Conformational states dynamically populated by a kinase determine its function

Activation dynamics of ubiquitin-specific protease 7

Ubiquitin-specific protease 7 (USP7) is a deubiquitinating enzyme that plays a crucial role in cellular processes, including the maintenance of genome stability and regulation of antiviral and immune responses. Its dysfunction is linked to various cancers and neurodevelopmental disorders such as Hao-Fountain syndrome. Unlike other USP-family enzymes, the triad of catalytic residues in USP7 adopts an inactive conformation and undergoes rearrangement into the active state upon substrate binding. Despite its potential importance for regulating the enzyme's activity, the dynamics of USP7 have not been explored. In this study, we combine advanced CPMG NMR relaxation dispersion measurements with the analysis of enzyme kinetics to investigate the conformational dynamics of USP7 in solution and its role in enzyme activation. Our results suggest that apo-USP7 exists in a dynamic equilibrium, transiently switching between inactive and low-populated active conformations, indicating that enzyme activation can occur spontaneously, even in the absence of a substrate. Furthermore, we show that the Hao-Fountain syndrome-associated variant G392D enhances the conformational dynamics of the enzyme, leading to a significant increase in its catalytic activity. This study captures the sparsely populated, "invisible" active conformation of USP7 and demonstrates how changes in enzyme dynamics can contribute to activity, offering broader insights into enzyme function and disease mechanisms.

Conformational Plasticity in dsRNA-Binding Domains Drives Functional Divergence in RNA Recognition

The functional specificity of proteins is often attributed to their sequence and structural homology while frequently neglecting the underlying conformational dynamics occurring at different time scales that can profoundly impact biological consequences. Using 15N-CEST NMR and RDC-corrected metainference molecular dynamics simulations, here, we reveal differential substrate recognition mechanisms in two dsRNA-binding domain (dsRBD) paralogs, DRB2D1 and DRB3D1. Despite their nearly identical solution structures and conserved dsRNA interaction interfaces, DRB3D1 demonstrates structural plasticity that enables it to recognize conformationally flexible dsRNA, a feature notably absent in the more rigid DRB2D1. We present the pivotal role of intrinsic structural dynamics in driving functional divergence and provide insights into the mechanisms that govern specificity in dsRBD:dsRNA interactions. Importantly, our combined experimental and computational approach captures a cluster of intermediate conformations, complementing conventional methods to resolve the dominant ground state and sparsely populated excited states.

Temperature vaulting: A method for screening of slow- and tight-binding inhibitors that selectively target kinases in their non-native state

A polypeptide folds into its protein tertiary structure in the native state through a folding intermediate in the non-native state. The transition between these states is thermodynamically driven. A folding intermediate of dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) autophosphorylates intramolecularly, whereas DYRK1A in the native state no longer catalyzes this reaction. The alteration in substrate specificity suggests a conformational transition of DYRK1A during its folding process. Consistent with this hypothesis, we identified FINDY (1), which inhibits the intramolecular autophosphorylation but not the intermolecular phosphorylation, suggesting that DYRK1A in the non-native state possesses an alternative inhibitor-binding site. Meanwhile, it remains an issue that the methods for approaching the alternative binding site require an intricate assay tailored to the individual target. Here we show a method, designated as "temperature vaulting," for inhibitor screening that targets the non-native state. Transient heating of recombinant DYRK1A protein drove the reversible transition between the native state and the non-native state targeted by FINDY (1). At physiological temperature, FINDY (1) slowly bound to the DYRK1A protein. These results indicate that transient heating accelerates the slow-binding process by assisting the protein to overcome the high-energy barrier leading to the target non-native state. The energy barrier also slowed down the dissociation, resulting in tight binding between DYRK1A and FINDY (1). Structure-activity relationship revealed that both the methoxy group and the alkyne moiety underlie the selectivity of FINDY (1) toward DYRK1A in the non-native state. Furthermore, this study suggests that the dissociation rate underlies the inhibition selectivity of FINDY (1) between DYRK1A and its family kinase DYRK1B. This method could leverage conventional assays to identify slow- and tight-binding inhibitors.

Structure and Dynamics of the ABL1 Tyrosine Kinase and Its Important Role in Chronic Myeloid Leukemia

Abelson (ABL1) tyrosine kinase is an essential component of non-receptor tyrosine kinases and is associated with numerous cellular processes, including differentiation and proliferation. The structural features of ABL1 include a distinct N-terminal cap region, a C-terminal tail, a bilobed kinase, SH2, and SH3 domains. These domains enable its engagement in several signaling cascades and dynamic control. The pathophysiology of chronic myeloid leukemia (CML) is mainly driven by the BCR-ABL1 oncoprotein, arising from dysregulation of ABL1 kinase, namely through its fusion to the breakpoint cluster region (BCR) gene. ABL1 is a crucial target in the treatment of CML as the BCR-ABL1 fusion causes uncontrolled cellular proliferation and resistance to apoptosis. Tyrosine kinase inhibitors (TKIs) targeting the ABL1 tyrosine kinase are playing a critical role in the treatment of CML through the inhibition of persistently activated signaling pathways mediated by the BCR-ABL1 fusion protein. The article examines the structural characteristics of ABL1, how they relate to CML, and the interactions between ABL1 and the current FDA-approved TKIs, emphasizing the kinase's critical function in carcinogenesis and its possible target status for tyrosine kinase inhibitors.

Advances in uncovering the mechanisms of macromolecular conformational entropy

During protein folding, proteins transition from a disordered polymer into a globular structure, markedly decreasing their conformational degrees of freedom, leading to a substantial reduction in entropy. Nonetheless, folded proteins retain substantial entropy as they fluctuate between the conformations that make up their native state. This residual entropy contributes to crucial functions like binding and catalysis, supported by growing evidence primarily from NMR and simulation studies. Here, we propose three major ways that macromolecules use conformational entropy to perform their functions; first, prepaying entropic cost through ordering of the ground state; second, spatially redistributing entropy, in which a decrease in entropy in one area is reciprocated by an increase in entropy elsewhere; third, populating catalytically competent ensembles, in which conformational entropy within the enzymatic scaffold aids in lowering transition state barriers. We also provide our perspective on how solving the current challenge of structurally defining the ensembles encoding conformational entropy will lead to new possibilities for controlling binding, catalysis and allostery.

Path sampling challenges in large biomolecular systems: RETIS and REPPTIS for ABL-imatinib kinetics

Predicting the kinetics of drug-protein interactions is crucial for understanding drug efficacy, particularly in personalized medicine, where protein mutations can significantly alter drug residence times. This study applies replica exchange transition interface sampling and its partial path variant to investigate the dissociation kinetics of imatinib from Abelson nonreceptor tyrosine kinase (ABL) and mutants relevant to chronic myeloid leukemia therapy. These path sampling methods offer a bias-free alternative to conventional approaches requiring qualitative predefined reaction coordinates. Nevertheless, the complex free energy landscape of ABL-imatinib dissociation presents significant challenges. Multiple metastable states and orthogonal barriers lead to parallel unbinding pathways, complicating convergence in transition interface sampling-based methods. Despite employing computational efficiency strategies such as asynchronous replica exchange, full convergence remained elusive. This work provides a critical assessment of path sampling in high-dimensional biological systems, discussing the need for enhanced initialization strategies, advanced Monte Carlo path generation moves, and machine learning-derived reaction coordinates to improve kinetic predictions of drug dissociation with minimal prior knowledge.

