reinforcement learning in bioinformaticsreinforcement learning in bioinformatics

Meanwhile, VAEs that performed conditional generation through the introduction of a representative latent space were also shown to be very useful for protein backbone design [8789]. The earliest routines redesigned existing native protein structures to get possible backbones with improved structural stability and perhaps new functions [68, 69] or systematically sampled helical bundles [23, 70, 71] under the constraints of Cricks parameterization. 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]. , Lin S.-C. Proteins with desired functions and properties are important in fields like nanotechnology and biomedicine. backbone generation, sequence fitness and candidate scoring, exemplified by Top 7 [20], the first globular protein that was designed without natural homologs, as well as other famous related works. To maximize the usage of limited exhibition space in this paper, we only choose one research as representative from a bunch of researches with similar objectives or procedures. Thus, deep learning with its intrinsic advantages and training techniques accumulated in earlier researches could substantially mitigate the limitations of regular procedures either by replacing the entire optimization routine or by eliciting local amelioration within their frameworks. Combing the advantages of Bayesian method, VAE with its elegant mathematical foundation, simple structure as well as satisfactory training cost and model performance, gradually becomes one of the common options for generative models and influences bioinformatics a lot. 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. In: Unsal S, Atas H, Albayrak M, et al. Evaluation of methods for protein representation learning: a quantitative analysis. Although these two frameworks have a large intersection, the VAE architecture could be trained for some models that GANs could not and vice versa. The widely used dimensionality reduction methods, such as principal component analysis, may not work well with such data because of those properties. , et al. 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]. 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 In combination with different kinds of top models, these representation vectors could be used for either protein sequence design or other analysis tasks (top). Low correlation among these meta learners indicates that these learners truly have complementary predictive capabilities, and the ablation analysis indicates that these learners differentially interacted and contributed to the final meta model. A wide variety of native proteins such as nuclear proteins, membrane proteins, hemoproteins, lipoproteins, heat-shock proteins, contractile proteins, etc. Modern practical implementations of CNN often involve huge networks containing architectural variants and millions of units. Haoyang Li, Shuye Tian3 and Yu Li contributed equally to this work. We will detail these novelties, illustrate the differences between these approaches and conventional knowledge-based ones, and articulate corresponding significance in the following sections. Within the trajectory, a random single residue substitution was initiated at an arbitrary position and the distance distribution map of this mutated sequence would be immediately predicted by trRosetta for every time step. One possible solution to overcome these obstacles is the representation learning using protein language models. Using few-shot learning algorithms, a model can be trained with reasonable performance on some difficult problems by utilizing only the existing limited data. , Donti P.L. It constructed a complicated representation space for protein sequences. ESM-1b Transformer integrated residue contexts across the entire input protein sequence through many stacked self-attention modules. Although this work is originally proposed to address the protein structure prediction problem, the underlying fundamental concept could be easily generalized to protein design. Web. He focuses on protein bioinformatics and molecular dynamics simulations. For example, a low-N protein engineering method [34] was reported based on representation learning of UniRep [33]. Implemented with customized temporal convolutional network [129] and self-attention mechanism [130], ProteinGAN could not only learn useful sequence motifs and critical long-range inter-residue interactions simultaneously, but also concentrate on functional areas like catalytic centers. 