Discovery of cryptic allosteric sites using reversed allosteric communication by a combined computational and experimental strategy

Pathways and mechanism of MRTX1133 binding to KRAS G12D elucidated by molecular dynamics simulations and Markov state models

KRAS G12D is the most common oncogenic mutation identified in several types of cancer. Therefore, design of inhibitors targeting KRAS G12D represents a promising strategy for anticancer therapy. MRTX1133 is a highly potent inhibitor (approximate experiment Kd ≈ 0.0002 nM) of KRAS G12D and is currently in Phase 1/2 study, however, pathways of the compound binding to KRAS G12D has remained unknown, and the mechanism underlying the complicated dynamic process are challenging to capture experimentally, which hinder the structure-based anti-cancer drug design. Here, using MRTX1133 as a probe, unbiased molecular dynamics (MD) was used to simulate the process of MRTX1133 spontaneously binding to KRAS G12D. In six of 42 independent MD simulation (a total of 99 μs), MRTX1133 was observed to successfully associate with KRAS G12D. The kinetically metastable states refer to the potential pathways of MRTX1133 binding to KRAS G12D were revealed by Markov state models (MSM) analysis. Additionally, 8 key residues that are essential for MRTX1133 recognition and tight binding at the preferred low energy states were identified by MM/GBSA analysis. In sum, this study provides a new perspective on understanding the pathways and mechanism of MRTX1133 binding to KRAS G12D.

Leveraging Bidirectional Nature of Allostery To Inhibit Protein-Protein Interactions (PPIs): A Case Study of PCSK9-LDLR Interaction

The protein PCSK9 (proprotein convertase subtilisin/Kexin type 9) negatively regulates the recycling of LDLR (low-density lipoprotein receptor), leading to an elevated plasma level of LDL. Inhibition of PCSK9-LDLR interaction has emerged as a promising therapeutic strategy to manage hypercholesterolemia. However, the large interaction surface area between PCSK9 and LDLR makes it challenging to identify a small molecule competitive inhibitor. An alternative strategy would be to identify distal cryptic sites as targets for allosteric inhibitors that can remotely modulate PCSK9-LDLR interaction. Using several microseconds long molecular dynamics (MD) simulations, we demonstrate that on binding with LDLR, there is a significant conformational change (population shift) in a distal loop (residues 211-222) region of PCSK9. Consistent with the bidirectional nature of allostery, we establish a clear correlation between the loop conformation and the binding affinity with LDLR. Using a thermodynamic argument, we establish that the loop conformations predominantly present in the apo state of PCSK9 would have lower LDLR binding affinity, and they would be potential targets for designing allosteric inhibitors. We elucidate the molecular origin of the allosteric coupling between this loop and the LDLR binding interface in terms of the population shift in a set of salt bridges and hydrogen bonds. Overall, our work provides a general strategy toward identifying allosteric hotspots: compare the conformational ensemble of the receptor between the apo and bound states of the protein and identify distal conformational changes, if any. The inhibitors should be designed to bind and stabilize the apo-specific conformations.

Identification of new potent NLRP3 inhibitors by multi-level in-silico approaches

Nod-like receptor protein 3 (NLRP-3), is an intracellular sensor that is involved in inflammasome activation, and the aberrant expression of NLRP3 is responsible for diabetes mellitus, its complications, and many other inflammatory diseases. NLRP3 is considered a promising drug target for novel drug design. Here, a pharmacophore model was generated from the most potent inhibitor, and its validation was performed by the Gunner-Henry scoring method. The validated pharmacophore was used to screen selected compounds databases. As a result, 646 compounds were mapped on the pharmacophore model. After applying Lipinski's rule of five, 391 hits were obtained. All the hits were docked into the binding pocket of target protein. Based on docking scores and interactions with binding site residues, six compounds were selected potential hits. To check the stability of these compounds, 100 ns molecular dynamic (MD) simulations were performed. The RMSD, RMSF, DCCM and hydrogen bond analysis showed that all the six compounds formed stable complex with NLRP3. The binding free energy with the MM-PBSA approach suggested that electrostatic force, and van der Waals interactions, played a significant role in the binding pattern of these compounds. Thus, the outcomes of the current study could provide insights into the identification of new potential NLRP3 inflammasome inhibitors against diabetes and its related disorders.

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

Kinetic Ensemble of Tau Protein through the Markov State Model and Deep Learning Analysis

The ordered assembly of Tau protein into filaments characterizes Alzheimer's and other neurodegenerative diseases, and thus, stabilization of Tau protein is a promising avenue for tauopathies therapy. To dissect the underlying aggregation mechanisms on Tau, we employ a set of molecular simulations and the Markov state model to determine the kinetics of ensemble of K18. K18 is the microtubule-binding domain of Tau protein and plays a vital role in the microtubule assembly, recycling processes, and amyloid fibril formation. Here, we efficiently explore the conformation of K18 with about 150 μs lifetimes in silico. Our results observe that all four repeat regions (R1-R4) are very dynamic, featuring frequent conformational conversion and lacking stable conformations, and the R2 region is more flexible than the R1, R3, and R4 regions. Additionally, it is worth noting that residues 300-310 in R2-R3 and residues 319-336 in R3 tend to form sheet structures, indicating that K18 has a broader functional role than individual repeat monomers. Finally, the simulations combined with Markov state models and deep learning reveal 5 key conformational states along the transition pathway and provide the information on the microsecond time scale interstate transition rates. Overall, this study offers significant insights into the molecular mechanism of Tau pathological aggregation and develops novel strategies for both securing tauopathies and advancing drug discovery.

Structural and energetic insights into the selective inhibition of PKMYT1 against WEE1

Protein kinase, membrane-associated tyrosine/threonine 1 (PKMYT1), a member of the WEE family and responsible for the regulation of CDK1 phosphorylation, has been considered a promising therapeutic target for cancer therapy. However, the highly structural conservation of the ATP-binding sites of the WEE family poses a challenge to the design of selective inhibitors for PKMYT1. Here, molecular docking, multiple microsecond-length molecular dynamics (MD) simulations and end-point free energy calculations were performed to uncover the molecular mechanism of the binding selectivity of RP-6306 toward PKMYT1 over its highly homologous kinase WEE1. The binding specificity of RP-6306 reported in previous experimental bioassays was clarified by MD simulations and binding free energy calculations. Further, the binding free energy prediction indicated that the binding selectivity of RP-6306 largely derived from the difference in the protein-ligand electrostatic interactions. The per-residue free energy decomposition suggested that the non-conserved gatekeeper residue in the hinge domain of PKMYT1/WEE1, Thr187/Asn376, is the critical factor responsible for the binding selectivity of RP-6306 toward PKMYT1. In addition, a water-mediated hydrogen bond was formed between RP-6306 and Gly191 at the hinge domain in the PKMYT1/RP-6306 complex, which was absent in the WEE1/RP-6306 complex. This study is expected to offer useful information for the design of more potent and selective PKMYT1 inhibitors.Communicated by Ramaswamy H. Sarma.

