Three-dimensional Structure Prediction Research Articles

Deep generative design of RNA aptamers using structural predictions.

RNAs represent a class of programmable biomolecules capable of performing diverse biological functions. Recent studies have developed accurate RNA three-dimensional structure prediction methods, which may enable new RNAs to be designed in a structure-guided manner. Here, we develop a structure-to-sequence deep learning platform for the de novo generative design of RNA aptamers. We show that our approach can design RNA aptamers that are predicted to be structurally similar, yet sequence dissimilar, to known light-up aptamers that fluoresce in the presence of small molecules. We experimentally validate several generated RNA aptamers to have fluorescent activity, show that these aptamers can be optimized for activity in silico, and find that they exhibit a mechanism of fluorescence similar to that of known light-up aptamers. Our results demonstrate how structural predictions can guide the targeted and resource-efficient design of new RNA sequences.

Identification of Shemin pathway genes for tetrapyrrole biosynthesis in bacteriophage sequences from aquatic environments

Tetrapyrroles such as heme, chlorophyll, and vitamin B12 are essential for various metabolic pathways. They derive from 5-aminolevulinic acid (5-ALA), which can be synthesized by a single enzyme (5-ALA synthase or AlaS, Shemin pathway) or by a two-enzyme pathway. The genomes of some bacteriophages from aquatic environments carry various tetrapyrrole biosynthesis genes. Here, we analyze available metagenomic datasets and identify alaS homologs (viral alaS, or valaS) in sequences corresponding to marine and freshwater phages. The genes are found individually or as part of complete or truncated three-gene loci encoding heme-catabolizing enzymes. Amino-acid sequence alignments and three-dimensional structure prediction support that the valaS sequences likely encode functional enzymes. Indeed, we demonstrate that is the case for a freshwater phage valaS sequence, as it can complement an Escherichia coli 5-ALA auxotroph, and an E. coli strain overexpressing the gene converts the typical AlaS substrates glycine and succinyl-CoA into 5-ALA. Thus, our work identifies valaS as an auxiliary metabolic gene in phage sequences from aquatic environments, further supporting the importance of tetrapyrrole metabolism in bacteriophage biology.

Identification and biochemical characterization of a novel halolysin from Halorubellus sp. PRR65 with a relatively high temperature activity.

Extracellular proteases from haloarchaea, also referred to as halolysins, are in increasing demand and are studied for their various applications in condiments and leather industries. In this study, an extracellular protease encoding gene from the haloarchaeon Halorubellus sp. PRR65, hly65, was cloned and heterologously expressed in E. coli. The novel halolysin Hly65 from the genus Halorubellus was characterized by complete inhibition of phenylmethanesulfonyl fluoride (PMSF) on its enzyme activity. Experimental determination revealed a triad catalytic active center consisting of Asp154-His193-Ser348. Deletion of the C-terminal extension (CTE) resulted in loss of enzyme activity, while dithiothreitol (DTT) did not inhibit the enzyme activity, suggesting that Hly65 may function as a monomer. The Km, Vmax and Kcat for the Hly65 were determined to be 2.91mM, 1230.47 U·mg-1 and 1538.09 S-1, respectively, under 60°C, pH 8.0 and 4.0M NaCl using azocasecin as a substrate. Furthermore, a three-dimensional structure prediction based on functional domains was obtained in this study which will facilitate modification and reorganization of halolysins to generate mutants with new physiological activities.

Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs.

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

An Overview of Molecular Docking

The computational modeling of structural complexes formed by two or more interacting molecules is known as molecular docking. Prediction of an interesting three-dimensional structure is the primary goal of molecular docking. Software for molecular docking is mostly employed in the development of drugs. Molecules and the simple use of structural databases caused damage to an important mechanism. Several expensive tools for drug design and research are provided by molecular docking. Simple molecular prediction as well as rapid access to structural databases have become important components on the medicinal chemist's desktop. Virtual screening is the most important contribution of molecular docking. Numerous docking programs were used to visualize the three-dimensional structure of the molecule, and different computational techniques can be used to analyze docking gain. In structural molecular biology and computer-aided drug design, molecular docking is a key tool. Docking is useful for lead optimization because it can be used to do virtual screening on huge collections of compounds, rate the results, and provide structural ideas for how the ligands affect the target.

