Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution

The quaternary question: Determining allostery in spastin through dynamics classification learning and bioinformatics

The nanomachine from the ATPases associated with various cellular activities superfamily, called spastin, severs microtubules during cellular processes. To characterize the functionally important allostery in spastin, we employed methods from evolutionary information, to graph-based networks, to machine learning applied to atomistic molecular dynamics simulations of spastin in its monomeric and the functional hexameric forms, in the presence or absence of ligands. Feature selection, using machine learning approaches, for transitions between spastin states recognizes all the regions that have been proposed as allosteric or functional in the literature. The analysis of the composition of the Markov State Model macrostates in the spastin monomer, and the analysis of the direction of change in the top machine learning features for the transitions, indicate that the monomer favors the binding of ATP, which primes the regions involved in the formation of the inter-protomer interfaces for binding to other protomer(s). Allosteric path analysis of graph networks, built based on the cross-correlations between residues in simulations, shows that perturbations to a hub specific for the pre-hydrolysis hexamer propagate throughout the structure by passing through two obligatory regions: the ATP binding pocket, and pore loop 3, which connects the substrate binding site to the ATP binding site. Our findings support a model where the changes in the terminal protomers due to the binding of ligands play an active role in the force generation in spastin. The secondary structures in spastin, which are found to be highly degenerative within the network paths, are also critical for feature transitions of the classification models, which can guide the design of allosteric effectors to enhance or block allosteric signaling.

Probing conformational landscapes and mechanisms of allosteric communication in the functional states of the ABL kinase domain using multiscale simulations and network-based mutational profiling of allosteric residue potentials

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

Electronic and structural study of T315I mutated form in DFG-out conformation of BCR-ABL inhibitors

In this work, the four main drugs for the treatment of chronic myeloid leukemia were analyzed, being imatinib, dasatinib, nilotinib and ponatinib followed by four derivative molecules of nilotinib and ponatinib. For these derivative molecules, the fluorine atoms were replaced by hydrogen and chlorine atoms in order to shade light to the structural effects on this set of inhibitors. Electronic studies were performed at density functional theory level with the B3LYP functional and 6-311+G(d,p) basis set. The frontier molecular orbitals, gap HOMO-LUMO, and NBO were analyzed and compared to docking studies for mutant T315I tyrosine kinase protein structure code 3IK3, in the DFG-out conformation. Structural similarities were pointed out, such as the presence of groups common to all inhibitors and modifications raised up on new generations of imatinib-based inhibitors. One of them is the trifluoromethyl group present in nilotinib and later included in ponatinib, in addition to the 1-methylpiperazin-1-ium group that is present in imatinib and ponatinib. The frontier molecular orbitals of imatinib and ponatinib are contributing to the same amino acid residues, and the ineffectiveness of imatinib against the T315I mutation was discussed.Communicated by Ramaswamy H. Sarma.

Coevolution, Dynamics and Allostery Conspire in Shaping Cooperative Binding and Signal Transmission of the SARS-CoV-2 Spike Protein with Human Angiotensin-Converting Enzyme 2

Binding to the host receptor is a critical initial step for the coronavirus SARS-CoV-2 spike protein to enter into target cells and trigger virus transmission. A detailed dynamic and energetic view of the binding mechanisms underlying virus entry is not fully understood and the consensus around the molecular origins behind binding preferences of SARS-CoV-2 for binding with the angiotensin-converting enzyme 2 (ACE2) host receptor is yet to be established. In this work, we performed a comprehensive computational investigation in which sequence analysis and modeling of coevolutionary networks are combined with atomistic molecular simulations and comparative binding free energy analysis of the SARS-CoV and SARS-CoV-2 spike protein receptor binding domains with the ACE2 host receptor. Different from other computational studies, we systematically examine the molecular and energetic determinants of the binding mechanisms between SARS-CoV-2 and ACE2 proteins through the lens of coevolution, conformational dynamics, and allosteric interactions that conspire to drive binding interactions and signal transmission. Conformational dynamics analysis revealed the important differences in mobility of the binding interfaces for the SARS-CoV-2 spike protein that are not confined to several binding hotspots, but instead are broadly distributed across many interface residues. Through coevolutionary network analysis and dynamics-based alanine scanning, we established linkages between the binding energy hotspots and potential regulators and carriers of signal communication in the virus-host receptor complexes. The results of this study detailed a binding mechanism in which the energetics of the SARS-CoV-2 association with ACE2 may be determined by cumulative changes of a number of residues distributed across the entire binding interface. The central findings of this study are consistent with structural and biochemical data and highlight drug discovery challenges of inhibiting large and adaptive protein-protein interfaces responsible for virus entry and infection transmission.

