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A Benchmarking Platform for Assessing Protein Language Models on Function-Related Prediction Tasks.

Elif Çevrim1,2, Melih Gökay Yiğit1,3, Erva Ulusoy1,2

  • 1Biological Data Science Lab, Department of Computer Engineering, Hacettepe University, Ankara, Turkey.

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Summary
This summary is machine-generated.

We introduce PROBE, a new tool to benchmark protein representations for function prediction. PROBE evaluates classical methods and protein language models (PLMs), aiding researchers in selecting optimal approaches for biological insights.

Keywords:
Benchmarking frameworksGene ontologyProtein familiesProtein function predictionProtein language modelsProtein–protein interactionsRepresentation learning

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Area of Science:

  • Biochemistry and Molecular Biology
  • Bioinformatics and Computational Biology

Background:

  • Proteins are fundamental to biological processes, necessitating accurate function annotation for scientific advancement and biotechnological innovation.
  • Traditional homology-based methods and emerging machine learning (ML) approaches, particularly protein language models (PLMs), are key to predicting protein function.
  • PLMs excel at capturing complex sequence-structure-function relationships through specialized deep learning architectures.

Purpose of the Study:

  • To introduce the Protein Representation Benchmark (PROBE), a framework for evaluating protein representations on function prediction tasks.
  • To provide a detailed protocol for using the PROBE framework via GitHub and a web service.
  • To demonstrate PROBE's utility by evaluating established and novel multimodal PLMs.

Main Methods:

  • Development of the PROBE benchmarking framework with four core tasks: semantic similarity inference, ontology-based function prediction, drug target family classification, and protein-protein binding affinity estimation.
  • Implementation of a user-friendly web service and a GitHub repository for PROBE accessibility.
  • Evaluation of protein representations, including classical methods and PLMs like ESM2, ESM3, ProstT5, and SaProt, using diverse data types (sequence and structure).

Main Results:

  • PROBE effectively benchmarks diverse protein representations, including classical methods and advanced PLMs.
  • Multimodal PLMs demonstrate capabilities in integrating sequence and structural information for enhanced function prediction.
  • The study highlights the potential of PLMs in advancing protein function prediction accuracy and utility.

Conclusions:

  • The PROBE tool offers a standardized framework for evaluating protein representations, crucial for advancing biological understanding.
  • Protein language models show significant promise for improving protein function prediction, benefiting both model developers and end-users.
  • PROBE facilitates informed selection of protein representation methods, accelerating research in biotechnology and therapeutics.