Computational Tools for Allosteric Drug Discovery: Site Identification and Focus Library Design

MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model

A crucial mechanism for controlling the actions of proteins is allostery. Allosteric modulators have the potential to provide many benefits compared to orthosteric ligands, such as increased selectivity and saturability of their effect. The identification of new allosteric sites presents prospects for the creation of innovative medications and enhances our comprehension of fundamental biological mechanisms. Allosteric sites are increasingly found in different protein families through various techniques, such as machine learning applications, which opens up possibilities for creating completely novel medications with a diverse variety of chemical structures. Machine learning methods, such as PASSer, exhibit limited efficacy in accurately finding allosteric binding sites when relying solely on 3D structural information.Scientific ContributionPrior to conducting feature selection for allosteric binding site identification, integration of supporting amino-acid-based information to 3D structural knowledge is advantageous. This approach can enhance performance by ensuring accuracy and robustness. Therefore, we have developed an accurate and robust model called Multimodel Ensemble Feature Selection for Allosteric Site Identification (MEF-AlloSite) after collecting 9460 relevant and diverse features from the literature to characterise pockets. The model employs an accurate and robust multimodal feature selection technique for the small training set size of only 90 proteins to improve predictive performance. This state-of-the-art technique increased the performance in allosteric binding site identification by selecting promising features from 9460 features. Also, the relationship between selected features and allosteric binding sites enlightened the understanding of complex allostery for proteins by analysing selected features. MEF-AlloSite and state-of-the-art allosteric site identification methods such as PASSer2.0 and PASSerRank have been tested on three test cases 51 times with a different split of the training set. The Student's t test and Cohen's D value have been used to evaluate the average precision and ROC AUC score distribution. On three test cases, most of the p-values ( < 0.05 ) and the majority of Cohen's D values ( > 0.5 ) showed that MEF-AlloSite's 1-6% higher mean of average precision and ROC AUC than state-of-the-art allosteric site identification methods are statistically significant.

The value of protein allostery in rational anticancer drug design: an update

INTRODUCTION

Allosteric drugs are advantageous. However, they still face hurdles, including identification of allosteric sites that will effectively alter the active site. Current strategies largely focus on identifying pockets away from the active sites into which the allosteric ligand will dock and do not account for exactly how the active site is altered. Favorable allosteric inhibitors dock into sites that are nearby the active sites and follow nature, mimicking diverse allosteric regulation strategies.

AREAS COVERED

The following article underscores the immense significance of allostery in drug design, describes current allosteric strategies, and especially offers a direction going forward. The article concludes with the authors' expert perspectives on the subject.

EXPERT OPINION

To select a productive venue in allosteric inhibitor development, we should learn from nature. Currently, useful strategies follow this route. Consider, for example, the mechanisms exploited in relieving autoinhibition and in harnessing allosteric degraders. Mimicking compensatory, or rescue mutations may also fall into such a thesis, as can molecular glues that capture features of scaffolding proteins. Capturing nature and creatively tailoring its mimicry can continue to innovate allosteric drug discovery.

Allo-targeting of the kinase domain: Insights from in silico studies and comparison with experiments

The eukaryotic protein kinase domain has been a broadly explored target for drug discovery, despite limitations imposed by its high sequence conservation as a shared modular domain and the development of resistance to drugs. One way of addressing those limitations has been to target its potential allosteric sites, shortly called allo-targeting, in conjunction with, or separately from, its conserved catalytic/orthosteric site that has been widely exploited. Allosteric regulation has gained importance as an alternative to overcome the drawbacks associated with the indiscriminate effect of targeting the active site, and it turned out to be particularly useful for these highly promiscuous and broadly shared kinase domains. Yet, allo-targeting often faces challenges as the allosteric sites are not as clearly defined as its orthosteric sites, and the effect on the protein function may not be unambiguously assessed. A robust understanding of the consequence of site-specific allo-targeting on the conformational dynamics of the target protein is essential to design effective allo-targeting strategies. Recent years have seen important advances in in silico identification of druggable sites and distinguishing among them those sites expected to allosterically mediate conformational switches essential to signal transmission. The present opinion underscores the utility of such computational approaches applied to the kinase domain, with the help of comparison between computational predictions and experimental observations.

