Pub Date : 2026-01-27DOI: 10.1021/acs.jcim.5c02954
Dmitri G Fedorov,Katarzyna J Zator,Julia Contreras-García,Seiji Mori
A new formulation of the many-body expansion of the electron density expressed in terms of the wave function data is developed in the framework of the fragment molecular orbital (FMO) method for the purpose of visualizing noncovalent interactions (NCI) in large systems. This expansion can also be used for a selected site of interest, such as a ligand binding site in a protein. The site formulation is shown to be both accurate and efficient, as demonstrated for a small protein-ligand complex (Trp-cage protein, PDB: 1L2Y) and a large complex of prostaglandin H2 synthase-1 (1EQG) with ibuprofen. In addition, the FMO/NCI methodology is extended to treat periodic boundary conditions, with an application to study packing effects in the crystal of crambin (1CBN).
{"title":"Noncovalent Interactions in Solvated Proteins and Protein Crystals Studied with the Fragment Molecular Orbital Method.","authors":"Dmitri G Fedorov,Katarzyna J Zator,Julia Contreras-García,Seiji Mori","doi":"10.1021/acs.jcim.5c02954","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02954","url":null,"abstract":"A new formulation of the many-body expansion of the electron density expressed in terms of the wave function data is developed in the framework of the fragment molecular orbital (FMO) method for the purpose of visualizing noncovalent interactions (NCI) in large systems. This expansion can also be used for a selected site of interest, such as a ligand binding site in a protein. The site formulation is shown to be both accurate and efficient, as demonstrated for a small protein-ligand complex (Trp-cage protein, PDB: 1L2Y) and a large complex of prostaglandin H2 synthase-1 (1EQG) with ibuprofen. In addition, the FMO/NCI methodology is extended to treat periodic boundary conditions, with an application to study packing effects in the crystal of crambin (1CBN).","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"274 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1021/acs.jcim.5c02630
Guillaume Bret,François Sindt,Didier Rognan
The recently released Boltz-2 cofolding model is generating high expectations by enabling both protein-ligand structure and binding affinity predictions. When applied to a recently described and challenging data set of ultralarge-virtual-screening hits, Boltz-2 excels at discriminating true from false positives, overcoming by a large margin all scoring functions tested so far on raw docking poses. Strikingly, affinity predictions seem to be relatively independent of pose quality but are not biased by obvious chemical similarity to known compounds sharing comparable binding potencies. To ascertain that Boltz-2 truly relies on the physics of intermolecular interactions, we challenged affinity predictions with biologically meaningful challenges (target mutation and target shuffling). Binary classification of active vs inactive compounds remains insensitive to key binding site mutations and even in some cases to target exchange, raising concerns on the hidden features governing Boltz-2 affinity predictions.
{"title":"Assessing Boltz-2 Performance for the Binding Classification of Docking Hits.","authors":"Guillaume Bret,François Sindt,Didier Rognan","doi":"10.1021/acs.jcim.5c02630","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02630","url":null,"abstract":"The recently released Boltz-2 cofolding model is generating high expectations by enabling both protein-ligand structure and binding affinity predictions. When applied to a recently described and challenging data set of ultralarge-virtual-screening hits, Boltz-2 excels at discriminating true from false positives, overcoming by a large margin all scoring functions tested so far on raw docking poses. Strikingly, affinity predictions seem to be relatively independent of pose quality but are not biased by obvious chemical similarity to known compounds sharing comparable binding potencies. To ascertain that Boltz-2 truly relies on the physics of intermolecular interactions, we challenged affinity predictions with biologically meaningful challenges (target mutation and target shuffling). Binary classification of active vs inactive compounds remains insensitive to key binding site mutations and even in some cases to target exchange, raising concerns on the hidden features governing Boltz-2 affinity predictions.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"1 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1021/acs.jcim.5c02594
Juliana Castro-Amorim,Maria J. Ramos,Pedro A. Fernandes