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.

Spectral analysis of molecular dynamics simulations on PDZ: MD sectors

The idea of protein "sectors" posits that sparse subsets of amino acid residues form cooperative networks that are key elements of protein stability, ligand binding, and allosterism. To date, protein sectors have been calculated by the statistical coupling analysis (SCA) method of Ranganathan and co-workers via the spectral analysis of conservation-weighted evolutionary covariance matrices obtained from a multiple sequence alignments of homologous families of proteins. SCA sectors, a knowledge-based protocol, have been indentified with functional properties and allosterism for a number of systems. In this study, we investigate the utility of the sector idea for the analysis of physics-based molecular dynamics (MD) trajectories of proteins. Our test case for this procedure is PSD95- PDZ3, one of the smallest proteins for which allosterism has been observed. It has served previously as a model system for a number of prediction algorithms, and is well characterized by X-ray crystallography, NMR spectroscopy and site specific mutagenisis. All-atom MD simulations were performed for a total of 500 nanoseconds using AMBER, and MD-calculated covariance matrices for the fluctuations of residue displacements and non-bonded interaction energies were subjected to spectral analysis in a manner analogous to that of SCA. The composition of MD sectors was compared with results from SCA, site specific mutagenesis, and allosterism. The concordance indicates that MD sectors are a viable protocol for analyzing MD trajectories and provide insight into the physical origin of the phenomenon.Communicated by Ramaswamy H. Sarma.

A combined computational and experimental strategy identifies mutations conferring resistance to drugs targeting the BCR-ABL fusion protein

Drug resistance is of increasing concern, especially during the treatments of infectious diseases and cancer. To accelerate the drug discovery process in combating issues of drug resistance, here we developed a computational and experimental strategy to predict drug resistance mutations. Using BCR-ABL as a case study, we successfully recaptured the clinically observed mutations that confer resistance imatinib, nilotinib, dasatinib, bosutinib, and ponatinib. We then experimentally tested the predicted mutants in vitro. We found that although all mutants showed weakened binding strength as expected, the binding constants alone were not a good indicator of drug resistance. Instead, the half-maximal inhibitory concentration (IC50) was shown to be a good indicator of the incidence of the predicted mutations, together with change in catalytic efficacy. Our suggested strategy for predicting drug-resistance mutations includes the computational prediction and in vitro selection of mutants with increased IC50 values beyond the drug safety window.

Combining the Allosteric Inhibitor Asciminib with Ponatinib Suppresses Emergence of and Restores Efficacy against Highly Resistant BCR-ABL1 Mutants

BCR-ABL1 point mutation-mediated resistance to tyrosine kinase inhibitor (TKI) therapy in Philadelphia chromosome-positive (Ph+) leukemia is effectively managed with several approved drugs, including ponatinib for BCR-ABL1T315I-mutant disease. However, therapy options are limited for patients with leukemic clones bearing multiple BCR-ABL1 mutations. Asciminib, an allosteric inhibitor targeting the myristoyl-binding pocket of BCR-ABL1, is active against most single mutants but ineffective against all tested compound mutants. We demonstrate that combining asciminib with ATP site TKIs enhances target inhibition and suppression of resistant outgrowth in Ph+ clinical isolates and cell lines. Inclusion of asciminib restores ponatinib's effectiveness against currently untreatable compound mutants at clinically achievable concentrations. Our findings support combining asciminib with ponatinib as a treatment strategy for this molecularly defined group of patients.

Data-driven computational analysis of allosteric proteins by exploring protein dynamics, residue coevolution and residue interaction networks

BACKGROUND

Computational studies of allosteric interactions have witnessed a recent renaissance fueled by the growing interest in modeling of the complex molecular assemblies and biological networks. Allosteric interactions in protein structures allow for molecular communication in signal transduction networks.