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.

Allosteric regulation and inhibition of protein kinases

The human genome encodes more than 500 different protein kinases: signaling enzymes with tightly regulated activity. Enzymatic activity within the conserved kinase domain is influenced by numerous regulatory inputs including the binding of regulatory domains, substrates, and the effect of post-translational modifications such as autophosphorylation. Integration of these diverse inputs occurs via allosteric sites that relate signals via networks of amino acid residues to the active site and ensures controlled phosphorylation of kinase substrates. Here, we review mechanisms of allosteric regulation of protein kinases and recent advances in the field.

Structure and chemistry of enzymatic active sites that play a role in the switch and conformation mechanism

Enzymes, which are biological molecules, are constructed from polypeptide chains, and these molecules are activated through reaction mechanisms. It is the role of enzymes to speed up chemical reactions that are used to build or break down cell structures. Activation energy is reduced by the enzymes' selective binding of substrates in a protected environment. In enzyme tertiary structures, the active sites are commonly situated in a "cleft," which necessitates the diffusion of substrates and products. The amino acid residues of the active site may be far apart in the primary structure owing to the folding required for tertiary structure. Due to their critical role in substrate binding and attraction, changes in amino acid structure at or near the enzyme's active site usually alter enzyme activity. At the enzyme's active site, or where the chemical reactions occur, the substrate is bound. Enzyme substrates are the primary targets of the enzyme's active site, which is designed to assist in the chemical reaction. This chapter elucidates the summary of structure and chemistry of enzymes, their active site features, charges and role of water in the structures to clarify the biochemistry of the enzymes in the depth of atomic features.

Mechanism of activation and the rewired network: New drug design concepts

Precision oncology benefits from effective early phase drug discovery decisions. Recently, drugging inactive protein conformations has shown impressive successes, raising the cardinal questions of which targets can profit and what are the principles of the active/inactive protein pharmacology. Cancer driver mutations have been established to mimic the protein activation mechanism. We suggest that the decision whether to target an inactive (or active) conformation should largely rest on the protein mechanism of activation. We next discuss the recent identification of double (multiple) same-allele driver mutations and their impact on cell proliferation and suggest that like single driver mutations, double drivers also mimic the mechanism of activation. We further suggest that the structural perturbations of double (multiple) in cis mutations may reveal new surfaces/pockets for drug design. Finally, we underscore the preeminent role of the cellular network which is deregulated in cancer. Our structure-based review and outlook updates the traditional Mechanism of Action, informs decisions, and calls attention to the intrinsic activation mechanism of the target protein and the rewired tumor-specific network, ushering innovative considerations in precision medicine.

Dynamic Cellular Cartography: Mapping the Local Determinants of Oligodendrocyte Transcription Factor 2 (OLIG2) Function in Live Cells Using Massively Parallel Fluorescence Correlation Spectroscopy Integrated with Fluorescence Lifetime Imaging Microscopy (mpFCS/FLIM)

