CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities

Targeting the SHOC2-RAS interaction in RAS-mutant cancers

Activating mutations in the rat sarcoma (RAS) genes HRAS, NRAS and KRAS collectively represent the most frequent oncogenic driver in human cancer1. They have previously been considered undruggable, but advances in the past few years have led to the clinical development of agents that target KRAS(G12C) and KRAS(G12D) mutants, yielding promises of therapeutic responses at tolerated doses2. However, clinical agents that selectively target NRAS(Q61*) mutants (* represents 'any'), the second-most-frequent oncogenic driver in melanoma, are still lacking. Here we identify SHOC2, a component of the SHOC2-MRAS-PP1C complex, as a dependency of RAS(Q61*) tumours in a nucleotide-state-dependent and isoform-agnostic manner. Mechanistically, we found that oncogenic NRAS(Q61R) forms a direct interaction with SHOC2, evidenced by X-ray co-crystal structure. In vitro high-throughput screening enabled the discovery of small molecules that bind to SHOC2 and disrupt the interaction with NRAS(Q61*). Structure-based optimization led to a cellularly active tool compound that shows inhibition of mitogen-activated protein kinase (MAPK) signalling and proliferation in RAS-mutant cancer models, most notably in NRAS(Q61*) settings. These findings provide evidence for a neomorph SHOC2-(canonical)RAS protein interaction that is pharmacologically actionable and relevant to cancer sustenance. Overall, this work provides the concept validation and foundation for developing new therapies at the core of the RAS signalling pathway.

SP-DTI: subpocket-informed transformer for drug-target interaction prediction

MOTIVATION

Drug-target interaction (DTI) prediction is crucial for drug discovery, significantly reducing costs and time in experimental searches across vast drug compound spaces. While deep learning has advanced DTI prediction accuracy, challenges remain: (i) existing methods often lack generalizability, with performance dropping significantly on unseen proteins and cross-domain settings; and (ii) current molecular relational learning often overlooks subpocket-level interactions, which are vital for a detailed understanding of binding sites.

RESULTS

We introduce SP-DTI, a subpocket-informed transformer model designed to address these challenges through: (i) detailed subpocket analysis using the Cavity Identification and Analysis Routine for interaction modeling at both global and local levels, and (ii) integration of pre-trained language models into graph neural networks to encode drugs and proteins, enhancing generalizability to unlabeled data. Benchmark evaluations show that SP-DTI consistently outperforms state-of-the-art models, achieving an area under the receiver operating characteristic curve of 0.873 in unseen protein settings, an 11% improvement over the best baseline.

AVAILABILITY AND IMPLEMENTATION

The model scripts are available at https://github.com/Steven51516/SP-DTI.

Activation of the bacterial defense-associated sirtuin system

The NADase activity of the defense-associated sirtuins (DSRs) is activated by the phage tail tube protein (TTP). Herein, we report cryo-EM structures of a free-state Bacillus subtilis DSR2 tetramer and a fragment of the tetramer, a phage SPR tail tube, and two DSR2-TTP complexes. DSR2 contains an N-terminal SIR2 domain, a middle domain (MID) and a C-terminal domain (CTD). The DSR2 CTD harbors the α-solenoid tandem-repeats like the HEAT-repeat proteins. DSR2 assembles into a tetramer with four SIR2 clustered at the center, and two intertwined MID-CTD chains flank the SIR2 core. SPR TTPs self-assemble into a tube-like complex. Upon DSR2 binding, the D1 domain of SPR TTP is captured between the HEAT-repeats domains of DSR2, which conflicts with TTPs self-assembly. Binding of TTPs induces conformational changes in DSR2 tetramer, resulting in increase of the NAD+ pocket volume in SIR2, thus activates the NADase activity and leads to cellular NAD+ depletion.