METHODS

In this work, we performed a large scale comprehensive and multi-faceted analysis of >300 diverse allosteric proteins and complexes with allosteric modulators. By modeling and exploring coarse-grained dynamics, residue coevolution, and residue interaction networks for allosteric proteins, we have determined unifying molecular signatures shared by allosteric systems.

RESULTS

The results of this study have suggested that allosteric inhibitors and allosteric activators may differentially affect global dynamics and network organization of protein systems, leading to diverse allosteric mechanisms. By using structural and functional data on protein kinases, we present a detailed case study that that included atomic-level analysis of coevolutionary networks in kinases bound with allosteric inhibitors and activators.

CONCLUSIONS

We have found that coevolutionary networks can form direct communication pathways connecting functional regions and can recapitulate key regulatory sites and interactions responsible for allosteric signaling in the studied protein systems. The results of this computational investigation are compared with the experimental studies and reveal molecular signatures of known regulatory hotspots in protein kinases.

GENERAL SIGNIFICANCE

This study has shown that allosteric inhibitors and allosteric activators can have a different effect on residue interaction networks and can exploit distinct regulatory mechanisms, which could open up opportunities for probing allostery and new drug combinations with broad range of activities.

ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism

ProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

Atomistic Modeling of the ABL Kinase Regulation by Allosteric Modulators Using Structural Perturbation Analysis and Community-Based Network Reconstruction of Allosteric Communications

In this work, we have examined the molecular mechanisms of allosteric regulation of the ABL tyrosine kinase at the atomic level. Atomistic modeling of the ABL complexes with a panel of allosteric modulators has been performed using a combination of molecular dynamics simulations, structural residue perturbation scanning, and a novel community analysis of the residue interaction networks. Our results have indicated that allosteric inhibitors and activators may exert a differential control on allosteric signaling between the kinase binding sites and functional regions. While the inhibitor binding can strengthen the closed ABL state and induce allosteric communications directed from the allosteric pocket to the ATP binding site, the DPH activator may induce a more dynamic open form and activate allosteric couplings between the ATP and substrate binding sites. By leveraging a network-centric theoretical framework, we have introduced a novel community analysis method and global topological parameters that have unveiled the hierarchical modularity and the intercommunity bridging sites in the residue interaction network. We have found that allosteric functional hotspots responsible for the kinase regulation may serve the intermodular bridges in the global interaction network. The central conclusion from this analysis is that the regulatory switch centers play a fundamental role in the modular network organization of ABL as the unique intercommunity bridges that connect the SH2 and SH3 domains with the catalytic core into a functional kinase assembly. The hierarchy of network organization in the ABL regulatory complexes may allow for the synergistic action of dense intercommunity links required for the robust signal transfer in the catalytic core and sparse network bridges acting as the regulatory control points that orchestrate allosteric transitions between the inhibited and active kinase forms.

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

The catalytic activity of Abl1 single and compound mutations: Implications for the mechanism of drug resistance mutations in chronic myeloid leukaemia

BACKGROUND

Abl1 is a protein tyrosine kinase whose aberrant activation due to mutations is the culprit of several cancers, most notably chronic myeloid leukaemia. Several Abl1 inhibitors are used as anti-cancer drugs. Unfortunately, drug resistance limits their effectiveness. The main cause for drug resistance is mutations in the kinase domain (KD) of Abl1 that evolve in patients. The T315I mutation confers resistance against all clinically-available inhibitors except ponatinib. Resistance to ponatinib can develop by compound (double) mutations.

METHODS

Kinetic measurements of the KD of Abl1 and its mutants were carried out to examine their catalytic activity. Specifically, mutants that lead to drug resistance against ponatinib were considered. Molecular dynamics simulations and multiple sequence analysis were used for explanation of the experimental findings.

RESULTS

The catalytic efficiency of the T315I pan-resistance mutant is more than two times lower than that of the native KD. All ponatinib resistant mutations restore the catalytic efficiency of the enzyme. Two of them (G250E/T315I and Y253H/E255V) have a catalytic efficiency that is more than five times that of the native KD.

