Structure Prediction Research Articles

Mobility inhibition of arsenic in the soil: the role of green synthesized silica nanoparticles.

The studies showed the effectiveness of green-synthesized SiO2NPs in mitigating the toxicity of Arsenic. Density Functional Theory (DFT) is a computational method used to determine electronic structure, energy gap, and toxicity prediction. Experimentally, silicon nanoparticles of 0 (S0) and 100% v/v (S100) were applied to the surface of the soil. 150 mL of Arsenic trioxide was applied twice at a rate of 0 (As0) and 3.2 g/mL (As3.2) at an interval of three weeks. Green synthesized SiO2NPs possessed a higher chemical potential (µ) and electrophilicity index; consequently, charges could be transferred and easily polarized. The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) levels of the green synthesized SiO2NPs enable them to donate electrons and complex with arsenic, reducing their bioavailability and toxicity. Evidence from the studies further showed that SiO2NPs had buffered the soil acidity and electric conductivity, posing a high binding site and reactivity with exchangeable cations and micronutrients due to their smaller energy gap. Furthermore, the catalytic activities of the soil enzymes dehydrogenase (DHA) and peroxidase (POD) were greatly increased, which enhanced the electrostatic interaction between the SiO2NPs and As.

The Development and Opportunities of Predictive Biotechnology.

Recent advances in bioeconomy allow a holistic view of existing and new process chains and enable novel production routines continuously advanced by academia and industry. All this progress benefits from a growing number of prediction tools that have found their way into the field. For example, automated genome annotations, tools for building model structures of proteins, and structural protein prediction methods such as AlphaFold2TM or RoseTTAFold have gained popularity in recent years. Recently, it has become apparent that more and more AI-based tools are being developed and used for biocatalysis and biotechnology. This is an excellent opportunity for academia and industry to accelerate advancements in the field further. Biotechnology, as a rapidly growing interdisciplinary field, stands to benefit greatly from these developments.

Topology Prediction of Gas-Separating Metal-Organic Frameworks with Low Symmetry Vertices.

Topology serves as a blueprint for the construction of reticular structures such as metal-organic frameworks, especially for those based on building blocks with highly symmetrical shapes. However, it remains a challenge to predict the topology of the frameworks from less symmetrical units, because their corresponding vertex figures are largely deformed from the perfect geometries with no "default" net embedding. Furthermore, vertices involving flexible units may have multiple shape choices, and the competition among their designated topologies makes the structure prediction in large uncertainty. Herein, the deformation index is proposed to characterize the symmetry loss of the vertex figure by comparing it with its ideal geometry. The mathematical index is employed to predict the shapes of two in situ formed Co-based metalloligands (pseudo-tetrahedron and pseudo-square), which further dictate the framework topology (flu and scu) when they are joined with the [Zr6O8]-based cuboid units. The two frameworks with very similar constituents provide an ideal platform to investigate how the pore shapes and interconnectivity influence the gas separation. The net with cylindrical channels outperforms the other with discreate cages in C3H8/C2H6/CH4 separation, benefiting from the facile accessibility of its interaction sites to the guests imposed by the specific framework topology.

Freeprotmap: waiting-free prediction method for protein distance map

BackgroundProtein residue–residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue–residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed.ResultIn this study, we propose a learning framework named FreeProtMap. In terms of protein representation processing, the proposed group pooling in FreeProtMap effectively mitigates issues arising from high-dimensional sparseness in protein representation. In terms of model structure, we have made several careful designs. Firstly, it is designed based on the locality of protein structures and triangular inequality distance constraints to improve prediction accuracy. Secondly, inference speed is improved by using additive attention and lightweight design. Besides, the generalization ability is improved by using bottlenecks and a neural network block named local microformer. As a result, FreeProtMap can predict protein residue–residue distances in tens of milliseconds and has higher precision than the best structure prediction method.ConclusionSeveral groups of comparative experiments and ablation experiments verify the effectiveness of the designs. The results demonstrate that FreeProtMap significantly outperforms other state-of-the-art methods in accurate protein residue–residue distance prediction, which is beneficial for lots of protein research works. It is worth mentioning that we could scan all proteins discovered each year based on FreeProtMap to find structurally similar proteins in a short time because the fact that the structure similarity calculation method based on distance maps is much less time-consuming than algorithms based on 3D structures.

A Perspective on Protein Structure Prediction Using Quantum Computers.

Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

Quorum sensing in bacteria: in silico protein analysis, ecophysiology, and reconstruction of their evolutionary history

BackgroundQuorum sensing (QS) is a sophisticated cell-to-cell signalling mechanism that allows the coordination of important processes in microbial populations. The AI-1 and AI-2 autoinducer systems are among the best characterized bacterial QS systems at the genetic level.ResultsIn this study, we present data derived from in silico screening of QS proteins from bacterial genomes available in public databases. Sequence analyses allowed identifying candidate sequences of known QS systems that were used to build phylogenetic trees. Eight categories were established according to the number of genes from the two major QS systems present in each genome, revealing a correlation with specific taxa, lifestyles or metabolic traits. Many species had incomplete QS systems, encoding the receptor protein but not the biosynthesis of the quorum sensing molecule (QSMs). Reconstruction of the evolutionary history of the LuxR family and prediction of the 3D structure of the ancestral protein suggested their monomeric configuration in the absence of the signal molecule and the presence of a cavity for its binding.ConclusionsHere we correlate the taxonomic affiliation and lifestyle of bacteria from different genera with the QS systems encoded in their genomes. Moreover, we present the first ancestral reconstruction of the LuxR QS receptors, providing further insight in their evolutionary history.

Exploring the fragmentation efficiency of proteins analyzed by MALDI-TOF-TOF tandem mass spectrometry using computational and statistical analyses.

Matrix-assisted laser desorption/ionization time-of-flight-time-of-flight (MALDI-TOF-TOF) tandem mass spectrometry (MS/MS) is a rapid technique for identifying intact proteins from unfractionated mixtures by top-down proteomic analysis. MS/MS allows isolation of specific intact protein ions prior to fragmentation, allowing fragment ion attribution to a specific precursor ion. However, the fragmentation efficiency of mature, intact protein ions by MS/MS post-source decay (PSD) varies widely, and the biochemical and structural factors of the protein that contribute to it are poorly understood. With the advent of protein structure prediction algorithms such as Alphafold2, we have wider access to protein structures for which no crystal structure exists. In this work, we use a statistical approach to explore the properties of bacterial proteins that can affect their gas phase dissociation via PSD. We extract various protein properties from Alphafold2 predictions and analyze their effect on fragmentation efficiency. Our results show that the fragmentation efficiency from cleavage of the polypeptide backbone on the C-terminal side of glutamic acid (E) and asparagine (N) residues were nearly equal. In addition, we found that the rearrangement and cleavage on the C-terminal side of aspartic acid (D) residues that result from the aspartic acid effect (AAE) were higher than for E- and N-residues. From residue interaction network analysis, we identified several local centrality measures and discussed their implications regarding the AAE. We also confirmed the selective cleavage of the backbone at D-proline bonds in proteins and further extend it to N-proline bonds. Finally, we note an enhancement of the AAE mechanism when the residue on the C-terminal side of D-, E- and N-residues is glycine. To the best of our knowledge, this is the first report of this phenomenon. Our study demonstrates the value of using statistical analyses of protein sequences and their predicted structures to better understand the fragmentation of the intact protein ions in the gas phase.

Community dynamics and metagenomic analyses reveal Bacteroidota's role in widespread enzymatic Fucus vesiculosus cell wall degradation

Enzymatic degradation of algae cell wall carbohydrates by microorganisms is under increasing investigation as marine organic matter gains more value as a sustainable resource. The fate of carbon in the marine ecosystem is in part driven by these degradation processes. In this study, we observe the microbiome dynamics of the macroalga Fucus vesiculosus in 25-day-enrichment cultures resulting in partial degradation of the brown algae. Microbial community analyses revealed the phylum Pseudomonadota as the main bacterial fraction dominated by the genera Marinomonas and Vibrio. More importantly, a metagenome-based Hidden Markov model for specific glycosyl hydrolyses and sulphatases identified Bacteroidota as the phylum with the highest potential for cell wall degradation, contrary to their low abundance. For experimental verification, we cloned, expressed, and biochemically characterised two α-L-fucosidases, FUJM18 and FUJM20. While protein structure predictions suggest the highest similarity to a Bacillota origin, protein–protein blasts solely showed weak similarities to defined Bacteroidota proteins. Both enzymes were remarkably active at elevated temperatures and are the basis for a potential synthetic enzyme cocktail for large-scale algal destruction.

