Minghao Xu

ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts

Xinyu Yuan

Santiago Miret

Current protein language models (PLMs) learn protein representations mainly based on their sequences, thereby well capturing co-evolutionary… (voir plus) information, but they are unable to explicitly acquire protein functions, which is the end goal of protein representation learning. Fortunately, for many proteins, their textual property descriptions are available, where their various functions are also described. Motivated by this fact, we first build the ProtDescribe dataset to augment protein sequences with text descriptions of their functions and other important properties. Based on this dataset, we propose the ProtST framework to enhance Protein Sequence pre-training and understanding by biomedical Texts. During pre-training, we design three types of tasks, i.e., unimodal mask prediction, multimodal representation alignment and multimodal mask prediction, to enhance a PLM with protein property information with different granularities and, at the same time, preserve the PLM's original representation power. On downstream tasks, ProtST enables both supervised learning and zero-shot prediction. We verify the superiority of ProtST-induced PLMs over previous ones on diverse representation learning benchmarks. Under the zero-shot setting, we show the effectiveness of ProtST on zero-shot protein classification, and ProtST also enables functional protein retrieval from a large-scale database without any function annotation.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

proceedings.mlr.press

A Systematic Study of Joint Representation Learning on Protein Sequences and Structures

Chuanrui Wang

Vijil Chenthamarakshan

Aurelie Lozano

Payel Das

Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequenc… (voir plus)e representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a challenge. In contrast, structure-based methods leverage 3D structural information with graph neural networks and geometric pre-training methods show potential in function prediction tasks, but still suffers from the limited number of available structures. To bridge this gap, our study undertakes a comprehensive exploration of joint protein representation learning by integrating a state-of-the-art PLM (ESM-2) with distinct structure encoders (GVP, GearNet, CDConv). We introduce three representation fusion strategies and explore different pre-training techniques. Our method achieves significant improvements over existing sequence- and structure-based methods, setting new state-of-the-art for function annotation. This study underscores several important design choices for fusing protein sequence and structure information. Our implementation is available at https://github.com/DeepGraphLearning/ESM-GearNet.

2023-03-10

ArXiv (prépublication)

arxiv.org

Enhancing Protein Language Model with Structure-based Encoder and Pre-training

Aurelie Lozano

Vijil Chenthamarakshan

Payel Das

Protein language models (PLMs) pre-trained on large-scale protein sequence corpora have achieved impressive performance on various downstrea… (voir plus)m protein understanding tasks. Despite the ability to implicitly capture inter-residue contact information, transformer-based PLMs cannot encode protein structures explicitly for better structure-aware protein representations. Besides, the power of pre-training on available protein structures has not been explored for improving these PLMs, though structures are important to determine functions. To tackle these limitations, in this work, we enhance the PLM with structure-based encoder and pre-training. We first explore feasible model architectures to combine the advantages of a state-of-the-art PLM (i.e., ESM-1b) and a state-of-the-art protein structure encoder (i.e., GearNet). We empirically verify the ESM-GearNet that connects two encoders in a series way as the most effective combination model. To further improve the effectiveness of ESM-GearNet, we pre-train it on massive unlabeled protein structures with contrastive learning, which aligns representations of co-occurring subsequences so as to capture their biological correlation. Extensive experiments on EC and GO protein function prediction benchmarks demonstrate the superiority of ESM-GearNet over previous PLMs and structure encoders, and clear performance gains are further achieved by structure-based pre-training upon ESM-GearNet. The source code will be made public upon acceptance.

