Arsenate reductase of Rufibacter tibetensis is a metallophosphoesterase evolved to catalyze redox reactions.

An arsenate reductase (Car1) from the Bacteroidetes species Rufibacter tibetensis 1351T was isolated from the Tibetan Plateau. The strain exhibits resistance to arsenite [As(III)] and arsenate [As(V)] and reduces As(V) to As(III). Here we shed light on the mechanism of enzymatic reduction by Car1. AlphaFold2 structure prediction, active site energy minimization, and steady-state kinetics of wild-type and mutant enzymes give insight into the catalytic mechanism. Car1 is structurally related to calcineurin-like metallophosphoesterases (MPPs). It functions as a binuclear metal hydrolase with limited phosphatase activity, particularly relying on the divalent metal Ni2+. As an As(V) reductase, it displays metal promiscuity and is coupled to the thioredoxin redox cycle, requiring the participation of two cysteine residues, Cys74 and Cys76. These findings suggest that Car1 evolved from a common ancestor of extant phosphatases by incorporating a redox function into an existing MPP catalytic site. Its proposed mechanism of arsenate reduction involves Cys74 initiating a nucleophilic attack on arsenate, leading to the formation of a covalent intermediate. Next, a nucleophilic attack of Cys76 leads to the release of As(III) and the formation of a surface-exposed Cys74-Cys76 disulfide, ready for reduction by thioredoxin.

Respiratory and C4-photosynthetic NAD-malic enzyme coexist in bundle sheath cell mitochondria and evolved via association of differentially adapted subunits.

In plant mitochondria, nicotinamide adenine dinucleotide-malic enzyme (NAD-ME) has a housekeeping function in malate respiration. In different plant lineages, NAD-ME was independently co-opted in C4 photosynthesis. In the C4 Cleome species, Gynandropsis gynandra and Cleome angustifolia, all NAD-ME genes (NAD-MEα, NAD-MEß1, and NAD-MEß2) were affected by C4 evolution and are expressed at higher levels than their orthologs in the C3 species Tarenaya hassleriana. In T. hassleriana, the NAD-ME housekeeping function is performed by two heteromers, NAD-MEα/ß1 and NAD-MEα/ß2, with similar biochemical properties. In both C4 species, this role is restricted to NAD-MEα/ß2. In the C4 species, NAD-MEα/ß1 is exclusively present in the leaves, where it accounts for most of the enzymatic activity. Gynandropsis gynandra NAD-MEα/ß1 (GgNAD-MEα/ß1) exhibits high catalytic efficiency and is differentially activated by the C4 intermediate aspartate, confirming its role as the C4-decarboxylase. During C4 evolution, NAD-MEß1 lost its catalytic activity; its contribution to the enzymatic activity results from a stabilizing effect on the associated α-subunit and the acquisition of regulatory properties. We conclude that in bundle sheath cell mitochondria of C4 species, the functions of NAD-ME as C4 photosynthetic decarboxylase and as a housekeeping enzyme coexist and are performed by isoforms that combine the same α-subunit with differentially adapted ß-subunits.

New simulation insights on the structural transition mechanism of bovine rhodopsin activation.

Inactive rhodopsin can absorb photons, which induces different structural transitions that finally activate rhodopsin. We have examined the change in spatial configurations and physicochemical factors that result during the transition mechanism from the inactive to the active rhodopsin state via intermediates. During the activation process, many existing atomic contacts are disrupted, and new ones are formed. This is related to the movement of Helix 5, which tilts away from Helix 3 in the intermediate state in lumirhodopsin and moves closer to Helix 3 again in the active state. Similar patterns of changing atomic contacts are observed between Helices 3 and 5 of the adenosine and neurotensin receptors. In addition, residues 220-238 of rhodopsin, which are disordered in the inactive state, fold in the active state before binding to the Gα, where it catalyzes GDP/GTP exchange on the Gα subunit. Finally, molecular dynamics simulations in the membrane environment revealed that the arrestin binding region adopts a more flexible extended conformation upon phosphorylation, likely promoting arrestin binding and inactivation. In summary, our results provide additional structural understanding of specific rhodopsin activation which might be relevant to other Class A G protein-coupled receptor proteins.

