Rapid prediction of crucial hotspot interactions for icosahedral viral capsid self-assembly by energy landscape atlasing validated by mutagenesis

Wu, Ruijin; Prabhu, Rahul; Ozkan, Aysegul; Sitharam, Meera

doi:10.1371/journal.pcbi.1008357

Cited by 4 publications

(9 citation statements)

References 86 publications

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“…These bounds have been empirically verified for a wide range of molecular geometries and sizes. 26,36 Thus, the results in this work, used in conjunction with the results in refs 26 and 36, directly give meaningful estimates of the performance of EASAL on arbitrarily large molecules.…”

Section: Resultsmentioning

confidence: 70%

“…In addition, the EASAL software has recently been employed to predict crucial intermonomeric interface interactions for viral capsid assembly of three viruses (Minute Virus of Mice (MVM), Adeno-Associated Virus (AAV), and Bromo-Mosaic Virus (BMV)) 35,36 and for high-performance sampling toward entropy integral computation, using an adaptive Jacobian strategy along with convex Cayley parametrization. 80 A recent parallelized version of the EASAL software uses a modified version of the original EASAL algorithm to make use of modern multicore CPUs for achieving further speedup.…”

Section: Accuracy Of Weighted Coverage Of Lower Energy Regionsmentioning

confidence: 99%

“…These theoretical bounds are also empirically proven, 26 which allow the results in this article to be used in conjunction with the results in ref 26 directly to give meaningful estimates of the performance of EASAL on larger assembly systems. 36 Further, the EASAL run times reported here are those achieved on the single threaded implementation of the EASAL methodology. A more recent parallelized version purports to speed this up even further.…”

Section: Measurements For Localized Sampling Of Individual Active Con...mentioning

confidence: 99%

“…A rigorous analysis of how EASAL's complexity and performance scales with the size and number of rigid assembling units has been thoroughly studied in ref 26, which gives theoretical bounds that have been empirically verified for a wide range of molecular geometries and sizes. 26,36 This analysis subsumes flexible molecules that are decomposed into their rigid components. Additionally, even for larger molecules, many statistical mechanical computations are restricted to specific regions and energy basins of the landscape.…”

Section: Introductionmentioning

confidence: 99%

“…The first original contribution of this article is comparing and contrasting specific sampling characteristics of the recently developed geometric methodology EASAL (Efficient Atlasing of Assembly Landscapes) ,,− against MC sampling. EASAL uses graph rigidity, distance geometry, and convex analysis to obtain an atlas of desired equipotential landscape regions and their neighborhood relationships.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Baseline Comparisons of Complementary Sampling Methods for Assembly Driven by Short-Ranged Pair Potentials toward Fast and Flexible Hybridization

Ozkan

Sitharam

Flores-Canales

et al. 2021

J. Chem. Theory Comput.

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This work measures baseline sampling characteristics that highlight fundamental differences between sampling methods for assembly driven by short-ranged pair potentials. Such granular comparison is essential for fast, flexible, and accurate hybridization of complementary methods. Besides sampling speed, efficiency, and accuracy of uniform grid coverage, other sampling characteristics measured are (i) accuracy of covering narrow low energy regions that have low effective dimension (ii) ability to localize sampling to specific basins, and (iii) flexibility in sampling distributions. As a proof of concept, we compare a recently developed geometric methodology EASAL (Efficient Atlasing and Search of Assembly Landscapes) and the traditional Monte Carlo (MC) method for sampling the energy landscape of two assembling trans-membrane helices, driven by short-range pair potentials. By measuring the above-mentioned sampling characteristics, we demonstrate that EASAL provides localized and accurate coverage of crucial regions of the energy landscape of low effective dimension, under flexible sampling distributions, with much fewer samples and computational resources than MC sampling. EASAL’s empirically validated theoretical guarantees permit credible extrapolation of these measurements and comparisons to arbitrary number and size of assembling units. Promising avenues for hybridizing the complementary advantages of the two methods are discussed.

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Section: Resultsmentioning

confidence: 70%

Section: Accuracy Of Weighted Coverage Of Lower Energy Regionsmentioning

confidence: 99%

Section: Measurements For Localized Sampling Of Individual Active Con...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Baseline Comparisons of Complementary Sampling Methods for Assembly Driven by Short-Ranged Pair Potentials toward Fast and Flexible Hybridization

Ozkan

Sitharam

Flores-Canales

et al. 2021

J. Chem. Theory Comput.

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Atlasing of Assembly Landscapes using Distance Geometry and Graph Rigidity

