Allosteric Methods and Their Applications: Facilitating the Discovery of Allosteric Drugs and the Investigation of Allosteric Mechanisms

Comprehensive insights into carbonic anhydrase inhibition: A triad of In vitro, In silico, and In vivo perspectives

Carbonic anhydrases (CAs) are zinc-dependent metalloenzymes essential for sustaining physiological balance by facilitating the reversible conversion of carbon dioxide to its hydrated form. Their biological significance, coupled with their involvement in a wide array of pathological conditions, makes them attractive targets for therapeutic intervention. This review presents a comprehensive analysis of carbonic anhydrase inhibition through an integrated triad of in vitro, In silico, and In vivo perspectives. In vitro studies provide critical insights into the mechanisms of enzyme inhibition, enabling the identification and optimization of potent inhibitors while elucidating their structure-activity relationships. In silico methodologies, including docking, molecular dynamics (MD) simulation, virtual screening, ADMET, and QSAR analyses, have emerged as invaluable tools in rational drug design, streamlining the discovery and development of isoform-specific inhibitors. Complementing these efforts, In vivo investigations validate the pharmacokinetics, pharmacodynamics, and therapeutic efficacy of CA inhibitors (CAIs) in disease models, bridging the gap between laboratory findings and clinical applications. The therapeutic relevance of CAIs extends across multiple domains, including glaucoma, epilepsy, cancer, metabolic disorders, and infectious diseases. Emerging applications, such as their potential use in combating antimicrobial resistance and modulating immune responses, further underscore their versatility. However, challenges such as achieving isoform selectivity, minimizing off-target effects, and translating preclinical findings into clinical success persist. Advances in fragment-based drug design, artificial intelligence-driven discovery, and innovative experimental techniques are poised to address these limitations, paving the way for the next generation of CAIs.

The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives

Protein allostery is a fundamental biological regulatory mechanism that allows communication between distant locations within a protein, modifying its function in response to signals. Experimental techniques, such as NMR spectroscopy and cryo-electron microscopy (cryo-EM), are critical validation tools for computational predictions and provide valuable insights into dynamic conformational changes. Combining these approaches has greatly improved our understanding of classical conformational allostery and complex dynamic coupling mechanisms. Recent advances in machine learning and enhanced sampling methods have broadened the scope of allostery research, identifying cryptic allosteric sites and directing new drug discovery approaches. Despite progress, bridging static structural data with dynamic functional states remains challenging. This review underscores the importance of combining experimental and computational approaches to comprehensively understand protein allostery and its diverse applications in biology and medicine.

Assessing the role of deep eutectic solvents in Yarrowia lipolytica inhibition

Yarrowia lipolytica has gained recognition as a microorganism with biological relevance and extensive biotechnological applications. Some of its features include a high enzyme secretion capacity and a high cell-density fermentation mode. Hexokinase (YlHxk) is a vital enzyme in Y. lipolytica growth since it catalyzes glucose metabolism through phosphorylation in the glycolytic pathway. Given the potential application of deep eutectic solvents (DES) as novel solvents in biotechnological processes, this study evaluated the influence of eighteen DES on the growth of Y. lipolytica. Furthermore, this work examined the effects of individual ions on the YlHxk enzyme by analyzing its enzymatic tunnel structure, molecule transport, and molecular docking. The results revealed a significant reduction in yeast growth in the presence of most DES compared to the control (medium without DES), with the exception of the [N8881]Cl: hexanoic acid (1:1) DES. The growth varied between 11.95 ± 0.60 and 0.68 ± 0.17 g dry cell weight L-1. According to the enzymatic tunnel analysis, DES components associated with the lowest microbial growth values were transported through tunnel 1. On the other hand, DES components had their pathway facilitated through tunnel 2 ([N8881]+ and hexanoic acid) and showed growth values close to the control. Molecular docking analysis identified a similarity between all the ligands in this tunnel (including substrate and product), presenting binding interactions with the ASN273 amino acid of the YlHxk active site. Combining experimental results with computational tools provided promising insights at the molecular level, while also potentially reducing analysis costs and time, paving the way for similar approaches in broad biocatalytic reactions.

A novel allosteric driver mutation of β-glucuronidase promotes head and neck squamous cell carcinoma progression through STT3B-mediated PD-L1 N-glycosylation

Head and neck squamous cell carcinoma (HNSCC) develops and advances because of the accumulation of somatic mutations located in orthosteric and allosteric areas. However, the biological effects of allosteric driver mutations during tumorigenesis are mostly unknown. Here, we mapped somatic mutations generated from 10 tumor-normal matched HNSCC samples into allosteric sites to prioritize the mutated allosteric proteins via whole-exome sequencing and AlloDriver, identifying the specific mutation H351Q in β-glucuronidase (GUSB), a lysosomal enzyme, as a novel allosteric driver mutation, which considerably encouraged HNSCC progression both in vitro and in vivo. Mechanistically, the allosteric mutation of H351Q remarkably attenuated protein trafficking from the endoplasmic reticulum (ER) to lysosomes, leading to ER retention, in which GUSB-H351Q facilitated the aberrant N-glycosylation of PD-L1 through increasing protein stability and mRNA transcripts of the STT3 oligosaccharyltransferase complex catalytic subunit B, an oligosaccharyltransferase complex. Moreover, GUSB-H351Q reshaped a more immunosuppressive microenvironment featuring increased infiltration of exhausted CD8+ T cells and remodeled tumor metabolism, characterized by increased activity of the purine metabolism pathways and pyruvic acid accumulation. This study provides a mechanism-driven approach to overcoming HNSCC progression and immune evasion and identifies novel druggable targets based on the presence of GUSB allosteric driver mutation.

Global hinge sites of proteins as target sites for drug binding

Hinge sites of proteins play a key role in mediating conformational mechanics. Among them, those involved in the most collective modes of motion, also called global hinges, are of particular interest, as they support cooperative rearrangements that are often functional. Yet, the utility of targeting global hinges for modulating function remains to be established. We present here a systematic study of a series of proteins resolved in drug-bound forms to examine the probabilistic occurrence of spatial overlaps between hinge sites and drug-binding pockets. Our analysis reveals a high propensity of drug binding to hinge sites compared to random. Notably, one-third of currently approved drugs are colocalized with hinge sites. These mechanosensitive sites are predictable by simple models such as the Gaussian Network Model. Their targeting thus emerges as a viable strategy for developing a new class of drugs that would exploit and modulate the target proteins' intrinsic dynamics, and potentially alleviate drug-resistance when used in combination with orthosteric or allosteric drugs.