Propagating uncertainty about molecular evolution models and prior distributions to phylogenetic trees.

Phylogenetic trees constructed from molecular sequence data rely on largely arbitrary assumptions about the substitution model, the distribution of substitution rates across sites, the version of the molecular clock, and, in the case of Bayesian inference, the prior distribution. Those assumptions affect results reported in the form of clade probabilities and error bars on divergence times and substitution rates. Overlooking the uncertainty in the assumptions leads to overly confident conclusions in the form of inflated clade probabilities and short confidence intervals or credible intervals. This paper demonstrates how to propagate that uncertainty by combining the models considered along with all of their assumptions, including their prior distributions. The combined models incorporate much more of the uncertainty than Bayesian model averages since the latter tend to settle on a single model due to the higher-level assumption that one of the models is true. Nucleotide sequence data illustrates the proposed model combination method.

Propagating clade and model uncertainty to confidence intervals of divergence times and branch lengths.

Confidence intervals of divergence times and branch lengths do not reflect uncertainty about their clades or about the prior distributions and other model assumptions on which they are based. Uncertainty about the clade may be propagated to a confidence interval by multiplying its confidence level by the bootstrap proportion of its clade or by another probability that the clade is correct. (If the confidence level is 95% and the bootstrap proportion is 90%, then the uncertainty-adjusted confidence level is (0.95)(0.90) = 86%.) Uncertainty about the model can be propagated to the confidence interval by reporting the union of the confidence intervals from all the plausible models. Unless there is no overlap between the confidence intervals, that results in an uncertainty-adjusted interval that has as its lower and upper limits the most extreme limits of the models. The proposed methods of uncertainty quantification may be used together.

Posttranslational Modification of the NADP-Malic Enzyme Involved in C4 Photosynthesis Modulates the Enzymatic Activity during the Day.

Evolution of the C4 photosynthetic pathway involved in some cases recruitment of housekeeping proteins through gene duplication and their further neofunctionalization. NADP-malic enzyme (ME), the most widespread C4 decarboxylase, has increased its catalytic efficiency and acquired regulatory properties that allowed it to participate in the C4 pathway. Here, we show that regulation of maize (Zea mays) C4-NADP-ME activity is much more elaborate than previously thought. Using mass spectrometry, we identified phosphorylation of the Ser419 residue of C4-NADP-ME in protein extracts of maize leaves. The phosphorylation event increases in the light, with a peak at Zeitgeber time 2. Phosphorylation of ZmC4-NADP-ME drastically decreases its activity as shown by the low residual activity of the recombinant phosphomimetic mutant. Analysis of the crystal structure of C4-NADP-ME indicated that Ser419 is involved in the binding of NADP at the active site. Molecular dynamics simulations and effective binding energy computations indicate a less favorable binding of the cofactor NADP in the phosphomimetic and the phosphorylated variants. We propose that phosphorylation of ZmC4-NADP-ME at Ser419 during the first hours in the light is a cellular mechanism that fine tunes the enzymatic activity to coordinate the carbon concentration mechanism with the CO2 fixation rate, probably to avoid CO2 leakiness from bundle sheath cells.

C-terminal modulators of heat shock protein of 90 kDa (HSP90): State of development and modes of action.