AI told you so: navigating protein structure prediction in the era of machine learning

Every protein in our cells has evolved to fold into a specific structure to perform its functions. However, determining these structures experimentally is often challenging, in some cases taking years. Recently, machine-learning algorithms have been designed to predict a protein’s structure directly from its amino acid sequence in minutes to hours. Since the release of the first of these algorithms, AlphaFold and RoseTTAFold, several more have been developed. These have been complemented by tools that leverage the outputs to give structural context to biochemical data, screen for novel protein–protein interactions or even help solve experimental structures. In addition, several public resources have incorporated the predictions into their databases, making the data open to all. Here, we provide a user-focused perspective on machine-learning protein structure prediction, covering some of the popular applications and highlighting caveats. Used effectively, predictive programs offer the potential to speed up research and guide experimental design.

The full spectrum of SLC22 OCT1 mutations illuminates the bridge between drug transporter biophysics and pharmacogenomics.

Mutations in transporters can impact an individual's response to drugs and cause many diseases. Few variants in transporters have been evaluated for their functional impact. Here, we combine saturation mutagenesis and multi-phenotypic screening to dissect the impact of 11,213 missense single-amino-acid deletions, and synonymous variants across the 554 residues of OCT1, a key liver xenobiotic transporter. By quantifying in parallel expression and substrate uptake, we find that most variants exert their primary effect on protein abundance, a phenotype not commonly measured alongside function. Using our mutagenesis results combined with structure prediction and molecular dynamic simulations, we develop accurate structure-function models of the entire transport cycle, providing biophysical characterization of all known and possible human OCT1 polymorphisms. This work provides a complete functional map of OCT1 variants along with a framework for integrating functional genomics, biophysical modeling, and human genetics to predict variant effects on disease and drug efficacy.

Mechanical evaluation of thermal barrier coatings by indentation: A review

Thermal barrier coatings (TBCs) play an important role in the thermal protection of alloy components in high temperature industries. Accurately characterizing and extracting mechanical properties of TBCs are crucial for structural design, performance optimization, failure prediction and lifespan assessment. Indentation test is an effective method for experimentally obtaining the mechanical properties and provides valuable insights into the elastic, plastic and fracture behaviors of TBCs at elevated temperatures. This paper aims to provide an overview of indentation mechanical evaluation of TBCs, including the mechanical properties at different temperatures, the mechanical property anisotropy, the microstructural and mechanical behavior under thermal exposure and/or thermal cycling, as well as the comparison of functional performance between conventional and nanostructured TBCs. Furthermore, this paper points out the main challenges and future directions from indentation investigations of TBCs. Finally, this paper is summarized and prospected.

1H, 15N and 13C resonance backbone and side-chain assignments and secondary structure determination of the BRCT domain of Mtb LigA.

The BRCA1 carboxyl-terminal (BRCT) domain, an evolutionarily conserved structural motif, is ubiquitous in a multitude of proteins spanning prokaryotic and eukaryotic organisms. In Mycobacterium tuberculosis (Mtb), BRCT domain plays a pivotal role in the catalytic activity of the NAD+-dependent DNA ligase (LigA). LigA is pivotal in DNA replication, catalyzing the formation of phosphodiester bonds in Okazaki fragments and repairing single-strand breaks in damaged DNA, essential for the survival of Mtb. Structural and functional aspects of LigA unveil its character as a highly modular protein, undergoing substantial conformational changes during its catalytic cycle. Although the BRCT domain of Mtb LigA plays an essential role in DNA binding and protein-protein interactions, the precise mechanism of action remains poorly understood. Unravelling the structure of the BRCT domain holds the promise of advancing our understanding of this pivotal domain. Additionally, it will facilitate further exploration of the protein-protein interactions and enhance our understanding of inter domain interactions within LigA, specifically between BRCT and the Adenylation domain. In this study, we demonstrate the overexpression of the BRCT domain of Mtb LigA and conduct its analysis using solution NMR spectroscopy, revealing a well-folded structure and we present the nearly complete chemical shift assignments of both backbone and sidechains. In addition, a secondary structure prediction by TALOS N predicts BRCT consisting of 3 α-helices and 4 β-sheets, closely resembling the typical structural topology of most BRCT domains.

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 <i>in vitro</i> and <i>in vivo</i> trials.