2023-03-05

ICLR.cc/2023/Workshop/MLDD (poster)

EurNet: Efficient Multi-Range Relational Modeling of Protein Structure

Yuanfan Guo

Yi Xu

Xinlei Chen

Yuandong Tian

Modeling the 3D structures of proteins is critical for obtaining effective protein structure representations, which further boosts protein f… (voir plus)unction understanding. Existing protein structure encoders mainly focus on modeling short-range interactions within protein structures, while they neglect modeling the interactions at multiple length scales that are actually complete interactive patterns in protein structures. To attain complete interaction modeling with efficient computation, we introduce the EurNet for Efficient multi-range relational modeling. In EurNet, we represent the protein structure as a multi-relational residue-level graph with different types of edges for modeling short-range, medium-range and long-range interactions. To efficiently process these different interactive relations, we propose a novel modeling layer, called Gated Relational Message Passing (GRMP), as the basic building block of EurNet. GRMP can capture multiple interactive relations in protein structures with little extra computational cost. We verify the state-of-the-art performance of EurNet on EC and GO protein function prediction benchmarks, and the proposed GRMP layer is proved to achieve better efficiency-performance trade-off than the widely-used relational graph convolution.

2023-03-05

ICLR.cc/2023/Workshop/MLDD (poster)

Protein Representation Learning by Geometric Structure Pretraining

Arian Jamasb

Vijil Chenthamarakshan

Aurelie Lozano

Payel Das

Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Ex… (voir plus)isting approaches usually pretrain protein language models on a large number of unlabeled amino acid sequences and then finetune the models with some labeled data in downstream tasks. Despite the effectiveness of sequence-based approaches, the power of pretraining on known protein structures, which are available in smaller numbers only, has not been explored for protein property prediction, though protein structures are known to be determinants of protein function. In this paper, we propose to pretrain protein representations according to their 3D structures. We first present a simple yet effective encoder to learn the geometric features of a protein. We pretrain the protein graph encoder by leveraging multiview contrastive learning and different self-prediction tasks. Experimental results on both function prediction and fold classification tasks show that our proposed pretraining methods outperform or are on par with the state-of-the-art sequence-based methods, while using much less pretraining data. Our implementation is available at https://github.com/DeepGraphLearning/GearNet.

2023-01-31

ICLR.cc/2023/Conference (poster)

Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction

Aurelie Lozano

Vijil Chenthamarakshan

Payel Das

Self-supervised pre-training methods on proteins have recently gained attention, with most approaches focusing on either protein sequences o… (voir plus)r structures, neglecting the exploration of their joint distribution, which is crucial for a comprehensive understanding of protein functions by integrating co-evolutionary information and structural characteristics. In this work, inspired by the success of denoising diffusion models in generative tasks, we propose the DiffPreT approach to pre-train a protein encoder by sequence-structure joint diffusion modeling. DiffPreT guides the encoder to recover the native protein sequences and structures from the perturbed ones along the joint diffusion trajectory, which acquires the joint distribution of sequences and structures. Considering the essential protein conformational variations, we enhance DiffPreT by a method called Siamese Diffusion Trajectory Prediction (SiamDiff) to capture the correlation between different conformers of a protein. SiamDiff attains this goal by maximizing the mutual information between representations of diffusion trajectories of structurally-correlated conformers. We study the effectiveness of DiffPreT and SiamDiff on both atom- and residue-level structure-based protein understanding tasks. Experimental results show that the performance of DiffPreT is consistently competitive on all tasks, and SiamDiff achieves new state-of-the-art performance, considering the mean ranks on all tasks. Our implementation is available at https://github.com/DeepGraphLearning/SiamDiff.

2022-12-31

arXiv.org (prépublication)

PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding

We are now witnessing significant progress of deep learning methods in a variety of tasks (or datasets) of proteins. However, there is a lac… (voir plus)k of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field. In this paper, we propose such a benchmark called PEER, a comprehensive and multi-task benchmark for Protein sEquence undERstanding. PEER provides a set of diverse protein understanding tasks including protein function prediction, protein localization prediction, protein structure prediction, protein-protein interaction prediction, and protein-ligand interaction prediction. We evaluate different types of sequence-based methods for each task including traditional feature engineering approaches, different sequence encoding methods as well as large-scale pre-trained protein language models. In addition, we also investigate the performance of these methods under the multi-task learning setting. Experimental results show that large-scale pre-trained protein language models achieve the best performance for most individual tasks, and jointly training multiple tasks further boosts the performance. The datasets and source codes of this benchmark are all available at https://github.com/DeepGraphLearning/PEER_Benchmark

2021-12-31

Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (publié)