Cells constantly need to adopt to changing environmental conditions, maintaining homeostasis and proteostasis. Heat shock proteins are a diverse class of molecular chaperones that assist proteins in folding to prevent stress-induced misfolding and aggregation. The heat shock protein of 90 kDa (HSP90) is the most abundant heat shock protein. While basal expression of HSP90 is essential for cell survival, in many tumors elevated HSP90 levels are found, which is often associated with bad prognosis. Therefore, HSP90 has emerged as a major target in tumor therapy. The HSP90 machinery is very complex in that it involves large conformational changes during the chaperoning cycle and a variety of co-chaperones. At the same time, this complexity offers a plethora of possibilities to interfere with HSP90 function. The best characterized class of HSP90 modulators are competitive inhibitors targeting the N-terminal ATP-binding pocket. Nineteen compounds of this class entered clinical trials. However, due to severe adverse effects, including induction of the heat shock response, no N-terminal inhibitor has been approved by the FDA so far. As alternatives, compounds commonly referred to as "C-terminal inhibitors" have been developed, either as natural product-based analogues or by rational design, which employ multiple mechanisms to modulate HSP90 function, including modulation of the interaction with co-chaperones, induction of conformational changes that influence the chaperoning cycle, or inhibition of C-terminal dimerization. In this review, we summarize the current development state of characteristic C-terminal inhibitors, with an emphasis on their (proposed) molecular modes of action and binding sites.

The tetrahydroxanthone-dimer phomoxanthone A is a strong inducer of apoptosis in cisplatin-resistant solid cancer cells.

Platinum compounds are the first-line therapy for many types of cancer. However, drug resistance has frequently been reported for and is a major limitation of platinum-based chemotherapy in the clinic. In the current study, we examined the anti-tumor activity of phomoxanthone A (PXA), a tetrahydroxanthone dimer isolated from the endophytic fungus Phomopsis longicolla, in several solid cancer cell lines and their cisplatin-resistant sub-cell lines. PXA showed strong cytotoxic effects with IC50 values in the high nanomolar or low micromolar range in MTT assays. IC50 values of PXA were lower than those of cisplatin. Remarkably, equipotent anti-cancer activity was found in cisplatin-sensitive and respective cisplatin-resistant cells. Anticancer effects of PXA were studied in further detail in ovarian cancer (A2780) and bladder cancer (J82) cell pairs. PXA led to rapid depolarization of the mitochondrial membrane potential and strong activation of caspase 3 and 7, eventually resulting in strong induction of apoptosis. These effects occurred again both in sensitive and resistant cell lines. IC50 values of PXA from MTT and mitochondrial membrane depolarization assays were in good agreement. Configurational free energy computations indicate that both the neutral and singly negatively charged PXA show membrane partitioning and can penetrate the inner mitochondrial membrane. PXA treatment did not damage the plasma membranes of cancer cells, thus excluding unspecific membrane effects. Further, PXA had neither an effect on intracellular ROS nor on reduction of ROS after hydrogen peroxide treatment. In conclusion, our studies present PXA as a natural compound with strong apoptotic anticancer effects against platinum-resistant solid cancers. This may open new treatment options in clinically resistant malignancies.

Muscle uncoupling protein 3 overexpression mimics endurance training and reduces circulating biomarkers of incomplete ß-oxidation.

Exercise substantially improves metabolic health, making the elicited mechanisms important targets for novel therapeutic strategies. Uncoupling protein 3 (UCP3) is a mitochondrial inner membrane protein highly selectively expressed in skeletal muscle. Here we report that moderate UCP3 overexpression (roughly 3-fold) in muscles of UCP3 transgenic (UCP3 Tg) mice acts as an exercise mimetic in many ways. UCP3 overexpression increased spontaneous activity (∼40%) and energy expenditure (∼5-10%) and decreased oxidative stress (∼15-20%), similar to exercise training in wild-type (WT) mice. The increase in complete fatty acid oxidation (FAO; ∼30% for WT and ∼70% for UCP3 Tg) and energy expenditure (∼8% for WT and 15% for UCP3 Tg) in response to endurance training was higher in UCP3 Tg than in WT mice, showing an additive effect of UCP3 and endurance training on these two parameters. Moreover, increases in circulating short-chain acylcarnitines in response to acute exercise in untrained WT mice were absent with training or in UCP3 Tg mice. UCP3 overexpression had the same effect as training in decreasing long-chain acylcarnitines. Outcomes coincided with a reduction in muscle carnitine acetyltransferase activity that catalyzes the formation of acylcarnitines. Overall, results are consistent with the conclusions that circulating acylcarnitines could be used as a marker of incomplete muscle FAO and that UCP3 is a potential target for the treatment of prevalent metabolic diseases in which muscle FAO is affected.

