AlphaFold and what is next: bridging functional, systems and structural biology

Expert Review of Proteomics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 17, 2025

The DeepMind's AlphaFold (AF) has revolutionized biomedical research by providing both experts and non-experts with an invaluable tool for predicting protein structures. However, while AF is highly effective structures of rigid globular proteins, it not able to fully capture the dynamics, conformational variability, interactions proteins ligands other biomacromolecules. In this review, we present a comprehensive overview latest advancements in 3D model predictions biomacromolecules using AF. We also provide detailed analysis its strengths limitations, explore more recent iterations, modifications, practical applications strategy. Moreover, map path forward expanding landscape toward every peptide proteome most physiologically relevant form. This discussion based on extensive literature search performed PubMed Google Scholar. While significant progress been made enhance AF's modeling capabilities, argue that combined approach integrating various silico vitro methods will be beneficial future structural biology, bridging gaps between static dynamic features their functions.

Язык: Английский

---

AlphaFold as a Prior: Experimental Structure Determination Conditioned on a Pretrained Neural Network

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Фев. 21, 2025

Abstract Advances in machine learning have transformed structural biology, enabling swift and accurate prediction of protein structure from sequence. However, challenges persist capturing sidechain packing, condition-dependent conformational dynamics, biomolecular interactions, primarily due to scarcity high-quality training data. Emerging techniques, including cryo-electron tomography (cryo-ET) high-throughput crystallography, promise vast new sources data, but translating raw experimental observations into mechanistically interpretable atomic models remains a key bottleneck. Here, we aim address these by improving the efficiency analysis through combining measurements with landmark method – AlphaFold2. We present an augmentation AlphaFold2, ROCKET, that refines its predictions using cryo-EM, cryo-ET, X-ray crystallography demonstrate this approach captures biologically important variation AlphaFold2 does not. By performing optimization space coevolutionary embeddings, rather than Cartesian coordinates, ROCKET automates difficult modeling tasks, such as flips functional loops domain rearrangements, are beyond scope current state-of-the-art methods and, some instances, even manual human modeling. The ability efficiently sample barrier-crossing rearrangements unlocks horizon for scalable automated model building. Crucially, not require retraining is readily adaptable multimers, ligand-cofolding, other data modalities. Conversely, our differentiable crystallographic cryo-EM target functions capable augmenting methods. thus provides extensible framework integration observables learning.

Язык: Английский

---

Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery

Biomolecules, Год журнала: 2025, Номер 15(3), С. 423 - 423

Опубликована: Март 17, 2025

Understanding protein structures can facilitate the development of therapeutic drugs. Traditionally, have been determined through experimental approaches such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy. While these methods are effective considered gold standard, they very resource-intensive time-consuming, ultimately limiting their scalability. However, with recent developments in computational biology artificial intelligence (AI), field prediction has revolutionized. Innovations like AlphaFold RoseTTAFold enable structure predictions to be made directly from amino acid sequences remarkable speed accuracy. Despite enormous enthusiasm associated newly developed AI-approaches, true potential structure-based drug discovery remains uncertain. In fact, although algorithms generally predict overall well, essential details for ligand docking, exact location side chains within binding pocket, not predicted necessary Additionally, docking methodologies more a hypothesis generator rather than precise predictor ligand–target interactions, thus, usually identify many false-positive hits among only few correctly interactions. this paper, we reviewing latest cutting-edge emphasis on GPCR target class assess role AI discovery.

Язык: Английский

---

A Folding-Docking-Affinity framework for protein-ligand binding affinity prediction

Communications Chemistry, Год журнала: 2025, Номер 8(1)

Опубликована: Апрель 7, 2025

Accurate protein-ligand binding affinity prediction is crucial in drug discovery. Existing methods are predominately docking-free, without explicitly considering atom-level interaction between proteins and ligands scenarios where crystallized conformations unavailable. Now, with breakthroughs deep learning AI-based protein folding conformation prediction, can we improve prediction? This study introduces a framework, Folding-Docking-Affinity (FDA), which folds proteins, determines conformations, predicts affinities from three-dimensional structures. Our experimental results indicate that FDA performs comparably to state-of-the-art docking-free methods. We anticipate our proposed framework serves as starting point for integrating structures more accurate prediction.

Язык: Английский

---

Predicting inhibitors of OATP1B1 via heterogeneous OATP-ligand interaction graph neural network (HOLIgraph)

Journal of Cheminformatics, Год журнала: 2025, Номер 17(1)

Опубликована: Май 5, 2025

Organic anion transporting polypeptides (OATPs) are membrane transporters crucial for drug uptake and distribution in the human body. OATPs can mediate drug-drug interactions (DDIs) which interaction of one with an OATP impairs another drug, resulting potentially fatal pharmacological effects. Predicting OATP-mediated DDIs is challenging, due to limited information on inhibition mechanisms inconsistent experimental data across different studies. This study introduces Heterogeneous OATP-Ligand Interaction Graph Neural Network (HOLIgraph), a novel computational model that integrates molecular modeling graph neural network enhance prediction drug-induced inhibition. By combining ligand (i.e., drug) features protein-ligand from rigorous docking simulations, HOLIgraph outperforms traditional DDI models rely solely features. achieved median balanced accuracy over 90 percent when predicting inhibitors OATP1B1, significantly outperforming purely ligand-based models. Beyond improving prediction, used train enable characterization protein residues involved inhibitory drug-OATP interactions. We identified certain OATP1B1 preferentially interact inhibitors, including I46 K49. anticipate such will be valuable future structural mechanistic investigations OATP1B1.

Язык: Английский

---