Multiple Protein Conformations Research Articles

Synthesis of New Imidazo[1,2‐a]pyridine Triazole Hybrid Molecules as Potential Apoptotic Antitumor Agents

AbstractNovel imidazo[1,2‐a]pyridines bearing 1,2,3‐triazole moieties at the C3 position were synthesized. After the characterization of the synthesized compounds, their in vitro therapeutic activities were evaluated in various cancer cell lines (MCF7, A549, HePG2 and T98G). Methoxy substituted derivative was identified as the most potent compound based on the results of its anti‐proliferative activity on various cancer cell lines, as well as showing no cytotoxicity on the healthy human fibroblast cell line (MRC‐5). As an indicator of apoptosis, a significant decrease in the level of PARP protein was observed in the MCF7 cells treated with this derivative. Molecular docking studies were conducted on wide range of targets such as phosphoinositide 3‐kinase (PI3K), cyclin‐independent kinase 2 (CDK2), mitogen‐activated protein kinase (MEK), insulin‐like growth‐factor‐1 (IGF‐1), tubulin, DNA topoisomerase, poly (ADP‐ribose) polymerase (PARP) and B‐cell lymphoma‐2 (BCL2). All the compounds tested showed the lowest binding energies with target PARP1. Moreover, CDK2 and tubulin displayed relatively good binding scores. The docking poses and scores were cross‐checked with two different software and multiple protein conformations were included to incorporate flexible protein docking features. Finally, drug‐likeness properties of the compounds are further tested via Swiss‐ADME software.

Quantitatively Accounting for Protein Reorganization in Computer-Aided Drug Design.

Structure-based drug design frequently operates under the assumption that a single holo structure is relevant. However, a large number of crystallographic examples clearly show that multiple conformations are possible. In those cases, the protein reorganization free energy must be known to accurately predict binding free energies for ligands. Only then can the energetic preference among these multiple protein conformations be utilized to design ligands with stronger binding potency and selectivity. Here, we present a computational method to quantify these protein reorganization free energies. We test it on two retrospective drug design cases, Abl kinase and HSP90, and illustrate how alternative holo conformations can be derisked and lead to large boosts in affinity. This method will allow computer-aided drug design to better support complex protein targets.

Machine Learning‐Enabled Virtual Screening with Multiple Protein Structures toward the Discovery of Novel JAK3 Inhibitors: Integration of Molecular Docking, Pharmacophore, and Naïve Bayesian Classification

AbstractExtensive research has accumulated suggesting that Janus kinase 3 (JAK3) is closely related to the occurrence and development of various human diseases, making JAK3 a highly potential drug target. However, JAK3 has high homology with other members of the JAK family, making the development of JAK3 inhibitors full of challenges. Thus, here, a naïve Bayesian classification (NBC) model based on multiple JAK3 protein conformations, which integrates molecular docking, pharmacophore, and molecular descriptors, is developed to find novel JAK3 inhibitors. First, the validation set is used to prove whether molecular docking or pharmacophore, integrating multiple JAK3 conformations always has higher prediction accuracy than that of any single conformation. Second, external prediction reveals that the NBC model combining molecular docking, pharmacophore, and important molecular features could significantly improve the enrichment of active JAK3 inhibitors. Finally, the optimal NBC model is utilized for virtual screening against a large chemical database and some compounds with high Bayesian scores are identified. Altogether, the machine learning‐based virtual screening protocol not only has strong efficiency but also has high screening accuracy. It is hoped that the developed virtual screening strategy could provide valuable guidance for the discovery of novel JAK3 inhibitors.

SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2.

The unprecedented performance of Deepmind's Alphafold2 in predicting protein structure in CASP XIV and the creation of a database of structures for multiple proteomes and protein sequence repositories is reshaping structural biology. However, because this database returns a single structure, it brought into question Alphafold's ability to capture the intrinsic conformational flexibility of proteins. Here we present a general approach to drive Alphafold2 to model alternate protein conformations through simple manipulation of the multiple sequence alignment via in silico mutagenesis. The approach is grounded in the hypothesis that the multiple sequence alignment must also encode for protein structural heterogeneity, thus its rational manipulation will enable Alphafold2 to sample alternate conformations. A systematic modeling pipeline is benchmarked against canonical examples of protein conformational flexibility and applied to interrogate the conformational landscape of membrane proteins. This work broadens the applicability of Alphafold2 by generating multiple protein conformations to be tested biologically, biochemically, biophysically, and for use in structure-based drug design.