The effect of phosphorylation on the conformational dynamics and allostery of the association of death-associated protein kinase with calmodulin

Protein phosphorylation plays an important role in the signal transduction and is capable of regulation of cell activity. The death-associated protein kinase 1 (DAPK1), as a Ser/Thr kinase, interacts with calmodulin (CaM) to regulate apoptotic and autophagic signaling. Autophosphorylation of DAPK1 at Ser308 located at the autoregulatory domain (ARD) blocks CaM binding and inhibits kinase catalytic activity. However, the mechanism underlying the influence of Ser308 phosphorylation (pS308) on the DAPK1 activity remains unclear. Here, we performed multiple, microsecond length molecular dynamics (MD) simulations, the molecular mechanics generalized Born/surface area (MM-GBSA) binding free energy calculations, principal component analysis, and dynamic cross-correlation analysis to unravel the conformational dynamics and allostery of the DAPK1 - CaM interaction triggered by the pS308 at the ARD. MD simulations showed that pS308 affected the conformational stability of the DAPK1 - CaM complex. Further energetic and structural exploration revealed that pS308 weakened the association of the phosphorylated DAPK1 to CaM, which lowered the susceptibility of DAPK1 to be activated by CaM. This result can provide mechanistic insights into the molecular underpinning through which the DAPK1 kinase activity is modulated by the auto-phosphorylation.Communicated by Ramaswamy H. Sarma.

Unraveling the Interplay of Extracellular Domain Conformational Changes and Parathyroid Hormone Type 1 Receptor Activation in Class B1 G Protein-Coupled Receptors: Integrating Enhanced Sampling Molecular Dynamics Simulations and Markov State Models

Parathyroid hormone (PTH) type 1 receptor (PTH1R), as a typical class B1 G protein-coupled receptor (GPCR), is responsible for regulating bone turnover and maintaining calcium homeostasis, and its dysregulation has been implicated in the development of several diseases. The extracellular domain (ECD) of PTH1R is crucial for the recognition and binding of ligands, and the receptor may exhibit an autoinhibited state with the closure of the ECD in the absence of ligands. However, the correlation between ECD conformations and PTH1R activation remains unclear. Thus, this study combines enhanced sampling molecular dynamics (MD) simulations and Markov state models (MSMs) to reveal the possible relevance between the ECD conformations and the activation of PTH1R. First, 22 intermediate structures are generated from the autoinhibited state to the active state and conducted for 10 independent 200 ns simulations each. Then, the MSM is constructed based on the cumulative 44 μs simulations with six identified microstates. Finally, the potential interplay between ECD conformational changes and PTH1R activation as well as cryptic allosteric pockets in the intermediate states during receptor activation is revealed. Overall, our findings reveal that the activation of PTH1R has a specific correlation with ECD conformational changes and provide essential insights for GPCR biology and developing novel allosteric modulators targeting cryptic sites.

Decoupling the dynamic mechanism revealed by FGFR2 mutation-induced population shift

The fibroblast growth factor receptor 2 (FGFR2) is a key component in cellular signaling networks, and its dysfunctional activation has been implicated in various diseases including cancer and developmental disorders. Mutations at the activation loop (A-loop) have been suggested to trigger an increased basal kinase activity. However, the molecular mechanism underlying this highly dynamic process has not been fully understood due to the limitation of static structural information. Here, we conducted multiple, large-scale Gaussian accelerated molecular dynamics simulations of five (K659E, K659N, K659M, K659Q, and K659T) FGFR2 mutants at the A-loop, and comprehensively analyzed the dynamic molecular basis of FGFR2 activation. The results quantified the population shift of each system, revealing that all mutants had a higher proportion of active-like states. Using Markov state models, we extracted the representative structure of different conformational states and identified key residues related to the increased kinase activity. Furthermore, community network analysis showed enhanced information connections in the mutants, highlighting the long-range allosteric communication between the A-loop and the hinge region. Our findings may provide insights into the dynamic mechanism for FGFR2 dysfunctional activation and allosteric drug discovery.Communicated by Ramaswamy H. Sarma.

The allosteric mechanism of mTOR activation can inform bitopic inhibitor optimization

mTOR serine/threonine kinase is a cornerstone in the PI3K/AKT/mTOR pathway. Yet, the detailed mechanism of activation of its catalytic core is still unresolved, likely due to mTOR complexes' complexity. Its dysregulation was implicated in cancer and neurodevelopmental disorders. Using extensive molecular dynamics (MD) simulations and compiled published experimental data, we determine exactly how mTOR's inherent motifs can control the conformational changes in the kinase domain, thus kinase activity. We also chronicle the critical regulation by the unstructured negative regulator domain (NRD). When positioned inside the catalytic cleft (NRD IN state), mTOR tends to adopt a deep and closed catalytic cleft. This is primarily due to the direct interaction with the FKBP-rapamycin binding (FRB) domain which restricts it, preventing substrate access. Conversely, when outside the catalytic cleft (NRD OUT state), mTOR favors an open conformation, exposing the substrate-binding site on the FRB domain. We further show how an oncogenic mutation (L2427R) promotes shifting the mTOR ensemble toward the catalysis-favored state. Collectively, we extend mTOR's "active-site restriction" mechanism and clarify mutation action. In particular, our mechanism suggests that RMC-5552 (RMC-6272) bitopic inhibitors may benefit from adjustment of the (PEG8) linker length when targeting certain mTOR variants. In the cryo-EM mTOR/RMC-5552 structure, the distance between the allosteric and orthosteric inhibitors is ∼22.7 Å. With a closed catalytic cleft, this linker bridges the sites. However, in our activation mechanism, in the open cleft it expands to ∼24.7 Å, offering what we believe to be the first direct example of how discovering an activation mechanism can potentially increase the affinity of inhibitors targeting mutants.

ASD2023: towards the integrating landscapes of allosteric knowledgebase

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66 589 potential allosteric sites, covering > 80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leads and (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic exploration and drug discovery.

A Structure-Based Allosteric Modulator Design Paradigm

Importance: Allosteric drugs bound to topologically distal allosteric sites hold a substantial promise in modulating therapeutic targets deemed undruggable at their orthosteric sites. Traditionally, allosteric modulator discovery has predominantly relied on serendipitous high-throughput screening. Nevertheless, the landscape has undergone a transformative shift due to recent advancements in our understanding of allosteric modulation mechanisms, coupled with a significant increase in the accessibility of allosteric structural data. These factors have extensively promoted the development of various computational methodologies, especially for machine-learning approaches, to guide the rational design of structure-based allosteric modulators. Highlights: We here presented a comprehensive structure-based allosteric modulator design paradigm encompassing 3 critical stages: drug target acquisition, allosteric binding site, and modulator discovery. The recent advances in computational methods in each stage are encapsulated. Furthermore, we delve into analyzing the successes and obstacles encountered in the rational design of allosteric modulators. Conclusion: The structure-based allosteric modulator design paradigm holds immense potential for the rational design of allosteric modulators. We hope that this review would heighten awareness of the use of structure-based computational methodologies in advancing the field of allosteric drug discovery.

