Characterization of lignin degrading enzyme PmdC, which catalyzes a key step in the synthesis of polymer precursor 2-pyrone-4,6-dicarboxylic acid (PDC).

Pyrone-2,4-dicarboxylic acid (PDC) is a valuable polymer precursor that can be derived from the microbial degradation of lignin. The key enzyme in the microbial production of PDC is CHMS dehydrogenase, which acts on the substrate 4-carboxy-2-hydroxymuconate-6-semialdehyde (CHMS). We present the crystal structure of CHMS dehydrogenase (PmdC from Comamonas testosteroni) bound to the cofactor NADP, shedding light on its three-dimensional architecture, and revealing residues responsible for binding NADP. Using a combination of structural homology, molecular docking, and quantum chemistry calculations we have predicted the binding site of CHMS. Key histidine residues in a conserved sequence are identified as crucial for binding the hydroxyl group of CHMS and facilitating dehydrogenation with NADP. Mutating these histidine residues results in a loss of enzyme activity, leading to a proposed model for the enzyme's mechanism. These findings are expected to help guide efforts in protein and metabolic engineering to enhance PDC yields in biological routes to polymer feedstock synthesis.

Structural prediction of GluN3 NMDA receptors.

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.

AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease.

Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind's AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.

Development and tissue specific expression of RAPGEF1 (C3G) transcripts having exons encoding disordered segments with predicted regulatory function.

BACKGROUND: The ubiquitously expressed Guanine nucleotide exchange factor, RAPGEF1 (C3G), is essential for early development of mouse embryos. It functions to regulate gene expression and cytoskeletal reorganization, thereby controlling cell proliferation and differentiation. While multiple transcripts have been predicted, their expression in mouse tissues has not been investigated in detail. METHODS & RESULTS: Full length RAPGEF1 isoforms primarily arise due to splicing at two hotspots, one involving exon-3, and the other involving exons 12-14 incorporating amino acids immediately following the Crk binding region of the protein. These isoforms vary in expression across embryonic and adult organs. We detected the presence of unannotated, and unpredicted transcripts with incorporation of cassette exons in various combinations, specifically in the heart, brain, testis and skeletal muscle. Isoform switching was detected as myocytes in culture and mouse embryonic stem cells were differentiated to form myotubes, and embryoid bodies respectively. The cassette exons encode a serine-rich polypeptide chain, which is intrinsically disordered, and undergoes phosphorylation. In silico structural analysis using AlphaFold indicated that the presence of cassette exons alters intra-molecular interactions, important for regulating catalytic activity. LZerD based docking studies predicted that the isoforms with one or more cassette exons differ in interaction with their target GTPase, RAP1A. CONCLUSIONS: Our results demonstrate the expression of novel RAPGEF1 isoforms, and predict cassette exon inclusion as an additional means of regulating RAPGEF1 activity in various tissues and during differentiation.

Structural Classification Insights Into the Plant Defensive Peptides.

This study presents a structural phylogenetic analysis of plant defensive peptides, revealing their evolutionary relationships, structural diversification, and functional adaptations. Utilizing a robust dataset comprising both experimental and predicted structures sourced from the RCSB Protein Data Bank and AlphaFold DB, we constructed a detailed phylogenetic tree to elucidate the distinct evolutionary paths of plant defensive peptide families. Our findings showcase the evolutionary intricacies of defensive peptides, highlighting their diversity and the conservation of key structural motifs critical to their antimicrobial or defensive functions. The results also underscore the adaptive significance of defensive peptides in plant evolution, highlighting their roles in responding to ecological pressures and pathogen interactions.

Exploring domain architectures of human glycosyltransferases: Highlighting the functional diversity of non-catalytic add-on domains.