Development of Drug Discovery Platforms Using Artificial Intelligence and Cheminformatics.

Recently, remarkable progress has been achieved in artificial intelligence (AI), including machine learning. Various AI models have been proposed for drug discovery, including the design of small molecules, activity prediction, and three-dimensional (3D) structure prediction of proteins. AI consists of diverse elements, including information retrieval and machine learning, and can be used in a wide range of drug discovery scenarios. In this review, we focused on AI for small-molecule drug discovery with respect to molecular design, activity prediction, and prediction of the binding poses of compounds to target molecules. We also discussed the applications of AI in academic drug discovery.

Intracellular bactericidal activity and action mechanism of MDP1 antimicrobial peptide against VRSA and MRSA in human endothelial cells.

Staphylococcus aureus is a prominent cause of postoperative infections, often persisting within host cells, leading to chronic infections. Conventional antibiotics struggle to eliminate intracellular S. aureus due to poor cell penetration. Antimicrobial peptides are a new hope for tackling intracellular bacteria. Accordingly, this study examines the antimicrobial peptide MDP1, derived from melittin, for its efficacy against intracellular S. aureus. In this study, the physiochemical properties (Prediction of three-dimensional structure, circular dichroism and helical wheel projection analysis) were investigated. Extracellular antibacterial activity and cytotoxicity of MDP1 were also assessed. The mechanism of interaction of MDP1 with S. aureus was evaluated by molecular dynamic simulation, atomic force and confocal microscopy. Bacterial internalization into an endothelial cell model was confirmed through culture and transmission electron microscopy. The effect of the peptide on intracellular bacteria was investigated by culture and epi-fluorescence microscopy. 3D structural prediction proved the conformation of MDP1 as an α-helix peptide. Helical-wheel projection analysis indicated the proper orientation of hydrophobic amino acid residues for membrane interaction. CD spectroscopy of MDP1 showed that MDP1 in SDS 10 and 30 mM adopted 87 and 91% helical conformation. Atomic force and confocal microscopy assessments as well as molecular dynamics studies revealed the peptide-bacterial membrane interaction. MDP1, at the concentration of 0.32 μg mL-1, demonstrated a fold reduction of 21.7 ± 1.8, 1.7 ± 0.2, and 7.3 ± 0.8 in intracellular bacterial load for ATCC, VRSA, and MRSA, respectively. Molecular dynamics results demonstrate a preferential interaction of MDP1 with POPG/POPE membranes, primarily driven by electrostatic forces and hydrogen bonding. In POPC systems, two out of four MDP1 interacted effectively, while all four MDP1 engaged with POPG/POPE membranes. Gathering all data together, MDP1 is efficacious in the reduction of intracellular VRSA and MRSA proved by culture and epi-fluorescent microscopy although further studies should be performed to increase the intracellular activity of MDP1.

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.

Structures prediction and replica exchange molecular dynamics simulations of α-synuclein: A case study for intrinsically disordered proteins

In recent years, a variety of three-dimensional structure prediction tools, including AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold, have been employed in the investigation of intrinsically disordered proteins. However, a comprehensive validation of these tools specifically for intrinsically disordered proteins has yet to be conducted. In this study, we utilize AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold to predict the structure of a model intrinsically disordered α-synuclein protein. Additionally, extensive replica exchange molecular dynamics simulations of the intrinsically disordered protein are conducted. The resulting structures from both structure prediction tools and replica exchange molecular dynamics simulations are analyzed for radius of gyration, secondary and tertiary structure properties, as well as Cα and Hα chemical shift values. A comparison of the obtained results with experimental data reveals that replica exchange molecular dynamics simulations provide results in excellent agreement with experimental observations. However, none of the structure prediction tools utilized in this study can fully capture the structural characteristics of the model intrinsically disordered protein. This study shows that a cluster of ensembles are required for intrinsically disordered proteins. Artificial-intelligence based structure prediction tools such as AlphaFold3 and C-I-TASSER could benefit from stochastic sampling or Monte Carlo simulations for generating an ensemble of structures for intrinsically disordered proteins.

