Singular dielectric nanolaser with atomic-scale field localization.

Compressing the optical field to the atomic scale opens up possibilities for directly observing individual molecules, offering innovative imaging and research tools for both physical and life sciences. However, the diffraction limit imposes a fundamental constraint on how much the optical field can be compressed, based on the achievable photon momentum. In contrast to dielectric structures, plasmonics offer superior field confinement by coupling the light field with the oscillations of free electrons in metals.

Reconfigurable moiré nanolaser arrays with phase synchronization.

Miniaturized lasers play a central role in the infrastructure of modern information society. The breakthrough in laser miniaturization beyond the wavelength scale has opened up new opportunities for a wide range of applications, as well as for investigating light-matter interactions in extreme-optical-field localization and lasing-mode engineering. An ultimate objective of microscale laser research is to develop reconfigurable coherent nanolaser arrays that can simultaneously enhance information capacity and functionality.

Twisted lattice nanocavity with theoretical quality factor exceeding 200 billion.

Simultaneous localization of light to extreme spatial and spectral scales is of high importance for testing fundamental physics and various applications. However, there is a longstanding trade-off between localizing a light field in space and in frequency. Here we discover a new class of twisted lattice nanocavities based on mode locking in momentum space.

RDbC2: an improved method to identify the residue-residue pairing in β strands.

Background: Despite the great advance of protein structure prediction, accurate prediction of the structures of mainly β proteins is still highly challenging, but could be assisted by the knowledge of residue-residue pairing in β strands. Previously, we proposed a ridge-detection-based algorithm RDbC that adopted a multi-stage random forest framework to predict the β-β pairing given the amino acid sequence of a protein.

Results: In this work, we developed a second version of this algorithm, RDbC2, by employing the residual neural network to further enhance the prediction accuracy.

DeepConPred2: An Improved Method for the Prediction of Protein Residue Contacts.

Information of residue-residue contacts is essential for understanding the mechanism of protein folding, and has been successfully applied as special topological restraints to simplify the conformational sampling in de novo protein structure prediction. Prediction of protein residue contacts has experienced amazingly rapid progresses recently, with prediction accuracy approaching impressively high levels in the past two years. In this work, we introduce a second version of our residue contact predictor, DeepConPred2, which exhibits substantially improved performance and sufficiently reduced running time after model re-optimization and feature updates.

Identification of residue pairing in interacting β-strands from a predicted residue contact map.

Background: Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in β strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in β-β interactions. This information may benefit the tertiary structure prediction of mainly β proteins.

Predicting the helix-helix interactions from correlated residue mutations.

Helix-helix interactions are crucial in the structure assembly, stability and function of helix-rich proteins including many membrane proteins. In spite of remarkable progresses over the past decades, the accuracy of predicting protein structures from their amino acid sequences is still far from satisfaction. In this work, we focused on a simpler problem, the prediction of helix-helix interactions, the results of which could facilitate practical protein structure prediction by constraining the sampling space.

Comparative study of the effectiveness and limitations of current methods for detecting sequence coevolution.

Motivation: With rapid accumulation of sequence data on several species, extracting rational and systematic information from multiple sequence alignments (MSAs) is becoming increasingly important. Currently, there is a plethora of computational methods for investigating coupled evolutionary changes in pairs of positions along the amino acid sequence, and making inferences on structure and function. Yet, the significance of coevolution signals remains to be established.

Evol and ProDy for bridging protein sequence evolution and structural dynamics.

Unlabelled: Correlations between sequence evolution and structural dynamics are of utmost importance in understanding the molecular mechanisms of function and their evolution. We have integrated Evol, a new package for fast and efficient comparative analysis of evolutionary patterns and conformational dynamics, into ProDy, a computational toolbox designed for inferring protein dynamics from experimental and theoretical data. Using information-theoretic approaches, Evol coanalyzes conservation and coevolution profiles extracted from multiple sequence alignments of protein families with their inferred dynamics.

ATPase subdomain IA is a mediator of interdomain allostery in Hsp70 molecular chaperones.

The versatile functions of the heat shock protein 70 (Hsp70) family of molecular chaperones rely on allosteric interactions between their nucleotide-binding and substrate-binding domains, NBD and SBD. Understanding the mechanism of interdomain allostery is essential to rational design of Hsp70 modulators. Yet, despite significant progress in recent years, how the two Hsp70 domains regulate each other's activity remains elusive.