Hyaluronidases are widely distributed in nature being ubiquitous in snake species (svHyal). They catalyze the hydrolysis of β-1,4-glycosidic bonds in hyaluronic acid, a critical constituent of the extracellular matrix. This facilitates the spread of venom toxins into the bloodstream, exacerbating tissue damage and systemic toxicity─a rationale for their common designation as “spreading factors”. While svHyals are not directly toxic, they substantially contribute to the morbidity and mortality associated with snakebite envenomation, the world’s most lethal neglected tropical disease. Despite their important role in tissue penetration, the atomic–level reaction mechanism of these enzymes remains poorly understood. To bridge this knowledge gap, we studied the chemical mechanism of the Hyal-1 enzyme isolated from the Puff Adder viper (Bitis arietans), likely the major contributor to snakebite mortality in sub-Saharan Africa. We evaluated two alternative mechanistic scenarios, based on different protonation states for the active site “assisting residue” (Asp110), and conducted umbrella sampling QM/MM MD simulations (PBE/DZVP-GTH-PBE: AMBER). Our findings indicate that the pathway starting from a neutral Asp110 yields an activation free energy barrier of 20.34 kcal·mol–1─nearly half that of the alternative pathway that considers an ionised Asp110. The deglycosylation step of the most favorable pathway yielded a free energy barrier of 13.94 kcal·mol–1. Our simulations also support an induced-fit mechanism for the svHyal/hyaluronic acid complex, with substrate distortion (chair → boat/skew-boat) favoring a conformation that closely mimics the transition state. This distortion, along with a prealignment of Glu112, lowers the activation free energy, enhancing the susceptibility of the glycosidic bond to nucleophilic attack. The results are likely transferable to all svHyal given their high degree of interspecific similarity (>90% sequence identity). This study highlights the importance of understanding mechanistics, including detailed stereoelectronic conformations and subsite-specific interactions, for the design of novel and effective inhibitors with broad clinical and biotechnological applications.
{"title":"Mechanism of Hyaluronic Acid Hydrolysis Catalyzed by Snake Venom Hyaluronidase","authors":"Juliana Castro-Amorim,Maria J. Ramos,Pedro A. Fernandes","doi":"10.1021/acs.jcim.5c02594","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02594","url":null,"abstract":"Hyaluronidases are widely distributed in nature being ubiquitous in snake species (svHyal). They catalyze the hydrolysis of β-1,4-glycosidic bonds in hyaluronic acid, a critical constituent of the extracellular matrix. This facilitates the spread of venom toxins into the bloodstream, exacerbating tissue damage and systemic toxicity─a rationale for their common designation as “spreading factors”. While svHyals are not directly toxic, they substantially contribute to the morbidity and mortality associated with snakebite envenomation, the world’s most lethal neglected tropical disease. Despite their important role in tissue penetration, the atomic–level reaction mechanism of these enzymes remains poorly understood. To bridge this knowledge gap, we studied the chemical mechanism of the Hyal-1 enzyme isolated from the Puff Adder viper (Bitis arietans), likely the major contributor to snakebite mortality in sub-Saharan Africa. We evaluated two alternative mechanistic scenarios, based on different protonation states for the active site “assisting residue” (Asp110), and conducted umbrella sampling QM/MM MD simulations (PBE/DZVP-GTH-PBE: AMBER). Our findings indicate that the pathway starting from a neutral Asp110 yields an activation free energy barrier of 20.34 kcal·mol–1─nearly half that of the alternative pathway that considers an ionised Asp110. The deglycosylation step of the most favorable pathway yielded a free energy barrier of 13.94 kcal·mol–1. Our simulations also support an induced-fit mechanism for the svHyal/hyaluronic acid complex, with substrate distortion (chair → boat/skew-boat) favoring a conformation that closely mimics the transition state. This distortion, along with a prealignment of Glu112, lowers the activation free energy, enhancing the susceptibility of the glycosidic bond to nucleophilic attack. The results are likely transferable to all svHyal given their high degree of interspecific similarity (>90% sequence identity). This study highlights the importance of understanding mechanistics, including detailed stereoelectronic conformations and subsite-specific interactions, for the design of novel and effective inhibitors with broad clinical and biotechnological applications.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"40 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146044946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding similarities between protein binding sites has long been of great interest, as such comparisons can reveal functional relationships that transcend sequence or fold. However, systematic comparison remains challenging due to the difficulty of defining active sites consistently and developing descriptors that are both general and discriminative. We present binding site vectors, a computational framework for a high-resolution comparison of macromolecular binding sites that integrates both structural and electrostatic properties. The vectors extend spherically from the center of the pocket, terminating at its surface to capture shape and electrostatic features in a multidimensional manner. Geometrically anchored, they enable a systematic comparison of binding sites across diverse systems. We applied this approach to cytochrome P450 (CYP) enzymes, analyzing over 600 human and plant CYP structures and a subset of 23 extensive structural ensembles obtained through molecular dynamics (MD) simulation. Comparisons based on binding site vectors reveal structural–functional relationships missed by sequence- or backbone-based groupings, particularly when full conformational ensembles are included. This demonstrates that binding site vectors provide a robust framework for both functional classification and deep mechanistic insights into macromolecular systems.
