Emerging Computational Methods for the Rational Discovery of Allosteric Drugs

AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics

Elucidating collective variables (CVs) for biomolecular dynamics is crucial for understanding numerous biological processes. By leveraging the tensor-train data structure, a multilinear version of the AMUSE (Algorithm for Multiple Unknown Signals) algorithm for Koopman approximation (AMUSEt) was recently developed to identify CVs for biomolecular dynamics. To find slow CVs, AMUSEt transforms input features (e.g., pairwise atomic distances) into nonlinear basis functions (e.g., Gaussian functions) and encodes these nonlinear basis functions within a tensor-train structure via time-lagged correlation functions. Due to the need to fit these tensor-train data structures into computer memory, AMUSEt can handle only a limited number of input features. Consequently, AMUSEt relies on manually selecting and ranking features based on physical intuition to fully capture the slow dynamics. However, when applied to complex biological systems with numerous features, this selection and ranking process becomes increasingly challenging. To address this challenge, here we present AMUSET-TICA (AMUSEt-based Time-lagged Independent Component Analysis), a CV-identification method using time-structure-independent components (tICs) as the input features for AMUSEt. The key insight of AMUSET-TICA lies in its highly effective embedding of high-dimensional atomistic protein conformations, achieved by expanding orthogonal tICs into overlapping Gaussian basis functions through a tensor-product data structure. This eliminates the need for manually selecting and ranking input features for a wide range of biomolecular systems. We demonstrate that AMUSET-TICA consistently and significantly outperforms AMUSEt and tICA in identifying slow CVs for three different biomolecular systems: alanine dipeptide, the N-terminal domain of L9 (NTL9), and the FIP35 WW domain. For all these systems, the CVs generated by AMUSET-TICA accurately describe the slowest dynamical modes underlying these biological conformational changes. Furthermore, we show that AMUSET-TICA achieves performance comparable to deep-learning approaches like VAMPnets in identifying the slowest dynamical modes, while being significantly more computationally efficient in terms of CPU time. In addition, the CVs yielded by AMUSET-TICA provide insights into the folding mechanisms of NTL9 and the FIP35 WW domain, including CV3 and CV4 of the WW domain, which capture its two parallel folding pathways. We expect AMUSET-TICA can be widely applied to facilitate the investigation of biomolecular dynamics.

Computer-aided drug design for the double-membrane vesicle pore complex inhibitors against SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of the ongoing global pandemic, has constituted the worst global health disaster in recent years. However, no antiviral drugs have proved clinically efficacious to combat SARS-CoV-2 infection. The SARS-CoV-2 double-membrane vesicle (DMV) pore complex, particularly for its positively charged residues R1613, R1614, R303, R305, and R306, which are highly conserved across β-coronaviruses and play a critical role in mediating RNA transport and virus replication, has been validated as an effective drug target. Here, we employed computer-aided drug design (CADD) techniques for the first time to screen the antiviral compounds against SARS-CoV-2 by targeting the crystal structure of the SARS-CoV-2 DMV nsp3-4 pore complex. A total of 486,387 drug compounds were subjected to virtual screening, such as toxicity prediction, ADMET prediction, molecular docking, and target analysis. The six compounds (three for each binding site) were selected based on their lowest binding energies. Notably, Compound 391 demonstrated the strongest binding affinity to the critical positively charged residues R1613 and R1614 at binding site 1, meanwhile, Compound 5,157 exhibited the most stable interactions with the essential positively charged residues R303, R305, and R306 at binding site 2. These results demonstrate that Compound 391 and Compound 5,157 exhibit greater potential antiviral effects, which provide a theoretical basis for further confirmation against SARS-CoV-2 in vitro and in vivo studies.

Conformational dynamics and activation of membrane-associated human Group IVA cytosolic phospholipase A 2 (cPLA 2 )

Cytosolic phospholipase A 2 (cPLA 2 ) associates with membranes where it hydrolyzes phospholipids containing arachidonic acid to initiate an inflammatory cascade. All-atom molecular dynamics simulations were employed to understand the activation process when cPLA 2 associates with the endoplasmic reticulum (ER) membrane of macrophages where it acts. We found that membrane association causes the lid region of cPLA 2 to undergo a closed-to-open state transition that is accompanied by the sideways movement of loop 495-540, allowing the exposure of a cluster of lysine residues (K488, K541, K543, and K544), which binds the allosteric activator PIP 2 in the membrane. The active site of the open form of cPLA 2 , containing the catalytic dyad residues S228 and D549, exhibited a three-fold larger cavity than the closed form of cPLA 2 in aqueous solution. These findings provide mechanistic insight as to how cPLA 2 ER membrane association promotes major transitions between conformational states critical to allosteric activation and enzymatic phospholipid hydrolysis.

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.

Benzylidenehydrazine Derivatives: Synthesis, Antidiabetic Evaluation, Antioxidation, Mode of Inhibition, DFT and Molecular Docking Studies

Diabetes mellitus (DM) is a metabolic condition that is a profound health concern across the globe due to its contribution to the increased mortality rate. It affects millions of people around the world and is associated with severe complications among people diagnosed with it. Among the array of approaches used for the management of DM, inhibition of enzymes viz. α-amylase and α-glucosidase which are responsible for sugars hydrolysis is regarded as a feasible therapeutic protocol for the management of DM. Herein, we report the synthesis of benzylidenehydrazine derivatives as well as their evaluation as α-glucosidase and α-amylase inhibitors including their antioxidant testing. Generally, all the synthesized derivatives were more potent inhibitors of α-amylase than of α-glucosidase. Specifically, 2,4 fluoro substituted analogue 9 (IC50 =116.19 μM) emerged as the strongest α-amylase inhibitor with ~5-fold superior activity in comparison to the standard drug, acarbose (IC50 =600 μM). Compounds 18 (IC50 =240.59) and 19 (IC50 =198.32 μM) displayed the strongest NO scavenging activity compared to Trolox (IC50 =272.36 μM). In addition, the enzyme kinetic studies indicated that compound 9 acts as a non-competitive inhibitor of α-amylase. Finally, density functional theory and molecular docking studies for compound 9 were conducted to explore its structural and electronic properties as well as to determine protein-ligand interactions, respectively to decipher its observed activity. The obtained findings present promising possibilities for developing new drug candidates to control postprandial glucose levels in persons with diabetes.

