Predicting inhibitors of OATP1B1 via heterogeneous OATP-ligand interaction graph neural network (HOLIgraph) DOI Creative Commons
Mehrsa Mardikoraem, Joelle Eaves, Theodore Belecciu

и другие.

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

Semiconducting polymer dots for multifunctional integrated nanomedicine carriers DOI Creative Commons
Ze Zhang, Chenhao Yu, Yuyang Wu

и другие.

Materials Today Bio, Год журнала: 2024, Номер 26, С. 101028 - 101028

Опубликована: Март 24, 2024

The expansion applications of semiconducting polymer dots (Pdots) among optical nanomaterial field have long posed a challenge for researchers, promoting their intelligent application in multifunctional nano-imaging systems and integrated nanomedicine carriers diagnosis treatment. Despite notable progress, several inadequacies still persist the Pdots, including development simplified near-infrared (NIR) nanoprobes, elucidation inherent biological behavior, integration information processing nanotechnology into biomedical applications. This review aims to comprehensively elucidate current status Pdots as classical nanophotonic material by discussing its advantages limitations terms biocompatibility, adaptability microenvironments vivo, etc. Multifunctional surface chemistry play crucial roles realizing Pdots. Information visualization based on physicochemical properties is pivotal achieving detection, sensing, labeling probes. Therefore, we refined underlying mechanisms constructed multiple comprehensive original mechanism summaries establish benchmark. Additionally, explored cross-linking interactions between nanomedicine, potential yet complete metabolic pathways, future research directions, innovative solutions integrating treatment strategies. presents possible expectations valuable insights advancing specifically from chemical, medical, photophysical practitioners' standpoints.

Язык: Английский

Процитировано

8
Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs DOI Open Access

Nada K. Alhumaid,

Essam A. Tawfik

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(18), С. 10139 - 10139

Опубликована: Сен. 21, 2024

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in field computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough structural biology been established by developing artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 provides state-of-the-art protein structures from nearly all known sequences with high accuracy. This study examined reliability models compared to experimental drug discovery, focusing on common drug-targeted classes as G protein-coupled receptors (GPCRs) class A. total 32 representative targets were selected, including X-ray crystallographic and Cryo-EM their corresponding models. quality was assessed using different validation tools, pLDDT score, RMSD value, MolProbity percentage Ramachandran favored, QMEAN Z-score, QMEANDisCo Global. molecular docking performed Genetic Optimization Ligand Docking (GOLD) software. models’ virtual screening determined ability predict ligand binding poses closest native pose assessing Root Mean Square Deviation (RMSD) metric scoring function. function evaluated enrichment factor (EF). Furthermore, capability identify hits key protein–ligand interactions analyzed. posing power results showed that successfully predicted (RMSD < 2 Å). However, they exhibited lower power, average EF values 2.24, 2.42, 1.82 X-ray, Cryo-EM, structures, respectively. Moreover, our revealed can competitive inhibitors. In conclusion, this found provided comparable particularly certain GPCR targets, could potentially significantly impact discovery.

Язык: Английский

Процитировано

8
From GPUs to AI and quantum: three waves of acceleration in bioinformatics DOI Creative Commons
Bertil Schmidt, Andreas Hildebrandt

Drug Discovery Today, Год журнала: 2024, Номер 29(6), С. 103990 - 103990

Опубликована: Апрель 23, 2024

The enormous growth in the amount of data generated by life sciences is continuously shifting field from model-driven science towards data-driven science. need for efficient processing has led to adoption massively parallel accelerators such as graphics units (GPUs). Consequently, development bioinformatics methods nowadays often heavily depends on effective use these powerful technologies. Furthermore, progress computational techniques and architectures continues be highly dynamic, involving novel deep neural network models artificial intelligence (AI) accelerators, potentially quantum future. These are expected disruptive a whole drug discovery particular. Here, we identify three waves acceleration their applications context: (i) GPU computing, (ii) AI (iii) next-generation computers.

Язык: Английский

Процитировано

7
Predicting substrates for orphan solute carrier proteins using multi-omics datasets DOI Creative Commons
Yimo Zhang, Simon Newstead, Peter Sarkies

и другие.

BMC Genomics, Год журнала: 2025, Номер 26(1)

Опубликована: Фев. 11, 2025

Abstract Solute carriers (SLC) are integral membrane proteins responsible for transporting a wide variety of metabolites, signaling molecules and drugs across cellular membranes. Despite key roles in metabolism, pharmacology, around one third SLC ‘orphans’ whose substrates unknown. Experimental determination is technically challenging, given the range possible physiological candidates. Here, we develop predictive algorithm to identify correlations between expression levels intracellular metabolite concentrations by leveraging existing cancer multi-omics datasets. Our predictions recovered known SLC-substrate pairs with high sensitivity specificity compared simulated random pairs. CRISPR-Cas9 dependency screen data metabolic pathway adjacency further improved performance our algorithm. In parallel, combined drug profiles predict new SLC-drug interactions. Together, provide novel bioinformatic pipeline substrate SLCs, offering opportunities de-orphanise SLCs important implications understanding their health disease.

