The Art and Science of Molecular Docking.

Molecular docking has become an essential part of a structural biologist's and medicinal chemist's toolkits. Given a chemical compound and the three-dimensional structure of a molecular target-for example, a protein-docking methods fit the compound into the target, predicting the compound's bound structure and binding energy. Docking can be used to discover novel ligands for a target by screening large virtual compound libraries. Docking can also provide a useful starting point for structure-based ligand optimization or for investigating a ligand's mechanism of action. Advances in computational methods, including both physics-based and machine learning approaches, as well as in complementary experimental techniques, are making docking an even more powerful tool. We review how docking works and how it can drive drug discovery and biological research. We also describe its current limitations and ongoing efforts to overcome them.

Structure-based discovery of CFTR potentiators and inhibitors.

The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, whereas its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here, we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify CFTR modulators. We docked ∼155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered mid-nanomolar potentiators, as well as inhibitors, that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery.

Localisation and function of key axonemal microtubule inner proteins and dynein docking complex members reveal extensive diversity among vertebrate motile cilia.

Vertebrate motile cilia are classified as (9+2) or (9+0), based on the presence or absence of the central pair apparatus, respectively. Cryogenic electron microscopy analyses of (9+2) cilia have uncovered an elaborate axonemal protein composition. The extent to which these features are conserved in (9+0) cilia remains unclear. CFAP53, a key axonemal filamentous microtubule inner protein (fMIP) and a centriolar satellites component, is essential for motility of (9+0), but not (9+2) cilia. Here, we show that in (9+2) cilia, CFAP53 functions redundantly with a paralogous fMIP, MNS1. MNS1 localises to ciliary axonemes, and combined loss of both proteins in zebrafish and mice caused severe outer dynein arm loss from (9+2) cilia, significantly affecting their motility. Using immunoprecipitation, we demonstrate that, whereas MNS1 can associate with itself and CFAP53, CFAP53 is unable to self-associate. We also show that additional axonemal dynein-interacting proteins, two outer dynein arm docking (ODAD) complex members, show differential localisation between types of motile cilia. Together, our findings clarify how paralogous fMIPs, CFAP53 and MNS1, function in regulating (9+2) versus (9+0) cilia motility, and further emphasise extensive structural diversity among these organelles.

ABAG-docking benchmark: a non-redundant structure benchmark dataset for antibody-antigen computational docking.

Accurate prediction of antibody-antigen complex structures is pivotal in drug discovery, vaccine design and disease treatment and can facilitate the development of more effective therapies and diagnostics. In this work, we first review the antibody-antigen docking (ABAG-docking) datasets. Then, we present the creation and characterization of a comprehensive benchmark dataset of antibody-antigen complexes. We categorize the dataset based on docking difficulty, interface properties and structural characteristics, to provide a diverse set of cases for rigorous evaluation. Compared with Docking Benchmark 5.5, we have added 112 cases, including 14 single-domain antibody (sdAb) cases and 98 monoclonal antibody (mAb) cases, and also increased the proportion of Difficult cases. Our dataset contains diverse cases, including human/humanized antibodies, sdAbs, rodent antibodies and other types, opening the door to better algorithm development. Furthermore, we provide details on the process of building the benchmark dataset and introduce a pipeline for periodic updates to keep it up to date. We also utilize multiple complex prediction methods including ZDOCK, ClusPro, HDOCK and AlphaFold-Multimer for testing and analyzing this dataset. This benchmark serves as a valuable resource for evaluating and advancing docking computational methods in the analysis of antibody-antigen interaction, enabling researchers to develop more accurate and effective tools for predicting and designing antibody-antigen complexes. The non-redundant ABAG-docking structure benchmark dataset is available at https://github.com/Zhaonan99/Antibody-antigen-complex-structure-benchmark-dataset.

GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking.

Protein-ligand interactions (PLIs) are essential for cellular activities and drug discovery. But due to the complexity and high cost of experimental methods, there is a great demand for computational approaches to recognize PLI patterns, such as protein-ligand docking. In recent years, more and more models based on machine learning have been developed to directly predict the root mean square deviation (RMSD) of a ligand docking pose with reference to its native binding pose. However, new scoring methods are pressingly needed in methodology for more accurate RMSD prediction. We present a new deep learning-based scoring method for RMSD prediction of protein-ligand docking poses based on a Graphormer method and Shell-like graph architecture, named GSScore. To recognize near-native conformations from a set of poses, GSScore takes atoms as nodes and then establishes the docking interface of protein-ligand into multiple bipartite graphs within different shell ranges. Benefiting from the Graphormer and Shell-like graph architecture, GSScore can effectively capture the subtle differences between energetically favorable near-native conformations and unfavorable non-native poses without extra information. GSScore was extensively evaluated on diverse test sets including a subset of PDBBind version 2019, CASF2016 as well as DUD-E, and obtained significant improvements over existing methods in terms of RMSE, $R$ (Pearson correlation coefficient), Spearman correlation coefficient and Docking power.

RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method.

Small molecule drugs can be used to target nucleic acids (NA) to regulate biological processes. Computational modeling methods, such as molecular docking or scoring functions, are commonly employed to facilitate drug design. However, the accuracy of the scoring function in predicting the closest-to-native docking pose is often suboptimal. To overcome this problem, a machine learning model, RmsdXNA, was developed to predict the root-mean-square-deviation (RMSD) of ligand docking poses in NA complexes. The versatility of RmsdXNA has been demonstrated by its successful application to various complexes involving different types of NA receptors and ligands, including metal complexes and short peptides. The predicted RMSD by RmsdXNA was strongly correlated with the actual RMSD of the docked poses. RmsdXNA also outperformed the rDock scoring function in ranking and identifying closest-to-native docking poses across different structural groups and on the testing dataset. Using experimental validated results conducted on polyadenylated nuclear element for nuclear expression triplex, RmsdXNA demonstrated better screening power for the RNA-small molecule complex compared to rDock. Molecular dynamics simulations were subsequently employed to validate the binding of top-scoring ligand candidates selected by RmsdXNA and rDock on MALAT1. The results showed that RmsdXNA has a higher success rate in identifying promising ligands that can bind well to the receptor. The development of an accurate docking score for a NA-ligand complex can aid in drug discovery and development advancements. The code to use RmsdXNA is available at the GitHub repository https://github.com/laiheng001/RmsdXNA.

Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration.

Understanding the protein structures is invaluable in various biomedical applications, such as vaccine development. Protein structure model building from experimental electron density maps is a time-consuming and labor-intensive task. To address the challenge, machine learning approaches have been proposed to automate this process. Currently, the majority of the experimental maps in the database lack atomic resolution features, making it challenging for machine learning-based methods to precisely determine protein structures from cryogenic electron microscopy density maps. On the other hand, protein structure prediction methods, such as AlphaFold2, leverage evolutionary information from protein sequences and have recently achieved groundbreaking accuracy. However, these methods often require manual refinement, which is labor intensive and time consuming. In this study, we present DeepTracer-Refine, an automated method that refines AlphaFold predicted structures by aligning them to DeepTracers modeled structure. Our method was evaluated on 39 multi-domain proteins and we improved the average residue coverage from 78.2 to 90.0% and average local Distance Difference Test score from 0.67 to 0.71. We also compared DeepTracer-Refine with Phenixs AlphaFold refinement and demonstrated that our method not only performs better when the initial AlphaFold model is less precise but also surpasses Phenix in run-time performance.

Phosphorylation of GCN2 by mTOR confers adaptation to conditions of hyper-mTOR activation under stress.

Adaptation to the shortage in free amino acids (AA) is mediated by 2 pathways, the integrated stress response (ISR) and the mechanistic target of rapamycin (mTOR). In response to reduced levels, primarily of leucine or arginine, mTOR in its complex 1 configuration (mTORC1) is suppressed leading to a decrease in translation initiation and elongation. The eIF2α kinase general control nonderepressible 2 (GCN2) is activated by uncharged tRNAs, leading to induction of the ISR in response to a broader range of AA shortage. ISR confers a reduced translation initiation, while promoting the selective synthesis of stress proteins, such as ATF4. To efficiently adapt to AA starvation, the 2 pathways are cross-regulated at multiple levels. Here we identified a new mechanism of ISR/mTORC1 crosstalk that optimizes survival under AA starvation, when mTORC1 is forced to remain active. mTORC1 activation during acute AA shortage, augmented ATF4 expression in a GCN2-dependent manner. Under these conditions, enhanced GCN2 activity was not dependent on tRNA sensing, inferring a different activation mechanism. We identified a labile physical interaction between GCN2 and mTOR that results in a phosphorylation of GCN2 on serine 230 by mTOR, which promotes GCN2 activity. When examined under prolonged AA starvation, GCN2 phosphorylation by mTOR promoted survival. Our data unveils an adaptive mechanism to AA starvation, when mTORC1 evades inhibition.