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. Deng J, Dong W, Socher R, et al. ImageNet: a large-scale hierarchical image database. A good meta learning model should generalize to a new task even if the task has never been encountered during the training time. manifest strikingly excellent properties compared with man-made machines, including extremely high efficiency, economy and precision in operation, self-assembly upon synthesis and so on. The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), under award numbers FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, URF/1/3450-01-01, URF/1/3454-01-01, URF/1/4098-01-01, URF/1/4077-01-01, and REI/1/0018-01-01. , et al. Candidate rating is thus often simplified as identifying sequencestructure pairs with the lowest energies. For example, secondary and tertiary structural properties could be identified from the generated sequence representations. With copious valuable achievements, de novo protein design was nominated as one of the top 10 annual breakthroughs by Science in 2016 [19]. Authors Srivamshi Pittala 1 , Chris Bailey-Kellogg 1 Affiliation 1 Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA. This review covers all aspects of retrosynthesis, including datasets, models and In: Mnih V, Kavukcuoglu K, Silver D, et al., Mahmud M, Kaiser MS, Hussain A, et al., Hanson J, Peliwal K, Litfin T, et al., Tunyasuvunakool K, Adler J, Wu Z, et al., Baek M, DiMaio F, Anishchenko I, et al., Jiang L, Althoff EA, Clemente FR, et al., Huang P-S, Oberdorfer G, Xu C, et al., Rocklin GJ, Chidyausiku TM, Goreshnik I, et al., Dou J, Vorobieva AA, Sheffler W, et al., Marcos E, Chidyausiku TM, McShan AC, et al., Lin Y-R, Koga N, Tatsumi-Koga R, et al., Marcos E, Basanta B, Chidyausiku TM, et al., Park K, Shen BW, Parmeggiani F, et al., Huang P-S, Feldmeier K, Parmeggiani F, et al., Cooper S, Khatib F, Treuille A, et al., Koepnick B, Flatten J, Husain T, et al., Yang C, Sesterhenn F, Bonet J, et al.. His research interests include sequence analysis in molecular biology and bioinformatics. It is noteworthy that the relationship between GANs and VAEs is complicated. 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]. When training a VAE, a low-dimensional latent representation of the raw data with latent variables can be learned, which were assumed to generate the real data. 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]. Almost all information of a protein is encoded in its sequence. , Pappu A.S. Google Scholar Digital Library; Cited By View all. Through this procedure, diverse sequences and designable structures not observed in nature were generated. 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). 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). Furthermore, since native proteins are optimized gradually through millions of years of evolution under the selective pressure of nature, they in principle are unlikely to handle challenges arising from human society within hundreds of years. Advances in immune signaling [2, 3], targeted therapeutics [4, 5], sense-response systems [6], protein switches [7, 8], self-assembly materials [9, 10] and other fields not mentioned here have shown the exciting potential of utilizing proteins as functional and reproducible materials. , Huang C. , Garg V.K. 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. The first one is the expensive cost of protein characterization, which leads to the data scarcity of sequence-label pairs for the training of deep neural networks. For example, a RNN was tuned by a policy-based reinforcement learning approach to generate desirable compounds [134]. WebWe have recently designed such a prototype system, but to maintain efficiency and a manageable metadata table, free formatted fields were designed as table entries. Besides, the hydrogen-bond network is also an important point that should be carefully attended to in sequence optimization procedures [104]. Some summaries have been articulated in the last two sections since this step has a close relationship with previous steps and many researches integrate them all together. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. the Cartesian coordinates of atoms stored in PDB files, protein structures are perfect media for the bidirectional mapping between sequences and functions. In the absent of any evolutionary, structural, physicochemical and other kinds of related data explicitly, representation vectors of protein sequences encoded by UniRep intrinsically contained the required information and thus could be easily clustered by these properties. Meta learning (Finn etal., 2017), also known as learn-to-learn, attempts to produce such models, which can quickly learn a new task with a few training samples based on models trained for related tasks. Completely random sequences of 100 residues were fed into trRosetta network [32], a well-performed predictor of protein inter-residue geometric properties based on sequence alignments, to derive the background inter-residue distance distributions. Representation vectors derived from this space carried distinguishable protein features of the corresponding sequences. ], Alshahrani M. Chevalier A, Silva D-A, Rocklin GJ, et al., Cao L, Goreshnik I, Coventry B, et al., Glasgow AA, Huang Y-M, Mandell DJ, et al., Savile CK, Janey JM, Mundorff EC, et al., Hammer SC, Kubik G, Watkins E, et al., Khoury GA, Smadbeck J, Kieslich CA, et al., Kuhlman B, Dantas G, Ireton GC, et al., Huang P-S, Ban Y-EA, Richter F, et al., Koga N, Tatsumi-Koga R, Liu G, et al., Berman HM, Westbrook J, Feng Z, et al., Bateman A, Martin MJ, O'Donovan C, et al.. For instance, the ability of an antibody to respond to an antigen depends on the antibodys specific recognition of an epitope (Hu etal., 2014). In the bioinformatics field, symbolic reasoning is applied and evaluated on structured biological knowledge, which can be used for data integration, retrieval, and federated queries in the knowledge graph (Alshahrani etal., 2017). and X.G. School of Artificial Intelligence, Nanjing University of Information Science and Technology, School of Future Technology, Nanjing University of Information Science and Technology, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing Advanced Innovation Center for Structural Biology, Tsinghua University. 