The TIR1/AFB Family in Solanum melongena: Genome-Wide Identification and Expression Profiling under Stresses and Picloram Treatment

TIR1/AFB proteins are a class of auxin receptors with key roles in plant development and biotic and abiotic stress responses; several have been identified as targets of the auxin-mimicking herbicide picloram. In this study, we identified five putative TIR1/AFB gene family members in the important vegetable crop Solanum melongena (eggplant) and characterized them using bioinformatics tools and gene expression analyses. Phylogenetic analysis of the TIR1/AFBs classified them into three subgroups based on their Arabidopsis and Solanum lycopersicum homologs. AFB6 homologs were present only in S. melongena and S. lycopersicum, whereas AFB2/3 homologs were found only in Arabidopsis. One pair of S. melongena TIR1 homologs were located in syntenic regions in the genome and appeared to have arisen by segmental duplication. Promoter analysis revealed 898 cis-elements in the TIR1/AFB promoters, 125 of which were related to hormones, stress, light, or growth responses, but only SmAFB5 had a cis-acting regulatory element involved in auxin responsiveness (AuxRR-core). RNA sequencing and expression profiling showed that the TIR1/AFB genes were differentially expressed at different growth stages and in response to light, temperature, and drought. Only SmTIR1A expression was significantly induced by picloram treatment and different growth stages. TIR1/AFB expression is regulated by microRNAs (miRNAs) in other plant species, and we identified 6 or 29 miRNAs that potentially targeted the five TIR1/AFB genes on the basis of comparisons with S. lycopersicum and S. tuberosum miRNAs, respectively. Three-dimensional protein structure predictions revealed that all the TIR1/AFB proteins were very similar in structure, differing only in the numbers of alpha helices and in one angle linking an α helix and a β sheet. For measuring the function of TIR1/AFB genes in response to drought, SmAFB5 was selected, and knockdown by virus-induced gene silence (VIGS) 35S::SmAFB5 lines showed resistance to drought compared to controls. These analyses provide insight into the potential functions of TIR1/AFBs during growth and in response to stress; they highlight differences among the SmTIR1/AFBs that may be useful for eggplant breeding.

Purification, characterization and three-dimensional structure prediction of multicopper oxidase Laccases from Trichoderma lixii FLU1 and Talaromyces pinophilus FLU12

Broad-spectrum biocatalysts enzymes, Laccases, have been implicated in the complete degradation of harmful pollutants into less-toxic compounds. In this study, two extracellularly produced Laccases were purified to homogeneity from two different Ascomycetes spp. Trichoderma lixii FLU1 (TlFLU1) and Talaromyces pinophilus FLU12 (TpFLU12). The purified enzymes are monomeric units, with a molecular mass of 44 kDa and 68.7 kDa for TlFLU1 and TpFLU12, respectively, on SDS-PAGE and zymogram. It reveals distinct properties beyond classic protein absorption at 270–280 nm, with TlFLU1's peak at 270 nm aligning with this typical range of type II Cu site (white Laccase), while TpFLU12's unique 600 nm peak signifies a type I Cu2+ site (blue Laccase), highlighting the diverse spectral fingerprints within the Laccase family. The Km and kcat values revealed that ABTS is the most suitable substrate as compared to 2,6-dimethoxyphenol, caffeic acid and guaiacol for both Laccases. The bioinformatics analysis revealed critical His, Ile, and Arg residues for copper binding at active sites, deviating from the traditional two His and a Cys motif in some Laccases. The predicted biological functions of the Laccases include oxidation–reduction, lignin metabolism, cellular metal ion homeostasis, phenylpropanoid catabolism, aromatic compound metabolism, cellulose metabolism, and biological adhesion. Additionally, investigation of degradation of polycyclic aromatic hydrocarbons (PAHs) by purified Laccases show significant reductions in residual concentrations of fluoranthene and anthracene after a 96-h incubation period. TlFLU1 Laccase achieved 39.0% and 44.9% transformation of fluoranthene and anthracene, respectively, while TpFLU12 Laccase achieved 47.2% and 50.0% transformation, respectively. The enzyme structure–function relationship study provided insights into the catalytic mechanism of these Laccases for possible biotechnological and industrial applications.