CONCLUSIONS

The measurements and analysis suggest that resistance is at least partially due to the development of a highly efficient kinase through subsequent mutations. The simulations highlight modifications in two structurally important regions of Abl1, the activation and phosphate binding loops, upon mutations.

GENERAL SIGNIFICANCE

Experimental and computational methods were used together to explain how mutations in the kinase domain of Abl1 lead to resistance against the most advanced drug currently in use to treat chronic myeloid leukaemia.

Network Analysis of RAD51 Proteins in Metazoa and the Evolutionary Relationships With Their Archaeal Homologs

The RAD51 (DNA repair protein RAD51) recombinases are essential for homologous recombination, DNA repair, and genome stability. Overexpression of RAD51 proteins has been observed in many cancer cells, such as thyroid carcinoma, breast cancer, pancreatic cancer, and others. In Metazoa, there are multiple members of RAD51 (RAD51, RAD51B, RAD51C, RAD51D, DMC1) (DNA meiotic recombinase 1), XRCC2 (X-ray repair cross-complementing 2), and XRCC3. In this study, we used a protein sequence similarity network (SSN) to analyze the evolutionary relationship within this protein family. The SSN based on the RAD51 proteins from Metazoa indicated that there are several proteins that have yet to be functionally defined. The SSN based on the distribution of the proteins supports the hypothesis that horizontal gene transfer plays an important role in the evolution of RAD51 proteins. Multiple sequence alignments with structural information revealed that the amino acid residues for ATP and Mg²⁺ are highly conserved. The seven RAD51 proteins in humans are under different selective pressure: RAD51 and DMC1 are under stringent negative selection, while other proteins are subject to relatively relaxed negative selection. Furthermore, the expression levels of the seven genes in different tissues showed that the genes in the same cluster in the phylogenetic tree showed similar expression profiles. Finally, the SSN based on the RAD51 proteins from both eukaryotes and prokaryotes suggested that the eukaryotic RAD51 recombinases share a common ancestor with the archaeal homologs, but XRCC2 may have a different origin. These findings expand the understanding of the evolution and diversity of RAD51 recombinases in Metazoa.

Protein Kinase Targets in Breast Cancer

With 1.67 million new cases and 522,000 deaths in the year 2012, breast cancer is the most common type of diagnosed malignancy and the second leading cause of cancer death in women around the world. Despite the success of screening programs and the development of adjuvant therapies, a significant percentage of breast cancer patients will suffer a metastatic disease that, to this day, remains incurable and justifies the research of new therapies to improve their life expectancy. Among the new therapies that have been developed in recent years, the emergence of targeted therapies has been a milestone in the fight against cancer. Over the past decade, many studies have shown a causal role of protein kinase dysregulations or mutations in different human diseases, including cancer. Along these lines, cancer research has demonstrated a key role of many protein kinases during human tumorigenesis and cancer progression, turning these molecules into valid candidates for new targeted therapies. The subsequent discovery and introduction in 2001 of the kinase inhibitor imatinib, as a targeted treatment for chronic myelogenous leukemia, revolutionized cancer genetic pathways research, and lead to the development of multiple small-molecule kinase inhibitors against various malignancies, including breast cancer. In this review, we analyze studies published to date about novel small-molecule kinase inhibitors and evaluate if they would be useful to develop new treatment strategies for breast cancer patients.

Ensemble-based modeling and rigidity decomposition of allosteric interaction networks and communication pathways in cyclin-dependent kinases: Differentiating kinase clients of the Hsp90-Cdc37 chaperone

