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Zuobai Zhang

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Str2str: A Score-Based Framework for Zero-Shot Protein Conformation Sampling
Jiarui Lu
Bozitao Zhong
Zuobai Zhang
Jian Tang
The dynamic nature of proteins is crucial for determining their biological functions and properties, for which Monte Carlo (MC) and molecula… (voir plus)r dynamics (MD) simulations stand as predominant tools to study such phenomena. By utilizing empirically derived force fields, MC or MD simulations explore the conformational space through numerically evolving the system via Markov chain or Newtonian mechanics. However, the high-energy barrier of the force fields can hamper the exploration of both methods by the rare event, resulting in inadequately sampled ensemble without exhaustive running. Existing learning-based approaches perform direct sampling yet heavily rely on target-specific simulation data for training, which suffers from high data acquisition cost and poor generalizability. Inspired by simulated annealing, we propose Str2Str, a novel structure-to-structure translation framework capable of zero-shot conformation sampling with roto-translation equivariant property. Our method leverages an amortized denoising score matching objective trained on general crystal structures and has no reliance on simulation data during both training and inference. Experimental results across several benchmarking protein systems demonstrate that Str2Str outperforms previous state-of-the-art generative structure prediction models and can be orders of magnitude faster compared to long MD simulations. Our open-source implementation is available at https://github.com/lujiarui/Str2Str
2024-05-06
International Conference on Learning Representations (Accept (poster))
doi.org
openreview.net
Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations
Jiarui Lu
Zuobai Zhang
Bozitao Zhong
Chence Shi
Jian Tang
The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consum… (voir plus)ing molecular dynamics (MD) simulations *in silico*. Recently, generative models has been leveraged as a surrogate sampler to obtain conformation ensembles with orders of magnitude faster and without requiring any simulation data (a "zero-shot" inference). However, being agnostic of the underlying energy landscape, the accuracy of such generative model may still be limited. In this work, we explore the few-shot setting of such pre-trained generative sampler which incorporates MD simulations in a tractable manner. Specifically, given a target protein of interest, we first acquire some seeding conformations from the pre-trained sampler followed by a number of physical simulations in parallel starting from these seeding samples. Then we fine-tuned the generative model using the simulation trajectories above to become a target-specific sampler. Experimental results demonstrated the superior performance of such few-shot conformation sampler at a tractable computational cost.
2024-03-03
ICLR.cc/2024/Workshop/GEM (poster)
openreview.net
openreview.net
Structure-Informed Protein Language Model
Zuobai Zhang
Jiarui Lu
Vijil Chenthamarakshan
Aurelie Lozano
Payel Das
Jian Tang
Protein language models are a powerful tool for learning protein representations through pre-training on vast protein sequence datasets. Ho… (voir plus)wever, traditional protein language models lack explicit structural supervision, despite its relevance to protein function. To address this issue, we introduce the integration of remote homology detection to distill structural information into protein language models without requiring explicit protein structures as input. We evaluate the impact of this structure-informed training on downstream protein function prediction tasks. Experimental results reveal consistent improvements in function annotation accuracy for EC number and GO term prediction. Performance on mutant datasets, however, varies based on the relationship between targeted properties and protein structures. This underscores the importance of considering this relationship when applying structure-aware training to protein function prediction tasks. Code and model weights will be made available upon acceptance.
2024-03-03
GEM @ International Conference on Learning Representations (poster)
doi.org
openreview.net
Evaluating Representation Learning on the Protein Structure Universe
Arian R. Jamasb
Alex Morehead
Chaitanya K. Joshi
Zuobai Zhang
Kieran Didi
Simon Mathis
Charles Harris
Jian Tang
Jianlin Cheng
Pietro Lio
Tom L. Blundell
We introduce ProteinWorkshop, a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural … (voir plus)Networks. We consider large-scale pre-training and downstream tasks on both experimental and predicted structures to enable the systematic evaluation of the quality of the learned structural representation and their usefulness in capturing functional relationships for downstream tasks. We find that: (1) large-scale pretraining on AlphaFold structures and auxiliary tasks consistently improve the performance of both rotation-invariant and equivariant GNNs, and (2) more expressive equivariant GNNs benefit from pretraining to a greater extent compared to invariant models. We aim to establish a common ground for the machine learning and computational biology communities to rigorously compare and advance protein structure representation learning. Our open-source codebase reduces the barrier to entry for working with large protein structure datasets by providing: (1) storage-efficient dataloaders for large-scale structural databases including AlphaFoldDB and ESM Atlas, as well as (2) utilities for constructing new tasks from the entire PDB. ProteinWorkshop is available at: github.com/a-r-j/ProteinWorkshop.
