Structure-based Drug Design Research Articles

Solution Ionic Strength Can Modulate Functional Loop Conformations in E. coli Dihydrofolate Reductase.

The observation of multiple conformations of a functional loop (termed M20) in the Escherichia coli dihydrofolate reductase (ecDHFR) enzyme triggered the proposition that large-scale motions of protein structural elements contribute to enzyme catalysis. The transition of the M20 loop from a closed conformation to an occluded conformation was thought to aid the rate-limiting release of the products. However, the influence of charged species in the solution environment on the observed M20 loop conformations, independent of charged ligands bound to the enzyme, had not been considered. Molecular dynamics simulations of ecDHFR in model CaCl2 solutions of varying molar ionic strengths IM reveal a substantial free energy barrier between occluded and closed M20 loop states at IM exceeding the E. coli threshold (∼0.24 M). This barrier may facilitate crystallization of ecDHFR in the occluded state, consistent with ecDHFR structures obtained at IM exceeding 0.3 M. At lower IM (≤0.15 M), the M20 loop can explore the occluded state, but prefers an open/partially closed conformation, again consistent with ecDHFR structures. Our findings caution against using ecDHFR structures obtained at nonphysiological ionic strengths in interpreting catalytic events or in structure-based drug design.

Development of Prolinol Containing Inhibitors of Hypoxanthine-Guanine-Xanthine Phosphoribosyltransferase: Rational Structure-Based Drug Design.

Inhibition of hypoxanthine-guanine-xanthine phosphoribosyltransferase activity decreases the pool of 6-oxo and 6-amino purine nucleoside monophosphates required for DNA and RNA synthesis, resulting in a reduction in cell growth. Therefore, inhibitors of this enzyme have potential to control infections, caused by Plasmodium falciparum and Plasmodium vivax, Trypanosoma brucei, Mycobacterium tuberculosis, and Helicobacter pylori. Five compounds synthesized here that contain a purine base covalently linked by a prolinol group to one or two phosphonate groups have Ki values ranging from 3 nM to >10 μM, depending on the structure of the inhibitor and the biological origin of the enzyme. X-ray crystal structures show that, on binding, these prolinol-containing inhibitors stimulated the movement of active site loops in the enzyme. Against TBr in cell culture, a prodrug exhibited an EC50 of 10 μM. Thus, these compounds are excellent candidates for further development as drug leads against infectious diseases as well as being potential anticancer agents.

Prospective de novo drug design with deep interactome learning

De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the “zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.

Computational analysis of affinity dynamics between the variants of SARS-CoV-2 spike protein (RBD) and human ACE-2 receptor

The novel coronavirus SARS-CoV-2 resulted in a significant worldwide health emergency known as the COVID-19 pandemic. This crisis has been marked by the widespread of various variants, with certain ones causing notable apprehension. In this study, we harnessed computational techniques to scrutinize these Variants of Concern (VOCs), including various Omicron subvariants. Our approach involved the use of protein structure prediction algorithms and molecular docking techniques, we have investigated the effects of mutations within the Receptor Binding Domain (RBD) of SARS-CoV-2 and how these mutations influence its interactions with the human angiotensin-converting enzyme 2 (hACE-2) receptor. Further we have predicted the structural alterations in the RBD of naturally occurring SARS-CoV-2 variants using the tr-Rosetta algorithm. Subsequent docking and binding analysis employing HADDOCK and PRODIGY illuminated crucial interactions occurring at the Receptor-Binding Motif (RBM). Our findings revealed a hierarchy of increased binding affinity between the human ACE2 receptor and the various RBDs, in the order of wild type (Wuhan-strain) < Beta < Alpha < Gamma < Omicron-B.1.1.529 < Delta < Omicron-BA.2.12.1 < Omicron-BA.5.2.1 < Omicron-BA.1.1. Notably, Omicron-BA.1.1 demonstrated the highest binding affinity of -17.4 kcal mol−1 to the hACE2 receptor when compared to all the mutant complexes. Additionally, our examination indicated that mutations occurring in active residues of the Receptor Binding Domain (RBD) consistently improved the binding affinity and intermolecular interactions in all mutant complexes. Analysis of the differences among variants has laid a foundation for the structure-based drug design targeting the RBD region of SARS-CoV-2.