{"title":"Binding Site Vectors Enable Mapping of Cytochrome P450 Functional Landscapes","authors":"Tea Kuvek,Zuzana Jandová,Klaus-Juergen Schleifer,Chris Oostenbrink","doi":"10.1021/acs.jcim.5c02705","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02705","url":null,"abstract":"Understanding similarities between protein binding sites has long been of great interest, as such comparisons can reveal functional relationships that transcend sequence or fold. However, systematic comparison remains challenging due to the difficulty of defining active sites consistently and developing descriptors that are both general and discriminative. We present binding site vectors, a computational framework for a high-resolution comparison of macromolecular binding sites that integrates both structural and electrostatic properties. The vectors extend spherically from the center of the pocket, terminating at its surface to capture shape and electrostatic features in a multidimensional manner. Geometrically anchored, they enable a systematic comparison of binding sites across diverse systems. We applied this approach to cytochrome P450 (CYP) enzymes, analyzing over 600 human and plant CYP structures and a subset of 23 extensive structural ensembles obtained through molecular dynamics (MD) simulation. Comparisons based on binding site vectors reveal structural–functional relationships missed by sequence- or backbone-based groupings, particularly when full conformational ensembles are included. This demonstrates that binding site vectors provide a robust framework for both functional classification and deep mechanistic insights into macromolecular systems.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"1 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146044945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1021/acs.jcim.5c03230
Thereza A. Soares*, and , Kenneth Merz Jr.*,
{"title":"Beyond Summary: Reviews That Shape the Field of Computational Molecular Sciences","authors":"Thereza A. Soares*, and , Kenneth Merz Jr.*, ","doi":"10.1021/acs.jcim.5c03230","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c03230","url":null,"abstract":"","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 2","pages":"809–811"},"PeriodicalIF":5.3,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-25DOI: 10.1021/acs.jcim.5c01166
Yue Jian,Curtis Wu,Danny Reidenbach,Aditi S. Krishnapriyan
Structure-based drug design (SBDD) aims to generate ligands that bind strongly and specifically to target protein pockets. Recent diffusion models have advanced SBDD by capturing the distributions of atomic positions and types, yet they often underemphasize binding affinity control during generation. To address this limitation, we introduce BADGER, a general binding-affinity guidance framework for diffusion models in SBDD. BADGER incorporates binding affinity awareness through two complementary strategies: (1) classifier guidance, which applies gradient-based affinity signals during sampling in a plug-and-play fashion, and (2) classifier-free guidance, which integrates affinity conditioning directly into diffusion model training. Together, these approaches enable controllable ligand generation guided by binding affinity. BADGER achieves up to a 60% improvement in ligand–protein binding affinity of sampled molecules over prior methods. Furthermore, we extend the framework to multiconstraint diffusion guidance, jointly optimizing for binding affinity, drug-likeness (QED), and synthetic accessibility (SA) to design realistic and synthesizable drug candidates.