Vanilloid-dependent TRPV1 opening trajectory from cryoEM ensemble analysis

Single particle cryo-EM often yields multiple protein conformations within a single dataset, but experimentally deducing the temporal relationship of these conformers within a conformational trajectory is not trivial. Here, we use thermal titration methods and cryo-EM in an attempt to obtain temporal resolution of the conformational trajectory of the vanilloid receptor TRPV1 with resiniferatoxin (RTx) bound. Based on our cryo-EM ensemble analysis, RTx binding to TRPV1 appears to induce intracellular gate opening first, followed by selectivity filter dilation, then pore loop rearrangement to reach the final open state. This apparent conformational wave likely arises from the concerted, stepwise, additive structural changes of TRPV1 over many subdomains. Greater understanding of the RTx-mediated long-range allostery of TRPV1 could help further the therapeutic potential of RTx, which is a promising drug candidate for pain relief associated with advanced cancer or knee arthritis.

NMR Analysis of Protein Folding Interaction Networks.

Theory and experimental evidence unequivocally indicate that protein folding is far more complex than the two-state (all-or-none) model that is usually assumed in the analysis of folding experiments. Proteins tend to fold hierarchically by forming secondary structure elements, followed by supersecondary arrangements, and other intermediate states that ultimately adopt the native tertiary fold as a result of a delicate balance between interatomic interactions and entropic contributions. However, small proteins with simple folds typically follow downhill folding, characterized by very small energetic barriers (<3RT) that allow multiple protein conformations to be populated along the folding path down the free energy landscape, reaching the native fold at the lowest energy level.Here we describe the use of solution-state nuclear magnetic resonance (NMR) for the analysis of protein folding interaction networks at atomic resolution. The assignment of NMR spectra acquired at different unfolding conditions provides hundreds of atomic unfolding curves that are analyzed to infer the network of folding interactions. The method is particularly useful to study small proteins that fold autonomously in the sub-millisecond timescale. The information obtained from the application of this method can potentially unveil the basic relationships between protein structure and folding.

MDock: A Suite for Molecular Inverse Docking and Target Prediction.

Molecular docking is commonly used for identification of drug candidates targeting a specified protein of known structure. With the increasing emphasis on drug repurposing over recent decades, molecular inverse docking has been widely applied to prediction of the potential protein targets of a specified molecule. In practice, inverse docking has many advantages, including early supervision of drugs' side effects and toxicity. MDock developed from our laboratory is a protein-ligand docking software based on a knowledge-based scoring function and has numerous applications to lead identification. In addition to its computational efficiency on ensemble docking for multiple protein conformations, MDock is well suited for inverse docking. In this chapter, we focus on introducing the protocol of inverse docking with MDock. For academic users, the MDock package is freely available at http://zoulab.dalton.missouri.edu/mdock.htm .

GTP hydrolysis is modulated by Arg34 in the RASopathy-associated KRASP34R.

RAS proteins are commonly mutated in cancerous tumors, but germline RAS mutations are also found in RASopathy syndromes such as Noonan syndrome (NS) and cardiofaciocutaneous (CFC) syndrome. Activating RAS mutations can be subclassified based on their activating mechanisms. Understanding the structural basis for these mechanisms may provide clues for how to manage associated health conditions. We determined high-resolution X-ray structures of the RASopathy mutant KRASP34R seen in NS and CFCS. GTP and GDP-bound KRASP34R crystallized in multiple forms, with each lattice consisting of multiple protein conformations. In all GTP-bound conformations, the switch regions are not compatible with GAP binding, suggesting a structural mechanism for the GAP insensitivity of this RAS mutant. However, GTP-bound conformations are compatible with intrinsic nucleotide hydrolysis, including one that places R34 in a position analogous to the GAP arginine finger or intrinsic arginine finger found in heterotrimeric G proteins, which may support intrinsic GTP hydrolysis. We also note that the affinity between KRASP34R and RAF-RBD is decreased, suggesting another possible mechanism for dampening of RAS signaling. These results may provide a foothold for development of new mutation-specific strategies to address KRASP34R -driven diseases.