Inside the Borate Anomaly: Leveraging a Predictive Modelling Approach to Navigate Complex Composition–Structure–Property Relationships in Oxyhalide Borate Glasses

This study employs a systematic and predictive modelling approach to investigate the structure and properties of multi-component borate glasses. In particular, this work is focused on understanding the individual and interaction effects of multiple constituents on several material properties. By leveraging advanced modeling techniques, this work examines how the inclusion and variation of B2O3, CaF2, TiO2, ZnO, and Na2CO3 influence the glass network, with particular attention to modifier fractions ≥ 30 mol%. This research addresses the gap in knowledge regarding the complex behavior of borate glasses in this high modifier fraction range, known as the borate anomaly, where prediction of glass structure and properties becomes particularly challenging. The use of a design of mixtures (DoM) approach facilitated the generation of polynomial equations indicating the influence of mixture components on various responses, enabling the prediction and optimization of glass properties over broad compositional ranges despite being within the anomalous region. This methodical approach not only advances our understanding of borate glass systems but also underscores the importance of predictive modelling in the accelerated design and development of novel glass materials for diverse applications.

Reevaluating the splice-altering variant in TECTA as a cause of nonsyndromic hearing loss DFNA8/12 by functional analysis of RNA.

The aim of this study was to determine the genetic cause of early onset autosomal dominant hearing loss segregating in five-generation kindred of Chinese descent and provide preimplantation genetic testing (PGT)for them. Clinical examination, pedigree analysis and exome sequencing were carried out on the family. Minigene-based splicing analysis, in vivo RNA analysis and protein structure prediction by molecular modeling were conducted on the candidate variant. PGT for the causative variation and chromosome aneuploidis based on SNP analysis has been used for avoidance of hearing loss in this family. All the affected individuals presented with moderate down-sloping hearing loss and whole-exome sequencing identified a novel splice-site variant c.5383+6T>A in the tested subjects within the TECTA locus. Genotyping of all the 32 family members confirmed segregation of this variant and the hearing loss phenotype in the extended family. Functional analysis of RNA and molecular modeling indicates that c.5383+6T>A is a pathogenic splice-site variant and should be considered as genetic cause of the hearing loss. Furthermore, a successful singleton pregnancy with no variation in TECTA c.5383+6 was established and a healthy male child was born by PGT. We have identified a novel variant c.5383+6T>A in TECTA ZA-ZP inter-domain, which could be attributable to the early-onset autosomal dominant hearing loss. The implications of our study are valuable in elucidating the disrupted RNA splicing and uncovering the genetic cause of hearing loss with TECTA pathogenic variants, as well as providing reproductive approaches to healthy offspring.

Functional and Genetic Analyses Unveil the Implication of CDC27 in Hemifacial Microsomia

Hemifacial microsomia (HFM) is a rare congenital genetic syndrome primarily affecting the first and second pharyngeal arches, leading to defects in the mandible, external ear, and middle ear. The pathogenic genes remain largely unidentified. Whole-exome sequencing (WES) was conducted on 12 HFM probands and their unaffected biological parents. Predictive structural analysis of the target gene was conducted using PSIPRED (v3.3) and SWISS-MODEL, while STRING facilitated protein-to-protein interaction predictions. CRISPR/Cas9 was applied for gene knockout in zebrafish. In situ hybridization (ISH) was employed to examine the spatiotemporal expression of the target gene and neural crest cell (NCC) markers. Immunofluorescence with PH3 and TUNEL assays were used to assess cell proliferation and apoptosis. RNA sequencing was performed on mutant and control embryos, with rescue experiments involving target mRNA injections and specific gene knockouts. CDC27 was identified as a novel candidate gene for HFM, with four nonsynonymous de novo variants detected in three unrelated probands. Structural predictions indicated significant alterations in the secondary and tertiary structures of CDC27. cdc27 knockout in zebrafish resulted in craniofacial malformation, spine deformity, and cardiac edema, mirroring typical HFM phenotypes. Abnormalities in somatic cell apoptosis, reduced NCC proliferation in pharyngeal arches, and chondrocyte differentiation issues were observed in cdc27−/− mutants. cdc27 mRNA injections and cdkn1a or tp53 knockout significantly rescued pharyngeal arch cartilage dysplasia, while sox9a mRNA administration partially restored the defective phenotypes. Our findings suggest a functional link between CDC27 and HFM, primarily through the inhibition of CNCC proliferation and disruption of pharyngeal chondrocyte differentiation.

Receptor-recognition and antiviral mechanisms of retrovirus-derived human proteins.

