Harnessing machine learning to guide phylogenetic-tree search algorithms

Azouri, Dana; Abadi, Shiran; Mansour, Yishay; Mayrose, Itay; Pupko, Tal

doi:10.1038/s41467-021-22073-8

Cited by 28 publications

(23 citation statements)

References 53 publications

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“…These works reveal that MSAs contain crucial underlying relationships that couple sequences with numerous plausible conformations ( Wang and Barth, 2015 ), which could be leveraged for expanded functional capacity design. A sequence generation approach that also harnesses MSA data could recognise the divergence of structural states from phylogenetic trees of extensive protein families constructed using existing modern methods ( Azouri et al, 2021 ). Here, the goal would be to learn the general motif changes in key sites that lead to the adoption of multiple states, and exploit that in design.…”

Section: Accounting For Flexibility In Deep Learning Protein Designmentioning

confidence: 99%

Deep learning approaches for conformational flexibility and switching properties in protein design

Rudden

Hijazi

Barth

2022

Front. Mol. Biosci.

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Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel structure and function. The inherent flexibility of proteins, from side-chain motion to larger conformational reshuffling, poses a challenge to design methods, where the ideal approach must consider both the spatial and temporal evolution of proteins in the context of their functional capacity. In this review, we highlight existing methods for protein design before discussing how methods at the forefront of deep learning-based design accommodate flexibility and where the field could evolve in the future.

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Section: Accounting For Flexibility In Deep Learning Protein Designmentioning

confidence: 99%

Deep learning approaches for conformational flexibility and switching properties in protein design

Rudden

Hijazi

Barth

2022

Front. Mol. Biosci.

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“…Alternatively, more complex Bayesian methods and mixture models may be used where the associated probabilities for events may vary for different sequence regions. The topology of a phylogenetic tree may be initialized randomly from the MSA and is often inferred by hierarchical clustering with a maximum likelihood estimator (MLE) [401]. In practice, phylogenetic tree methods are central to evolutionary biology [53] and understanding the evolutionary dynamics of clonal populations in cancer [402] (among a multitude of other applications), the latter of which has significant clinical relevance to the targeting of precision therapeutics and characterization of treatment resistance.…”

Section: Bioinformaticsmentioning

confidence: 99%

Biology and medicine in the landscape of quantum advantages

Cordier¹,

Sawaya²,

Guerreschi³

et al. 2021

Preprint

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Quantum computing holds significant potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning approaches for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is typically contingent on a reduction in the consumption of a computational resource, such as time, space, or data. Here, we distill the concept of a quantum advantage into a simple framework that we hope will aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to i) assess the potential of quantum advantages in specific application areas and ii) identify gaps that may be addressed with novel quantum approaches. Bearing in mind the rapid pace of change in the fields of quantum computing and classical algorithms, we aim to provide an extensive survey of applications in biology and medicine that may lead to practical quantum advantages.

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“…Furthermore, it was previously shown that it is beneficial not to compute the likelihood of neighboring trees whose estimated sum of branch lengths highly deviates from that of the current tree ( Hordijk and Gascuel, 2005 ). We have recently shown that machine-learning algorithms can be efficiently utilized to accurately rank neighboring trees without computing their likelihood, thus potentially increasing the computational efficiency of tree inference ( Azouri et al , 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A LASSO-based approach to sample sites for phylogenetic tree search

et al. 2022

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Motivation In recent years, full-genome sequences have become increasingly available and as a result many modern phylogenetic analyses are based on very long sequences, often with over 100 000 sites. Phylogenetic reconstructions of large-scale alignments are challenging for likelihood-based phylogenetic inference programs and usually require using a powerful computer cluster. Current tools for alignment trimming prior to phylogenetic analysis do not promise a significant reduction in the alignment size and are claimed to have a negative effect on the accuracy of the obtained tree. Results Here, we propose an artificial-intelligence-based approach, which provides means to select the optimal subset of sites and a formula by which one can compute the log-likelihood of the entire data based on this subset. Our approach is based on training a regularized Lasso-regression model that optimizes the log-likelihood prediction accuracy while putting a constraint on the number of sites used for the approximation. We show that computing the likelihood based on 5% of the sites already provides accurate approximation of the tree likelihood based on the entire data. Furthermore, we show that using this Lasso-based approximation during a tree search decreased running-time substantially while retaining the same tree-search performance. Availability and implementation The code was implemented in Python version 3.8 and is available through GitHub (https://github.com/noaeker/lasso_positions_sampling). The datasets used in this paper were retrieved from Zhou et al. (2018) as described in section 3. Supplementary information Supplementary data are available at Bioinformatics online.

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Harnessing machine learning to guide phylogenetic-tree search algorithms

Cited by 28 publications

References 53 publications

Deep learning approaches for conformational flexibility and switching properties in protein design

Deep learning approaches for conformational flexibility and switching properties in protein design

Biology and medicine in the landscape of quantum advantages

A LASSO-based approach to sample sites for phylogenetic tree search

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