Artificial intelligence for nailfold capillaroscopy analyses - a proof of concept application in juvenile dermatomyositis.

Background: Biomarkers for idiopathic inflammatory myopathies are difficult to identify and may involve expensive laboratory tests. We assess the potential for artificial intelligence (AI) to differentiate children with juvenile dermatomyositis (JDM) from healthy controls using nailfold capillaroscopy (NFC) images. We also assessed the potential of NFC images to reflect the range of disease activity with JDM.

Probing Mechanisms of Binding and Allostery in the SARS-CoV-2 Spike Omicron Variant Complexes with the Host Receptor: Revealing Functional Roles of the Binding Hotspots in Mediating Epistatic Effects and Communication with Allosteric Pockets.

In this study, we performed all-atom MD simulations of RBD-ACE2 complexes for BA.1, BA.1.

Interpretable Machine Learning Models for Molecular Design of Tyrosine Kinase Inhibitors Using Variational Autoencoders and Perturbation-Based Approach of Chemical Space Exploration.

In the current study, we introduce an integrative machine learning strategy for the autonomous molecular design of protein kinase inhibitors using variational autoencoders and a novel cluster-based perturbation approach for exploration of the chemical latent space. The proposed strategy combines autoencoder-based embedding of small molecules with a cluster-based perturbation approach for efficient navigation of the latent space and a feature-based kinase inhibition likelihood classifier that guides optimization of the molecular properties and targeted molecular design. In the proposed generative approach, molecules sharing similar structures tend to cluster in the latent space, and interpolating between two molecules in the latent space enables smooth changes in the molecular structures and properties.

Integrating Conformational Dynamics and Perturbation-Based Network Modeling for Mutational Profiling of Binding and Allostery in the SARS-CoV-2 Spike Variant Complexes with Antibodies: Balancing Local and Global Determinants of Mutational Escape Mechanisms.

In this study, we combined all-atom MD simulations, the ensemble-based mutational scanning of protein stability and binding, and perturbation-based network profiling of allosteric interactions in the SARS-CoV-2 spike complexes with a panel of cross-reactive and ultra-potent single antibodies (B1-182.1 and A23-58.1) as well as antibody combinations (A19-61.

Frustration-driven allosteric regulation and signal transmission in the SARS-CoV-2 spike omicron trimer structures: a crosstalk of the omicron mutation sites allosterically regulates tradeoffs of protein stability and conformational adaptability.

Dissecting the regulatory principles underlying function and activity of the SARS-CoV-2 spike protein at the atomic level is of paramount importance for understanding the mechanisms of virus transmissibility and immune escape. In this work, we introduce a hierarchical computational approach for atomistic modeling of allosteric mechanisms in the SARS-CoV-2 Omicron spike proteins and present evidence of a frustration-based allostery as an important energetic driver of the conformational changes and spike activation. By examining conformational landscapes and the residue interaction networks in the SARS-CoV-2 Omicron spike protein structures, we have shown that the Omicron mutational sites are dynamically coupled and form a central engine of the allosterically regulated spike machinery that regulates the balance and tradeoffs between conformational plasticity, protein stability, and functional adaptability.

Computer Simulations and Network-Based Profiling of Binding and Allosteric Interactions of SARS-CoV-2 Spike Variant Complexes and the Host Receptor: Dissecting the Mechanistic Effects of the Delta and Omicron Mutations.

In this study, we combine all-atom MD simulations and comprehensive mutational scanning of S-RBD complexes with the angiotensin-converting enzyme 2 (ACE2) host receptor in the native form as well as the S-RBD Delta and Omicron variants to (a) examine the differences in the dynamic signatures of the S-RBD complexes and (b) identify the critical binding hotspots and sensitivity of the mutational positions. We also examined the differences in allosteric interactions and communications in the S-RBD complexes for the Delta and Omicron variants. Through the perturbation-based scanning of the allosteric propensities of the SARS-CoV-2 S-RBD residues and dynamics-based network centrality and community analyses, we characterize the global mediating centers in the complexes and the nature of local stabilizing communities.

Landscape-Based Protein Stability Analysis and Network Modeling of Multiple Conformational States of the SARS-CoV-2 Spike D614G Mutant: Conformational Plasticity and Frustration-Induced Allostery as Energetic Drivers of Highly Transmissible Spike Variants.

The structural and functional studies of the SARS-CoV-2 spike protein variants revealed an important role of the D614G mutation that is shared across many variants of concern (VOCs), suggesting the effect of this mutation on the enhanced virus infectivity and transmissibility. The recent structural and biophysical studies provided important evidence about multiple conformational substates of the D614G spike protein. The development of a plausible mechanistic model that can explain the experimental observations from a more unified thermodynamic perspective is an important objective of the current work.