Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 8 appraisals
Journal of chemical theory and computation
Predicting and Decoding Allosteric Binding Sites Using Protein Language Models and Structure-Based Machine Learning: An Energy Landscape-Guided Explainable AI Framework
Computational prediction of allosteric binding sites in protein structures remains a persistent challenge, as these regulatory pockets evade detection by both sequence-based and structure-based algorithms. Both computational and physical origins of this predictive asymmetry remain insufficiently understood. In this study, we systematically examine the determinants of binding site predictability using a dual framework that integrates a fine-tuned protein language model and the structure-based method P2Rank as complementary tools probing a diverse data set of 453 human kinases, together with a physics-based interpretability layer derived from energy landscape frustration analysis. Both predictors exhibit a sharp and reproducible dichotomy on protein kinases, in which orthosteric ATP-binding sites can be identified with high precision, whereas allosteric sites are detected with substantially lower confidence across kinase structures and distinct conformational states. To decode this divergence, we deploy energy landscape-based explainable AI approach that integrates local frustration analysis as an independent physical interpretability layer, mapping predictive behavior to the underlying energetic organization of protein structures. This analysis reveals that predictive success is governed by the local energetic embedding of binding sites within the protein energy landscape. Orthosteric pockets are located in minimally frustrated basins that generate strong evolutionary and structural signatures, whereas allosteric pockets occupy predominantly neutrally frustrated zones associated with conformational plasticity and reduced evolutionary constraint. By integrating the prediction results with energy landscape analysis, our framework converts predictive performance into physically interpretable descriptors of binding site organization in protein kinases. These results establish energy landscape frustration as a potentially important determinant of algorithmic visibility and an interpretability layer providing a feasible strategy for diagnosing the limits of current prediction methods.
27 May 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Predictions from deep learning propose substantial protein-carbohydrate interplay
Noncovalent interaction between proteins and carbohydrates (sugars, glycans) is the basis for biological functions from metabolic regulation to intercellular recognition. It is a grand challenge to identify the protein-carbohydrate interactomes in organisms. Direct experiments would require extensive libraries of glycans to distinguish binding from nonbinding proteins. Computational screening of proteins for carbohydrate binding potential provides an attractive alternative. Current estimates propose that <5% of proteins bind carbohydrates, a number that is not well established. We therefore developed a neural network, "Protein interaction of Carbohydrates Predictor" (PiCAP), to predict whether a protein noncovalently binds to a carbohydrate. We trained PiCAP on a manually curated dataset of known carbohydrate binders and proteins that we identified as likely not to bind carbohydrates (transcription factors, cytoskeletal components, and small-molecule-binding proteins). PiCAP achieves 90% balanced accuracy on protein-level predictions of carbohydrate binding/nonbinding. Using the same datasets, we developed Carbohydrate Protein Site Identifier 2 (CAPSIF2) to predict protein residues that interact noncovalently with carbohydrates. CAPSIF2 achieves a Dice coefficient of 0.57 on residue-level predictions on our independent test dataset, outperforming previous models. To demonstrate the models' biological applicability, we investigated human cell surface proteins and further predicted the likelihood of carbohydrate binding in six proteomes (Escherichia coli, Mus musculus, Homo sapiens, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster). PiCAP predicts that ~35 to 40% of proteins in these proteomes bind carbohydrates, with 75% of extracellular and cell surface proteins predicted to bind. The PiCAP predicted binders are enriched for functions including growth factor receptor binding, inflammation, and cell-cell adhesion.
