The interaction of a protein with its environment can be understood and controlled via its 3D structure. Experimental methods for protein structure determination, such as X-ray crystallography or cryogenic electron microscopy, shed light on biological processes but introduce challenging inverse problems. Learning-based approaches have emerged as accurate and efficient methods to solve these inverse problems for 3D structure determination, but are specialized for a predefined type of measurement. Here, we introduce a versatile framework to turn biophysical measurements, such as cryo-EM density maps, into 3D atomic models. Our method combines a physics-based forward model of the measurement process with a pretrained generative model providing a task-agnostic, data-driven prior. Our method outperforms posterior sampling baselines on linear and non-linear inverse problems. In particular, it is the first diffusion-based method for refining atomic models from cryo-EM maps and building atomic models from sparse distance matrices.
Mask prior-guided denoising diffusion improves inverse protein folding
School of Computer Science, University of Sheffield（谢菲尔德大学计算机科学学院）；Biologics Engineering, Oncology R&D, AstraZeneca（阿斯利康生物制药工程与肿瘤研发部）；Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca（阿斯利康医药化学与研发部）；School of Biological Sciences, University of Southampton（南安普顿大学生物科学学院）；Centre for Machine Intelligence, University of Sheffield（谢菲尔德大学智能中心）
RIGA-Fold: A General Framework for Protein Inverse Folding via Recurrent Interaction and Geometric Awareness
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China（深圳大学计算机科学与软件工程学院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China（大数据系统计算技术国家工程实验室）
Property-driven Protein Inverse Folding With Multi-Objective Preference Alignment
School of Computer Science, Peking University（北京大学计算机科学系）；BioGeometry；Mila - Québec AI Institute（魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；HEC Montréal（蒙特利尔HEC商学院）；CIFAR AI Research Chair（CIFAR人工智能研究主席）
ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；AI Lab, Research Center for Industries of the Future, Westlake University（未来产业研究院人工智能实验室）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高陵人工智能学院）
Bridge-IF: Learning Inverse Protein Folding with Markov Bridges
Liangzhu Laboratory, Zhejiang University（浙江大学良渚实验室）；College of Pharmaceutical Sciences, Zhejiang University（浙江大学药学院）；Alibaba Cloud Computing（阿里云计算）；School of Public Health, Zhejiang University（浙江大学公共卫生学院）；School of Artificial Intelligence and Data Science, University of Science and Technology of China（中国科学技术大学人工智能与数据科学学院）；The Second Affiliated Hospital Zhejiang University School of Medicine（浙江大学医学院附属第二医院）
Ben-Gurion University of the Negev（本-古里安大学）；The University of British Columbia（不列颠哥伦比亚大学）
Recent advancements in machine learning techniques for protein folding motivate better results in its inverse problem -- protein design. In this work we introduce a new graph mimetic neural network, MimNet, and show that it is possible to build a reversible architecture that solves the structure and design problems in tandem, allowing to improve protein design when the structure is better estimated. We use the ProteinNet data set and show that the state of the art results in protein design can be improved, given recent architectures for protein folding.
Zhejiang University（浙江大学）；Westlake University（西湖大学）；Westlake Institute for Advanced Study（西湖高等研究院）
The linear sequence of amino acids determines protein structure and function. Protein design, known as the inverse of protein structure prediction, aims to obtain a novel protein sequence that will fold into the defined structure. Recent works on computational protein design have studied designing sequences for the desired backbone structure with local positional information and achieved competitive performance. However, similar local environments in different backbone structures may result in different amino acids, indicating that protein structure's global context matters. Thus, we propose t…
Iowa State University（爱荷华州立大学）；University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；New York University（纽约大学）；University of California Riverside（加州大学河滨分校）
The traditional approach of hand-crafting priors (such as sparsity) for solving inverse problems is slowly being replaced by the use of richer learned priors (such as those modeled by deep generative networks). In this work, we study the algorithmic aspects of such a learning-based approach from a theoretical perspective. For certain generative network architectures, we establish a simple non-convex algorithmic approach that (a) theoretically enjoys linear convergence guarantees for certain linear and nonlinear inverse problems, and (b) empirically improves upon conventional techniques such as…
UT Austin（德克萨斯大学奥斯汀分校）；Princeton University（普林斯顿大学）
We study image inverse problems with a normalizing flow prior. Our formulation views the solution as the maximum a posteriori estimate of the image conditioned on the measurements. This formulation allows us to use noise models with arbitrary dependencies as well as non-linear forward operators. We empirically validate the efficacy of our method on various inverse problems, including compressed sensing with quantized measurements and denoising with highly structured noise patterns. We also present initial theoretical recovery guarantees for solving inverse problems with a flow prior.
