A dataset of recorded electricity outages by United States county 2014-2022.

In this Data Descriptor, we present county-level electricity outage estimates at 15-minute intervals from 2014 to 2022. By 2022 92% of customers in the 50 US States, Washington DC, and Puerto Rico are represented. These data have been produced by the Environment for Analysis of Geo-Located Energy Information (EAGLE-ITM), a geographic information system and data visualization platform created at Oak Ridge National Laboratory to map the population experiencing electricity outages every 15 minutes at the county level. Although these data do not cover every US customer, they represent the most comprehensive outage information ever compiled for the United States. The rate of coverage increases through time between 2014 and 2022. We present a quantitative Data Quality Index for these data for the years 2018-2022 to demonstrate temporal changes in customer coverage rates by FEMA region and indicators of data collection gaps or other errors.

Rules of Physical Mathematics Govern Intrinsically Disordered Proteins.

In stark contrast to foldable proteins with a unique folded state, intrinsically disordered proteins and regions (IDPs) persist in perpetually disordered ensembles. Yet an IDP ensemble has conformational features-even when averaged-that are specific to its sequence. In fact, subtle changes in an IDP sequence can modulate its conformational features and its function. Recent advances in theoretical physics reveal a set of elegant mathematical expressions that describe the intricate relationships among IDP sequences, their ensemble conformations, and the regulation of their biological functions. These equations also describe the molecular properties of IDP sequences that predict similarities and dissimilarities in their functions and facilitate classification of sequences by function, an unmet challenge to traditional bioinformatics. These physical sequence-patterning metrics offer a promising new avenue for advancing synthetic biology at a time when multiple novel functional modes mediated by IDPs are emerging.

Critical Comparison of MaxCal and Other Stochastic Modeling Approaches in Analysis of Gene Networks.

Learning the underlying details of a gene network with feedback is critical in designing new synthetic circuits. Yet, quantitative characterization of these circuits remains limited. This is due to the fact that experiments can only measure partial information from which the details of the circuit must be inferred. One potentially useful avenue is to harness hidden information from single-cell stochastic gene expression time trajectories measured for long periods of time-recorded at frequent intervals-over multiple cells. This raises the feasibility vs. accuracy dilemma while deciding between different models of mining these stochastic trajectories. We demonstrate that inference based on the Maximum Caliber (MaxCal) principle is the method of choice by critically evaluating its computational efficiency and accuracy against two other typical modeling approaches: (i) a detailed model (DM) with explicit consideration of multiple molecules including protein-promoter interaction, and (ii) a coarse-grain model (CGM) using Hill type functions to model feedback. MaxCal provides a reasonably accurate model while being significantly more computationally efficient than DM and CGM. Furthermore, MaxCal requires minimal assumptions since it is a top-down approach and allows systematic model improvement by including constraints of higher order, in contrast to traditional bottom-up approaches that require more parameters or ad hoc assumptions. Thus, based on efficiency, accuracy, and ability to build minimal models, we propose MaxCal as a superior alternative to traditional approaches (DM, CGM) when inferring underlying details of gene circuits with feedback from limited data.

Intrachain interaction topology can identify functionally similar intrinsically disordered proteins.

Functionally similar IDPs (intrinsically disordered proteins) often have little sequence similarity. This is in stark contrast to folded proteins and poses a challenge for the inverse problem, functional classification of IDPs using sequence alignment. The problem is further compounded because of the lack of structure in IDPs, preventing structural alignment as an alternate tool for classification. Recent advances in heteropolymer theory unveiled a powerful set of sequence-patterning metrics bridging molecular interaction with chain conformation. Focusing only on charge patterning, these set of metrics yield a sequence charge decoration matrix (SCDM). SCDMs can potentially identify functionally similar IDPs not apparent from sequence alignment alone. Here, we illustrate how these information-rich "molecular blueprints" encoded in SCDMs can be used for functional classification of IDPs with specific application in three protein families-Ste50, PSC, and RAM-in which electrostatics is known to be important. For both the Ste50 and PSC protein family, the set of metrics appropriately classifies proteins in functional and nonfunctional groups in agreement with experiment. Furthermore, our algorithm groups synthetic variants of the disordered RAM region of the Notch receptor protein-important in gene expression-in reasonable accordance with classification based on experimentally measured binding constants of RAM and transcription factor. Taken together, the novel classification scheme reveals the critical role of a high-dimensional set of metrics-manifest in self-interaction maps and topology-in functional annotation of IDPs even when there is low sequence homology, providing the much-needed alternate to a traditional sequence alignment tool.

