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.1.1, BA.2, and BA.3 Omicron subvariants, conducted a systematic mutational scanning of the RBD–ACE2 binding interfaces and analysis of electrostatic effects. The binding free energy computations of the Omicron RBD–ACE2 complexes and comprehensive examination of the electrostatic interactions quantify the driving forces of binding and provide new insights into energetic mechanisms underlying evolutionary differences between Omicron variants. A systematic mutational scanning of the RBD residues determines the protein stability centers and binding energy hotpots in the Omicron RBD–ACE2 complexes. By employing the ensemble-based global network analysis, we propose a community-based topological model of the Omicron RBD interactions that characterized functional roles of the Omicron mutational sites in mediating non-additive epistatic effects of mutations. Our findings suggest that non-additive contributions to the binding affinity may be mediated by R493, Y498, and Y501 sites and are greater for the Omicron BA.1.1 and BA.2 complexes that display the strongest ACE2 binding affinity among the Omicron subvariants. A network-centric adaptation model of the reversed allosteric communication is unveiled in this study, which established a robust connection between allosteric network hotspots and potential allosteric binding pockets. Using this approach, we demonstrated that mediating centers of long-range interactions could anchor the experimentally validated allosteric binding pockets. Through an array of complementary approaches and proposed models, this comprehensive and multi-faceted computational study revealed and quantified multiple functional roles of the key Omicron mutational site R493, R498, and Y501 acting as binding energy hotspots, drivers of electrostatic interactions as well as mediators of epistatic effects and long-range communications with the allosteric pockets

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. The results demonstrated that the proposed strategy can efficiently explore the latent space of small molecules and kinase inhibitors along interpretable directions to guide the generation of novel family-specific kinase molecules that display a significant scaffold diversity and optimal biochemical properties. Through assessment of the latent-based and chemical feature-based binary and multiclass classifiers, we developed a robust probabilistic evaluator of kinase inhibition likelihood that is specifically tailored to guide the molecular design of novel SRC kinase molecules. The generated molecules originating from LCK and ABL1 kinase inhibitors yielded ~40% of novel and valid SRC kinase compounds with high kinase inhibition likelihood probability values (p \u3e 0.75) and high similarity (Tanimoto coefficient \u3e 0.6) to the known SRC inhibitors. By combining the molecular perturbation design with the kinase inhibition likelihood analysis and similarity assessments, we showed that the proposed molecular design strategy can produce novel valid molecules and transform known inhibitors of different kinase families into potential chemical probes of the SRC kinase with excellent physicochemical profiles and high similarity to the known SRC kinase drugs. The results of our study suggest that task-specific manipulation of a biased latent space may be an important direction for more effective task-oriented and target-specific autonomous chemical design models

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

n 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 (A19-61.1/B1-182.1 and A19-46.1/B1-182.1). Using this approach, we quantify the local and global effects of mutations in the complexes, identify protein stability centers, characterize binding energy hotspots, and predict the allosteric control points of long-range interactions and communications. Conformational dynamics and distance fluctuation analysis revealed the antibody-specific signatures of protein stability and flexibility of the spike complexes that can affect the pattern of mutational escape. A network-based perturbation approach for mutational profiling of allosteric residue potentials revealed how antibody binding can modulate allosteric interactions and identified allosteric control points that can form vulnerable sites for mutational escape. The results show that the protein stability and binding energetics of the SARS-CoV-2 spike complexes with the panel of ultrapotent antibodies are tolerant to the effect of Omicron mutations, which may be related to their neutralization efficiency. By employing an integrated analysis of conformational dynamics, binding energetics, and allosteric interactions, we found that the antibodies that neutralize the Omicron spike variant mediate the dominant binding energy hotpots in the conserved stability centers and allosteric control points in which mutations may be restricted by the requirements of the protein folding stability and binding to the host receptor. This study suggested a mechanism in which the patterns of escape mutants for the ultrapotent antibodies may not be solely determined by the binding interaction changes but are associated with the balance and tradeoffs of multiple local and global factors, including protein stability, binding affinity, and long-range interactions

Probing Conformational Landscapes and Mechanisms of Allosteric Communication in the Functional States of the ABL Kinase Domain Using Multiscale Simulations and Network-Based Mutational Profiling of Allosteric Residue Potentials

