N-methyl-D-aspartate (NMDA) receptors are heterotetrametric ion channels composed of two obligatory GluN1 subunits and two alternative GluN2 or GluN3 subunits, forming GluN1-N2, GluN1-N3, and GluN1-N2-N3 type of NMDA receptors. Extensive research has focused on the functional and structural properties of conventional GluN1-GluN2 NMDA receptors due to their early discovery and high expression levels. However, the knowledge of unconventional GluN1-N3 NMDA receptors remains limited. In this study, we modeled the GluN1-N3A, GluN1-N3B, and GluN1-N3A-N3B NMDA receptors using deep-learned protein-language predication algorithms AlphaFold and RoseTTAFold All-Atom. We then compared these structures with GluN1-N2 and GluN1-N3A receptor cryo-EM structures and found that GluN1-N3 receptors have distinct properties in subunit arrangement, domain swap, and domain interaction. Furthermore, we predicted the agonist- or antagonist-bound structures, highlighting the key molecular-residue interactions. Our findings shed new light on the structural and functional diversity of NMDA receptors and provide a new direction for drug development. This study uses advanced AI algorithms to model GluN1-N3 NMDA receptors, revealing unique structural properties and interactions compared to conventional GluN1-N2 receptors. By highlighting key molecular-residue interactions and predicting ligand-bound structures, our research enhances the understanding of NMDA receptor diversity and offers new insights for targeted drug development.

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BACKGROUND: Protein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance.
RESULTS: In this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set.
CONCLUSIONS: GATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol .

Residue contact maps provide a condensed two-dimensional representation of three-dimensional protein structures, serving as a foundational framework in structural modeling but also as an effective tool in their own right in identifying inter-helical binding sites and drawing insights about protein function. Treating contact maps primarily as an intermediate step for 3D structure prediction, contact prediction methods have limited themselves exclusively to sequential features. Now that AlphaFold2 predicts 3D structures with good accuracy in general, we examine (1) how well predicted 3D structures can be directly used for deciding residue contacts, and (2) whether features from 3D structures can be leveraged to further improve residue contact prediction. With a well-known benchmark dataset, we tested predicting inter-helical residue contact based on AlphaFold2\'s predicted structures, which gave an 83% average precision, already outperforming a sequential features-based state-of-the-art model. We then developed a procedure to extract features from atomic structure in the neighborhood of a residue pair, hypothesizing that these features will be useful in determining if the residue pair is in contact, provided the structure is decently accurate, such as predicted by AlphaFold2. Training on features generated from experimentally determined structures, we leveraged knowledge from known structures to significantly improve residue contact prediction, when testing using the same set of features but derived using AlphaFold2 structures. Our results demonstrate a remarkable improvement over AlphaFold2, achieving over 91.9% average precision for a held-out subset and over 89.5% average precision in cross-validation experiments.

BEND family transcription factors directly interact with DNA through BEN domains and have been found across metazoan species. Interestingly, certain insect and mammalian viruses have also hijacked Bend genes into their genome. However, the phylogenetic classification and evolution of these viral BEN domains remain unclear. Building on our previous finding that in silico method accurately determine the 3D model of BEN domains, we used AlphaFold2 to predict the tertiary structures of poxviral BEN domains for comprehensive homologous comparison. We revealed that the majority of poxviral BEN modules exhibit characteristics of type II BEN. Additionally, electrostatic surface potential analysis found various poxviral BEN domains, including the first BEN of OPG067 in Orthopoxvirus, the third BEN of OPG067 in Yatapoxvirus and the third BEN of MC036R in MCV, have positively charged protein surfaces, indicating a structural basis for DNA loading. Notably, MC036R shares structural resemblance with human BEND3, as they both contain four BEN domains and an intrinsically disordered region. In summary, our discoveries provide deeper insights into the functional roles of BEN proteins within poxviruses.

Highly accurate protein structure prediction can generate accurate models of protein and protein-protein complexes in X-ray crystallography. However, the question of how to make more effective use of predicted models for completing structure analysis, and which strategies should be employed for the more challenging cases such as multi-helical structures, multimeric structures and extremely large structures, both in the model preparation and in the completion steps, remains open for discussion. In this paper, a new strategy is proposed based on the framework of direct methods and dual-space iteration, which can greatly simplify the pre-processing steps of predicted models both in normal and in challenging cases. Following this strategy, full-length models or the conservative structural domains could be used directly as the starting model, and the phase error and the model bias between the starting model and the real structure would be modified in the direct-methods-based dual-space iteration. Many challenging cases (from CASP14) have been tested for the general applicability of this constructive strategy, and almost complete models have been generated with reasonable statistics. The hybrid strategy therefore provides a meaningful scheme for X-ray structure determination using a predicted model as the starting point.

