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Pancreatic ductal adenocarcinoma (PDAC) is characterized by poor response to all therapeutic modalities and dismal prognosis. The presence of tertiary lymphoid structures (TLSs) in various solid cancers is of crucial prognostic significance, highlighting the intricate interplay between the tumor microenvironment and immune cells aggregation. However, the extent to which TLSs and immune status affect PDAC prognosis remains incompletely understood. Here, we sought to unveil the unique properties of TLSs in PDAC by leveraging both single-cell and bulk transcriptomics, culminating in a risk model that predicts clinical outcomes. We used TLS scores based on a 12-gene (CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13) and 9-gene (PTGDS, RBP5, EIF1AY, CETP, SKAP1, LAT, CCR6, CD1D, and CD79B) signature, respectively, and examined their distribution in cell clusters of single-cell data from PDAC samples. The markers involved in these clusters were selected to develop a prognostic model using The Cancer Genome Atlas Program database as the training cohort and Gene Expression Omnibus database as the validation cohort. Further, we compared the immune infiltration, drug sensitivity, and enriched and differentially expressed genes between the high- and low-risk groups in our model. Therefore, we established a risk model that has significant implications for the prognostic assessment of PADC patients with remarkable differences in immune infiltration and chemosensitivity between the low- and high-risk groups. This paradigm established by TLS-related cell marker genes provides a prognostic prediction and a panel of novel therapeutic targets for exploring potential immunotherapy. [ABSTRACT FROM AUTHOR]

Motivation Noncoding RNAs (ncRNAs) express their functions by adopting molecular structures. Specifically, RNA secondary structures serve as a relatively stable intermediate step before tertiary structures, offering a reliable signature of molecular function. Consequently, within an RNA functional family, secondary structures are generally more evolutionarily conserved than sequences. Conversely, homologous RNA families grouped within an RNA clan share ancestors but typically exhibit structural differences. Inferring the evolution of RNA structures within RNA families and clans is crucial for gaining insights into functional adaptations over time and providing clues about the Ancient RNA World Hypothesis. Results We introduce the median problem and the small parsimony problem for ncRNA families, where secondary structures are represented as leaf-labeled trees. We utilize the Robinson-Foulds (RF) tree distance, which corresponds to a specific edit distance between RNA trees, and a new metric called the Internal-Leafset (IL) distance. While the RF tree distance compares sets of leaves descending from internal nodes of two RNA trees, the IL distance compares the collection of leaf-children of internal nodes. The latter is better at capturing differences in structural elements of RNAs than the RF distance, which is more focused on base pairs. We also consider a more general tree edit distance that allows the mapping of base pairs that are not perfectly aligned. We study the theoretical complexity of the median problem and the small parsimony problem under the three distance metrics and various biologically relevant constraints, and we present polynomial-time maximum parsimony algorithms for solving some versions of the problems. Our algorithms are applied to ncRNA families from the RFAM database, illustrating their practical utility. Availability and implementation https://github.com/bmarchand/rna\_small\_parsimony. [ABSTRACT FROM AUTHOR]

N -Methylisothiazolinone (MIT) is a thiol group modifier and antimicrobial agent. Arthrobacter sarcosine oxidase (SoxA), a diagnostic enzyme for assaying creatinine, loses its activity upon the addition of MIT, and its inactivation mechanism remains unclear. In this study, SoxA was chemically modified using MIT (mo-SoxA), and its structural and chemical properties were characterized. Spectral analysis data, oxygen consumption rates, and reactions were compared between intact SoxA and mo-SoxA. These demonstrate that the oxidative half-reaction toward oxygen is inhibited by MIT modification. The oxidase activity of mo-SoxA was approximately 2.1% of that of intact SoxA, and its dehydrogenase activity was approximately 4.2 times higher. The C-to-S mutants revealed that cooperative modification of 2 specific cysteine residues caused a drastic change in the enzyme reaction mode. Based on the modeled tertiary structures, the putative entrance for oxygen uptake is predicted to be blocked by the chemical modification of the 2 cysteine residues. [ABSTRACT FROM AUTHOR]