Advances of Predicting Allosteric Mechanisms Through Protein Contact in New Technologies and Their Application

Allostery is an intriguing phenomenon wherein the binding activity of a biological macromolecule is modulated via non-canonical binding site, resulting in synchronized functional changes. The mechanics underlying allostery are relatively complex and this review is focused on common methodologies used to study allostery, such as X-ray crystallography, NMR spectroscopy, and HDXMS. Different methodological approaches are used to generate data in different scenarios. For example, X-ray crystallography provides high-resolution structural information, NMR spectroscopy offers dynamic insights into allosteric interactions in solution, and HDXMS provides information on protein dynamics. The residue transition state (RTS) approach has emerged as a critical tool in understanding the energetics and conformational changes associated with allosteric regulation. Allostery has significant implications in drug discovery, gene transcription, disease diagnosis, and enzyme catalysis. Enzymes' catalytic activity can be modulated by allosteric regulation, offering opportunities to develop novel therapeutic alternatives. Understanding allosteric mechanisms associated with infectious organisms like SARS-CoV and bacterial pathogens can aid in the development of new antiviral drugs and antibiotics. Allosteric mechanisms are crucial in the regulation of a variety of signal transduction and cell metabolism pathways, which in turn govern various cellular processes. Despite progress, challenges remain in identifying allosteric sites and characterizing their contribution to a variety of biological processes. Increased understanding of these mechanisms can help develop allosteric systems specifically designed to modulate key biological mechanisms, providing novel opportunities for the development of targeted therapeutics. Therefore, the current review aims to summarize common methodologies that are used to further our understanding of allosteric mechanisms. In conclusion, this review provides insights into the methodologies used for the study of allostery, its applications in in silico modeling, the mechanisms underlying antibody allostery, and the ongoing challenges and prospects in advancing our comprehension of this intriguing phenomenon.

GETdb: A comprehensive database for genetic and evolutionary features of drug targets

The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.

Negative allosteric modulator of Group Ⅰ mGluRs: Recent advances and therapeutic perspective for neuropathic pain

Neuropathic pain (NP) is a widespread public health problem that existing therapeutic treatments cannot manage adequately; therefore, novel treatment strategies are urgently required. G-protein-coupled receptors are important for intracellular signal transduction, and widely participate in physiological and pathological processes, including pain perception. Group I metabotropic glutamate receptors (mGluRs), including mGluR1 and mGluR5, are predominantly implicated in central sensitization, which can lead to hyperalgesia and allodynia. Many orthosteric site antagonists targeting Group I mGluRs have been found to alleviate NP, but their poor efficacy, low selectivity, and numerous side effects limit their development in NP treatment. Here we reviewed the advantages of Group I mGluRs negative allosteric modulators (NAMs) over orthosteric site antagonists based on allosteric modulation mechanism, and the challenges and opportunities of Group I mGluRs NAMs in NP treatment. This article aims to elucidate the advantages and future development potential of Group I mGluRs NAMs in the treatment of NP.

Adaptive Workflows of Machine Learning Illuminate the Sequential Operation Mechanism of the TAK1's Allosteric Network

Allostery is a fundamental mechanism driving biomolecular processes that holds significant therapeutic concern. Our study rigorously investigates how two distinct machine-learning algorithms uniquely classify two already close-to-active DFG-in states of TAK1, differing just by the presence or absence of its allosteric activator TAB1, from an ensemble mixture of conformations (obtained from 2.4 μs molecular dynamics (MD) simulations). The novelty, however, lies in understanding the deeper algorithmic potentials to systematically derive a diverse set of differential residue connectivity features that reconstruct the essential mechanistic architecture for TAK1-TAB1 allostery in such a close-to-active biochemical scenario. While the recursive, random forest-based workflow displays the potential of conducting discretized, hierarchical derivation of allosteric features, a multilayer perceptron-based approach gains considerable efficacy in revealing fluid connected patterns of features when hybridized with mutual information scoring. Interestingly, both pipelines benchmark similar directions of functional conformational changes for TAK1's activation. The findings significantly advance the depth of mechanistic understanding by highlighting crucial activation signatures along a directed C-lobe → activation loop → ATP pocket channel of information flow, including (1) the αF-αE biterminal alignments and (2) the "catalytic" drift of the activation loop toward kinase active site. Besides, some novel allosteric hotspots (K253, Y206, N189, etc.) are further recognized as TAB1 sensors, transducers, and responders, including a benchmark E70 mutation site, precisely mapping the important structural segments for sequential allosteric execution. Hence, our work demonstrates how to navigate through greater structural depths and dimensions of dynamic allosteric machineries just by leveraging standard ML methods in suitable streamlined workflows adaptive to the specific system and objectives.

Allosteric Activation of α7 Nicotinic Acetylcholine Receptors by Novel 2-Arylamino-thiazole-5-carboxylic Acid Amide Derivatives for the Improvement of Cognitive Deficits in Mice

Enhancing α7 nAChR function serves as a therapeutic strategy for cognitive disorders. Here, we report the synthesis and evaluation of 2-arylamino-thiazole-5-carboxylic acid amide derivatives 6-9 that as positive allosteric modulators (PAMs) activate human α7 nAChR current expressed in Xenopus ooctyes. Among the 4-amino derivatives, a representative atypical type I PAM 6p exhibits potent activation of α7 current with an EC50 of 1.3 μM and the maximum activation effect on the current over 48-fold in the presence of acetylcholine (100 μM). The structure-activity relationship (SAR) analysis reveals that the 4-amino group is crucial for the allosteric activation of α7 currents by compound 6p as the substitution of 4-methyl group results in its conversion to compound 7b (EC50 = 2.1 μM; max effect: 58-fold) characterized as a typical type I PAM. Furthermore, both 6p and 7b are able to rescue auditory gating deficits in mouse schizophrenia-like model of acoustic startle prepulse inhibition.