Development of Software for the In-Depth Analysis of Protein Dynamics as Determined by MALDI Mass Spectrometry-Based Hydrogen/Deuterium Exchange.

Hydrogen/deuterium exchange (HDX) coupled with pepsin digestion is useful for rapidly analyzing the kinetic properties of small amounts of protein. However, the analysis of HDX by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) is time-consuming due to a lack of dedicated software. Currently available software programs mainly calculate average mass shifts, even though the isotopic distribution width contains information regarding multiple protein conformations. Moreover, HDX reaction samples are typically composed of peptides that contain various numbers of deuterium atoms, which also hinders the rapid and comprehensive analysis of protein dynamics. We report here on the development of a software program “Scipas DX” that can be used to automatically analyze the hydrogen–deuterium isotopic distribution in peaks in HDX spectra and calculate the average number of atoms exchanged, the average deuteration ratio, the abundance ratio for exchanged atoms, and their fitted spectra with a high degree of accuracy within a few minutes. Analysis of the abundance ratio for exchanged atoms of a model protein, adenylate kinase 1, using Scipas DX indicate that the local structure at residues 83–106 and 107–117 are in a slow equilibrium, suggesting that these regions adopt multiple conformations that are involved in the stability and in switching between the active and inactive forms. Furthermore, precise HDX kinetics of the average deuteration ratio both confirmed the known induced conformations of two regions (residues 46–75 and 131–165) that are responsible for ligand binding and verified the novel structural dynamics of residues 107–117 and 166–196 following ligand binding to ligand-binding pockets 1 and 2, respectively. Collectively, these results highlight the usefulness and versatility of Scipas DX in MALDI-MS HDX-based analyses of protein dynamics.

ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance.

Protein flexibility and solvation pose major challenges to docking algorithms and scoring functions. One established strategy for addressing these challenges is to use multiple protein conformations for docking (all‐against‐all ensemble docking). Recent studies have shown that the performance of ensemble docking can be improved by selecting the most relevant protein structures for docking. In search for a robust approach to protein structure selection, we have come up with an integrated mAchine Learning AnD DockINg approach (ALADDIN). ALADDIN employs a battery of random forest classifiers to select, individually for each compound of interest, from an ensemble of protein structures, the single most suitable protein structure for docking. ALADDIN outperformed the best single‐structure docking runs, ensemble docking and a similarity‐based docking approach on three out of four investigated targets, with up to 0.15, 0.11 and 0.16 higher area under the receiver operating characteristic curve (AUC) values, respectively. Only in the case of cytochrome P450 3A4, ALADDIN, like any of the other tested approaches, failed to obtain decent performance. ALADDIN can be particularly useful for structure‐based virtual screening of malleable proteins, including kinases, some viral enzymes and anti‐targets.

Three-dimensional structure of Megabalanus rosa Cement Protein 20 revealed by multi-dimensional NMR and molecular dynamics simulations.

Barnacles employ a protein-based cement to firmly attach to immersed substrates. The cement proteins (CPs) have previously been identified and sequenced. However, the molecular mechanisms of adhesion are not well understood, in particular, because the three-dimensional molecular structure of CPs remained unknown to date. Here, we conducted multi-dimensional nuclear magnetic resonance (NMR) studies and molecular dynamics (MD) simulations of recombinant Megabalanus rosa Cement Protein 20 (rMrCP20). Our NMR results show that rMrCP20 contains three main folded domain regions intervened by two dynamic loops, resulting in multiple protein conformations that exist in equilibrium. We found that 12 out of 32 Cys in the sequence engage in disulfide bonds that stabilize the β-sheet domains owing to their placement at the extremities of β-strands. Another feature unveiled by NMR is the location of basic residues in turn regions that are exposed to the solvent, playing an important role for intermolecular contact with negatively charged surfaces. MD simulations highlight a highly stable and conserved β-motif (β7-β8), which may function as nuclei for amyloid-like nanofibrils previously observed in the cured adhesive cement. To the best of our knowledge, this is the first report describing the tertiary structure of an extracellular biological adhesive protein at the molecular level. This article is part of the theme issue 'Transdisciplinary approaches to the study of adhesion and adhesives in biological systems'.