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]. , Barzilay R. After that, candidates are scored, rated and selected to generate the final design outputs [21]. Structure-based protein design could be treated as the reverse process of protein structure prediction. His research focuses on bioinformatics and machine learning. , Czibula G. (, Hong Z. Taking pharmacy and therapeutics as an example, although conventional drug discovery methodologies concentrated on molecular dynamics simulations and molecular docking [40] have made great achievements, protein design approaches are gradually showing their impressive capability and promising future. For the latter, some potential structures should be modeled for a given sequence, while for the former, some feasible sequences should be optimized for a backbone with the designed topology (Figure 2). Structure-based de novo protein design usually has three domains or stages, i.e. Recently, the introduction of deep learning has shown preliminary but transformative influence to the field of protein design. The key idea is that when training a model is finished, the model needs to be exposed to a new task during the testing phase, several steps of fine-tuning are performed, and then the models performance on the new task is checked. Radford A, Jozefowicz R, Sutskever I. 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. With gradients backpropagated from the predefined structures to input protein sequences through the trRosetta network [32], sequences and structures could be optimized simultaneously [110]. Due to the strictly limited working environment and relatively short operation life, native proteins, however, cannot meet the surging demands of human beings satisfactorily. Guo X, Du Y, Tadepalli S, et al. Generating tertiary protein structures via an interpretative variational autoencoder. The complex nature of information derivation from such data has posed great challenges to other ML methods but has been handled well by ANNs. WebThis study aims to perform generation of targeted molecules by training the recurrent neural network to learn the building rules of production of valid molecules in the form of SMILES strings and optimize it to produce molecules with 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. In this work, we summarize the advances of graph representation learning and its representative applications in bioinformatics. , et al. Examples could be found everywhere in our daily life, including designed small-molecule binding proteins that are used in in vivo biosensors [136, 137], designed biomedical inhibitors that aim to prevent viral infections [138], designed enzymes that have attractive catalytic efficiencies [139141], designed highly symmetric self-assembly materials that endow vaccine applications with multivalent presentation of antigens [10, 142], etc. , et al. The translation from protein inter-residue geometric matrices to backbone coordinates could also be undertaken by approaches related to deep learning [32, 61, 92]. , Delong A. , Khan M.A. Second, computational power has been increasing rapidly with affordable costs, including the development of new computing devices, such as graphics processing units and field programmable gate arrays. The scarcity of training data would hinder the accurate design, consequently leading to the demand for additional experimental optimization. No matter which case it is, proteins are important gifts from nature to mankind, and with the blueprints glimpsed by deep learning, we could craft desired tools as we want to make our world a better place after iterations of trials and errors. In: Anishchenko I, Pellock SJ, Chidyausiku TM, et al.. 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. Bai S, Kolter JZ, Koltun V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. , Ramsundar B. This review could help people get familiar with this field and promote relevant researches. 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. one-hot encoding for RNA, DNA, or protein sequences) into another representation of the sequence. It is noteworthy that ProteinSolver was only trained and tested with the constraints derived from existing proteins, and thus, its ability to sample reasonable sequences of novel proteins still needs further validation. Results: Recently, deep reinforcement learning algorithms have become very popular. In our paper, we also use this method to train the number of cells and control signals. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. 