Biology subjects

Alshahrani, M.

Publications and source records attributed to Alshahrani, M..

27 records · Page 2Linked to original sources

bioRxiv2023

Markov State Models and Perturbation-Based Approaches Reveal Distinct Dynamic Signatures and Hidden Allosteric Pockets in the Emerging SARS-Cov-2 Spike Omicron Variants Complexes with the Host Receptor: The Interplay of Dynamics and Convergent Evolution Modulates Allostery and Functional Mechanisms

The new generation of SARS-CoV-2 Omicron variants displayed a significant growth advantage and the increased viral fitness by acquiring convergent mutations, suggesting that the immune pressure can promote convergent evolution leading to the sudden acceleration of SARS-CoV-2 evolution. In the current study, we combined structural modeling, extensive microsecond MD simulations and Markov state models to characterize conformational landscapes and identify specific dynamic signatures of the SARS-CoV-2 spike complexes with the host receptor ACE2 for the recently emerged highly transmissible XBB.1, XBB.1.5, BQ.1, and BQ.1.1 Omicron variants. Microsecond simulations and Markovian modeling provided a detailed characterization of the conformational landscapes and revealed the increased thermodynamic stabilization of the XBB.1.5 subvariant which is contrasted to more dynamic BQ.1 and BQ.1.1 subvariants. Despite considerable structural similarities, Omicron mutations can induce unique dynamic signatures and specific distributions of conformational states. The results suggested that variant-specific changes of conformational mobility in the functional interfacial loops of the spike receptor binding domain can be fine-tuned through cross-talk between convergent mutations thereby providing an evolutionary path for modulation of immune escape. By combining atomistic simulations and Markovian modeling analysis with perturbation-based approaches, we determined important complementary roles of convergent mutation sites as effectors and receivers of allosteric signaling involved in modulating conformational plasticity at the binding interface and regulating allosteric responses. This study also characterized the dynamics-induced evolution of allosteric pockets in the Omicron complexes that revealed hidden allosteric pockets and suggested that convergent mutation sites could control evolution and distribution of allosteric pockets through modulation of conformational plasticity in the flexible adaptable regions. Through integrative computational approaches, this investigation provides a systematic analysis and comparison of the effects of Omicron subvariants on conformational dynamics and allosteric signaling in the complexes with the ACE2 receptor. For Table of Contents Use Only O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/541592v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@2729a7org.highwire.dtl.DTLVardef@11fa88forg.highwire.dtl.DTLVardef@1737e4eorg.highwire.dtl.DTLVardef@1695eb6_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Probing Conformational Landscapes of Binding and Allostery in the SARS-CoV-2 Omicron Variant Complexes Using Microsecond Atomistic Simulations and Perturbation-Based Profiling Approaches: Hidden Role of Omicron Mutations as Modulators of Allosteric Signaling and Epistatic Relationships

In this study, we systematically examine the conformational dynamics, binding and allosteric communications in the Omicron BA.1, BA.2, BA.3 and BA.4/BA.5 complexes with the ACE2 host receptor using molecular dynamics simulations and perturbation-based network profiling approaches. Microsecond atomistic simulations provided a detailed characterization of the conformational landscapes and revealed the increased thermodynamic stabilization of the BA.2 variant which is contrasted with the BA.4/BA.5 variants inducing a significant mobility of the complexes. Using ensemble-based mutational scanning of binding interactions, we identified binding affinity and structural stability hotspots in the Omicron complexes. Perturbation response scanning and network-based mutational profiling approaches probed the effect of the Omicron variants on allosteric communications. The results of this analysis revealed specific roles of Omicron mutations as "plastic and evolutionary adaptable" modulators of binding and allostery which are coupled to the major regulatory positions through interaction networks. Through perturbation network scanning of allosteric residue potentials in the Omicron variant complexes, which is performed in the background of the original strain, we identified that the key Omicron binding affinity hotspots N501Y and Q498R could mediate allosteric interactions and epistatic couplings. Our results suggested that the synergistic role of these hotspots in controlling stability, binding and allostery can enable for compensatory balance of fitness tradeoffs with conformationally and evolutionary adaptable immune-escape Omicron mutations. Through integrative computational approaches, this study provides a systematic analysis of the effects of Omicron mutations on thermodynamics, binding and allosteric signaling in the complexes with ACE2 receptor. The findings support a mechanism in which Omicron mutations can evolve to balance thermodynamic stability and conformational adaptability in order to ensure proper tradeoff between stability, binding and immune escape.

biophysics↗

bioRxiv2023

Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages : Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes

The evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, protein stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and identified energetic hotspots that can mediate the predicted increased stability and the enhanced binding affinity of the BA.2.75 and XBB.1.5 complexes. The results suggested a mechanism driven by the stability hotspots and a spatially localized group of the Omicron binding affinity centers, while allowing for functionally beneficial neutral Omicron mutations in other binding interface positions. A network-based community model for the analysis of non-additive epistatic contributions in the Omicron complexes is proposed revealing the key role of the binding hotspots R498 and Y501 in mediating community-based epistatic couplings with other Omicron sites and allowing for compensatory dynamics and binding energetic changes. The results also showed that mutations in the convergent evolutionary hotspot F486 can modulate not only local interactions but also rewire the global network of local communities in this region allowing the F486P mutation to restore both the stability and binding affinity of the XBB.1.5 variant which may explain the growth advantages over the XBB.1 variant. The results of this study are consistent with a broad range of functional studies rationalizing functional roles of the Omicron mutation sites that form a coordinated network of hotspots enabling balance of multiple fitness tradeoffs and shaping up a complex functional landscape of virus transmissibility.

biophysics↗

bioRxiv2023

Coarse-Grained Molecular Simulations and Ensemble-Based Mutational Profiling of Protein Stability in the Different Functional Forms of the SARS-CoV-2 Spike Trimers : Balancing Stability and Adaptability in BA.1, BA.2 and BA.2.75 Variants

The evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity, conformational plasticity, protein stability and allosteric modulation. In this study, we embarked on a systematic comparative analysis of the conformational dynamics, electrostatics, protein stability and allostery in the different functional states of spike trimers for BA.1, BA.2, and BA.2.75 variants. Using efficient and accurate coarse-grained simulations and atomistic reconstruction of the ensembles, we examined conformational dynamics of the spike trimers that agrees with the recent functional studies, suggesting that BA.2.75 trimers are the most stable among these variants. A systematic mutational scanning of the inter-protomer interfaces in the spike trimers revealed a group of conserved structural stability hotspots that play a key role in modulation of functional dynamics and are also involved in the inter-protomer couplings through local contacts and interaction networks with the Omicron mutational sites. The results of mutational scanning provided evidence that BA.2.75 trimers are more stable than BA.2 and comparable in stability to BA.1 variant. Using dynamic network modeling of the S Omicron BA.1, BA.2 and BA.2.75 trimers we showed that the key network positions driving long-range signaling are associated with the major stability hotspots that are inter-connected along potential communication pathways, while sites of Omicron mutations may often correspond to weak spots of stability and allostery but are coupled to the major stability hotspots through interaction networks. The presented analysis of the BA.1, BA.2 and BA.2.75 trimers suggested that thermodynamic stability of BA.1 and BA.2.75 variants may be intimately linked with the residue interaction network organization that allows for a broad ensemble of allosteric communications in which signaling between structural stability hotspots may be modulated by the Omicron mutational sites. The findings provided plausible rationale for mechanisms in which Omicron mutations can evolve to balance thermodynamic stability and conformational adaptability in order to ensure proper tradeoff between stability, binding and immune escape.

biophysics↗

bioRxiv2020

New mechanisms of radioiodide uptake revealed via a novel high throughput drug screening approach in thyroid cancer

New combinatorial drug strategies are urgently needed to improve radioiodide (RAI) uptake and efficiently ablate thyroid cancer cells, thereby addressing recurrent and metastatic disease. Cellular iodide uptake is accomplished solely by the sodium iodide symporter (NIS), but the complexity of NIS functional regulation and a lack of amenable high-throughput screening assays has impeded progress. We utilised mutated yellow fluorescent protein (YFP) as a surrogate biosensor of intracellular iodide for [~]1200 FDA-approved drugs, allowing us to appraise the impact of 73 leading compounds at 10 doses on 125I uptake in thyroid cancer cell lines. Subsequent mechanistic analysis suggests three predominant modes of drug action: Firstly, a number of drugs inhibited specific regulation of NIS function by the protein VCP. Secondly, some drugs enhanced transcriptional or post-transcriptional regulation of NIS expression. Thirdly, several drugs strongly implicated proteasomal degradation and the unfolded protein response in the cellular processing of NIS. Exploiting these mechanistic insights, multiple compounds gave striking increases in radioiodide uptake when combined with the drug SAHA. Importantly, our new drug combination strategies were also effective in human primary thyrocytes, suggesting they target endogenous NIS physiology. In patients with papillary thyroid cancer, genes involved in proteostasis were remarkably altered and predicted significantly worse outcome, but only in those patients who received RAI therapy. Collectively, we therefore propose a new model of intracellular NIS processing, and identify key nodes which may now be druggable in patients with aggressive thyroid cancer. SUMMARYOur data identify FDA-approved drugs that enhance radioiodide uptake outside of the canonical pathways of NIS processing, leading to a new mechanistic understanding of endogenous NIS function which is subverted in cancer.

cancer biology↗

bioRxiv2020

Pseudomonas aeruginosa biofilms display carbohydrate ligands for CD206 and CD209 that interfere with their receptor function