Mechanisms of Cytochrome P450-Catalyzed Oxidations.

Enzymes are complex biological catalysts and are critical to life. Most oxidations of chemicals are catalyzed by cytochrome P450 (P450, CYP) enzymes, which generally utilize mixed-function oxidase stoichiometry, utilizing pyridine nucleotides as electron donors: NAD(P)H + O2 + R → NAD(P)+ + RO + H2O (where R is a carbon substrate and RO is an oxidized product). The catalysis of oxidations is largely understood in the context of the heme iron-oxygen complex generally referred to as Compound I, formally FeO3+, whose basis was in peroxidase chemistry. Many X-ray crystal structures of P450s are now available (≥ 822 structures from ≥146 different P450s) and have helped in understanding catalytic specificity. In addition to hydroxylations, P450s catalyze more complex oxidations, including C-C bond formation and cleavage. Enzymes derived from P450s by directed evolution can even catalyze more unusual reactions, e.g. cyclopropanation. Current P450 questions under investigation include the potential role of the intermediate Compound 0 (formally FeIII-O2 -) in catalysis of some reactions, the roles of high- and low-spin forms of Compound I, the mechanism of desaturation, the roles of open and closed structures of P450s in catalysis, the extent of processivity in multi-step oxidations, and the role of the accessory protein cytochrome b 5. More global questions include exactly how structure drives function, prediction of catalysis, and roles of multiple protein conformations.

Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach

Ligand docking to flexible protein molecules can be efficiently carried out through ensemble docking to multiple protein conformations, either from experimental X-ray structures or from in silico simulations. The success of ensemble docking often requires the careful selection of complementary protein conformations, through docking and scoring of known co-crystallized ligands. False positives, in which a ligand in a wrong pose achieves a better docking score than that of native pose, arise as additional protein conformations are added. In the current study, we developed a new ligand-biased ensemble receptor docking method and composite scoring function which combine the use of ligand-based atomic property field (APF) method with receptor structure-based docking. This method helps us to correctly dock 30 out of 36 ligands presented by the D3R docking challenge. For the six mis-docked ligands, the cognate receptor structures prove to be too different from the 40 available experimental Pocketome conformations used for docking and could be identified only by receptor sampling beyond experimentally explored conformational subspace.

Cosolvent-Based Molecular Dynamics for Ensemble Docking: Practical Method for Generating Druggable Protein Conformations.

Protein flexibility is a major hurdle in current structure-based virtual screening (VS). In spite of the recent advances in high-performance computing, protein-ligand docking methods still demand tremendous computational cost to take into account the full degree of protein flexibility. In this context, ensemble docking has proven its utility and efficiency for VS studies, but it still needs a rational and efficient method to select and/or generate multiple protein conformations. Molecular dynamics (MD) simulations are useful to produce distinct protein conformations without abundant experimental structures. In this study, we present a novel strategy that makes use of cosolvent-based molecular dynamics (CMD) simulations for ensemble docking. By mixing small organic molecules into a solvent, CMD can stimulate dynamic protein motions and induce partial conformational changes of binding pocket residues appropriate for the binding of diverse ligands. The present method has been applied to six diverse target proteins and assessed by VS experiments using many actives and decoys of DEKOIS 2.0. The simulation results have revealed that the CMD is beneficial for ensemble docking. Utilizing cosolvent simulation allows the generation of druggable protein conformations, improving the VS performance compared with the use of a single experimental structure or ensemble docking by standard MD with pure water as the solvent.

Discovery of Novel Nonactive Site Inhibitors of the Prothrombinase Enzyme Complex.

