Structure-based design of non-hypertrophic apelin receptor modulator.

Apelin is a key hormone in cardiovascular homeostasis that activates the apelin receptor (APLNR), which is regarded as a promising therapeutic target for cardiovascular disease. However, adverse effects through the ß-arrestin pathway limit its pharmacological use. Here, we report cryoelectron microscopy (cryo-EM) structures of APLNR-Gi1 complexes bound to three agonists with divergent signaling profiles. Combined with functional assays, we have identified "twin hotspots" in APLNR as key determinants for signaling bias, guiding the rational design of two exclusive G-protein-biased agonists WN353 and WN561. Cryo-EM structures of WN353- and WN561-stimulated APLNR-G protein complexes further confirm that the designed ligands adopt the desired poses. Pathophysiological experiments have provided evidence that WN561 demonstrates superior therapeutic effects against cardiac hypertrophy and reduced adverse effects compared with the established APLNR agonists. In summary, our designed APLNR modulator may facilitate the development of next-generation cardiovascular medications.

Structural mechanisms of α7 nicotinic receptor allosteric modulation and activation.

The α7 nicotinic acetylcholine receptor is a pentameric ligand-gated ion channel that plays an important role in cholinergic signaling throughout the nervous system. Its unique physiological characteristics and implications in neurological disorders and inflammation make it a promising but challenging therapeutic target. Positive allosteric modulators overcome limitations of traditional α7 agonists, but their potentiation mechanisms remain unclear. Here, we present high-resolution structures of α7-modulator complexes, revealing partially overlapping binding sites but varying conformational states. Structure-guided functional and computational tests suggest that differences in modulator activity arise from the stable rotation of a channel gating residue out of the pore. We extend the study using a time-resolved cryoelectron microscopy (cryo-EM) approach to reveal asymmetric state transitions for this homomeric channel and also find that a modulator with allosteric agonist activity exploits a distinct channel-gating mechanism. These results define mechanisms of α7 allosteric modulation and activation with implications across the pentameric receptor superfamily.

Molecular recognition of morphine and fentanyl by the human µ-opioid receptor.

Morphine and fentanyl are among the most used opioid drugs that confer analgesia and unwanted side effects through both G protein and arrestin signaling pathways of µ-opioid receptor (µOR). Here, we report structures of the human µOR-G protein complexes bound to morphine and fentanyl, which uncover key differences in how they bind the receptor. We also report structures of µOR bound to TRV130, PZM21, and SR17018, which reveal preferential interactions of these agonists with TM3 side of the ligand-binding pocket rather than TM6/7 side. In contrast, morphine and fentanyl form dual interactions with both TM3 and TM6/7 regions. Mutations at the TM6/7 interface abolish arrestin recruitment of µOR promoted by morphine and fentanyl. Ligands designed to reduce TM6/7 interactions display preferential G protein signaling. Our results provide crucial insights into fentanyl recognition and signaling of µOR, which may facilitate rational design of next-generation analgesics.

From structure to clinic: Design of a muscarinic M1 receptor agonist with potential to treatment of Alzheimer's disease.

Current therapies for Alzheimer's disease seek to correct for defective cholinergic transmission by preventing the breakdown of acetylcholine through inhibition of acetylcholinesterase, these however have limited clinical efficacy. An alternative approach is to directly activate cholinergic receptors responsible for learning and memory. The M1-muscarinic acetylcholine (M1) receptor is the target of choice but has been hampered by adverse effects. Here we aimed to design the drug properties needed for a well-tolerated M1-agonist with the potential to alleviate cognitive loss by taking a stepwise translational approach from atomic structure, cell/tissue-based assays, evaluation in preclinical species, clinical safety testing, and finally establishing activity in memory centers in humans. Through this approach, we rationally designed the optimal properties, including selectivity and partial agonism, into HTL9936-a potential candidate for the treatment of memory loss in Alzheimer's disease. More broadly, this demonstrates a strategy for targeting difficult GPCR targets from structure to clinic.

Ribosome-Targeting Antibiotics: Modes of Action, Mechanisms of Resistance, and Implications for Drug Design.

Genetic information is translated into proteins by the ribosome. Structural studies of the ribosome and of its complexes with factors and inhibitors have provided invaluable information on the mechanism of protein synthesis. Ribosome inhibitors are among the most successful antimicrobial drugs and constitute more than half of all medicines used to treat infections. However, bacterial infections are becoming increasingly difficult to treat because the microbes have developed resistance to the most effective antibiotics, creating a major public health care threat. This has spurred a renewed interest in structure-function studies of protein synthesis inhibitors, and in few cases, compounds have been developed into potent therapeutic agents against drug-resistant pathogens. In this review, we describe the modes of action of many ribosome-targeting antibiotics, highlight the major resistance mechanisms developed by pathogenic bacteria, and discuss recent advances in structure-assisted design of new molecules.