Human glycosyltransferases (GTs) play crucial roles in glycan biosynthesis, exhibiting diverse domain architectures. This study explores the functional diversity of "add-on" domains within human GTs, using data from the AlphaFold Protein Structure Database. Among 215 annotated human GTs, 74 contain one or more add-on domains in addition to their catalytic domain. These domains include lectin folds, fibronectin type III, and thioredoxin-like domains and contribute to substrate specificity, oligomerization, and consequent enzymatic activity. Notably, certain GTs possess dual enzymatic functions due to catalytic add-on domains. The analysis highlights the importance of add-on domains in enzyme functionality and disease implications, such as congenital disorders of glycosylation. This comprehensive overview enhances our understanding of GT domain organization, providing insights into glycosylation mechanisms and potential therapeutic targets.

Strengths and limitations of web servers for the modeling of TCRpMHC complexes.

Cellular immunity relies on the ability of a T-cell receptor (TCR) to recognize a peptide (p) presented by a class I major histocompatibility complex (MHC) receptor on the surface of a cell. The TCR-peptide-MHC (TCRpMHC) interaction is a crucial step in activating T-cells, and the structural characteristics of these molecules play a significant role in determining the specificity and affinity of this interaction. Hence, obtaining 3D structures of TCRpMHC complexes offers valuable insights into various aspects of cellular immunity and can facilitate the development of T-cell-based immunotherapies. Here, we aimed to compare three popular web servers for modeling the structures of TCRpMHC complexes, namely ImmuneScape (IS), TCRpMHCmodels, and TCRmodel2, to examine their strengths and limitations. Each method employs a different modeling strategy, including docking, homology modeling, and deep learning. The accuracy of each method was evaluated by reproducing the 3D structures of a dataset of 87 TCRpMHC complexes with experimentally determined crystal structures available on the Protein Data Bank (PDB). All selected structures were limited to human MHC alleles, presenting a diverse set of peptide ligands. A detailed analysis of produced models was conducted using multiple metrics, including Root Mean Square Deviation (RMSD) and standardized assessments from CAPRI and DockQ. Special attention was given to the complementarity-determining region (CDR) loops of the TCRs and to the peptide ligands, which define most of the unique features and specificity of a given TCRpMHC interaction. Our study provides an optimistic view of the current state-of-the-art for TCRpMHC modeling but highlights some remaining challenges that must be addressed in order to support the future application of these tools for TCR engineering and computer-aided design of TCR-based immunotherapies.

Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics.

Google DeepMind Technologies Limited (London, United Kingdom) recently released its new version of the biomolecular structure predictor artificial intelligence (AI) model named AlphaFold 3. Superior in accuracy and more powerful than its predecessor AlphaFold 2, this innovation has astonished the world with its capacity and speed. It takes humans years to determine the structure of various proteins and how the shape works with the receptors but AlphaFold 3 predicts the same structure in seconds. The version's utility is unimaginable in the field of drug discoveries, vaccines, enzymatic processes, and determining the rate and effect of different biological processes. AlphaFold 3 uses similar machine learning and deep learning models such as Gemini (Google DeepMind Technologies Limited). AlphaFold 3 has already established itself as a turning point in the field of computational biochemistry and drug development along with receptor modulation and biomolecular development. With the help of AlphaFold 3 and models similar to this, researchers will gain unparalleled insights into the structural dynamics of proteins and their interactions, opening up new avenues for scientists and doctors to exploit for the benefit of the patient. The integration of AI models like AlphaFold 3, bolstered by rigorous validation against high-standard research publications, is set to catalyze further innovations and offer a glimpse into the future of biomedicine.

In Silico Assessment of LIR Motif-Containing Proteins Binding to ATG8-Family Proteins.

Selective autophagic degradation of cellular components has been shown to be mediated by the interaction of LIR motif-containing proteins with ATG8-family proteins. Here, we present a detailed methodology for the in silico evaluation of potential binding between LIR motif-containing proteins and ATG8-family proteins. We visualize AlphaFold-predicted protein complexes using PyMOL to assess potential interactions, providing an effective computational tool for this purpose.

The AP-1 adaptor complex is essential for intracellular trafficking of the ORF2 capsid protein and assembly of Hepatitis E virus.