An analytical theory to describe sequence-specific inter-residue distance profiles for polyampholytes and intrinsically disordered proteins.

Intrinsically Disordered Proteins (IDPs), unlike folded proteins, lack a unique folded structure and rapidly interconvert among ensembles of disordered states. However, they have specific conformational properties when averaged over their ensembles of disordered states. It is critical to develop a theoretical formalism to predict these ensemble average conformational properties that are encoded in the IDP sequence (the specific order in which amino acids/residues are linked). We present a general heteropolymer theory that analytically computes the ensemble average distance profiles (⟨Rij 2⟩) between any two (i, j) monomers (amino acids for IDPs) as a function of the sequence. Information rich distance profiles provide a detailed description of the IDP in contrast to typical metrics such as scaling exponents, radius of gyration, or end-to-end distance. This generalized formalism supersedes homopolymer-like models or models that are built only on the composition of amino acids but ignore sequence details. The prediction of these distance profiles for highly charged polyampholytes and naturally occurring IDPs unmasks salient features that are hidden in the sequence. Moreover, the model reveals strategies to modulate the entire distance map to achieve local or global swelling/compaction by subtle changes/modifications-such as phosphorylation, a biologically relevant process-in specific hotspots in the sequence. Sequence-specific distance profiles and their modulation have been benchmarked against all-atom simulations. Our new formalism also predicts residue-pair specific coil-globule transitions. The analytical nature of the theory will facilitate design of new sequences to achieve specific target distance profiles with broad applications in synthetic biology and polymer science.

Modulating charge patterning and ionic strength as a strategy to induce conformational changes in intrinsically disordered proteins.

We present an analytical theory to describe conformational changes as a function of salt for polymers with a given sequence of charges. We apply this model to describe Intrinsically Disordered Proteins (IDPs) by explicitly accounting for charged residues and their exact placement in the primary sequence while approximating the effect of non-electrostatic interactions at a mean-field level by effective short-range (two body and three-body) interaction parameters. The effect of ions is introduced by treating electrostatic interactions within Debye-Huckle approximation. Using typical values of the short-range mean-field parameters derived from all-atom Monte Carlo simulations (at zero salt), we predict the conformational changes as a function of salt concentration. We notice that conformational transitions in response to changes in ionic strength strongly depend on sequence specific charge patterning. For example, globule to coil transition can be observed upon increasing salt concentration, in stark contrast to uniformly charged polyelectrolyte theories based on net charge only. In addition, it is possible to observe non-monotonic behavior with salt as well. Drastic differences in salt-induced conformational transitions is also evident between two doubly phosphorylated sequences-derived from the same wild type sequence-that only differ in the site of phosphorylation. Similar effects are also predicted between two sequences derived from the same parent sequence differing by a single site mutation where a negative charge is replaced by a positive charge. These effects are purely a result of charge decoration and can only be understood in terms of metrics based on specific placement of charges, and cannot be explained by models based on charge composition alone. Identifying sequences and hot spots within sequences-for post translational modification or charge mutation-using our high-throughput theory will yield fundamental insights into design and biological regulation mediated by phosphorylation and/or local changes in salt concentration.

Ancient thioredoxins evolved to modern-day stability-function requirement by altering native state ensemble.