Flavonoids as Potential Modulators of Pancreatic Lipase Catalytic Activity

Background/Objectives: Obesity has reached pandemic proportions, with predictions suggesting that, by 2030, over 1.5 billion people will be affected. Pancreatic lipase (PL), the enzyme primarily responsible for the absorption of dietary lipids, presents a potential target for obesity management. However, while porcine pancreatic lipase (PPL) is commonly used as the enzyme source for screening potential inhibitors, its effect on human pancreatic lipase (HPL) is rarely reported. This work aimed to screen the inhibitory effects of a library of flavonoids with different functional groups on the activity of PL from the human pancreas (triacylglycerol acyl hydrolase, EC 3.1.1.3) and compare it to the effects of the porcine pancreas (type II, EC 3.1.1.3), establishing, whenever possible, a structure-activity relationship. Methods: The inhibitory effects of a library of 48 flavonoids with different hydroxy, glycosyl, rutinosyl, galloyl, and extended alkyl groups were evaluated against PPL and HPL. The kinetic parameters and inhibitory mechanisms of the most active flavonoids were determined, and in silico docking studies of the more potent flavonoids were also performed, using the active site of HPL. Results/Conclusions: Variations in enzyme catalytic activity were observed depending on the source of the enzyme. The inhibitory effect was particularly influenced by the presence of extended alkyl groups at the C-3 of the C-ring and the C2=C3 double bond of the C-ring and the presence of a pyrogallol group at the C-2', C-3' and C-4' of the B-ring. Docking results showed a strong correlation between docking scores and observed inhibitory activities, highlighting the critical role of specific substituents on the flavonoid backbone in enhancing detailed interaction dynamics with key amino acids. Compounds 28, 29, and 30, with alkyl groups, showed the highest docking scores, interacting with residues HIS151, PHE215, ARG256, and HIS263. Further analysis also revealed that specific substituents improved pocket occupancy and formed additional interactions with residues TYR114, PRO180, ILE209, and PHE215, which are crucial for inhibition. These binding characteristics closely mimic those observed with orlistat, reinforcing their mechanistic similarities in inhibiting HPL and validating their inhibitory activities.

1,3,4-oxadiazoles as inhibitors of the atypical member of the BET family bromodomain factor 3 from Trypanosoma cruzi (TcBDF3)

Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, affects millions globally, with increasing urban cases outside of Latin America. Treatment is based on two compounds, namely, benznidazole (BZ) and nifurtimox, but chronic cases pose several challenges. Targeting lysine acetylation, particularly bromodomain-containing proteins, shows promise as a novel antiparasitic target. Our research focuses on TcBDF3, a cytoplasmic protein, which is crucial for parasite differentiation that recognizes acetylated alpha-tubulin. In our previous study, A1B4 was identified as a high-affinity binder of TcBDF3, showing significant trypanocidal activity with low host toxicity in vitro. In this report, the binding of TcBDF3 to A1B4 was validated using differential scanning fluorescence, fluorescence polarization, and molecular modeling, confirming its specific interaction. Additionally, two new 1,3,4-oxadiazoles derived from A1B4 were identified, which exhibited improved trypanocide activity and cytotoxicity profiles. Furthermore, TcBDF3 was classified for the first time as an atypical divergent member of the bromodomain extraterminal family found in protists and plants. These results make TcBDF3 a unique target due to its localization and known functions not shared with higher eukaryotes, which holds promise for Chagas disease treatment.

Novel DNA Repair Inhibitors Targeting XPG to Enhance Cisplatin Therapy in Non-Small Cell Lung Cancer: Insights from In Silico and Cell-Based Studies

NSCLC is marked by low survival and resistance to platinum-based chemotherapy. The XPG endonuclease has emerged as a promising biomarker for predicting the prognosis of cisplatin-treated patients and its downregulation having been reported to increase cisplatin efficacy. This study presents an integrated strategy for identifying small molecule inhibitors of XPG to improve cisplatin therapy in NSCLC. A structure-based virtual screening approach was adopted, including a structural and physicochemical analysis of the protein, and a library of small molecules with reported inhibitory activities was retrieved. This analysis identified Lys84 as a crucial residue for XPG activity by targeting its interaction with DNA. After molecular docking and virtual screening calculations, 61 small molecules were selected as potential XPG inhibitors, acquired from the ChemBridge database and then validated in H1299 cells, a NSCLC cell line exhibiting the highest ERCC5 expression. The MTS assay was performed as a first screening approach to determine whether these potential inhibitors could enhance cisplatin-induced cytotoxicity. Overall, among the eight compounds identified as the most promising, three of them revealed to significantly increase the impact of cisplatin. The inherent cytotoxicity of these compounds was further investigated in a non-tumoral lung cell line (BEAS-2B cells), which resulted in the identification of two non-cytotoxic candidates to be used in combination with cisplatin in order to improve its efficacy in NSCLC therapy.