Although the Hepatitis E virus (HEV) is an emerging global health burden, little is known about its interaction with the host cell. HEV genome encodes three proteins including the ORF2 capsid protein that is produced in different forms, the ORF2i protein which is the structural component of viral particles, and the ORF2g/c proteins which are massively secreted but are not associated with infectious material. We recently demonstrated that the endocytic recycling compartment (ERC) is hijacked by HEV to serve as a viral factory. However, host determinants involved in the subcellular shuttling of viral proteins to viral factories are unknown. Here, we demonstrate that the AP-1 adaptor complex plays a pivotal role in the targeting of ORF2i protein to viral factories. This complex belongs to the family of adaptor proteins that are involved in vesicular transport between the trans-Golgi network and early/recycling endosomes. An interplay between the AP-1 complex and viral protein(s) has been described for several viral lifecycles. In the present study, we demonstrated that the ORF2i protein colocalizes and interacts with the AP-1 adaptor complex in HEV-producing or infected cells. We showed that silencing or drug-inhibition of the AP-1 complex prevents ORF2i protein localization in viral factories and reduces viral production in hepatocytes. Modeling of the ORF2i/AP-1 complex also revealed that the S domain of ORF2i likely interacts with the σ1 subunit of AP-1 complex. Hence, our study identified for the first time a host factor involved in addressing HEV proteins (i.e. ORF2i protein) to viral factories.

ARCIMBOLDO at low resolution: Verification for coiled coils and globular proteins.

Crystallography at low resolution must determine the atomic model from less experimental observations, which is challenging in the absence of a model. In addition, model bias is more severe when independent experimental data are scarce. Our methods solve the phase problem by combining the location of accurate model fragments using Phaser with density modification and interpretation of the resulting maps using SHELXE. From a partial, correct structure, the density modification process and the stereochemical constraints draw the rest of the structure, validating the result. This same principle is now exploited at low resolution. Coiled coils are important, ubiquitous structures but notoriously difficult to phase and to predict. Both correct solutions and incorrect ones are poorly discriminated by the crystallographic figures of merit as long as helices are correctly oriented. We incorporate coiled-coil verification, designed to set up competing, incompatible structural hypotheses to probe both the results and establish the power of the data to discriminate them. Efficiency of coiled-coil phasing and validation in test cases from 3 to 4 Å is demonstrated in ARCIMBOLDO_LITE, placing single helices, and in ARCIMBOLDO_SHREDDER, with fragments derived from AlphaFold models. SHELXE tracing at low resolution has been enhanced, maintaining its local character but extending the environment assessment. For non-helical structures, verification is demonstrated in the fragment location process. Its use is exemplified with the solution of the VSR1 structure at 3.5 Å, depending on LLG optimization and the emergence of new features in the electron density. Relying on verification, we have extended the use of the ARCIMBOLDO software to low resolution.

Using AlphaFold Multimer to discover interkingdom protein-protein interactions.

Structural prediction by artificial intelligence can be powerful new instruments to discover novel protein-protein interactions, but the community still grapples with the implementation, opportunities and limitations. Here, we discuss and re-analyse our in silico screen for novel pathogen-secreted inhibitors of immune hydrolases to illustrate the power and limitations of structural predictions. We discuss strategies of curating sequences, including controls, and reusing sequence alignments and highlight important limitations caused by different platforms, sequence depth and computing times. We hope these experiences will support similar interactomic screens by the research community.

AlphaFold 2-based stacking model for protein solubility prediction and its transferability on seed storage proteins.