Decoupling the dynamic mechanism revealed by FGFR2 mutation-induced population shift

The fibroblast growth factor receptor 2 (FGFR2) is a key component in cellular signaling networks, and its dysfunctional activation has been implicated in various diseases including cancer and developmental disorders. Mutations at the activation loop (A-loop) have been suggested to trigger an increased basal kinase activity. However, the molecular mechanism underlying this highly dynamic process has not been fully understood due to the limitation of static structural information. Here, we conducted multiple, large-scale Gaussian accelerated molecular dynamics simulations of five (K659E, K659N, K659M, K659Q, and K659T) FGFR2 mutants at the A-loop, and comprehensively analyzed the dynamic molecular basis of FGFR2 activation. The results quantified the population shift of each system, revealing that all mutants had a higher proportion of active-like states. Using Markov state models, we extracted the representative structure of different conformational states and identified key residues related to the increased kinase activity. Furthermore, community network analysis showed enhanced information connections in the mutants, highlighting the long-range allosteric communication between the A-loop and the hinge region. Our findings may provide insights into the dynamic mechanism for FGFR2 dysfunctional activation and allosteric drug discovery.Communicated by Ramaswamy H. Sarma.

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.

Designing drugs and chemical probes with the dualsteric approach

Traditionally, drugs are monovalent, targeting only one site on the protein surface. This includes orthosteric and allosteric drugs, which bind the protein at orthosteric and allosteric sites, respectively. Orthosteric drugs are good in potency, whereas allosteric drugs have better selectivity and are solutions to classically undruggable targets. However, it would be difficult to simultaneously reach high potency and selectivity when targeting only one site. Also, both kinds of monovalent drugs suffer from mutation-caused drug resistance. To overcome these obstacles, dualsteric modulators have been proposed in the past twenty years. Compared to orthosteric or allosteric drugs, dualsteric modulators are bivalent (or bitopic) with two pharmacophores. Each of the two pharmacophores bind the protein at the orthosteric and an allosteric site, which could bring the modulator with special properties beyond monovalent drugs. In this study, we comprehensively review the current development of dualsteric modulators. Our main effort reason and illustrate the aims to apply the dualsteric approach, including a "double win" of potency and selectivity, overcoming mutation-caused drug resistance, developments of function-biased modulators, and design of partial agonists. Moreover, the strengths of the dualsteric technique also led to its application outside pharmacy, including the design of highly sensitive fluorescent tracers and usage as molecular rulers. Besides, we also introduced drug targets, designing strategies, and validation methods of dualsteric modulators. Finally, we detail the conclusions and perspectives.

PASSerRank: Prediction of allosteric sites with learning to rank

Allostery plays a crucial role in regulating protein activity, making it a highly sought-after target in drug development. One of the major challenges in allosteric drug research is the identification of allosteric sites. In recent years, many computational models have been developed for accurate allosteric site prediction. Most of these models focus on designing a general rule that can be applied to pockets of proteins from various families. In this study, we present a new approach using the concept of Learning to Rank (LTR). The LTR model ranks pockets based on their relevance to allosteric sites, that is, how well a pocket meets the characteristics of known allosteric sites. After the training and validation on two datasets, the Allosteric Database (ASD) and CASBench, the LTR model was able to rank an allosteric pocket in the top three positions for 83.6% and 80.5% of test proteins, respectively. The model outperforms other common machine learning models with higher F1 scores (0.662 in ASD and 0.608 in CASBench) and Matthews correlation coefficients (0.645 in ASD and 0.589 in CASBench). The trained model is available on the PASSer platform (https://passer.smu.edu) to aid in drug discovery research.

Markov State Models and Perturbation-Based Approaches Reveal Distinct Dynamic Signatures and Hidden Allosteric Pockets in the Emerging SARS-Cov-2 Spike Omicron Variant Complexes with the Host Receptor: The Interplay of Dynamics and Convergent Evolution Modulates Allostery and Functional Mechanisms

The new generation of SARS-CoV-2 Omicron variants displayed a significant growth advantage and increased viral fitness by acquiring convergent mutations, suggesting that the immune pressure can promote convergent evolution leading to the sudden acceleration of SARS-CoV-2 evolution. In the current study, we combined structural modeling, microsecond molecular dynamics simulations, and Markov state models to characterize conformational landscapes and identify specific dynamic signatures of the SARS-CoV-2 spike complexes with the host receptor ACE2 for the recently emerged highly transmissible XBB.1, XBB.1.5, BQ.1, and BQ.1.1 Omicron variants. Microsecond simulations and Markovian modeling provided a detailed characterization of the functional conformational states and revealed the increased thermodynamic stabilization of the XBB.1.5 subvariant, which can be contrasted to more dynamic BQ.1 and BQ.1.1 subvariants. Despite considerable structural similarities, Omicron mutations can induce unique dynamic signatures and specific distributions of the conformational states. The results suggested that variant-specific changes of the conformational mobility in the functional interfacial loops of the receptor-binding domain in the SARS-CoV-2 spike protein can be fine-tuned through crosstalk between convergent mutations which could provide an evolutionary path for modulation of immune escape. By combining atomistic simulations and Markovian modeling analysis with perturbation-based approaches, we determined important complementary roles of convergent mutation sites as effectors and receivers of allosteric signaling involved in modulation of conformational plasticity and regulation of allosteric communications. This study also revealed hidden allosteric pockets and suggested that convergent mutation sites could control evolution and distribution of allosteric pockets through modulation of conformational plasticity in the flexible adaptable regions.

Glycomimetics for the inhibition and modulation of lectins

Carbohydrates are essential mediators of many processes in health and disease. They regulate self-/non-self- discrimination, are key elements of cellular communication, cancer, infection and inflammation, and determine protein folding, function and life-times. Moreover, they are integral to the cellular envelope for microorganisms and participate in biofilm formation. These diverse functions of carbohydrates are mediated by carbohydrate-binding proteins, lectins, and the more the knowledge about the biology of these proteins is advancing, the more interfering with carbohydrate recognition becomes a viable option for the development of novel therapeutics. In this respect, small molecules mimicking this recognition process become more and more available either as tools for fostering our basic understanding of glycobiology or as therapeutics. In this review, we outline the general design principles of glycomimetic inhibitors (Section 2). This section is then followed by highlighting three approaches to interfere with lectin function, i.e. with carbohydrate-derived glycomimetics (Section 3.1), novel glycomimetic scaffolds (Section 3.2) and allosteric modulators (Section 3.3). We summarize recent advances in design and application of glycomimetics for various classes of lectins of mammalian, viral and bacterial origin. Besides highlighting design principles in general, we showcase defined cases in which glycomimetics have been advanced to clinical trials or marketed. Additionally, emerging applications of glycomimetics for targeted protein degradation and targeted delivery purposes are reviewed in Section 4.