Multivalent Small-Molecule Pan-RAS Inhibitors.

Design of small molecules that disrupt protein-protein interactions, including the interaction of RAS proteins and their effectors, may provide chemical probes and therapeutic agents. We describe here the synthesis and testing of potential small-molecule pan-RAS ligands, which were designed to interact with adjacent sites on the surface of oncogenic KRAS. One compound, termed 3144, was found to bind to RAS proteins using microscale thermophoresis, nuclear magnetic resonance spectroscopy, and isothermal titration calorimetry and to exhibit lethality in cells partially dependent on expression of RAS proteins. This compound was metabolically stable in liver microsomes and displayed anti-tumor activity in xenograft mouse cancer models. These findings suggest that pan-RAS inhibition may be an effective therapeutic strategy for some cancers and that structure-based design of small molecules targeting multiple adjacent sites to create multivalent inhibitors may be effective for some proteins.

Structure of the Adenosine A1 Receptor Reveals the Basis for Subtype Selectivity.

The adenosine A1 receptor (A1-AR) is a G-protein-coupled receptor that plays a vital role in cardiac, renal, and neuronal processes but remains poorly targeted by current drugs. We determined a 3.2 Å crystal structure of the A1-AR bound to the selective covalent antagonist, DU172, and identified striking differences to the previously solved adenosine A2A receptor (A2A-AR) structure. Mutational and computational analysis of A1-AR revealed a distinct conformation of the second extracellular loop and a wider extracellular cavity with a secondary binding pocket that can accommodate orthosteric and allosteric ligands. We propose that conformational differences in these regions, rather than amino-acid divergence, underlie drug selectivity between these adenosine receptor subtypes. Our findings provide a molecular basis for AR subtype selectivity with implications for understanding the mechanisms governing allosteric modulation of these receptors, allowing the design of more selective agents for the treatment of ischemia-reperfusion injury, renal pathologies, and neuropathic pain.

The p53 Pathway: Origins, Inactivation in Cancer, and Emerging Therapeutic Approaches.

Inactivation of the transcription factor p53, through either direct mutation or aberrations in one of its many regulatory pathways, is a hallmark of virtually every tumor. In recent years, screening for p53 activators and a better understanding of the molecular mechanisms of oncogenic perturbations of p53 function have opened up a host of novel avenues for therapeutic intervention in cancer: from the structure-guided design of chemical chaperones to restore the function of conformationally unstable p53 cancer mutants, to the development of potent antagonists of the negative regulators MDM2 and MDMX and other modulators of the p53 pathway for the treatment of cancers with wild-type p53. Some of these compounds have now moved from proof-of-concept studies into clinical trials, with prospects for further, personalized anticancer medicines. We trace the structural evolution of the p53 pathway, from germ-line surveillance in simple multicellular organisms to its pluripotential role in humans.

Unlocking translational machinery for antitubercular drug development.

Targeting translational factor proteins (TFPs) presents significant promise for the development of innovative antitubercular drugs. Previous insights from antibiotic binding mechanisms and recently solved 3D crystal structures of Mycobacterium tuberculosis (Mtb) elongation factor thermo unstable-GDP (EF-Tu-GDP), elongation factor thermo stable-EF-Tu (EF-Ts-EF-Tu), and elongation factor G-GDP (EF-G-GDP) have opened up new avenues for the design and development of potent antituberculosis (anti-TB) therapies.

Targeting SLC transporters: small molecules as modulators and therapeutic opportunities.

Solute carrier (SLCs) transporters mediate the transport of a broad range of solutes across biological membranes. Dysregulation of SLCs has been associated with various pathologies, including metabolic and neurological disorders, as well as cancer and rare diseases. SLCs are therefore emerging as key targets for therapeutic intervention with several recently approved drugs targeting these proteins. Unlocking this large and complex group of proteins is essential to identifying unknown SLC targets and developing next-generation SLC therapeutics. Recent progress in experimental and computational techniques has significantly advanced SLC research, including drug discovery. Here, we review emerging topics in therapeutic discovery of SLCs, focusing on state-of-the-art approaches in structural, chemical, and computational biology, and discuss current challenges in transporter drug discovery.

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.

Antibiotic class with potent in vivo activity targeting lipopolysaccharide synthesis in Gram-negative bacteria.