26 May 2026
Read appraisal →Journal of molecular modeling
Carbonic anhydrase under the computational lens: a review of advances, challenges, and future directions
CONTEXT: Carbonic anhydrases (CAs) are zinc metalloenzymes responsible for the catalysis of the reversible hydration of carbon dioxide and play pivotal roles in many physiological and pathological processes. The complexity of CA isoforms with distinct expression patterns and catalytic characteristics has aroused fierce interest in the development of isoform-selective inhibitors and activators for therapeutic purposes and diagnostics. In this review, a systematic overview of computational and theoretical methods utilized to determine the structure, mechanism, inhibition, and activation of CAs was given. It reviews the evolution and utilization of computational methods in deciphering catalytic and proton transfer mechanisms as well as active-site interactions into atomic resolution and also features recent developments in drug design and screening strategies engineered for CA isoforms, particularly those involved in cancers and Alzheimer's disease. This study can be a reference for multidisciplinary researchers and inspire the future rational design of CA modulators. METHODS: This review summarizes the literature that employed QM and QM/MM by using like B3LYP, BE0-D3BJ, and MD simulations, GROMACS and Desmond packages, and various force fields: OPLS-AA, AMBER14, and CHARMM36. Molecular docking was performed using such as Glide, AutoDock Vina, and GOLD. Machine learning and AI techniques including SVM, LightGBM, SHAP, and neural networks were also reviewed. This review addresses the limitations in these models as well as prospects with newly emerging instruments such as polarizable force fields and real-time DFT as well as explainable AI frameworks, too. Incorporating data-driven insights with physics-based simulations, the next era of work is primed to provide predictive, scalable platforms for rational design of next-generation CA modulators.
15 May 2026
Read appraisal →European journal of medicinal chemistry
AI-driven identification of a selective dual function inhibitor blocking HK2 activity and HK2-VDAC1 interaction displaying enhanced anticancer efficacy under hypoxia
Selective inhibition of hexokinase 2 (HK2) represents a promising therapeutic strategy due to the pivotal role of HK2 in the Warburg effect, enhancement of glycolysis and anti-apoptosis via HK2-Voltage-Dependent Anion Channel 1 (VDAC1) protein-protein interaction. Moreover, HK2 initiates glycolysis to generate lactate, hence this central enzyme can be pharmacologically targeted to enhance therapy outcomes. Currently, no HK2 inhibitors (HK2is) exist in the clinic. Herein, we employed GCVec, an artificial intelligence (AI)-based compound-protein interaction (CPI) prediction tool, along with molecular docking, to identify the HK2i, 106. This compound exhibited an IC50 of 0.79 ± 0.07 μM and a consistent Kd of 0.41 ± 0.03 μM against HK2 enzyme. It also apparently blocked HK2-VDAC1 interaction as indicated by the disrupted colocalization of HK2-GFP and VDAC1-mCherry. Furthermore, 106 demonstrated enhanced anticancer efficacy under hypoxia in tumor cells with elevated HIF-1α/HK2 and VDAC1 levels. Compound 106 selectively targeted SW480 colorectal cancer cells with high HK2 expression, achieving a growth inhibition IC50 value of 5.00 ± 0.94 μM. Consistently, knockout of HK2 in these tumor cells significantly rescued the IC50 values and eliminated the glycolytic inhibition induced by 106. We further showed that 106 reduced lactate and ATP levels and induced markers of apoptosis, including increased p-AMPK/AMPK ratio and increased Bax levels, as well as decreased Bcl2 levels. Collectively, our findings highlight the potential of GCVec in identifying 106, a first in class dual-function HK2i which emerges as a promising lead compound for further development into a possible anticancer therapeutic agent.
11 May 2026
Read appraisal →Protein science : a publication of the Protein Society
AlphaFold-driven discovery of oxysterol-binding protein-related protein-phosphoinositide 3-, 4-, and 5-phosphatase interactions using new generation confidence scores
Non-vesicular lipid transport contributes to the regulation of membrane composition and organelle function at membrane contact sites. OSBP-related proteins (ORPs) are central to this process, yet their interaction networks remain incompletely defined. Here, we systematically screened potential interactions between ORPs and phosphoinositide 3-, 4-, and 5-phosphatases using AlphaPulldown2, AlphaFold2-Multimer, and AlphaFold3. We established a protocol for model generation by combining AlphaFold2-Multimer predictions (including five-replicates) with an AlphaPulldown2 interaction screen across around 200 protein pairs, and with AlphaFold3 predictions including lipid-bound and multimeric assemblies. Interface confidence was assessed for consistency using the weighted ipTM + pTM metric, actifpTM, new generation ipSAE scoring, and FoldSeek-Multimer clustering. We further evaluated the protein pairs' biological plausibility based on subcellular localization data, in silico membrane insertion, evolutionary conservation via ConSurf, and protein binding interface analysis using the deep learning tool PeSTo. This integrative protocol uncovered functionally conserved binding modes in the SAC1 lipid phosphatase with the ORP family, particularly with ORP11, and predicted functionally relevant protein-lipid interfaces.