Covalent-Allosteric Inhibitors: Do We Get the Best of Both Worlds?

Covalent-allosteric inhibitors (CAIs) may achieve the best of both worlds: increased potency, long-lasting effects, and reduced drug resistance typical of covalent ligands, along with enhanced specificity and decreased toxicity inherent in allosteric modulators. Therefore, CAIs can be an effective strategy to transform many undruggable targets into druggable ones. However, CAIs are challenging to design. In this perspective, we analyze the discovery of known CAIs targeting three protein families: protein phosphatases, protein kinases, and GTPases. We also discuss how computational methods and tools can play a role in addressing the practical challenges of rational CAI design.

The promise of cyclic AMP modulation to restore cognitive function in neurodevelopmental disorders

Cyclic AMP (cAMP) is a key regulator of synaptic function and is dysregulated in both neurodevelopmental (NDD) and neurodegenerative disorders. Due to the ease of diffusion and promiscuity of downstream effectors, cAMP signaling is restricted within spatiotemporal domains to localize activation. Among the best-studied mechanisms is the feedback inhibition of cAMP-dependent protein kinase (PKA) activity by phosphodiesterases 4 (PDE4s) at synapses controlling neuronal plasticity, which is largely regulated by PDE4D. In fact, genetic variants in genes for multiple PKA subunits and PDE4D lead to NDDs. Here, we discuss the rationale for choosing PDE4D as a candidate for the design of selective allosteric inhibitors and the recent advances in clinical trials. These new compounds improve cognitive function in preclinical animal models due to improved selectivity and more physiological inhibition of the active enzyme. We also discuss opportunities for better understanding of PDE4D function in general, and for the development of next-generation inhibitors.

Allosteric activation of AMPK ADaM's site by structural analogs of Epigallocatechin and Galegine: computational molecular modeling investigation

5'-Adenosine Monophosphate Protein Kinase (AMPK) is a central protein involved in cellular energy homeostasis, turning on catabolic pathways when the energy level is depleted and inhibiting anabolic pathways utilizing ATP. AMPK is implicated in several diseases including but not limited to diabetes, cancer, and cardiovascular diseases. Regulation of AMPK is cogent for restoring cellular energy levels which mediates the pathways leading to these diseases. Allosteric activation of AMPK via a novel ADaM site is intended for study in this case. In the search for AMPK activators, this study engaged a database for a virtual screening campaign through the ZINC15 database involving pharmacophoric modeling of two reported natural bioactive AMPK activators- Galegine and Epigallocatechin. Generated pharmacophores were targeted against the AMPK-ADaM site by employing various tools within the structure-based drug discovery process among which include consensus molecular docking, physicochemical profiling, ADMET, and molecular dynamics simulation. Advanced methods such as molecular mechanics (MM/GBSA) and quantitative structure-activity relationship (QSAR) were also performed. This investigation revealed promising pharmacophores that show better interactions and pharmacokinetic properties compared to the standards. This study proposes further development of these pharmacophores into potential drugs with better efficacies that could enhance the activation of the AMPK-ADaM site in ameliorating the aforementioned diseases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00311-x.

Allostery

Allostery describes the ability of biological macromolecules to transmit signals spatially through the molecule from an allosteric site – a site that is distinct from orthosteric binding sites of primary, endogenous ligands – to the functional or active site. This review starts with a historical overview and a description of the classical example of allostery – hemoglobin – and other well-known examples (aspartate transcarbamoylase, Lac repressor, kinases, G-protein-coupled receptors, adenosine triphosphate synthase, and chaperonin). We then discuss fringe examples of allostery, including intrinsically disordered proteins and inter-enzyme allostery, and the influence of dynamics, entropy, and conformational ensembles and landscapes on allosteric mechanisms, to capture the essence of the field. Thereafter, we give an overview over central methods for investigating molecular mechanisms, covering experimental techniques as well as simulations and artificial intelligence (AI)-based methods. We conclude with a review of allostery-based drug discovery, with its challenges and opportunities: with the recent advent of AI-based methods, allosteric compounds are set to revolutionize drug discovery and medical treatments.

Graph theory approaches for molecular dynamics simulations

Graph theory, a branch of mathematics that focuses on the study of graphs (networks of nodes and edges), provides a robust framework for analysing the structural and functional properties of biomolecules. By leveraging molecular dynamics (MD) simulations, atoms or groups of atoms can be represented as nodes, while their dynamic interactions are depicted as edges. This network-based approach facilitates the characterization of properties such as connectivity, centrality, and modularity, which are essential for understanding the behaviour of molecular systems. This review details the application and development of graph theory-based models in studying biomolecular systems. We introduce key concepts in graph theory and demonstrate their practical applications, illustrating how innovative graph theory approaches can be employed to design biomolecular systems with enhanced functionality. Specifically, we explore the integration of graph theoretical methods with MD simulations to gain deeper insights into complex biological phenomena, such as allosteric regulation, conformational dynamics, and catalytic functions. Ultimately, graph theory has proven to be a powerful tool in the field of molecular dynamics, offering valuable insights into the structural properties, dynamics, and interactions of molecular systems. This review establishes a foundation for using graph theory in molecular design and engineering, highlighting its potential to transform the field and drive advancements in the understanding and manipulation of biomolecular systems.