Recent studies in the rice genome-wide have established that de novo genes, evolving from noncoding sequences, enhance protein diversity through a stepwise process. However, the pattern and rate of their evolution in protein structure over time remain unclear. Here, we addressed these issues within a surprisingly short evolutionary timescale (<1 million years for 97% of Oryza de novo genes) with comparative approaches to gene duplicates. We found that de novo genes evolve faster than gene duplicates in the intrinsically disordered regions (such as random coils), secondary structure elements (such as α helix and β strand), hydrophobicity, and molecular recognition features. In de novo proteins, specifically, we observed an 8% to 14% decay in random coils and intrinsically disordered region lengths and a 2.3% to 6.5% increase in structured elements, hydrophobicity, and molecular recognition features, per million years on average. These patterns of structural evolution align with changes in amino acid composition over time as well. We also revealed higher positive charges but smaller molecular weights for de novo proteins than duplicates. Tertiary structure predictions showed that most de novo proteins, though not typically well folded on their own, readily form low-energy and compact complexes with other proteins facilitated by extensive residue contacts and conformational flexibility, suggesting a faster-binding scenario in de novo proteins to promote interaction. These analyses illuminate a rapid evolution of protein structure in de novo genes in rice genomes, originating from noncoding sequences, highlighting their quick transformation into active, protein complex-forming components within a remarkably short evolutionary timeframe. [ABSTRACT FROM AUTHOR]

Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features while streamlining the redundant data in primary sequences and bypassing the time-consuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-of-the-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO. [ABSTRACT FROM AUTHOR]

A fundamental goal in evolutionary biology and population genetics is to understand how selection shapes the fate of new mutations. Here, we test the null hypothesis that insertion–deletion (indel) events in protein-coding regions occur randomly with respect to secondary structures. We identified indels across 11,444 sequence alignments in mouse, rat, human, chimp, and dog genomes and then quantified their overlap with four different types of secondary structure—alpha helices, beta strands, protein bends, and protein turns—predicted by deep-learning methods of AlphaFold2. Indels overlapped secondary structures 54% as much as expected and were especially underrepresented over beta strands, which tend to form internal, stable regions of proteins. In contrast, indels were enriched by 155% over regions without any predicted secondary structures. These skews were stronger in the rodent lineages compared to the primate lineages, consistent with population genetic theory predicting that natural selection will be more efficient in species with larger effective population sizes. Nonsynonymous substitutions were also less common in regions of protein secondary structure, although not as strongly reduced as in indels. In a complementary analysis of thousands of human genomes, we showed that indels overlapping secondary structure segregated at significantly lower frequency than indels outside of secondary structure. Taken together, our study shows that indels are selected against if they overlap secondary structure, presumably because they disrupt the tertiary structure and function of a protein. [ABSTRACT FROM AUTHOR]

Summary RNA (ribonucleic acid) molecules have secondary and tertiary structures in vivo which play a crucial role in cellular processes such as the regulation of gene expression, RNA processing and localization. The ability to investigate these structures will enhance our understanding of their function and contribute to the diagnosis and treatment of diseases caused by RNA dysregulation. However, there are no mature pipelines or packages for processing and analyzing complex in vivo RNA structural data. Here, we present rnaCrosslinkOO (RNA Crosslink Object-Oriented), a novel software package for the comprehensive analysis of data derived from the COMRADES (Crosslinking of Matched RNA and Deep Sequencing) method. rnaCrosslinkOO offers a comprehensive pipeline from raw sequencing reads to the identification and comparison of RNA structural features. It includes read processing and alignment, clustering of duplexes, data exploration, folding and comparisons of RNA structures. rnaCrosslinkOO also enables comparisons between conditions, the identification of inter-RNA interactions, and the incorporation of reactivity data to improve structure prediction. Availability and implementation rnaCrosslinkOO is freely available to noncommercial users and implemented in R, with the source code and documentation accessible at https://CRAN.R-project.org/package=rnaCrosslinkOO. The software is supported on Linux, macOS, and Windows platforms. [ABSTRACT FROM AUTHOR]