In-silico design, molecular docking, molecular dynamic simulations, Molecular mechanics with generalised Born and surface area solvation study, and pharmacokinetic prediction of novel diclofenac as anti-inflammatory compounds

The prostaglandins inside inflamed tissues are produced by cyclooxygenase-2 (COX-2), making it an important target for improving anti-inflammatory medications over a long period. Adverse effects have been related to the traditional usage of non-steroidal anti-inflammatory drugs (NSAIDs) for the treatment of inflammation, mainly centered around gastrointestinal (GI) complications. The current research involves the creation of a virtual library of innovative molecules showing similar drug properties via a structure-based drug design. A library that includes five novel derivatives of Diclofenac was designed. Subsequently, molecular docking through the Glide module and determining the binding free energy implementing the Prime-MMGBSA module by the Schrödinger software package was used to identify compounds that showed marked specificity towards the COX-2 isoform. In addition, the ligands are subject to evaluation of their drug-like properties and ADMET (absorption, distribution, metabolism, excretion, and toxicity) characteristics using the QikProp module. Finally, molecular dynamics simulation has been calculated for the best molecule. The docking results indicated that all compounds own a predictive capability for specific binding to the COX-2 enzyme compared to the standard drug with a docking score range from -10.07 to -10.66 Kcal/mole, thus potentially overcoming the limitations imposed previously by the drugs currently used in clinical use. The ADMET analysis of the virtually active compounds demonstrated an acceptable drug-like profile and desirable pharmacokinetics properties. MM/GBSA calculation revealed that all the suggested compounds exhibited favorable free binding energies (-49.150 to - 60.185 Kcal/mole), indicating their strong potential to fit well into the COX-2 receptor. Finally, the MD simulation study revealed that compound 1 had perfect alignment with COX-2 receptor. The findings indicated that the compounds possess a predictive capability for specific binding to the COX-2 enzyme, thus potentially surmounting the restrictions imposed by the drugs currently employed in clinical use.

Integrative phytochemical, ligand structure based drug design nephroprotective potential of Annona muricata flower-petals

This study was aimed at investigating the ability of extract of Annona muricata (AM) flower-petals in ameliorating the toxic effects of acetaminophen on the kidneys of albino rats. The biochemical results showed a marked increase in AM 200 mg (32.84 ± 0.14) and AM 400 mg (32.64 ± 0.78). Increase levels of total protein in AM 200 mg (77.00 ± 5.65) displays nephroprotective potential of the flower extract. Reduction of renal activities of serum urea in AM 400 mg group (6.41 ± 0.22) indicates its protective potency against acetaminophen induced kidney damage. Increased activities of SOD levels at 200 mg (4.97 ± 0.05) and CAT levels at 200 mg (23.39 ± 1.13). This study showed that A. muricata has good prospects of being a nephroprotective drug candidate.

Upgrade of crystallography beamline BL19U1 at the Shanghai Synchrotron Radiation Facility

BL19U1, an energy-tunable protein complex crystallography beamline at the Shanghai Synchrotron Radiation Facility, has emerged as one of the most productive MX beamlines since opening to the public in July 2015. As of October 2023, it has contributed to over 2000 protein structures deposited in the Protein Data Bank (PDB), resulting in the publication of more than 1000 scientific papers. In response to increasing interest in structure-based drug design utilizing X-ray crystallography for fragment library screening, enhancements have been implemented in both hardware and data collection systems on the beamline to optimize efficiency. Hardware upgrades include the transition from MD2 to MD2S for the diffractometer, alongside the installation of a humidity controller featuring a rapid nozzle exchanger. This allows users to opt for either low-temperature or room-temperature data collection modes. The control system has been upgraded from Blu-Ice to MXCuBE3, which supports website-mode data collection, providing enhanced compatibility and easy expansion with new features. An automated data processing pipeline has also been developed to offer users real-time feedback on data quality.