AI Research and Innovation Lab, Westlake University（西湖大学人工智能研究与创新实验室）；Zhejiang University（浙江大学）
While DeepMind has tentatively solved protein folding, its inverse problem -- protein design which predicts protein sequences from their 3D structures -- still faces significant challenges. Particularly, the lack of large-scale standardized benchmark and poor accuray hinder the research progress. In order to standardize comparisons and draw more research interest, we use AlphaFold DB, one of the world's largest protein structure databases, to establish a new graph-based benchmark -- AlphaDesign. Based on AlphaDesign, we propose a new method called ADesign to improve accuracy by introducing pro…
Soft robotic manipulators provide numerous advantages over conventional rigid manipulators in fragile environments such as the marine environment. However, developing analytic inverse models necessary for shape, motion, and force control of such robots remains a challenging problem. As an alternative to analytic models, numerical models can be learned using powerful machine learned methods. In this paper, the Kinematics Transformer is proposed for developing accurate and precise inverse kinematic models of soft robotic limbs. The proposed method re-casts the inverse kinematics problem as a seq…
National Technical University of Athens（雅典国立技术大学）；European University Cyprus（塞浦路斯欧洲大学）
We propose an end-to-end approach for solving inverse problems for a class of complex astronomical signals, namely Spectral Energy Distributions (SEDs). Our goal is to reconstruct such signals from scarce and/or unreliable measurements. We achieve that by leveraging a learned structural prior in the form of a Deep Generative Network. Similar methods have been tested almost exclusively for images which display useful properties (e.g., locality, periodicity) that are implicitly exploited. However, SEDs lack such properties which make the problem more challenging. We manage to successfully extend…
We propose quadratic residual networks (QRes) as a new type of parameter-efficient neural network architecture, by adding a quadratic residual term to the weighted sum of inputs before applying activation functions. With sufficiently high functional capacity (or expressive power), we show that it is especially powerful for solving forward and inverse physics problems involving partial differential equations (PDEs). Using tools from algebraic geometry, we theoretically demonstrate that, in contrast to plain neural networks, QRes shows better parameter efficiency in terms of network width and de…
The linear inverse problem is fundamental to the development of various scientific areas. Innumerable attempts have been carried out to solve different variants of the linear inverse problem in different applications. Nowadays, the rapid development of deep learning provides a fresh perspective for solving the linear inverse problem, which has various well-designed network architectures results in state-of-the-art performance in many applications. In this paper, we present a comprehensive survey of the recent progress in the development of deep learning for solving various linear inverse probl…
We present a directed percolation inverse problem for diode networks: Given information about which pairs of nodes allow current to percolate from one to the other, can one find a configuration of diodes consistent with the observed currents? We implement a divide-and-concur iterative projection method for solving the problem and demonstrate the supremacy of our method over an exhaustive approach for nontrivial instances of the problem. We find that the problem is most difficult when some but not all of the percolation data are hidden, and that the most difficult networks to reconstruct genera…
George Mason University（乔治梅森大学）；University of Maryland, College Park（马里兰大学帕克分校）
We consider the simulation of Bayesian statistical inverse problems governed by large-scale linear and nonlinear partial differential equations (PDEs). Markov chain Monte Carlo (MCMC) algorithms are standard techniques to solve such problems. However, MCMC techniques are computationally challenging as they require several thousands of forward PDE solves. The goal of this paper is to introduce a fractional deep neural network based approach for the forward solves within an MCMC routine. Moreover, we discuss some approximation error estimates and illustrate the efficiency of our approach via sev…