In the current study, multiscale simulation approaches and dynamic network methods are employed to examine the dynamic and energetic details of conformational landscapes and allosteric interactions in the ABL kinase domain that determine the kinase functions. Using a plethora of synergistic computational approaches, we elucidate how conformational transitions between the active and inactive ABL states can employ allosteric regulatory switches to modulate intramolecular communication networks between the ATP site, the substrate binding region, and the allosteric binding pocket. A perturbation-based network approach that implements mutational profiling of allosteric residue propensities and communications in the ABL states is proposed. Consistent with biophysical experiments, the results reveal functionally significant shifts of the allosteric interaction networks in which preferential communication paths between the ATP binding site and substrate regions in the active ABL state become suppressed in the closed inactive ABL form, which in turn features favorable allosteric coupling between the ATP site and the allosteric binding pocket. By integrating the results of atomistic simulations with dimensionality reduction methods and Markov state models, we analyze the mechanistic role of macrostates and characterize kinetic transitions between the ABL conformational states. Using network-based mutational scanning of allosteric residue propensities, this study provides a comprehensive computational analysis of long-range communications in the ABL kinase domain and identifies conserved regulatory hotspots that modulate kinase activity and allosteric crosstalk between the allosteric pocket, ATP binding site, and substrate binding regions

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. We show that a constellation of mutational sites (G496S, Q498R, N501Y and Y505H) correspond to key binding energy hotspots and also contribute decisively to the key interfacial communities that mediate allosteric communications between S-RBD and ACE2. These Omicron mutations are responsible for both favorable local binding interactions and long-range allosteric interactions, providing key functional centers that mediate the high transmissibility of the virus. At the same time, our results show that other mutational sites could provide a “flexible shield” surrounding the stable community network, thereby allowing the Omicron virus to modulate immune evasion at different epitopes, while protecting the integrity of binding and allosteric interactions in the RBD–ACE2 complexes. This study suggests that the SARS-CoV-2 S protein may exploit the plasticity of the RBD to generate escape mutants, while engaging a small group of functional hotspots to mediate efficient local binding interactions and long-range allosteric communications with ACE2

Speckle pattern analysis of security holograms and related foils for quality assessment and authentication.

A speckle pattern is produced by the mutual interference of a set of coherent wavefronts. Speckle patterns typically occur in diffuse reflections of monochromatic light such a laser light. When a rough surface is illuminated by a coherent light is imaged, a speckle pattern is observed in the image plane. This study involves the quality assessment and authentication of security holograms and its related foils by analyzing the speckle pattern generated from the specimen itself. Speckle pattern from various type of security holograms and foils are taken. By processing the image of the speckle pattern, the size of the speckles is analyzed using MATLAB software. By evaluating the size of the speckle generated, the feasibility of analyzing the quality and authenticity of the security hologram is assessed. The paper discusses about the experimental setup, image capturing, and processing method and the result obtained in detail

Maternal experience-dependent cortical plasticity in mice is circuit- and stimulus-specific and requires MECP2

ABSTRACT The neurodevelopmental disorder Rett syndrome is caused by mutations in the gene Mecp2 . Misexpression of the protein MECP2 is thought to contribute to neuropathology by causing dysregulation of plasticity. Female heterozygous Mecp2 mutants ( Mecp2 het ) failed to acquire a learned maternal retrieval behavior when exposed to pups, an effect linked to disruption of parvalbumin-expressing inhibitory interneurons (PV+) in the auditory cortex. However, the consequences of dysregulated PV+ networks during early maternal experience for auditory cortical sensory activity are unknown. Here we show that maternal experience in wild-type adult female mice ( Mecp2 wt ) triggers suppression of PV+ auditory responses. We also observe concomitant disinhibition of auditory responses in deep-layer pyramidal neurons that is selective for behaviorally-relevant pup vocalizations. These neurons also exhibit sharpened tuning for pup vocalizations following maternal experience. All of these neuronal changes are abolished in Mecp2 het , yet a genetic manipulation of GABAergic networks that restores accurate retrieval behavior in Mecp2 het also restores maternal experience-dependent plasticity of PV+. Our data are consistent with a growing body of evidence that cortical networks are particularly vulnerable to mutations of Mecp2 in PV+ neurons