Research progress of indole-fused derivatives as allosteric modulators: Opportunities for drug development

Allosteric modulation is a direct and effective method for regulating the function of biological macromolecules, which play vital roles in various cellular activities. Unlike orthosteric modulators, allosteric modulators bind to sites distant from the protein's orthosteric/active site and can have specific effects on the protein's function or activity without competing with endogenous ligands. Compared to traditional orthosteric modulators, allosteric modulators offer several advantages, including reduced side effects, greater specificity, and lower toxicity, making them a promising strategy for developing novel drugs. Indole-fused architectures are widely distributed in natural products and bioactive drug leads, displaying diverse biological activities that attract the interest of both chemists and biologists in drug discovery. Currently, an increasing number of indole-fused compounds have exhibited potent activities in allosteric modulation. In this review, we provide a brief summary of examples of allosteric modulators based on the indole-fused complex architecture, highlighting the strategies for drug design/discovery and the structure-activity relationships of allosteric modulators from the perspective of medicinal chemistry.

Markov State Models and Perturbation-Based Approaches Reveal Distinct Dynamic Signatures and Hidden Allosteric Pockets in the Emerging SARS-Cov-2 Spike Omicron Variants Complexes with the Host Receptor: The Interplay of Dynamics and Convergent Evolution Modulates Allostery and Functional Mechanisms

The new generation of SARS-CoV-2 Omicron variants displayed a significant growth advantage and the increased viral fitness by acquiring convergent mutations, suggesting that the immune pressure can promote convergent evolution leading to the sudden acceleration of SARS-CoV-2 evolution. In the current study, we combined structural modeling, extensive microsecond MD simulations and Markov state models to characterize conformational landscapes and identify specific dynamic signatures of the SARS-CoV-2 spike complexes with the host receptor ACE2 for the recently emerged highly transmissible XBB.1, XBB.1.5, BQ.1, and BQ.1.1 Omicron variants. Microsecond simulations and Markovian modeling provided a detailed characterization of the conformational landscapes and revealed the increased thermodynamic stabilization of the XBB.1.5 subvariant which is contrasted to more dynamic BQ.1 and BQ.1.1 subvariants. Despite considerable structural similarities, Omicron mutations can induce unique dynamic signatures and specific distributions of conformational states. The results suggested that variant-specific changes of conformational mobility in the functional interfacial loops of the spike receptor binding domain can be fine-tuned through cross-talk between convergent mutations thereby providing an evolutionary path for modulation of immune escape. By combining atomistic simulations and Markovian modeling analysis with perturbation-based approaches, we determined important complementary roles of convergent mutation sites as effectors and receivers of allosteric signaling involved in modulating conformational plasticity at the binding interface and regulating allosteric responses. This study also characterized the dynamics-induced evolution of allosteric pockets in the Omicron complexes that revealed hidden allosteric pockets and suggested that convergent mutation sites could control evolution and distribution of allosteric pockets through modulation of conformational plasticity in the flexible adaptable regions. Through integrative computational approaches, this investigation provides a systematic analysis and comparison of the effects of Omicron subvariants on conformational dynamics and allosteric signaling in the complexes with the ACE2 receptor.

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From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field.

Machine learning and protein allostery

The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric mechanisms will have an increasingly important role in bridging a wide spectrum of data-intensive experimental and theoretical technologies.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Recent applications of computational methods to allosteric drug discovery

Interest in exploiting allosteric sites for the development of new therapeutics has grown considerably over the last two decades. The chief driving force behind the interest in allostery for drug discovery stems from the fact that in comparison to orthosteric sites, allosteric sites are less conserved across a protein family, thereby offering greater opportunity for selectivity and ultimately tolerability. While there is significant overlap between structure-based drug design for orthosteric and allosteric sites, allosteric sites offer additional challenges mostly involving the need to better understand protein flexibility and its relationship to protein function. Here we examine the extent to which structure-based drug design is impacting allosteric drug design by highlighting several targets across a variety of target classes.

Phytate-Induced Dose-Response Auto-Activation of Enzyme in Commercial Recombinant Phytase From Escherichia coli

Background

Microbial phytase is one of the most widely used enzymes in food industries like cattle, poultry, and aquaculture food. Therefore, understanding the kinetic properties of the enzyme is very important to evaluate and predict its behavior in the digestive system of livestock. Working on phytase is one of the most challenging experiments because of some problems, including free inorganic phosphate (FIP) impurity in phytate (substrate) and interference reaction of the reagent with both phosphates (product and phytate impurity).

Objective

In the present study, FIP impurity of phytate was removed, and then it was shown that the substrate (phytate) has a dual role in enzyme kinetics: substrate and activator.

Material and Methods

phytate impurity was decreased by two-step recrystallization prior to the enzyme assay. The impurity removal was estimated by the ISO30024:2009 method and confirmed by Fourier-transform infrared (FTIR) spectroscopy. The kinetic behavior of phytase activity was evaluated using the purified phytate as substrate by non-Michaelis-Menten analysis, including Eadie-Hofstee, Clearance, and Hill plots. The possibility of an allosteric site on phytase was assessed by molecular docking.

Results

The results showed a 97.2% decrease in FIP due to recrystallization. The phytase saturation curve had a sigmoidal appearance, and Lineweaver-Burk plot with a negative y-intercept indicated the positive homotropic effect of the substrate on the enzyme activity. A right-side concavity of Eadie-Hofstee plot confirmed it. Hill coefficient was calculated to be 2.26. Molecular docking also showed that Escherichia coli phytase molecule has another binding site for phytate very close to the active site, called "allosteric site".

