Dynamics-based community analysis and perturbation response scanning of allosteric interaction networks in the TRAP1 chaperone structures dissect molecular linkage between conformational asymmetry and sequential ATP hydrolysis

TRAP1 S-nitrosylation as a model of population-shift mechanism to study the effects of nitric oxide on redox-sensitive oncoproteins

S-nitrosylation is a post-translational modification in which nitric oxide (NO) binds to the thiol group of cysteine, generating an S-nitrosothiol (SNO) adduct. S-nitrosylation has different physiological roles, and its alteration has also been linked to a growing list of pathologies, including cancer. SNO can affect the function and stability of different proteins, such as the mitochondrial chaperone TRAP1. Interestingly, the SNO site (C501) of TRAP1 is in the proximity of another cysteine (C527). This feature suggests that the S-nitrosylated C501 could engage in a disulfide bridge with C527 in TRAP1, resembling the well-known ability of S-nitrosylated cysteines to resolve in disulfide bridge with vicinal cysteines. We used enhanced sampling simulations and in-vitro biochemical assays to address the structural mechanisms induced by TRAP1 S-nitrosylation. We showed that the SNO site induces conformational changes in the proximal cysteine and favors conformations suitable for disulfide bridge formation. We explored 4172 known S-nitrosylated proteins using high-throughput structural analyses. Furthermore, we used a coarse-grained model for 44 protein targets to account for protein flexibility. This resulted in the identification of up to 1248 proximal cysteines, which could sense the redox state of the SNO site, opening new perspectives on the biological effects of redox switches. In addition, we devised two bioinformatic workflows ( https://github.com/ELELAB/SNO_investigation_pipelines ) to identify proximal or vicinal cysteines for a SNO site with accompanying structural annotations. Finally, we analyzed mutations in tumor suppressors or oncogenes in connection with the conformational switch induced by S-nitrosylation. We classified the variants as neutral, stabilizing, or destabilizing for the propensity to be S-nitrosylated and undergo the population-shift mechanism. The methods applied here provide a comprehensive toolkit for future high-throughput studies of new protein candidates, variant classification, and a rich data source for the research community in the NO field.

Augmented Therapeutic Potential of EC-Synthetic Retinoids in Caco-2 Cancer Cells Using an In Vitro Approach

Colorectal cancer therapies have produced promising clinical responses, but tumor cells rapidly develop resistance to these drugs. It has been previously shown that EC19 and EC23, two EC-synthetic retinoids, have single-agent preclinical anticancer activity in colorectal carcinoma. Here, isobologram analysis revealed that they have synergistic cytotoxicity with retinoic acid receptor (RAR) isoform-selective agonistic retinoids such as AC261066 (RARβ2-selective agonist) and CD437 (RARγ-selective agonist) in Caco-2 cells. This synergism was confirmed by calculating the combination index (lower than 1) and the dose reduction index (higher than 1). Flow cytometry of combinatorial IC₅₀ (the concentration causing 50% cell death) confirmed the cell cycle arrest at the SubG₀-G₁ phase with potentiated apoptotic and necrotic effects. The reported synergistic anticancer activity can be attributed to their ability to reduce the expression of ATP-binding cassette (ABC) transporters including P-glycoprotein (P-gp1), breast cancer resistance protein (BCRP) and multi-drug resistance-associated protein-1 (MRP1) and Heat Shock Protein 70 (Hsp70). This adds up to the apoptosis-promoting activity of EC19 and EC23, as shown by the increased Caspase-3/7 activities and DNA fragmentation leading to DNA double-strand breaks. This study sheds the light on the possible use of EC-synthetic retinoids in the rescue of multi-drug resistance in colorectal cancer using Caco-2 as a model and suggests new promising combinations between different synthetic retinoids. The current in vitro results pave the way for future studies on these compounds as possible cures for colorectal carcinoma.