In Silico Design and Evaluation of a Novel Therapeutic Agent Against the Spike Protein as a Novel Treatment Strategy for COVID-19 Treatment.

Coronavirus disease 2019 (COVID-19) is a viral respiratory disease that is associated with severe damage to other human organs. It causes by a novel coronavirus, and it is spreading all over the world. To date, there is some approved vaccine or therapeutic agent which could be effective against this disease. But their effectiveness against mutated strains is not studied completely. The spike glycoprotein on the surface of the coronaviruses gives the virus the ability to bind to host cell receptors and enter cells. Inhibition of attachment of these spikes can lead to virus neutralization by inhibiting viral entrance. In this study, we tried to use the virus entrance strategy against itself by utilizing virus receptor (ACE-2) in order to design an engineered protein consisting of a human Fc antibody fragment and a part of ACE-2, which reacts with virus RBD, and we also evaluated this interaction by computational methods and in silico methods. Subsequently, we have designed a new protein structure to bind with this site and inhibit the virus from attaching to its cell receptor, mechanically or chemically. Various in silico software, bioinformatics, and patent databases were used to retrieve the requested gene and protein sequences. The physicochemical properties and possibility of allergenicity were also examined. Three-dimensional structure prediction and molecular docking were also performed to develop the most suitable therapeutic protein. The designed protein consisted of a total of 256 amino acids with a molecular weight of 28984.62 and 5.92 as a theoretical isoelectric point. Instability and aliphatic index and grand average of hydropathicity are 49.99, 69.57 and -0.594, respectively. In silico studies can provide a good opportunity to study viral proteins and new drugs or compounds since they do not need direct exposure to infectious agents or equipped laboratories. The suggested therapeutic agent should be further characterized in vitro and in vivo.

Phylum-level studies of bacterial cutinases for unravelling enzymatic specificity toward PET degradation: an in silico approach.

The overwhelming use of PET plastic in various day-to-day activities led to the voluminous increase in PET waste and growing environmental hazards. A plethora of methods have been used that are associated with secondary pollutants. Therefore, microbial degradation of PET provides a sustainable approach due to its versatile metabolic diversity and capacity. The present work highlights the cutinase enzyme's role in PET degradation. This study focuses on the bacterial cutinases homologs screened from 43 reported phylum of bacteria. The reported bacterial cutinases for plastic degradation have been chosen as reference sequences, and 917 sequences have shown homology across the bacterial phyla. The dienelactone hydrolase (DLH) domain was identified for attaining specificity towards PET binding in 196 of 917 sequences. Various computational tools have been used for the physicochemical characterization of 196 sequences. The analysis revealed that most selected sequences are hydrophilic, extracellular, and thermally stable. Based on this analysis, 17 sequences have been further pursued for three-dimensional structure prediction and validation. The molecular docking studies of 17 selected sequences revealed efficient PET binding with the three sequences derived from the phylum Bacteroidota, the lowest binding energy of -5.9kcal/mol, Armatimonadota, and Nitrososphaerota with -5.8kcal/mol. The two enzyme sequences retrieved from the phylum Bacteroidota and Armatimonadota are metagenomically derived. Therefore, the present studies concluded that there is a high probability of finding cutinase homologs in various environmental resources that can be further explored for PET degradation.

SimRNAweb v2.0: a web server for RNA folding simulations and 3D structure modeling, with optional restraints and enhanced analysis of folding trajectories.

Research on ribonucleic acid (RNA) structures and functions benefits from easy-to-use tools for computational prediction and analyses of RNA three-dimensional (3D) structure. The SimRNAweb server version 2.0 offers an enhanced, user-friendly platform for RNA 3D structure prediction and analysis of RNA folding trajectories based on the SimRNA method. SimRNA employs a coarse-grained model, Monte Carlo sampling and statistical potentials to explore RNA conformational space, optionally guided by spatial restraints. Recognized for its accuracy in RNA 3D structure prediction in RNA-Puzzles and CASP competitions, SimRNA is particularly useful for incorporating restraints based on experimental data. The new server version introduces performance optimizations and extends user control over simulations and the processing of results. It allows the application of various hard and soft restraints, accommodating alternative structures involving canonical and noncanonical base pairs and unpaired residues, while also integrating data from chemical probing methods. Enhanced features include an improved analysis of folding trajectories, offering advanced clustering options and multiple analyses of the generated trajectories. These updates provide comprehensive tools for detailed RNA structure analysis. SimRNAweb v2.0 significantly broadens the scope of RNA modeling, emphasizing flexibility and user-defined parameter control. The web server is available at https://genesilico.pl/SimRNAweb.