The overarching goal of delineating molecular principles underlying differentiation of protein kinase clients and chaperone-based modulation of kinase activity is fundamental to understanding activity of many oncogenic kinases that require chaperoning of Hsp70 and Hsp90 systems to attain a functionally competent active form. Despite structural similarities and common activation mechanisms shared by cyclin-dependent kinase (CDK) proteins, members of this family can exhibit vastly different chaperone preferences. The molecular determinants underlying chaperone dependencies of protein kinases are not fully understood as structurally similar kinases may often elicit distinct regulatory responses to the chaperone. The regulatory divergences observed for members of CDK family are of particular interest as functional diversification among these kinases may be related to variations in chaperone dependencies and can be exploited in drug discovery of personalized therapeutic agents. In this work, we report the results of a computational investigation of several members of CDK family (CDK5, CDK6, CDK9) that represented a broad repertoire of chaperone dependencies—from nonclient CDK5, to weak client CDK6, and strong client CDK9. By using molecular simulations of multiple crystal structures we characterized conformational ensembles and collective dynamics of CDK proteins. We found that the elevated dynamics of CDK9 can trigger imbalances in cooperative collective motions and reduce stability of the active fold, thus creating a cascade of favorable conditions for chaperone intervention. The ensemble-based modeling of residue interaction networks and community analysis determined how differences in modularity of allosteric networks and topography of communication pathways can be linked with the client status of CDK proteins. This analysis unveiled depleted modularity of the allosteric network in CDK9 that alters distribution of communication pathways and leads to impaired signaling in the client kinase. According to our results, these network features may uniquely define chaperone dependencies of CDK clients. The perturbation response scanning and rigidity decomposition approaches identified regulatory hotspots that mediate differences in stability and cooperativity of allosteric interaction networks in the CDK structures. By combining these synergistic approaches, our study revealed dynamic and network signatures that can differentiate kinase clients and rationalize subtle divergences in the activation mechanisms of CDK family members. The therapeutic implications of these results are illustrated by identifying structural hotspots of pathogenic mutations that preferentially target regions of the increased flexibility to enable modulation of activation changes. Our study offers a network-based perspective on dynamic kinase mechanisms and drug design by unravelling relationships between protein kinase dynamics, allosteric communications and chaperone dependencies.

Evolutionary, computational, and biochemical studies of the salicylaldehyde dehydrogenases in the naphthalene degradation pathway

Salicylaldehyde (SAL) dehydrogenase (SALD) is responsible for the oxidation of SAL to salicylate using nicotinamide adenine dinucleotide (NAD⁺) as a cofactor in the naphthalene degradation pathway. We report the use of a protein sequence similarity network to make functional inferences about SALDs. Network and phylogenetic analyses indicated that SALDs and the homologues are present in bacteria and fungi. The key residues in SALDs were analyzed by evolutionary methods and a molecular simulation analysis. The results showed that the catalytic residue is most highly conserved, followed by the residues binding NAD⁺ and then the residues binding SAL. A molecular simulation analysis demonstrated the binding energies of the amino acids to NAD⁺ and/or SAL and showed that a conformational change is induced by binding. A SALD from Alteromonas naphthalenivorans (SALDan) that undergoes trimeric oligomerization was characterized enzymatically. The results showed that SALDan could catalyze the oxidation of a variety of aromatic aldehydes. Site-directed mutagenesis of selected residues binding NAD⁺ and/or SAL affected the enzyme’s catalytic efficiency, but did not eliminate catalysis. Finally, the relationships among the evolution, catalytic mechanism, and functions of SALD are discussed. Taken together, this study provides an expanded understanding of the evolution, functions, and catalytic mechanism of SALD.

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication

Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks and signaling pathways. We found that functional sites involved in allosteric regulation of Hsp70 may be characterized by structural stability, proximity to global hinge centers and local structural environment that is enriched by highly coevolving flexible residues. These specific characteristics may be necessary for regulation of allosteric structural transitions and could distinguish regulatory sites from nonfunctional conserved residues. The observed confluence of dynamics correlations and coevolutionary residue couplings with global networking features may determine modular organization of allosteric interactions and dictate localization of key mediating sites. Community analysis of the residue interaction networks revealed that concerted rearrangements of local interacting modules at the inter-domain interface may be responsible for global structural changes and a population shift in the DnaK chaperone. The inter-domain communities in the Hsp70 structures harbor the majority of regulatory residues involved in allosteric signaling, suggesting that these sites could be integral to the network organization and coordination of structural changes. Using a network-based formalism of allostery, we introduced a community-hopping model of allosteric communication. Atomistic reconstruction of signaling pathways in the DnaK structures captured a direction-specific mechanism and molecular details of signal transmission that are fully consistent with the mutagenesis experiments. The results of our study reconciled structural and functional experiments from a network-centric perspective by showing that global properties of the residue interaction networks and coevolutionary signatures may be linked with specificity and diversity of allosteric regulation mechanisms.