Simple estimators of false discovery rates given as few as one or two p-values without strong parametric assumptions.

Multiple comparison procedures that control a family-wise error rate or false discovery rate provide an achieved error rate as the adjusted p-value or q-value for each hypothesis tested. However, since achieved error rates are not understood as probabilities that the null hypotheses are true, empirical Bayes methods have been employed to estimate such posterior probabilities, called local false discovery rates (LFDRs) to emphasize that their priors are unknown and of the frequency type. The main approaches to LFDR estimation, relying either on fully parametric models to maximize likelihood or on the presence of enough hypotheses for nonparametric density estimation, lack the simplicity and generality of adjusted p-values. To begin filling the gap, this paper introduces simple methods of LFDR estimation with proven asymptotic conservatism without assuming the parameter distribution is in a parametric family. Simulations indicate that they remain conservative even for very small numbers of hypotheses. One of the proposed procedures enables interpreting the original FDR control rule in terms of LFDR estimation, thereby facilitating practical use of the former. The most conservative of the new procedures is applied to measured abundance levels of 20 proteins.

Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences.

Phosphorylations are the most common and extensively studied post-translational modification (PTM) of proteins in eukaryotes. They constitute a major regulatory mechanism, modulating protein function, protein-protein interactions, as well as subcellular localization. Phosphorylation sites are preferably located in intrinsically disordered regions and have been shown to trigger structural rearrangements and order-to-disorder transitions. They can therefore have a significant effect on protein backbone dynamics or conformation, but only sparse experimental data are available. To obtain a more general description of how and when phosphorylations have a significant effect on protein behavior, molecular dynamics (MD) currently provides the only suitable framework to study these effects at a large scale in atomistic detail. This study develops a systematic MD simulation framework to explore the influence of phosphorylations on the local backbone dynamics and conformational propensities of proteins. Through a series of glycine-backbone peptides, we studied the effects of amino acid residues including the three most common phosphorylations (Ser, Thr, and Tyr), on local backbone dynamics and conformational propensities. We further extended our study to investigate the interactions of all such residues between position i to positions i + 1, i + 2, i + 3, and i + 4 in such peptides. The final data set comprises structural ensembles for 3393 sequences with more than 1 µs of sampling for each ensemble. To validate the relevance of the results, the structural and conformational properties extracted from the MD simulations are compared to NMR data from the Biological Magnetic Resonance Data Bank. The systematic nature of this study enables the projection of the gained knowledge onto any phosphorylation site in the proteome and provides a general framework for the study of further PTMs. The full data set is publicly available, as a training and reference set.

Data-driven probabilistic definition of the low energy conformational states of protein residues.

Protein dynamics and related conformational changes are essential for their function but difficult to characterise and interpret. Amino acids in a protein behave according to their local energy landscape, which is determined by their local structural context and environmental conditions. The lowest energy state for a given residue can correspond to sharply defined conformations, e.g. in a stable helix, or can cover a wide range of conformations, e.g. in intrinsically disordered regions. A good definition of such low energy states is therefore important to describe the behaviour of a residue and how it changes with its environment. We propose a data-driven probabilistic definition of six low energy conformational states typically accessible for amino acid residues in proteins. This definition is based on solution NMR information of 1322 proteins through a combined analysis of structure ensembles with interpreted chemical shifts. We further introduce a conformational state variability parameter that captures, based on an ensemble of protein structures from molecular dynamics or other methods, how often a residue moves between these conformational states. The approach enables a different perspective on the local conformational behaviour of proteins that is complementary to their static interpretation from single structure models.

Empirical Bayes estimation of posterior probabilities of enrichment: a comparative study of five estimators of the local false discovery rate.