The cluster of differentiation 36 (CD36) domain defines the characteristic ectodomain associated with class B scavenger receptor (SR-B) proteins. In bilaterians, SR-Bs play critical roles in diverse biological processes including innate immunity functions such as pathogen recognition and apoptotic cell clearance, as well as metabolic sensing associated with fatty acid uptake and cholesterol transport. Although previous studies suggest this protein family is ancient, SR-B diversity across Eukarya has not been robustly characterized. We analyzed SR-B homologs identified from the genomes and transcriptomes of 165 diverse eukaryotic species. The presence of highly conserved amino acid motifs across major eukaryotic supergroups supports the presence of a SR-B homolog in the last eukaryotic common ancestor. Our comparative analyses of SR-B protein structure identify the retention of a canonical asymmetric beta barrel tertiary structure within the CD36 ectodomain across Eukarya. We also identify multiple instances of independent lineage-specific sequence expansions in the apex region of the CD36 ectodomain—a region functionally associated with ligand-sensing. We hypothesize that a combination of both sequence expansion and structural variation in the CD36 apex region may reflect the evolution of SR-B ligand-sensing specificity between diverse eukaryotic clades. [ABSTRACT FROM AUTHOR]

The evolution and diversification of proteins capable of remodeling domains has been critical for transcriptional reprogramming during cell fate determination in multicellular eukaryotes. Chromatin remodeling proteins of the CHD3 family have been shown to have important and antagonistic impacts on seed development in the model plant, Arabidopsis thaliana , yet the basis of this functional divergence remains unknown. In this study, we demonstrate that genes encoding the CHD3 proteins PICKLE (PKL) and PICKLE-RELATED 2 (PKR2) originated from a duplication event during the diversification of crown Brassicaceae, and that these homologs have undergone distinct evolutionary trajectories since this duplication, with PKR2 fast evolving under positive selection, while PKL is subject to purifying selection. We find that the rapid evolution of PKR2 under positive selection reduces the encoded protein's intrinsic disorder, possibly suggesting a tertiary structure configuration which differs from that of PKL. Our whole genome transcriptome analysis in seeds of pkr2 and pkl mutants reveals that they act antagonistically on the expression of specific sets of genes, providing a basis for their differing roles in seed development. Our results provide insights into how gene duplication and neofunctionalization can lead to differing and antagonistic selective pressures on transcriptomes during plant reproduction, as well as on the evolutionary diversification of the CHD3 family within seed plants. [ABSTRACT FROM AUTHOR]

Motivation The tertiary structures of an increasing number of biological macromolecules have been determined using cryo-electron microscopy (cryo-EM). However, there are still many cases where the resolution is not high enough to model the molecular structures with standard computational tools. If the resolution obtained is near the empirical borderline (3–4.5 Å), improvement in the map quality facilitates structure modeling. Results We report EM-GAN, a novel approach that modifies an input cryo-EM map to assist protein structure modeling. The method uses a 3D generative adversarial network (GAN) that has been trained on high- and low-resolution density maps to learn the density patterns, and modifies the input map to enhance its suitability for modeling. The method was tested extensively on a dataset of 65 EM maps in the resolution range of 3–6 Å and showed substantial improvements in structure modeling using popular protein structure modeling tools. Availability and implementation https://github.com/kiharalab/EM-GAN , Google Colab: https://tinyurl.com/3ccxpttx. [ABSTRACT FROM AUTHOR]

In recent years, protein structure problems have become a hotspot for understanding protein folding and function mechanisms. It has been observed that most of the protein structure works rely on and benefit from co-evolutionary information obtained by multiple sequence alignment (MSA). As an example, AlphaFold2 (AF2) is a typical MSA-based protein structure tool which is famous for its high accuracy. As a consequence, these MSA-based methods are limited by the quality of the MSAs. Especially for orphan proteins that have no homologous sequence, AlphaFold2 performs unsatisfactorily as MSA depth decreases, which may pose a barrier to its widespread application in protein mutation and design problems in which there are no rich homologous sequences and rapid prediction is needed. In this paper, we constructed two standard datasets for orphan and de novo proteins which have insufficient/none homology information, called Orphan62 and Design204, respectively, to fairly evaluate the performance of the various methods in this case. Then, depending on whether or not utilizing scarce MSA information, we summarized two approaches, MSA-enhanced and MSA-free methods, to effectively solve the issue without sufficient MSAs. MSA-enhanced model aims to improve poor MSA quality from the data source by knowledge distillation and generation models. MSA-free model directly learns the relationship between residues on enormous protein sequences from pre-trained models, bypassing the step of extracting the residue pair representation from MSA. Next, we evaluated the performance of four MSA-free methods (trRosettaX-Single, TRFold, ESMFold and ProtT5) and MSA-enhanced (Bagging MSA) method compared with a traditional MSA-based method AlphaFold2, in two protein structure-related prediction tasks, respectively. Comparison analyses show that trRosettaX-Single and ESMFold which belong to MSA-free method can achieve fast prediction (⁠|$\sim\! 40$| s) and comparable performance compared with AF2 in tertiary structure prediction, especially for short peptides, |$\alpha $| -helical segments and targets with few homologous sequences. Bagging MSA utilizing MSA enhancement improves the accuracy of our trained base model which is an MSA-based method when poor homology information exists in secondary structure prediction. Our study provides biologists an insight of how to select rapid and appropriate prediction tools for enzyme engineering and peptide drug development. Contact guofei@csu.edu.cn , jj.tang@siat.ac.cn [ABSTRACT FROM AUTHOR]

