[Paper Review] CCPL: Cross-modal Contrastive Protein Learning

Jiangbin Zheng, Stan Z. Li|arXiv (Cornell University)|Mar 19, 2023

Machine Learning in Bioinformatics4 citations

TL;DR

This paper proposes CCPL, a novel unsupervised contrastive protein learning framework that leverages a pretrained protein language model to guide structure model pretraining via cross-modal contrastive alignment between sequences and 3D structures. By introducing a self-supervised contact map prediction loss and evaluating on internal (contact map prediction, retrieval alignment) and external (protein design) tasks, the method achieves state-of-the-art performance with lower perplexity and higher sequence recovery, demonstrating strong generalization and robustness in structure representation learning.

ABSTRACT

Effective protein representation learning is crucial for predicting protein functions. Traditional methods often pretrain protein language models on large, unlabeled amino acid sequences, followed by finetuning on labeled data. While effective, these methods underutilize the potential of protein structures, which are vital for function determination. Common structural representation techniques rely heavily on annotated data, limiting their generalizability. Moreover, structural pretraining methods, similar to natural language pretraining, can distort actual protein structures. In this work, we introduce a novel unsupervised protein structure representation pretraining method, cross-modal contrastive protein learning (CCPL). CCPL leverages a robust protein language model and uses unsupervised contrastive alignment to enhance structure learning, incorporating self-supervised structural constraints to maintain intrinsic structural information. We evaluated our model across various benchmarks, demonstrating the framework's superiority.

Motivation & Objective

• To address the lack of generalizable, unsupervised protein structure pretraining methods that avoid damaging real spatial structure representations.
• To leverage the rich prior knowledge from large-scale pretrained protein language models for initializing and guiding protein structure representation learning.
• To develop a self-supervised structural constraint that strengthens intrinsic structural information learning without requiring labeled data.
• To design a comprehensive evaluation protocol combining internal and downstream tasks to validate the robustness and generalization of the pretrained structure model.
• To demonstrate that cross-modal contrastive alignment between sequence and structure modalities enables effective, scalable, and generalizable protein structure representation learning.

Proposed method

• The framework uses a pretrained protein language model to guide the training of a protein structure model via unsupervised contrastive alignment between sequence and structure representations.
• It constructs mini-batches of random structure-sequence pairs to create positive and negative samples for contrastive learning, enabling cross-modal alignment.
• A self-supervised contact map prediction head is introduced, using virtual Cβ atoms computed from N, Cα, and O atoms to enforce structural consistency in the latent space.
• The model is trained end-to-end using a contrastive loss that aligns the embedding spaces of sequences and structures, with the language model providing strong inductive bias.
• The structure encoder is jointly optimized with the contact map prediction head to preserve intrinsic structural topology and spatial relationships.
• Evaluation includes internal tasks (contact map prediction, retrieval alignment) and external downstream tasks (non-autoregressive protein design) to assess representation quality and generalization.

Experimental results

Research questions

• RQ1 Can a pretrained protein language model effectively guide the pretraining of a protein structure model without requiring labeled data?
• RQ2 Does cross-modal contrastive alignment between protein sequences and 3D structures improve the generalization and robustness of structure representations?
• RQ3 Can a self-supervised contact map prediction head enhance the structural inductive bias of the model without external annotations?
• RQ4 How does the proposed framework compare to existing methods in downstream protein design tasks in terms of sequence recovery and perplexity?
• RQ5 To what extent does the contrastive alignment loss improve the alignment between sequence and structure embedding spaces?

Key findings

• The residue-level and protein-level pretrained models achieve high P@L accuracy (over 90% on most datasets) in contact map prediction, indicating strong structural representation learning.
• The protein-level model shows superior retrieval alignment performance with higher accuracy and lower KL divergence, confirming effective cross-modal alignment.
• In downstream protein design, the model achieves lower perplexity and higher sequence recovery than strong baselines like ProteinMPNN and GVP-Transformer.
• The non-autoregressive decoder enables faster sampling speed while maintaining high performance, demonstrating efficiency and scalability.
• Fine-tuning with the pretrained structure model significantly improves performance over a non-pretrained backbone, confirming its strong generalization ability.
• The performance gap between residue-level and protein-level pretrained models narrows during fine-tuning, suggesting effective knowledge transfer.