In: Rohl CA, Strauss CEM, Misura KMS, et al., Alford RF, Leaver-Fay A, Jeliazkov JR, et al., Bateman A, Martin M-J, Orchard S, et al.. Compared with traditional potential terms, energy functions learned by deep neural networks evaluate designs more precisely but slowly. In the hierarchical architecture, the meta learner of each level will input the meta features outputted from a low level and output the meta features to successive levels until the top level which will output the final classification result. This type of reinforcement learning has recently been incorporated into the DL paradigm, referred to as deep reinforcement learning. Thus, meta learning can be used in B-cell conformational epitope prediction in continuously evolving viruses, which is useful for vaccine design. This method combines symbolic methods, in particular, knowledge representation using symbolic logic and automated reasoning, with neural networks that encode for related information within knowledge graphs, and these embeddings can be applied to predict the edges in the knowledge graph, such as drugtarget relations. , Garg V.K. Recently, deep learning techniques have shown preliminary but impressive impacts to the field of protein design. For example, information encoded in a protein sequence is hard to extract from the target sequence alone, since it is simply a permutation or combination of 20 kinds of amino acid residues. Since proteins need to form particular tertiary structures to perform their specific functions and structures usually contain richer information, e.g. In addition, massive protein structural data accumulated from previous researches, such as protein fold classification, consequent clustering and reaction mechanism information described by binding interfaces, catalytic centers and allosteric regulations, would also be extremely helpful. As we searched, one-shot learning has been used to significantly lower the quantity of data required and achieves precise predictions in drug discovery (Altae-Tran etal., 2017). The training procedure of this so-called FBGAN system contained two parts. Therefore, in combination with downstream generative models or methods, proteins of desired functions but with unseen sequences could be generated in a high throughput manner. UniRep was trained with approximately 24 million sequences from the UniRef50 database [118] and its self-supervised training procedure [119] utilized input sequences themselves as the corresponding labels. In this section, we will focus on two major aspects of direct protein sequence design with concrete cases to look through the past achievements and anticipate the future trends. Radford A, Jozefowicz R, Sutskever I. Protein homology plays an important role in protein structure prediction, providing massive evolutionary information for precise inferences. Protein representations with fundamental features are obtained through protein language models (bottom). We will detail these novelties, illustrate the differences between these approaches and conventional knowledge-based ones, and articulate corresponding significance in the following sections. Second, the clinical expect accuracy of computational model related to the healthcare or disease diagnosis is 98%99% and it is tough to reach that high accuracy. WebReinforcement learning models provide an excellent example of how a computational process approach can help organize ideas and understanding of underlying neurobiology. (A) An illustration of the natural protein synthesis process. GANs also played an important role in direct protein sequence generation. Guo X, Du Y, Tadepalli S, et al. Generating tertiary protein structures via an interpretative variational autoencoder. The earlier protein design approaches such as directed evolution [11, 12] and the following rational engineering [13, 14] mainly focus on the imitation and/or acceleration of natural evolutionary processes. Unlike discriminative models widely used in protein researches that construct mappings from the space of the input data to that of the output label by maximizing the respective likelihood of samples, generative models such as generative adversarial networks (GANs) [42] and variational auto-encoders (VAEs) [43] try to capture the underlying data distribution of training set and sample brand new instances according to the learned distribution. Predicting potential microbe-disease associations based on multi-source features and deep learning. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, ProJect: a powerful mixed-model missing value imputation method, EnGens: a computational framework for generation and analysis of representative protein conformational ensembles, From contigs towards chromosomes: automatic improvement of long read assemblies (ILRA), Predicting potential microbedisease associations based on multi-source features and deep learning, Briefings of deep learning techniques related to this review, Deep learning in structure-based protein design, https://doi.org/10.1101/2021.07.18.452833, https://doi.org/10.48550/arXiv.1704.01444, https://doi.org/10.48550/arXiv.1706.03762, https://doi.org/10.1101/2021.12.08.471762, https://doi.org/10.48550/arXiv.1606.05908, https://doi.org/10.48550/arXiv.1511.06434, https://doi.org/10.48550/arXiv.2004.07119, https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html, https://doi.org/10.1101/2020.08.07.242347, https://doi.org/10.1101/2020.01.06.895466, https://doi.org/10.1101/2020.07.23.218917, https://doi.org/10.48550/arXiv.1810.04805, https://doi.org/10.48550/arXiv.2007.06225, https://proceedings.neurips.cc/paper/2015/file/8d55a249e6baa5c06772297520da2051-Paper.pdf, https://doi.org/10.48550/arXiv.1903.00458, https://doi.org/10.1101/2020.10.28.359828, https://doi.org/10.48550/arXiv.1803.01271, https://doi.org/10.48550/arXiv.1805.08318, https://doi.org/10.48550/arXiv.1707.06347, https://doi.org/10.48550/arXiv.1503.02531, https://creativecommons.org/licenses/by-nc/4.0/, Receive