Here, we describe the identification of an antibiotic class acting via LpxH, a clinically unexploited target in lipopolysaccharide synthesis. The lipopolysaccharide synthesis pathway is essential in most Gram-negative bacteria and there is no analogous pathway in humans. Based on a series of phenotypic screens, we identified a hit targeting this pathway that had activity on efflux-defective strains of Escherichia coli. We recognized common structural elements between this hit and a previously published inhibitor, also with activity against efflux-deficient bacteria. With the help of X-ray structures, this information was used to design inhibitors with activity on efflux-proficient, wild-type strains. Optimization of properties such as solubility, metabolic stability and serum protein binding resulted in compounds having potent in vivo efficacy against bloodstream infections caused by the critical Gram-negative pathogens E. coli and Klebsiella pneumoniae. Other favorable properties of the series include a lack of pre-existing resistance in clinical isolates, and no loss of activity against strains expressing extended-spectrum-ß-lactamase, metallo-ß-lactamase, or carbapenemase-resistance genes. Further development of this class of antibiotics could make an important contribution to the ongoing struggle against antibiotic resistance.

Identification of highly selective SIK1/2 inhibitors that modulate innate immune activation and suppress intestinal inflammation.

The salt-inducible kinases (SIK) 1-3 are key regulators of pro- versus anti-inflammatory cytokine responses during innate immune activation. The lack of highly SIK-family or SIK isoform-selective inhibitors suitable for repeat, oral dosing has limited the study of the optimal SIK isoform selectivity profile for suppressing inflammation in vivo. To overcome this challenge, we devised a structure-based design strategy for developing potent SIK inhibitors that are highly selective against other kinases by engaging two differentiating features of the SIK catalytic site. This effort resulted in SIK1/2-selective probes that inhibit key intracellular proximal signaling events including reducing phosphorylation of the SIK substrate cAMP response element binding protein (CREB) regulated transcription coactivator 3 (CRTC3) as detected with an internally generated phospho-Ser329-CRTC3-specific antibody. These inhibitors also suppress production of pro-inflammatory cytokines while inducing anti-inflammatory interleukin-10 in activated human and murine myeloid cells and in mice following a lipopolysaccharide challenge. Oral dosing of these compounds ameliorates disease in a murine colitis model. These findings define an approach to generate highly selective SIK1/2 inhibitors and establish that targeting these isoforms may be a useful strategy to suppress pathological inflammation.

Discovery of lirafugratinib (RLY-4008), a highly selective irreversible small-molecule inhibitor of FGFR2.

Fibroblast growth factor receptor (FGFR) kinase inhibitors have been shown to be effective in the treatment of intrahepatic cholangiocarcinoma and other advanced solid tumors harboring FGFR2 alterations, but the toxicity of these drugs frequently leads to dose reduction or interruption of treatment such that maximum efficacy cannot be achieved. The most common adverse effects are hyperphosphatemia caused by FGFR1 inhibition and diarrhea due to FGFR4 inhibition, as current therapies are not selective among the FGFRs. Designing selective inhibitors has proved difficult with conventional approaches because the orthosteric sites of FGFR family members are observed to be highly similar in X-ray structures. In this study, aided by analysis of protein dynamics, we designed a selective, covalent FGFR2 inhibitor. In a key initial step, analysis of long-timescale molecular dynamics simulations of the FGFR1 and FGFR2 kinase domains allowed us to identify differential motion in their P-loops, which are located adjacent to the orthosteric site. Using this insight, we were able to design orthosteric binders that selectively and covalently engage the P-loop of FGFR2. Our drug discovery efforts culminated in the development of lirafugratinib (RLY-4008), a covalent inhibitor of FGFR2 that shows substantial selectivity over FGFR1 (~250-fold) and FGFR4 (~5,000-fold) in vitro, causes tumor regression in multiple FGFR2-altered human xenograft models, and was recently demonstrated to be efficacious in the clinic at doses that do not induce clinically significant hyperphosphatemia or diarrhea.

Structures of Leukotriene Biosynthetic Enzymes and Development of New Therapeutics.

Leukotrienes are potent immune-regulating lipid mediators with patho-genic roles in inflammatory and allergic diseases, particularly asthma. These autacoids also contribute to low-grade inflammation, a hallmark of cardiovascular, neurodegenerative, metabolic, and tumor diseases. Biosynthesis of leukotrienes involves release and oxidative metabolism of arachidonic acid and proceeds via a set of cytosolic and integral membrane enzymes that are typically expressed by cells of the innate immune system. In activated cells, these enzymes traffic and assemble at the endoplasmic and perinuclear membrane, together comprising a biosynthetic complex. Here we describe recent advances in our molecular understanding of the protein components of the leukotriene-synthesizing enzyme machinery and also briefly touch upon the leukotriene receptors. Moreover, we discuss emerging opportunities for pharmacological intervention and development of new therapeutics.

A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation.

Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://github.com/gersteinlab/GenAI4Drug.

Structure prediction of linear and cyclic peptides using CABS-flex.