Dynamic networks connect the USP14 active site region with the proteasome interaction surface

Ubiquitin-specific protease 14 (USP14) is a member of the USP family responsible for the catalytic removal of ubiquitin (Ub) from proteins directed to the proteasome, implicated in the pathogenesis of neurodegeneration and cancer. Crystallography and cryo-EM analysis have identified loop regions crucial for the deubiquitinase activity of USP14, specifically those involved in Ub and proteasome binding. However, the structural changes in USP14 upon ligand binding to these regions are minimal, indicating significant yet uncharacterized dynamic contributions to its function. In this study, through structural and dynamical NMR experiments and functional evaluation, we demonstrate that small mutations designed to impact Ub binding and catalytic activity without disturbing the USP structure display both local and long-range effects. The affected residues connect the catalytic site and the Ub binding region with the proteasome interaction surface through a network of loops, which show varied dynamics on the ps-ms time scale. Collectively, our findings experimentally reveal different aspects of dynamic connections within USP14, suggesting the presence of allosteric networks that link enzyme activity with regulatory function. The identification of coupled clusters of possible allostery participants in the free USP domain provides new insights into the dynamic regulation of USP14, with potential implications for understanding its role in cellular processes.

One-pot dual protein labeling for simultaneous mechanical and fluorescent readouts in optical tweezers

Optical tweezers are widely used in the study of biological macromolecules but are limited by their one-directional probing capability, potentially missing critical conformational changes. Combining fluorescence microscopy with optical tweezers, employing Förster resonance energy transfer (FRET) pairs, addresses this issue. When integrating fluorescence microscopy with optical tweezers, orthogonal protein conjugation methods are needed to enable simultaneous, site-specific attachment of fluorophores and DNA handles, commonly used to apply force to molecules of interest. In this study, we utilized commercially available reagents for dual site-specific labeling of the homodimeric heat shock protein 90 (Hsp90) using thiol-maleimide and inverse electron demand Diels-Alder cycloaddition (IEDDAC) bioorthogonal reactions. In a one-pot approach, Hsp90 modified with a cysteine mutation and the non-canonical amino acid cyclopropene-L-lysine (CpK) was labeled with the FRET pair maleimide-Atto 550 and maleimide-Atto 647N, alongside single-stranded methyltetrazine-modified DNA oligonucleotide. Optical tweezers experiments with this labeled Hsp90 construct revealed structural transitions consistent with previous studies, validating the approach. Fluorescence measurements confirmed the proximity of FRET pairs in the N-terminally closed state of Hsp90 in this experimental setup. This integrative method provides a powerful tool for probing complex protein conformational dynamics beyond the limitations of traditional optical tweezers.

Prospective Evaluation of Structure-Based Simulations Reveal Their Ability to Predict the Impact of Kinase Mutations on Inhibitor Binding

Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as ΔΔG). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We show that physics-based simulations are best suited to estimate ΔΔG of mutations that are distal to the kinase active site. To probe modes of failure, we retrospectively investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.

Leveraging Sequence Purification for Accurate Prediction of Multiple Conformational States with AlphaFold2

AlphaFold2 (AF2) has transformed protein structure prediction by harnessing co-evolutionary constraints embedded in multiple sequence alignments (MSAs). MSAs not only encode static structural information, but also hold critical details about protein dynamics, which underpin biological functions. However, these subtle coevolutionary signatures, which dictate conformational state preferences, are often obscured by noise within MSA data and thus remain challenging to decipher. Here, we introduce AF-ClaSeq, a systematic framework that isolates these co-evolutionary signals through sequence purification and iterative enrichment. By extracting sequence subsets that preferentially encode distinct structural states, AF-ClaSeq enables high-confidence predictions of alternative conformations. Our findings reveal that the successful sampling of alternative states depends not on MSA depth but on sequence purity. Intriguingly, purified sequences encoding specific structural states are distributed across phylogenetic clades and superfamilies, rather than confined to specific lineages. Expanding upon AF2's transformative capabilities, AF-ClaSeq provides a powerful approach for uncovering hidden structural plasticity, advancing allosteric protein and drug design, and facilitating dynamics-based protein function annotation.

Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding

Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as ΔΔG). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We show that physics-based simulations are best suited to estimate ΔΔG of mutations that are distal to the kinase active site. To probe modes of failure, we retrospectively investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.

PIM-1L Kinase Binds to and Inactivates SRPK1: A Biochemical and Molecular Dynamics Study

SR/RS dipeptide repeats vary in both length and position, and are phosphorylated by SR protein kinases (SRPKs). PIM-1L, the long isoform of PIM-1 kinase, the splicing of which has been implicated in acute myeloid leukemia, contains a domain that consists largely of repeating SR/RS and SH/HS dipeptides (SR/SH-rich). In order to extend our knowledge on the specificity and cellular functions of SRPK1, here we investigate whether PIM-1L could act as substrate of SRPK1 by a combination of biochemical and computational approaches. Our biochemical data showed that the SR/SH-rich domain of PIM-1L was able to associate with SRPK1, yet it could not act as a substrate but, instead, inactivated the kinase. In line with our biochemical data, molecular modeling followed by a microsecond-scale all-atom molecular dynamics (MD) simulation suggests that the SR/SH-rich domain acts as a pseudo-docking peptide that binds to the same acidic docking-groove used in other SRPK1 interactions and induces inactive SRPK1 conformations. Comparative community network analysis of the MD trajectories, unraveled the dynamic architecture of apo SRPK1 and notable alterations of allosteric communications upon PIM-1L peptide binding. This analysis also allowed us to identify key SRPK1 residues, including unique ones, with a pivotal role in mediating allosteric signal propagation within the kinase core. Interestingly, most of the identified amino acids correspond to cancer-associated amino acid changes, validating our results. In total, this work provides insights not only on the details of SRPK1 inhibition by the PIM-1L SR/SH-domain, but also contributes to an in-depth understanding of SRPK1 regulation.

Allostery in Disease: Anticancer Drugs, Pockets, and the Tumor Heterogeneity Challenge

Charting future innovations is challenging. Yet, allosteric and orthosteric anticancer drugs are undergoing a revolution and taxing unresolved dilemmas await. Among the imaginative innovations, here we discuss cereblon and thalidomide derivatives as a means of recruiting neosubstrates and their degradation, allosteric heterogeneous bifunctional drugs like PROTACs, drugging phosphatases, inducers of targeted posttranslational protein modifications, antibody-drug conjugates, exploiting membrane interactions to increase local concentration, stabilizing the folded state, and more. These couple with harnessing allosteric cryptic pockets whose discovery offers more options to modulate the affinity of orthosteric, active site inhibitors. Added to these are strategies to counter drug resistance through drug combinations co-targeting pathways to bypass signaling blockades. Here, we discuss on the molecular and cellular levels, such inspiring advances, provide examples of their applications, their mechanisms and rational. We start with an overview on difficult to target proteins and their properties-rarely, if ever-conceptualized before, discuss emerging innovative drugs, and proceed to the increasingly popular allosteric cryptic pockets-their advantages-and critically, issues to be aware of. We follow with drug resistance and in-depth discussion of tumor heterogeneity. Heterogeneity is a hallmark of highly aggressive cancers, the core of drug resistance unresolved challenge. We discuss potential ways to target heterogeneity by predicting it. The increase in experimental and clinical data, computed (cell-type specific) interactomes, capturing transient cryptic pockets, learned drug resistance, workings of regulatory mechanisms, heterogeneity, and resistance-based cell signaling drug combinations, assisted by AI-driven reasoning and recognition, couple with creative allosteric drug discovery, charting future innovations.