Selective inhibition of overactive warmth-sensitive Ca2+-permeable TRPV3 channels by antispasmodic agent flopropione for alleviation of skin inflammation.

The temperature-sensitive Ca2+-permeable TRPV3 ion channel is robustly expressed in the skin keratinocytes, and its gain-of-function mutations are involved in the pathology of skin lesions. Here, we report the identification of an antispasmodic agent flopropione that alleviates skin inflammation by selective inhibition of TRPV3. In whole-cell patch clamp recordings, flopropione selectively inhibits macroscopic TRPV3 currents in a concentration-dependent manner with an IC50 value of 17.8 ± 3.5 µM. At the single-channel level, flopropione inhibits TRPV3 channel open probability without alteration of its unitary conductance. In an in vivo mouse model of skin inflammation induced by the skin sensitizer DNFB, flopropione also alleviates dorsal skin lesions and ear skin swelling. Further molecular docking combined with site-directed mutagenesis reveals that two residues E501 and I505 in the channel S2-helix are critical for flopropione-mediated inhibition of TRPV3. Taken together, our findings demonstrate that the spasmolytic drug flopropione as a selective inhibitor of TRPV3 channel not only provides a valuable tool molecule for understanding of TRPV3 channel pharmacology but also holds repurposing potential for therapy of skin disorders, such as dermatitis and pruritus.

Deciphering the SAM- and Metal-Dependent Mechanism of O-Methyltransferases in Cystargolide and Belactosin Biosynthesis: A Structure-Activity Relationship Study.

Cystargolides and belactosins are natural products with a distinct dipeptide structure and an electrophilic ß-lactone warhead. They are known to inhibit proteases such as the proteasome or caseinolytic protease P, highlighting their potential in treating cancers and neurodegenerative diseases. Recent genetic analyses have shown homology between the biosynthetic pathways of the two inhibitors. Here, we characterize the O-methyltransferases BelI and CysG, which catalyze the initial step of ß-lactone formation. Employing techniques such as crystallography, computational analysis, mutagenesis, and activity assays, we identified a His-His-Asp (HHD) motif in the active sites of the two enzymes, which is crucial for binding a catalytically active calcium ion. Our findings thus elucidate a conserved divalent metal-dependent mechanism in both biosynthetic pathways that distinguishes BelI and CysG from previously characterized O-methyltransferases.

Kinetic and in silico structural characterization of norbelladine O-methyltransferase of Amaryllidaceae alkaloids biosynthesis.

Amaryllidaceae alkaloids (AAs) are a diverse group of alkaloids exclusively reported from the Amaryllidaceae plant family. In planta, their biosynthesis is still not fully characterized, however, a labeling study established 4'-O-methylnorbelladine as the key intermediate compound of the pathway. Previous reports have characterized O-methyltransferases from several Amaryllidaceae species. Nevertheless, the formation of the different O-methylnorbelladine derivatives (3'-O-methylnorbelladine, 4'-O-methylnorbelladine, and 3'4'-O-dimethylnorbelladine), the role, and the preferred substrates of O-methyltransferases are not clearly understood. In this study, we performed the biochemical characterization of an O-methyltransferase candidate from Narcissus papyraceus (NpOMT) in vitro and in vivo, following biotransformation of norbelladine in Nicotiana benthamiana having transient expression of NpOMT. Docking analysis was further used to investigate substrate preferences, as well as key interacting residues of NpOMT. Our study shows that NpOMT methylates norbelladine preferentially at the 4'-OH position in vitro and in planta. Interestingly, NpOMT also catalyzed the synthesis of 3',4'-O-dimethylnorbelladine from norbelladine and 4'-O-methylnorbelladine during in vitro enzymatic assay. Furthermore, we show that NpOMT methylates 3,4-dihydroxybenzylaldehyde and caffeic acid in a non-regiospecific manner to produce meta/para monomethylated products. This study reveals a novel catalytic potential of an Amaryllidaceae O-methyltransferase and its ability to regioselectively methylate norbelladine in the heterologous host Nicotiana benthamiana.

High-throughput virtual search of small molecules for controlling the mechanical stability of human CD4.