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.

Maternal lipid mobilization is essential for embryonic development in the malaria vector Anopheles gambiae

Lipid metabolism is an essential component in reproductive physiology. While lipid mobilization has been implicated in the growth of Plasmodium falciparum malaria parasites in their Anopheles vectors, the role of this process in the reproductive biology of these mosquitoes remains elusive. Here, we show that impairing lipolysis in Anopheles gambiae, the major malaria vector, leads to embryonic lethality. Embryos derived from females in which we silenced the triglyceride lipase AgTL2 or the lipid storage droplet AgLSD1 develop normally during early embryogenesis but fail to hatch due to severely impaired metabolism. Embryonic lethality is efficiently recapitulated by exposing adult females to broad-spectrum lipase inhibitors prior to blood feeding, unveiling lipolysis as a potential target for inducing mosquito sterility. Our findings provide mechanistic insights into the importance of maternal lipid mobilization in embryonic health that may inform studies on human reproduction.

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.

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.

Toward the Design of Allosteric Effectors: Gaining Comprehensive Control of Drug Properties and Actions

While the therapeutic potential of allosteric drugs is increasingly realized, the discovery of effectors is largely incidental. The rational design of allosteric effectors requires new state-of-the-art approaches to account for the distinct characteristics of allosteric ligands and their modes of action. We present a broadly applicable computational framework for obtaining allosteric site-effector pairs, providing targeted, highly specific, and tunable regulation to any functional site. We validated the framework using the main protease from SARS-CoV-2 and the K-RasG12D oncoprotein. High-throughput per-residue quantification of the energetics of allosteric signaling and effector binding revealed known drugs capable of inducing the required modulation upon binding. Starting from fragments of known well-characterized drugs, allosteric effectors and binding sites were designed and optimized simultaneously to achieve targeted and specific signaling to distinct functional sites, such as, for example, the switch regions of K-RasG12D. The generic framework proposed in this work will be instrumental in developing allosteric therapies aligned with a precision medicine approach.

Cracking the chaperone code through the computational microscope

The heat shock protein 90 kDa (Hsp90) chaperone machinery plays a crucial role in maintaining cellular homeostasis. Beyond its traditional role in protein folding, Hsp90 is integral to key pathways influencing cellular function in health and disease. Hsp90 operates through the modular assembly of large multiprotein complexes, with their composition, stability, and localization adapting to the cell's needs. Its functional dynamics are finely tuned by ligand binding and post-translational modifications (PTMs). Here, we discuss how to disentangle the intricacies of the complex code that governs the crosstalk between dynamics, binding, PTMs, and the functions of the Hsp90 machinery using computer-based approaches. Specifically, we outline the contributions of computational and theoretical methods to the understanding of Hsp90 functions, ranging from providing atomic-level insights into its dynamics to clarifying the mechanisms of interactions with protein clients, cochaperones, and ligands. The knowledge generated in this framework can be actionable for the design and development of chemical tools and drugs targeting Hsp90 in specific disease-associated cellular contexts. Finally, we provide our perspective on how computation can be integrated into the study of the fine-tuning of functions in the highly complex Hsp90 landscape, complementing experimental methods for a comprehensive understanding of this important chaperone system.

Structural Models of Human Norepinephrine Transporter Ensemble Reveal the Allosteric Sites and Ligand-Binding Mechanism

The norepinephrine transporter (NET) plays a pivotal role in recycling norepinephrine (NE) from the synaptic cleft. However, the structures referring to the conformational heterogeneity of NET during the transport cycle remain poorly understood. Here, three structural models of NE bound to the orthosteric site of NET in outward-open (OOholo), outward-occluded (OCholo), and inward-open (IOholo) conformations were first obtained using the multistate structures of serotonin transporter as templates and further characterized through Gaussian-accelerated molecular dynamics and free energy reweighting. Analysis of the structures revealed eight potential allosteric sites on the functional-specific states of NET. One of the pharmacologically relevant pockets located at the extracellular vestibule was further verified by simulating the binding behaviors of a clinical trial drug χ-MrIA that is allosterically regulating NET. These structural and energetic insights into NET advanced our understanding of NE reuptake and paved the way for discovering novel molecules targeting the allosteric sites.

Identification of Orthosteric and Allosteric Pharmacological Chaperones for Mucopolysaccharidosis Type IIIB

Mucopolysaccharidosis type IIIB (MPS IIIB) is an autosomal inherited disease caused by mutations in gene encoding the lysosomal enzyme N-acetyl-alpha-glucosaminidase (NAGLU). These mutations result in reduced NAGLU activity, preventing it from catalyzing the hydrolysis of the glycosaminoglycan heparan sulfate (HS). There are currently no approved treatments for MPS IIIB. A novel approach in the treatment of lysosomal storage diseases is the use of pharmacological chaperones (PC). In this study, we used a drug repurposing approach to identify and characterize novel potential PCs for NAGLU enzyme. We modeled the interaction of natural and artificial substrates within the active cavity of NAGLU (orthosteric site) and predicted potential allosteric sites. We performed a virtual screening for both the orthosteric and the predicted allosteric site against a curated database of human tested molecules. Considering the binding affinity and predicted blood-brain barrier permeability and gastrointestinal absorption, we selected atovaquone and piperaquine as orthosteric and allosteric PCs. The PCs were evaluated by their capacity to bind NAGLU and the ability to restore the enzymatic activity in human MPS IIIB fibroblasts These results represent novel PCs described for MPS IIIB and demonstrate the potential to develop novel therapeutic alternatives for this and other protein deficiency diseases.

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.