Conclusions

The observations strongly propose the existence of an intrinsic molecular mechanism in Escherichia coli phytase molecules to be promoted for more activity by its substrate, phytate (positive homotropic allosteric effect). In silico analysis showed that phytate binding to the allosteric site caused new substrate-mediated inter-domain interactions, which seems to lead to a more active conformation of phytase. Our results provide a strong basis for animal feed development strategies, especially in the case of poultry food and supplements, regarding a short food passage time in their gastrointestinal tract and variable concentration of phytate along with it. Additionally, the results strengthen our understanding of phytase auto-activation as well as allosteric regulation of monomeric proteins in general.

Molecular Insights into the Heterotropic Allosteric Mechanism in Cytochrome P450 3A4-Mediated Midazolam Metabolism

Cytochrome P450 3A4 (CYP3A4) is the main P450 enzyme for drug metabolism and drug-drug interactions (DDIs), as it is involved in the metabolic process of approximately 50% of drugs. A detailed mechanistic elucidation of DDIs mediated by CYP3A4 is commonly believed to be critical for drug optimization and rational use. Here, two typical probes, midazolam (MDZ, substrate) and testosterone (TST, allosteric effector), are used to investigate the molecular mechanism of CYP3A4-mediated heterotropic allosteric interactions, through conventional molecular dynamics (cMD) and well-tempered metadynamics (WT-MTD) simulations. Distance monitoring shows that TST can stably bind in two potential peripheral sites (Site 1 and Site 2) of CYP3A4. The binding of TST at these two sites can induce conformational changes in CYP3A4 flexible loops on the basis of conformational analysis, thereby promoting the transition of the MDZ binding mode and affecting the ratio of MDZ metabolites. According to the results of the residue interaction network, multiple allosteric communication pathways are identified that can provide vivid and applicable insights into the heterotropic allostery of TST on MDZ metabolism. Comparing the regulatory effects and the communication pathways, the allosteric effect caused by TST binding in Site 2 seems to be more pronounced than in Site 1. Our findings could provide a deeper understanding of CYP3A4-mediated heterotropic allostery at the atomic level and would be helpful for rational drug use as well as the design of new allosteric modulators.

Exploring the druggability of the binding site of aurovertin, an exogenous allosteric inhibitor of FOF1-ATP synthase

In addition to playing a central role in the mitochondria as the main producer of ATP, F_OF₁-ATP synthase performs diverse key regulatory functions in the cell membrane. Its malfunction has been linked to a growing number of human diseases, including hypertension, atherosclerosis, cancer, and some neurodegenerative, autoimmune, and aging diseases. Furthermore, inhibition of this enzyme jeopardizes the survival of several bacterial pathogens of public health concern. Therefore, F_OF₁-ATP synthase has emerged as a novel drug target both to treat human diseases and to combat antibiotic resistance. In this work, we carried out a computational characterization of the binding sites of the fungal antibiotic aurovertin in the bovine F₁ subcomplex, which shares a large identity with the human enzyme. Molecular dynamics simulations showed that although the binding sites can be described as preformed, the inhibitor hinders inter-subunit communications and exerts long-range effects on the dynamics of the catalytic site residues. End-point binding free energy calculations revealed hot spot residues for aurovertin recognition. These residues were also relevant to stabilize solvent sites determined from mixed-solvent molecular dynamics, which mimic the interaction between aurovertin and the enzyme, and could be used as pharmacophore constraints in virtual screening campaigns. To explore the possibility of finding species-specific inhibitors targeting the aurovertin binding site, we performed free energy calculations for two bacterial enzymes with experimentally solved 3D structures. Finally, an analysis of bacterial sequences was carried out to determine conservation of the aurovertin binding site. Taken together, our results constitute a first step in paving the way for structure-based development of new allosteric drugs targeting F_OF₁-ATP synthase sites of exogenous inhibitors.

Design, synthesis and biological evaluation of 2-aminopyridine derivatives as USP7 inhibitors

A series of novel 2-aminopyridine derivatives 1-26 have been designed and synthesized by structural modifications on a lead USP7 inhibitor, GNE6640. All the compounds were evaluated for their USP7 inhibitory activities. The results showed that most of the compounds have good USP7 inhibitory activities at the concentration of 50 μM. Among them, compounds 7, 14 and 21 are the most potential ones from each category with the IC₅₀ values of 7.6 ± 0.1 μM, 17.0 ± 0.2 μM and 11.6 ± 0.5 μM, respectively. Compounds 7 and 21 expressed significant binding interactions with USP7 by surface plasmon resonance (SPR)-based binding assay, but both of them presented moderate antiproliferative activities against HCT116 cells. They could effectively promote MDM2 degradation, p53 stabilization and p21 gene expression in the western blot analysis.

Computer-Aided Drug Design Boosts RAS Inhibitor Discovery

The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.

Mechanistic insights into the clinical Y96D mutation with acquired resistance to AMG510 in the KRASG12C

Special oncogenic mutations in the RAS proteins lead to the aberrant activation of RAS and its downstream signaling pathways. AMG510, the first approval drug for KRAS, covalently binds to the mutated cysteine 12 of KRASG12C protein and has shown promising antitumor activity in clinical trials. Recent studies have reported that the clinically acquired Y96D mutation could severely affect the effectiveness of AMG510. However, the underlying mechanism of the drug-resistance remains unclear. To address this, we performed multiple microsecond molecular dynamics simulations on the KRASG12C−AMG510 and KRASG12C/Y96D−AMG510 complexes at the atomic level. The direct interaction between the residue 96 and AMG510 was impaired owing to the Y96D mutation. Moreover, the mutation yielded higher flexibility and more coupled motion of the switch II and α3-helix, which led to the departing motion of the switch II and α3-helix. The resulting departing motion impaired the interaction between the switch II and α3-helix and subsequently induced the opening and loosening of the AMG510 binding pocket, which further disrupted the interaction between the key residues in the pocket and AMG510 and induced an increased solvent exposure of AMG510. These findings reveal the resistance mechanism of AMG510 to KRASG12C/Y96D, which will help to offer guidance for the development of KRAS targeted drugs to overcome acquired resistance.