The risk of serious bleeding is a major liability of anticoagulant drugs that are active-site competitive inhibitors targeting the Factor Xa (FXa) prothrombin (PT) binding site. The present work identifies several new classes of small molecule anticoagulants that can act as nonactive site inhibitors of the prothrombinase (PTase) complex composed of FXa and Factor Va (FVa). These new classes of anticoagulants were identified, using a novel agnostic computational approach to identify previously unrecognized binding pockets at the FXa-FVa interface. From about three million docking calculations of 281,128 compounds in a conformational ensemble of FXa heavy chains identified by molecular dynamics (MD) simulations, 97 compounds and their structural analogues were selected for experimental validation, through a series of inhibition assays. The compound selection was based on their predicted binding affinities to FXa and their ability to successfully bind to multiple protein conformations while showing selectivity for particular binding sites at the FXa/FVa interface. From these, thirty-one (31) compounds were experimentally identified as nonactive site inhibitors. Concentration-based assays further identified 10 compounds represented by four small-molecule families of inhibitors that achieve dose-independent partial inhibition of PTase activity in a nonactive site-dependent and self-limiting mechanism. Several compounds were identified for their ability to bind to protein conformations only seen during MD, highlighting the importance of accounting for protein flexibility in structure-based drug discovery approaches.

Balancing Protein Stability and Activity in Cancer: A New Approach for Identifying Driver Mutations Affecting CBL Ubiquitin Ligase Activation.

Oncogenic mutations in the monomeric Casitas B-lineage lymphoma (Cbl) gene have been found in many tumors, but their significance remains largely unknown. Several human c-Cbl (CBL) structures have recently been solved, depicting the protein at different stages of its activation cycle and thus providing mechanistic insight underlying how stability-activity tradeoffs in cancer-related proteins-may influence disease onset and progression. In this study, we computationally modeled the effects of missense cancer mutations on structures representing four stages of the CBL activation cycle to identify driver mutations that affect CBL stability, binding, and activity. We found that recurrent, homozygous, and leukemia-specific mutations had greater destabilizing effects on CBL states than random noncancer mutations. We further tested the ability of these computational models, assessing the changes in CBL stability and its binding to ubiquitin-conjugating enzyme E2, by performing blind CBL-mediated EGFR ubiquitination assays in cells. Experimental CBL ubiquitin ligase activity was in agreement with the predicted changes in CBL stability and, to a lesser extent, with CBL-E2 binding affinity. Two thirds of all experimentally tested mutations affected the ubiquitin ligase activity by either destabilizing CBL or disrupting CBL-E2 binding, whereas about one-third of tested mutations were found to be neutral. Collectively, our findings demonstrate that computational methods incorporating multiple protein conformations and stability and binding affinity evaluations can successfully predict the functional consequences of cancer mutations on protein activity, and provide a proof of concept for mutations in CBL.

Improving docking results via reranking of ensembles of ligand poses in multiple X-ray protein conformations with MM-GBSA.

There is a tendency in the literature to be critical of scoring functions when docking programs perform poorly. The assumption is that existing scoring functions need to be enhanced or new ones developed in order to improve the performance of docking programs for tasks such as pose prediction and virtual screening. However, failures can result from either sampling or scoring (or a combination of the two), although less emphasis tends to be given to the former. In this work, we use the programs GOLD and Glide on a high-quality data set to explore whether failures in pose prediction and binding affinity estimation can be attributable more to sampling or scoring. We show that identification of the correct pose (docking power) can be improved by incorporating ligand strain into the scoring function or rescoring an ensemble of diverse docking poses with MM-GBSA in a postprocessing step. We explore the use of nondefault docking settings and find that enhancing ligand sampling also improves docking power, again suggesting that sampling is more limiting than scoring for the docking programs investigated in this work. In cross-docking calculations (docking a ligand to a noncognate receptor structure) we observe a significant reduction in the accuracy of pose ranking, as expected and has been reported by others; however, we demonstrate that these alternate poses may in fact be more complementary between the ligand and the rigid receptor conformation, emphasizing that treating the receptor rigidly is an artificial constraint on the docking problem. We simulate protein flexibility by the use of multiple crystallographic conformations of a protein and demonstrate that docking results can be improved with this level of protein sampling. This work indicates the need for better sampling in docking programs, especially for the receptor. This study also highlights the variable descriptive value of RMSD as the sole arbiter of pose replication quality. It is shown that ligand poses within 2 Å of the crystallographic one can show dramatic differences in calculated relative protein-ligand energies. MM-GBSA rescoring of distinct poses overcomes some of the sensitivities of pose ranking experienced by the docking scoring functions due to protein preparation and binding site definition.