The three-dimensional structure of RNA molecules plays a critical role in a wide range of cellular processes encompassing functions from riboswitches to epigenetic regulation. These RNA structures are incredibly dynamic and can indeed be described aptly as an ensemble of structures that shifts in distribution depending on different cellular conditions. Thus, the computational prediction of RNA structure poses a unique challenge, even as computational protein folding has seen great advances. In this review, we focus on a variety of machine learning-based methods that have been developed to predict RNA molecules' secondary structure, as well as more complex tertiary structures. We survey commonly used modeling strategies, and how many are inspired by or incorporate thermodynamic principles. We discuss the shortcomings that various design decisions entail and propose future directions that could build off these methods to yield more robust, accurate RNA structure predictions. [ABSTRACT FROM AUTHOR]

CapF, a staphylococcal capsule-producing enzyme, binds Zn2+ ion and NADPH using its C-terminal domain (CTD) and N-terminal domain (NTD), respectively. To elucidate the roles of cofactors and domains, we have systematically investigated the related recombinant proteins, rCapF, rCTD, recombinant NTD (rNTD) and the Zn2+-free rCapF/rCTD, Apo-rCapF/Apo-rCTD. The results show that the secondary structure, tertiary structure, shape and surface hydrophobicity of Apo-rCapF and Apo-rCTD are different from those of rCapF and rCTD. The removal of Zn2+ made rCapF thermo-sensitive, whereas both rCTD and Apo-rCTD are thermo-resistant proteins. Further, Apo-rCapF and rCapF existed as the dimers, whereas rCTD and Apo-rCTD formed a mixture of dimers and tetramers in the aqueous solution. Zn2+ maintained the structure of NTD as well. The NADPH binding activity and Cys accessibility of rNTD, rCapF and Apo-rCapF were significantly different from each other. The binding of NADPH to the above three proteins freely occurred, liberated heat at 25°C and increased their diameters. In addition, the structure, stability, shape and oligomerization ability of rNTD, rCTD and rCapF little resembled each other. Collectively, the domains and cofactors of CapF contribute to preserving its conformation, stability, shape and dimerization ability. [ABSTRACT FROM AUTHOR]

The diverse GTPases of the dynamin superfamily play various roles in the cell, as exemplified by the dynamin-related proteins (DRPs) Mgm1 and Opa1, which remodel the mitochondrial inner membrane in fungi and metazoans, respectively. Via an exhaustive search of genomic and metagenomic databases, we found previously unknown DRP types occurring in diverse eukaryotes and giant viruses (phylum Nucleocytoviricota). One novel DRP clade, termed MidX, combined hitherto uncharacterized proteins from giant viruses and six distantly related eukaryote taxa (Stramenopiles, Telonemia, Picozoa, Amoebozoa, Apusomonadida, and Choanoflagellata). MidX stood out because it was not only predicted to be mitochondria-targeted but also to assume a tertiary structure not observed in other DRPs before. To understand how MidX affects mitochondria, we exogenously expressed MidX from Hyperionvirus in the kinetoplastid Trypanosoma brucei , which lacks Mgm1 or Opa1 orthologs. MidX massively affected mitochondrial morphology from inside the matrix, where it closely associates with the inner membrane. This unprecedented mode of action contrasts to those of Mgm1 and Opa1, which mediate inner membrane remodeling in the intermembrane space. We speculate that MidX was acquired in Nucleocytoviricota evolution by horizontal gene transfer from eukaryotes and is used by giant viruses to remodel host mitochondria during infection. MidX's unique structure may be an adaptation for reshaping mitochondria from the inside. Finally, Mgm1 forms a sister group to MidX and not Opa1 in our phylogenetic analysis, throwing into question the long-presumed homology of these DRPs with similar roles in sister lineages. [ABSTRACT FROM AUTHOR]