exclusive offers and updates from Oxford Academic, Hallucinate novel proteins through protein structure prediction networks, Completely arbitrary protein sequences with fixed length of 100 amino acids, trRosetta network within residue substitution step of a simulated annealing trajectory, Generate coordinates of immunoglobulin backbones, Generate protein sequence with given geometric and amino acid constraints, Proteins extracted from UniProt database, sequence repository Gene3D, Optimize over protein sequences and structures simultaneously by backpropagating gradients through protein structure prediction networks, Proteins collected from a structure-refinement research (redundancy with trRosetta training set were reduced), Rate candidate predicted structures without explicit standards and answers, Extract fundamental features of unlabeled protein sequences into a statistical representation, Protein sequences from UniRef50 database, Train a deep contextual protein language model to produce generalized features, Build precise virtual protein fitness landscape based on protein sequence representation, A few mutants of natural target protein and their functional characterizations, Single-layer linear regression model on the top of UniRep, Generate synthetic genes coding proteins with desirable functions or biophysical properties, Peptides with 550 residues from UniProt dataset, Generate functional protein sequences by learning natural sequence diversity, Bacterial MDH sequences from UniProt dataset, Tailored GAN with temporal convolution and self-attention, Copyright 2023 Oxford University Press. Despite the significant achievements [2224], these conventional approaches are mainly knowledge based, relying on physical principles and statistical rules [25]. Tremendous number of unlabeled sequences in combination with the powerful capability of deep learning for feature extraction, pattern recognition and objective generation make it possible and valuable to directly explore the sequence space and improve the protein design paradigm. Reinforcement learning (RL) aims to build intelligent agents able to optimally act after the training process to solve a given goal task in an autonomous and non Xianggen Liu is currently a Ph.D. student at Tsinghua University. Basically, deep reinforcement learning divides the world into two parts, an environment and an agent. In comparison to traditional knowledge-based energy functions that are typically combinations of statistical and empirical potential terms [21, 97, 108], deep learning models could provide a more general and more accurate description of the multidimensional potential functions in the real world. One possible procedure is designing a modular protein sensor-actuator switch where small ligands could directly change downstream transductions of corresponding cellular signal pathway by binding to the designed targets [6, 73, 149]. , et al. With the application of more advanced technologies, these methods can help us excavate more intrinsic principles of proteins and get more high-quality functional protein materials. In recent years, ML researchers have developed a number of methods to incorporate symbolic reasoning with DL. Other deep-learning-based methods constructed their networks with various architectures including auto-encoders [105], 3D convolutional neural networks [106], DenseNets [107] and GANs [84] to predict sequence probability profiles from a given backbone structure. , Czibula G. Another important direction to overcome this deficiency might be the few-shot learning [145, 146], and to our knowledge, related exploration has not been tried yet. Reinforcement Learning New methods for the automated design of compounds against profiles of multiple properties are thus of great In brief, meta learning outputs an ML model that can learn quickly. Generally, protein fitness landscape searching methods cluster residue side-chain conformations as different rotamers [96], abstract the sequence optimization of a given backbone to a discrete energy minimization problem and then search combinations of rotamers around the global minimum [97]. (B) Protein sequence generation from left to right by deep reinforcement learning. Therefore, generative models are more widely used in this area compared with discriminative ones (as exhibited in Table 2). Hence, a work introduced a set of protein bioinformatics tasks with clear definitions, data and assessing metrics to construct a standard evaluation system for protein transfer learning [126]. [Supplementary material is available at Journal of Molecular Cell Biology online. In: Anishchenko I, Pellock SJ, Chidyausiku TM, et al.. Attention mechanisms can potentially be used in a wide range of biosequence analysis problems, such as RNA sequence analysis and prediction (Park etal., 2017), protein structure and function prediction from amino acid sequences (Zou etal., 2018), and identification of enhancerpromoter interactions (EPIs) (Hong etal., 2020). In: Desmet J, De Maeyer M, Hazes B, et al.. This feedback-loop mechanism finetuned the distribution mapping between the latent space and the real DNA sequence space for specific downstream optimization objectives. secondary structures) has been validated and further related researches would surely acquire a greater depth in the coming future. Introduction of Reinforcement Learning in Other direct sequence generative models adopted different architectures suitable for specific generation demands [87, 88, 122, 131]. Here, we