Bacterial biofilms represent a challenge to the healthcare system because of their resilience against antimicrobials and immune attack. Biofilms consist of bacterial aggregates embedded in an extracellular polymeric substance (EPS) composed of carbohydrate polymers, nucleic acids and proteins. Carbohydrates within P. aeruginosa biofilms include neutral and mannose-rich Psl, and cationic Pel composed of N-acetyl-galactosamine and N-acetyl-glucosamine. Here we show that P. aeruginosa biofilms display ligands for the C-type lectin receptors mannose receptor (MR, CD206) and Dendritic Cell-Specific Intercellular adhesion molecule-3-Grabbing Non-integrin (DC-SIGN, CD209). Binding of MR and DC-SIGN to P. aeruginosa biofilms is carbohydrate-and calcium-dependent and extends to biofilms formed by clinical isolates. Confocal analysis of P. aeruginosa biofilms shows abundant DC-SIGN ligands among bacteria aggregates while MR ligands concentrate into discrete clusters. DC-SIGN ligands are also detected in planktonic P. aeruginosa cultures and depend on the presence of the common polysaccharide antigen. Carbohydrates purified from P. aeruginosa biofilms are recognised by DC-SIGN and MR; both receptors preferentially bind the high molecular weight fraction (HMW; >132,000Da) with KDs in the nM range. HMW preparations contain 74.9-80.9% mannose, display -mannan segments and alter the morphology of human dendritic cells without causing obvious changes in cytokine responses. Finally, HMW interferes with the endocytic activity of cell-associated MR and DC-SIGN. This work identifies MR and DC-SIGN as receptors for bacterial biofilms and highlights the potential for biofilm-associated carbohydrates as immunomodulators through engagement of C-type lectin receptors. Author SummarySelective engagement of pattern recognition receptors during infection guides the decision-making process during induction of immune responses. This work identifies mannose-rich carbohydrates within bacterial biofilms as novel molecular patterns associated with bacterial infections. P. aeruginosa biofilms and biofilm-derived carbohydrates bind two important lectin receptors, MR (CD206) and DC-SIGN (CD209), involved in recognition of self and immune evasion. Abundance of MR and DC-SIGN ligands in the context of P. aeruginosa biofilms could impact immune responses and promote chronic infection.

microbiology↗

bioRxiv2019

Pharmacologically targeting a novel pathway of sodium iodide symporter trafficking to enhance radioiodine uptake

Radioiodine treatment fails [≥]25% of patients with thyroid cancer and has been proposed as a potential treatment for breast cancer. Cellular iodide uptake is governed by the sodium iodide symporter (NIS), which is frequently mislocalized in thyroid and breast tumours. However, the trafficking of NIS to the plasma membrane (PM) is ill-defined. Through mass spectrometry, co-immunoprecipitation, cell surface biotinylation and proximity ligation assays we identify two proteins which control NIS subcellular trafficking: ADP-ribosylation factor 4 (ARF4) and valosin-containing protein (VCP). HiLo microscopy revealed ARF4 enhanced NIS trafficking in co-incident PM vesicles, governed by a C-terminal VXPX motif, whilst papillary thyroid cancers (PTC) demonstrate repressed ARF4 expression. In contrast, VCP, the central protein in ER-associated degradation, specifically bound NIS and decreased its PM localization. Five chemically distinct allosteric VCP inhibitors all overcame VCP-mediated repression of NIS function. In mice, two re-purposed FDA-approved VCP inhibitors significantly enhanced radioiodine uptake into thyrocytes, whilst human primary thyrocytes showed similar increases. Critically, PTC patients with high tumoural VCP expression who received radioiodine had strikingly worse disease-free survival. These studies now delineate the mechanisms of NIS trafficking, and for the first time open the therapeutic possibility of systemically enhancing radioiodine uptake in patients via FDA-approved drugs.\n\nOne Sentence SummaryNovel NIS interactors ARF4 and VCP alter NIS trafficking in vitro, and FDA-approved VCP inhibitors can significantly enhance radioiodine uptake.

cancer biology↗

bioRxiv2018

Drug repurposing through joint learning on knowledge graphs and literature

MotivationDrug repurposing is the problem of finding new uses for known drugs, and may either involve finding a new protein target or a new indication for a known mechanism. Several computational methods for drug repurposing exist, and many of these methods rely on combinations of different sources of information, extract hand-crafted features and use a computational model to predict targets or indications for a drug. One of the distinguishing features between different drug repurposing systems is the selection of features. Recently, a set of novel machine learning methods have become available that can efficiently learn features from datasets, and these methods can be applied, among others, to text and structured data in knowledge graphs.\n\nResultsWe developed a novel method that combines information in literature and structured databases, and applies feature learning to generate vector space embeddings. We apply our method to the identification of drug targets and indications for known drugs based on heterogeneous information about drugs, target proteins, and diseases. We demonstrate that our method is able to combine complementary information from both structured databases and from literature, and we show that our method can compete with well-established methods for drug repurposing. Our approach is generic and can be applied to other areas in which multi-modal information is used to build predictive models.\n\nAvailabilityhttps://github.com/bio-ontology-research-group/multi-drug-embedding\n\nContactrobert.hoehndorf@kaust.edu.sa

bioinformatics↗

bioRxiv2018

Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes

MotivationIn the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a diseases (or patients) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse.\n\nResultsWe developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprising of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network.\n\nAvailabilityhttps://github.com/bio-ontology-research-group/SmuDGE\n\nContactrobert.hoehndorf@kaust.edu.sa

bioinformatics↗