  arXiv  Detail & Related papers  (2025-04-17T16:37:41Z)
• Computational Protein Science in the Era of Large Language Models (LLMs) [54.35488233989787]
  Computational protein science is dedicated to revealing knowledge and developing applications within the protein sequence-structure-function paradigm.<n>Recently, Language Models (pLMs) have emerged as a milestone in AI due to their unprecedented language processing & generalization capability.
  arXiv  Detail & Related papers  (2025-01-17T16:21:18Z)
• Unifying Sequences, Structures, and Descriptions for Any-to-Any Protein Generation with the Large Multimodal Model HelixProtX [14.927425008686692]
  We introduce HelixProtX, a system built upon the large multimodal model, to support any-to-any protein modality generation. HelixProtX consistently achieves superior accuracy across a range of protein-related tasks, outperforming existing state-of-the-art models.
  arXiv  Detail & Related papers  (2024-07-12T14:03:02Z)
• Learning the Language of Protein Structure [8.364087723533537]
  We introduce an approach using a vector-quantized autoencoder that effectively tokenizes protein structures into discrete representations.<n>To demonstrate the efficacy of our learned representations, we show that a simple GPT model trained on our codebooks can generate novel, diverse, and designable protein structures.
  arXiv  Detail & Related papers  (2024-05-24T16:03:47Z)
• xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein [74.64101864289572]
  We propose a unified protein language model, xTrimoPGLM, to address protein understanding and generation tasks simultaneously.<n>xTrimoPGLM significantly outperforms other advanced baselines in 18 protein understanding benchmarks across four categories.<n>It can also generate de novo protein sequences following the principles of natural ones, and can perform programmable generation after supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-01-11T15:03:17Z)
• A Latent Diffusion Model for Protein Structure Generation [50.74232632854264]
  We propose a latent diffusion model that can reduce the complexity of protein modeling. We show that our method can effectively generate novel protein backbone structures with high designability and efficiency.
  arXiv  Detail & Related papers  (2023-05-06T19:10:19Z)
• Structure-informed Language Models Are Protein Designers [69.70134899296912]
  We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs) We conduct a structural surgery on pLMs, where a lightweight structural adapter is implanted into pLMs and endows it with structural awareness. Experiments show that our approach outperforms the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2023-02-03T10:49:52Z)
• Learning Geometrically Disentangled Representations of Protein Folding Simulations [72.03095377508856]
  This work focuses on learning a generative neural network on a structural ensemble of a drug-target protein. Model tasks involve characterizing the distinct structural fluctuations of the protein bound to various drug molecules. Results show that our geometric learning-based method enjoys both accuracy and efficiency for generating complex structural variations.
  arXiv  Detail & Related papers  (2022-05-20T19:38:00Z)
• Deep Generative Modeling for Protein Design [0.0]
  Deep learning approaches have produced breakthroughs in fields such as image classification and natural language processing. generative models of proteins have been developed that encompass all known protein sequences, model specific protein families, or extrapolate the dynamics of individual proteins. We discuss five classes of generative models that have been most successful at modeling proteins and provide a framework for model guided protein design.
  arXiv  Detail & Related papers  (2021-08-31T14:38:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.