Probing allosteric communication with combined molecular dynamics simulations and network analysis

Understanding the allosteric mechanisms within biomolecules involved in diseases is of paramount importance for drug discovery. Indeed, characterizing communication pathways and critical hotspots in signal transduction can guide a rational approach to leverage allosteric modulation for therapeutic purposes. While the atomistic signatures of allosteric processes are difficult to determine experimentally, computational methods can be a remarkable resource. Network analysis built on Molecular Dynamics simulation data is particularly suited in this respect and is gradually becoming of routine use. Herein, we collect the recent literature in the field, discussing different aspects and available options for network construction and analysis. We further highlight interesting refinements and extensions, eventually providing our perspective on this topic.

AI approaches for the discovery and validation of drug targets

Artificial intelligence (AI) holds immense promise for accelerating and improving all aspects of drug discovery, not least target discovery and validation. By integrating a diverse range of biological data modalities, AI enables the accurate prediction of drug target properties, ultimately illuminating biological mechanisms of disease and guiding drug discovery strategies. Despite the indisputable potential of AI in drug target discovery, there are many challenges and obstacles yet to be overcome, including dealing with data biases, model interpretability and generalisability, and the validation of predicted drug targets, to name a few. By exploring recent advancements in AI, this review showcases current applications of AI for drug target discovery and offers perspectives on the future of AI for the discovery and validation of drug targets, paving the way for the generation of novel and safer pharmaceuticals.

A Fresh Look at the Normal Mode Analysis of Proteins: Introducing Allosteric Co-Vibrational Modes

We propose a new way of utilizing normal modes to study protein conformational transitions. Instead of considering individual modes independently, we show that a weighted mixture of low-frequency vibrational modes can reveal dynamic information about the conformational mechanism in more detail than any single mode can. The weights in the mixed mode, termed the allosteric covibrational mode, are determined using a simple model where the conformational transition is viewed as a perturbation of the coupled harmonic oscillator associated with either of the two conformations. We demonstrate our theory in a biologically relevant example of high pharmaceutical interest involving the V617F mutation of Janus 2 tyrosine kinase (JAK2).

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

Tutorial on how to build non-Markovian dynamic models from molecular dynamics simulations for studying protein conformational changes

Protein conformational changes play crucial roles in their biological functions. In recent years, the Markov State Model (MSM) constructed from extensive Molecular Dynamics (MD) simulations has emerged as a powerful tool for modeling complex protein conformational changes. In MSMs, dynamics are modeled as a sequence of Markovian transitions among metastable conformational states at discrete time intervals (called lag time). A major challenge for MSMs is that the lag time must be long enough to allow transitions among states to become memoryless (or Markovian). However, this lag time is constrained by the length of individual MD simulations available to track these transitions. To address this challenge, we have recently developed Generalized Master Equation (GME)-based approaches, encoding non-Markovian dynamics using a time-dependent memory kernel. In this Tutorial, we introduce the theory behind two recently developed GME-based non-Markovian dynamic models: the quasi-Markov State Model (qMSM) and the Integrative Generalized Master Equation (IGME). We subsequently outline the procedures for constructing these models and provide a step-by-step tutorial on applying qMSM and IGME to study two peptide systems: alanine dipeptide and villin headpiece. This Tutorial is available at https://github.com/xuhuihuang/GME_tutorials. The protocols detailed in this Tutorial aim to be accessible for non-experts interested in studying the biomolecular dynamics using these non-Markovian dynamic models.

Chiral Induced Spin Selectivity

Since the initial landmark study on the chiral induced spin selectivity (CISS) effect in 1999, considerable experimental and theoretical efforts have been made to understand the physical underpinnings and mechanistic features of this interesting phenomenon. As first formulated, the CISS effect refers to the innate ability of chiral materials to act as spin filters for electron transport; however, more recent experiments demonstrate that displacement currents arising from charge polarization of chiral molecules lead to spin polarization without the need for net charge flow. With its identification of a fundamental connection between chiral symmetry and electron spin in molecules and materials, CISS promises profound and ubiquitous implications for existing technologies and new approaches to answering age old questions, such as the homochiral nature of life. This review begins with a discussion of the different methods for measuring CISS and then provides a comprehensive overview of molecules and materials known to exhibit CISS-based phenomena before proceeding to identify structure-property relations and to delineate the leading theoretical models for the CISS effect. Next, it identifies some implications of CISS in physics, chemistry, and biology. The discussion ends with a critical assessment of the CISS field and some comments on its future outlook.

Allosteric drugs: New principles and design approaches

Focusing on an important biomedical implication of allostery - design of allosteric drugs, we describe characteristics of allosteric sites, effectors, and their modes of actions distinguishing them from the orthosteric counterparts and calling for new principles and protocols in the quests for allosteric drugs. We show the importance of considering both binding affinity and allosteric signaling in establishing the structure-activity relationships (SARs) toward design of allosteric effectors, arguing that pairs of allosteric sites and their effector ligands - the site-effector pairs - should be generated and adjusted simultaneously in the framework of what we call directed design protocol. Key ideas and approaches for designing allosteric effectors including reverse perturbation, targeted and agnostic analysis are also discussed here. Several promising computational approaches are highlighted, along with the need for and potential advantages of utilizing generative models to facilitate discovery/design of new allosteric drugs.

Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 ± 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC50 value of 70 ± 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and other emerging viruses.

Discovery of a Potential Allosteric Site in the SARS-CoV-2 Spike Protein and Targeting Allosteric Inhibitor to Stabilize the RBD Down State using a Computational Approach

BACKGROUND

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a worldwide public health crisis. At present, the development of effective drugs and/or related therapeutics is still the most urgent and important task for combating the virus. The viral entry and associated infectivity mainly rely on its envelope spike protein to recognize and bind to the host cell receptor angiotensin-converting enzyme 2 (ACE2) through a conformational switch of the spike receptor binding domain (RBD) from inactive to active state. Thus, it is of great significance to design an allosteric inhibitor targeting spike to lock it in the inactive and ACE2-inaccessible state.