{"title":"General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design","authors":"Yue Jian,Curtis Wu,Danny Reidenbach,Aditi S. Krishnapriyan","doi":"10.1021/acs.jcim.5c01166","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01166","url":null,"abstract":"Structure-based drug design (SBDD) aims to generate ligands that bind strongly and specifically to target protein pockets. Recent diffusion models have advanced SBDD by capturing the distributions of atomic positions and types, yet they often underemphasize binding affinity control during generation. To address this limitation, we introduce BADGER, a general binding-affinity guidance framework for diffusion models in SBDD. BADGER incorporates binding affinity awareness through two complementary strategies: (1) classifier guidance, which applies gradient-based affinity signals during sampling in a plug-and-play fashion, and (2) classifier-free guidance, which integrates affinity conditioning directly into diffusion model training. Together, these approaches enable controllable ligand generation guided by binding affinity. BADGER achieves up to a 60% improvement in ligand–protein binding affinity of sampled molecules over prior methods. Furthermore, we extend the framework to multiconstraint diffusion guidance, jointly optimizing for binding affinity, drug-likeness (QED), and synthetic accessibility (SA) to design realistic and synthesizable drug candidates.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"1 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146044947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-25DOI: 10.1021/acs.jcim.5c02584
Bo Liu,Likun Zhao,Lingling Wang,Xiaoqing Gong,Xiaojun Yao,Huanxiang Liu,Qianqian Zhang
Kinases are key molecules in cell signal transduction. Their abnormal activation is closely related to cancer, inflammation, and metabolic diseases, making them important drug targets. However, the high sequence conservation of the kinase family limits the selectivity of inhibitors, and the dynamic conformational changes of kinases profoundly affect drug binding. Receptor-interacting protein kinase 1 (RIPK1) is a core factor in regulating cell necrosis. Its unique DLG (Asp-Leu-Gly) motif replaces the DFG (Asp-Phe-Gly) motif in traditional kinases. It is worth noting that, in the known RIPK1 crystal structure, the DLG motif is always in the "out" conformation, while its flipping mechanism and its regulatory mechanism on drug activity have not yet been elucidated. This study combined conventional molecular dynamics simulation and metadynamics simulation to deeply explore the conformational flipping process of the DLG motif in RIPK1 and its effect on protein conformation and drug binding. The results show that the flipping of the DLG motif occurs in coordination with the rotation of the αC helix, significantly changing the hydrophobicity and spatial volume of the ATP binding pocket, thereby regulating the affinity of drug molecules. In addition, the DLG flipping also reshapes the allosteric communication network of RIPK1, especially affecting the allosteric connection of the hinge region. The study further revealed the differential effects of different types of inhibitors on the conformational flipping of the DLG motif. This work not only provides a new structural perspective and theoretical basis for the design of highly selective RIPK1 inhibitors, but also provides important insights for the development of inhibitors targeting other kinases containing atypical DLG motifs.
{"title":"Metadynamics Simulation Reveals Allosteric Communication Effects of the Flipping Process of the Atypical DLG Motif in RIPK1.","authors":"Bo Liu,Likun Zhao,Lingling Wang,Xiaoqing Gong,Xiaojun Yao,Huanxiang Liu,Qianqian Zhang","doi":"10.1021/acs.jcim.5c02584","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02584","url":null,"abstract":"Kinases are key molecules in cell signal transduction. Their abnormal activation is closely related to cancer, inflammation, and metabolic diseases, making them important drug targets. However, the high sequence conservation of the kinase family limits the selectivity of inhibitors, and the dynamic conformational changes of kinases profoundly affect drug binding. Receptor-interacting protein kinase 1 (RIPK1) is a core factor in regulating cell necrosis. Its unique DLG (Asp-Leu-Gly) motif replaces the DFG (Asp-Phe-Gly) motif in traditional kinases. It is worth noting that, in the known RIPK1 crystal structure, the DLG motif is always in the \"out\" conformation, while its flipping mechanism and its regulatory mechanism on drug activity have not yet been elucidated. This study combined conventional molecular dynamics simulation and metadynamics simulation to deeply explore the conformational flipping process of the DLG motif in RIPK1 and its effect on protein conformation and drug binding. The results show that the flipping of the DLG motif occurs in coordination with the rotation of the αC helix, significantly changing the hydrophobicity and spatial volume of the ATP binding pocket, thereby regulating the affinity of drug molecules. In addition, the DLG flipping also reshapes the allosteric communication network of RIPK1, especially affecting the allosteric connection of the hinge region. The study further revealed the differential effects of different types of inhibitors on the conformational flipping of the DLG motif. This work not only provides a new structural perspective and theoretical basis for the design of highly selective RIPK1 inhibitors, but also provides important insights for the development of inhibitors targeting other kinases containing atypical DLG motifs.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"57 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146044694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1021/acs.jcim.5c02426