Decoding the deactivation mechanism of R192W mutation of ZAP-70 using molecular dynamics simulations and binding free energy calculations

CONTEXT

ZAP-70 (zeta-chain-associated protein of 70 kDa), serving as a critical regulator for T cell antigen receptor signaling, represents an attractive therapeutic target for autoimmunity disease. How the mechanistical mechanism of ZAP-70 to a human autoimmune syndrome-associated R192W mutation remains unclear. The results indicated that the R192W mutation of ZAP-70 clearly affected the conformational flexibility of the N-terminal ITAM-Y2P. Structural analysis unveiled that the R192W mutation of ZAP-70 caused the exposure of the N-terminal ITAM-Y2P to the solvent. MM-GBSA binding free energy calculations exhibited that the R192W mutation decreased the binding affinity of ITAM-Y2P to the ZAP-70 mutant. Residue-based free energy decomposition further revealed that the protein-peptide interaction networks involving electrostatic interactions provide significant contributions for complex formation. The energy unfavorable residues include Arg43, Arg192, Tyr240, and Lys244 from ZAP-70 and Asn301, Leu303, pY304, and pY315 from ITAM-Y2P in the R192W mutant. Our obtained results may help the understanding of the deactivation mechanism of ZAP-70 induced by the R192W mutation.

METHODS

In the work, multiple replica molecular dynamics simulations and molecular mechanics-generalized Born surface area (MM-GBSA) method were performed to reveal the doubly phosphorylated ITAMs (ITAM-Y2P)-mediated deactivation mechanism of ZAP-70 induced by the R192W mutation.

Decoding molecular mechanism of species-selective targeting of fungal versus human HSP90 using multiple replica molecular dynamics simulations and binding free energy calculations

As a highly evolutionarily conserved molecular chaperone, heat shock protein (HSP90), plays an important role in virulence traits, representing a therapeutic target for the treatment of fungal infections. The close evolutionary relationship between fungi and their human hosts poses a key challenge for the development of selective antifungal agents. In this work, molecular docking, multiple replica microsecond-based molecular dynamics (MD) simulations, and binding free energy calculations were performed to decode molecular mechanism of species-selective targeting of fungal versus human HSP90 triggered by the compound A11. MD simulations reveal that binding of compound A11 to human HSP90 nucleotide-binding domain (NBD) leads to obvious conformational changes relative to fungal HSP90 NBD. Binding free energy calculations show that the binding of compound A11 to fungal HSP90 NBD is stronger than that to human HSP90 NBD. Per residue-based free energy decomposition analysis was used to evaluate the inhibitor - residue interaction profile. The results efficiently identify the hot spot residues that play vital roles in favorable binding of compound A11 to fungal HSP90 NBD. This study is expected to provide a useful guidance for the development of selective inhibitors toward fungal HSP90.Communicated by Ramaswamy H. Sarma.

Exploring Conformational Landscapes and Cryptic Binding Pockets in Distinct Functional States of the SARS-CoV-2 Omicron BA.1 and BA.2 Trimers: Mutation-Induced Modulation of Protein Dynamics and Network-Guided Prediction of Variant-Specific Allosteric Binding Sites

A significant body of experimental structures of SARS-CoV-2 spike trimers for the BA.1 and BA.2 variants revealed a considerable plasticity of the spike protein and the emergence of druggable binding pockets. Understanding the interplay of conformational dynamics changes induced by the Omicron variants and the identification of cryptic dynamic binding pockets in the S protein is of paramount importance as exploring broad-spectrum antiviral agents to combat the emerging variants is imperative. In the current study, we explore conformational landscapes and characterize the universe of binding pockets in multiple open and closed functional spike states of the BA.1 and BA.2 Omicron variants. By using a combination of atomistic simulations, a dynamics network analysis, and an allostery-guided network screening of binding pockets in the conformational ensembles of the BA.1 and BA.2 spike conformations, we identified all experimentally known allosteric sites and discovered significant variant-specific differences in the distribution of binding sites in the BA.1 and BA.2 trimers. This study provided a structural characterization of the predicted cryptic pockets and captured the experimentally known allosteric sites, revealing the critical role of conformational plasticity in modulating the distribution and cross-talk between functional binding sites. We found that mutational and dynamic changes in the BA.1 variant can induce the remodeling and stabilization of a known druggable pocket in the N-terminal domain, while this pocket is drastically altered and may no longer be available for ligand binding in the BA.2 variant. Our results predicted the experimentally known allosteric site in the receptor-binding domain that remains stable and ranks as the most favorable site in the conformational ensembles of the BA.2 variant but could become fragmented and less probable in BA.1 conformations. We also uncovered several cryptic pockets formed at the inter-domain and inter-protomer interface, including functional regions of the S2 subunit and stem helix region, which are consistent with the known role of pocket residues in modulating conformational transitions and antibody recognition. The results of this study are particularly significant for understanding the dynamic and network features of the universe of available binding pockets in spike proteins, as well as the effects of the Omicron-variant-specific modulation of preferential druggable pockets. The exploration of predicted druggable sites can present a new and previously underappreciated opportunity for therapeutic interventions for Omicron variants through the conformation-selective and variant-specific targeting of functional sites involved in allosteric changes.

Grasping cryptic binding sites to neutralize drug resistance in the field of anticancer

Drug resistance is a significant obstacle to successful cancer treatment. The utilization and development of cryptic binding sites (CBSs) in proteins involved in cancer-related drug-resistance (CRDR) could help to overcome that drug resistance. However, there is no comprehensive review of the successful use of CBSs in addressing CRDR. Here, we have systematically summarized and analyzed the opportunities and challenges of using CBSs in addressing CRDR and revealed the key role that CBSs have in targeting CRDR. First, we have identified the CRDR targets and the corresponding CBSs. Second, we discuss the mechanisms by which CBSs can overcome CRDR. Finally, we have provided examples of successful CBS applications in addressing CRDR. We hope that this approach will provide guidance to biologists and chemists in effectively utilizing CBSs for the development of new drugs to alleviate CRDR.

Markov State Models and Perturbation-Based Approaches Reveal Distinct Dynamic Signatures and Hidden Allosteric Pockets in the Emerging SARS-Cov-2 Spike Omicron Variant 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 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, microsecond molecular dynamics 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 functional conformational states and revealed the increased thermodynamic stabilization of the XBB.1.5 subvariant, which can be 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 the conformational states. The results suggested that variant-specific changes of the conformational mobility in the functional interfacial loops of the receptor-binding domain in the SARS-CoV-2 spike protein can be fine-tuned through crosstalk between convergent mutations which could provide 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 modulation of conformational plasticity and regulation of allosteric communications. This study also 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.

