11,691 research outputs found

    Machine learning-guided directed evolution for protein engineering

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    Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. ML methods use data to predict how sequence maps to function without requiring a detailed model of the underlying physics or biological pathways. To demonstrate ML-guided directed evolution, we introduce the steps required to build ML sequence-function models and use them to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to using ML for protein engineering as well as the current literature and applications of this new engineering paradigm. ML methods accelerate directed evolution by learning from information contained in all measured variants and using that information to select sequences that are likely to be improved. We then provide two case studies that demonstrate the ML-guided directed evolution process. We also look to future opportunities where ML will enable discovery of new protein functions and uncover the relationship between protein sequence and function.Comment: Made significant revisions to focus on aspects most relevant to applying machine learning to speed up directed evolutio

    DeePSLiM: A Deep Learning Approach to Identify Predictive Short-linear Motifs for Protein Sequence Classification

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    With the increasing quantity of biological data, it is important to develop algorithms that can quickly find patterns in large databases of DNA, RNA and protein sequences. Previous research has been very successful at applying deep learning methods to the problems of motif detection as well as classification of biological sequences. There are, however, limitations to these approaches. Most are limited to finding motifs of a single length. In addition, most research has focused on DNA and RNA, both of which use a four letter alphabet. A few of these have attempted to apply deep learning methods on the larger, twenty letter, alphabet of proteins. We present an enhanced deep learning model, called DeePSLiM, capable of detecting predictive, short linear motifs (SLiM) in protein sequences. The model is a shallow network that can be trained quickly on large amounts of data. The SLiMs are predictive because they can be used to classify the sequences into their respective families. The model was able to reach scores of 94.5% on accuracy, precision, recall, F1-Score and Matthews-correlation coefficient, as well as 99.9% area under the receiver operator characteristic curve (AUROC)

    Knowledge discovery in biological databases : a neural network approach

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    Knowledge discovery, in databases, also known as data mining, is aimed to find significant information from a set of data. The knowledge to be mined from the dataset may refer to patterns, association rules, classification and clustering rules, and so forth. In this dissertation, we present a neural network approach to finding knowledge in biological databases. Specifically, we propose new methods to process biological sequences in two case studies: the classification of protein sequences and the prediction of E. Coli promoters in DNA sequences. Our proposed methods, based oil neural network architectures combine techniques ranging from Bayesian inference, coding theory, feature selection, dimensionality reduction, to dynamic programming and machine learning algorithms. Empirical studies show that the proposed methods outperform previously published methods and have excellent performance on the latest dataset. We have implemented the proposed algorithms into an infrastructure, called Genome Mining, developed for biosequence classification and recognition

    Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints

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    The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even for shallow sequence alignments. Here we introduce DMPfold, which uses deep learning to predict inter-atomic distance bounds, the main chain hydrogen bond network, and torsion angles, which it uses to build models in an iterative fashion. DMPfold produces more accurate models than two popular methods for a test set of CASP12 domains, and works just as well for transmembrane proteins. Applied to all Pfam domains without known structures, confident models for 25% of these so-called dark families were produced in under a week on a small 200 core cluster. DMPfold provides models for 16% of human proteome UniProt entries without structures, generates accurate models with fewer than 100 sequences in some cases, and is freely available.Comment: JGG and SMK contributed equally to the wor
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