Compartmentalization and integration of molecular processes through diffusion are basic mechanisms through which cells perform biological functions. To characterize these mechanisms in live cells, quantitative and ultrasensitive analytical methods with high spatial and temporal resolution are needed. Here, we present quantitative scanning-free confocal microscopy with single-molecule sensitivity, high temporal resolution (∼10 μs/frame), and fluorescence lifetime imaging capacity, developed by integrating massively parallel fluorescence correlation spectroscopy with fluorescence lifetime imaging microscopy (mpFCS/FLIM); we validate the method, use it to map in live cell location-specific variations in the concentration, diffusion, homodimerization, DNA binding, and local environment of the oligodendrocyte transcription factor 2 fused with the enhanced Green Fluorescent Protein (OLIG2-eGFP), and characterize the effects of an allosteric inhibitor of OLIG2 dimerization on these determinants of OLIG2 function. In particular, we show that cytoplasmic OLIG2-eGFP is largely monomeric and freely diffusing, with the fraction of freely diffusing OLIG2-eGFP molecules being f_D,free^cyt = (0.75 ± 0.10) and the diffusion time τ_D,free^cyt = (0.5 ± 0.3) ms. In contrast, OLIG2-eGFP homodimers are abundant in the cell nucleus, constituting ∼25% of the nuclear pool, some f_D,bound^nuc = (0.65 ± 0.10) of nuclear OLIG2-eGFP is bound to chromatin DNA, whereas freely moving OLIG2-eGFP molecules diffuse at the same rate as those in the cytoplasm, as evident from the lateral diffusion times τ_D,free^nuc = τ_D,free^cyt = (0.5 ± 0.3) ms. OLIG2-eGFP interactions with chromatin DNA, revealed through their influence on the apparent diffusion behavior of OLIG2-eGFP, τ_D,bound^nuc (850 ± 500) ms, are characterized by an apparent dissociation constant K_d,app^OLIG2-DNA = (45 ± 30) nM. The apparent dissociation constant of OLIG2-eGFP homodimers was estimated to be K_d,app^{(OLIG2-eGFP)2} ≈ 560 nM. The allosteric inhibitor of OLIG2 dimerization, compound NSC 50467, neither affects OLIG2-eGFP properties in the cytoplasm nor does it alter the overall cytoplasmic environment. In contrast, it significantly impedes OLIG2-eGFP homodimerization in the cell nucleus, increasing five-fold the apparent dissociation constant, K_{d,app,NSC50467}^{(OLIG2-eGFP)2} ≈ 3 μM, thus reducing homodimer levels to below 7% and effectively abolishing OLIG2-eGFP specific binding to chromatin DNA. The mpFCS/FLIM methodology has a myriad of applications in biomedical research and pharmaceutical industry. For example, it is indispensable for understanding how biological functions emerge through the dynamic integration of location-specific molecular processes and invaluable for drug development, as it allows us to quantitatively characterize the interactions of drugs with drug targets in live cells.

Differential miRNAs in acute spontaneous coronary artery dissection: Pathophysiological insights from a potential biomarker

BACKGROUND

Spontaneous Coronary Artery Dissection (SCAD) is an important cause of acute coronary syndromes, particularly in young to middle-aged women. Differentiating acute SCAD from coronary atherothrombosis remains a major clinical challenge.

METHODS

A case-control study was used to explore the usefulness of circulating miRNAs to discriminate both clinical entities. The profile of miRNAs was evaluated using an unbiased human RT-PCR platform and confirmed using individual primers. miRNAs were evaluated in plasma samples from acute SCAD and atherothrombotic acute myocardial infarction (AT-AMI) from two independent cohorts; discovery cohort (SCAD n = 15, AT-AMI n = 15), and validation cohort (SCAD n = 11, AT-AMI n = 41) with 9 healthy control subjects. Plasma levels of IL-8, TGFB1, TGBR1, Endothelin-1 and MMP2 were analysed by ELISA assays.

FINDINGS

From 15 differentially expressed miRNAs detected in cohort 1, we confirmed in cohort 2 the differential expression of 4 miRNAs: miR-let-7f-5p, miR-146a-5p, miR-151a-3p and miR-223-5p, whose expression was higher in SCAD compared to AT-AMI. The combined expression of these 4 miRNAs showed the best predictive value to distinguish between both entities (AUC: 0.879, 95% CI 0.72-1.0) compared to individual miRNAs. Functional profiling of target genes identified an association with blood vessel biology, TGF-beta pathway and cytoskeletal traction force. ELISA assays showed high plasma levels of IL-8, TGFB1, TGFBR1, Endothelin-1 and MMP2 in SCAD patients compared to AT-AMI.

INTERPRETATION

We present a novel signature of plasma miRNAs in patients with SCAD. miR-let-7f-5p, miR-146a-5p, miR-151a-3p and miR-223-5p discriminate SCAD from AT-AMI patients and also shed light on the pathological mechanisms underlying this condition.

FUNDING

Spanish Ministry of Economy and Competitiveness (MINECO): Plan Nacional de Salud SAF2017-82886-R, Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV). Fundación BBVA a equipos de Investigación Científica 2018 and from Caixa Banking Foundation under the project code HR17-00016 to F.S.M. Instituto de Salud Carlos III (AES 2019): PI19/00565 to F.R, PI19/00545 to P.M. CAM (S2017/BMD-3671-INFLAMUNE-CM) from Comunidad de Madrid to FSM and PM. The UK SCAD study was supported by BeatSCAD, the British Heart Foundation (BHF) PG/13/96/30608 the NIHR rare disease translational collaboration and the Leicester NIHR Biomedical Research Centre.