Quantitative Characterization of the Impact of Protein-Protein Interactions on Ligand-Protein Binding: A Multi-Chain Dynamics Perturbation Analysis Method

Many protein-protein interactions (PPIs) affect the ways in which small molecules bind to their constituent proteins, which can impact drug efficacy and regulatory mechanisms. While recent advances have improved our ability to independently predict both PPIs and ligand-protein interactions (LPIs), a comprehensive understanding of how PPIs affect LPIs is still lacking. Here, we examined 63 pairs of ligand-protein complexes in a benchmark dataset for protein-protein docking studies and quantified six typical effects of PPIs on LPIs. A multi-chain dynamics perturbation analysis method, called mcDPA, was developed to model these effects and used to predict small-molecule binding regions in protein-protein complexes. Our results illustrated that the mcDPA can capture the impact of PPI on LPI to varying degrees, with six similar changes in its predicted ligand-binding region. The calculations showed that 52% of the examined complexes had prediction accuracy at or above 50%, and 55% of the predictions had a recall of not less than 50%. When applied to 33 FDA-approved protein-protein-complex-targeting drugs, these numbers improved to 60% and 57% for the same accuracy and recall rates, respectively. The method developed in this study may help to design drug-target interactions in complex environments, such as in the case of protein-protein interactions.

G Protein-Coupled Estrogen Receptor-Mediated Anti-Inflammatory and Mucosal Healing Activity of a Trimethylpyridinol Analogue in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is characterized by abnormal immune responses, including elevated proinflammatory cytokines, such as tumor necrosis factor-α (TNFα) and interleukin-6 (IL-6) in the gastrointestinal (GI) tract. This study presents the synthesis and anti-inflammatory evaluation of 2,4,5-trimethylpyridin-3-ol analogues, which exhibit dual inhibition of TNFα- and IL-6-induced inflammation. Analysis using in silico methods, including 3D shape-based target identification, modeling, and docking, identified G protein-coupled estrogen receptor 1 (GPER) as the molecular target for the most effective analogue, 6-26, which exhibits remarkable efficacy in ameliorating inflammation and restoring colonic mucosal integrity. This was further validated by surface plasmon resonance (SPR) assay results, which showed direct binding to GPER, and by the results showing that GPER knockdown abolished the inhibitory effects of 6-26 on TNFα and IL-6 actions. Notably, 6-26 displayed no cytotoxicity, unlike G1 and G15, a well-known GPER agonist and an antagonist, respectively, which induced necroptosis independently of GPER. These findings suggest that the GPER-selective compound 6-26 holds promise as a therapeutic candidate for IBD.

A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the kinome at various multiple sequence alignment (MSA) depths showed a bias within the predicted structures of kinases in DFG-in conformations, particularly those controlled by the DFG motif, based on their overabundance in the PDB. We demonstrate that predicting kinase structures using AF2 at lower MSA depths explored these alternative conformations more extensively, including identifying previously unobserved conformations for 398 kinases. Ligand enrichment analyses for 23 kinases showed that, on average, docked models distinguished between active molecules and decoys better than random (average AUC (avgAUC) of 64.58), but select models perform well (e.g., avgAUCs for PTK2 and JAK2 were 79.28 and 80.16, respectively). Further analysis explained the ligand enrichment discrepancy between low- and high-performing kinase models as binding site occlusions that would preclude docking. The overall results of our analyses suggested that, although AF2 explored previously uncharted regions of the kinase conformational space and select models exhibited enrichment scores suitable for rational drug discovery, rigorous refinement of AF2 models is likely still necessary for drug discovery campaigns.

Benzbromarone Induces Targeted Degradation of HSP47 Protein and Improves Hypertrophic Scar Formation

Fibrotic diseases are characterized by the abnormal accumulation of collagen in the extracellular matrix, leading to the functional impairment of various organs. In the skin, excessive collagen deposition manifests as hypertrophic scars and keloids, placing a substantial burden on patients and the healthcare system worldwide. HSP47 is essential for proper collagen assembly and contributes to fibrosis. However, identifying clinically applicable HSP47 inhibitors has been a major pharmaceutical challenge. In this study, we identified benzbromarone (BBR) as an HSP47 inhibitor for hypertrophic scarring treatment. BBR inhibited collagen production and secretion in fibroblasts from patients with keloid by binding to HSP47 and inhibiting the interaction between HSP47 and collagen. Interestingly, BBR not only inhibits HSP47 but also acts as a molecular glue degrader that promotes its proteasome-dependent degradation. Through these molecular mechanisms, BBR effectively reduced hypertrophic scarring in mini pigs and rats with burns and/or excisional skin damage. Thus, these findings suggest that BBR can be used to clinically treat hypertrophic scars and, more generally, fibrotic diseases.