Precision Vaccine Development: Unraveling the Immunoinformatics Code Against Epstein-Barr Virus's T41 Glycoprotein 350/220

Epstein-Barr Virus (EBV) poses a substantial global health threat, with a 90% morbidity rate in adults worldwide. Linked to infectious mononucleosis, autoimmune diseases, and cancers, there is an imperative for effective preventive measures, given the absence of current vaccines or medications. This study employs a bioinformatics strategy to design an epitope vaccine targeting EBV. The primary target for immunity is the EBV glycoprotein 350/220 (gp350), which mediates attachment to B cells through complement receptor 2 (CR2/CD21). The selected protein, EBV glycoprotein 350/220 was chosen based on its role in pathogenicity. In the post-therapeutic approach, three-dimensional structure prediction, validation, and verification processes were conducted, followed by molecular docking studies to identify potential drug-binding sites and suitable therapeutic inhibitors for the target protein. The protein's ability to stimulate humoral and cell-mediated immunity was assessed, leading to the identification of a peptide segment ATNLFLLEL from positions 342 to 356 as the predominant T cell epitope. This epitope demonstrated compatibility with various HLA profiles, enhanced by its interaction with eight HLA-A and HLA-B alleles. Population coverage analysis revealed broad responsiveness across geographies, suggesting its potential as a widely applicable vaccine component. Its non-allergic classification further supports its potential efficacy for diverse individuals. Docking studies with HLA-B*3501 confirmed its significant affinity for MHC-I molecules, establishing it as a promising vaccination candidate. Additionally, a region from amino acid positions 253 to 310 was identified as containing potential B-cell epitopes, with the sequence "LGAGELALTMRSKKLP" showing promise due to its antigenic potential. This computational approach to epitope-based peptide vaccine design and target site prediction against EBV represents a pioneering advancement in EBV virus research, emphasizing the need for validation through &lt;i&gt;in vitro&lt;/i&gt; and &lt;i&gt;in vivo&lt;/i&gt; trials.

Interferon-induced transmembrane protein-1 competitively blocks Ephrin receptor A2-mediated Epstein-Barr virus entry into epithelial cells.

Epstein-Barr virus (EBV) can infect both B cells and epithelial cells (ECs), causing diseases such as mononucleosis and cancer. It enters ECs via Ephrin receptor A2 (EphA2). The function of interferon-induced transmembrane protein-1 (IFITM1) in EBV infection of ECs remains elusive. Here we report that IFITM1 inhibits EphA2-mediated EBV entry into ECs. RNA-sequencing and clinical sample analysis show reduced IFITM1 in EBV-positive ECs and a negative correlation between IFITM1 level and EBV copy number. IFITM1 depletion increases EBV infection and vice versa. Exogenous soluble IFITM1 effectively prevents EBV infection in vitro and in vivo. Furthermore, three-dimensional structure prediction and site-directed mutagenesis demonstrate that IFITM1 interacts with EphA2 via its two specific residues, competitively blocking EphA2 binding to EBV glycoproteins. Finally, YTHDF3, an m6A reader, suppresses IFITM1 via degradation-related DEAD-box protein 5 (DDX5). Thus, this study underscores IFITM1's crucial role in blocking EphA2-mediated EBV entry into ECs, indicating its potential in preventing EBV infection.

Characterization of a novel gammapartitivirus infecting the phytopathogenic fungus Pyricularia oryzae.

In this study, we identified a novel double-strand RNA (dsRNA) mycovirus in Pyricularia oryzae, designated "Magnaporthe oryzae partitivirus 4" (MoPV4). The genome of MoPV4 consists of a dsRNA-1 segment encoding an RNA-dependent RNA polymerase (RdRP) and a dsRNA-2 segment encoding a capsid protein (CP). Phylogenetic analysis indicated that MoPV4 belongs to the genus Gammapartitivirus within family Partitiviridae. The particles of MoPV4 are isometric with a diameter of about 32.4 nm. Three-dimensional structure predictions indicated that the RdRP of MoPV4 forms a classical right-handed conformation, while the CP has a reclining-V shape.