Nanog1 in NTERA-2 and Recombinant NanogP8 from Somatic Cancer Cells Adopt Multiple Protein Conformations and Migrate at Multiple M.W Species

Human Nanog1 is a 305-amino acid (aa) homeodomain-containing transcription factor critical for the pluripotency of embryonic stem (ES) and embryonal carcinoma (EC) cells. Somatic cancer cells predominantly express a retrogene homolog of Nanog1 called NanogP8, which is ∼99% similar to Nanog at the aa level. Although the predicted M.W of Nanog1/NanogP8 is ∼35 kD, both have been reported to migrate, on Western blotting (WB), at apparent molecular masses of 29–80 kD. Whether all these reported protein bands represent authentic Nanog proteins is unclear. Furthermore, detailed biochemical studies on Nanog1/NanogpP8 have been lacking. By combining WB using 8 anti-Nanog1 antibodies, immunoprecipitation, mass spectrometry, and studies using recombinant proteins, here we provide direct evidence that the Nanog1 protein in NTERA-2 EC cells exists as multiple M.W species from ∼22 kD to 100 kD with a major 42 kD band detectable on WB. We then demonstrate that recombinant NanogP8 (rNanogP8) proteins made in bacteria using cDNAs from multiple cancer cells also migrate, on denaturing SDS-PAGE, at ∼28 kD to 180 kD. Interestingly, different anti-Nanog1 antibodies exhibit differential reactivity towards rNanogP8 proteins, which can spontaneously form high M.W protein species. Finally, we show that most long-term cultured cancer cell lines seem to express very low levels of or different endogenous NanogP8 protein that cannot be readily detected by immunoprecipitation. Altogether, the current study reveals unique biochemical properties of Nanog1 in EC cells and NanogP8 in somatic cancer cells.

CYP 2D6 binding affinity predictions using multiple ligand and protein conformations.

Because of the large flexibility and malleability of Cytochrome P450 enzymes (CYPs), in silico prediction of CYP binding affinities to drugs and other xenobiotic compounds is a true challenge. In the current work, we use an iterative linear interaction energy (LIE) approach to compute CYP binding affinities from molecular dynamics (MD) simulation. In order to improve sampling of conformational space, we combine results from simulations starting with different relevant protein-ligand geometries. For calculated binding free energies of a set of thiourea compounds binding to the flexible CYP 2D6 isoform, improved correlation with experiment was obtained by combining results ofMDruns starting from distinct protein conformations and ligand-binding orientations. This accuracy was obtained from relatively short MD simulations, which makes our approach computationally attractive for automated calculations of ligand-binding affinities to flexible proteins such as CYPs.

Coevolutionary signals across protein lineages help capture multiple protein conformations

A long-standing problem in molecular biology is the determination of a complete functional conformational landscape of proteins. This includes not only proteins' native structures, but also all their respective functional states, including functionally important intermediates. Here, we reveal a signature of functionally important states in several protein families, using direct coupling analysis, which detects residue pair coevolution of protein sequence composition. This signature is exploited in a protein structure-based model to uncover conformational diversity, including hidden functional configurations. We uncovered, with high resolution (mean ~1.9 Å rmsd for nonapo structures), different functional structural states for medium to large proteins (200-450 aa) belonging to several distinct families. The combination of direct coupling analysis and the structure-based model also predicts several intermediates or hidden states that are of functional importance. This enhanced sampling is broadly applicable and has direct implications in protein structure determination and the design of ligands or drugs to trap intermediate states.