focus on the ongoing trends and future directions of modern DL, perspective on future developments and potential new applications to biology and biomedicine. The positive rewards encourage the agent to strengthen its policy, i.e. Subsequent in vitro synthesis showed that these protein hallucinations were monomeric and stable, possessing designed structural elements. Anand N, Eguchi R, Huang PS. After each action, the state can change. bioinformatics Therefore, artificial protein modification, and even one step further, the design of brand-new proteins from scratch emerges as the times require. An illustration of protein representation learning, direct protein sequence design and related downstream protein analysis applications. Compared with the classical molecular mechanics force fields with great complexity and cost, this data-driven method only needed a few hours for training, which exhibited its practical applicability and huge potentiality. In the present paper, we use a deep reinforcement learning (DRL) approach for solving the multiple sequence alignment problem which is an NP-complete problem. Within every training step, the agent chooses an available action according to its own policy, which slightly changes the environment, and then receives feedbacks called rewards from the environment. Recently, the introduction of deep learning has shown preliminary but transformative influence to the field of protein design. In: Yang J, Anishchenko I, Park H, et al., Alley EC, Khimulya G, Biswas S, et al., Biswas S, Khimulya G, Alley EC, et al.. Representation learning has laid a solid foundation for sequence generation. Although these two frameworks have a large intersection, the VAE architecture could be trained for some models that GANs could not and vice versa. Note that a key distinguishing feature is that users do not have to predefine all the states, and a model can be trained in an end-to-end manner, which has become an increasingly active research field with numerous algorithms being developed. In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. In: Goodfellow I, Pouget-Abadie J, Mirza M, et al.. Deep learning could provide fast, high-throughput and precise in silico protein design methodologies. Web. A simple supervised top model taking the sequence representations as input was trained on a limited number (as few as 24 sequences, and this is the source of its name low-N) of functionally assayed random mutants of the target protein to rate arbitrary sequences. Rau M, Renaud N, Xue LC, et al. DeepRank-GNN: a graph neural network framework to learn patterns in protein-protein interfaces. In: Silver D, Huang A, Maddison CJ, et al.. Many impressive achievements have been made through protein design over the past decade, which intensively impacted and promoted synthetic biology in both academia and industry. Kenta Nakai is a professor in the Institute of Medical Science, the University of Tokyo. , Choi H.-S. Furthermore, with the rapid accumulation of protein sequence data and the usage of network architectures with higher complexity and capability, the future versions of ESM-1b were expected to have additional improvements in protein sequence representation. Reinforcement Learning Results: Recently, deep reinforcement learning algorithms have become very popular. and X.G. In combination with different kinds of top models, these representation vectors could be used for either protein sequence design or other analysis tasks (top). Haoyang Li, Shuye Tian3 and Yu Li contributed equally to this work. Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making. These successes deepened our understanding of the sequencestructure relationship for proteins, which is also the foundation of structure-based design, and provided a bunch of practical tools that could be directly used in design problems. Scoring functions in the Rosetta program range from statistical potentials established using Bayesian methods [111] to complicated modern force fields [112]. Before the completion of sequence generation, the reward to the agent remained 0. (, Mnih V. This perspective may shed new light on the foreseeable future applications of modern DL methods in bioinformatics. Goodfellow IJ, Mirza M, Xiao D, et al. An empirical investigation of catastrophic forgetting in gradient-based neural networks. After each action, the state can change. EPIVAN (Hong etal., 2020) was designed to predict long-range EPIs using only genomic sequences via DL methods and attention mechanisms. During these iterations, rotamers are substituted randomly based on the energy functions, following the Metropolis-Hastings algorithm. For instance, the ability of an antibody to respond to an antigen depends on the antibodys specific recognition of an epitope (Hu etal., 2014). , Czibula I. Reinforcement learning interfaces for biomedical database systems 95 (95% confidence interval = 1.21.6 ) compared with the 3.5 r.m.s.d. GANs are used as an inpainting tool to repair the inter-residue distance map for a corrupted protein structure. Nevertheless, designable backbones usually cluster into minute regions that disperse sparsely in the space [67], because protein domains stabilized by complicated atom-level forces like hydrogen bonds and hydrophobic interactions have to adopt exquisite shapes with well packed cores and properly exposed interfaces. Through their incredible power of extracting and integrating statistical patterns within existing protein data, artificial deep neural networks learn fundamental protein features, store them in billions of parameters and generalize them for inferences in different sub-fields. It is notable that some researches skipped two-dimensional structural representations and generated