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
Yangtian Zhang
Zuobai Zhang
Bozitao Zhong
Sanchit Misra
Jian Tang
Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions. Ac… (voir plus)curately predicting the conformation of protein side-chains given their backbones is important for applications in protein structure prediction, design and protein-protein interactions. Traditional methods are computationally intensive and have limited accuracy, while existing machine learning methods treat the problem as a regression task and overlook the restrictions imposed by the constant covalent bond lengths and angles. In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space. To avoid issues arising from simultaneous perturbation of all four torsional angles, we propose autoregressively generating the four torsional angles from
2023-12-11
Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net
Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction
Zuobai Zhang
Minghao Xu
Aurelie Lozano
Vijil Chenthamarakshan
Payel Das
Jian Tang
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.
2023-12-11
Conference on Neural Information Processing Systems (Accept (spotlight))
doi.org
openreview.net
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design
Chuanrui Wang
Bozitao Zhong
Zuobai Zhang
Narendra Chaudhary
Sanchit Misra
Jian Tang
Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years. However, a univers… (voir plus)ally accepted method for evaluation has not been established, since the wet-lab validation can be overly time-consuming for the development of new algorithms, and the
2023-10-24
NeurIPS.cc/2023/Workshop/AI4D3 (poster)
openreview.net
openreview.net
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Songtao Liu
Zhengkai Tu
Minkai Xu
Zuobai Zhang
Lu Lin
Rex Ying
Jian Tang
Peilin Zhao
Dinghao Wu
Retrosynthetic planning aims to devise a complete multi-step synthetic route from starting materials to a target molecule. Current strategie… (voir plus)s use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route. In this work, we propose a novel framework that utilizes context information for improved retrosynthetic planning. We view synthetic routes as reaction graphs and propose to incorporate context through three principled steps: encode molecules into embeddings, aggregate information over routes, and readout to predict reactants. Our approach is the first attempt to utilize in-context learning for retrosynthesis prediction in retrosynthetic planning. The entire framework can be efficiently optimized in an end-to-end fashion and produce more practical and accurate predictions. Comprehensive experiments demonstrate that by fusing in the context information over routes, our model significantly improves the performance of retrosynthetic planning over baselines that are not context-aware, especially for long synthetic routes. Code is available at https://github.com/SongtaoLiu0823/FusionRetro.
2023-07-24
International Conference on Machine Learning (Accept (Poster))
doi.org
proceedings.mlr.press
Protein Representation Learning by Geometric Structure Pretraining
Zuobai Zhang
Minghao Xu
Arian Jamasb
Vijil Chenthamarakshan
Aurelie Lozano
Payel Das
Jian Tang
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.
2023-04-30
International Conference on Learning Representations (Accept: poster)
doi.org
openreview.net
A Systematic Study of Joint Representation Learning on Protein Sequences and Structures
Zuobai Zhang
Chuanrui Wang
Minghao Xu
Vijil Chenthamarakshan
Aurelie Lozano
Payel Das
Jian Tang
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
arxiv.org
Enhancing Protein Language Model with Structure-based Encoder and Pre-training
Zuobai Zhang
Minghao Xu
Aurelie Lozano
Vijil Chenthamarakshan
Payel Das
Jian Tang
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)
openreview.net
openreview.net
FusionRetro: Molecule Representation Fusion via Reaction Graph for Retrosynthetic Planning
Songtao Liu
Zhengkai Tu
Minkai Xu
Zuobai Zhang
Peilin Zhao
Jian Tang
Rex Ying
Lu Lin
Dinghao Wu
Retrosynthetic planning is a fundamental problem in drug discovery and organic chemistry, which aims to find a complete multi-step syntheti… (voir plus)c route from a set of starting materials to the target molecule, determining crucial process flow in chemical production. Existing approaches combine single-step retrosynthesis models and search algorithms to find synthetic routes. However, these approaches generally consider the two pieces in a decoupled manner, taking only the product as the input to predict the reactants per planning step and largely ignoring the important context information from other intermediates along the synthetic route. In this work, we perform a series of experiments to identify the limitations of this decoupled view and propose a novel retrosynthesis framework that also exploits context information for retrosynthetic planning. We view synthetic routes as reaction graphs, and propose to incorporate the context by three principled steps: encode molecules into embeddings, aggregate information over routes, and readout to predict reactants. The whole framework can be efficiently optimized in an end-to-end fashion. Comprehensive experiments show that by fusing in context information over routes, our model sig-nificantly improves the performance of retrosyn-thetic planning over baselines that are not context-aware, especially for long synthetic routes.
2022-12-31
(publié)
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