Structural insights into angiotensin receptor signaling modulation by balanced and biased agonists

The peptide hormone angiotensin II regulates blood pressure mainly through the type 1 angiotensin II receptor AT1 R and its downstream signaling proteins Gq and β-arrestin. AT1 R blockers, clinically used as antihypertensive drugs, inhibit both signaling pathways, whereas AT1 R β-arrestin-biased agonists have shown great potential for the treatment of acute heart failure. Here, we present a cryo-electron microscopy (cryo-EM) structure of the human AT1 R in complex with a balanced agonist, Sar1 -AngII, and Gq protein at 2.9 Å resolution. This structure, together with extensive functional assays and computational modeling, reveals the molecular mechanisms for AT1 R signaling modulation and suggests that a major hydrogen bond network (MHN) inside the receptor serves as a key regulator of AT1 R signal transduction from the ligand-binding pocket to both Gq and β-arrestin pathways. Specifically, we found that the MHN mutations N1113.35 A and N2947.45 A induce biased signaling to Gq and β-arrestin, respectively. These insights should facilitate AT1 R structure-based drug discovery for the treatment of cardiovascular diseases.

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.

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Machine learning and protein allostery

The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric mechanisms will have an increasingly important role in bridging a wide spectrum of data-intensive experimental and theoretical technologies.

Decoding the Conformational Selective Mechanism of FGFR Isoforms: A Comparative Molecular Dynamics Simulation

Fibroblast growth factor receptors (FGFRs) play critical roles in the regulation of cell growth, differentiation, and proliferation. Specifically, FGFR2 gene amplification has been implicated in gastric and breast cancer. Pan-FGFR inhibitors often cause large toxic side effects, and the highly conserved ATP-binding pocket in the FGFR1/2/3 isoforms poses an immense challenge in designing selective FGFR2 inhibitors. Recently, an indazole-based inhibitor has been discovered that can selectively target FGFR2. However, the detailed mechanism involved in selective inhibition remains to be clarified. To this end, we performed extensive molecular dynamics simulations of the apo and inhibitor-bound systems along with multiple analyses, including Markov state models, principal component analysis, a cross-correlation matrix, binding free energy calculation, and community network analysis. Our results indicated that inhibitor binding induced the phosphate-binding loop (P-loop) of FGFR2 to switch from the open to the closed conformation. This effect enhanced extensive hydrophobic FGFR2-inhibitor contacts, contributing to inhibitor selectivity. Moreover, the key conformational intermediate states, dynamics, and driving forces of this transformation were uncovered. Overall, these findings not only provided a structural basis for understanding the closed P-loop conformation for therapeutic potential but also shed light on the design of selective inhibitors for treating specific types of cancer.

How protein topology controls allosteric regulations

Allostery is an important regulatory mechanism of protein functions. Among allosteric proteins, certain protein structure types are more observed. However, how allosteric regulation depends on protein topology remains elusive. In this study, we extracted protein topology graphs at the fold level and found that known allosteric proteins mainly contain multiple domains or subunits and allosteric sites reside more often between two or more domains of the same fold type. Only a small fraction of fold-fold combinations are observed in allosteric proteins, and homo-fold-fold combinations dominate. These analyses imply that the locations of allosteric sites including cryptic ones depend on protein topology. We further developed TopoAlloSite, a novel method that uses the kernel support vector machine to predict the location of allosteric sites on the overall protein topology based on the subgraph-matching kernel. TopoAlloSite successfully predicted known cryptic allosteric sites in several allosteric proteins like phosphopantothenoylcysteine synthetase, spermidine synthase, and sirtuin 6, demonstrating its power in identifying cryptic allosteric sites without performing long molecular dynamics simulations or large-scale experimental screening. Our study demonstrates that protein topology largely determines how its function can be allosterically regulated, which can be used to find new druggable targets and locate potential binding sites for rational allosteric drug design.

Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches

Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

The allosteric effect of the upper half of SENP1 contributes to its substrate selectivity for SUMO1 over SUMO2

SUMOylation regulates various cellular process and SENP1 (SUMO-specific protease 1) serves as a SUMO (small ubiquitin-related modifier) specific protease that participates in the SUMO cycle. Given its extensive influences on metabolic activities, SENP1 has gained more and more attentions in clinical treatments. However, there remains a question on why does the SENP1 prefer to process SUMO1 rather than SUMO2. Here, we performed molecular dynamics simulations of SENP1-SUMO1, SENP1-SUMO2, and apo SENP1 systems and observed distinct conformational dynamics in the upper half of the clamp and the three loops in the catalytic center of the SENP1. Principal component analysis revealed that the most prominent canonical variable represented the spatial distribution of the upper half of the clamp, while the openness of the cleft was closely related to the catalytic ability of SENP1. Further analysis of the SENP1-SUMO interactions revealed that the extensive and strong interactions between the SENP1 and SUMO1 were both in the interface of the upper half region and the catalytic center. Dynamic cross-correlation matrix analysis demonstrated that the inter-residue correlations in the SUMO1 system was much stronger, especially in the two essential regions belonging to the upper and lower half of cleft. Based on these observations, we proposed an allosteric propagation model and further testified it using the community analysis. These results revealed the propagation pathway of allosteric communication that contributed to the substrate discrimination of SENP1 upon SUMO1 and SUMO2.Communicated by Ramaswamy H. Sarma.

Structure-Based Design of the Indole-Substituted Triazolopyrimidines as New EED-H3K27me3 Inhibitors for the Treatment of Lymphoma

Interrupting the embryonic ectoderm development (EED)-H3K27me3 interaction represents a promising strategy to allosterically inhibit polycomb repressive complex 2 (PRC2) for cancer therapy. In this work, we report the structure-based design of new triazolopyrimidine-based EED inhibitors, which structurally feature the electron-rich indole ring at the C8 position. Particularly, ZJH-16 directly binds to EED (HTRF IC₅₀ = 2.72 nM, BLI K_D = 4.4 nM) and potently inhibits the growth of KARPAS422 and Pfeiffer cells. In both cells, ZJH-16 is selectively engaged with EED and reduces H3K27 trimethylation levels. ZJH-16 inhibits the gene silencing function of PRC2 in KARPAS422 cells. ZJH-16 possesses favorable pharmacokinetic (PK) profiles with an excellent oral bioavailability (F = 94.7%). More importantly, ZJH-16 shows robust tumor regression in the KARPAS422 xenograft model after oral administration with the tumor growth inhibition reaching nearly 100%. The robust antitumor efficacy and favorable PK profiles of ZJH-16 warrant further advanced preclinical development for lymphoma treatment.