Discovery of a pocket network on the domain 5 of the TrkB receptor - A potential new target in the quest for the new ligands.

The important role that the neurotrophin tyrosine kinase receptor - TrkB has in the pathogenesis of several neurodegenerative conditions such are Alzheimer's disease, Parkinson's disease, Huntington's disease, has been well described. This shouldn't be a surprise, since in the physiological conditions, once activated by brain-derived neurotrophic factor (BDNF) and neurotrophin-4/5 (NT-4/5), the TrkB receptor promotes neuronal survival, differentiation and synaptic function. Considering that the natural ligands for TrkB receptor are large proteins, it is a challenge to discover small molecule capable to mimic their effects. Even though, the surface of receptor that is interacting with BDNF or NT-4/5 is known, there was always a question which pocket and interaction is responsible for activation of it. In order to answer this challenging question, we have used molecular dynamic (MD) simulations and Pocketron algorithm which enabled us to detect, for the first time, a pocket network existing in the interacting domain (d5) of the receptor; to describe them and to see how they are communicating with each other. This new discovery gave us potential new areas on receptor that can be targeted and used for structure-based drug design approach in the development of the new ligands.

Discovery and biological characterization of a novel mesoionic insecticide fenmezoditiaz.

Many piercing-sucking insects have developed resistance or cross-resistance to many insecticides targeting insect neural nicotinic acetylcholine receptor (nAChR). Here we are aiming to present the discovery of a novel mesoionic insecticide, fenmezoditiaz, by BASF through structure-based drug design (SBDD) approaches. It has recently been added to the Insecticide Resistance Action Committee mode of classification (IRAC 4E). It is being developed for plant protection against piercing-sucking pests, especially rice hopper complex. The soluble acetylcholine binding protein (AChBP) from the sea slug Aplysia californica was modified using site-directed mutagenesis and based on putative aphid nAChR subunit sequences to create soluble insect-like AChBPs. Among them, insect-like β1 AChBP and native aphid membrane preparation showed the highest correlated biochemical affinity toward structurally diverse ligands. This mutant AChBP was used to understand how insect nAChRs structurally interact with mesoionics, which was then utilized to design novel mesoionics including fenmezoditiaz. It is an excellent systemic insecticide with diverse application methods and has a broad insecticidal spectrum, especially against piercing/sucking insects. It lacks cross-resistance for neonicotinoid resistant plant hoppers. Field-collected brown plant hopper populations from Asian countries showed high susceptibility. Fenmezoditiaz is a systemic insecticide with a broad spectrum, lack of cross-resistance and it could be an additional tool for integrated pest management and insecticide resistance management, especially for the rice hopper complex. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Pyrano[2,3-c]pyrazole fused spirooxindole-linked 1,2,3-triazoles as antioxidant agents: Exploring their utility in the development of antidiabetic drugs via inhibition of α-amylase and DPP4 activity