Probing the Relation Between Community Evolution in Dynamic Residue Interaction Networks and Xylanase Thermostability

Residue-residue interactions are the basis of protein thermostability. The molecular conformations of Streptomyces lividans xylanase (xyna_strli) and Thermoascus aurantiacus xylanase (xyna_theau) at 300K, 325K and 350K were obtained by Molecular Dynamics (MD) simulations. Dynamic weighted residue interaction networks were constructed and the rigid-communities were detected using the ESPRA algorithm and the Evolving Graph+Fast-Newman algorithm. The residues in the rigid-communities are primarily located in loop2, short helixes α2^', α3^', α4^' and helixes α3 and α4. Thus, the rigid-community is close to the N-terminus of xylanase, which is usually stabilized to increase thermostability using site-directed mutagenesis. The evolution of the rigid-community with increasing temperature shows a stable synergistic interaction between loop2, α2^', α3^' and α4^' in xyna_theau. In particular, the short helixes α2^' and α3^' form a "thermo helix" to promote thermostability. In addition, tight global interactions between loop2, α2^', α3^', α3, α4^' and α4 of xyna_theau are identified, consisting mainly of hydrogen bonds, van der Waals forces and π-π stacking. These residue interactions are more resistant to high temperatures than those in xyna_strli. Robust residue interactions within these secondary structures are key factors influencing xyna_strli and xyna_theau thermostability. Analyzing the rigid-community can elucidate the cooperation of secondary structures, which cannot be discovered from sequence and 3D structure alone.

Synchronous and Asynchronous Response in Dynamically Perturbed Proteins

We present a dynamic perturbation-response model of proteins based on the Gaussian Network Model, where a residue is perturbed periodically, and the dynamic response of other residues is determined. The model shows that periodic perturbation causes a synchronous response in phase with the perturbation and an asynchronous response that is out of phase. The asynchronous component results from the viscous effects of the solvent and other dispersive factors in the system. The model is based on the solution of the Langevin equation in the presence of solvent, noise, and perturbation. We introduce several novel ideas: The concept of storage and loss compliance of the protein and their dependence on structure and frequency; the amount of work lost and the residues that contribute significantly to the lost work; new dynamic correlations that result from perturbation; causality, that is, the response of j when i is perturbed is not equal to the response of i when j is perturbed. As examples, we study two systems, namely, bovine rhodopsin and the class of nanobodies. The general results obtained are (i) synchronous and asynchronous correlations depend strongly on the frequency of perturbation, their magnitude decreases with increasing frequency, (ii) time-delayed mean-squared fluctuations of residues have only synchronous components. Asynchronicity is present only in cross correlations, that is, correlations between different residues, (iii) perturbation of loop residues leads to a large dissipation of work, (iv) correlations satisfy the hypothesis of pre-existing pathways according to which information transfer by perturbation rides on already existing equilibrium correlations in the system, (v) dynamic perturbation can introduce a selective response in the system, where the perturbation of each residue excites different sets of responding residues, and (vi) it is possible to identify nondissipative residues whose perturbation does not lead to dissipation in the protein. Despite its simplicity, the model explains several features of allosteric manipulation.

Identification of Allosteric Effects in Proteins by Elastic Network Models

Allostery is a fundamental regulatory mechanism in the majority of biological processes of molecular machines. Allostery is well-known as a dynamic-driven process, and thus, the molecular mechanism of allosteric signal transmission needs to be established. Elastic network models (ENMs) provide efficient methods for investigating the intrinsic dynamics and allosteric communication pathways in proteins. In this chapter, two ENM methods including Gaussian network model (GNM) coupled with Markovian stochastic model, as well as the anisotropic network model (ANM), were introduced to identify allosteric effects in hemoglobins. Techniques on model parameters, scripting and calculation, analysis, and visualization are shown step by step.

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.

Both intra and inter-domain interactions define the intrinsic dynamics and allosteric mechanism in DNMT1s

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Dynamics and allosteric potentials of the RFTS domain are proposed.

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Hinge sites located at the RFTS-CD interface are key regulators for inter-domain interactions.

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Network analysis reveals local allosteric networks and inter-domain communication pathways in DNMT1.

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A potential allosteric site at the TRD interface for DNMT1 is identified.