Protein mechanical stability determines the function of a myriad of proteins, especially proteins from the extracellular matrix. Failure to maintain protein mechanical stability may result in diseases and disorders such as cancer, cardiomyopathies, or muscular dystrophy. Thus, developing mutation-free approaches to enhance and control the mechanical stability of proteins using pharmacology-based methods may have important implications in drug development and discovery. Here, we present the first approach that employs computational high-throughput virtual screening and molecular docking to search for small molecules in chemical libraries that function as mechano-regulators of the stability of human cluster of differentiation 4, receptor of HIV-1. Using single-molecule force spectroscopy, we prove that these small molecules can increase the mechanical stability of CD4D1D2 domains over 4-fold in addition to modifying the mechanical unfolding pathways. Our experiments demonstrate that chemical libraries are a source of mechanoactive molecules and that drug discovery approaches provide the foundation of a new type of molecular function, that is, mechano-regulation, paving the way toward mechanopharmacology.

Structural and biochemical basis for regiospecificity of the flavonoid glycosyltransferase UGT95A1.

Glycosylation is a predominant strategy plants employ to fine-tune the properties of small molecule metabolites to affect their bioactivity, transport, and storage. It is also important in biotechnology and medicine as many glycosides are utilized in human health. Small molecule glycosylation is largely carried out by family 1 glycosyltransferases. Here, we report a structural and biochemical investigation of UGT95A1, a family 1 GT enzyme from Pilosella officinarum that exhibits a strong, unusual regiospecificity for the 3'-O position of flavonoid acceptor substrate luteolin. We obtained an apo crystal structure to help drive the analyses of a series of binding site mutants, revealing that while most residues are tolerant to mutations, key residues M145 and D464 are important for overall glycosylation activity. Interestingly, E347 is crucial for maintaining the strong preference for 3'-O glycosylation, while R462 can be mutated to increase regioselectivity. The structural determinants of regioselectivity were further confirmed in homologous enzymes. Our study also suggests that the enzyme contains large, highly dynamic, disordered regions. We showed that while most disordered regions of the protein have little to no implication in catalysis, the disordered regions conserved among investigated homologues are important to both the overall efficiency and regiospecificity of the enzyme. This report represents a comprehensive in-depth analysis of a family 1 GT enzyme with a unique substrate regiospecificity and may provide a basis for enzyme functional prediction and engineering.

The small molecule activator S3969 stimulates the epithelial sodium channel by interacting with a specific binding pocket in the channel's ß-subunit.

The epithelial sodium channel (ENaC) is essential for mediating sodium absorption in several epithelia. Its impaired function leads to severe disorders, including pseudohypoaldosteronism type 1 and respiratory distress. Therefore, pharmacological ENaC activators have potential therapeutic implications. Previously, a small molecule ENaC activator (S3969) was developed. So far, little is known about molecular mechanisms involved in S3969-mediated ENaC stimulation. Here, we identified an S3969-binding site in human ENaC by combining structure-based simulations with molecular biological methods and electrophysiological measurements of ENaC heterologously expressed in Xenopus laevis oocytes. We confirmed a previous observation that the extracellular loop of ß-ENaC is essential for ENaC stimulation by S3969. Molecular dynamics simulations predicted critical residues in the thumb domain of ß-ENaC (Arg388, Phe391, and Tyr406) that coordinate S3969 within a binding site localized at the ß-γ-subunit interface. Importantly, mutating each of these residues reduced (R388H; R388A) or nearly abolished (F391G; Y406A) the S3969-mediated ENaC activation. Molecular dynamics simulations also suggested that S3969-mediated ENaC stimulation involved a movement of the α5 helix of the thumb domain of ß-ENaC away from the palm domain of γ-ENaC. Consistent with this, the introduction of two cysteine residues (ßR437C - γS298C) to form a disulfide bridge connecting these two domains prevented ENaC stimulation by S3969 unless the disulfide bond was reduced by DTT. Finally, we demonstrated that S3969 stimulated ENaC endogenously expressed in cultured human airway epithelial cells (H441). These new findings may lead to novel (patho-)physiological and therapeutic concepts for disorders associated with altered ENaC function.

Identification of small-molecule ligand-binding sites on and in the ARNT PAS-B domain.