Molecular principles underlying aggressive cancers

Aggressive tumors pose ultra-challenges to drug resistance. Anti-cancer treatments are often unsuccessful, and single-cell technologies to rein drug resistance mechanisms are still fruitless. The National Cancer Institute defines aggressive cancers at the tissue level, describing them as those that spread rapidly, despite severe treatment. At the molecular, foundational level, the quantitative biophysics discipline defines aggressive cancers as harboring a large number of (overexpressed, or mutated) crucial signaling proteins in major proliferation pathways populating their active conformations, primed for their signal transduction roles. This comprehensive review explores highly aggressive cancers on the foundational and cell signaling levels, focusing on the differences between highly aggressive cancers and the more treatable ones. It showcases aggressive tumors as harboring massive, cancer-promoting, catalysis-primed oncogenic proteins, especially through certain overexpression scenarios, as predisposed aggressive tumor candidates. Our examples narrate strong activation of ERK1/2, and other oncogenic proteins, through malfunctioning chromatin and crosslinked signaling, and how they activate multiple proliferation pathways. They show the increased cancer heterogeneity, plasticity, and drug resistance. Our review formulates the principles underlying cancer aggressiveness on the molecular level, discusses scenarios, and describes drug regimen (single drugs and drug combinations) for PDAC, NSCLC, CRC, HCC, breast and prostate cancers, glioblastoma, neuroblastoma, and leukemia as examples. All show overexpression scenarios of master transcription factors, transcription factors with gene fusions, copy number alterations, dysregulation of the epigenetic codes and epithelial-to-mesenchymal transitions in aggressive tumors, as well as high mutation loads of vital upstream signaling regulators, such as EGFR, c-MET, and K-Ras, befitting these principles.

Lifetime of ground conformational state determines the activity of structured RNA

Biomolecules continually sample alternative conformations. Consequently, even the most energetically favored ground conformational state has a finite lifetime. Here, we show that, in addition to the three-dimensional (3D) structure, the lifetime of a ground conformational state determines its biological activity. Using hydrogen-deuterium exchange nuclear magnetic resonance spectroscopy, we found that Zika virus exoribonuclease-resistant RNA (xrRNA) encodes a ground conformational state with a lifetime that is ~105-107 longer than that of canonical base pairs. Mutations that shorten the apparent lifetime of the ground state without affecting its 3D structure decreased exoribonuclease resistance in vitro and impaired virus replication in cells. Additionally, we observed this exceptionally long-lived ground state in xrRNAs from diverse infectious mosquito-borne flaviviruses. These results demonstrate the biological importance of the lifetime of a preorganized ground state and further suggest that elucidating the lifetimes of dominant 3D structures of biomolecules may be crucial for understanding their behaviors and functions.

Enhanced sampling of protein conformational changes via true reaction coordinates from energy relaxation

The bottleneck in enhanced sampling lies in finding collective variables that effectively accelerate protein conformational changes; true reaction coordinates that accurately predict the committor are the well-recognized optimal choice. However, identifying them requires unbiased natural reactive trajectories, which, paradoxically, require effective enhanced sampling. Using the generalized work functional method, we uncover that true reaction coordinates control both conformational changes and energy relaxation, enabling us to compute them from energy relaxation simulations. Biasing true reaction coordinates accelerates conformational changes and ligand dissociation in PDZ2 domain and HIV-1 protease by 105 to 1015-fold. The resulting trajectories follow natural transition pathways, enabling efficient generation of unbiased reactive trajectories. In contrast, biased trajectories from empirical collective variables display non-physical features. Furthermore, our method uses a single protein structure as input, enabling predictive sampling of conformational changes. These findings unlock access to a broader range of protein functional processes in molecular dynamics simulations.

Leveraging Cryptic Ligand Envelopes through Enhanced Molecular Simulations

Protein-bound ligands can adopt a range of different conformations, collectively defining a ligand envelope that has proven to be crucial for the design of potent and selective drugs. Yet, the cryptic nature of this ligand envelope makes it difficult to visualize, characterize, and ultimately exploit for drug design. Using enhanced molecular dynamics simulations, here, we provide a general framework to reconstruct the cryptic ligand envelope that is dynamically accessible by protein-bound small molecules in solution. We apply this approach to quantify hidden conformational heterogeneity in structurally complex ligands including the marine natural product plitidepsin. The computed conformational heterogeneity expands the small-molecule footprint beyond that typically observed in experiments, also revealing key thermodynamic and kinetic properties of single ligand-target interactions. The model agrees quantitatively with solution NMR, X-ray crystallography, and biochemical measurements, showcasing a versatile strategy to integrate receptor-bound ligand conformational ensembles in molecular design.

The conformational space of RNase P RNA in solution

RNA conformational diversity has fundamental biological roles1-5, but direct visualization of its full conformational space in solution has not been possible using traditional biophysical techniques. Using solution atomic force microscopy, a deep neural network and statistical analyses, we show that the ribonuclease P (RNase P) RNA adopts heterogeneous conformations consisting of a conformationally invariant core and highly flexible peripheral structural elements that sample a broad conformational space, with amplitudes as large as 20-60 Å in a multitude of directions, with very low net energy cost. Increasing Mg2+ drives compaction and enhances enzymatic activity, probably by narrowing the conformational space. Moreover, analyses of the correlations and anticorrelations between spatial flexibility and sequence conservation suggest that the functional roles of both the structure and dynamics of key regions are embedded in the primary sequence. These findings reveal the structure-dynamics basis for the embodiment of both enzymatic precision and substrate promiscuity in the RNA component of the RNase P. Mapping the conformational space of the RNase P RNA demonstrates a new general approach to studying RNA structure and dynamics.

A combined NMR and deep neural network approach for enhancing the spectral resolution of aromatic side chains in proteins

Nuclear magnetic resonance (NMR) spectroscopy is an important technique for deriving the dynamics and interactions of macromolecules; however, characterizations of aromatic residues in proteins still pose a challenge. Here, we present a deep neural network (DNN), which transforms NMR spectra recorded on simple uniformly 13C-labeled samples to yield high-quality 1H-13C correlation maps of aromatic side chains. Key to the success of the DNN is the design of NMR experiments that produce data with unique features to aid the DNN produce high-resolution spectra. The methodology was validated experimentally on protein samples ranging from 7 to 40 kDa in size, where it accurately reconstructed multidimensional aromatic 1H-13C correlation maps, to facilitate 1H-13C chemical shift assignments and to quantify kinetics. More generally, we believe that the strategy of designing new NMR experiments in combination with customized DNNs represents a substantial advance that will have a major impact on the study of molecules using NMR in the years to come.

Mapping the FF domain folding pathway via structures of transiently populated folding intermediates

Despite the tremendous accomplishments of AlpaFold2/3 in predicting biomolecular structure, the protein folding problem remains unsolved in the sense that accurate atomistic models of how protein molecules fold into their native conformations from an unfolded ensemble are still elusive. Here, using chemical exchange saturation transfer (CEST) NMR experiments and a comprehensive four-state kinetic model of the folding trajectory of a 71 residue four-helix bundle FF domain from human HYPA/FBP11 we present an atomic resolution structure of a transiently formed intermediate, I2, that along with the structure of a second intermediate, I1, provides a description of the FF domain folding trajectory. By recording CEST profiles as a function of urea concentration the extent of compaction along the folding pathway is evaluated. Our data establish that unlike the partially disordered I1 state, the I2 intermediate that is also formed before the rate-limiting folding barrier is well ordered and compact like the native conformer, while retaining nonnative interactions similar to those found in I1. The slow-interconversion from I2 to F, involving changes in secondary structure and the breaking of nonnative interactions, proceeds via a compact transition-state. Interestingly, the native state of the FF1 domain from human p190-A Rho GAP resembles the I2 conformation, suggesting that well-ordered folding intermediates can be repurposed by nature in structurally related proteins to assume functional roles. It is anticipated that the strategy for elucidation of sparsely populated and transiently formed structures of intermediates along kinetic pathways described here will be of use in other studies of protein dynamics.