Green and efficient one-pot three-component synthesis of novel drug-like furo[2,3–d]pyrimidines as potential active site inhibitors and putative allosteric hotspots modulators of both SARS-CoV-2 MPro and PLPro

In this paper, an environmentally benign, convenient, and efficient one-pot three-component reaction has been developed for the regioselective synthesis of novel 5-aroyl(or heteroaroyl)-6-(alkylamino)-1,3-dimethylfuro[2,3-d]pyrimidine-2,4(1H,3H)-diones (4a‒n) through the sequential condensation of aryl(or heteroaryl)glyoxal monohydrates (1a‒g), 1,3-dimethylbarbituric acid (2), and alkyl(viz. cyclohexyl or tert-butyl)isocyanides (3a or 3b) catalyzed by ultra-low loading ZrOCl₂•8H₂O (just 2 mol%) in water at 50 ˚C. After synthesis and characterization of the mentioned furo[2,3-d]pyrimidines (4a‒n), their multi-targeting inhibitory properties were investigated against the active site and putative allosteric hotspots of both SARS-CoV-2 main protease (M^Pro) and papain-like protease (PL^Pro) based on molecular docking studies and compare the attained results with various medicinal compounds which approximately in three past years were used, introduced, and or repurposed to fight against COVID-19. Furthermore, drug-likeness properties of the mentioned small heterocyclic frameworks (4a‒n) have been explored using in silico ADMET analyses. Interestingly, the molecular docking studies and ADMET-related data revealed that the novel series of furo[2,3-d]pyrimidines (4a‒n), especially 5-(3,4-methylendioxybenzoyl)-6-(cyclohexylamino)-1,3-dimethylfuro[2,3-d]pyrimidine-2,4(1H,3H)-dione (4g) as hit one is potential COVID-19 drug candidate, can subject to further in vitro and in vivo studies. It is worthwhile to note that the protein-ligand-type molecular docking studies on the human body temperature-dependent M^Pro protein that surprisingly contains zinc^II (Zn^II) ion between His41/Cys145 catalytic dyad in the active site, which undoubtedly can make new plans for designing novel SARS-CoV-2 M^Pro inhibitors, is performed for the first time in this paper, to the best of our knowledge.

Insights into pralsetinib resistance to the non-gatekeeper RET kinase G810C mutation through molecular dynamics simulations

OBJECTIVE

RET (rearranged during transfection) kinase, as a transmembrane receptor tyrosine kinase, is a therapeutic target for several human cancer such as non-small cell lung cancer (NSCLC) and thyroid cancer. Pralsetinib is a recently approved drug for the treatment of RET-driven NSCLC and thyroid cancers. A single point mutation G810C at the C-lobe of the RET kinase causes pralsetinib resistance to this non-gatekeeper variant. However, the detailed mechanism remains poorly understood.

METHODS

Here, multiple microsecond molecular dynamics (MD) simulations, molecular mechanics/generalized born surface area (MM/GBSA) binding free energy calculations, and community network analysis were performed to reveal the mechanism of pralsetinib resistance to the RET G810C mutant.

RESULTS

The simulations showed that the G810C mutation had a minor effect on the overall conformational dynamics of the RET kinase domain. Energetic analysis suggested that the G810C mutation reduced the binding affinity of pralsetinib to the mutant. Per-residue energy contribution and structural analyses revealed that the hydrogen bonding interactions between pralsetinib and the hinge residues Glu805 and Ala807 were disrupted in the G810C mutant, which were responsible for the decreased binding affinity of pralsetinib to the mutant.

CONCLUSIONS

The obtained results may provide understanding of the mechanism of pralsetinib resistance to the non-gatekeeper RET G810C mutant.

Mechanistic insights into the role of calcium in the allosteric regulation of the calmodulin-regulated death-associated protein kinase

Calcium (Ca²⁺) signaling plays an important role in the regulation of many cellular functions. Ca²⁺-binding protein calmodulin (CaM) serves as a primary effector of calcium function. Ca²⁺/CaM binds to the death-associated protein kinase 1 (DAPK1) to regulate intracellular signaling pathways. However, the mechanism underlying the influence of Ca²⁺ on the conformational dynamics of the DAPK1-CaM interactions is still unclear. Here, we performed large-scale molecular dynamics (MD) simulations of the DAPK1-CaM complex in the Ca²⁺-bound and-unbound states to reveal the importance of Ca²⁺. MD simulations revealed that removal of Ca²⁺ increased the anti-correlated inter-domain motions between DAPK1 and CaM, which weakened the DAPK1-CaM interactions. Binding free energy calculations validated the decreased DAPK1-CaM interactions in the Ca²⁺-unbound state. Structural analysis further revealed that Ca²⁺ removal caused the significant conformational changes at the DAPK1-CaM interface, especially the helices α1, α2, α4, α6, and α7 from the CaM and the basic loop and the phosphate-binding loop from the DAPK1. These results may be useful to understand the biological role of Ca²⁺ in physiological processes.

Mechanistic Insights into the Protection Effect of Argonaute-RNA Complex on the HCV Genome

While host miRNA usually plays an antiviral role, the relentless tides of viral evolution have carved out a mechanism to recruit host miRNA as a viral protector. By complementing miR-122 at the 5' end of the genome, the hepatitis C virus (HCV) gene can form a complex with Argonaute 2 (Ago2) protein to protect the 5' end of HCV RNA from exonucleolytic attacks. Experiments showed that the disruption of the stem-loop 1(SL1) structure and the 9th nucleotide (T9) of HCV site 1 RNA could enhance the affinity of the Ago2 protein to the HCV site 1 RNA (target RNA). However, the underlying mechanism of how the conformation and dynamics of the Ago2: miRNA: target RNA complex is affected by the SL1 and T9 remains unclear. To address this, we performed large-scale molecular dynamics simulations on the AGO2-miRNA complex binding with the WT target, T9-abasic target and SL1-disruption target, respectively. The results revealed that the T9 and SL1 structures could induce the departing motion of the PAZ, PIWI and N domains, propping up the mouth of the central groove which accommodates the target RNA, causing the instability of the target RNA and disrupting the Ago2 binding. The coordinated motion among the PAZ, PIWI and N domains were also weakened by the T9 and SL1 structures. Moreover, we proposed a new model wherein the Ago2 protein could adopt a more constraint conformation with the proximity and more correlated motions of the PAZ, N and PIWI domains to protect the target RNA from dissociation. These findings reveal the mechanism of the Ago2-miRNA complex's protective effect on the HCV genome at the atomic level, which will offer guidance for the design of drugs to confront the protection effect and engineering of Ago2 as a gene-regulation tool.

