Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 11 appraisals
Current opinion in chemical biology
Prediction of protein-protein interactions and co-complex models with deep learning
Protein-protein interactions (PPIs) are fundamental to cellular processes, and essential for understanding biological function and disease mechanisms. In this review, we emphasize recent deep learning-based methods for protein interaction study. Focusing on three closely related tasks of proteome-wide PPI prediction, PPI interface prediction, and PPI co-complex structure prediction, we discuss how emerging concepts and computation approaches have evolved to shape these fields We categorize recent approaches according to their methodological paradigms, summarize their strengths and limitations, and further explore diverse biological and biomedical applications, highlighting how computational methods in PPI prediction, PPI interface prediction, and PPI structure prediction jointly contribute to understanding of complex biological systems.
3 Aug 2026
Read appraisal →Bioconjugate chemistry
Artificial Intelligence for Discovery in Life Sciences
Artificial intelligence is becoming a transformative tool in life sciences, not just by improving the results of existing technologies but also by introducing fundamental new ways of discovery. Initially applied to denoising, segmentation, or pattern recognition, it now extends across microscopy, structural biology, protein engineering, experimental design, and hypothesis generation. In imaging, deep learning enhances fluorescence, cryo-EM, and expansion microscopy and increasingly links optical and non-optical modalities. Beyond imaging, AI accelerates fluorescent probe development, while large language models and multi-agent systems are beginning to synthesize literature, generate hypotheses, and guide experiments. We survey these developments across imaging and non-imaging domains, from microscopy and structural biology to molecular design, hypothesis generation, and autonomous experimentation. We discuss the convergence of AI with tools from chemistry to instrumentation and explain challenges in validation, interpretability, generalizability, and autonomy. We conclude that AI is beginning to connect measurement, design, and reasoning to accelerate biological discovery.
16 July 2026
Read appraisal →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 →Cell reports methods
A tri-modal contrastive learning framework for protein representation learning
Protein foundation models, particularly protein language models, have shown strong success in learning meaningful protein representations using transformer architectures pretrained on large-scale datasets through self-supervised learning. These representations have proven effective for downstream tasks such as predicting protein functions and properties. However, most existing models focus solely on amino acid sequences, overlooking other informative modalities such as 3D structures and literature text. While some recent efforts incorporate multiple modalities, they often suffer from limitations in modality coverage or training strategy. To address this gap, we propose a multimodal pretraining framework that integrates three complementary modalities-protein sequences, structures, and literature text. Our method uses the sequence modality as an anchor and aligns the other two modalities to it via contrastive learning, enabling the model to capture richer and more holistic protein representations. Across a diverse set of downstream tasks, ProteinAligner outperforms state-of-the-art foundation models in predicting protein functions and properties.
20 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 →Journal of computer-aided molecular design
Resolving the ambiguous binding site of quercetin at the calcineurin subunit junction using funnel metadynamics with deep learning collective variables
Calcineurin represents a prominent target for immunosuppressive drugs, where conventional macrocyclic inhibitors utilize an immunophilin-dependent mechanism for their inhibition but are consequently hindered by adverse effects and variable pharmacokinetics. The flavonoid quercetin has been shown to inhibit calcineurin in a non-competitive, immunophilin-independent manner, but its exact binding site remains ambiguous at the junction between calcineurin subunits A and B, with the open solvent-exposed nature of this region proving challenging to model. In this study, we employ funnel metadynamics with collective variables derived from a deep learning model to identify the binding site of quercetin. A selective strategy using multiple simulations with stricter funnel definitions and a smaller number of carefully chosen descriptors for model training proved more effective than a broad-based approach. These simulations were able to effectively distinguish the binding site of quercetin from three experimentally suggested sites, with a calculated free energy of binding of -8.36 ± 0.60 kcal/mol showing excellent agreement with experiment. Ligand-tryptophan distances similarly corroborated measurements from FRET assays with a r2 of 0.85. The corresponding binding pose showed that quercetin inserts itself into a channel between Arg122, Gly123, Tyr124 on one side and Phe160, Thr161, and Asn345 on the other, stabilizing its binding through a network of hydrogen bonds. The findings of this study provide insights into the modelling of challenging binding sites and ligands using deep learning driven metadynamics simulations and provide the foundations for rational development of immunophilin-independent inhibitors of calcineurin.