Human syncytin-1 and suppressyn are cellular proteins of retroviral origin involved in cell-cell fusion events to establish the maternal-fetal interface in the placenta. In cell culture, they restrict infections from members of the largest interference group of vertebrate retroviruses, and are regarded as host immunity factors expressed during development. At the core of the syncytin-1 and suppressyn functions are poorly understood mechanisms to recognize a common cellular receptor, the membrane transporter ASCT2. Here, we present cryo-electron microscopy structures of human ASCT2 in complexes with the receptor-binding domains of syncytin-1 and suppressyn. Despite their evolutionary divergence, the two placental proteins occupy similar positions in ASCT2, and are stabilized by the formation of a hybrid β-sheet or 'clamp' with the receptor. Structural predictions of the receptor-binding domains of extant retroviruses indicate overlapping binding interfaces and clamping sites with ASCT2, revealing a competition mechanism between the placental proteins and the retroviruses. Our work uncovers a common ASCT2 recognition mechanism by a large group of endogenous and disease-causing retroviruses, and provides high-resolution views on how placental human proteins exert morphological and immunological functions.

A new reagent for in vivo structure probing of RNA G and U residues that improves RNA structure prediction alone and combined with DMS.

A key to understanding the roles of RNA in regulating gene expression is knowing their structures in vivo. One way to obtain this information is through probing structures of RNA with chemicals. To probe RNA structure directly in cells, membrane-permeable reagents that modify the Watson-Crick (WC) face of unpaired nucleotides can be used. While dimethyl sulfate (DMS) has led to substantial insight into RNA structure, it has limited nucleotide specificity in vivo, with WC face reactivity only at adenine (A) and cytosine (C) at neutral pH. The reagent 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) was recently shown to modify the WC face of guanine (G) and uracil (U). While useful at lower concentrations in experiments that measure chemical modifications by reverse transcription stops, at higher concentrations necessary for detection by mutational profiling (MaP), EDC treatment leads to degradation of RNA. Here we demonstrate EDC-stimulated degradation of RNA in Gram-negative and Gram-positive bacteria. In an attempt to overcome these limitations, we developed a new carbodiimide reagent, 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide methiodide (ETC), which we show specifically modifies unpaired Gs and Us in vivo without substantial degradation of RNA. We establish ETC as a probe for MaP and optimize the reverse transcription conditions and computational analysis in Escherichia coli Importantly, we demonstrate the utility of ETC as a probe for improving RNA structure prediction both alone and with DMS.

StructGNN: An efficient graph neural network framework for static structural analysis

In the field of structural analysis prediction via supervised learning, neural networks are widely employed. Recent advances in graph neural networks (GNNs) have expanded their capabilities, enabling the prediction of structures with diverse geometries by utilizing graph representations and GNNs' message-passing mechanism. However, conventional message-passing in GNNs doesn't align with structural properties, resulting in inefficient computation and limited generalization to extrapolated datasets. To address this, a novel structural graph representation, incorporating pseudo nodes as rigid diaphragms in each story, alongside an efficient GNN framework called StructGNN is proposed. StructGNN employs an adaptive message-passing mechanism tailored to the structure's story count, enabling seamless transmission of input loading features across the structural graph. Extensive experiments validate the effectiveness of this approach, achieving over 99% accuracy in predicting displacements, bending moments, and shear forces. StructGNN also exhibits strong generalization over non-GNN models, with an average accuracy of 96% on taller, unseen structures. These results highlight StructGNN's potential as a reliable, computationally efficient tool for static structural response prediction, offering promise for addressing challenges associated with dynamic seismic loads in structural analysis.

Insights into conformational changes in cytochrome bduring the early steps of its maturation.

Membrane proteins carrying redox cofactors are key subunits of respiratory chain complexes, yet the exact path of their folding and maturation remains poorly understood. Here, using cryo-EM and structure prediction via Alphafold2, we generated models of early assembly intermediates of cytochrome b (Cytb), a central subunit of complex III. The predicted structure of the first assembly intermediate suggests how the binding of Cytb to the assembly factor Cbp3-Cbp6 imposes an open configuration to facilitate the acquisition of its heme cofactors. Moreover, structure predictions of the second intermediate indicate how hemes get stabilized by binding of the assembly factor Cbp4, with a concomitant weakening of the contact between Cbp3-Cbp6 and Cytb, preparing for the release of the fully hemylated protein from the assembly factors.