Mechanistic Insights into the Long-range Allosteric Regulation of KRAS Via Neurofibromatosis Type 1 (NF1) Scaffold Upon SPRED1 Loading

Allosteric regulation is the most direct and efficient way of regulating protein function, wherein proteins transmit the perturbations at one site to another distinct functional site. Deciphering the mechanism of allosteric regulation is of vital importance for the comprehension of both physiological and pathological events in vivo as well as the rational allosteric drug design. However, it remains challenging to elucidate dominant allosteric signal transduction pathways, especially for large and multi-component protein machineries where long-range allosteric regulation exits. One of the quintessential examples having long-range allosteric regulation is the ternary complex, SPRED1-RAS-neurofibromin type 1 (NF1, a RAS GTPase-activating protein), in which SPRED1 facilitates RAS-GTP hydrolysis by interacting with NF1 at a distal, allosteric site from the RAS binding site. To address the underlying mechanism, we performed extensive Gaussian accelerated molecular dynamics simulations and Markov state model analysis of KRAS-NF1 complex in the presence and absence of SPRED1. Our findings suggested that SPRED1 loading allosterically enhanced KRAS-NF1 binding, but hindered conformational transformation of the NF1 catalytic center for RAS hydrolysis. Moreover, we unveiled the possible allosteric pathways upon SPRED1 binding through difference contact network analysis. This study not only provided an in-depth mechanistic insight into the allosteric regulation of KRAS by SPRED1, but also shed light on the investigation of long-range allosteric regulation among complex macromolecular systems.

The Role of Conformational Dynamics and Allostery in the Control of Distinct Efficacies of Agonists to the Glucocorticoid Receptor

Glucocorticoid receptor (GR) regulates various cellular functions. Given its broad influence on metabolic activities, it has been the target of drug discovery for decades. However, how drugs induce conformational changes in GR has remained elusive. Herein, we used five GR agonists (dex, AZ938, pred, cor, and dibC) with different efficacies to investigate which aspect of the ligand induced the differences in efficacy. We performed molecular dynamics simulations on the five systems (dex-, AZ938-, pred-, cor-, and dibC-bound systems) and observed a distinct discrepancy in the conformation of the cofactor TIF2. Moreover, we discovered ligand-induced differences regarding the level of conformational changes posed by the binding of cofactor TIF2 and identified a pair of essential residues D590 and T39. We further found a positive correlation between the efficacies of ligands and the interaction of the two binding pockets' domains, where D590 and T739 were involved, implying their significance in the participation of allosteric communication. Using community network analysis, two essential communities containing D590 and T739 were identified with their connectivity correlating to the efficacy of ligands. The potential communication pathways between these two residues were revealed. These results revealed the underlying mechanism of allosteric communication between the ligand-binding and cofactor-binding pockets and identified a pair of important residues in the allosteric communication pathway, which can serve as a guide for future drug discovery.

Insights into the Allosteric Effect of SENP1 Q597A Mutation on the Hydrolytic Reaction of SUMO1 via an Integrated Computational Study

Small ubiquitin-related modifier (SUMO)-specific protease 1 (SENP1) is a cysteine protease that catalyzes the cleavage of the C-terminus of SUMO1 for the processing of SUMO precursors and deSUMOylation of target proteins. SENP1 is considered to be a promising target for the treatment of hepatocellular carcinoma (HCC) and prostate cancer. SENP1 Gln597 is located at the unstructured loop connecting the helices α4 to α5. The Q597A mutation of SENP1 allosterically disrupts the hydrolytic reaction of SUMO1 through an unknown mechanism. Here, extensive multiple replicates of microsecond molecular dynamics (MD) simulations, coupled with principal component analysis, dynamic cross-correlation analysis, community network analysis, and binding free energy calculations, were performed to elucidate the detailed mechanism. Our MD simulations showed that the Q597A mutation induced marked dynamic conformational changes in SENP1, especially in the unstructured loop connecting the helices α4 to α5 which the mutation site occupies. Moreover, the Q597A mutation caused conformational changes to catalytic Cys603 and His533 at the active site, which might impair the catalytic activity of SENP1 in processing SUMO1. Moreover, binding free energy calculations revealed that the Q597A mutation had a minor effect on the binding affinity of SUMO1 to SENP1. Together, these results may broaden our understanding of the allosteric modulation of the SENP1-SUMO1 complex.

Unmasking allosteric binding sites: Novel targets for GPCR drug discovery

INTRODUCTION

Unexpected non-apparent and hidden allosteric binding sites are non-classical and non-apparent allosteric centers in 3-D X-ray protein structures until orthosteric or allosteric ligands bind to them. The orthosteric center of one protomer that modulates binding centers of the other protomers within an oligomer is also an unexpected allosteric site. Furthermore, another partner protein can also produce these effects, acting as an unexpected allosteric modulator.

AREAS COVERED

This review summarizes both classical and non-classical allosterism. The authors focus on G protein-coupled receptor (GPCR) oligomers as a paradigm of allosteric molecules. Moreover, they show several examples of unexpected allosteric sites such as hidden allosteric sites in a protomer that appear after the interaction with other molecules and the allosterism exerted between orthosteric sites within GPCR oligomer, emphasizing on the allosteric modulations that can occur between binding sites.

EXPERT OPINION

The study of these new non-classical allosteric sites will expand the diversity of allosteric control on the function of orthosteric sites within proteins, whether GPCRs or other receptors, enzymes or transporters. Moreover, the design of new drugs targeting these hidden allosteric sites or already known orthosteric sites acting as allosteric sites in protein homo- or hetero-oligomers will increase the therapeutic potential of allosterism.

Delineating the conformational landscape and intrinsic properties of the angiotensin II type 2 receptor using a computational study

As a key regulator for the renin-angiotensin system, a class A G protein-coupled receptor (GPCR), AngII type 2 receptor (AT₂R), plays a pivotal role in the homeostasis of the cardiovascular system. Compared with other GPCRs, AT₂R has a unique antagonist-bound conformation and its mechanism is still an enigma. Here, we applied combined dynamic and evolutional approaches to investigate the conformational space and intrinsic properties of AT₂R. With molecular dynamic simulations, Markov State Models, and statistics coupled analysis, we captured the conformational landscape of AT₂R and identified its uniquity from both dynamical and evolutional viewpoints. A cryptic pocket was also discovered in the intermediate state during conformation transitions. These findings offer a deeper understanding of the AT₂R mechanism at an atomic level and provide hints for the design of novel AT₂R modulators.