Accurate protein solubility prediction is crucial in screening suitable candidates for food application. Existing models often rely only on sequences, overlooking important structural details. In this study, a regression model for protein solubility was developed using both the sequences and predicted structures of 2983 E. coli proteins. The sequence and structural level properties of the proteins were bioinformatically extracted and subjected to multilayer perceptron (MLP). Moreover, residue level features and contact maps were utilized to construct a graph convolutional network (GCN). The out-of-fold predictions of the two models were combined and fed into multiple meta-regressors to create a stacking model. The stacking model with support vector regressor (SVR) achieved R2 of 0.502 and 0.468 on test and external validation datasets, respectively, displaying higher performance compared to existing regression models. Based on the improved performance compared to its based models, the stacking model effectively captured the strength of its base models as well as the significance of the different features used. Furthermore, the model's transferability was indirectly validated on a dataset of seed storage proteins using Osborne definition as well as on a case study using molecular dynamic simulation, showing potential for application beyond microbial proteins to food and agriculture-related ones.

Artificial intelligence in fusion protein three-dimensional structure prediction: Review and perspective.

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action of new chimeric proteins. However, predicting their 3D structure using a template-based model is challenging because known template structures are often unavailable in databases. Deep learning (DL) models that utilize multi-level protein information have revolutionized the prediction of protein 3D structures. In this review paper, we highlighted the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using DL models. We aim to explore both the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta and D-I-TASSER for modelling the 3D structures. HIGHLIGHTS: This review provides the overall pipeline and landscape of the prediction of the 3D structure of fusion protein. This review provides the factors that should be considered in predicting the 3D structures of fusion proteins using AI approaches in each step. This review highlights the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using deep learning models. This review explores the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta, and D-I-TASSER to model 3D structures.

Structural basis for coupling of the WASH subunit FAM21 with the endosomal SNX27-Retromer complex.

Endosomal membrane trafficking is mediated by specific protein coats and formation of actin-rich membrane domains. The Retromer complex coordinates with sorting nexin (SNX) cargo adaptors including SNX27, and the SNX27-Retromer assembly interacts with the Wiskott-Aldrich syndrome protein and SCAR homolog (WASH) complex which nucleates actin filaments establishing the endosomal recycling domain. Crystal structures, modeling, biochemical, and cellular validation reveal how the FAM21 subunit of WASH interacts with both Retromer and SNX27. FAM21 binds the FERM domain of SNX27 using acidic-Asp-Leu-Phe (aDLF) motifs similar to those found in the SNX1 and SNX2 subunits of the ESCPE-1 complex. Overlapping FAM21 repeats and a specific Pro-Leu containing motif bind three distinct sites on Retromer involving both the VPS35 and VPS29 subunits. Mutation of the major VPS35-binding site does not prevent cargo recycling; however, it partially reduces endosomal WASH association indicating that a network of redundant interactions promote endosomal activity of the WASH complex. These studies establish the molecular basis for how SNX27-Retromer is coupled to the WASH complex via overlapping and multiplexed motif-based interactions required for the dynamic assembly of endosomal membrane recycling domains.

AI-Driven Deep Learning Techniques in Protein Structure Prediction.

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein-protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation.

Cellulose binding and the timing of expression influence protein targeting to the double-layered cyst wall of Acanthamoeba.