The diversity of allosteric mechanisms in the Hsp70 proteins could range from modulation of the inter-domain interactions and conformational dynamics to fine-tuning of the Hsp70 interactions with co-chaperones. The goal of this study is to present a systematic computational analysis of the dynamic and evolutionary factors underlying allosteric structural transformations of the Hsp70 proteins. We investigated the relationship between functional dynamics, residue coevolution, and network organization of residue interactions in the Hsp70 proteins. The results of this study revealed that conformational dynamics of the Hsp70 proteins may be linked with coevolutionary propensities and mutual information dependencies of the protein residues. Modularity and connectivity of allosteric interactions in the Hsp70 chaperones are coordinated by stable functional sites that feature unique coevolutionary signatures and high network centrality. The emergence of the inter-domain communities that are coordinated by functional centers and include highly coevolving residues could facilitate structural transitions through cooperative reorganization of the local interacting modules. We determined that the differences in the modularity of the residue interactions and organization of coevolutionary networks in DnaK may be associated with variations in their allosteric mechanisms. The network signatures of the DnaK structures are characteristic of a population-shift allostery that allows for coordinated structural rearrangements of local communities. A dislocation of mediating centers and insufficient coevolutionary coupling between functional regions may render a reduced cooperativity and promote a limited entropy-driven allostery in the Sse1 chaperone that occurs without structural changes. The results of this study showed that a network-centric framework and a community-hopping model of allosteric communication pathways may provide novel insights into molecular and evolutionary principles of allosteric regulation in the Hsp70 proteins.

Network-based modelling and percolation analysis of conformational dynamics and activation in the CDK2 and CDK4 proteins: dynamic and energetic polarization of the kinase lobes may determine divergence of the regulatory mechanisms

Network modeling and percolation analysis of conformational dynamics and energetics of regulatory mechanisms in cyclin-dependent kinases.

Integrative View of the Diversity and Evolution of SWEET and SemiSWEET Sugar Transporters

Sugars Will Eventually be Exported Transporter (SWEET) and SemiSWEET are recently characterized families of sugar transporters in eukaryotes and prokaryotes, respectively. SemiSWEETs contain 3 transmembrane helices (TMHs), while SWEETs contain 7. Here, we performed sequence-based comprehensive analyses for SWEETs and SemiSWEETs across the biosphere. In total, 3,249 proteins were identified and ≈60% proteins were found in green plants and Oomycota, which include a number of important plant pathogens. Protein sequence similarity networks indicate that proteins from different organisms are significantly clustered. Of note, SemiSWEETs with 3 or 4 TMHs that may fuse to SWEET were identified in plant genomes. 7-TMH SWEETs were found in bacteria, implying that SemiSWEET can be fused directly in prokaryote. 15-TMH extraSWEET and 25-TMH superSWEET were also observed in wild rice and oomycetes, respectively. The transporters can be classified into 4, 2, 2, and 2 clades in plants, Metazoa, unicellular eukaryotes, and prokaryotes, respectively. The consensus and coevolution of amino acids in SWEETs were identified by multiple sequence alignments. The functions of the highly conserved residues were analyzed by molecular dynamics analysis. The 19 most highly conserved residues in the SWEETs were further confirmed by point mutagenesis using SWEET1 from Arabidopsis thaliana. The results proved that the conserved residues located in the extrafacial gate (Y57, G58, G131, and P191), the substrate binding pocket (N73, N192, and W176), and the intrafacial gate (P43, Y83, F87, P145, M161, P162, and Q202) play important roles for substrate recognition and transport processes. Taken together, our analyses provide a foundation for understanding the diversity, classification, and evolution of SWEETs and SemiSWEETs using large-scale sequence analysis and further show that gene duplication and gene fusion are important factors driving the evolution of SWEETs.

