Challenge: Pre-trained protein language models have been used in protein engineering, but their adoption is limited due to data collection, task benchmarking, and application challenges.
Approach: They propose a versatile engine that integrates biological data retrieval, standardized task benchmarking, and modular fine-tuning of PLMs.
Outcome: The proposed engine integrates biological data retrieval, task benchmarking, and modular fine-tuning of PLMs.

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Rethinking Text-based Protein Understanding: Retrieval or LLM? (2025.emnlp-main)

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Challenge: Recent studies have focused on integrating protein-related knowledge into large language models through continued pretraining and multi-modal alignment.
Approach: They propose a retrieval-enhanced method which significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios.
Outcome: The proposed method significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios.
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering (2025.coling-industry)

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Challenge: Deep learning models are often inefficient and resource-intensive for biologists without specialized computational expertise.
Approach: They propose an agent framework that leverages large language models for multimodal automated machine learning (AutoML) in protein engineering.
Outcome: The proposed framework demonstrates significant improvements in performance over previous approaches in two real-world protein engineering tasks.
Petals: Collaborative Inference and Fine-tuning of Large Models (2023.acl-demo)

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Challenge: Recent studies show that pretrained language models can solve practical tasks using more than 100 billion parameters.
Approach: They propose a system for inference and fine-tuning of large models collaboratively by joining the resources of multiple parties.
Outcome: The proposed system outperforms offloading for very large models running on consumer GPUs with 1 step per second, enough for many interactive LLM applications.
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models (2024.acl-demos)

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Challenge: Efficient fine-tuning of large language models requires non-trivial efforts to implement these methods on different models.
Approach: They propose a framework that democratizes the fine-tuning of large language models by integrating a suite of efficient training methods into one framework.
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Protein Large Language Models: A Comprehensive Survey (2025.findings-emnlp)

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Challenge: Existing studies focus on specific aspects or applications, but this study provides a comprehensive overview of Protein-specific large language models.
Approach: This paper proposes a structured taxonomy of state-of-the-art ProteinLLMs . they analyze how they leverage large-scale protein sequence data for improved accuracy .
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ProtT3: Protein-to-Text Generation for Text-based Protein Understanding (2024.acl-long)

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Challenge: Language Models excel in understanding textual descriptions of proteins, but struggle to process texts.
Approach: They propose a framework for Protein-to-Text Generation for Text-based Protein Understanding that integrates a PLM as its protein understanding module.
Outcome: The proposed framework surpasses existing baselines and is highly efficient in protein-to-text generation.
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (2026.acl-long)

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Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Ďurech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolčec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Inés Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, Imanol Schlag
Challenge: Apertus is a fully open suite of large language models (LLMs) designed to address responsibility shortcomings in today’s open model ecosystem, namely data responsibility and global representation.
Approach: They propose to release a fully open suite of large language models (LLMs) that address data responsibility and global representation shortcomings in today’s open model ecosystem.
Outcome: The proposed model is pretrained on openly available data and suppresses verbatim recall of data while retaining task performance.
InstructProtein: Aligning Human and Protein Language via Knowledge Instruction (2024.acl-long)

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Challenge: Large Language Models (LLMs) are a promising new approach to understanding biological sequences such as proteins.
Approach: They propose an LLM that can generate protein sequences in human and protein languages by pre-training an Lm on protein and natural language corpora and supervised instruction tuning to facilitate alignment.
Outcome: The proposed model outperforms state-of-the-art LLMs on protein-text generation tasks by a large margin.
LM2Protein: A Structure-to-Token Protein Large Language Model (2025.findings-emnlp)

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Challenge: RNA-binding proteins are critical for various molecular functions, relying on their precise tertiary structures.
Approach: They propose a method to integrate protein 3D structural data within a sequence processing framework.
Outcome: The proposed method achieves high sequence recovery in inverse folding and protein-conditioned RNA design.

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