Targeting CDK9 in Cancer: An Integrated Approach of Combining In Silico Screening with Experimental Validation for Novel Degraders

The persistent threat of cancer remains a significant hurdle for global health, prompting the exploration of innovative approaches in the quest for successful therapeutic interventions. Cyclin-dependent kinase 9 (CDK9), a central player in transcription regulation and cell cycle progression, has emerged as a promising target to combat cancer. Its pivotal role in oncogenic pathways and the pressing need for novel cancer treatments has propelled CDK9 into the spotlight of drug discovery efforts. This article presents a comprehensive study that connects a multidisciplinary approach, combining computational methodologies, experimental validation, and the transformative Proteolysis-Targeting Chimera (PROTAC) technology. By uniting these diverse techniques, we aim to identify, characterize, and optimize a new class of degraders targeting CDK9. We explore these compounds for targeted protein degradation, offering a novel and potentially effective approach to cancer therapy. This cohesive strategy utilizes the combination of computational predictions and experimental insights, with the goal of advancing the development of effective anticancer therapeutics, targeting CDK9.

Systematic identification and repurposing of FDA-approved drugs as antibacterial agents against Streptococcus pyogenes: In silico and in vitro studies

Streptococcus pyogenes (Group A Streptococcus - GAS) is a human pathogen causing wide range of infections and toxin-mediated diseases in human beings of all age groups with fatality of 10-30 %. The limited success of antibiotics and the non-availability of vaccines makes GAS a global burden. The multi-subunit RNA polymerase (RNAP) is a validated bacterial therapeutic target as it is involved in transcription and can arrest growth. Of the five subunits of this enzyme complex, the β-subunit (RpoC) has attracted specific attention as a drug target, particularly in the switch region. Here we attempt to repurpose non-antimicrobial drugs to act as RpoC inhibitors against S. pyogenes. In this study, 1826 FDA approved drugs have been identified through high-throughput virtual screening. Free Energy Perturbation (FEP) based binding free energy calculations have been performed at the final step of the virtual screening funnel to ensure high accuracy in silico results. Three compounds identified have been tested for susceptibility of S. pyogenes MTCC 442 strain and two antibiotic-resistant clinical isolates of S. pyogenes using microdilution assay. Among the three, two drugs Amlodipine Besylate (Amd) and Ranitidine hydrochloride (Rnt) have shown inhibition against all the tested strains and its mechanism of interaction with RpoC has been studied. The docked complexes were analyzed to understand the binding mode of the drugs to the target. Classical Molecular Dynamics studies for RpoC-Rnt complex and the two stable conformations of RpoC-Amd complex was carried out. Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (RoG) and Solvent Accessible Surface Area (SASA) of the complexes were plotted and studied. The thermodynamic parameters of protein-drug were experimentally determined using Isothermal Titration Calorimetry (ITC). Infrared spectroscopic studies and Fluorescence quenching studies provided insights into the secondary structural changes in RpoC on binding to the drugs.

CRAFT: a web-integrated cavity prediction tool based on flow transfer algorithm

Numerous computational methods, including evolutionary-based, energy-based, and geometrical-based methods, are utilized to identify cavities inside proteins. Cavity information aids protein function annotation, drug design, poly-pharmacology, and allosteric site investigation. This article introduces "flow transfer algorithm" for rapid and effective identification of diverse protein cavities through multidimensional cavity scan. Initially, it identifies delimiter and susceptible tetrahedra to establish boundary regions and provide seed tetrahedra. Seed tetrahedron faces are precisely scanned using the maximum circle radius to transfer seed flow to neighboring tetrahedra. Seed flow continues until terminated by boundaries or forbidden faces, where a face is forbidden if the estimated maximum circle radius is less or equal to the user-defined maximum circle radius. After a seed scanning, tetrahedra involved in the flow are clustered to locate the cavity. The CRAFT web interface integrates this algorithm for protein cavity identification with enhanced user control. It supports proteins with cofactors, hydrogens, and ligands and provides comprehensive features such as 3D visualization, cavity physicochemical properties, percentage contribution graphs, and highlighted residues for each cavity. CRAFT can be accessed through its web interface at http://pitools.niper.ac.in/CRAFT , complemented by the command version available at https://github.com/PGlab-NIPER/CRAFT/ .Scientific contribution: Flow transfer algorithm is a novel geometric approach for accurate and reliable prediction of diverse protein cavities. This algorithm employs a distinct concept involving maximum circle radius within the 3D Delaunay triangulation to address diverse van der Waals radii while existing methods overlook atom specific van der Waals radii or rely on complex weighted geometric techniques.