Accounting for Fast vs Slow Exchange in Single Molecule FRET Experiments Reveals Hidden Conformational States

Proteins are dynamic systems whose structural preferences determine their function. Unfortunately, building atomically detailed models of protein structural ensembles remains challenging, limiting our understanding of the relationships between sequence, structure, and function. Combining single molecule Förster resonance energy transfer (smFRET) experiments with molecular dynamics simulations could provide experimentally grounded, all-atom models of a protein's structural ensemble. However, agreement between the two techniques is often insufficient to achieve this goal. Here, we explore whether accounting for important experimental details like averaging across structures sampled during a given smFRET measurement is responsible for this apparent discrepancy. We present an approach to account for this time-averaging by leveraging the kinetic information available from Markov state models of a protein's dynamics. This allows us to accurately assess which time scales are averaged during an experiment. We find this approach significantly improves agreement between simulations and experiments in proteins with varying degrees of dynamics, including the well-ordered protein T4 lysozyme, the partially disordered protein apolipoprotein E (ApoE), and a disordered amyloid protein (Aβ40). We find evidence for hidden states that are not apparent in smFRET experiments because of time averaging with other structures, akin to states in fast exchange in nuclear magnetic resonance, and evaluate different force fields. Finally, we show how remaining discrepancies between computations and experiments can be used to guide additional simulations and build structural models for states that were previously unaccounted for. We expect our approach will enable combining simulations and experiments to understand the link between sequence, structure, and function in many settings. Understanding protein dynamics is crucial for understanding protein function, yet few methodologies report on protein motion at an atomic level. Combining single molecule Förster resonance energy transfer (smFRET) experiments with computer simulations could provide atomistic models of protein ensembles which are grounded in experiments, however, there has been limited agreement between the two methods to date. Here, we present an algorithm to recapitulate smFRET experiments from molecular dynamics simulations. This approach significantly improves agreement between simulations and experiments for proteins across the ordered spectrum. Moreover, with this approach we can confidently create atomic models for states observed during smFRET experiments which were otherwise difficult to model due to high amounts of flexibility, disorder, or large deviations from crystal-like states.

Structure-Guided Drug Design Targeting Abl Kinase: How Structure and Regulation Can Assist in Designing New Drugs

The human protein Abelson kinase (Abl), a tyrosine kinase, plays a pivotal role in developing chronic myeloid leukemia (CML). Abl's involvement in various signaling pathways underscores its significance in regulating fundamental biological processes, including DNA damage responses, actin polymerization, and chromatin structural changes. The discovery of the Bcr-Abl oncoprotein, resulting from a chromosomal translocation in CML patients, revolutionized the understanding and treatment of the disease. The introduction of targeted therapies, starting with interferon-alpha and culminating in the development of tyrosine kinase inhibitors (TKIs) like imatinib, significantly improved patient outcomes. However, challenges such as drug resistance and side effects persist, indicating the necessity of research into novel therapeutic strategies. This review describes advancements in Abl kinase inhibitor development, emphasizing rational compound design from structural and regulatory information. Strategies, including bivalent inhibitors, PROTACs, and compounds targeting regulatory domains, promise to overcome resistance and minimize side effects. Additionally, leveraging the intricate structure and interactions of Bcr-Abl may provide insights into developing inhibitors for other kinases. Overall, this review highlights the importance of continued research into Abl kinase inhibition and its broader implications for therapeutic interventions targeting kinase-driven diseases. It provides valuable insights and strategies that may guide the development of next-generation therapies.

In silico and in vitro study of FLT3 inhibitors and their application in acute myeloid leukemia

Acute myeloid leukemia (AML) is the most common hematological cancer in the adult population worldwide. Approximately 35% of patients with AML present internal tandem duplication (ITD) mutations in the FMS‑like tyrosine kinase 3 (FLT3) receptor associated with poor prognosis, and thus, this receptor is a relevant target for potential therapeutics. Tyrosine kinase inhibitors (TKIs) are used to treat AML; however, their molecular interactions and effects on leukemic cells are poorly understood. The present study aimed to gain insights into the molecular interactions and affinity forces of four TKI drugs (sorafenib, midostaurin, gilteritinib and quizartinib) with the wild‑type (WT)‑FLT3 and ITD‑mutated (ITD‑FLT3) structural models of FLT3, in its inactive aspartic acid‑phenylalanine‑glycine motif (DFG‑out) and active aspartic acid‑phenylalanine‑glycine motif (DFG‑in) conformations. Furthermore, the present study evaluated the effects of the second‑generation TKIs gilteritinib and quizartinib on cancer cell viability, apoptosis and proliferation in the MV4‑11 (ITD‑FLT3) and HL60 (WT‑FLT3) AML cell lines. Peripheral blood mononuclear cells (PBMCs) from a healthy volunteer were included as an FLT3‑negative group. Molecular docking analysis indicated higher affinities of second‑generation TKIs for WT‑FLT3/DFG‑out and WT‑FLT3/DFG‑in compared with those of the first‑generation TKIs. However, the ITD mutation changed the affinity of all TKIs. The in vitro data supported the in silico predictions: MV4‑11 cells presented high selective sensibility to gilteritinib and quizartinib compared with the HL60 cells, whereas the drugs had no effect on PBMCs. Thus, the current study presented novel information about molecular interactions between the FLT3 receptors (WT or ITD‑mutated) and some of their inhibitors. It also paves the way for the search for novel inhibitory molecules with potential use against AML.

A Fast Method to Monitor Tyrosine Kinase Inhibitor Mechanisms

Methionine residues within the kinase domain of Src serve as unique NMR probes capable of distinguishing between distinct conformational states of full-length Src, including alternative drug-inhibited forms. This approach offers a rapid method to differentiate between various inhibition mechanisms at any stage of drug development, eliminating the need to resolve the structure of Src-drug complexes. Using selectively 13C-methyl-enriched methionine, spectra can be acquired in under an hour, while natural abundance spectra with comparable information are achievable within a few hours.

Allosteric Changes in the Conformational Landscape of Src Kinase upon Substrate Binding

Precise regulation of protein kinase activity is crucial in cell functions, and its loss is implicated in various diseases. The kinase activity is regulated by interconverting active and inactive states in the conformational landscape. However, how protein kinases switch conformations in response to different signals such as the binding at distinct sites remains incompletely understood. Here, we predict the binding mode for the peptide substrate to Src tyrosine kinase using enhanced conformational sampling simulations (totaling 24 μs) and then investigate changes in the conformational landscape upon substrate binding by conducting unbiased molecular dynamics simulations (totaling 50 μs) initiated from the apo and substrate-bound forms. Unexpectedly, the peptide substrate binding significantly facilitates the transitions from active to inactive conformations in which the αC helix is directed outward, the regulatory spine is broken, and the ATP-binding domain is perturbed. We also explore an underlying residue-contact network responsible for the allosteric conformational changes. Our results are in accord with the recent experiments reporting the negative cooperativity between the peptide substrate and ATP binding to tyrosine kinases and will contribute to advancing our understanding of the regulation mechanisms for kinase activity.