Environment-benign, multicomponent synthetic methodologies are vital in modern pharmaceutical research and facilitates multi-targeted drug development via synergistic approach. Herein, we reported green and efficient synthesis of pyrano[2,3-c]pyrazole fused spirooxindole linked 1,2,3-triazoles using a tea waste supported copper catalyst (TWCu). The synthetic approach involves a one-pot, five-component reaction using N-propargylated isatin, hydrazine hydrate, ethyl acetoacetate, malononitrile/ethyl cyanoacetate and aryl azides as model substrates. Mechanistically, the reaction was found to proceed via in situ pyrazolone formation followed by Knoevenagel condensation, azide alkyne cycloaddition and Michael's addition reactions. The molecules were developed using structure-based drug design. The primary goal is to identifying anti-oxidant molecules with potential ability to modulate α-amylase and DPP4 (dipeptidyl-peptidase 4) activity. The anti-oxidant analysis, as determined via DPPH, suggested that the synthesized compounds, A6 and A10 possessed excellent anti-oxidant potential compared to butylated hydroxytoluene (BHT). In contrast, compounds A3, A5, A8, A9, A13, A15, and A18 were found to possess comparable anti-oxidant potential. Among these, A3 and A13 possessed potential α-amylase inhibitory activity compared to the acarbose, and A3 further emerged as dual inhibitors of both DPP4 and α-amylase with anti-oxidant potential. The relationship of functionalities on their anti-oxidant and enzymatic inhibition was explored in context to their SAR that was further corroborated using in silico techniques and enzyme kinetics.

Abstract CT056: Development of a novel next-generation RET inhibitor

Abstract Oncogenic RET alterations confer constitutive activation in multiple types of human cancers. Two first-generation selective RET inhibitors selpercatinib and pralsetinib have been approved for patients with RET-driven solid tumors including non-small cell lung cancer (NSCLC) and thyroid cancer. However, critical unmet medical needs remain due to drug resistance caused by RET mutations in patients treated with the first-generation RET inhibitors. To address this emerging clinical issue, we discovered a highly potent and selective RET inhibitor with novel chemical scaffold through structure-based drug design, APS03118. Preclinical studies showed APS03118 had stronger in vitro and in vivo biological activity than the first-generation RET inhibitors targeting a broad range of RET fusions and mutations. Importantly, APS03118 was highly potent against a series of resistant RET mutations including the solvent front mutations RET G810C/R/S (IC50 0.04-5.72 nM) and gatekeeper mutations RET V804E/L/M (IC50 0.04-1.36 nM). Off-target screen analysis using the 468-kinase panel demonstrated APS03118 was highly selective for RET. APS03118 was orally bioavailable with good ADME and PK properties and markedly suppressed tumor growth in various RET-driven mouse xenograft tumor models (tumor growth inhibition 87-108% at 10-30 mg/kg BID). In addition, APS03118 exhibited excellent brain penetrating properties with strong efficacy in the in vivo orthotopic brain tumor model. GLP toxicity studies showed acceptable safety profiles. APS03118 received IND approval from both US FDA and China NMPA, and was granted the fast-track designation by FDA. We recently achieved First-in-Human milestone for APS03118 and the Ph 1 clinical trial is ongoing. As of January 5, 2024, three dose levels have been tested. Plasma exposure of APS03118 increased proportionally following single and repeated administration of APS03118 at different doses. APS03118 was well tolerated in NSCLC and thyroid cancer patients harboring RET fusions or mutations relapsed on the chemotherapy, multi-kinase inhibitor and first-generation RET inhibitor therapy. In addition to the favorable safety profiles, we observed sign of efficacy in the RET altered cancer patients in response to APS03118 treatment even in the early clinical trial testing. Partial response and stable disease were observed in patients 2-4 months after administration of APS03118 and dose limiting toxicity (DLT) dose has not been reached. Dose escalation for APS03118 is continuing. Taken together, APS03118 is a novel highly potent and selective next-generation RET inhibitor overcoming resistance following treatment with the first-generation RET inhibitors. APS03118 warrants further clinical development. Citation Format: Jun Zhong, Ruxue Xiao, Wenya Xie, Minchun Chen, Xiaohu Chen, Hao Wang, Yongbo Liu, Qin Gao, Xiupeng Wang, Zongbao Wang, Qian Chen, Jing Yang, Yizhe Ji, Yuxun Wang. Development of a novel next-generation RET inhibitor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT056.