Pan-KRAS inhibitors suppress proliferation through feedback regulation in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is currently one of the most lethal cancers worldwide. Several basic studies have confirmed that Kirsten rat sarcoma virus (KRAS) is a key driver gene for the occurrence of PDAC, and KRAS mutations have also been found in most patients in clinical studies. In this study, two pan-KRAS inhibitors, BI-2852 and BAY-293, were chosen as chemical probes to investigate their antitumor potency in PDAC. Their inhibitory effects on KRAS activation were validated in vitro and their antiproliferative potency in PDAC cell lines were profiled, with half-maximal inhibitory concentration (IC50) values of approximately 1 μM, demonstrating the therapeutic potential of pan-KRAS inhibitors in the treatment of PDAC. However, feedback regulation in the KRAS pathway weakened inhibitor activity, which was observed by a 50 times difference in BAY-293 from in vitro activity. Furthermore, pan-KRAS inhibitors effectively inhibited cell proliferation in 3D organoids cultured from PDAC patient samples; however, there were some variations between individuals. These results provide a sufficient theoretical foundation for KRAS as a clinical therapeutic target and for the application of pan-KRAS inhibitors in the treatment of PDAC, with important scientific significance in translational medicine.

From Myricetin to the Discovery of Novel Natural Human ENPP1 Inhibitors: A Virtual Screening, Molecular Docking, Molecular Dynamics Simulation, and MM/GBSA Study

It was recently revealed that naturally occurring myricetin can inhibit ectonucleotidase ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), which, in turn, can treat ischemic cardiac injury. However, due to myricetin’s poor druggability, its further developments are relatively limited, which necessitates the discovery of novel ENPP1-inhibiting myricetin analogs as alternatives. In this study, the binding model of myricetin with ENPP1 was elucidated by molecular docking and molecular dynamics studies. Subsequently, virtual screening on the self-developed flavonoid natural product database (FNPD), led to the identification of two flavonoid glycosides (Cas No: 1397173-50-0 and 1169835-58-8), as potential ENPP1 inhibitors. Docking scores and MM/GBSA binding energies predicted that they might have higher inhibitory effects than myricetin. This study provides a strong foundation for the future development of ischemic cardiac injury drugs.

Computer-Aided Drug Design Boosts RAS Inhibitor Discovery

The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.

Mechanistic Understanding of the Palmitoylation of Go Protein in the Allosteric Regulation of Adhesion Receptor GPR97

Adhesion G-protein-coupled receptors (aGPCRs)—a major family of GPCRs—play critical roles in the regulation of tissue development and cancer progression. The orphan receptor GPR97, activated by glucocorticoid stress hormones, is a prototypical aGPCR. Although it has been established that the palmitoylation of the C-terminal G_o protein is essential for G_o’s efficient engagement with the active GPR97, the detailed allosteric mechanism remains to be clarified. Hence, we performed extensive large-scale molecular dynamics (MD) simulations of the GPR97−G_o complex in the presence or absence of G_o palmitoylation. The conformational landscapes analyzed by Markov state models revealed that the overall conformation of GPR97 is preferred to be fully active when interacting with palmitoylated G_o protein. Structural and energetic analyses indicated that the palmitoylation of G_o can allosterically stabilize the critical residues in the ligand-binding pocket of GPR97 and increase the affinity of the ligand for GPR97. Furthermore, the community network analysis suggests that the palmitoylation of G_o not only allosterically strengthens the internal interactions between G_αo and G_βγ, but also enhances the coupling between G_o and GPR97. Our study provides mechanistic insights into the regulation of aGPCRs via post-translational modifications of the G_o protein, and offers guidance for future drug design of aGPCRs.

Markov State Models and Molecular Dynamics Simulations Reveal the Conformational Transition of the Intrinsically Disordered Hypervariable Region of K-Ras4B to the Ordered Conformation

K-Ras4B, the most frequently mutated Ras isoform in human tumors, plays a vital part in cell growth, differentiation, and survival. Its tail, the C-terminal hypervariable region (HVR), is involved in anchoring K-Ras4B at the cellular plasma membrane and in isoform-specific protein-protein interactions and signaling. In the inactive guanosine diphosphate-bound state, the intrinsically disordered HVR interacts with the catalytic domain at the effector-binding region, rendering K-Ras4B in its autoinhibited state. Activation releases the HVR from the catalytic domain, with its ensemble favoring an ordered α-helical structure. The large-scale conformational transition of the HVR from the intrinsically disordered to the ordered conformation remains poorly understood. Here, we deploy a computational scheme that integrates a transition path-generation algorithm, extensive molecular dynamics simulation, and Markov state model analysis to investigate the conformational landscape of the HVR transition pathway. Our findings reveal a stepwise pathway for the HVR transition and uncover several key conformational substates along the transition pathway. Importantly, key interactions between the HVR and the catalytic domain are unraveled, highlighting the pathogenesis of K-Ras4B mild mutations in several congenital developmental anomaly syndromes. Together, these findings provide a deeper understanding of the HVR transition mechanism and the regulation of K-Ras4B activity at an atomic level.

Mechanistic insights into the clinical Y96D mutation with acquired resistance to AMG510 in the KRASG12C

Special oncogenic mutations in the RAS proteins lead to the aberrant activation of RAS and its downstream signaling pathways. AMG510, the first approval drug for KRAS, covalently binds to the mutated cysteine 12 of KRASG12C protein and has shown promising antitumor activity in clinical trials. Recent studies have reported that the clinically acquired Y96D mutation could severely affect the effectiveness of AMG510. However, the underlying mechanism of the drug-resistance remains unclear. To address this, we performed multiple microsecond molecular dynamics simulations on the KRASG12C−AMG510 and KRASG12C/Y96D−AMG510 complexes at the atomic level. The direct interaction between the residue 96 and AMG510 was impaired owing to the Y96D mutation. Moreover, the mutation yielded higher flexibility and more coupled motion of the switch II and α3-helix, which led to the departing motion of the switch II and α3-helix. The resulting departing motion impaired the interaction between the switch II and α3-helix and subsequently induced the opening and loosening of the AMG510 binding pocket, which further disrupted the interaction between the key residues in the pocket and AMG510 and induced an increased solvent exposure of AMG510. These findings reveal the resistance mechanism of AMG510 to KRASG12C/Y96D, which will help to offer guidance for the development of KRAS targeted drugs to overcome acquired resistance.