Motivation Tertiary structure alignment is one of the main challenges in the computer-aided comparative study of molecular structures. Its aim is to optimally overlay the 3D shapes of two or more molecules in space to find the correspondence between their nucleotides. Alignment is the starting point for most algorithms that assess structural similarity or find common substructures. Thus, it has applications in solving a variety of bioinformatics problems, e.g. in the search for structural patterns, structure clustering, identifying structural redundancy, and evaluating the prediction accuracy of 3D models. To date, several tools have been developed to align 3D structures of RNA. However, most of them are not applicable to arbitrarily large structures and do not allow users to parameterize the optimization algorithm. Results We present two customizable heuristics for flexible alignment of 3D RNA structures, geometric search (GEOS), and genetic algorithm (GENS). They work in sequence-dependent/independent mode and find the suboptimal alignment of expected quality (below a predefined RMSD threshold). We compare their performance with those of state-of-the-art methods for aligning RNA structures. We show the results of quantitative and qualitative tests run for all of these algorithms on benchmark sets of RNA structures. Availability and implementation Source codes for both heuristics are hosted at https://github.com/RNApolis/rnahugs. [ABSTRACT FROM AUTHOR]

This study attempts to identify the significant role played by the secondary and tertiary structure of collagen-derived peptides that are involved in lipid peroxide quenching in food products. Fish collagen hydrolysate (CH) was extracted with an efficiency of 70%. The constituent peptides of CH (8.2–9.7 kDa) existed in a polyproline-II (PP-II) conformation and at a minimum concentration of 1 mg ml−1 and pH range 7 to 8, assembled into a stable, hierarchical, quasi-fibrillar (QF) network. The peroxide quenching activity of this QF-CH increased with increasing ionic stability of the assembly and decreased upon proteolytic dismantling. Upon being used as an additive, the QF-CH reduced peroxide formation by 84.5% to 98.9% in both plant and fish-based oil and increased the shelf life of soya oil by a factor of 5 after 6 months of storage. The addition of QF-CH to cultured cells quenched peroxide ions generated in situ and decreased stressor activity by a factor of 12.16 abundant peptides were identified from the CH. The reason behind the high efficacy displayed by CH was attributed to its unique charge distribution, prevalence of proton-donating amino acid residues and proximal charge delocalization by the QF network, making fish derived CH a suitable substitute for antiperoxide agents in lipid-rich food. [ABSTRACT FROM AUTHOR]

IL‐33, an epithelial‐derived cytokine, functions as an alarmin for the immune system in the tumor microenvironment (TME). However, the expression and role of IL‐33 on cervical cancer remain unclear. The aim of this study was to investigate the expression of IL‐33 and its relationship with clinicopathologic features, tertiary lymphoid structures (TLS), and programmed cell death 1 (PD‐1)/programmed cell death 1 ligand (PD‐L1) immune checkpoints by immunohistochemistry in 93 cervical cancer patient specimens. Down‐regulation of IL‐33 expression was observed in tumor tissues compared with adjacent tissues. More importantly, IL‐33 was detected in the cytoplasm of tumor fraction. IL‐33 expression in tumor cytoplasm was associated with tumor size and the invasive depth of tumors (p < 0.05). Meanwhile, IL‐33 expression in tumor cytoplasm was positively correlated with infiltration of CD3+ T cells, CD8+ T cells, and PD‐L1 expression in tumor tissues (p < 0.05). The number of TLS strongly correlated with the depth of tumor invasion, preoperative chemotherapy, human papillomavirus infection, and high level of PD‐1 (p < 0.05). However, there was no significant relationship between IL‐33 and TLS. Kaplan–Meier survival curves showed that the formation of TLS was associated with a better prognosis (p = 0.008). In multivariable Cox regression modeling, high expression of PD‐L1 in tumor tissues was correlated with poor prognosis (HR = 0.128; 95% CI: 0.026–0.646; p = 0.013), whereas the high expression of IL‐33 in tumor tissues was associated with better prognosis (HR = 5.097; 95% CI:1.050–24.755; p = 0.043). These results indicate that IL‐33, TLS, and PD‐L1 are potentially valuable prognostic predictor for cervical cancer. IL‐33 has potential for combination with PD‐L1‐related antitumor therapy. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is the most prevalent motor neuron disorder worldwide. In ALS, progressing disease can result from misfolding and aggregation of superoxide dismutase‐1 (SOD1) or TAR DNA‐binding protein 43 kDa (TDP43). An efficient immunotherapy for ALS should spare intact SOD1 while eliminating its dysfunctional variant. We utilized advanced immunoinformatics to suggest a potential vaccine candidate against ALS by proposing a model of dynamic TLR4 mediation and induction of a specific Th2‐biased shift against mutant SOD1, TDP43, and TRAF6, a protein that specifically interacts with dysfunctional SOD1. SOD1, TDP43, and TRAF6 were retrieved in FASTA. Immune Epitopes Database and CTLpred suggested T/B‐cell epitopes from disease‐specific regions of selected antigens. A TLR4‐mediating adjuvant, RS01, was used. Sequences were assembled via suitable linkers. Tertiary structure of the protein was calculated. Refined protein structure and physicochemical features of the 3D structure were verified in silico. Differential immune induction was assessed via C‐ImmSim. GROningen MAchine for Chemical Simulation was used to assess evolution of the docked vaccine–TLR4 complex in blood. Our protein showed high structural quality and was nonallergenic and immune inducing. Also, the vaccine–TLR4 complex stability was verified by RMSD, RMSF, gyration, and visual analyses of the molecular dynamic trajectory. Contact residues in the vaccine–TLR4 complex showed favorable binding energies. Immune stimulation analyses of the proposed candidate demonstrated a sustained memory cell response and a strong adaptive immune reaction. We proposed a potential vaccine candidate against ALS and verified its physicochemical and immune inducing features. Future studies should assess this vaccine in animal studies. [ABSTRACT FROM AUTHOR]