UNASSIGNED: Residue contacts maps offer a 2-d reduced representation of 3-d protein structures and constitute a structural constraint and scaffold in structural modeling. In addition, contact maps are also an effective tool in identifying interhelical binding sites and drawing insights about protein function. While most works predict contact maps using features derived from sequences, we believe information from known structures can be leveraged for a prediction improvement in unknown structures where decent approximate structures such as ones predicted by AlphaFold2 are available.
UNASSIGNED: Alphafold2\'s predicted structures are found to be quite accurate at inter-helical residue contact prediction task, achieving 83% average precision. We adopt an unconventional approach, using features extracted from atomic structures in the neighborhood of a residue pair and use them to predicting residue contact. We trained on features derived from experimentally determined structures and predicted on features derived from AlphaFold2\'s predicted structures. Our results demonstrate a remarkable improvement over AlphaFold2 achieving over 91.9% average precision for held-out and over 89.5% average precision in cross validation experiments.
UNASSIGNED: Training on features generated from experimentally determined structures, we were able to leverage knowledge from known structures to significantly improve the contacts predicted using AlphaFold2 structures. We demonstrated that using coordinates directly (instead of the proposed features) does not lead to an improvement in contact prediction performance.

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

BACKGROUND: Grifola frondosa is a Basidiomycete fungus belonging to the family of Grifolaceae and the order of Polyporales. β-Glucans are the main polymers in G. frondosa, playing a crucial role in the physiology and representing the healthy benefits for humans. The membrane-integrated β-1, 3-glucan synthase (GLS) is responsible for glucan synthesis, cell wall assembly, differentiation and growth of the edible fungi. However, the structural/catalytic characteristics and mechanisms of β-1, 3-glucan synthases in G. frondosa are still unknown due to their extremely complex structures with multi-transmembranes and large molecular masses.
RESULTS: Herein, a β-1, 3-glucan synthase (GFGLS2) was purified and identified from the cultured mycelia with a specific activity of 60.01 pmol min-1 μg-1 for the first time. The GFGLS2 showed a strict specificity to UDP-glucose with a Vmax value of 1.29 ± 0.04 µM min-1 at pH 7.0 and synthesized β-1, 3-glucan with a maximum degree of polymerization (DP) of 62. Sequence Similarity Network (SSN) analysis revealed that GFGLS2 has a close relationship with others in Ganoderma sinense, Trametes coccinea, Polyporus brumalis, and Trametes pubescens. With the assistance of 3D structure modelling by AlphaFold 2, molecular docking and molecular dynamics simulations, the central hydrophilic domain (Class III) in GFGLS2 was the main active sites through binding the substrate UDP-glucose to 11 amino acid residues via hydrogen bonds, π-stacking and salt bridges.
CONCLUSIONS: The biochemical, 3D structural characterization and potential catalytic mechanism of a membrane-bound β-1, 3-glucan synthase GFGLS2 from cultured mycelia of G. frondosa were well investigated and would provide a reasonable full picture of β-1, 3-glucan synthesis in fungi.

OBJECTIVE: Transient pregnancy-induced Cushing\'s syndrome is a rare condition characterized by the manifestation of symptoms solely during pregnancy, which typically resolve spontaneously following delivery or miscarriage. While it has been established that GNAS is associated with adrenal tumors, its specific role in the pathogenesis of pregnancy-induced Cushing\'s syndrome remains uncertain.This work aims to examine the association between GNAS mutation and pregnancy-induced Cushing\'s syndrome.
METHODS: DNA was extracted from patients\' peripheral blood and tumor tissues for whole-exome sequencing (WES) and Sanger sequencing. We used AlphaFold to predict the protein structure of wild-type and mutant GNAS and to make functional predictions, and immunohistochemistry was used to detect disease-associated protein expression. A review and summary of reported cases of transient pregnancy-induced Cushing\'s syndrome induced by pregnancy was conducted.
RESULTS: Using WES, we identified a somatic mutation in GNAS (NM_000516, c.C601T, p.R201C) that was predicted to have a deleterious effect using computational methods, such as AlphaFold. Human chorionic gonadotropin (hCG) stimulation tests had weakly positive results, and immunohistochemical staining of adrenal adenoma tissue also revealed positivity for luteinizing hormone/chorionic gonadotropin receptor (LHCGR) and cytochrome P450 family 11 subfamily B member 1 (CYP11B1). We reviewed 15 published cases of transient Cushing\'s syndrome induced by pregnancy. Among these cases, immunohistochemical staining of the adrenal gland showed positive LHCGR expression in 3 case reports, similar to our findings.
CONCLUSIONS: Transient pregnancy-induced Cushing\'s syndrome may be associated with somatic GNAS mutations and altered adrenal pathology due to abnormal activation of LHCGR.