Viral Capsid Assembly: A Quantified Uncertainty Approach

Clement, Nathan; Rasheed, Muhibur; Bajaj, Chandrajit L.

doi:10.1089/cmb.2017.0218

Cited by 5 publications

(5 citation statements)

References 46 publications

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“…We showed that, under this simplified model of small perturbations, there can be a wide variance in simple QOI. In [13], we further showed that perturbations under this simplistic model can propagate to further uncertainties in the viral assembly problem.…”

Section: Uncertainty Quantificationmentioning

confidence: 77%

Quantified uncertainty of flexible protein-protein docking algorithms

Clement

2019

Preprint

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The strength or weakness of an algorithm is ultimately governed by the confidence of its result. When the domain of the problem is large (e.g. traversal of a high-dimensional space), a perfect solution cannot be obtained, so approximations must be made. These approximations often lead to a reported quantity of interest (QOI) which varies between runs, decreasing the confidence of any single run. When the algorithm further computes this final QOI based on uncertain or noisy data, the variability (or lack of confidence) of the final QOI increases. Unbounded, these two sources of uncertainty (algorithmic approximations and uncertainty in input data) can result in a reported statistic that has low correlation with ground truth.In biological applications, this is especially applicable, as the search space is generally approximated at least to some degree (e.g. a high percentage of protein structures are invalid or energetically unfavorable) and the explicit conversion from continuous to discrete space for protein representation implies some uncertainty in the input data. This research applies uncertainty quantification techniques to the difficult protein-protein docking problem, first showing the variability that exists in existing software, and then providing a method for computing probabilistic certificates in the form of Chernoff-like bounds. Finally, this paper leverages these probabilistic certificates to accurately bound the uncertainty in docking from two docking algorithms, providing a QOI that is both robust and statistically meaningful. ACM Subject ClassificationApplied computing → Molecular structural biology; Mathematics of computing → Hypothesis testing and confidence interval computation; Computing methodologies → Uncertainty quantification Keywords and phrases protein-protein docking, uncertainty quantification, protein flexibility, lowdiscrepancy sampling, high-dimensional sampling Acknowledgements I would like to thank all those who have supported and helped advise on this work, for their valuable feedback and suggestions for improvement.1

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Section: Uncertainty Quantificationmentioning

confidence: 77%

Quantified uncertainty of flexible protein-protein docking algorithms

Clement

2019

Preprint

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“…The present work focuses on fixed structures for the dimer subunits of cowpea chlorotic mottle virus (CCMV) formed from the A − B and C − C chains (PDB 1za7, [ 40 ]), as well as a model of the pentamer subunits of generic dodecamer assembly. Clement et al [ 41 ] examines the energetic effects of allowing the subunits to come from a distribution of possible configurations rather than a single PDB structure.…”

Section: Methodsmentioning

confidence: 99%

A method for efficient Bayesian optimization of self-assembly systems from scattering data

Thomas

Schwartz

2018

BMC Syst Biol

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BackgroundThe ability of collections of molecules to spontaneously assemble into large functional complexes is central to all cellular processes. Using the viral capsid as a model system for complicated macro-molecular assembly, we develop methods for probing fine details of the process by learning kinetic rate parameters consistent with experimental measures of assembly. We have previously shown that local rule based stochastic simulation methods in conjunction with bulk indirect experimental data can meaningfully constrain the space of possible assembly trajectories and allow inference of experimentally unobservable features of the real system.ResultsIn the present work, we introduce a new Bayesian optimization framework using multi-Gaussian process model regression. We also extend our prior work to encompass small-angle X-ray/neutron scattering (SAXS/SANS) as a possibly richer experimental data source than the previously used static light scattering (SLS). Method validation is based on synthetic experiments generated using protein data bank (PDB) structures of cowpea chlorotic mottle virus. We also apply the same approach to computationally cheaper differential equation based simulation models.ConclusionsWe present a flexible approach for the global optimization of computationally costly objective functions associated with dynamic, multidimensional models. When applied to the stochastic viral capsid system, our method outperforms a current state of the art black box solver tailored for use with noisy objectives. Our approach also has wide applicability to general stochastic optimization problems.

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“…However, other optimization approaches can be employed when the measurement matrix is highly coherent when 1 minimization is not necessarily optimal. Finally, it would be interesting to employ the developed DSRAR approach for UQ study in other complex biological systems [94,95]. Such results will be presented in a future publication.…”

Section: Uq Study Of a Molecule System Under Non-gaussian Conformatio...mentioning

confidence: 96%

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness

Lei

Gao

et al. 2019

Computer Methods in Applied Mechanics and Engineering

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The challenge of quantifying uncertainty propagation in real-world systems is rooted in the highdimensionality of the stochastic input and the frequent lack of explicit knowledge of its probability distribution. Traditional approaches show limitations for such problems, especially when the size of the training data is limited. To address these difficulties, we have developed a general framework of constructing surrogate models on spaces of stochastic input with arbitrary probability measure irrespective of the mutual dependencies between individual components of the random inputs and the analytical form. The present Data-driven Sparsity-enhancing Rotation for Arbitrary Randomness (DSRAR) framework includes a data-driven construction of multivariate polynomial basis for arbitrary mutually dependent probability measure and a sparsity enhancement rotation procedure.This sparsity enhancement method was initially proposed in our previous work [1] for Gaussian density distributions, which may not be feasible for non-Gaussian distributions due to the loss of orthogonality after the rotation. To remedy such difficulties, we developed a new data-driven approach to construct orthonormal polynomials for polynomials for arbitrary mutually dependent (amdP) randomness, ensuring the constructed basis maintains the orthogonality/near-orthogonality with respect to the density of the rotated random vector, where directly applying the regular polynomial chaos including arbitrary polynomial chaos (aPC) [2] shows limitations due to the assumption of the mutual independence between the components of the random inputs. The developed DSRAR framework leads to accurate recovery, with only limited training data, of a sparse representation of the target functions. The effectiveness of our method is demonstrated in challenging problems such as PDEs and realistic molecular systems within high-dimensional conformational space (O(10))where the underlying density is implicitly represented by a large collection of sample data, as well as systems with explicitly given non-Gaussian probabilistic measures. * huan.lei@pnnl.gov † The first two authors contributed equally ‡ nathan.baker@pnnl.gov arXiv:1804.08609v4 [math.NA]

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Viral Capsid Assembly: A Quantified Uncertainty Approach

Cited by 5 publications

References 46 publications

Quantified uncertainty of flexible protein-protein docking algorithms

Quantified uncertainty of flexible protein-protein docking algorithms

A method for efficient Bayesian optimization of self-assembly systems from scattering data

A data-driven framework for sparsity-enhanced surrogates with arbitrary mutually dependent randomness

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