Characterizing RNA structures and functions have mostly been focused on 2D, secondary and 3D, tertiary structures. Recent advances in experimental and computational techniques for probing or predicting RNA solvent accessibility make this 1D representation of tertiary structures an increasingly attractive feature to explore. Here, we provide a survey of these recent developments, which indicate the emergence of solvent accessibility as a simple 1D property, adding to secondary and tertiary structures for investigating complex structure–function relations of RNAs. [ABSTRACT FROM AUTHOR]

Motivation Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accuracy of predicting quaternary structures of protein complexes consisting of multiple chains is still relatively low due to lack of advanced deep learning methods in the field. Because interchain residue–residue contacts can be used as distance restraints to guide quaternary structure modeling, here we develop a deep dilated convolutional residual network method (DRCon) to predict interchain residue–residue contacts in homodimers from residue–residue co-evolutionary signals derived from multiple sequence alignments of monomers, intrachain residue–residue contacts of monomers extracted from true/predicted tertiary structures or predicted by deep learning, and other sequence and structural features. Results Tested on three homodimer test datasets (Homo_std dataset, DeepHomo dataset and CASP-CAPRI dataset), the precision of DRCon for top L /5 interchain contact predictions (L : length of monomer in a homodimer) is 43.46%, 47.10% and 33.50% respectively at 6 Å contact threshold, which is substantially better than DeepHomo and DNCON2_inter and similar to Glinter. Moreover, our experiments demonstrate that using predicted tertiary structure or intrachain contacts of monomers in the unbound state as input, DRCon still performs well, even though its accuracy is lower than using true tertiary structures in the bound state are used as input. Finally, our case study shows that good interchain contact predictions can be used to build high-accuracy quaternary structure models of homodimers. Availability and implementation The source code of DRCon is available at https://github.com/jianlin-cheng/DRCon. The datasets are available at https://zenodo.org/record/5998532#.YgF70vXMKsB. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Modification of the domain architecture of galectins has been attempted to analyze their biological functions and to develop medical applications. Several types of galectin-1 repeat mutants were previously reported but, however, it was not clear whether the native structure of the wild type was retained. In this study, we determined the crystal structure of a galectin-1 tandem-repeat mutant with a short linker peptide, and compared the unfolding profiles of the wild type and mutant by chemical denaturation. The structure of the mutant was consistent with that of the dimer of the wild type, and both carbohydrate-binding sites were retained. The unfolding curve of the wild type with lactose suggested that the dimer dissociation and the tertiary structure unfolding was concomitant at micromolar protein concentrations. The midpoint denaturant concentration of the wild type was dependent on the protein concentration and lower than that of the mutant. Linking the two subunits significantly stabilized the tertiary structure. The mutant exhibited higher T-cell growth-inhibition activity and comparable hemagglutinating activity. Structural stabilization may prevent the oxidation of the internal cysteine residue. [ABSTRACT FROM AUTHOR]