Transcription factors are challenging to target with small-molecule inhibitors due to their structural plasticity and lack of catalytic sites. Notable exceptions include naturally ligand-regulated transcription factors, including our prior work with the hypoxia-inducible factor (HIF)-2 transcription factor, showing that small-molecule binding within an internal pocket of the HIF-2α Per-Aryl hydrocarbon Receptor Nuclear Translocator (ARNT)-Sim (PAS)-B domain can disrupt its interactions with its dimerization partner, ARNT. Here, we explore the feasibility of targeting small molecules to the analogous ARNT PAS-B domain itself, potentially opening a promising route to modulate several ARNT-mediated signaling pathways. Using solution NMR fragment screening, we previously identified several compounds that bind ARNT PAS-B and, in certain cases, antagonize ARNT association with the transforming acidic coiled-coil containing protein 3 transcriptional coactivator. However, these ligands have only modest binding affinities, complicating characterization of their binding sites. We address this challenge by combining NMR, molecular dynamics simulations, and ensemble docking to identify ligand-binding "hotspots" on and within the ARNT PAS-B domain. Our data indicate that the two ARNT/transforming acidic coiled-coil containing protein 3 inhibitors, KG-548 and KG-655, bind to a ß-sheet surface implicated in both HIF-2 dimerization and coactivator recruitment. Furthermore, while KG-548 binds exclusively to the ß-sheet surface, KG-655 can additionally bind within a water-accessible internal cavity in ARNT PAS-B. Finally, KG-279, while not a coactivator inhibitor, exemplifies ligands that preferentially bind only to the internal cavity. All three ligands promoted ARNT PAS-B homodimerization, albeit to varying degrees. Taken together, our findings provide a comprehensive overview of ARNT PAS-B ligand-binding sites and may guide the development of more potent coactivator inhibitors for cellular and functional studies.

Development of innovative multi-epitope mRNA vaccine against Pseudomonas aeruginosa using in silico approaches.

The rising issue of antibiotic resistance has made treating Pseudomonas aeruginosa infections increasingly challenging. Therefore, vaccines have emerged as a viable alternative to antibiotics for preventing P. aeruginosa infections in susceptible individuals. With its superior accuracy, high efficiency in stimulating cellular and humoral immune responses, and low cost, mRNA vaccine technology is quickly replacing traditional methods. This study aimed to design a novel mRNA vaccine by using in silico approaches against P. aeruginosa. The research team identified five surface and antigenic proteins and selected their appropriate epitopes with immunoinformatic tools. These epitopes were then examined for toxicity, allergenicity and homology. The researchers also checked their presentation and identification by major histocompatibility complex cells and other immune cells through valuable tools like molecular docking. They subsequently modeled a multi-epitope protein and optimized it. The mRNA was analyzed in terms of structure and stability, after which the immune system's response against the new vaccine was simulated. The results indicated that the designed mRNA construct could be an effective and promising vaccine that requires laboratory and clinical trials.

The novel function of an orphan pheromone receptor reveals the sensory specializations of two potential distinct types of sex pheromones in noctuid moth.

Sex pheromones play crucial role in mating behavior of moths, involving intricate recognition mechanisms. While insect chemical biology has extensively studied type I pheromones, type II pheromones remain largely unexplored. This study focused on Helicoverpa armigera, a representative species of noctuid moth, aiming to reassess its sex pheromone composition. Our research unveiled two previously unidentified candidate type II sex pheromones-3Z,6Z,9Z-21:H and 3Z,6Z,9Z-23:H-in H. armigera. Furthermore, we identified HarmOR11 as an orphan pheromone receptor of 3Z,6Z,9Z-21:H. Through AlphaFold2 structural prediction, molecular docking, and molecular dynamics simulations, we elucidated the structural basis and key residues governing the sensory nuances of both type I and type II pheromone receptors, particularly HarmOR11 and HarmOR13. This study not only reveals the presence and recognition of candidate type II pheromones in a noctuid moth, but also establishes a comprehensive structural framework for PRs, contributing to the understanding of connections between evolutionary adaptations and the emergence of new pheromone types.

Regulation of Rubisco activity by interaction with chloroplast metabolites.

Rubisco activity is highly regulated and frequently limits carbon assimilation in crop plants. In the chloroplast, various metabolites can inhibit or modulate Rubisco activity by binding to its catalytic or allosteric sites, but this regulation is complex and still poorly understood. Using rice Rubisco, we characterised the impact of various chloroplast metabolites which could interact with Rubisco and modulate its activity, including photorespiratory intermediates, carbohydrates, amino acids; as well as specific sugar-phosphates known to inhibit Rubisco activity - CABP (2-carboxy-d-arabinitol 1,5-bisphosphate) and CA1P (2-carboxy-d-arabinitol 1-phosphate) through in vitro enzymatic assays and molecular docking analysis. Most metabolites did not directly affect Rubisco in vitro activity under both saturating and limiting concentrations of Rubisco substrates, CO2 and RuBP (ribulose-1,5-bisphosphate). As expected, Rubisco activity was strongly inhibited in the presence of CABP and CA1P. High physiologically relevant concentrations of the carboxylation product 3-PGA (3-phosphoglyceric acid) decreased Rubisco activity by up to 30%. High concentrations of the photosynthetically derived hexose phosphates fructose 6-phosphate (F6P) and glucose 6-phosphate (G6P) slightly reduced Rubisco activity under limiting CO2 and RuBP concentrations. Biochemical measurements of the apparent Vmax and Km for CO2 and RuBP (at atmospheric O2 concentration) and docking interactions analysis suggest that CABP/CA1P and 3-PGA inhibit Rubisco activity by binding tightly and loosely, respectively, to its catalytic sites (i.e. competing with the substrate RuBP). These findings will aid the design and biochemical modelling of new strategies to improve the regulation of Rubisco activity and enhance the efficiency and sustainability of carbon assimilation in rice.