9 May 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Making invisible excited-state structures of pro-interleukin-18 visible by combining NMR and machine learning
NMR spectroscopy is a powerful tool for studies of protein dynamics over timescales extending more than twelve orders of magnitude, with motion queried at many of the backbone and sidechain positions in the molecule of interest. NMR experiments can, in principle, provide atomic resolution descriptions of excited conformational states that are sparsely populated and transiently formed-i.e., invisible, and there are examples of such structures in the literature. However, in other cases, the NMR data are not sufficient for generating structural ensembles of rare and transient higher energy conformers of the protein of interest. One example is provided by the precursor form of the proinflammatory cytokine interleukin-18, pro-IL-18, studied here. Although NMR studies show that pro-IL-18 adopts two sparsely populated (<0.5%) and transiently formed (ms lifetimes) excited-state conformations in exchange with a highly populated ground state conformer and localize regions undergoing exchange to a pair of short β-strands that are preserved in at least one of the excited states, additional structural information is not forthcoming. Here, we develop a protocol whereby the NMR data are used to select alternative conformers of pro-IL-18 from ensembles predicted by the generative machine learning model AlphaFlow that are then evaluated through further NMR experiments. The method identifies distinct conformers that correspond to the pair of excited states reported by NMR, combining experiment with computation to characterize the pro-IL-18 energy landscape in ways that are not possible with either approach alone.
20 Apr 2026
Read appraisal →Journal of agricultural and food chemistry
Dairy-Derived Aroma Release Modulated by Oral Mucin: In Vitro Study Insights from Multispectral, Computational Modeling, and Machine Learning
Salivary mucin plays a pivotal role in modulating aroma perception during dairy consumption. This study investigated the impact of mucin on the release of ten key milk fat-derived aroma compounds (including 3 aldehydes, 3 ketones, and 4 lactones) using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). The results demonstrated that mucin significantly inhibited the release of most compounds, including 2-heptanone, 2-nonanone, 2-pentadecanone, heptanal, δ-hexalactone, δ-octalactone, and δ-decalactone, with a maximum binding percentage of 34.79 ± 9.63%. Sensory analysis confirmed perceptible differences. Thermodynamic and spectroscopic data revealed that the binding was spontaneous and primarily driven by hydrophobic interactions and hydrogen bonds involving phenylalanine and tyrosine residues, which induced significant conformational changes in the mucin secondary structure. Shapley additive explanations analysis based on the support vector regression model identified the number of H-bond acceptors as the most critical molecular feature. These findings provide insights for modulating aroma perception in dairy products.
17 Apr 2026
Read appraisal →Journal of computer-aided molecular design
Identification of potential inhibitors of 3‑mercaptopyruvate sulfurtransferase with a deep-learning based screening of natural products
3-Mercaptopyruvate sulfurtransferase (3-MST), a key enzyme in sulfur metabolism, has recently gained attention as a potential anticancer target. However, reported 3-MST inhibitors remain limited, motivating the exploration of new scaffolds such as natural products. In this study, a library of 3744 natural products was virtually screened against human 3-MST using DiffDock (diffusion-model-based docking) followed by AutoDock Vina docking. Top-ranking candidates were further analyzed via molecular dynamics simulations and Molecular Mechanics Poisson-Boltzmann Surface Area binding free energy calculations. Methylophiopogonanone A (4), Daphnoretin (5), and L-asarinin (9) exhibited stable binding with favorable energetics, displaying binding free energies comparable to the reference ligand 7NC301. Binding mode analyses revealed that Methylophiopogonanone A primarily engaged in hydrophobic interactions, whereas Daphnoretin and L-asarinin formed extensive polar contacts, accompanied by higher desolvation penalties. In vitro cytotoxicity assays showed that Methylophiopogonanone A and L-asarinin reduced HCT116 cell viability by 40.3% and 26.3% at 25 µM, which is consistent with their inhibitory of 3-MST with IC50 = 5.83 ± 0.69 µM and IC50 = 19.11 ± 3.37 µM, respectively. These results suggested that the natural products identified in this study represent promising scaffolds for further optimization as potential 3-MST inhibitors. This work provides an AI-guided natural-product screening workflow for 3-MST and delivers prioritized inhibitor scaffolds for subsequent optimization and experimental validation.
6 Apr 2026
Read appraisal →