BACKGROUND: In investigating differentially expressed genes or other selected features, researchers conduct hypothesis tests to determine which biological categories, such as those of the Gene Ontology (GO), are enriched for the selected features. Multiple comparison procedures (MCPs) are commonly used to prevent excessive false positive rates. Traditional MCPs, e.g., the Bonferroni method, go to the opposite extreme: strictly controlling a family-wise error rate, resulting in excessive false negative rates. Researchers generally prefer the more balanced approach of instead controlling the false discovery rate (FDR). However, the q-values that methods of FDR control assign to biological categories tend to be too low to reliably estimate the probability that a biological category is not enriched for the preselected features. Thus, we study an application of the other estimators of that probability, which is called the local FDR (LFDR). RESULTS: We considered five LFDR estimators for detecting enriched GO terms: a binomial-based estimator (BBE), a maximum likelihood estimator (MLE), a normalized MLE (NMLE), a histogram-based estimator assuming a theoretical null hypothesis (HBE), and a histogram-based estimator assuming an empirical null hypothesis (HBE-EN). Since NMLE depends not only on the data but also on the specified value of π0, the proportion of non-enriched GO terms, it is only advantageous when either π0 is already known with sufficient accuracy or there are data for only 1 GO term. By contrast, the other estimators work without specifying π0 but require data for at least 2 GO terms. Our simulation studies yielded the following summaries of the relative performance of each of those four estimators. HBE and HBE-EN produced larger biases for 2, 4, 8, 32, and 100 GO terms than BBE and MLE. BBE has the lowest bias if π0 is 1 and if the number of GO terms is between 2 and 32. The bias of MLE is no worse than that of BBE for 100 GO terms even when the ideal number of components in its underlying mixture model is unknown, but has high bias when the number of GO terms is small compared to the number of estimated parameters. For unknown values of π0, BBE has the lowest bias for a small number of GO terms (2-32 GO terms), and MLE has the lowest bias for a medium number of GO terms (100 GO terms). CONCLUSIONS: For enrichment detection, we recommend estimating the LFDR by MLE given at least a medium number of GO terms, by BBE given a small number of GO terms, and by NMLE given either only 1 GO term or precise knowledge of π0.

Empirical Bayes interval estimates that are conditionally equal to unadjusted confidence intervals or to default prior credibility intervals.

Problems involving thousands of null hypotheses have been addressed by estimating the local false discovery rate (LFDR). A previous LFDR approach to reporting point and interval estimates of an effect-size parameter uses an estimate of the prior distribution of the parameter conditional on the alternative hypothesis. That estimated prior is often unreliable, and yet strongly influences the posterior intervals and point estimates, causing the posterior intervals to differ from fixed-parameter confidence intervals, even for arbitrarily small estimates of the LFDR. That influence of the estimated prior manifests the failure of the conditional posterior intervals, given the truth of the alternative hypothesis, to match the confidence intervals. Those problems are overcome by changing the posterior distribution conditional on the alternative hypothesis from a Bayesian posterior to a confidence posterior. Unlike the Bayesian posterior, the confidence posterior equates the posterior probability that the parameter lies in a fixed interval with the coverage rate of the coinciding confidence interval. The resulting confidence-Bayes hybrid posterior supplies interval and point estimates that shrink toward the null hypothesis value. The confidence intervals tend to be much shorter than their fixed-parameter counterparts, as illustrated with gene expression data. Simulations nonetheless confirm that the shrunken confidence intervals cover the parameter more frequently than stated. Generally applicable sufficient conditions for correct coverage are given. In addition to having those frequentist properties, the hybrid posterior can also be motivated from an objective Bayesian perspective by requiring coherence with some default prior conditional on the alternative hypothesis. That requirement generates a new class of approximate posteriors that supplement Bayes factors modified for improper priors and that dampen the influence of proper priors on the credibility intervals. While that class of posteriors intersects the class of confidence-Bayes posteriors, neither class is a subset of the other. In short, two first principles generate both classes of posteriors: a coherence principle and a relevance principle. The coherence principle requires that all effect size estimates comply with the same probability distribution. The relevance principle means effect size estimates given the truth of an alternative hypothesis cannot depend on whether that truth was known prior to observing the data or whether it was learned from the data.

Estimators of the local false discovery rate designed for small numbers of tests.