Sakari Pirnes,Veera Hägg,Mykhailo Girych,Ilpo Vattulainen,Giray Enkavi
Understanding the relative orientation of protein secondary structure elements is crucial for elucidating their tertiary organization, function, and interactions. Here, we introduce HelixSide, a comprehensive method for systematically quantifying geometrical metrics of helical secondary structures, including widely used measures, such as tilt and kink angles. Additionally, to characterize the orientation of secondary structure motifs relative to each other or to the helical axis, we introduce a new quantity, the side angle. HelixSide computes these metrics at both single-residue and whole-protein levels, revealing local and global conformational features of the system. We demonstrate the method's utility through case studies of two well-characterized single-pass transmembrane proteins: insulin receptor and glycophorin A. These analyses showcase HelixSide's ability to capture tertiary structural characteristics and compare conformational states. HelixSide is open source and available on GitHub at https://github.com/SakariPirnes/helixside. It is applicable to experimental structures, theoretical models, and molecular dynamics trajectories of membrane and soluble proteins, and can be used as a featurization tool for machine learning.
{"title":"HelixSide: A Comprehensive Method for Local and Global Orientational Analysis of Proteins.","authors":"Sakari Pirnes,Veera Hägg,Mykhailo Girych,Ilpo Vattulainen,Giray Enkavi","doi":"10.1021/acs.jcim.5c02426","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02426","url":null,"abstract":"Understanding the relative orientation of protein secondary structure elements is crucial for elucidating their tertiary organization, function, and interactions. Here, we introduce HelixSide, a comprehensive method for systematically quantifying geometrical metrics of helical secondary structures, including widely used measures, such as tilt and kink angles. Additionally, to characterize the orientation of secondary structure motifs relative to each other or to the helical axis, we introduce a new quantity, the side angle. HelixSide computes these metrics at both single-residue and whole-protein levels, revealing local and global conformational features of the system. We demonstrate the method's utility through case studies of two well-characterized single-pass transmembrane proteins: insulin receptor and glycophorin A. These analyses showcase HelixSide's ability to capture tertiary structural characteristics and compare conformational states. HelixSide is open source and available on GitHub at https://github.com/SakariPirnes/helixside. It is applicable to experimental structures, theoretical models, and molecular dynamics trajectories of membrane and soluble proteins, and can be used as a featurization tool for machine learning.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"284 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1021/acs.jcim.5c02855
Julia Kandler,Ayse Sıla Kantarçeken,Aljoša Smajić,Gerhard F Ecker
The sodium-iodide symporter (NIS, SLC5A5) plays a crucial role in thyroid hormone synthesis. Especially during brain development, correct thyroid signaling is of critical importance. Hence, inhibition of this transporter can lead to neurodevelopmental disorders, such as lowered IQ or autism. In order to uncover environmental chemicals with the potential of causing developmental neurotoxicity (DNT), NIS was selected for modeling. To support next-generation risk assessment, in silico-based methods were utilized. Docking-based virtual screening workflows of a library of compounds with experimentally determined inhibitory activity on NIS were applied. In addition, machine learning (ML) models based on random forest (RF), extreme gradient boosting (XGB), and support vector machines (SVM) were trained using extended-connectivity fingerprints 4 (ECFP4) and continuous and data-driven descriptors (CDDDs) with 9-fold cross validation to discriminate between NIS inhibiting and noninhibiting compounds. Ultimately, combining ML and docking predictions improved discrimination, achieving an area under the receiver operating characteristic curve (ROC AUC) of 0.77. Thresholds for optimal discrimination between actives and inactives were determined using kernel density estimate plots, at which a Matthews correlation coefficient (MCC) of 0.32, and a balanced accuracy (BA) of 0.78 were achieved on the internal test set. By combining ML predictions with docking scores and training on a larger, more diverse data set of 1412 compounds, this study provides a novel and robust framework for NIS inhibition prediction, which constitutes a new approach method in toxicological risk assessment.