MECP2 regulates cortical plasticity underlying a learned behavior in adult female mice

Neurodevelopmental disorders begin with the emergence of inappropriate synaptic connectivity early in life, yet how the sustained disruption of experience-dependent plasticity aggravates symptoms in adulthood is unclear. Here we used pup retrieval learning to assay adult cortical plasticity in a female mouse model of Rett syndrome (MeCP2het). We show that auditory cortical plasticity and retrieval learning are impaired in MeCP2het. Specifically, normal MECP2 expression in the adult auditory cortex is required for efficient retrieval learning. In wild-type mice, cohabitation with a mother and her pups triggered transient changes to auditory cortical inhibitory networks, including elevated levels of the GABA-synthesizing enzyme GAD67. However, MeCP2het further exhibited increased expression of parvalbumin (PV) and perineuronal nets (PNNs), events thought to suppress plasticity at the closure of critical periods and in adult learning. Averting these events with genetic and pharmacological manipulations of the GABAergic network restored retrieval behavior. We propose that adult retrieval learning triggers a transient episode of inhibitory plasticity in the auditory cortex that is dysregulated in MeCP2het. This window of heightened sensitivity to social sensory cues reveals a role of MeCP2 mutations in facilitating adult plasticity that is distinct from their effects on early development

Identification of Spt5 Target Genes in Zebrafish Development Reveals Its Dual Activity In Vivo

Spt5 is a conserved essential protein that represses or stimulates transcription elongation in vitro. Immunolocalization studies on Drosophila polytene chromosomes suggest that Spt5 is associated with many loci throughout the genome. However, little is known about the prevalence and identity of Spt5 target genes in vivo during development. Here, we identify direct target genes of Spt5 using fogsk8 zebrafish mutant, which disrupts the foggy/spt5 gene. We identified that fogsk8 and their wildtype siblings differentially express less than 5% of genes examined. These genes participate in diverse biological processes from stress response to cell fate specification. Up-regulated genes exhibit shorter overall gene length compared to all genes examined. Through chromatin immunoprecipitation in zebrafish embryos, we identified a subset of developmentally critical genes that are bound by both Spt5 and RNA polymerase II. The protein occupancy patterns on these genes are characteristic of both repressive and stimulatory elongation regulation. Together our findings establish Spt5 as a dual regulator of transcription elongation in vivo and identify a small but diverse set of target genes critically dependent on Spt5 during development

Perturbation Modeling for Molecular Design of Protein Tyrosine Kinase Inhibitors using Unsupervised Machine Learning

The field of computational drug discovery and development has grown, with the aid of new computational tools for novel molecule discovery. In specific, generative deep learning models have excelled as tools to aid in navigating the large space of known molecules and in the creation of new molecules. These models are fed various representations of molecules as inputs and learn to perform a variety of things, such as the optimization of these molecules towards a targeted property. Ultimately, these generative learning models allow us to build bridges between chemical and continuous spaces to understand the compromise between invoking small incremental changes to radical modifications and generate optimal molecules for therapeutic benefits. The goal of this study pertains to creating a perturbation modeling framework in which we can conduct transformations of small molecules to SRC kinase inhibitors using a combination of a generative learning technique called Variational Auto-Encoders and Unsupervised Machine Learning techniques. This study focuses specifically using the methods above to transform molecules from various kinase inhibitor families to SRC Kinase Inhibitors. These generated molecules are evaluated using various physicochemical metrics and similarity metrics for validity of transformation. The results of this study demonstrate that Machine Learning based perturbation techniques can aid in the evolution of molecules from one chemical space to another. By combining Generative Learning frameworks with targeted based alteration of the continuous space, we allow for the emergence of novel molecules that are structurally adjacent to the various molecular scaffolds of SRC Kinase Inhibitors. Our findings also suggested that these vi molecules exhibit chemical and drug-likeness similarity to known SRC Kinase Inhibitors, displaying potential for synthetic and therapeutic benefits. Further studies should be conducted to determine whether the novel molecules that are generated contain potential to be synthesized for practical use