OBJECTIVE

This study aims to discover the potential broad-spectrum allosteric inhibitors capable of binding and stabilizing the diverse spike variants, including the wild type, Delta, and Omicron, in the inactive RBD down state.

METHODS

In this work, we first detected a potential allosteric pocket within the SARS-CoV-2 spike protein. Then, we performed large-scale structure-based virtual screening by targeting the putative allosteric pocket to identify allosteric inhibitors that could stabilize the spike inactive state. Molecular dynamics simulations were further carried out to evaluate the effects of compound binding on the stability of spike RBD.

RESULTS

Finally, we identified three potential allosteric inhibitors, CPD3, CPD5, and CPD6, against diverse SARS-CoV-2 variants, including Wild-type, Delta, and Omicron variants. Our simulation results showed that the three compounds could stably bind the predicted allosteric site and effectively stabilize the spike in the inactive state.

CONCLUSION

The three compounds provide novel chemical structures for rational drug design targeting spike protein, which is expected to greatly assist in the development of new drugs against SARS-CoV-2.

Three Decades of Targeting Falcipains to Develop Antiplasmodial Agents: What have we Learned and What can be Done Next?

Malaria is a devastating infectious disease that affects large swathes of human populations across the planet's tropical regions. It is caused by parasites of the genus Plasmodium, with Plasmodium falciparum being responsible for the most lethal form of the disease. During the intraerythrocytic stage in the human hosts, malaria parasites multiply and degrade hemoglobin (Hb) using a battery of proteases, which include two cysteine proteases, falcipains 2 and 3 (FP-2 and FP-3). Due to their role as major hemoglobinases, FP-2 and FP-3 have been targeted in studies aiming to discover new antimalarials and numerous inhibitors with activity against these enzymes, and parasites in culture have been identified. Nonetheless, cross-inhibition of human cysteine cathepsins remains a serious hurdle to overcome for these compounds to be used clinically. In this article, we have reviewed key functional and structural properties of FP-2/3 and described different compound series reported as inhibitors of these proteases during decades of active research in the field. Special attention is also paid to the wide range of computer-aided drug design (CADD) techniques successfully applied to discover new active compounds. Finally, we provide guidelines that, in our understanding, will help advance the rational discovery of new FP-2/3 inhibitors.

Computing allostery: from the understanding of biomolecular regulation and the discovery of cryptic sites to molecular design

The concept of allostery has become a central tenet in the study of biological systems. In parallel, the discovery of allosteric drugs is generating new opportunities to selectively modulate difficult targets involved in pathologic mechanisms. Molecular simulations can provide atomistically detailed insight into the processes involved in allosteric regulation and signaling, and at the same time, they have the potential to unveil regulatory hotspots or cryptic sites that are not immediately evident from the analysis of static structures. In this context, computational approaches should be able to connect the study of allosteric regulation at different scales to the possibility of leveraging this knowledge to expand the chemical space of new, active drugs. Here, we will discuss recent advances in the study of allosteric regulation via computational methods and connect the mechanistic knowledge generated to the possibility of designing new small molecules that can tweak the functions of their receptors.

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery

Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.

Allosteric modulation of SHP2: Quest from known to unknown

Src homology-2 domain-containing protein tyrosine phosphatase-2 (SHP2) is a key regulatory factor in the cell cycle and its activating mutations play an important role in the development of various cancers, making it an important target for antitumor drugs. Due to the highly conserved amino acid sequence and positively charged nature of the active site of SHP2, it is difficult to discover inhibitors with high affinity for the catalytic site of SHP2 and sufficient cell permeability, making it considered an "undruggable" target. However, the discovery of allosteric regulation mechanisms provides new opportunities for transforming undruggable targets into druggable ones. Given the limitations of orthosteric inhibitors, SHP2 allosteric inhibitors have become a more selective and safer research direction. In this review, we elucidate the oncogenic mechanism of SHP2 and summarize the discovery methods of SHP2 allosteric inhibitors, providing new strategies for the design and improvement of SHP2 allosteric inhibitors.

Protein conformational ensembles in function: roles and mechanisms

The sequence-structure-function paradigm has dominated twentieth century molecular biology. The paradigm tacitly stipulated that for each sequence there exists a single, well-organized protein structure. Yet, to sustain cell life, function requires (i) that there be more than a single structure, (ii) that there be switching between the structures, and (iii) that the structures be incompletely organized. These fundamental tenets called for an updated sequence-conformational ensemble-function paradigm. The powerful energy landscape idea, which is the foundation of modernized molecular biology, imported the conformational ensemble framework from physics and chemistry. This framework embraces the recognition that proteins are dynamic and are always interconverting between conformational states with varying energies. The more stable the conformation the more populated it is. The changes in the populations of the states are required for cell life. As an example, in vivo, under physiological conditions, wild type kinases commonly populate their more stable "closed", inactive, conformations. However, there are minor populations of the "open", ligand-free states. Upon their stabilization, e.g., by high affinity interactions or mutations, their ensembles shift to occupy the active states. Here we discuss the role of conformational propensities in function. We provide multiple examples of diverse systems, including protein kinases, lipid kinases, and Ras GTPases, discuss diverse conformational mechanisms, and provide a broad outlook on protein ensembles in the cell. We propose that the number of molecules in the active state (inactive for repressors), determine protein function, and that the dynamic, relative conformational propensities, rather than the rigid structures, are the hallmark of cell life.