backbones with 3D atom coordinates directly. In: Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. Reinforcement learning is a amino acid embeddings, could be optimized and the representation of a protein sequence with its fundamental features could be inferred in a latent space. This lack of interpretability has limited their applications, particularly when their performance did not stand out among other more interpretable ML methods, such as linear regression, logistic regression, support vector machines, and decision trees. WebReinforcement Learning is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. However, they have been criticized for being black boxes. In a nutshell, deep learning trains an artificial neural network or a combination of related networks to approximate complicated unknown functions in a high-dimensional abstract space. Among them, deep learning techniques, which have revolutionized many other fields like natural language processing and computer vision [30], made the most significant impacts. As expected, image is the most commonly approached topic by DL, and disease and imaging follow closely. In addition, networks originally constructed for protein structure prediction could also be repurposed for sequence design by energy landscape optimization. Deep generative models can be applied to problems related to protein structure design (Anand and Huang, 2018; Ingraham etal., 2019), 3D compound design (Imrie etal., 2020), protein loop modelling (Li etal., 2017b), and DNA design (Killoran etal., 2017). Thus, in principle, directly mapping the spaces of protein sequence and function seems to be advantageous over design procedures that need predetermined structural topologies as intermedia. In: Unsal S, Atas H, Albayrak M, et al. Evaluation of methods for protein representation learning: a quantitative analysis. The performance combining symbolic methods outperforms traditional approaches. Wenze Ding is currently a research scientist in School of Artificial Intelligence and School of Future Technology, NUIST, interested in structural bioinformatics and protein design. According to the central dogma, the basic biological principle articulated by Francis Crick in 1958, proteins are the executive ends of information flow systems in living organisms, each performing one or a few specifically encoded functions that jointly define the corresponding organism in turn. , Lee L.J. This requires deep learning methods to design proteins with multiple and distinct energy minima. Machine learning (ML) is a multidisciplinary field that employs computer science, artificial intelligence, computational statistics and information theory to construct algorithms that can learn from existing data sets and to make predictions on new data set. Third, a range of proposed optimization algorithms have made deep ANNs stand out as an ideal technique for large and complex data analyses and information discovery compared to competing techniques in the big data era. D. Shen, and C. Tan. Another method based on conditional generative model and graph representation also improved the reliability and computational speed of sequence fitness compared with traditional methods like Rosetta [38]. Deep learning also contributes to the energy evaluation process of protein fitness landscape searching. , Umarov R. (, Li Y. Statistical sampling methods, exemplified by Monte Carlo simulations, have been used to solve this dilemma and could achieve acceptable approximations in practice [99]. Many researches of this field focused on algorithm development and in silico evaluation with barely few experimental verifications and practical applications. Information from unimproved variants is discarded. The results of applying one-shot models to a number of assay collections show strong performance compared to other methods, such as random forest and graph CNNs. During the past decade, three important advances in science and technology have led to the rejuvenation of ANNs, particularly via DL. Google Scholar Digital Library; Cited By View all. The adoption of more advanced and lightweight network architectures as well as knowledge distillation [147] and network pruning [148] may partially handle this dilemma. Wenze Ding and others, Protein design via deep learning, Briefings in Bioinformatics, Volume 23, Issue 3, May 2022, bbac102, https://doi.org/10.1093/bib/bbac102. reinforcement learning Devlin J, Chang M-W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding. (, Altae-Tran H. Almost all information of a protein is encoded in its sequence. Recently, a novel network called transformer that contains encoderdecoder architecture and attention mechanism exhibited its superior capability for sequence processing [36]. , et al. Language: All Sort: Most stars Developer-Y / cs-video-courses Star 57.2k Code Issues Pull requests List of Computer Science courses with video lectures. (, Wang P.