Motivation Inter-protein (interfacial) contact prediction is very useful for in silico structural characterization of protein–protein interactions. Although deep learning has been applied to this problem, its accuracy is not as good as intra-protein contact prediction. Results We propose a new deep learning method GLINTER (Graph Learning of INTER-protein contacts) for interfacial contact prediction of dimers, leveraging a rotational invariant representation of protein tertiary structures and a pretrained language model of multiple sequence alignments. Tested on the 13th and 14th CASP-CAPRI datasets, the average top L/10 precision achieved by GLINTER is 54% on the homodimers and 52% on all the dimers, much higher than 30% obtained by the latest deep learning method DeepHomo on the homodimers and 15% obtained by BIPSPI on all the dimers. Our experiments show that GLINTER-predicted contacts help improve selection of docking decoys. Availability and implementation The software is available at https://github.com/zw2x/glinter. The datasets are available at https://github.com/zw2x/glinter/data. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation In the last decade, de novo protein structure prediction accuracy for individual proteins has improved significantly by utilising deep learning (DL) methods for harvesting the co-evolution information from large multiple sequence alignments (MSAs). The same approach can, in principle, also be used to extract information about evolutionary-based contacts across protein–protein interfaces. However, most earlier studies have not used the latest DL methods for inter-chain contact distance prediction. This article introduces a fold-and-dock method based on predicted residue-residue distances with trRosetta. Results The method can simultaneously predict the tertiary and quaternary structure of a protein pair, even when the structures of the monomers are not known. The straightforward application of this method to a standard dataset for protein–protein docking yielded limited success. However, using alternative methods for generating MSAs allowed us to dock accurately significantly more proteins. We also introduced a novel scoring function, PconsDock, that accurately separates 98% of correctly and incorrectly folded and docked proteins. The average performance of the method is comparable to the use of traditional, template-based or ab initio shape-complementarity-only docking methods. Moreover, the results of conventional and fold-and-dock approaches are complementary, and thus a combined docking pipeline could increase overall docking success significantly. This methodology contributed to the best model for one of the CASP14 oligomeric targets, H1065. Availability and implementation All scripts for predictions and analysis are available from https://github.com/ElofssonLab/bioinfo-toolbox/ and https://gitlab.com/ElofssonLab/benchmark5/. All models joined alignments, and evaluation results are available from the following figshare repository https://doi.org/10.6084/m9.figshare.14654886.v2. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation An accurate estimation of the quality of protein model structures typifies as a cornerstone in protein structure prediction regimes. Despite the recent groundbreaking success in the field of protein structure prediction, there are certain prospects for the improvement in model quality estimation at multiple stages of protein structure prediction and thus, to further push the prediction accuracy. Here, a novel approach, named ProFitFun, for assessing the quality of protein models is proposed by harnessing the sequence and structural features of experimental protein structures in terms of the preferences of backbone dihedral angles and relative surface accessibility of their amino acid residues at the tripeptide level. The proposed approach leverages upon the backbone dihedral angle and surface accessibility preferences of the residues by accounting for its N-terminal and C-terminal neighbors in the protein structure. These preferences are used to evaluate protein structures through a machine learning approach and tested on an extensive dataset of diverse proteins. Results The approach was extensively validated on a large test dataset (n  = 25 005) of protein structures, comprising 23 661 models of 82 non-homologous proteins and 1344 non-homologous experimental structures. In addition, an external dataset of 40 000 models of 200 non-homologous proteins was also used for the validation of the proposed method. Both datasets were further used for benchmarking the proposed method with four different state-of-the-art methods for protein structure quality assessment. In the benchmarking, the proposed method outperformed some state-of-the-art methods in terms of Spearman's and Pearson's correlation coefficients, average GDT-TS loss, sum of z -scores and average absolute difference of predictions over corresponding observed values. The high accuracy of the proposed approach promises a potential use of the sequence and structural features in computational protein design. Availability and implementation http://github.com/KYZ-LSB/ProTerS-FitFun. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation The Estimation of Model Accuracy problem is a cornerstone problem in the field of Bioinformatics. As of CASP14, there are 79 global QA methods, and a minority of 39 residue-level QA methods with very few of them working on protein complexes. Here, we introduce ZoomQA, a novel, single-model method for assessing the accuracy of a tertiary protein structure/complex prediction at residue level, which have many applications such as drug discovery. ZoomQA differs from others by considering the change in chemical and physical features of a fragment structure (a portion of a protein within a radius |$r$| of the target amino acid) as the radius of contact increases. Fourteen physical and chemical properties of amino acids are used to build a comprehensive representation of every residue within a protein and grade their placement within the protein as a whole. Moreover, we have shown the potential of ZoomQA to identify problematic regions of the SARS-CoV-2 protein complex. Results We benchmark ZoomQA on CASP14, and it outperforms other state-of-the-art local QA methods and rivals state of the art QA methods in global prediction metrics. Our experiment shows the efficacy of these new features and shows that our method is able to match the performance of other state-of-the-art methods without the use of homology searching against databases or PSSM matrices. Availability http://zoomQA.renzhitech.com [ABSTRACT FROM AUTHOR]