Thioredoxins (THRXs)-small globular proteins that reduce other proteins-are ubiquitous in all forms of life, from Archaea to mammals. Although ancestral thioredoxins share sequential and structural similarity with the modern-day (extant) homologues, they exhibit significantly different functional activity and stability. We investigate this puzzle by comparative studies of their (ancient and modern-day THRXs') native state ensemble, as quantified by the dynamic flexibility index (DFI), a metric for the relative resilience of an amino acid to perturbations in the rest of the protein. Clustering proteins using DFI profiles strongly resemble an alternative classification scheme based on their activity and stability. The DFI profiles of the extant proteins are substantially different around the α3, α4 helices and catalytic regions. Likewise, allosteric coupling of the active site with the rest of the protein is different between ancient and extant THRXs, possibly explaining the decreased catalytic activity at low pH with evolution. At a global level, we note that the population of low-flexibility (called hinges) and high-flexibility sites increases with evolution. The heterogeneity (quantified by the variance) in DFI distribution increases with the decrease in the melting temperature typically associated with the evolution of ancient proteins to their modern-day counterparts.This article is part of a discussion meeting issue 'Allostery and molecular machines'.

All-Atom Simulations Reveal Protein Charge Decoration in the Folded and Unfolded Ensemble Is Key in Thermophilic Adaptation.

Thermophilic proteins denature at much higher temperature compared to their mesophilic homologues, in spite of high structural and sequential similarity. Computational approaches to understand this puzzle face three major challenges: (i) unfolded ensembles are usually neglected, (ii) simulation studies of the folded states are often too short, and (iii) the majority of investigations focus on a few protein pairs, obscuring the prevalence of different strategies across multiple protein systems. We address these concerns by carrying out all-atom simulations to characterize physicochemical properties of both the folded and the disordered ensemble in multiple (12) thermophilic-mesophilic homologous protein pairs. We notice two clear trends in most pairs (10 out of 12). First, specific distribution of charges in the native basin-sampled from multimicrosecond long Molecular Dynamics (MD) simulation trajectories-leads to more favorable electrostatic interaction energy in thermophiles compared to mesophiles. Next, thermophilic proteins have lowered electrostatic interaction in their unfolded state-generated using Monte Carlo (MC) simulation-compared to their mesophilic counterparts. The net contribution of interaction energy to folding stability, however, remains more favorable in thermophiles compared to mesophiles. The overall contribution of electrostatics quantified by combining the net interaction energy and the solvation penalty of folding-due to differential charge burial in the folded and the unfolded ensemble-is also mostly favorable in thermophilic proteins compared to mesophiles. The systems that deviate from this trend provide interesting test cases to learn more about alternate design strategies when modification of charges is not viable due to functional reasons. The unequal contribution of the unfolded state to the stability in thermophiles and mesophiles highlights the importance of modeling the disordered ensemble to understand thermophilic adaptation as well as protein stability, in general. Our integrated approach-combining finite element analysis with MC and MD-can be useful in designing charge mutations to alter protein stability.

Preoperative mapping of nonmelanoma skin cancer using spatial frequency domain and ultrasound imaging.

RATIONALE AND OBJECTIVES: The treatment of nonmelanoma skin cancer (NMSC) is usually by surgical excision or Mohs micrographic surgery and alternatively may include photodynamic therapy (PDT). To guide surgery and to optimize PDT, information about the tumor structure, optical parameters, and vasculature is desired. MATERIALS AND METHODS: Spatial frequency domain imaging (SFDI) can map optical absorption, scattering, and fluorescence parameters that can enhance tumor contrast and quantify light and photosensitizer dose. High frequency ultrasound (HFUS) imaging can provide high-resolution tumor structure and depth, which is useful for both surgery and PDT planning. RESULTS: Here, we present preliminary results from our recently developed clinical instrument for patients with NMSC. We quantified optical absorption and scattering, blood oxygen saturation (StO2), and total hemoglobin concentration (THC) with SFDI and lesion thickness with ultrasound. These results were compared to histological thickness of excised tumor sections. CONCLUSIONS: SFDI quantified optical parameters with high precision, and multiwavelength analysis enabled 2D mappings of tissue StO2 and THC. HFUS quantified tumor thickness that correlated well with histology. The results demonstrate the feasibility of the instrument for noninvasive mapping of optical, physiological, and ultrasound contrasts in human skin tumors for surgery guidance and therapy planning.