PI3K inhibitors: review and new strategies

The search is on for effective specific inhibitors for PI3Kα mutants. PI3Kα, a critical lipid kinase, has two subunits, catalytic and inhibitory. PIK3CA, the gene that encodes the p110α catalytic subunit is a highly mutated protein in cancer. Dysregulation of PI3Kα signalling is commonly associated with tumorigenesis and drug resistance. Despite its vast importance, only recently the FDA approved the first drug (alpelisib by Novartis) for breast cancer. A second (GDC0077), classified as PI3Kα isoform-specific, is undergoing clinical trials. Not surprisingly, these ATP-competitive drugs commonly elicit severe concentration-dependent side effects. Here we briefly review PI3Kα mutations, focus on PI3K drug repertoire and propose new, to-date unexplored PI3Kα therapeutic strategies. These include (1) an allosteric and orthosteric inhibitor combination and (2) taking advantage of allosteric rescue mutations to guide drug discovery.

Spectral analysis of molecular dynamics simulations on PDZ: MD sectors

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

Cosolvent Analysis Toolkit (CAT): a robust hotspot identification platform for cosolvent simulations of proteins to expand the druggable proteome

Cosolvent Molecular Dynamics (MD) simulations are increasingly popular techniques developed for prediction and characterization of allosteric and cryptic binding sites, which can be rendered “druggable” by small molecule ligands. Despite their conceptual simplicity and effectiveness, the analysis of cosolvent MD trajectories relies on pocket volume data, which requires a high level of manual investigation and may introduce a bias. In this work, we present CAT (Cosolvent Analysis Toolkit): an open-source, freely accessible analytical tool, suitable for automated analysis of cosolvent MD trajectories. CAT is compatible with commonly used molecular graphics software packages such as UCSF Chimera and VMD. Using a novel hybrid empirical force field scoring function, CAT accurately ranks the dynamic interactions between the macromolecular target and cosolvent molecules. To benchmark, CAT was used for three validated protein targets with allosteric and orthosteric binding sites, using five chemically distinct cosolvent molecules. For all systems, CAT has accurately identified all known sites. CAT can thus assist in computational studies aiming at identification of protein “hotspots” in a wide range of systems. As an easy-to-use computational tool, we expect that CAT will contribute to an increase in the size of the potentially ‘druggable’ human proteome.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Mechanism of Selective Enzyme Inhibition through Uncompetitive Regulation of an Allosteric Agonist

Classical uncompetitive inhibitors are potent pharmacological modulators of enzyme function. Since they selectively target enzyme-substrate complexes (E:S), their inhibitory potency is amplified by increasing substrate concentrations. Recently, an unconventional uncompetitive inhibitor, called CE3F4R, was discovered for the exchange protein activated by cAMP isoform 1 (EPAC1). Unlike conventional uncompetitive inhibitors, CE3F4R is uncompetitive with respect to an allosteric effector, cAMP, as opposed to the substrate (i.e., CE3F4R targets the E:cAMP rather than the E:S complex). However, the mechanism of CE3F4R as an uncompetitive inhibitor is currently unknown. Here, we elucidate the mechanism of CE3F4R's action using NMR spectroscopy. Due to limited solubility and line broadening, which pose major challenges for traditional structural determination approaches, we resorted to a combination of protein- and ligand-based NMR experiments to comparatively analyze EPAC mutations, inhibitor analogs, and cyclic nucleotide derivatives that trap EPAC at different stages of activation. We discovered that CE3F4R binds within the EPAC cAMP-binding domain (CBD) at a subdomain interface distinct from the cAMP binding site, acting as a wedge that stabilizes a cAMP-bound mixed-intermediate. The mixed-intermediate includes attributes of both the apo/inactive and cAMP-bound/active states. In particular, the intermediate targeted by CE3F4R traps a CBD's hinge helix in its inactive conformation, locking EPAC into a closed domain topology that restricts substrate access to the catalytic domain. The proposed mechanism of action also explains the isoform selectivity of CE3F4R in terms of a single EPAC1 versus EPAC2 amino acid difference that destabilizes the active conformation of the hinge helix.