Medicinal Chemistry Strategies for the Development of Bruton's Tyrosine Kinase Inhibitors against Resistance

Despite significant efficacy, one of the major limitations of small-molecule Bruton's tyrosine kinase (BTK) agents is the presence of clinically acquired resistance, which remains a major clinical challenge. This Perspective focuses on medicinal chemistry strategies for the development of BTK small-molecule inhibitors against resistance, including the structure-based design of BTK inhibitors targeting point mutations, e.g., (i) developing noncovalent inhibitors from covalent inhibitors, (ii) avoiding steric hindrance from mutated residues, (iii) making interactions with the mutated residue, (iv) modifying the solvent-accessible region, and (v) developing new scaffolds. Additionally, a comparative analysis of multi-inhibitions of BTK is presented based on cross-comparisons between 2916 unique BTK ligands and 283 other kinases that cover 7108 dual/multiple inhibitions. Finally, targeting the BTK allosteric site and uding proteolysis-targeting chimera (PROTAC) as two potential strategies are addressed briefly, while also illustrating the possibilities and challenges to find novel ligands of BTK.

Therapeutic Targeting the Allosteric Cysteinome of RAS and Kinase Families

Allosteric mechanisms are pervasive in nature, but human-designed allosteric perturbagens are rare. The history of KRAS^G12C inhibitor development suggests that covalent chemistry may be a key to expanding the armamentarium of allosteric inhibitors. In that effort, irreversible targeting of a cysteine converted a non-deal allosteric binding pocket and low affinity ligands into a tractable drugging strategy. Here we examine the feasibility of expanding this approach to other allosteric pockets of RAS and kinase family members, given that both protein families are regulators of vital cellular processes that are often dysregulated in cancer and other human diseases. Moreover, these heavily studied families are the subject of numerous drug development campaigns that have resulted, sometimes serendipitously, in the discovery of allosteric inhibitors. We consequently conducted a comprehensive search for cysteines, a commonly targeted amino acid for covalent drugs, using AlphaFold-generated structures of those families. This new analysis presents potential opportunities for allosteric targeting of validated and understudied drug targets, with an emphasis on cancer therapy.

Understanding the P-Loop Conformation in the Determination of Inhibitor Selectivity Toward the Hepatocellular Carcinoma-Associated Dark Kinase STK17B

As a member of the death-associated protein kinase family of serine/threonine kinases, the STK17B has been associated with diverse diseases such as hepatocellular carcinoma. However, the conformational dynamics of the phosphate-binding loop (P-loop) in the determination of inhibitor selectivity profile to the STK17B are less understood. Here, a multi-microsecond length molecular dynamics (MD) simulation of STK17B in the three different states (ligand-free, ADP-bound, and ligand-bound states) was carried out to uncover the conformational plasticity of the P-loop. Together with the analyses of principal component analysis, cross-correlation and generalized correlation motions, secondary structural analysis, and community network analysis, the conformational dynamics of the P-loop in the different states were revealed, in which the P-loop flipped into the ADP-binding site upon the inhibitor binding and interacted with the inhibitor and the C-lobe, strengthened the communication between the N- and C-lobes. These resulting interactions contributed to inhibitor selectivity profile to the STK17B. Our results may advance our understanding of kinase inhibitor selectivity and offer possible implications for the design of highly selective inhibitors for other protein kinases.

Computational Design of Inhibitors Targeting the Catalytic β Subunit of Escherichia coli FOF1-ATP Synthase

With the uncontrolled growth of multidrug-resistant bacteria, there is an urgent need to search for new therapeutic targets, to develop drugs with novel modes of bactericidal action. FoF1-ATP synthase plays a crucial role in bacterial bioenergetic processes, and it has emerged as an attractive antimicrobial target, validated by the pharmaceutical approval of an inhibitor to treat multidrug-resistant tuberculosis. In this work, we aimed to design, through two types of in silico strategies, new allosteric inhibitors of the ATP synthase, by targeting the catalytic β subunit, a centerpiece in communication between rotor subunits and catalytic sites, to drive the rotary mechanism. As a model system, we used the F1 sector of Escherichia coli, a bacterium included in the priority list of multidrug-resistant pathogens. Drug-like molecules and an IF1-derived peptide, designed through molecular dynamics simulations and sequence mining approaches, respectively, exhibited in vitro micromolar inhibitor potency against F1. An analysis of bacterial and Mammalia sequences of the key structural helix-turn-turn motif of the C-terminal domain of the β subunit revealed highly and moderately conserved positions that could be exploited for the development of new species-specific allosteric inhibitors. To our knowledge, these inhibitors are the first binders computationally designed against the catalytic subunit of FOF1-ATP synthase.

Allosteric pluripotency: challenges and opportunities

Allosteric pluripotency arises when the functional response of an allosteric receptor to an allosteric stimulus depends on additional allosteric modulators. Here, we discuss allosteric pluripotency as observed in the prototypical Protein Kinase A (PKA) as well as in other signaling systems, from typical multidomain signaling proteins to bacterial enzymes. We identify key drivers of pluripotent allostery and illustrate how hypothesizing allosteric pluripotency may solve apparent discrepancies currently present in the literature regarding the dual nature of known allosteric modulators. We also outline the implications of allosteric pluripotency for cellular signaling and allosteric drug design, and analyze the challenges and opportunities opened by the pluripotent nature of allostery.