Allostery at a Protein-Protein Interface Harboring an Intermolecular Motional Network

Motional properties of proteins govern recognition, catalysis, and regulation. The dynamics of tightly interacting residues can form intramolecular dynamic networks, dependencies fine-tuned by evolution to optimize a plethora of functional aspects. The constructive interaction of residues from different proteins to assemble intermolecular dynamic networks is a similarly likely case but has escaped thorough experimental assessment due to interfering association/dissociation dynamics. Here, we use fast-MAS solid-state 15N R1ρ NMR relaxation dispersion aided by molecular-dynamics simulations to mechanistically assess the hierarchy of individual μs timescale motions arising from a crystal-crystal contact, in the absence of translational motion. In contrast to the monomer, where particular mutations entail isolated perturbations, specific intermolecular interactions couple the motional properties between distant residues in the same protein. The mechanistic insights obtained from this conceptual work may improve our understanding on how intramolecular allostery can be tuned by intermolecular interactions via assembly of dynamic networks from previously isolated elements.

Probing Functional Allosteric States and Conformational Ensembles of the Allosteric Protein Kinase States and Mutants: Atomistic Modeling and Comparative Analysis of AlphaFold2, OmegaFold, and AlphaFlow Approaches and Adaptations

This study reports a comprehensive analysis and comparison of several AlphaFold2 adaptations and OmegaFold and AlphaFlow approaches in predicting distinct allosteric states, conformational ensembles, and mutation-induced structural effects for a panel of state-switching allosteric ABL mutants. The results revealed that the proposed AlphaFold2 adaptation with randomized alanine sequence scanning can generate functionally relevant allosteric states and conformational ensembles of the ABL kinase that qualitatively capture a unique pattern of population shifts between the active and inactive states in the allosteric ABL mutants. Consistent with the NMR experiments, the proposed AlphaFold2 adaptation predicted that G269E/M309L/T408Y mutant could induce population changes and sample a significant fraction of the fully inactive I2 form which is a low-populated, high-energy state for the wild-type ABL protein. We also demonstrated that other ABL mutants G269E/M309L/T334I and M309L/L320I/T334I that introduce a single activating T334I mutation can reverse equilibrium and populate exclusively the active ABL form. While the precise quantitative predictions of the relative populations of the active and various hidden inactive states in the ABL mutants remain challenging, our results provide evidence that AlphaFold2 adaptation with randomized alanine sequence scanning can adequately detect a spectrum of the allosteric ABL states and capture the equilibrium redistributions between structurally distinct functional ABL conformations. We further validated the robustness of the proposed AlphaFold2 adaptation for predicting the unique inactive architecture of the BSK8 kinase and structural differences between ligand-unbound apo and ATP-bound forms of BSK8. The results of this comparative study suggested that AlpahFold2, OmegaFold, and AlphaFlow approaches may be driven by structural memorization of existing protein folds and are strongly biased toward predictions of the thermodynamically stable ground states of the protein kinases, highlighting limitations and challenges of AI-based methodologies in detecting alternative functional conformations, accurate characterization of physically significant conformational ensembles, and prediction of mutation-induced allosteric structural changes.

Structural Plasticity as a Driver of the Maturation of Pro-Interleukin-18

Dynamics are often critical for biomolecular function. Herein we explore the role of motion in driving the maturation process of pro-IL-18, a potent pro-inflammatory cytokine that is cleaved by caspases-1 and -4 to generate the mature form of the protein. An NMR dynamics study of pro-IL-18, probing time scales over 12 orders of magnitude and focusing on 1H, 13C, and 15N spin probes along the protein backbone and amino-acid side chains, reveals a plastic structure, with millisecond time scale dynamics occurring in a pair of β-strands, β1 and β*, that show large structural variations in a comparison of caspase-free and bound pro-IL-18 states. Fits of the relaxation data to a three-site model of exchange showed that the ground state secondary structure is maintained in the excited conformers, with the side chain of I48 that undergoes a buried-to-exposed conformational change in the caspase-free to -bound transition of pro-IL-18, sampling a more extensive range of torsion angles in one of the excited states characterized, suggesting partial unpacking in this region. Hydrogen exchange measurements establish the occurrence of an additional process, whereby strands β1 and β* locally unfold. Our data are consistent with a hierarchy of dynamic events that likely prime pro-IL-18 for facile caspase binding.

A drug-mediated organic electrochemical transistor for robustly reusable biosensors

Reusable point-of-care biosensors offer a cost-effective solution for serial biomarker monitoring, addressing the critical demand for tumour treatments and recurrence diagnosis. However, their realization has been limited by the contradictory requirements of robust reusability and high sensing capability to multiple interactions among transducer surface, sensing probes and target analytes. Here we propose a drug-mediated organic electrochemical transistor as a robust, reusable epidermal growth factor receptor sensor with striking sensitivity and selectivity. By electrostatically adsorbing protonated gefitinib onto poly(3,4-ethylenedioxythiophene):polystyrene sulfonate and leveraging its strong binding to the epidermal growth factor receptor target, the device operates with a unique refresh-in-sensing mechanism. It not only yields an ultralow limit-of-detection concentration down to 5.74 fg ml-1 for epidermal growth factor receptor but, more importantly, also produces an unprecedented regeneration cycle exceeding 200. We further validate the potential of our devices for easy-to-use biomedical applications by creating an 8 × 12 diagnostic drug-mediated organic electrochemical transistor array with excellent uniformity to clinical blood samples.

Dynamic Interplay of Loop Motions Governs the Molecular Level Regulatory Dynamics in Spleen Tyrosine Kinase: Insights from Molecular Dynamics Simulations

The spleen tyrosine kinase (Syk) is a key regulator in immune cell signaling and is linked to various mechanisms in cancer and neurodegenerative diseases. Although most computational research on Syk focuses on novel drug design, the molecular-level regulatory dynamics remain unexplored. In this study, we utilized 5 × 1 μs all-atom molecular dynamics simulations of the Syk kinase domain, examining it in combinations of activation segment phosphorylated/unphosphorylated (at Tyr525, Tyr526) and the "DFG"-Asp protonated/deprotonated (at Asp512) states to investigate conformational variations and regulatory dynamics of various loops and motifs within the kinase domain. Our findings revealed that the formation and disruption of several electrostatic interactions among residues within and near the activation segment likely influenced its dynamics. The protein structure network analysis indicated that the N-terminal and C-terminal anchors were stabilized by connections with the nearby stable helical regions. The P-loop showed conformational variation characterized by movements toward and away from the conserved "HRD"-motif. Additionally, there was a significant correlation between the movement of the β3-αC loop and the P-loop, which controls the dimensions of the adenine-binding cavity of the C-spine region. Overall, understanding these significant motions of the Syk kinase domain enhances our knowledge of its functional regulatory mechanism and can guide future research.

Slow Conformational Exchange between Partially Folded and Near-Native States of Ubiquitin: Evidence for a Multistate Folding Model

The mechanism by which small proteins fold, i.e., via intermediates or via a two-state mechanism, is a subject of intense investigation. Intermediate states in the folding pathways of these proteins are sparsely populated due to transient lifetimes under normal conditions rendering them transparent to a majority of the biophysical methods employed for structural, thermodynamic, and kinetic characterization, which attributes are essential for understanding the cooperative folding/unfolding of such proteins. Dynamic NMR spectroscopy has enabled the characterization of folding intermediates of ubiquitin that exist in equilibrium under conditions of low pH and denaturants. At low pH, an unlocked state defined as N' is in fast exchange with an invisible state, U″, as observed by CEST NMR. Addition of urea to ubiquitin at pH 2 creates two new states F' and U', which are in slow exchange (kF'→U' = 0.14 and kU'→F' = 0.28 s-1) as indicated by longitudinal ZZ-magnetization exchange spectroscopy. High-resolution solution NMR structures of F' show it to be in an "unlocked" conformation with measurable changes in rotational diffusion, translational diffusion, and rotational correlational times. U' is characterized by the presence of just the highly conserved N-terminal β1-β2 hairpin. The folding of ubiquitin is cooperative and is nucleated by the formation of an N-terminal β-hairpin followed by significant hydrophobic collapse of the protein core resulting in the formation of bulk of the secondary structural elements stabilized by extensive tertiary contacts. U' and F' may thus be described as early and late folding intermediates in the ubiquitin folding pathway.