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. For example, DyNA PPO [132] was such a deep reinforcement learning model based on proximal-policy optimization [133] for sequence 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. For example, EPIs show great significance to human development because they are critical to the regulation of gene expression and are closely related to the occurrence of human diseases. Recently, deep reinforcement learning (DRL) has shown success in automatically learning good heuristics to solve graph optimization problems. , Gilligan J. In: Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. However, the training cost of such a huge protein language model would not be something that ordinary small research groups could afford and it would be meaningless to repeat the construction of these infrastructures for the whole academic community. Y.X. Abstract. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed. We are presenting how various machine learning approaches can be useful in early diagnosis of many diseases and explained where machine learning and deep learning can be used on electronically stored medical data. ML has been the main contributor to the recent resurgence of artificial intelligence. An illustration of two inverse processes, i.e. 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. Attention mechanisms, which were first proposed to conduct machine-based translation tasks (Vaswani etal., 2017), can alleviate the problems faced by RNNs when applied to bioinformatics problems, thus expanding their domain of applications in bioinformatics. inpainting a large distance map with small size-fixed patches (Figure 3). In biology, high-throughput omic data tend to have high dimensionality and be intrinsically noisy, such as single-cell transcriptomic data (Lopez etal., 2018). secondary structures) has been validated and further related researches would surely acquire a greater depth in the coming future. Then, in silico directed evolution was executed through a Markov Chain Monte Carlo procedure on the surrogate fitness landscape provided by sequence representations and the rating model. Graph optimization problems (such as minimum vertex cover, maximum cut, traveling salesman problems) appear in many fields including social sciences, power systems, chemistry, and bioinformatics. Anand N, Eguchi R, Huang PS. BMC Bioinformatics. Taking sequence space as an example, since all native protein sequences originated from a few ancient accidental events and gradually evolved with haphazard mutation and oriented selective pressure, they exist in the sequence space in the form of sprinkling clusters called protein families instead of even dispersion. D. Shen, and C. Tan. , et al. Since different researches of representation learning generally use self-built datasets and have no unified evaluation process or standard, it is difficult for people to compare them and consider the accuracy and efficiency, advantages and disadvantages of each [125]. Protein homology plays an important role in protein structure prediction, providing massive evolutionary information for precise inferences. Statistical sampling methods, exemplified by Monte Carlo simulations, have been used to solve this dilemma and could achieve acceptable approximations in practice [99]. XI. 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. 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. There are many roadmaps involving protein design in this field, which aim at various diseases afflicting human beings. For example, trained by sequences from the Swiss-Prot database, a VAE model called BioSeqVAE learned good sequence representations, which could be used as input features for multiple downstream applications [124]. From contigs towards chromosomes: automatic improvement of long read assemblies (ILRA). Furthermore, although constructed through trRosetta [32], this hallucination approach could be easily extended to more advanced protein structure prediction networks like AlphaFold2 [63] and RoseTTAFold [65] to improve its hallucinating power. The significance of this work is not limited in showing a feasible exploration for structure or sequence generation. Similarly, information of protein sequencestructure relationships stored in billions of parameters in the powerful protein structure prediction networks could also be utilized inversely to generate new sequences and structures [91]. Therefore, generative models are more widely used in this area compared with discriminative ones (as exhibited in Table 2). Recurrent neural network (RNN) is another classic network architecture suitable for processing sequential data like natural languages and protein sequences. We also comprehensively discuss the coming challenges and opportunities in the near future. , et al. Thus, inspired by natural language processing [115], protein language models treat a complete sequence as a paragraph or a sentence and the amino acids within it as single words [116, 117]. The most important inspiration from this research would be the attempt and success of decreasing the catastrophic forgetting risk [135], a common problem for protein generative models. The underlying idea to unfold recursive computation into a computational graph with repetitive structure naturally results in large-scale parameter sharing. , Umarov R. Thus, its practicability still needs to be testified in future researches. , Nguyen S.P. Besides, convolution also makes it possible to handle input data of variable sizes. 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