HIPPO: HIstogram-based Pseudo-POtential for scoring protein-ssRNA fragment-based docking poses.

BACKGROUND: The RNA-Recognition motif (RRM) is a protein domain that binds single-stranded RNA (ssRNA) and is present in as much as 2% of the human genome. Despite this important role in biology, RRM-ssRNA interactions are very challenging to study on the structural level because of the remarkable flexibility of ssRNA. In the absence of atomic-level experimental data, the only method able to predict the 3D structure of protein-ssRNA complexes with any degree of accuracy is ssRNA'TTRACT, an ssRNA fragment-based docking approach using ATTRACT. However, since ATTRACT parameters are not ssRNA-specific and were determined in 2010, there is substantial opportunity for enhancement. RESULTS: Here we present HIPPO, a composite RRM-ssRNA scoring potential derived analytically from contact frequencies in near-native versus non-native docking models. HIPPO consists of a consensus of four distinct potentials, each extracted from a distinct reference pool of protein-trinucleotide docking decoys. To score a docking pose with one potential, for each pair of RNA-protein coarse-grained bead types, each contact is awarded or penalised according to the relative frequencies of this contact distance range among the correct and incorrect poses of the reference pool. Validated on a fragment-based docking benchmark of 57 experimentally solved RRM-ssRNA complexes, HIPPO achieved a threefold or higher enrichment for half of the fragments, versus only a quarter with the ATTRACT scoring function. In particular, HIPPO drastically improved the chance of very high enrichment (12-fold or higher), a scenario where the incremental modelling of entire ssRNA chains from fragments becomes viable. However, for the latter result, more research is needed to make it directly practically applicable. Regardless, our approach already improves upon the state of the art in RRM-ssRNA modelling and is in principle extendable to other types of protein-nucleic acid interactions.

Human Cx50 Isoleucine177 prevents heterotypic docking and formation of functional gap junction channels with Cx43.

The human lens is an avascular organ, and its transparency is dependent on gap junction (GJ)-mediated microcirculation. Lens GJs are composed of three connexins with Cx46 and Cx50 being expressed in lens fiber cells and Cx43 and Cx50 in the epithelial cells. Impairment of GJ communication by either Cx46 or Cx50 mutations has been shown to be one of the main molecular mechanisms of congenital cataracts in mutant carrier families. The docking compatibility and formation of functional heterotypic GJs for human lens connexins have not been studied. Previous study on rodent lens connexins revealed that Cx46 can form functional heterotypic GJs with Cx50 and Cx43, but Cx50 cannot form heterotypic GJ with Cx43 due to its second extracellular (EL2) domain. To study human lens connexin docking and formation of functional heterotypic GJs, we developed a genetically engineered HEK293 cell line with endogenously expressed Cx43 and Cx45 ablated. The human lens connexins showed docking compatibility identical to those found in the rodent connexins. To reveal the structural mechanisms of the docking incompatibility between Cx50 and Cx43, we designed eight variants based on the differences between the EL2 of Cx50 and Cx46. We found that Cx50I177L is sufficient to establish heterotypic docking with Cx43 with some interesting gating properties. Our structure models indicate this residue is important for interdomain interactions within a single connexin, Cx50 I177L showed an increased interdomain interaction which might alter the docking interface structure to be compatible with Cx43.NEW & NOTEWORTHY The human lens is an avascular organ, and its transparency is partially dependent on gap junction (GJ) network composed of Cx46, Cx50, and Cx43. We found that human Cx46 can dock and form functional heterotypic GJs with Cx50 and Cx43, but Cx50 is unable to form functional heterotypic GJs with Cx43. Through mutagenesis and patch-clamp study of several designed variants, we found that Cx50 I177L was sufficient to form functional heterotypic GJs with Cx43.