Ponatinib: A Review of the History of Medicinal Chemistry behind Its Development

The primary treatment for chronic myeloid leukemia (CML) involves first- and second-generation tyrosine kinase inhibitors (TKIs), such as imatinib, nilotinib, bosutinib, and dasatinib. However, these medications are ineffective against mutations in the kinase domain of the ABL1 protein, particularly in the protein with the T315I mutation. To address this, ponatinib (PNT), a third-generation inhibitor, was developed. Despite its efficacy in treating the BCR-ABL1T315I mutation, the use of PNT was briefly suspended in 2013 due to serious adverse effects but was subsequently reintroduced to the market. During the drug discovery and development process, it is rare to consolidate all information into a single article, as is the case with ponatinib. This review aims to compile and chronologically organize the research on the discovery of ponatinib using medicinal chemistry tools and computational methods. It includes in silico calculations, such as the octanol/water partition coefficient (cLogP) via SwissAdme, and 2D maps of intermolecular interactions through molecular docking. This approach enhances understanding for both specialists and those interested in medicinal chemistry and pharmacology, while also contextualizing future directions for further optimizations of ponatinib, facilitating the development of new analogs of this crucial inhibitor for the treatment of CML and Philadelphia chromosome-positive acute lymphoblastic leukemia (ALL).

The future of integrated structural biology

Instruct-ERIC, "the European Research Infrastructure Consortium for Structural biology research," is a pan-European distributed research infrastructure making high-end technologies and methods in structural biology available to users. Here, we describe the current state-of-the-art of integrated structural biology and discuss potential future scientific developments as an impulse for the scientific community, many of which are located in Europe and are associated with Instruct. We reflect on where to focus scientific and technological initiatives within the distributed Instruct research infrastructure. This review does not intend to make recommendations on funding requirements or initiatives directly, neither at the national nor the European level. However, it addresses future challenges and opportunities for the field, and foresees the need for a stronger coordination within the European and international research field of integrated structural biology to be able to respond timely to thematic topics that are often prioritized by calls for funding addressing societal needs.

New ABL1 Kinase Domain Mutations in BCR::ABL1-Positive Acute Lymphoblastic Leukemia

BACKGROUND

Since the development of the first-generation Tyrosine Kinase Inhibitor (TKI), it has played a crucial role in the treatment of BCR::ABL1-positive acute lymphoblastic leukemia (ALL) and chronic myeloid leukemia (CML). However, ABL1 kinase domain (ABL1 KD) mutations confer resistance to several TKIs. These mutations have been extensively studied in chronic myeloid leukemia (CML) but less so in BCR::ABL1-positive acute lymphoblastic leukemia (ALL).

METHODS

Our study aimed to analyze the the ABL1 KD mutations in 97 consecutive newly-diagnosed adults with BCR::ABL1-positive ALL before therapy, in cytogenetic complete remission and at relapse with next generation sequencing (NGS). The relationship between ABL1 KD mutations and TKI selection was also analyzed.

RESULTS

Previously unreported ABL1 KD mutations R239G, F401V/L, R516L and K262T were the most prevalent in pre-therapy and cytogenetic remission samples, whereas T315I/P and P-loop mutations were most prevalent in relapse samples. R239G, F401V/L, R516L and K262T are related to the BCR::ABL1 structure, whereas T315I/P and P-loop mutations directly alter kinase activity. BaF3 cells transfected with ABL1 KD F401V, K262T, R239G, or R516L mutations were resistant to imatinib but strongly inhibited by olverembatinib with IC50 values of 0.73 to 1.52nM. Meanwhile, olverembatinib had advantages in increasing complete molecular response (CMR) and good prognosis.

CONCLUSION

Overall, our findings indicate the prevalence and impact of new ABL1 KD mutations in BCR::ABL1-positive ALL patients, highlighting the necessity for effective therapies targetingthese mutations.

Exploring the conformational landscape of protein kinases

Protein kinases are dynamic enzymes that display complex regulatory mechanisms. Although they possess a structurally conserved catalytic domain, significant conformational dynamics are evident both within a single kinase and across different kinases in the kinome. Here, we highlight methods for exploring this conformational space and its dynamics using kinase domains from ABL1 (Abelson kinase), PKA (protein kinase A), AurA (Aurora A), and PYK2 (proline-rich tyrosine kinase 2) as examples. Such experimental approaches combined with AI-driven methods, such as AlphaFold, will yield discoveries about kinase regulation, the catalytic process, substrate specificity, the effect of disease-associated mutations, as well as new opportunities for structure-based drug design.

A side-by-side comparison of variant function measurements using deep mutational scanning and base editing

Variant annotation is a crucial objective in mammalian functional genomics. Deep Mutational Scanning (DMS) is a well-established method for annotating human gene variants, but CRISPR base editing (BE) is emerging as an alternative. However, questions remain about how well high-throughput base editing measurements can annotate variant function and the extent of downstream experimental validation required. This study presents the first direct comparison of DMS and BE in the same lab and cell line. Results indicate that focusing on the most likely edits and highest efficiency sgRNAs enhances the agreement between a "gold standard" DMS dataset and a BE screen. A simple filter for sgRNAs making single edits in their window could sufficiently annotate a large proportion of variants directly from sgRNA sequencing of large pools. When multi-edit guides are unavoidable, directly measuring the variants created in the pool, rather than sgRNA abundance, can recover high-quality variant annotation measurements in multiplexed pools. Taken together, our data show a surprising degree of correlation between base editor data and gold standard deep mutational scanning.

Predicting Mutation-Induced Allosteric Changes in Structures and Conformational Ensembles of the ABL Kinase Using AlphaFold2 Adaptations with Alanine Sequence Scanning

Despite the success of AlphaFold2 approaches in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and have been challenged to accurately capture the effects of single point mutations that induced significant structural changes. We examined several implementations of AlphaFold2 methods to predict conformational ensembles for state-switching mutants of the ABL kinase. The results revealed that a combination of randomized alanine sequence masking with shallow multiple sequence alignment subsampling can significantly expand the conformational diversity of the predicted structural ensembles and capture shifts in populations of the active and inactive ABL states. Consistent with the NMR experiments, the predicted conformational ensembles for M309L/L320I and M309L/H415P ABL mutants that perturb the regulatory spine networks featured the increased population of the fully closed inactive state. The proposed adaptation of AlphaFold can reproduce the experimentally observed mutation-induced redistributions in the relative populations of the active and inactive ABL states and capture the effects of regulatory mutations on allosteric structural rearrangements of the kinase domain. The ensemble-based network analysis complemented AlphaFold predictions by revealing allosteric hotspots that correspond to state-switching mutational sites which may explain the global effect of regulatory mutations on structural changes between the ABL states. This study suggested that attention-based learning of long-range dependencies between sequence positions in homologous folds and deciphering patterns of allosteric interactions may further augment the predictive abilities of AlphaFold methods for modeling of alternative protein sates, conformational ensembles and mutation-induced structural transformations.

Overcoming clinical BCR-ABL1 compound mutant resistance with combined ponatinib and asciminib therapy

BCR-ABL1 compound mutations can lead to resistance to ABL1 inhibitors in chronic myeloid leukemia (CML), which could be targeted by combining the ATP-site inhibitor ponatinib and the allosteric inhibitor asciminib. Here, we report the clinical validation of this approach in a CML patient, providing a basis for combination therapy to overcome such resistance.