Integrative computational approaches for discovery and evaluation of lead compound for drug design

In the drug discovery and development, the identification of leadcompoundsplaysa crucial role in the quest for novel therapeutic agents. Leadcompounds are the initial molecules that show promising pharmacological activity againsta specific target and serve as the foundation for drug development. Integrativecomputational approaches have emerged as powerful tools in expediting this complex andresource-intensive process. They enable the efficient screening of vast chemical librariesand the rational design of potential drug candidates, significantly accelerating the drugdiscoverypipeline. This review paper explores the multi-layered landscape of integrative computationalmethodologies employed in lead compound discovery and evaluation. These approaches include various techniques, including molecular modelling, cheminformatics, structure-based drug design (SBDD), high-throughput screening, molecular dynamics simulations, ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction, anddrug-target interaction analysis. By revealing the critical role ofintegrative computational methods, this review highlights their potential to transformdrug discovery into a more efficient, cost-effective, and target-focused endeavour, ultimately paving the way for the development of innovative therapeutic agents to addressa multitude of medical challenges.

Abstract ND04: Discovery of ORIC-944, a novel inhibitor of PRC2 with best-in-class properties for the treatment of prostate cancer

Abstract First generation inhibitors of PRC2 (Polycomb Repressive Complex 2) targeting EZH2 (Enhancer of Zeste Homolog 2) achieved clinical proof-of-concept in follicular lymphoma and epithelioid sarcoma, and emerging clinical data recently revealed promising activity in prostate cancer in combination with AR pathway inhibitors (ARPIs). Here we describe the discovery and development of a second generation PRC2 inhibitor, ORIC-944, a potent, selective, allosteric inhibitor of PRC2 that binds the EED (Embryonic Ectoderm Development) subunit within the complex. Co-crystallization of previously disclosed triazolopyrimidine-based compounds demonstrated binding to EED in the histone H3K27me3 binding pocket, adopting a Lys211-in conformation. We evaluated the apo structure of EED and hypothesized that the Lys211-in conformation may be energetically unfavorable. Using structure-based drug design we pursued a strategy for differentiation of SAR and physicochemical properties by progressing from an early 2-substituted imidazopyrimidine that targets the cryptic pocket with a Lys211-out conformation. Key optimization criteria focused on metabolic stability and solubility, and resulted in the discovery of ORIC-944, an orally bioavailable inhibitor of PRC2 with best-in-class properties. Single agent tumor growth inhibition was observed for ORIC-944 in prostate cancer xenograft models. ORIC-944 also improved ARPI antitumor activity in preclinical combination studies and demonstrated in vitro synergy. Characterization of ORIC-944 revealed superiority to first generation clinical PRC2 inhibitors across a variety of preclinical assessments including potency, solubility, CYP inhibition/induction, PK and oral bioavailability. Together, the preclinical results indicate the desired properties were achieved in ORIC-944 to meet a superior target candidate profile, and the ongoing phase 1b trial (NCT05413421) demonstrates a clinical half-life of approximately 20 hours consistent with once daily dosing and a potential best-in-class profile. Citation Format: Matthew A. Marx, Aaron C. Burns, Nidhi Arora, Thomas P. Bobinski, David M. Briere, Andrew Calinisan, Robin J. Gunn, John M. Ketcham, David Lawson, Matthew Lee, Peter Olson, Christopher R. Smith, Niranjan Sudhakar, James G. Christensen, Chelsea X. Chen, Natalie Yuen, Livia Ulicna, Frank Duong, Christophe Colas, Anjana Ramnath, Ron Xu, Edna Chow Maneval, Pratik S. Multani, Rupal Patel, Archana Kumar, Anneleen Daemen, Anthony Romero, Melissa R. Junttila, Lori S. Friedman. Discovery of ORIC-944, a novel inhibitor of PRC2 with best-in-class properties for the treatment of prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr ND04.

Rational Design of Drugs Targeting G-Protein-Coupled Receptors: A Structural Biology Perspective.

