Information-theoretical measures identify accurate low-resolution representations of protein configurational space

Mele, Margherita; Covino, Roberto; Potestio, Raffaello

doi:10.1039/d2sm00636g

Cited by 5 publications

(4 citation statements)

References 55 publications

(72 reference statements)

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“…Conversely, CG representations that identified sites with α carbons along the protein backbone resulted in relatively high information loss (i.e., high H map ). These C-α mappings, which are widely adopted in CG protein models, presumably correspond to highly structured conditioned distributions, p r|R , since the atomic structure of the protein backbone can be accurately reconstructed from the α carbon coordinates. , Subsequently, Potestio and co-workers , related the mapping entropy to “resolution” and “relevance” metrics that have been employed to characterize deep learning . As they have emphasized, fundamental insights into the mapping operator may prove useful not only for determining CG representations but also for identifying order parameters to analyze and bias molecular simulations, and even much more generally for understanding complex interacting systems, such as economic markets or social networks. , …”

Section: Coarse-grained Representationmentioning

confidence: 99%

“…The large majority of these studies have focused on small molecules with relatively little conformational flexibility , or systems that fluctuate about a well-defined equilibrium conformation. ,− ,,,, Clementi and co-workers have provided important insights for modeling more complex systems that transition between diverse conformational states . By employing diffusion maps and Markov state methods, they identified coherent domains that persist in microsecond protein simulations with global folding and unfolding transitions. , In such systems, the optimal mapping may dynamically vary as the system transitions among diverse metastable conformations.…”

Section: Coarse-grained Representationmentioning

confidence: 99%

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Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

Noid

2023

J. Phys. Chem. B

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By averaging over atomic details, coarse-grained (CG) models provide profound computational and conceptual advantages for studying soft materials. In particular, bottom-up approaches develop CG models based upon information obtained from atomically detailed models. At least in principle, a bottom-up model can reproduce all the properties of an atomically detailed model that are observable at the resolution of the CG model. Historically, bottom-up approaches have accurately modeled the structure of liquids, polymers, and other amorphous soft materials, but have provided lower structural fidelity for more complex biomolecular systems. Moreover, they have also been plagued by unpredictable transferability and a poor description of thermodynamic properties. Fortunately, recent studies have reported dramatic advances in addressing these prior limitations. This Perspective reviews this remarkable progress, while focusing on its foundation in the basic theory of coarse-graining. In particular, we describe recent insights and advances for treating the CG mapping, for modeling many-body interactions, for addressing the state-point dependence of effective potentials, and even for reproducing atomic observables that are beyond the resolution of the CG model. We also outline outstanding challenges and promising directions in the field. We anticipate that the synthesis of rigorous theory and modern computational tools will result in practical bottom-up methods that not only are accurate and transferable but also provide predictive insight for complex systems.

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Section: Coarse-grained Representationmentioning

confidence: 99%

Section: Coarse-grained Representationmentioning

confidence: 99%

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

Noid

2023

J. Phys. Chem. B

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“…The critical variable selection method, also known as resolution-relevance 71 – 76 , has been successful in identifying optimal clustering for the reduction of complexity in the representation of biomolecules 77 or for a protein conformational landscape 78 .…”

Section: Daily Transition Matrixmentioning

confidence: 99%

“…The relevance is the Shannon entropy associated with this second empirical probability. For both limit cases of 1 and N clusters, , the relevance being non-negative otherwise 45 , 78 . The maximum relevance thus corresponds to an optimal clustering, i.e.…”

Section: Daily Transition Matrixmentioning

confidence: 99%

Quality assessment and community detection methods for anonymized mobility data in the Italian Covid context

Morand,

Yip,

Velegrakis

et al. 2024

Sci Rep

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We discuss how to assess the reliability of partial, anonymized mobility data and compare two different methods to identify spatial communities based on movements: Greedy Modularity Clustering (GMC) and the novel Critical Variable Selection (CVS). These capture different aspects of mobility: direct population fluxes (GMC) and the probability for individuals to move between two nodes (CVS). As a test case, we consider movements of Italians before and during the SARS-Cov2 pandemic, using Facebook users’ data and publicly available information from the Italian National Institute of Statistics (Istat) to construct daily mobility networks at the interprovincial level. Using the Perron-Frobenius (PF) theorem, we show how the mean stochastic network has a stationary population density state comparable with data from Istat, and how this ceases to be the case if even a moderate amount of pruning is applied to the network. We then identify the first two national lockdowns through temporal clustering of the mobility networks, define two representative graphs for the lockdown and non-lockdown conditions and perform optimal spatial community identification on both graphs using the GMC and CVS approaches. Despite the fundamental differences in the methods, the variation of information (VI) between them assesses that they return similar partitions of the Italian provincial networks in both situations. The information provided can be used to inform policy, for example, to define an optimal scale for lockdown measures. Our approach is general and can be applied to other countries or geographical scales.

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Surveying the energy landscape of coarse-grained mappings

Kidder,

Shell,

Noid

2024

The Journal of Chemical Physics

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Simulations of soft materials often adopt low-resolution coarse-grained (CG) models. However, the CG representation is not unique and its impact upon simulated properties is poorly understood. In this work, we investigate the space of CG representations for ubiquitin, which is a typical globular protein with 72 amino acids. We employ Monte Carlo methods to ergodically sample this space and to characterize its landscape. By adopting the Gaussian network model as an analytically tractable atomistic model for equilibrium fluctuations, we exactly assess the intrinsic quality of each CG representation without introducing any approximations in sampling configurations or in modeling interactions. We focus on two metrics, the spectral quality and the information content, that quantify the extent to which the CG representation preserves low-frequency, large-amplitude motions and configurational information, respectively. The spectral quality and information content are weakly correlated among high-resolution representations but become strongly anticorrelated among low-resolution representations. Representations with maximal spectral quality appear consistent with physical intuition, while low-resolution representations with maximal information content do not. Interestingly, quenching studies indicate that the energy landscape of mapping space is very smooth and highly connected. Moreover, our study suggests a critical resolution below which a “phase transition” qualitatively distinguishes good and bad representations.

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Information-theoretical measures identify accurate low-resolution representations of protein configurational space

Abstract: Different clustering strategies can produce qualitatively different low-resolution representations of a protein’s conformational space. The resolution-relevance framework pinpoints those that better preserve important, biologically relevant features.

Cited by 5 publications

References 55 publications

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

Quality assessment and community detection methods for anonymized mobility data in the Italian Covid context

Surveying the energy landscape of coarse-grained mappings

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