Worth the Weight: Sub-Pocket EXplorer (SubPEx), a Weighted Ensemble Method to Enhance Binding-Pocket Conformational Sampling

Structure-based virtual screening (VS) is an effective method for identifying potential small-molecule ligands, but traditional VS approaches consider only a single binding-pocket conformation. Consequently, they struggle to identify ligands that bind to alternate conformations. Ensemble docking helps address this issue by incorporating multiple conformations into the docking process, but it depends on methods that can thoroughly explore pocket flexibility. We here introduce Sub-Pocket EXplorer (SubPEx), an approach that uses weighted ensemble (WE) path sampling to accelerate binding-pocket sampling. As proof of principle, we apply SubPEx to three proteins relevant to drug discovery: heat shock protein 90, influenza neuraminidase, and yeast hexokinase 2. SubPEx is available free of charge without registration under the terms of the open-source MIT license: http://durrantlab.com/subpex/.

Combining structural and coevolution information to unveil allosteric sites

Understanding allosteric regulation in biomolecules is of great interest to pharmaceutical research and computational methods emerged during the last decades to characterize allosteric coupling. However, the prediction of allosteric sites in a protein structure remains a challenging task. Here, we integrate local binding site information, coevolutionary information, and information on dynamic allostery into a structure-based three-parameter model to identify potentially hidden allosteric sites in ensembles of protein structures with orthosteric ligands. When tested on five allosteric proteins (LFA-1, p38-α, GR, MAT2A, and BCKDK), the model successfully ranked all known allosteric pockets in the top three positions. Finally, we identified a novel druggable site in MAT2A confirmed by X-ray crystallography and SPR and a hitherto unknown druggable allosteric site in BCKDK validated by biochemical and X-ray crystallography analyses. Our model can be applied in drug discovery to identify allosteric pockets.

Zafirlukast Is a Promising Scaffold for Selectively Inhibiting TNFR1 Signaling

Tumor necrosis factor (TNF) plays an important role in the pathogenesis of inflammatory and autoimmune diseases such as rheumatoid arthritis and Crohn's disease. The biological effects of TNF are mediated by binding to TNF receptors, TNF receptor 1 (TNFR1), or TNF receptor 2 (TNFR2), and this coupling makes TNFR1-specific inhibition by small-molecule therapies essential to avoid deleterious side effects. Recently, we engineered a time-resolved fluorescence resonance energy transfer biosensor for high-throughput screening of small molecules that modulate TNFR1 conformational states and identified zafirlukast as a compound that inhibits receptor activation, albeit at low potency. Here, we synthesized 16 analogues of zafirlukast and tested their potency and specificity for TNFR1 signaling. Using cell-based functional assays, we identified three analogues with significantly improved efficacy and potency, each of which induces a conformational change in the receptor (as measured by fluorescence resonance energy transfer (FRET) in cells). The best analogue decreased NF-κB activation by 2.2-fold, IκBα efficiency by 3.3-fold, and relative potency by two orders of magnitude. Importantly, we showed that the analogues do not block TNF binding to TNFR1 and that binding to the receptor's extracellular domain is strongly cooperative. Despite these improvements, the best candidate's maximum inhibition of NF-κB is only 63%, leaving room for further improvements to the zafirlukast scaffold to achieve full inhibition and prove its potential as a therapeutic lead. Interestingly, while we find that the analogues also bind to TNFR2 in vitro, they do not inhibit TNFR2 function in cells or cause any conformational changes upon binding. Thus, these lead compounds should also be used as reagents to study conformational-dependent activation of TNF receptors.

Worth the weight: Sub-Pocket EXplorer (SubPEx), a weighted-ensemble method to enhance binding-pocket conformational sampling

Structure-based virtual screening (VS) is an effective method for identifying potential small-molecule ligands, but traditional VS approaches consider only a single binding-pocket conformation. Consequently, they struggle to identify ligands that bind to alternate conformations. Ensemble docking helps address this issue by incorporating multiple conformations into the docking process, but it depends on methods that can thoroughly explore pocket flexibility. We here introduce Sub-Pocket EXplorer (SubPEx), an approach that uses weighted ensemble (WE) path sampling to accelerate binding-pocket sampling. As proof of principle, we apply SubPEx to three proteins relevant to drug discovery: heat shock protein 90, influenza neuraminidase, and yeast hexokinase 2. SubPEx is available free of charge without registration under the terms of the open-source MIT license: http://durrantlab.com/subpex/.

Application of Mixed-Solvent Molecular Dynamics Simulations for Prediction of Allosteric Sites on G Protein-Coupled Receptors

The development of small molecule allosteric modulators acting at G protein-coupled receptors (GPCRs) is becoming increasingly attractive. Such compounds have advantages over traditional drugs acting at orthosteric sites on these receptors, in particular target specificity. However, the number and locations of druggable allosteric sites within most clinically relevant GPCRs are unknown. In the present study, we describe the development and application of a mixed-solvent molecular dynamics (MixMD)-based method for the identification of allosteric sites on GPCRs. The method employs small organic probes with druglike qualities to identify druggable hotspots in multiple replicate short-timescale simulations. As proof of principle, we first applied the method retrospectively to a test set of five GPCRs (cannabinoid receptor type 1, C-C chemokine receptor type 2, M2 muscarinic receptor, P2Y purinoceptor 1, and protease-activated receptor 2) with known allosteric sites in diverse locations. This resulted in the identification of the known allosteric sites on these receptors. We then applied the method to the μ-opioid receptor. Several allosteric modulators for this receptor are known, although the binding sites for these modulators are not known. The MixMD-based method revealed several potential allosteric sites on the mu-opioid receptor. Implementation of the MixMD-based method should aid future efforts in the structure-based drug design of drugs targeting allosteric sites on GPCRs. SIGNIFICANCE STATEMENT: Allosteric modulation of G protein-coupled receptors (GPCRs) has the potential to provide more selective drugs. However, there are limited structures of GPCRs bound to allosteric modulators, and obtaining such structures is problematic. Current computational methods utilize static structures and therefore may not identify hidden or cryptic sites. Here we describe the use of small organic probes and molecular dynamics to identify druggable allosteric hotspots on GPCRs. The results reinforce the importance of protein dynamics in allosteric site identification.