G protein-coupled receptors (GPCRs) play a key role in the transduction of extracellular signals to cells and regulation of many biological processes, which makes these membrane proteins one of the most important targets for pharmacological agents. A significant increase in the number of resolved atomic structures of GPCRs has opened the possibility of developing pharmaceuticals targeting these receptors via structure-based drug design (SBDD). SBDD employs information on the structure of receptor-ligand complexes to search for selective ligands without the need for an extensive high-throughput experimental ligand screening and can significantly expand the chemical space for ligand search. In this review, we describe the process of deciphering GPCR structures using X-ray diffraction analysis and cryoelectron microscopy as an important stage in the rational design of drugs targeting this receptor class. Our main goal was to present modern developments and key features of experimental methods used in SBDD of GPCR-targeting agents to a wide range of specialists.

A REVIEW ON MOLECULAR DOCKING AND ITS APPLICATION

Molecular Docking is powerful computer strategy, critical in drug development, structural biology, and biomolecular interaction researchproviding a thorough grasp of its importance in modern scientific study. Molecular docking involves forecasting how a small molecule, often a potential medication, will interact with a target biomolecule like DNA or a protein. This process examines the spatial and energetic compatibility of the ligand with the active site of the receptor, assisting in the discovery of new drug candidates, refining existing compounds, and understanding the intricate interactions between drugs and receptors. Molecular Docking a computational tool that evaluates and ranks different ligand-receptor conformations based on their binding energies. An accurate scoring function is imperative for distinguishing high-affinity ligands from low-affinity ones, this makes it possible to identify potential medication candidates for validation in experiments. Molecular docking finds application in various domains of Drug Progress, comprisingstructure-based medication development, virtual screening,lead optimization.When creating a medicine based on structure, the target biomolecules three-dimensional structure is employed to guide the ligand selection that will communicate with the region that is active. Virtual examination accelerates the discovery of potential drug properties by rapidly evaluating large compound libraries. Lead optimization, on the other hand, is facilitated through iterative docking studies aimed at enhancing binding affinity and pharmacological properties. The field of molecular docking has seen a proliferation of diverse techniques designed to address the unique challenges of structure-based drug design and biomolecular interaction analysis. This report provides an in-depth examination of thedistinct methodologies of docking, its principles, applications, and their importance in advancing our understanding of molecular interactions in different contexts.The diversity of docking methods arises from the need to accommodate different types of molecules, target structures, and research objectives.

Discovery and Optimization of Novel Nonbile Acid FXR Agonists as Preclinical Candidates for the Treatment of Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) is a multifactorial chronic inflammation of the intestine and has become a global public health concern. A farnesoid X receptor (FXR) was recently reported to play a key role in hepatic-intestinal circulation, intestinal metabolism, immunity, and microbial regulation, and thus, it becomes a promising therapeutic target for IBD. In this study, we identified a series of nonbile acid FXR agonists, in which 33 novel compounds were designed and synthesized by the structure-based drug design strategy from our previously identified hit compound. Compound 33 exhibited a potent FXR agonistic activity, high intestinal distribution, good anti-inflammatory activity, and the ability to repair the colon epithelium in a DSS-induced acute enteritis model. Based on the results of RNA-seq analysis, we further investigated the therapeutic potential of the combination of compound 33 with 5-ASA. Overall, the results indicated that compound 33 is a promising drug candidate for IBD treatment.

Native Mass Spectrometry Dissects the Structural Dynamics of an Allosteric Heterodimer of SARS-CoV-2 Nonstructural Proteins.