The structural modeling of peptides can be a useful aid in the discovery of new drugs and a deeper understanding of the molecular mechanisms of life. Here we present a novel multiscale protocol for the structure prediction of linear and cyclic peptides. The protocol combines two main stages: coarse-grained simulations using the CABS-flex standalone package and an all-atom reconstruction-optimization process using the Modeller program. We evaluated the protocol on a set of linear peptides and two sets of cyclic peptides, with cyclization through the backbone and disulfide bonds. A comparison with other state-of-the-art tools (APPTEST, PEP-FOLD, ESMFold and AlphaFold implementation in ColabFold) shows that for most cases, AlphaFold offers the highest resolution. However, CABS-flex is competitive, particularly when it comes to short linear peptides. As demonstrated, the protocol performance can be further improved by combination with the residue-residue contact prediction method or more efficient scoring. The protocol is included in the CABS-flex standalone package along with online documentation to aid users in predicting the structure of peptides and mini-proteins.

Introducing enzymatic cleavage features and transfer learning realizes accurate peptide half-life prediction across species and organs.

Peptide drugs are becoming star drug agents with high efficiency and selectivity which open up new therapeutic avenues for various diseases. However, the sensitivity to hydrolase and the relatively short half-life have severely hindered their development. In this study, a new generation artificial intelligence-based system for accurate prediction of peptide half-life was proposed, which realized the half-life prediction of both natural and modified peptides and successfully bridged the evaluation possibility between two important species (human, mouse) and two organs (blood, intestine). To achieve this, enzymatic cleavage descriptors were integrated with traditional peptide descriptors to construct a better representation. Then, robust models with accurate performance were established by comparing traditional machine learning and transfer learning, systematically. Results indicated that enzymatic cleavage features could certainly enhance model performance. The deep learning model integrating transfer learning significantly improved predictive accuracy, achieving remarkable R2 values: 0.84 for natural peptides and 0.90 for modified peptides in human blood, 0.984 for natural peptides and 0.93 for modified peptides in mouse blood, and 0.94 for modified peptides in mouse intestine on the test set, respectively. These models not only successfully composed the above-mentioned system but also improved by approximately 15% in terms of correlation compared to related works. This study is expected to provide powerful solutions for peptide half-life evaluation and boost peptide drug development.

DiffPROTACs is a deep learning-based generator for proteolysis targeting chimeras.

PROteolysis TArgeting Chimeras (PROTACs) has recently emerged as a promising technology. However, the design of rational PROTACs, especially the linker component, remains challenging due to the absence of structure-activity relationships and experimental data. Leveraging the structural characteristics of PROTACs, fragment-based drug design (FBDD) provides a feasible approach for PROTAC research. Concurrently, artificial intelligence-generated content has attracted considerable attention, with diffusion models and Transformers emerging as indispensable tools in this field. In response, we present a new diffusion model, DiffPROTACs, harnessing the power of Transformers to learn and generate new PROTAC linkers based on given ligands. To introduce the essential inductive biases required for molecular generation, we propose the O(3) equivariant graph Transformer module, which augments Transformers with graph neural networks (GNNs), using Transformers to update nodes and GNNs to update the coordinates of PROTAC atoms. DiffPROTACs effectively competes with existing models and achieves comparable performance on two traditional FBDD datasets, ZINC and GEOM. To differentiate the molecular characteristics between PROTACs and traditional small molecules, we fine-tuned the model on our self-built PROTACs dataset, achieving a 93.86% validity rate for generated PROTACs. Additionally, we provide a generated PROTAC database for further research, which can be accessed at https://bailab.siais.shanghaitech.edu.cn/service/DiffPROTACs-generated.tgz. The corresponding code is available at https://github.com/Fenglei104/DiffPROTACs and the server is at https://bailab.siais.shanghaitech.edu.cn/services/diffprotacs.

GLDM: hit molecule generation with constrained graph latent diffusion model.

Discovering hit molecules with desired biological activity in a directed manner is a promising but profound task in computer-aided drug discovery. Inspired by recent generative AI approaches, particularly Diffusion Models (DM), we propose Graph Latent Diffusion Model (GLDM)-a latent DM that preserves both the effectiveness of autoencoders of compressing complex chemical data and the DM's capabilities of generating novel molecules. Specifically, we first develop an autoencoder to encode the molecular data into low-dimensional latent representations and then train the DM on the latent space to generate molecules inducing targeted biological activity defined by gene expression profiles. Manipulating DM in the latent space rather than the input space avoids complicated operations to map molecule decomposition and reconstruction to diffusion processes, and thus improves training efficiency. Experiments show that GLDM not only achieves outstanding performances on molecular generation benchmarks, but also generates samples with optimal chemical properties and potentials to induce desired biological activity.