c-Abl and Parkinson's Disease: Mechanisms and Therapeutic Potential

Although the etiology of Parkinson's disease (PD) is poorly understood, oxidative stress has long been implicated in the pathogenesis of the disease. However, multifaceted and divergent signaling cascades downstream of oxidative stress have posed challenges for researchers to identify a central component of the oxidative stress-induced pathways causing neurodegeneration in PD. Since 2010, c-Abl-a non-receptor tyrosine kinase and an indicator of oxidative stress-has shown remarkable potential as a future promising drug target in PD therapeutics. Although, the constitutively active form of c-Abl, Bcr-Abl, has a long history in chronic myeloid leukemia and acute lymphocytic leukemia, the role of c-Abl in PD and relevant neurodegenerative diseases was completely unknown. Recently, others and we have identified and validated c-Abl as an important pathogenic mediator of the disease, where activated c-Abl emerges as a common link to various PD-related inducers of oxidative stress relevant to both sporadic and familial forms of PD and α-synucleinopathies. This review discusses the role of c-Abl in PD and the latest advancement on c-Abl as a drug target and as a prospective biomarker.

Exploring Molecular Mechanisms of Paradoxical Activation in the BRAF Kinase Dimers: Atomistic Simulations of Conformational Dynamics and Modeling of Allosteric Communication Networks and Signaling Pathways

The recent studies have revealed that most BRAF inhibitors can paradoxically induce kinase activation by promoting dimerization and enzyme transactivation. Despite rapidly growing number of structural and functional studies about the BRAF dimer complexes, the molecular basis of paradoxical activation phenomenon is poorly understood and remains largely hypothetical. In this work, we have explored the relationships between inhibitor binding, protein dynamics and allosteric signaling in the BRAF dimers using a network-centric approach. Using this theoretical framework, we have combined molecular dynamics simulations with coevolutionary analysis and modeling of the residue interaction networks to determine molecular determinants of paradoxical activation. We have investigated functional effects produced by paradox inducer inhibitors PLX4720, Dabrafenib, Vemurafenib and a paradox breaker inhibitor PLX7904. Functional dynamics and binding free energy analyses of the BRAF dimer complexes have suggested that negative cooperativity effect and dimer-promoting potential of the inhibitors could be important drivers of paradoxical activation. We have introduced a protein structure network model in which coevolutionary residue dependencies and dynamic maps of residue correlations are integrated in the construction and analysis of the residue interaction networks. The results have shown that coevolutionary residues in the BRAF structures could assemble into independent structural modules and form a global interaction network that may promote dimerization. We have also found that BRAF inhibitors could modulate centrality and communication propensities of global mediating centers in the residue interaction networks. By simulating allosteric communication pathways in the BRAF structures, we have determined that paradox inducer and breaker inhibitors may activate specific signaling routes that correlate with the extent of paradoxical activation. While paradox inducer inhibitors may facilitate a rapid and efficient communication via an optimal single pathway, the paradox breaker may induce a broader ensemble of suboptimal and less efficient communication routes. The central finding of our study is that paradox breaker PLX7904 could mimic structural, dynamic and network features of the inactive BRAF-WT monomer that may be required for evading paradoxical activation. The results of this study rationalize the existing structure-functional experiments by offering a network-centric rationale of the paradoxical activation phenomenon. We argue that BRAF inhibitors that amplify dynamic features of the inactive BRAF-WT monomer and intervene with the allosteric interaction networks may serve as effective paradox breakers in cellular environment.

Inherent formulation issues of kinase inhibitors

The small molecular Kinase Inhibitor (smKI) drug class is very promising and rapidly expanding. All of these drugs are administered orally. The clear relationship between structure and function has led to drugs with a general low intrinsic solubility. The majority of the commercial pharmaceutical formulations of the smKIs are physical mixtures that are limited by the low drug solubility of a salt form. This class of drugs is therefore characterized by an impaired and variable bioavailability rendering them costly and their therapies suboptimal. New formulations are sparingly being reported in literature and patents. The presented data suggests that continued research into formulation design can help to develop more efficient and cost-effective smKI formulation. Moreover, it may also be of help in the future design of the formulations of new smKIs.

Molecular dynamics simulations and modelling of the residue interaction networks in the BRAF kinase complexes with small molecule inhibitors: probing the allosteric effects of ligand-induced kinase dimerization and paradoxical activation

The computational analysis of residue interaction networks dissects the allosteric effects of inhibitor-induced BRAF kinase dimerization and paradoxical activation.