Structure-based drug design, which relies on precise understanding of the target protein and its interaction with the drug candidate, is dramatically expedited by advances in computational methods for candidate prediction. Yet, the accuracy needs to be improved with more structural data from high throughput experiments, which are challenging to generate, especially for dynamic and weak associations. Herein, we applied native mass spectrometry (native MS) to rapidly characterize ligand binding of an allosteric heterodimeric complex of SARS-CoV-2 nonstructural proteins (nsp) nsp10 and nsp16 (nsp10/16), a complex essential for virus survival in the host and thus a desirable drug target. Native MS showed that the dimer is in equilibrium with monomeric states in solution. Consistent with the literature, well characterized small cosubstrate, RNA substrate, and product bind with high specificity and affinity to the dimer but not the free monomers. Unsuccessfully designed ligands bind indiscriminately to all forms. Using neutral gas collision, the nsp16 monomer with bound cosubstrate can be released from the holo dimer complex, confirming the binding to nsp16 as revealed by the crystal structure. However, we observed an unusual migration of the endogenous zinc ions bound to nsp10 to nsp16 after collisional dissociation. The metal migration can be suppressed by using surface collision with reduced precursor charge states, which presumably resulted in minimal gas-phase structural rearrangement and highlighted the importance of complementary techniques. With minimal sample input (∼μg), native MS can rapidly detect ligand binding affinities and locations in dynamic multisubunit protein complexes, demonstrating the potential of an "all-in-one" native MS assay for rapid structural profiling of protein-to-AI-based compound systems to expedite drug discovery.

3D molecular generative framework for interaction-guided drug design

Deep generative modeling has a strong potential to accelerate drug design. However, existing generative models often face challenges in generalization due to limited data, leading to less innovative designs with often unfavorable interactions for unseen target proteins. To address these issues, we propose an interaction-aware 3D molecular generative framework that enables interaction-guided drug design inside target binding pockets. By leveraging universal patterns of protein-ligand interactions as prior knowledge, our model can achieve high generalizability with limited experimental data. Its performance has been comprehensively assessed by analyzing generated ligands for unseen targets in terms of binding pose stability, affinity, geometric patterns, diversity, and novelty. Moreover, the effective design of potential mutant-selective inhibitors demonstrates the applicability of our approach to structure-based drug design.

Guided diffusion for molecular generation with interaction prompt.

Molecular generative models have exhibited promising capabilities in designing molecules from scratch with high binding affinities in a predetermined protein pocket, offering potential synergies with traditional structural-based drug design strategy. However, the generative processes of such models are random and the atomic interaction information between ligand and protein are ignored. On the other hand, the ligand has high propensity to bind with residues called hotspots. Hotspot residues contribute to the majority of the binding free energies and have been recognized as appealing targets for designed molecules. In this work, we develop an interaction prompt guided diffusion model, InterDiff to deal with the challenges. Four kinds of atomic interactions are involved in our model and represented as learnable vector embeddings. These embeddings serve as conditions for individual residue to guide the molecular generative process. Comprehensive in silico experiments evince that our model could generate molecules with desired ligand-protein interactions in a guidable way. Furthermore, we validate InterDiff on two realistic protein-based therapeutic agents. Results show that InterDiff could generate molecules with better or similar binding mode compared to known targeted drugs.

Binding-Adaptive Diffusion Models for Structure-Based Drug Design

Structure-based drug design (SBDD) aims to generate 3D ligand molecules that bind to specific protein targets. Existing 3D deep generative models including diffusion models have shown great promise for SBDD. However, it is complex to capture the essential protein-ligand interactions exactly in 3D space for molecular generation. To address this problem, we propose a novel framework, namely Binding-Adaptive Diffusion Models (BindDM). In BindDM, we adaptively extract subcomplex, the essential part of binding sites responsible for protein-ligand interactions. Then the selected protein-ligand subcomplex is processed with SE(3)-equivariant neural networks, and transmitted back to each atom of the complex for augmenting the target-aware 3D molecule diffusion generation with binding interaction information. We iterate this hierarchical complex-subcomplex process with cross-hierarchy interaction node for adequately fusing global binding context between the complex and its corresponding subcomplex. Empirical studies on the CrossDocked2020 dataset show BindDM can generate molecules with more realistic 3D structures and higher binding affinities towards the protein targets, with up to -5.92 Avg. Vina Score, while maintaining proper molecular properties. Our code is available at https://github.com/YangLing0818/BindDM