Язык: Английский

Процитировано

1
AmIActive (AIA): A Large-scale QSAR Based Target Fishing and Polypharmacology Predictive Web Tool DOI

Luis Felipe de Morais Melo,

Luciano Pereira de Oliveira Filho,

Uilames de Assis Ferreira

и другие.

Journal of Molecular Biology, Год журнала: 2025, Номер unknown, С. 169090 - 169090

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

1
AlphaFold touted as next big thing for drug discovery — but is it? DOI

Carrie Arnold

Nature, Год журнала: 2023, Номер 622(7981), С. 15 - 17

Опубликована: Сен. 22, 2023

Язык: Английский

Процитировано

15
AlphaFold2 structures template ligand discovery DOI Creative Commons
Jiankun Lyu, Nicholas J. Kapolka, Ryan H. Gumpper

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Дек. 21, 2023

AlphaFold2 (AF2) and RosettaFold have greatly expanded the number of structures available for structure-based ligand discovery, even though retrospective studies cast doubt on their direct usefulness that goal. Here, we tested unrefined AF2 models prospectively, comparing experimental hit-rates affinities from large library docking against vs same screens targeting receptors. In σ2 5-HT2A receptors, struggled to recapitulate ligands had previously found receptors' structures, consistent with published results. Prospective models, however, yielded similar hit rates both receptors versus experimentally-derived structures; hundreds molecules were prioritized each model structure receptor. The success was achieved despite differences in orthosteric pocket residue conformations targets structures. Intriguingly, receptor most potent, subtype-selective agonists discovered via model, not structure. To understand this a molecular perspective, cryoEM determined one more potent selective emerge Our findings suggest may sample are relevant much extending domain applicability discovery.

Язык: Английский

Процитировано

15
AlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles DOI
Gregory R. Bowman

Annual Review of Biomedical Data Science, Год журнала: 2024, Номер 7(1), С. 51 - 57

Опубликована: Апрель 11, 2024

Like the black knight in classic Monty Python movie, grand scientific challenges such as protein folding are hard to finish off. Notably, AlphaFold is revolutionizing structural biology by bringing highly accurate structure prediction masses and opening up innumerable new avenues of research. Despite this enormous success, calling prediction, much less related problems, “solved” dangerous, doing so could stymie further progress. Imagine what world would be like if we had declared flight solved after first commercial airlines opened stopped investing research development. Likewise, there still important limitations that benefit from addressing. Moreover, limited our understanding diversity different structures a single can adopt (called conformational ensemble) dynamics which explores space. What clear ensembles critical function, aspect will advance ability design proteins drugs.

Язык: Английский

Процитировано

5
Computational methods for unlocking the secrets of potassium channels: Structure, mechanism, and drug design DOI
Lingling Wang, Qianqian Zhang, Henry H.Y. Tong

и другие.

Wiley Interdisciplinary Reviews Computational Molecular Science, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 1, 2024

Abstract Potassium (K + ) channels play vital roles in various physiological functions, including regulating K flow cell membranes, impacting nervous system signal transduction, neuronal firing, muscle contraction, neurotransmitters, and enzyme secretion. Their activation switch‐off are directly linked to diseases like arrhythmias, atrial fibrillation, pain etc. Although the experimental methods important studying structure function of channels, they still some limitations enclose dynamic molecular processes corresponding mechanisms conformational changes during ion transport, permeation, gating control. Relatively, computational have obvious advantages such problems compared with methods. Recently, more three‐dimensional structures been disclosed based on silico prediction methods, which provide a good chance study mechanism related functional regulations channels. Based these structural details, dynamics simulations together as enhanced sampling free energy calculations, widely used reveal dynamics, conductance, channel gating, ligand binding mechanisms. Additionally, accessibility also provides large space for structure‐based drug design. This review mainly addresses recent progress structure, mechanism, design After summarizing fields, we give our opinion future direction area research combined cutting edge article is categorized under: Molecular Statistical Mechanics > Dynamics Monte‐Carlo Methods Structure Mechanism Computational Biochemistry Biophysics Data Science Chemoinformatics

Язык: Английский

Процитировано

4
Utilization of an optimized AlphaFold protein model for structure‐based design of a selective HDAC11 inhibitor with anti‐neuroblastoma activity DOI Creative Commons
Fady Baselious, Sebastian Hilscher,

Sven Hagemann

и другие.

Archiv der Pharmazie, Год журнала: 2024, Номер 357(10)

Опубликована: Июль 12, 2024

AlphaFold is an artificial intelligence approach for predicting the three-dimensional (3D) structures of proteins with atomic accuracy. One challenge that limits use models drug discovery correct prediction folding in absence ligands and cofactors, which compromises their direct use. We have previously described optimization histone deacetylase 11 (HDAC11) model docking selective inhibitors such as FT895 SIS17. Based on predicted binding mode optimized HDAC11 model, a new scaffold was designed, resulting compounds were tested vitro against various HDAC isoforms. Compound 5a proved to be most active compound IC

Язык: Английский

Процитировано

3