Histogram-based empirical Bayes methods developed for analyzing data for large numbers of genes, SNPs, or other biological features tend to have large biases when applied to data with a smaller number of features such as genes with expression measured conventionally, proteins, and metabolites. To analyze such small-scale and medium-scale data in an empirical Bayes framework, we introduce corrections of maximum likelihood estimators (MLEs) of the local false discovery rate (LFDR). In this context, the MLE estimates the LFDR, which is a posterior probability of null hypothesis truth, by estimating the prior distribution. The corrections lie in excluding each feature when estimating one or more parameters on which the prior depends. In addition, we propose the expected LFDR (ELFDR) in order to propagate the uncertainty involved in estimating the prior. We also introduce an optimally weighted combination of the best of the corrected MLEs with a previous estimator that, being based on a binomial distribution, does not require a parametric model of the data distribution across features. An application of the new estimators and previous estimators to protein abundance data illustrates the extent to which different estimators lead to different conclusions about which proteins are affected by cancer. A simulation study was conducted to approximate the bias of the new estimators relative to previous LFDR estimators. Data were simulated for two different numbers of features (N), two different noncentrality parameter values or detectability levels (dalt), and several proportions of unaffected features (p0). One of these previous estimators is a histogram-based estimator (HBE) designed for a large number of features. The simulations show that some of the corrected MLEs and the ELFDR that corrects the HBE reduce the negative bias relative to the MLE and the HBE, respectively. For every method, we defined the worst-case performance as the maximum of the absolute value of the bias over the two different dalt and over various p0. The best worst-case methods represent the safest methods to be used under given conditions. This analysis indicates that the binomial-based method has the lowest worst-case absolute bias for high p0 and for N = 3, 12. However, the corrected MLE that is based on the minimum description length (MDL) principle is the best worst-case method when the value of p0 is more uncertain since it has one of the lowest worst-case biases over all possible values of p0 and for N = 3, 12. Therefore, the safest estimator considered is the binomial-based method when a high proportion of unaffected features can be assumed and the MDL-based method otherwise. A second simulation study was conducted with additional values of N. We found that HBE requires N to be at least 6-12 features to perform as well as the estimators proposed here, with the precise minimum N depending on p0 and dalt.

Co-targeting HSP90 alpha and CDK7 overcomes resistance against HSP90 inhibitors in BCR-ABL1+ leukemia cells.

HSP90 has emerged as an appealing anti-cancer target. However, HSP90 inhibitors (HSP90i) are characterized by limited clinical utility, primarily due to the resistance acquisition via heat shock response (HSR) induction. Understanding the roles of abundantly expressed cytosolic HSP90 isoforms (α and ß) in sustaining malignant cells' growth and the mechanisms of resistance to HSP90i is crucial for exploiting their clinical potential. Utilizing multi-omics approaches, we identified that ablation of the HSP90ß isoform induces the overexpression of HSP90α and extracellular-secreted HSP90α (eHSP90α). Notably, we found that the absence of HSP90α causes downregulation of PTPRC (or CD45) expression and restricts in vivo growth of BCR-ABL1+ leukemia cells. Subsequently, chronic long-term exposure to the clinically advanced HSP90i PU-H71 (Zelavespib) led to copy number gain and mutation (p.S164F) of the HSP90AA1 gene, and HSP90α overexpression. In contrast, acquired resistance toward other tested HSP90i (Tanespimycin and Coumermycin A1) was attained by MDR1 efflux pump overexpression. Remarkably, combined CDK7 and HSP90 inhibition display synergistic activity against therapy-resistant BCR-ABL1+ patient leukemia cells via blocking pro-survival HSR and HSP90α overexpression, providing a novel strategy to avoid the emergence of resistance against treatment with HSP90i alone.

Biophysical and pharmacokinetic characterization of a small-molecule inhibitor of RUNX1/ETO tetramerization with anti-leukemic effects.