Protein Multiple Conformation Prediction Using Multi-Objective Evolution Algorithm

The breakthrough of AlphaFold2 and the publication of AlphaFold DB represent a significant advance in the field of predicting static protein structures. However, AlphaFold2 models tend to represent a single static structure, and multiple-conformation prediction remains a challenge. In this work, we proposed a method named MultiSFold, which uses a distance-based multi-objective evolutionary algorithm to predict multiple conformations. To begin, multiple energy landscapes are constructed using different competing constraints generated by deep learning. Subsequently, an iterative modal exploration and exploitation strategy is designed to sample conformations, incorporating multi-objective optimization, geometric optimization and structural similarity clustering. Finally, the final population is generated using a loop-specific sampling strategy to adjust the spatial orientations. MultiSFold was evaluated against state-of-the-art methods using a benchmark set containing 80 protein targets, each characterized by two representative conformational states. Based on the proposed metric, MultiSFold achieves a remarkable success ratio of 56.25% in predicting multiple conformations, while AlphaFold2 only achieves 10.00%, which may indicate that conformational sampling combined with knowledge gained through deep learning has the potential to generate conformations spanning the range between different conformational states. In addition, MultiSFold was tested on 244 human proteins with low structural accuracy in AlphaFold DB to test whether it could further improve the accuracy of static structures. The experimental results demonstrate the performance of MultiSFold, with a TM-score better than that of AlphaFold2 by 2.97% and RoseTTAFold by 7.72%. The online server is at http://zhanglab-bioinf.com/MultiSFold .

Slower CDK4 and faster CDK2 activation in the cell cycle

Dysregulation of cyclin-dependent kinases (CDKs) impacts cell proliferation, driving cancer. Here, we ask why the cyclin-D/CDK4 complex governs cell cycle progression through the longer G1 phase, whereas cyclin-E/CDK2 regulates the shorter G1/S phase transition. We consider available experimental cellular and structural data including cyclin-E's high-level burst, sustained duration of elevated cyclin-D expression, and explicit solvent molecular dynamics simulations of the inactive monomeric and complexed states, to establish the conformational tendencies along the landscape of the distinct activation scenarios of cyclin-D/CDK4 and cyclin-E/CDK2 in the G1 phase and G1/S transition of the cell cycle, respectively. These lead us to propose slower activation of cyclin-D/CDK4 and rapid activation of cyclin-E/CDK2. We provide the mechanisms through which this occurs, offering innovative CDK4 drug design considerations. Our insightful mechanistic work addresses a compelling cell cycle regulation question and illuminates the distinct activation speeds between the G1 and the G1/S phases, which are crucial for function.

Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases

Protein kinases are molecular machines with rich sequence variation that distinguishes the two main evolutionary branches - tyrosine kinases (TKs) from serine/threonine kinases (STKs). Using a sequence co-variation Potts statistical energy model we previously concluded that TK catalytic domains are more likely than STKs to adopt an inactive conformation with the activation loop in an autoinhibitory folded conformation, due to intrinsic sequence effects. Here we investigate the structural basis for this phenomenon by integrating the sequence-based model with structure-based molecular dynamics (MD) to determine the effects of mutations on the free energy difference between active and inactive conformations, using a thermodynamic cycle involving many (n = 108) protein-mutation free energy perturbation (FEP) simulations in the active and inactive conformations. The sequence and structure-based results are consistent and support the hypothesis that the inactive conformation DFG-out Activation Loop Folded, is a functional regulatory state that has been stabilized in TKs relative to STKs over the course of their evolution via the accumulation of residue substitutions in the activation loop and catalytic loop that facilitate distinct substrate binding modes in trans and additional modes of regulation in cis for TKs.

An RNA aptamer exploits exosite-dependent allostery to achieve specific inhibition of coagulation factor IXa

Hemostasis relies on a reaction network of serine proteases and their cofactors to form a blood clot. Coagulation factor IXa (protease) plays an essential role in hemostasis as evident from the bleeding disease associated with its absence. RNA aptamers specifically targeting individual coagulation factors have potential as anticoagulants and as probes of the relationship between structure and function. Here, we report X-ray structures of human factor IXa without a ligand bound to the active site either in the apo-form or in complex with an inhibitory aptamer specific for factor IXa. The aptamer binds to an exosite in the catalytic domain and allosterically distorts the active site. Our studies reveal a conformational ensemble of IXa states, wherein large movements of Trp215 near the active site drive functional transitions between the closed (aptamer-bound), latent (apo), and open (substrate-bound) states. The latent state of the apo-enzyme may bear on the uniquely poor catalytic activity of IXa compared to other coagulation proteases. The exosite, to which the aptamer binds, has been implicated in binding VIIIa and heparin, both of which regulate IXa function. Our findings reveal the importance of exosite-driven allosteric modulation of IXa function and new strategies to rebalance hemostasis for therapeutic gain.

Exploring the conformational landscapes of protein kinases: perspectives from FRET and DEER

Conformational changes of catalytically-important structural elements are a key feature of the regulation mechanisms of protein kinases and are important for dictating inhibitor binding modes and affinities. The lack of widely applicable methods for tracking kinase conformational changes in solution has hindered our understanding of kinase regulation and our ability to design conformationally selective inhibitors. Here we provide an overview of two recently developed methods that detect conformational changes of the regulatory activation loop and αC-helix of kinases and that yield complementary information about allosteric mechanisms. An intramolecular Förster resonance energy transfer-based approach provides a scalable platform for detecting and classifying structural changes in high-throughput, as well as quantifying ligand binding cooperativity, shedding light on the energetics governing allostery. The pulsed electron paramagnetic resonance technique double electron-electron resonance provides lower throughput but higher resolution information on structural changes that allows for unambiguous assignment of conformational states and quantification of population shifts. Together, these methods are shedding new light on kinase regulation and drug interactions and providing new routes for the identification of novel kinase inhibitors and allosteric modulators.

Integration of a Randomized Sequence Scanning Approach in AlphaFold2 and Local Frustration Profiling of Conformational States Enable Interpretable Atomistic Characterization of Conformational Ensembles and Detection of Hidden Allosteric States in the ABL1 Protein Kinase

Despite the success of AlphaFold methods in predicting single protein structures, these methods showed intrinsic limitations in the characterization of multiple functional conformations of allosteric proteins. The recent NMR-based structural determination of the unbound ABL kinase in the active state and discovery of the inactive low-populated functional conformations that are unique for ABL kinase present an ideal challenge for the AlphaFold2 approaches. In the current study, we employ several adaptations of the AlphaFold2 methodology to predict protein conformational ensembles and allosteric states of the ABL kinase including randomized alanine sequence scanning combined with the multiple sequence alignment subsampling proposed in this study. We show that the proposed new AlphaFold2 adaptation combined with local frustration profiling of conformational states enables accurate prediction of the protein kinase structures and conformational ensembles, also offering a robust approach for interpretable characterization of the AlphaFold2 predictions and detection of hidden allosteric states. We found that the large high frustration residue clusters are uniquely characteristic of the low-populated, fully inactive ABL form and can define energetically frustrated cracking sites of conformational transitions, presenting difficult targets for AlphaFold2. The results of this study uncovered previously unappreciated fundamental connections between local frustration profiles of the functional allosteric states and the ability of AlphaFold2 methods to predict protein structural ensembles of the active and inactive states. This study showed that integration of the randomized sequence scanning adaptation of AlphaFold2 with a robust landscape-based analysis allows for interpretable atomistic predictions and characterization of protein conformational ensembles, providing a physical basis for the successes and limitations of current AlphaFold2 methods in detecting functional allosteric states that play a significant role in protein kinase regulation.