Memory Unlocks the Future of Biomolecular Dynamics: Transformative Tools to Uncover Physical Insights Accurately and Efficiently

Conformational changes underpin function and encode complex biomolecular mechanisms. Gaining atomic-level detail of how such changes occur has the potential to reveal these mechanisms and is of critical importance in identifying drug targets, facilitating rational drug design, and enabling bioengineering applications. While the past two decades have brought Markov state model techniques to the point where practitioners can regularly use them to glimpse the long-time dynamics of slow conformations in complex systems, many systems are still beyond their reach. In this Perspective, we discuss how including memory (i.e., non-Markovian effects) can reduce the computational cost to predict the long-time dynamics in these complex systems by orders of magnitude and with greater accuracy and resolution than state-of-the-art Markov state models. We illustrate how memory lies at the heart of successful and promising techniques, ranging from the Fokker-Planck and generalized Langevin equations to deep-learning recurrent neural networks and generalized master equations. We delineate how these techniques work, identify insights that they can offer in biomolecular systems, and discuss their advantages and disadvantages in practical settings. We show how generalized master equations can enable the investigation of, for example, the gate-opening process in RNA polymerase II and demonstrate how our recent advances tame the deleterious influence of statistical underconvergence of the molecular dynamics simulations used to parameterize these techniques. This represents a significant leap forward that will enable our memory-based techniques to interrogate systems that are currently beyond the reach of even the best Markov state models. We conclude by discussing some current challenges and future prospects for how exploiting memory will open the door to many exciting opportunities.

Turning up the heat mimics allosteric signaling in imidazole-glycerol phosphate synthase

Allosteric drugs have the potential to revolutionize biomedicine due to their enhanced selectivity and protection against overdosage. However, we need to better understand allosteric mechanisms in order to fully harness their potential in drug discovery. In this study, molecular dynamics simulations and nuclear magnetic resonance spectroscopy are used to investigate how increases in temperature affect allostery in imidazole glycerol phosphate synthase. Results demonstrate that temperature increase triggers a cascade of local amino acid-to-amino acid dynamics that remarkably resembles the allosteric activation that takes place upon effector binding. The differences in the allosteric response elicited by temperature increase as opposed to effector binding are conditional to the alterations of collective motions induced by either mode of activation. This work provides an atomistic picture of temperature-dependent allostery, which could be harnessed to more precisely control enzyme function.

Intermediate-aided allostery mechanism for α-glucosidase by Xanthene-11v as an inhibitor using residue interaction network analysis

Exploring allosteric inhibition and the discovery of new inhibitor binding sites are important studies in protein regulation mechanisms and drug discovery. Structural and network-based analyses of trajectories resulting from molecular dynamics (MD) simulations have been developed to discover protein dynamics, landscape, functions, and allosteric regions. Here, an experimentally suggested non-competitive inhibitor, xanthene-11v, was considered to explore its allosteric inhibition mechanism in α-glucosidase MAL12. Comparative structural and network analyses were applied to eight 250 ns independent MD simulations, four of which were performed in the free state and four of which were performed in ligand-bound forms. Projected two-dimensional free energy landscapes (FEL) were constructed from the probabilistic distribution of conformations along the first two principal components. The post-simulation analyses of the coordinates, side-chain torsion angles, non-covalent interaction networks, network communities, and their centralities were performed on α-glucosidase conformations and the intermediate sub-states. Important communities of residues have been found that connect the allosteric site to the active site. Some of these residues like Thr307, Arg312, TYR344, ILE345, Phe357, Asp406, Val407, Asp408, and Leu436 are the key messengers in the transition pathway between allosteric and active sites. Evaluating the probability distribution of distances between gate residues including Val407 in one community and Phe158, and Pro65 in another community depicted the closure of this gate due to the inhibitor binding. Six macro states of protein were deduced from the topology of FEL and analysis of conformational preference of free and ligand-bound systems to these macro states shows a combination of lock-and-key, conformational selection, and induced fit mechanisms are effective in ligand binding. All these results reveal structural states, allosteric mechanisms, and key players in the inhibition pathway of α-glucosidase by xanthene-11v.

Computational approaches streamlining drug discovery

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.

Coevolution-based prediction of key allosteric residues for protein function regulation

Allostery is fundamental to many biological processes. Due to the distant regulation nature, how allosteric mutations, modifications, and effector binding impact protein function is difficult to forecast. In protein engineering, remote mutations cannot be rationally designed without large-scale experimental screening. Allosteric drugs have raised much attention due to their high specificity and possibility of overcoming existing drug-resistant mutations. However, optimization of allosteric compounds remains challenging. Here, we developed a novel computational method KeyAlloSite to predict allosteric site and to identify key allosteric residues (allo-residues) based on the evolutionary coupling model. We found that protein allosteric sites are strongly coupled to orthosteric site compared to non-functional sites. We further inferred key allo-residues by pairwise comparing the difference of evolutionary coupling scores of each residue in the allosteric pocket with the functional site. Our predicted key allo-residues are in accordance with previous experimental studies for typical allosteric proteins like BCR-ABL1, Tar, and PDZ3, as well as key cancer mutations. We also showed that KeyAlloSite can be used to predict key allosteric residues distant from the catalytic site that are important for enzyme catalysis. Our study demonstrates that weak coevolutionary couplings contain important information of protein allosteric regulation function. KeyAlloSite can be applied in studying the evolution of protein allosteric regulation, designing and optimizing allosteric drugs, and performing functional protein design and enzyme engineering.