-W. Variants of RNN like long short-term memory (LSTM) and gated recurrent unit (GRU) also played an important role in natural language processing and bioinformatics during the past decade [3335]. A Wasserstein GAN (WGAN) [84] combined with a novel external feedback-loop mechanism (denoted as a function analyzer) was trained to generate DNA sequences encoding proteins [127]. Published by Oxford University Press on behalf of, This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, Transcriptional pausing induced by ionizing radiation enables the acquisition of radioresistance in nasopharyngeal carcinoma, Dynamic phosphorylation of CENP-N by CDK1 guides accurate chromosome segregation in mitosis, Large-scale data-driven and physics-based models offer insights into the relationships among the structures, dynamics, and functions of the chromosomes, A Targetable Pathway to Eliminate TRA-1-60, Targeted gene panel provides advantages over whole-exome sequencing for diagnosing obesity and diabetes mellitus, http://creativecommons.org/licenses/by-nc/4.0/, Receive exclusive offers and updates from Oxford Academic, The bioinformatics of next generation sequencing: a meeting report. As shown in Figure 1, GAN generally contains two main parts: a generator and a discriminator. Thi Reinforcement learning is known to be unstable or even to diverge, the specific reasons for it can be found in the [37, 38]. MediaWiki, 18 Sept. 2007. WebI am curious if it could potentially be useful, since it's more often used to control dynamic systems. Deng J, Dong W, Socher R, et al. ImageNet: a large-scale hierarchical image database. However, for protein design with a specific objective, related data of protein functions and properties are usually not only scarce but also collected without unified and standard experimental conditions. Furthermore, attention networks with the most advanced end-to-end training procedure developed by Google DeepMind shocked the public in the 14th Critical Assessment of protein Structure Prediction (CASP) experiments by providing a wonderful solution for the structure prediction of single-domain proteins [6365]. (, Li Z. Hierarchical reinforcement learning for automatic disease diagnosis | Bioinformatics | Oxford Academic AbstractMotivation. (, Hong Z. For each topic, the three bars show the number of publications mentioning the terms RNN, CNN, and deep learning, respectively. Motivation: Disease diagnosis-oriented dialog system models the interactive consultation procedure as the Markov decision process, and reinforcement learning algorithms are used to solve the problem. Search for other works by this author on: Department of Biology, Southern University of Science and Technology, Computational Bioscience Research Center (CBRC), Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), School of Computer Science and Technology, Hangzhou Dianzi University, Ningbo Institute of Information Technology Application, Chinese Academy of Sciences, Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Neuro-symbolic representation learning on biological knowledge graphs, Low data drug discovery with one-shot learning, Identifying enhancerpromoter interactions with neural network based on pre-trained DNA vectors and attention mechanism, Approximation capabilities of multilayer feedforward networks, Using deep reinforcement learning to speed up collective cell migration, A meta-learning approach for B-cell conformational epitope prediction, A fully automated high-throughput flow cytometry screening system enabling phenotypic drug discovery, Deep learning in bioinformatics: Introduction, application, and perspective in the big data era, DEEPre: sequence-based enzyme EC number prediction by deep learning, Deep generative modeling for single-cell transcriptomics, Human-level control through deep reinforcement learning, Deep reinforcement learning of cell movement in the early stage of C. elegans embryogenesis, mldeepre: multi-functional enzyme function prediction with hierarchical multi-label deep learning, The Author(s) (2020). In the last decade, protein design has achieved great successes, helping mankind deal with social challenges in multiple facets. Modern deep learning in bioinformatics - Oxford Academic A 3D convolutional neural network was trained in an autoregressive manner to learn the distribution of sequences conditioned on a predetermined backbone directly from the protein structure data [109]. The root-mean-square deviation score of their GAN method has 44% improvement compared to other tools, and their GAN method obtains the smallest standard deviation compared to other tools, which show the stability of their prediction. Supplementary data are available at Bioinformatics online. However, most of these reviews have focused on previous research, whereas current trends in the principled DL field and perspectives on their future developments and potential new applications to biology and biomedicine are still scarce. Observations about the set of change-of-state become guiding information for future actions. GNN has been widely applied to knowledge graphs, social networks, drug discovery and protein bioinformatics [3841]. A method called unified representation (UniRep) trained a multiplicative longshort-term-memory RNN (mLSTM RNN) [35] with 1900 hidden units to learn the fundamental representation of protein sequences and encode arbitrary sequences into length-fixed vectors [33]. Tag: reinforcement learning Biostats Colloquium with Eric Laber 4/27 Harvard Biostatistics Colloquium SeriesThursday, April 271:00-2:00pmFXB G11Eric LaberProfessor of Statistical ScienceDuke UniversityReinforcement Learning for Respondent-Driven Sampling Candidate rating is thus often simplified as identifying sequencestructure pairs with the lowest energies. This method has been tested on six cell lines, and the area under the receiver operating characteristic (AUROC) and area under the precision-recall curve (AUPR) values of EPIVAN are higher than those without the attention mechanism, which indicates that the attention mechanism is more concerned with cell line-specific features and can better capture the hidden information from the perspective of sequences. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. wrote the paper together; Q.F., R.T., Y.P., and C.H. , Donti P.L. Learning Superiority over other state-of-the-art input features across a wide range of applications like mutational effect prediction further testified its generalizability and advantages.

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