An interactive visualization tool for educational outreach in protein contact map overlap analysis.

Recent advancements in contact map-based protein three-dimensional (3D) structure prediction have been driven by the evolution of deep learning algorithms. However, the gap in accessible software tools for novices in this domain remains a significant challenge. This study introduces GoFold, a novel, standalone graphical user interface (GUI) designed for beginners to perform contact map overlap (CMO) problems for better template selection. Unlike existing tools that cater more to research needs or assume foundational knowledge, GoFold offers an intuitive, user-friendly platform with comprehensive tutorials. It stands out in its ability to visually represent the CMO problem, allowing users to input proteins in various formats and explore the CMO problem. The educational value of GoFold is demonstrated through benchmarking against the state-of-the-art contact map overlap method, map_align, using two datasets: PSICOV and CAMEO. GoFold exhibits superior performance in terms of TM-score and Z-score metrics across diverse qualities of contact maps and target difficulties. Notably, GoFold runs efficiently on personal computers without any third-party dependencies, thereby making it accessible to the general public for promoting citizen science. The tool is freely available for download for macOS, Linux, and Windows.

Characterization of a novel 3-quinuclidinone reductase possessing remarkable thermostability

The 3-quinuclidinone reductase plays an irreplaceable role in the biopreparation of (R)-3-quinuclidinol, an intermediate vital for synthesis of various pharmaceuticals. Thermal robustness is a critical factor for enzymatic synthesis in industrial applications. This study characterized a new 3-quinuclidinone reductase, named SaQR, with significant thermal stability. The SaQR was overexpressed in a GST-fused state, and substrate and cofactor screening were conducted. Additionally, three-dimensional structure prediction using AlphaFold and analysis were performed, along with relevant thermostability tests, and the evaluation of factors influencing enzyme activity. The findings highlight the remarkable thermostability of SaQR, retaining over 90% of its activity after 72 h at 50°C, with an optimal operational temperature of 85°C. SaQR showed typical structural traits of the SDR superfamily, with its cofactor-determining residue being aspartic acid, conferring nicotinamide adenine dinucleotide (NAD(H)) preference. Moreover, K+ and Na+, at a concentration of 400 mM, could significantly enhance the activity, while Mg2+ and Mn2+ only display inhibitory effects within the tested concentration range. The findings of molecular dynamics simulations suggest that high temperatures may disrupt the binding of enzyme to substrate by increasing the flexibility of residues 205–215. In conclusion, this study reports a novel 3-quinuclidinone reductase with remarkable thermostability.

Expression, purification, and characterization of transmembrane protein homogentisate solanesyltransferase

Homogentisate solanesyltransferase (HST) is a crucial enzyme in the plastoquinone biosynthetic pathway and has recently emerged as a promising target for herbicides. In this study, we successfully expressed and purified a stable and highly pure form of seven times transmembrane protein Chlamydomonas reinhardtii HST (CrHST). The final yield of CrHST protein obtained was 12.2 mg per liter of M9 medium. We evaluated the inhibitory effect on CrHST using Des-Morpholinocarbony Cyclopyrimorate (DMC) and found its IC50 value to be 3.63 ± 0.53 μM, indicating significant inhibitory potential. Additionally, we investigated the substrate affinity of CrHST with two substrates, determining the Km values as 22.76 ± 1.70 μM for FPP and 48.54 ± 3.89 μM for HGA. Through sequence alignment analyses and three-dimensional structure predictions, we identified conserved amino acid residues forming the active cavity in the enzyme. The results from molecular docking and binding energy calculations indicate that DMC has a greater binding affinity with HST compared to HGA. These findings represent substantial progress in understanding CrHST’s properties and potential for herbicide development.Key points• First high-yield transmembrane CrHST protein via E. coli system• Preliminarily identified active cavity composition via activity testing• Determined substrate and inhibitor modes via molecular docking