Prabhu

Sitharam

Ozkan

et al. 2020

J. Chem. Inf. Model.

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This Article describes a novel geometric methodology for analyzing free energy and kinetics of assembly driven by short-range pair-potentials in an implicit solvent and provides a proof-of-concept illustration of its unique capabilities. An atlas is a labeled partition of the assembly landscape into a roadmap of maximal, contiguous, nearly-equipotential-energy conformational regions or macrostates, together with their neighborhood relationships. The new methodology decouples the roadmap generation from sampling and produces: (1) a queryable atlas of local potential energy minima, their basin structure, energy barriers, and neighboring basins; (2) paths between a specified pair of basins, each path being a sequence of conformational regions or macrostates below a desired energy threshold; and (3) approximations of relative path lengths, basin volumes (configurational entropy), and path probabilities. Results demonstrating the core algorithm’s capabilities and high computational efficiency have been generated by a resource-light, curated open source software implementation EASAL (Efficient Atlasing and Search of Assembly Landscapes, 10.1145/3204472ACM Trans. Math. Softw.201844148; see software, Efficient Atlasing and Search of Assembly Landscapes2016; video, Video Illustrating the opensource software EASAL2016; and user guide, EASAL software user guide2016). Running on a laptop with Intel(R) Core(TM) i7-7700@3.60 GHz CPU with 16GB of RAM, EASAL atlases several hundred thousand conformational regions or macrostates in minutes using a single compute core. Subsequent path and basin computations each take seconds. A parallelized EASAL version running on the same laptop with 4 cores gives a 3× speedup for atlas generation. The core algorithm’s correctness, time complexity, and efficiency–accuracy trade-offs are formally guaranteed using modern distance geometry, geometric constraint systems and combinatorial rigidity. The methodology further links the shape of the input assembling units to a type of intuitive and queryable bar-code of the output atlas, which in turn determine stable assembled structures and kinetics. This succinct input–output relationship facilitates reverse analysis and control toward design. A novel feature that is crucial to both the high sampling efficiency and decoupling of roadmap generation from sampling is a recently developed theory of convex Cayley (distance-based) custom parametrizations specific to assembly, as opposed to folding. Representing microstates with macrostate-specific Cayley parameters, to generate microstate samples, avoids gradient-descent search used by all prevailing methods. Further, these parametrizations convexify conformational regions or macrostates. This ratchets up sampling efficiency, significantly reducing number of repeated and discarded samples. These features of the new stand-alone methodology can also be used to complement the strengths of prevailing methodologies including Molecular Dynamics, Monte Carlo, and Fast Fourier Transform based methods.

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A new discrete-geometry approach for integrative docking of proteins using chemical crosslinks

Zhang,

Jindal,

Viswanath

et al. 2024

Preprint

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The structures of protein complexes allow us to understand and modulate the biological functions of the proteins. Integrative docking is a computational method to obtain the structures of a protein complex, given the atomic structures of the constituent proteins along with other experimental data on the complex, such as chemical crosslinks or SAXS profiles. Here, we develop a new discrete geometry-based method, wall-EASAL, for integrative rigid docking of protein pairs given the structures of the constituent proteins and chemical crosslinks. The method is an adaptation of EASAL (Efficient Atlasing and Search of Assembly Landscapes), a state-of-the-art discrete geometry method for efficient and exhaustive sampling of macromolecular configurations under pairwise inter-molecular distance constraints. We provide a mathematical proof that the method finds a structure satisfying the crosslink constraints under a natural condition satisfied by energy landscapes. We compare wall-EASAL with IMP (Integrative Modeling Platform), a commonly used integrative modeling method, on a benchmark, varying the numbers, types, and sources of input crosslinks, and sources of monomer structures. The wall-EASAL method performs better than IMP in terms of the average satisfaction of the configurations to the input crosslinks and the average similarity of the configurations to their corresponding native structures. The ensembles from IMP exhibit greater variability in these two measures. Further, wall-EASAL is more efficient than IMP. Although the current study uses crosslinks, the method is general and any source of distance constraints can be used for integrative docking with wall-EASAL. However, the current implementation only supports binary rigid protein docking, i.e., assumes that the monomer structures are known and remain rigid. Additionally, the current implementation is deterministic, i.e., it does not account for uncertainties in the crosslinking data beyond using distance bounds. Neither of these appears to be a theoretical or algorithmic limitation of the EASAL methodology. Structures from wall-EASAL can be incorporated in methods for modeling large macromolecular assemblies, for example by suggesting rigid bodies or restraints for use in these methods. This will facilitate the characterization of assemblies and cellular neighborhoods at increased efficiency, accuracy, and precision. The wall-EASAL method is available at https://bitbucket.org/geoplexity/easal-dev/src/Crosslink and the benchmark is available at https://github.com/isblab/Integrative_docking_benchmark

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Rapid prediction of crucial hotspot interactions for icosahedral viral capsid self-assembly by energy landscape atlasing validated by mutagenesis

Cited by 4 publications

References 86 publications

Baseline Comparisons of Complementary Sampling Methods for Assembly Driven by Short-Ranged Pair Potentials toward Fast and Flexible Hybridization

Baseline Comparisons of Complementary Sampling Methods for Assembly Driven by Short-Ranged Pair Potentials toward Fast and Flexible Hybridization

Atlasing of Assembly Landscapes using Distance Geometry and Graph Rigidity

A new discrete-geometry approach for integrative docking of proteins using chemical crosslinks

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