3 May 2026
Read appraisal →PLoS computational biology
MsgaBpred: A B-cell epitope predictor integrating AlphaFold3-predicted structures with multi-scale GCNs and pre-trained language model ESM-C
Accurate prediction of B-cell epitopes plays a key role in facilitating advancements in vaccines, therapeutics, and diagnostics. In contrast to labor-intensive experimental approaches, computational strategies provide a more economical and efficient means of identifying potential epitopes. Existing methods are often limited by their reliance on experimentally resolved protein structures or by the use of lower-accuracy predicted structures. Sequence-based approaches, while fast, largely fail to capture the 3D spatial context essential for conformational epitopes. With the breakthroughs achieved by AlphaFold3 in predicting protein structures, we present MsgaBpred, the model to apply AlphaFold3-derived structures to B-cell epitope identification. Given only a protein sequence, our model employs a multi-scale graph convolutional network and additive attention to capture complex structural dependencies without relying on experimentally determined structures. The multi-scale design allows for effective modeling of both local and global contexts by aggregating information across different neighborhood ranges. Additionally, we leverage ESM-C, a more expressive protein language model than ESM-2, to enhance feature representation for B-cell epitope prediction. Extensive evaluations across multiple benchmark datasets demonstrate that MsgaBpred achieves competitive and robust performance; notably, it yields a statistically significant improvement in AUC compared to existing state-of-the-art methods. Moreover, the modular and scalable architecture of MsgaBpred holds promise for broader applications, including the structural analysis of other biomolecular entities such as nucleic acids and carbohydrates.
3 May 2026
Read appraisal →Science advances
Repertoire-scale antibody structural prediction informs therapeutic design
We present AF3-TurboAb, a scalable framework that makes a repertoire-scale antibody-antigen complex structural decoding routine for antibody engineering. By eliminating preprocessing bottlenecks, AF3-TurboAb enables end-to-end complex modeling in 0.5 minutes per seed on a single GPU while preserving near-experimental interface fidelity, as validated on ~1000 posttraining Protein Data Bank (PDB) benchmarks and 12 experimentally determined cryo-electron microscopy nanobody-antigen structures. Applying this capability to 275,371 immunization-derived antigen-specific nanobodies produced 28,013 high-confidence complex predictions, substantially expanding the structural landscape of antibody recognition. The resulting atlas reveals hundreds of previously unmapped epitopes, extensive coverage of solvent-exposed surfaces, and recurrent affinity hotspots enriched in aromatic and charged residues. Despite wide sequence diversity, we observe structural convergence at shared epitopes and consistent physicochemical and geometric features that complement and extend existing PDB entries. We demonstrate translational utility by (i) designing durable (escape-proof), multiepitope neutralizers against highly evolved viruses, (ii) identifying cross-species and glycoform-specific binders to a cancer checkpoint, and (iii) enabling near-real-time in silico binder triage. The models and metadata will be shared for community use, establishing repertoire-scale structural decoding as a practical design modality that transforms the scale and speed of structure-guided antibody engineering.
27 Apr 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 →Proceedings of the National Academy of Sciences of the United States of America
Proteome-wide prediction of interactions between structured domains and peptide motifs reveals functionally coherent subnetworks
Many protein-protein interactions (PPIs) are mediated by the binding of short linear motifs (SLiMs) to peptide recognition domains (PRDs). Here, we describe PrePPI-SLiM, a proteome-scale computational pipeline that leverages data from the Eukaryotic Linear Motif (ELM) database to predict whether two proteins will form a peptide-mediated complex. The ELM database defines classes of protein-peptide interactions with SLiMs represented by sequence motifs and PRDs represented by Pfam domains. PrePPI-SLiM systematically evaluates all pairwise combinations of proteins within a proteome and identifies PRD-SLiM pairs that occur in the same ELM class. This evidence together with disorder prediction and sequence conservation of the motif are integrated in a naïve Bayes framework to assign a likelihood for complex formation. To obtain potential PDB templates for atomistic models of PrePPI-SLiM interactions, we associate individual PPI predictions with homologous PDB complexes involving the same PRD Pfam domain and SLIM, and obtain PDB templates for 92% of our high-confidence predictions. Moreover, studies with AF3Complex suggest that prior knowledge of the interacting PRD and SLiM, as provided here, is a critical starting point for creating a 3D model of the specific sequences of the PRD and SLiM query proteins. Finally, we demonstrate that clustering of the high-confidence PrePPI-SLiM interactome yields functionally coherent PPI networks that reveal mechanistic insights into cellular processes. The PrePPI webserver provides convenient access to high-confidence PrePPI-SLiM predictions, PDB templates for modeling, and functional networks.
8 Apr 2026
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