Network Analysis of Molecular Dynamics Sectors in the p53 Protein

Design of allosteric regulators is an emergent field in the area of drug discovery holding promise for currently untreated diseases. Allosteric regulators bind to a protein in one location and affect a distant site. The ubiquitous presence of allosteric effectors in biology and the success of serendipitously identified allosteric compounds point to the potential they hold. Although the mechanism of transmission of an allosteric signal is not unequivocally determined, one hypothesis suggests that groups of evolutionarily covarying residues within a protein, termed sectors, are conduits. A long-term goal of our lab is to allosterically modulate the activity of proteins by binding small molecules at points of allosteric control. However, methods to consistently identify such points remain unclear. Sector residues on the surfaces of proteins are a promising source of allosteric targets. Recently, we introduced molecular dynamics (MD)-based sectors; MD sectors capitalize on covariance of motion, in place of evolutionary covariance. By focusing on motional covariance, MD sectors tap into the framework of statistical mechanics afforded by the Boltzmann ensemble of structural conformations comprising the underlying data set. We hypothesized that the method of MD sectors can be used to identify a cohesive network of motionally covarying residues capable of transmitting an allosteric signal in a protein. While our initial qualitative results showed promise for the method to predict sectors, that a network of cohesively covarying residues had been produced remained an untested assumption. In this work, we apply network theory to rigorously analyze MD sectors, allowing us to quantitatively assess the biologically relevant property of network cohesiveness of sectors in the context of the tumor suppressor protein, p53. We revised the methodology for assessing and improving MD sectors. Specifically, we introduce a metric to calculate the cohesive properties of the network. Our new approach separates residues into two categories: sector residues and non-sector residues. The relatedness within each respective group is computed with a distance metric. Cohesive sector networks are identified as those that have high relatedness among the sector residues which exceeds the relatedness of the residues to the non-sector residues in terms of the correlation of motions. Our major finding was that the revised means of obtaining sectors was more efficacious than previous iterations, as evidenced by the greater cohesion of the networks. These results are discussed in the context of the development of allosteric regulators of p53 in particular and the expected applicability of the method to the drug design field in general.

Allosteric Modulators of Dopamine D2 Receptors for Fine-Tuning of Dopaminergic Neurotransmission in CNS Diseases: Overview, Pharmacology, Structural Aspects and Synthesis

Allosteric modulation of G protein-coupled receptors (GPCRs) is nowadays a hot topic in medicinal chemistry. Allosteric modulators, i.e., compounds which bind in a receptor site topologically distinct from orthosteric sites, exhibit a number of advantages. They are more selective, safer and display a ceiling effect which prevents overdosing. Allosteric modulators of dopamine D₂ receptor are potential drugs against a number of psychiatric and neurological diseases, such as schizophrenia and Parkinson's disease. In this review, an insightful summary of current research on D₂ receptor modulators is presented, ranging from their pharmacology and structural aspects of ligand-receptor interactions to their synthesis.

Variable Regions of p53 Isoforms Allosterically Hard Code DNA Interaction

Allosteric regulation of protein activity pervades biology as the "second secret of life." We have been examining the allosteric regulation and mutant reactivation of the tumor suppressor protein p53. We have found that generalizing the definition of allosteric effector to include entire proteins and expanding the meaning of binding site to include the interface of a transcription factor with its DNA to be useful in understanding the modulation of protein activity. Here, we cast the variable regions of p53 isoforms as allosteric regulators of p53 interactions with its consensus DNA. We implemented molecular dynamics simulations and our lab's new techniques of molecular dynamics (MD) sectors and MD-Markov state models to investigate the effects of nine naturally occurring splice variant isoforms of p53. We find that all of the isoforms differ from wild type in their dynamic properties and how they interact with the DNA. We consider the implications of these findings on allostery and cancer treatment.

Proposal to Consider Chemical/Physical Microenvironment as a New Therapeutic Off-Target Approach

The molecular revolution could lead drug discovery from chance observation to the rational design of new classes of drugs that could simultaneously be more effective and less toxic. Unfortunately, we are witnessing some failure in this sense, and the causes of the crisis involve a wide range of epistemological and scientific aspects. In pharmacology, one key point is the crisis of the paradigm the “magic bullet”, which is to design therapies based on specific molecular targets. Drug repurposing is one of the proposed ways out of the crisis and is based on the off-target effects of known drugs. Here, we propose the microenvironment as the ideal place to direct the off-targeting of known drugs. While it has been extensively investigated in tumors, the generation of a harsh microenvironment is also a phenotype of the vast majority of chronic diseases. The hostile microenvironment, on the one hand, reduces the efficacy of both chemical and biological drugs; on the other hand, it dictates a sort of “Darwinian” selection of those cells armed to survive in such hostile conditions. This opens the way to the consideration of the microenvironment as a convenient target for pharmacological action, with a clear example in proton pump inhibitors.

Membrane Lipids Are an Integral Part of Transmembrane Allosteric Sites in GPCRs: A Case Study of Cannabinoid CB1 Receptor Bound to a Negative Allosteric Modulator, ORG27569, and Analogs

A growing number of G-protein-coupled receptor (GPCR) structures reveal novel transmembrane lipid-exposed allosteric sites. Ligands must first partition into the surrounding membrane and take lipid paths to these sites. Remarkably, a significant part of the bound ligands appears exposed to the membrane lipids. The experimental structures do not usually account for the surrounding lipids, and their apparent contribution to ligand access and binding is often overlooked and poorly understood. Using classical and enhanced molecular dynamics simulations, we show that membrane lipids are critical in the access and binding of ORG27569 and its analogs at the transmembrane site of cannabinoid CB1 receptor. The observed differences in the binding affinity and cooperativity arise from the functional groups that interact primarily with lipids. Our results demonstrate the significance of incorporating membrane lipids as an integral component of transmembrane sites for accurate characterization, binding-affinity calculations, and lead optimization in drug discovery.

Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs

Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative drug discovery.