The cyst wall of the eye pathogen Acanthamoeba castellanii contains cellulose and has ectocyst and endocyst layers connected by conical ostioles. Cyst walls contain families of lectins that localize to the ectocyst layer (Jonah) or the endocyst layer and ostioles (Luke and Leo). How lectins and an abundant laccase bind cellulose and why proteins go to locations in the wall are not known and are the focus of the studies here. Structural predictions identified ß-jelly-roll folds (BJRFs) of Luke and sets of four disulfide knots (4DKs) of Leo, each of which contains linear arrays of aromatic amino acids, also present in carbohydrate-binding modules of bacterial and plant endocellulases. Ala mutations showed that these aromatics are necessary for cellulose binding and proper localization of Luke and Leo in the Acanthamoeba cyst wall. BJRFs of Luke, 4DKs of Leo, a single ß-helical fold (BHF) of Jonah, and a copper oxidase domain of the laccase each bind to glycopolymers in both layers of deproteinated cyst walls. Promoter swaps showed that ectocyst localization does not just correlate with but is caused by early encystation-specific expression, while localization in the endocyst layer and ostioles is caused by later expression. Evolutionary studies showed distinct modes of assembly of duplicated domains in Luke, Leo, and Jonah lectins and suggested Jonah BHFs originated from bacteria, Luke BJRFs share common ancestry with slime molds, while 4DKs of Leo are unique to Acanthamoeba.IMPORTANCEAcanthamoebae is the only human parasite with cellulose in its cyst wall and conical ostioles that connect its inner and outer layers. Cyst walls are important virulence factors because they make Acanthamoebae resistant to surface disinfectants, hand sanitizers, contact lens sterilizers, and antibiotics applied to the eye. The goal here was to understand better how proteins are targeted to specific locations in the cyst wall. To this end, we identified three new proteins in the outer layer of the cyst wall, which may be targets for diagnostic antibodies in corneal scrapings. We used structural predictions and mutated proteins to show linear arrays of aromatic amino acids of two unrelated wall proteins are necessary for binding cellulose and proper wall localization. We showed early expression during encystation causes proteins to localize to the outer layer, while later expression causes proteins to localize to the inner layer and the ostioles.

Utilizing AfDesign for Developing a Small Molecule Inhibitor of PICK 1-PDZ.

INTRODUCTION: The PICK1 PDZ domain has been identified as a potential drug target for neurological disorders. After many years of effort, a few inhibitors, such as TAT-C5 and mPD5, have been discovered experimentally to bind to the PDZ domain with a relatively high binding affinity. With the rapid growth of computational research, there is an urgent need for more efficient computational methods to design viable ligands that target proteins. METHOD: Recently, a newly developed program called AfDesign (part of ColabDesign) at https:// github.com/sokrypton/ColabDesign), an open-source software built on AlphaFold, has been suggested to be capable of generating ligands that bind to targeted proteins, thus potentially facilitating the ligand development process. To evaluate the performance of this program, we explored its ability to target the PICK1 PDZ domain, given our current understanding of it. We found that the designated length of the ligand and the number of recycles play vital roles in generating ligands with optimal properties. RESULTS: Utilizing AfDesign with a sequence length of 5 for the ligand produced the highest comparable ligands to that of prior identified ligands. Moreover, these designed ligands displayed significantly lower binding energy compared to manually created sequences. CONCLUSION: This work demonstrated that AfDesign can potentially be a powerful tool to facilitate the exploration of the ligand space for the purpose of targeting PDZ domains.

T and Z, partial seed coat patterning genes in common bean, provide insight into the structure and protein interactions of a plant MBW complex.

Flavonoids are secondary metabolites associated with plant seed coat and flower color. These compounds provide health benefits to humans as anti-inflammatory and antioxidant compounds. The expression of the late biosynthetic genes in the flavonoid pathway is controlled by a ternary MBW protein complex consisting of interfacing MYB, beta-helix-loop-helix (bHLH), and WD40 Repeat (WDR) proteins. P, the master regulator gene of the flavonoid expression in common bean (Phaseolus vulgaris L.), was recently determined to encode a bHLH protein. The T and Z genes control the distribution of color in bean seeds and flowers and have historically been considered regulators of the flavonoid gene expression. T and Z candidates were identified using reverse genetics based on genetic mapping, phylogenetic analysis, and mutant analysis. Domain and AlphaFold2 structure analyses determined that T encodes a seven-bladed ß-propeller WDR protein, while Z encodes a R2R3 MYB protein. Deletions and SNPs in T and Z mutants, respectively, altered the 3D structure of these proteins. Modeling of the Z MYB/P bHLH/T WDR MBW complex identified interfacing sequence domains and motifs in all three genes that are conserved in dicots. One Z MYB motif is a possible beta-molecular recognition feature (ß-MoRF) that only appears in a structured state when Z MYB is modeled as a component of a MBW complex. Complexes containing mutant T and Z proteins changed the interaction of members of the complex in ways that would alter their role in regulating the expression of genes in the flavonoid pathway.