{"title":"Combined Modeling Approaches for Assessing Sodium-Iodide Symporter Inhibition.","authors":"Julia Kandler,Ayse Sıla Kantarçeken,Aljoša Smajić,Gerhard F Ecker","doi":"10.1021/acs.jcim.5c02855","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c02855","url":null,"abstract":"The sodium-iodide symporter (NIS, SLC5A5) plays a crucial role in thyroid hormone synthesis. Especially during brain development, correct thyroid signaling is of critical importance. Hence, inhibition of this transporter can lead to neurodevelopmental disorders, such as lowered IQ or autism. In order to uncover environmental chemicals with the potential of causing developmental neurotoxicity (DNT), NIS was selected for modeling. To support next-generation risk assessment, in silico-based methods were utilized. Docking-based virtual screening workflows of a library of compounds with experimentally determined inhibitory activity on NIS were applied. In addition, machine learning (ML) models based on random forest (RF), extreme gradient boosting (XGB), and support vector machines (SVM) were trained using extended-connectivity fingerprints 4 (ECFP4) and continuous and data-driven descriptors (CDDDs) with 9-fold cross validation to discriminate between NIS inhibiting and noninhibiting compounds. Ultimately, combining ML and docking predictions improved discrimination, achieving an area under the receiver operating characteristic curve (ROC AUC) of 0.77. Thresholds for optimal discrimination between actives and inactives were determined using kernel density estimate plots, at which a Matthews correlation coefficient (MCC) of 0.32, and a balanced accuracy (BA) of 0.78 were achieved on the internal test set. By combining ML predictions with docking scores and training on a larger, more diverse data set of 1412 compounds, this study provides a novel and robust framework for NIS inhibition prediction, which constitutes a new approach method in toxicological risk assessment.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"6 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1021/acs.jcim.5c02389
Yusaku Fukushima,Takashi Yoshidome
Hydration thermodynamic quantities are essential for understanding protein function from a free-energy perspective. The grid inhomogeneous solvation theory (GIST) enables the computation of spatial distributions of hydration energy, ΔEW(r), and hydration entropy, ΔSW(r), using molecular dynamics (MD) simulations, from which the distribution of the hydration free energy, ΔGW(r), is obtained as ΔGW(r) = ΔEW(r) - TΔSW(r), where T is the absolute temperature. However, GIST is computationally demanding, requiring tens of hours to compute these distributions. To overcome this bottleneck, we developed a set of deep learning models capable of predicting ΔEW(r), TΔSW(r), and ΔGW(r). Our deep learning models completed these predictions within tens of seconds using a single graphics processing unit. The resulting distributions achieved coefficient of determination values of 0.76-0.84 for ΔGW(r) when compared to GIST results, and lower values were obtained for ΔEW(r) and TΔSW(r). As a practical application, we examined the free energy change required for a water molecule to move from the bulk region to the ligand-binding site, ΔGW,replace, using both our deep learning model and GIST. A high correlation coefficient of 0.78 was observed between the predictions of our model and GIST, confirming its reliability. Furthermore, the results for a representative protein were consistent with experimental data of the corresponding protein-ligand complex: Water molecules with low ΔGW,replace values located near crystallographic waters, suggesting retention upon ligand binding, whereas those with unfavorable values overlapped with the ligand, indicating displacement upon the ligand binding. These findings demonstrate that our deep learning models provide an efficient and accurate alternative to GIST for predicting hydration thermodynamics and enable the consideration of protein conformational fluctuations, which is difficult to achieve with conventional GIST. The program called "Deep GIST" is available under the GNU General Public License from https://github.com/YoshidomeGroup-Hydration/Deep-GIST.
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