Accounting for fast vs slow exchange in single molecule FRET experiments reveals hidden conformational states

Proteins are dynamic systems whose structural preferences determine their function. Unfortunately, building atomically detailed models of protein structural ensembles remains challenging, limiting our understanding of the relationships between sequence, structure, and function. Combining single molecule Förster resonance energy transfer (smFRET) experiments with molecular dynamics simulations could provide experimentally grounded, all-atom models of a protein's structural ensemble. However, agreement between the two techniques is often insufficient to achieve this goal. Here, we explore whether accounting for important experimental details like averaging across structures sampled during a given smFRET measurement is responsible for this apparent discrepancy. We present an approach to account for this time-averaging by leveraging the kinetic information available from Markov state models of a protein's dynamics. This allows us to accurately assess which timescales are averaged during an experiment. We find this approach significantly improves agreement between simulations and experiments in proteins with varying degrees of dynamics, including the well-ordered protein T4 lysozyme, the partially disordered protein apolipoprotein E (ApoE), and a disordered amyloid protein (Aβ40). We find evidence for hidden states that are not apparent in smFRET experiments because of time averaging with other structures, akin to states in fast exchange in NMR, and evaluate different force fields. Finally, we show how remaining discrepancies between computations and experiments can be used to guide additional simulations and build structural models for states that were previously unaccounted for. We expect our approach will enable combining simulations and experiments to understand the link between sequence, structure, and function in many settings.

Prediction of Conformational Ensembles and Structural Effects of State-Switching Allosteric Mutants in the Protein Kinases Using Comparative Analysis of AlphaFold2 Adaptations with Sequence Masking and Shallow Subsampling

Despite the success of AlphaFold2 approaches in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and have been challenged to accurately capture of the effects of single point mutations that induced significant structural changes. We systematically examined several implementations of AlphaFold2 methods to predict conformational ensembles for state-switching mutants of the ABL kinase. The results revealed that a combination of randomized alanine sequence masking with shallow multiple sequence alignment subsampling can significantly expand the conformational diversity of the predicted structural ensembles and capture shifts in populations of the active and inactive ABL states. Consistent with the NMR experiments, the predicted conformational ensembles for M309L/L320I and M309L/H415P ABL mutants that perturb the regulatory spine networks featured the increased population of the fully closed inactive state. On the other hand, the predicted conformational ensembles for the G269E/M309L/T334I and M309L/L320I/T334I triple ABL mutants that share activating T334I gate-keeper substitution are dominated by the active ABL form. The proposed adaptation of AlphaFold can reproduce the experimentally observed mutation-induced redistributions in the relative populations of the active and inactive ABL states and capture the effects of regulatory mutations on allosteric structural rearrangements of the kinase domain. The ensemble-based network analysis complemented AlphaFold predictions by revealing allosteric mediating centers that often directly correspond to state-switching mutational sites or reside in their immediate local structural proximity, which may explain the global effect of regulatory mutations on structural changes between the ABL states. This study suggested that attention-based learning of long-range dependencies between sequence positions in homologous folds and deciphering patterns of allosteric interactions may further augment the predictive abilities of AlphaFold methods for modeling of alternative protein sates, conformational ensembles and mutation-induced structural transformations.

Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases

Protein kinases are molecular machines with rich sequence variation that distinguishes the two main evolutionary branches - tyrosine kinases (TKs) from serine/threonine kinases (STKs). Using a sequence co-variation Potts statistical energy model we previously concluded that TK catalytic domains are more likely than STKs to adopt an inactive conformation with the activation loop in an autoinhibitory "folded" conformation, due to intrinsic sequence effects. Here we investigated the structural basis for this phenomenon by integrating the sequence-based model with structure-based molecular dynamics (MD) to determine the effects of mutations on the free energy difference between active and inactive conformations, using a novel thermodynamic cycle involving many (n=108) protein-mutation free energy perturbation (FEP) simulations in the active and inactive conformations. The sequence and structure-based results are consistent and support the hypothesis that the inactive conformation "DFG-out Activation Loop Folded", is a functional regulatory state that has been stabilized in TKs relative to STKs over the course of their evolution via the accumulation of residue substitutions in the activation loop and catalytic loop that facilitate distinct substrate binding modes in trans and additional modes of regulation in cis for TKs.

Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases

Protein kinases are molecular machines with rich sequence variation that distinguishes the two main evolutionary branches - tyrosine kinases (TKs) from serine/threonine kinases (STKs). Using a sequence co-variation Potts statistical energy model we previously concluded that TK catalytic domains are more likely than STKs to adopt an inactive conformation with the activation loop in an autoinhibitory "folded" conformation, due to intrinsic sequence effects. Here we investigated the structural basis for this phenomenon by integrating the sequence-based model with structure-based molecular dynamics (MD) to determine the effects of mutations on the free energy difference between active and inactive conformations, using a novel thermodynamic cycle involving many (n=108) protein-mutation free energy perturbation (FEP) simulations in the active and inactive conformations. The sequence and structure-based results are consistent and support the hypothesis that the inactive conformation "DFG-out Activation Loop Folded", is a functional regulatory state that has been stabilized in TKs relative to STKs over the course of their evolution via the accumulation of residue substitutions in the activation loop and catalytic loop that facilitate distinct substrate binding modes in trans and additional modes of regulation in cis for TKs.

Synthesis, conformational analysis and antimicrobial activity of 10-benzyl-1,2,4-triazolo[4,3-b]1,2,4-triazino[5,6-b]indole acyclo C-nucleoside analogs

Condensation of 5-benzyl-3-hydrazino-1,2,4-triazino[5,6-b]indole with various sugar aldoses or ketoses gave the corresponding sugar hydrazones as single geometrical isomer or exist in E/Z tautomeric isomers. The hydrazones underwent heterocyclization with Fe(Ш)Cl3 to give the N2-adduct acyclo C-nucleosides: 3-(alditol-1yl)-10-benzyl-1,2,4-triazolo[4,3-b]1,2,4-triazino[5,6-b]indoles rather than the N4-adduct: 10-(alditol-1-yl)-3-benzyl-1,2,4-triazolo[3,4-c]1,2,4-triazino[5,6-b] indoles on the basis of chemical and UV spectral proofs. Conformational analysis of their polyacetates were studied. The new acyclo C-nucleosides were evaluated for antimicrobial activity.

Classifying protein kinase conformations with machine learning

Protein kinases are key actors of signaling networks and important drug targets. They cycle between active and inactive conformations, distinguished by a few elements within the catalytic domain. One is the activation loop, whose conserved DFG motif can occupy DFG-in, DFG-out, and some rarer conformations. Annotation and classification of the structural kinome are important, as different conformations can be targeted by different inhibitors and activators. Valuable resources exist; however, large-scale applications will benefit from increased automation and interpretability of structural annotation. Interpretable machine learning models are described for this purpose, based on ensembles of decision trees. To train them, a set of catalytic domain sequences and structures was collected, somewhat larger and more diverse than existing resources. The structures were clustered based on the DFG conformation and manually annotated. They were then used as training input. Two main models were constructed, which distinguished active/inactive and in/out/other DFG conformations. They considered initially 1692 structural variables, spanning the whole catalytic domain, then identified ("learned") a small subset that sufficed for accurate classification. The first model correctly labeled all but 3 of 3289 structures as active or inactive, while the second assigned the correct DFG label to all but 17 of 8826 structures. The most potent classifying variables were all related to well-known structural elements in or near the activation loop and their ranking gives insights into the conformational preferences. The models were used to automatically annotate 3850 kinase structures predicted recently with the Alphafold2 tool, showing that Alphafold2 reproduced the active/inactive but not the DFG-in proportions seen in the Protein Data Bank. We expect the models will be useful for understanding and engineering kinases.