Mechanistic Insights Into Co-Administration of Allosteric and Orthosteric Drugs to Overcome Drug-Resistance in T315I BCR-ABL1

Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm, driven by the BCR-ABL1 fusion oncoprotein. The discovery of orthosteric BCR-ABL1 tyrosine kinase inhibitors (TKIs) targeting its active ATP-binding pocket, such as first-generation Imatinib and second-generation Nilotinib (NIL), has profoundly revolutionized the therapeutic landscape of CML. However, currently targeted therapeutics still face considerable challenges with the inevitable emergence of drug-resistant mutations within BCR-ABL1. One of the most common resistant mutations in BCR-ABL1 is the T315I gatekeeper mutation, which confers resistance to most current TKIs in use. To resolve such conundrum, co-administration of orthosteric TKIs and allosteric drugs offers a novel paradigm to tackle drug resistance. Remarkably, previous studies have confirmed that the dual targeting BCR-ABL1 utilizing orthosteric TKI NIL and allosteric inhibitor ABL001 resulted in eradication of the CML xenograft tumors, exhibiting promising therapeutic potential. Previous studies have demonstrated the cooperated mechanism of two drugs. However, the conformational landscapes of synergistic effects remain unclear, hampering future efforts in optimizations and improvements. Hence, extensive large-scale molecular dynamics (MD) simulations of wide type (WT), WT-NIL, T315I, T315I-NIL, T315I-ABL001 and T315I-ABL001-NIL systems were carried out in an attempt to address such question. Simulation data revealed that the dynamic landscape of NIL-bound BCR-ABL1 was significantly reshaped upon ABL001 binding, as it shifted from an active conformation towards an inactive conformation. The community network of allosteric signaling was analyzed to elucidate the atomistic overview of allosteric regulation within BCR-ABL1. Moreover, binding free energy analysis unveiled that the affinity of NIL to BCR-ABL1 increased by the induction of ABL001, which led to its favorable binding and the release of drug resistance. The findings uncovered the in-depth structural mechanisms underpinning dual-targeting towards T315I BCR-ABL1 to overcome its drug resistance and will offer guidance for the rational design of next generations of BCR-ABL1 modulators and future combinatory therapeutic regimens.

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.

From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output

An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.

Elucidation of the conformational dynamics and assembly of Argonaute-RNA complexes by distinct yet coordinated actions of the supplementary microRNA

Argonaute (AGO) proteins, the core of RNA-induced silencing complex, are guided by microRNAs (miRNAs) to recognize target RNA for repression. The miRNA-target RNA recognition forms initially through pairing at the seed region while the additional supplementary pairing can enhance target recognition and compensate for seed mismatch. The extension of miRNA lengths can strengthen the target affinity when pairing both in the seed and supplementary regions. However, the mechanism underlying the effect of the supplementary pairing on the conformational dynamics and the assembly of AGO-RNA complex remains poorly understood. To address this, we performed large-scale molecular dynamics simulations of AGO-RNA complexes with different pairing patterns and miRNA lengths. The results reveal that the additional supplementary pairing can not only strengthen the interaction between miRNA and target RNA, but also induce the increased plasticity of the PAZ domain and enhance the domain connectivity among the PAZ, PIWI, N domains of the AGO protein. The strong community network between these domains tightens the mouth of the supplementary chamber of AGO protein, which prevents the escape of target RNA from the complex and shields it from solvent water attack. Importantly, the inner stronger matching pairs between the miRNA and target RNA can compensate for weaker mismatches at the edge of supplementary region. These findings provide guidance for the design of miRNA mimics and anti-miRNAs for both clinical and experimental use and open the way for further engineering of AGO proteins as a new tool in the field of gene regulation.

Targeting a cryptic allosteric site of SIRT6 with small-molecule inhibitors that inhibit the migration of pancreatic cancer cells

SIRT6 belongs to the conserved NAD⁺-dependent deacetylase superfamily and mediates multiple biological and pathological processes. Targeting SIRT6 by allosteric modulators represents a novel direction for therapeutics, which can overcome the selectivity problem caused by the structural similarity of orthosteric sites among deacetylases. Here, developing a reversed allosteric strategy AlloReverse, we identified a cryptic allosteric site, Pocket Z, which was only induced by the bi-directional allosteric signal triggered upon orthosteric binding of NAD⁺. Based on Pocket Z, we discovered an SIRT6 allosteric inhibitor named JYQ-42. JYQ-42 selectively targets SIRT6 among other histone deacetylases and effectively inhibits SIRT6 deacetylation, with an IC₅₀ of 2.33 μmol/L. JYQ-42 significantly suppresses SIRT6-mediated cancer cell migration and pro-inflammatory cytokine production. JYQ-42, to our knowledge, is the most potent and selective allosteric SIRT6 inhibitor. This study provides a novel strategy for allosteric drug design and will help in the challenging development of therapeutic agents that can selectively bind SIRT6.

Challenges in Discovering Drugs That Target the Protein-Protein Interactions of Disordered Proteins

Protein–protein interactions (PPIs) outnumber proteins and are crucial to many fundamental processes; in consequence, PPIs are associated with several pathological conditions including neurodegeneration and modulating them by drugs constitutes a potentially major class of therapy. Classically, however, the discovery of small molecules for use as drugs entails targeting individual proteins rather than targeting PPIs. This is largely because discovering small molecules to modulate PPIs has been seen as extremely challenging. Here, we review the difficulties and limitations of strategies to discover drugs that target PPIs directly or indirectly, taking as examples the disordered proteins involved in neurodegenerative diseases.

Allosteric binding on nuclear receptors: Insights on screening of non-competitive endocrine-disrupting chemicals

Endocrine-disrupting chemicals (EDCs) can compete with endogenous hormones and bind to the orthosteric site of nuclear receptors (NRs), affecting normal endocrine system function and causing severe symptoms. Recently, a series of pharmaceuticals and personal care products (PPCPs) have been discovered to bind to the allosteric sites of NRs and induce similar effects. However, it remains unclear how diverse EDCs work in this new way. Therefore, we have systematically summarized the allosteric sites and underlying mechanisms based on existing studies, mainly regarding drugs belonging to the PPCP class. Advanced methods, classified as structural biology, biochemistry and computational simulation, together with their advantages and hurdles for allosteric site recognition and mechanism insight have also been described. Furthermore, we have highlighted two available strategies for virtual screening of numerous EDCs, relying on the structural features of allosteric sites and lead compounds, respectively. We aim to provide reliable theoretical and technical support for a broader view of various allosteric interactions between EDCs and NRs, and to drive high-throughput and accurate screening of potential EDCs with non-competitive effects.

CDK9 Inhibitors in Cancer Research

Cyclin dependent kinase 9 (CDK9) played an essential role in regulating transcriptional elongation. Aberrations in CDK9 activity have been observed in various cancers, which made CDK9 was an attractive therapeutic...