Acute myeloid leukemia (AML) is a malignant disease of immature myeloid cells and the most prevalent acute leukemia among adults. The oncogenic homo-tetrameric fusion protein RUNX1/ETO results from the chromosomal translocation t(8;21) and is found in AML patients. The nervy homology region 2 (NHR2) domain of ETO mediates tetramerization; this oligomerization is essential for oncogenic activity. Previously, we identified the first-in-class small-molecule inhibitor of NHR2 tetramer formation, 7.44, which was shown to specifically interfere with NHR2, restore gene expression down-regulated by RUNX1/ETO, inhibit the proliferation of RUNX1/ETO-depending SKNO-1 cells, and reduce the RUNX1/ETO-related tumor growth in a mouse model. However, no biophysical and structural characterization of 7.44 binding to the NHR2 domain has been reported. Likewise, the compound has not been characterized as to physicochemical, pharmacokinetic, and toxicological properties. Here, we characterize the interaction between the NHR2 domain of RUNX1/ETO and 7.44 by biophysical assays and show that 7.44 interferes with NHR2 tetramer stability and leads to an increase in the dimer population of NHR2. The affinity of 7.44 with respect to binding to NHR2 is Klig = 3.75 ± 1.22 µM. By NMR spectroscopy combined with molecular dynamics simulations, we show that 7.44 binds with both heteroaromatic moieties to NHR2 and interacts with or leads to conformational changes in the N-termini of the NHR2 tetramer. Finally, we demonstrate that 7.44 has favorable physicochemical, pharmacokinetic, and toxicological properties. Together with biochemical, cellular, and in vivo assessments, the results reveal 7.44 as a lead for further optimization towards targeted therapy of t(8;21) AML.

Challenges in describing the conformation and dynamics of proteins with ambiguous behavior.

Traditionally, our understanding of how proteins operate and how evolution shapes them is based on two main data sources: the overall protein fold and the protein amino acid sequence. However, a significant part of the proteome shows highly dynamic and/or structurally ambiguous behavior, which cannot be correctly represented by the traditional fixed set of static coordinates. Representing such protein behaviors remains challenging and necessarily involves a complex interpretation of conformational states, including probabilistic descriptions. Relating protein dynamics and multiple conformations to their function as well as their physiological context (e.g., post-translational modifications and subcellular localization), therefore, remains elusive for much of the proteome, with studies to investigate the effect of protein dynamics relying heavily on computational models. We here investigate the possibility of delineating three classes of protein conformational behavior: order, disorder, and ambiguity. These definitions are explored based on three different datasets, using interpretable machine learning from a set of features, from AlphaFold2 to sequence-based predictions, to understand the overlap and differences between these datasets. This forms the basis for a discussion on the current limitations in describing the behavior of dynamic and ambiguous proteins.

Development of a First-in-Class Small-Molecule Inhibitor of the C-Terminal Hsp90 Dimerization.

Heat shock proteins 90 (Hsp90) are promising therapeutic targets due to their involvement in stabilizing several aberrantly expressed oncoproteins. In cancerous cells, Hsp90 expression is elevated, thereby exerting antiapoptotic effects, which is essential for the malignant transformation and tumor progression. Most of the Hsp90 inhibitors (Hsp90i) under investigation target the ATP binding site in the N-terminal domain of Hsp90. However, adverse effects, including induction of the prosurvival resistance mechanism (heat shock response or HSR) and associated dose-limiting toxicity, have so far precluded their clinical approval. In contrast, modulators that interfere with the C-terminal domain (CTD) of Hsp90 do not inflict HSR. Since the CTD dimerization of Hsp90 is essential for its chaperone activity, interfering with the dimerization process by small-molecule protein-protein interaction inhibitors is a promising strategy for anticancer drug research. We have developed a first-in-class small-molecule inhibitor (5b) targeting the Hsp90 CTD dimerization interface, based on a tripyrimidonamide scaffold through structure-based molecular design, chemical synthesis, binding mode model prediction, assessment of the biochemical affinity, and efficacy against therapy-resistant leukemia cells. 5b reduces xenotransplantation of leukemia cells in zebrafish models and induces apoptosis in BCR-ABL1+ (T315I) tyrosine kinase inhibitor-resistant leukemia cells, without inducing HSR.