Understanding gilteritinib resistance to FLT3-F691L mutation through an integrated computational strategy

FMS-like tyrosine kinase 3 (FLT3) serves as an important drug target for acute myeloid leukemia (AML), and gene mutations of FLT3 have been closely associated with AML patients with an incidence rate of ~ 30%. However, the mechanism of the clinically relevant F691L gatekeeper mutation conferred resistance to the drug gilteritinib remained poorly understood. In this study, multiple microsecond molecular dynamics (MD) simulations, end-point free energy calculations, and dynamic correlated and network analyses were performed to investigate the molecular basis of gilteritinib resistance to the FLT3-F691L mutation. The simulations revealed that the resistant mutation largely induced the conformational changes of the activation loop (A-loop), the phosphate-binding loop, and the helix αC of the FLT3 protein. The binding abilities of the gilteritinib to the wild-type and the F691L mutant were different through the binding free energy prediction. The simulation results further indicated that the driving force to determine the binding affinity of gilteritinib was derived from the differences in the energy terms of electrostatic and van der Waals interactions. Moreover, the per-residue free energy decomposition suggested that the four residues (Phe803, Gly831, Leu832, and Ala833) located at the A-loop of FLT3 had a significant impact on the binding affinity of gilteritinib to the F691L mutant. This study may provide useful information for the design of novel FLT3 inhibitors specially targeting the F691L gatekeeper mutant.

Mechanistic Insights into the Long-range Allosteric Regulation of KRAS Via Neurofibromatosis Type 1 (NF1) Scaffold Upon SPRED1 Loading

Allosteric regulation is the most direct and efficient way of regulating protein function, wherein proteins transmit the perturbations at one site to another distinct functional site. Deciphering the mechanism of allosteric regulation is of vital importance for the comprehension of both physiological and pathological events in vivo as well as the rational allosteric drug design. However, it remains challenging to elucidate dominant allosteric signal transduction pathways, especially for large and multi-component protein machineries where long-range allosteric regulation exits. One of the quintessential examples having long-range allosteric regulation is the ternary complex, SPRED1-RAS-neurofibromin type 1 (NF1, a RAS GTPase-activating protein), in which SPRED1 facilitates RAS-GTP hydrolysis by interacting with NF1 at a distal, allosteric site from the RAS binding site. To address the underlying mechanism, we performed extensive Gaussian accelerated molecular dynamics simulations and Markov state model analysis of KRAS-NF1 complex in the presence and absence of SPRED1. Our findings suggested that SPRED1 loading allosterically enhanced KRAS-NF1 binding, but hindered conformational transformation of the NF1 catalytic center for RAS hydrolysis. Moreover, we unveiled the possible allosteric pathways upon SPRED1 binding through difference contact network analysis. This study not only provided an in-depth mechanistic insight into the allosteric regulation of KRAS by SPRED1, but also shed light on the investigation of long-range allosteric regulation among complex macromolecular systems.

Mechanistic Insights into the Mechanism of Inhibitor Selectivity toward the Dark Kinase STK17B against Its High Homology STK17A

As a member of the death-associated protein kinase (DAPK) family, STK17B plays an important role in the regulation of cellular apoptosis and has been considered as a promising drug target for hepatocellular carcinoma. However, the highly conserved ATP-binding site of protein kinases represents a challenge to design selective inhibitors for a specific DAPK isoform. In this study, molecular docking, multiple large-scale molecular dynamics (MD) simulations, and binding free energy calculations were performed to decipher the molecular mechanism of the binding selectivity of PKIS43 toward STK17B against its high homology STK17A. MD simulations revealed that STK17A underwent a significant conformational arrangement of the activation loop compared to STK17B. The binding free energy predictions suggested that the driving force to control the binding selectivity of PKIS43 was derived from the difference in the protein–ligand electrostatic interactions. Furthermore, the per-residue free energy decomposition unveiled that the energy contribution from Arg41 at the phosphate-binding loop of STK17B was the determinant factor responsible for the binding specificity of PKIS43. This study may provide useful information for the rational design of novel and potent selective inhibitors toward STK17B.

The Role of Conformational Dynamics and Allostery in the Control of Distinct Efficacies of Agonists to the Glucocorticoid Receptor

Glucocorticoid receptor (GR) regulates various cellular functions. Given its broad influence on metabolic activities, it has been the target of drug discovery for decades. However, how drugs induce conformational changes in GR has remained elusive. Herein, we used five GR agonists (dex, AZ938, pred, cor, and dibC) with different efficacies to investigate which aspect of the ligand induced the differences in efficacy. We performed molecular dynamics simulations on the five systems (dex-, AZ938-, pred-, cor-, and dibC-bound systems) and observed a distinct discrepancy in the conformation of the cofactor TIF2. Moreover, we discovered ligand-induced differences regarding the level of conformational changes posed by the binding of cofactor TIF2 and identified a pair of essential residues D590 and T39. We further found a positive correlation between the efficacies of ligands and the interaction of the two binding pockets’ domains, where D590 and T739 were involved, implying their significance in the participation of allosteric communication. Using community network analysis, two essential communities containing D590 and T739 were identified with their connectivity correlating to the efficacy of ligands. The potential communication pathways between these two residues were revealed. These results revealed the underlying mechanism of allosteric communication between the ligand-binding and cofactor-binding pockets and identified a pair of important residues in the allosteric communication pathway, which can serve as a guide for future drug discovery.

Insights into the Allosteric Effect of SENP1 Q597A Mutation on the Hydrolytic Reaction of SUMO1 via an Integrated Computational Study

Small ubiquitin-related modifier (SUMO)-specific protease 1 (SENP1) is a cysteine protease that catalyzes the cleavage of the C-terminus of SUMO1 for the processing of SUMO precursors and deSUMOylation of target proteins. SENP1 is considered to be a promising target for the treatment of hepatocellular carcinoma (HCC) and prostate cancer. SENP1 Gln597 is located at the unstructured loop connecting the helices α4 to α5. The Q597A mutation of SENP1 allosterically disrupts the hydrolytic reaction of SUMO1 through an unknown mechanism. Here, extensive multiple replicates of microsecond molecular dynamics (MD) simulations, coupled with principal component analysis, dynamic cross-correlation analysis, community network analysis, and binding free energy calculations, were performed to elucidate the detailed mechanism. Our MD simulations showed that the Q597A mutation induced marked dynamic conformational changes in SENP1, especially in the unstructured loop connecting the helices α4 to α5 which the mutation site occupies. Moreover, the Q597A mutation caused conformational changes to catalytic Cys603 and His533 at the active site, which might impair the catalytic activity of SENP1 in processing SUMO1. Moreover, binding free energy calculations revealed that the Q597A mutation had a minor effect on the binding affinity of SUMO1 to SENP1. Together, these results may broaden our understanding of the allosteric modulation of the SENP1−SUMO1 complex.

The N Terminus of Adhesion G Protein–Coupled Receptor GPR126/ADGRG6 as Allosteric Force Integrator

The adhesion G protein–coupled receptor (aGPCR) GPR126/ADGRG6 plays an important role in several physiological functions, such as myelination or peripheral nerve repair. This renders the receptor an attractive pharmacological target. GPR126 is a mechano-sensor that translates the binding of extracellular matrix (ECM) molecules to its N terminus into a metabotropic intracellular signal. To date, the structural requirements and the character of the forces needed for this ECM-mediated receptor activation are largely unknown. In this study, we provide this information by combining classic second-messenger detection with single-cell atomic force microscopy. We established a monoclonal antibody targeting the N terminus to stimulate GPR126 and compared it to the activation through its known ECM ligands, collagen IV and laminin 211. As each ligand uses a distinct mode of action, the N terminus can be regarded as an allosteric module that can fine-tune receptor activation in a context-specific manner.