SiteFerret: Beyond Simple Pocket Identification in Proteins

We present a novel method for the automatic detection of pockets on protein molecular surfaces. The algorithm is based on an ad hoc hierarchical clustering of virtual probe spheres obtained from the geometrical primitives used by the NanoShaper software to build the solvent-excluded molecular surface. The final ranking of putative pockets is based on the Isolation Forest method, an unsupervised learning approach originally developed for anomaly detection. A detailed importance analysis of pocket features provides insight into which geometrical (clustering) and chemical (amino acidic composition) properties characterize a good binding site. The method also provides a segmentation of pockets into smaller subpockets. We prove that subpockets are a convenient representation to pinpoint the binding site with great precision. SiteFerret is outstanding in its versatility, accurately predicting a wide range of binding sites, from those binding small molecules to those binding peptides, including difficult shallow sites.

A multilayer dynamic perturbation analysis method for predicting ligand-protein interactions

BACKGROUND

Ligand-protein interactions play a key role in defining protein function, and detecting natural ligands for a given protein is thus a very important bioengineering task. In particular, with the rapid development of AI-based structure prediction algorithms, batch structural models with high reliability and accuracy can be obtained at low cost, giving rise to the urgent requirement for the prediction of natural ligands based on protein structures. In recent years, although several structure-based methods have been developed to predict ligand-binding pockets and ligand-binding sites, accurate and rapid methods are still lacking, especially for the prediction of ligand-binding regions and the spatial extension of ligands in the pockets.

RESULTS

In this paper, we proposed a multilayer dynamics perturbation analysis (MDPA) method for predicting ligand-binding regions based solely on protein structure, which is an extended version of our previously developed fast dynamic perturbation analysis (FDPA) method. In MDPA/FDPA, ligand binding tends to occur in regions that cause large changes in protein conformational dynamics. MDPA, examined using a standard validation dataset of ligand-protein complexes, yielded an averaged ligand-binding site prediction Matthews coefficient of 0.40, with a prediction precision of at least 50% for 71% of the cases. In particular, for 80% of the cases, the predicted ligand-binding region overlaps the natural ligand by at least 50%. The method was also compared with other state-of-the-art structure-based methods.

CONCLUSIONS

MDPA is a structure-based method to detect ligand-binding regions on protein surface. Our calculations suggested that a range of spaces inside the protein pockets has subtle interactions with the protein, which can significantly impact on the overall dynamics of the protein. This work provides a valuable tool as a starting point upon which further docking and analysis methods can be used for natural ligand detection in protein functional annotation. The source code of MDPA method is freely available at: https://github.com/mingdengming/mdpa .

Estimating the Similarity between Protein Pockets

With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.

A Molecular Docking Study of Human STEAP2 for the Discovery of New Potential Anti-Prostate Cancer Chemotherapeutic Candidates

Prostate cancer is a rising health concern and accounts for 3.8% of all cancer deaths globally. Uganda has one of the highest incidence rates of the disease in Africa at 5.2% with the majority of diagnosed patients found to have advanced disease. This study aimed to use the STEAP2 protein (prostate cancer-specific biomarker) for the discovery of new targeted therapy. To determine the most likely compound that can bind to the STEAP2 protein, we docked the modeled STEAP2 3D structure against 2466 FDA (Food and Drug Administration)-approved drug candidates using AutoDock Vina. Protein basic local alignment search tool (BLASTp) search, multiple sequence alignment (MSA), and phylogenetics were further carried out to analyze the diversity of this marker and determine its conserved domains as suitable target regions. Six promising drug candidates (ligands) were identified. Triptorelin had the highest binding energy (-12.1 kcal/mol) followed by leuprolide (docking energy: -11.2 kcal/mol). All the top two drug candidates interacted with residues Ser-372 and Gly-369 in close proximity with the iron-binding domain (an important catalyst of metal reduction). The two drugs had earlier been approved for the treatment of advanced prostate cancer with an elusive mode of action. Through this study, further insight into figuring out their interaction with STEAP2 might be important during treatment.

Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications

Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.