DNA methyltransferase 1 (DNMT1), a large multidomain enzyme, is believed to be involved in the passive transmission of genomic methylation patterns via methylation maintenance. Yet, the molecular mechanism of interaction networks underlying DNMT1 structures, dynamics, and its biological significance has yet to be fully characterized. In this work, we used an integrated computational strategy that combined coarse-grained and atomistic simulations with coevolution information and network modeling of the residue interactions for the systematic investigation of allosteric dynamics in DNMT1. The elastic network modeling has proposed that the high plasticity of RFTS has strengthened the correlated behaviors of DNMT1 structures through the hinge sites located at the RFTS-CD interface, which mediate the collective motions between domains. The perturbation response scanning (PRS) analysis combined with the enrichment analysis of disease mutations have further highlighted the allosteric potential of the RFTS domain. Furthermore, the long-range paths connect the intra-domain interactions through the TRD interface and catalytic interface, emphasizing some key inter-domain interactions as the bridges in the global allosteric regulation of DNMT1. The observed interplay between conserved intra-domain networks and dynamical plasticity encoded by inter-domain interactions provides insights into the intrinsic dynamics and functional evolution, as well as the design of allosteric modulators of DNMT1 based on the TRD interface.

Integrated Computational Approaches and Tools forAllosteric Drug Discovery

Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.

Role of protein-protein interactions in allosteric drug design for DNA methyltransferases

DNA methyltransferases (DNMTs) not only play key roles in epigenetic gene regulation, but also serve as emerging targets for several diseases, especially for cancers. Due to the multi-domains of DNMT structures, targeting allosteric sites of protein-protein interactions (PPIs) is becoming an attractive strategy in epigenetic drug discovery. This chapter aims to review the major contemporary approaches utilized for the drug discovery based on PPIs in different dimensions, from the enumeration of allosteric mechanism to the identification of allosteric pockets. These include the construction of protein structure networks (PSNs) based on molecular dynamics (MD) simulations, performing elastic network models (ENMs) and perturbation response scanning (PRS) calculation, the sequence-based conservation and coupling analysis, and the allosteric pockets identification. Furthermore, we complement this methodology by highlighting the role of computational approaches in promising practical applications for the computer-aided drug design, with special focus on two DNMTs, namely, DNMT1 and DNMT3A.

Dynamics, nanomechanics and signal transduction in reelin repeats

Reelin is a large glycoprotein controlling brain development and cell adhesion. It regulates the positioning of neurons, as well as neurotransmission and memory formation. Perturbations in reelin signaling are linked to psychiatric disorders. Reelin participates in signal transduction by binding to the lipoprotein receptors VLDLR and ApoER2 through its central region. This part is rich in repeating BNR-EGF-BNR modules. We used standard molecular dynamics, steered molecular dynamics, and perturbation response scanning computational methods to characterize unique dynamical properties of reelin modules involved in signaling. Each module has specific sensors and effectors arranged in a similar topology. In the modules studied, disulfide bridges play a protective role, probably making both selective binding and protease activity of reelin possible. Results of single reelin molecule stretching by atomic force microscopy provide the first data on the mechanical stability of individual reelin domains. The forces required for partial unfolding of the modules studied are below 60 pN.

Ligand Binding, Unbinding, and Allosteric Effects: Deciphering Small-Molecule Modulation of HSP90

The molecular chaperone HSP90 oversees the functional activation of a large number of client proteins. Because of its role in multiple pathways linked to cancer and neurodegeneration, drug discovery targeting HSP90 has been actively pursued. Yet, a number of inhibitors failed to meet expectations due to induced toxicity problems. In this context, allosteric perturbation has emerged as an alternative strategy for the pharmacological modulation of HSP90 functions. Specifically, novel allosteric stimulators showed the interesting capability of accelerating HSP90 closure dynamics and ATPase activities while inducing tumor cell death. Here, we gain atomistic insight into the mechanisms of allosteric ligand recognition and their consequences on the functional dynamics of HSP90, starting from the fully unbound state. We integrate advanced computational sampling methods based on FunnelMetadynamics, with the analysis of internal dynamics of the structural ensembles visited during the simulations. We observe several binding/unbinding events, and from these, we derive an accurate estimation of the absolute binding free energy. Importantly, we show that different binding poses induce different dynamics states. Our work for the first time explicitly correlates HSP90 responses to binding/unbinding of an allosteric ligand to the modulation of functionally oriented protein motions.