Delineating the conformational landscape and intrinsic properties of the angiotensin II type 2 receptor using a computational study

As a key regulator for the renin-angiotensin system, a class A G protein-coupled receptor (GPCR), AngII type 2 receptor (AT₂R), plays a pivotal role in the homeostasis of the cardiovascular system. Compared with other GPCRs, AT₂R has a unique antagonist-bound conformation and its mechanism is still an enigma. Here, we applied combined dynamic and evolutional approaches to investigate the conformational space and intrinsic properties of AT₂R. With molecular dynamic simulations, Markov State Models, and statistics coupled analysis, we captured the conformational landscape of AT₂R and identified its uniquity from both dynamical and evolutional viewpoints. A cryptic pocket was also discovered in the intermediate state during conformation transitions. These findings offer a deeper understanding of the AT₂R mechanism at an atomic level and provide hints for the design of novel AT₂R modulators.

Understanding the P-Loop Conformation in the Determination of Inhibitor Selectivity Toward the Hepatocellular Carcinoma-Associated Dark Kinase STK17B

As a member of the death-associated protein kinase family of serine/threonine kinases, the STK17B has been associated with diverse diseases such as hepatocellular carcinoma. However, the conformational dynamics of the phosphate-binding loop (P-loop) in the determination of inhibitor selectivity profile to the STK17B are less understood. Here, a multi-microsecond length molecular dynamics (MD) simulation of STK17B in the three different states (ligand-free, ADP-bound, and ligand-bound states) was carried out to uncover the conformational plasticity of the P-loop. Together with the analyses of principal component analysis, cross-correlation and generalized correlation motions, secondary structural analysis, and community network analysis, the conformational dynamics of the P-loop in the different states were revealed, in which the P-loop flipped into the ADP-binding site upon the inhibitor binding and interacted with the inhibitor and the C-lobe, strengthened the communication between the N- and C-lobes. These resulting interactions contributed to inhibitor selectivity profile to the STK17B. Our results may advance our understanding of kinase inhibitor selectivity and offer possible implications for the design of highly selective inhibitors for other protein kinases.

A cryptic pocket in Ebola VP35 allosterically controls RNA binding

Protein-protein and protein-nucleic acid interactions are often considered difficult drug targets because the surfaces involved lack obvious druggable pockets. Cryptic pockets could present opportunities for targeting these interactions, but identifying and exploiting these pockets remains challenging. Here, we apply a general pipeline for identifying cryptic pockets to the interferon inhibitory domain (IID) of Ebola virus viral protein 35 (VP35). VP35 plays multiple essential roles in Ebola's replication cycle but lacks pockets that present obvious utility for drug design. Using adaptive sampling simulations and machine learning algorithms, we predict VP35 harbors a cryptic pocket that is allosterically coupled to a key dsRNA-binding interface. Thiol labeling experiments corroborate the predicted pocket and mutating the predicted allosteric network supports our model of allostery. Finally, covalent modifications that mimic drug binding allosterically disrupt dsRNA binding that is essential for immune evasion. Based on these results, we expect this pipeline will be applicable to other proteins.

Insights into Fluctuations of Structure of Proteins: Significance of Intermediary States in Regulating Biological Functions

Proteins are indispensable to cellular communication and metabolism. The structure on which cells and tissues are developed is deciphered from proteins. To perform functions, proteins fold into a three-dimensional structural design, which is specific and fundamentally determined by their characteristic sequence of amino acids. Few of them have structural versatility, allowing them to adapt their shape to the task at hand. The intermediate states appear momentarily, while protein folds from denatured (D) ⇔ native (N), which plays significant roles in cellular functions. Prolific effort needs to be taken in characterizing these intermediate species if detected during the folding process. Protein folds into its native structure through definite pathways, which involve a limited number of transitory intermediates. Intermediates may be essential in protein folding pathways and assembly in some cases, as well as misfolding and aggregation folding pathways. These intermediate states help to understand the machinery of proper folding in proteins. In this review article, we highlight the various intermediate states observed and characterized so far under in vitro conditions. Moreover, the role and significance of intermediates in regulating the biological function of cells are discussed clearly.

Autopromotion of K-Ras4B Feedback Activation Through an SOS-Mediated Long-Range Allosteric Effect

The Ras-specific guanine nucleotide exchange factors Son of Sevenless (SOS) regulates Ras activation by converting inactive GDP-bound to active GTP-bound states. The catalytic activity of Ras is further allosterically regulated by GTP−Ras bound to a distal site through a positive feedback loop. To address the mechanism underlying the long-range allosteric activation of the catalytic K-Ras4B by an additional allosteric GTP–Ras through SOS, we employed molecular dynamics simulation of the K-Ras4B^G13D•SOS^cat complex with and without an allosteric GTP-bound K-Ras4B^G13D. We found that the binding of an allosteric GTP−K-Ras4B^G13D enhanced the affinity between the catalytic K-Ras4B^G13D and SOS^cat, forming a more stable conformational state. The peeling away of the switch I from the nucleotide binding site facilitated the dissociation of GDP, thereby contributing to the increased nucleotide exchange rate. The community networks further showed stronger edge connection upon allosteric GTP−K-Ras4B^G13D binding, which represented an increased interaction between catalytic K-Ras4B^G13D and SOS^cat. Moreover, GTP−K-Ras4B^G13D binding transmitted allosteric signaling pathways though the Cdc25 domain of SOS that enhanced the allosteric regulatory from the K-Ras4B^G13D allosteric site to the catalytic site. This study may provide an in-depth mechanism for abnormal activation and allosteric regulation of K-Ras4B^G13D.

Membrane Hormone Receptors and Their Signaling Pathways as Targets for Endocrine Disruptors

The endocrine disruptors are mostly small organic molecules developed for numerous and very diverse industrial applications. They essentially act through nuclear receptors with small and hydrophobic endogenous ligands. Nevertheless, potential adverse effects through membrane hormone receptors cannot be ruled out, and have indeed been observed. The present paper reviews how orthosteric and allosteric binding sites of the different families of membrane receptors can be targets for man-made hydrophobic molecules (components of plastics, paints, flame retardants, herbicides, pesticides, etc.). We also review potential target proteins for such small hydrophobic molecules downstream of membrane receptors at the level of their intracellular signaling pathways. From the currently available information, although endocrine disruptors primarily affect nuclear receptors’ signaling, membrane receptors for hormones, cytokines, neuro-mediators, and growth factors can be affected as well and deserve attention.