Motivation Protein structure modeling can be improved by the use of distance constraints between amino acid residues, provided such data reflects—at least partially—the native tertiary structure of the target system. In fact, only a small subset of the native contact map is necessary to successfully drive the model conformational search, so one important goal is to obtain the set of constraints with the highest true-positive rate, lowest redundancy and greatest amount of information. In this work, we introduce a constraint evaluation and selection method based on the point-biserial correlation coefficient, which utilizes structural information from an ensemble of models to indirectly measure the power of each constraint in biasing the conformational search toward consensus structures. Results Residue contact maps obtained by direct coupling analysis are systematically improved by means of discriminant analysis, reaching in some cases accuracies often seen only in modern deep-learning-based approaches. When combined with an iterative modeling workflow, the proposed constraint classification optimizes the selection of the constraint set and maximizes the probability of obtaining successful models. The use of discriminant analysis for the valorization of the information of constraint datasets is a general concept with possible applications to other constraint types and modeling problems. Availability and implementation MSA for the targets in this work is available on https://github.com/m3g/2021%5fBottino%5fBiserial. Modeling data supporting the findings of this study was generated at the Center for Computing in Engineering and Sciences, and is available from the corresponding author LM on request. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Good knowledge of a peptide's tertiary structure is important for understanding its function and its interactions with its biological targets. APPTEST is a novel computational protocol that employs a neural network architecture and simulated annealing methods for the prediction of peptide tertiary structure from the primary sequence. APPTEST works for both linear and cyclic peptides of 5–40 natural amino acids. APPTEST is computationally efficient, returning predicted structures within a number of minutes. APPTEST performance was evaluated on a set of 356 test peptides; the best structure predicted for each peptide deviated by an average of 1.9Å from its experimentally determined backbone conformation, and a native or near-native structure was predicted for 97% of the target sequences. A comparison of APPTEST performance with PEP-FOLD, PEPstrMOD and PepLook across benchmark datasets of short, long and cyclic peptides shows that on average APPTEST produces structures more native than the existing methods in all three categories. This innovative, cutting-edge peptide structure prediction method is available as an online web server at https://research.timmons.eu/apptest , facilitating in silico study and design of peptides by the wider research community. [ABSTRACT FROM AUTHOR]

Motivation The accuracy of RNA secondary and tertiary structure prediction can be significantly improved by using structural restraints derived from evolutionary coupling or direct coupling analysis. Currently, these coupling analyses relied on manually curated multiple sequence alignments collected in the Rfam database, which contains 3016 families. By comparison, millions of non-coding RNA sequences are known. Here, we established RNAcmap, a fully automatic pipeline that enables evolutionary coupling analysis for any RNA sequences. The homology search was based on the covariance model built by INFERNAL according to two secondary structure predictors: a folding-based algorithm RNAfold and the latest deep-learning method SPOT-RNA. Results We showed that the performance of RNAcmap is less dependent on the specific evolutionary coupling tool but is more dependent on the accuracy of secondary structure predictor with the best performance given by RNAcmap (SPOT-RNA). The performance of RNAcmap (SPOT-RNA) is comparable to that based on Rfam-supplied alignment and consistent for those sequences that are not in Rfam collections. Further improvement can be made with a simple meta predictor RNAcmap (SPOT-RNA/RNAfold) depending on which secondary structure predictor can find more homologous sequences. Reliable base-pairing information generated from RNAcmap, for RNAs with high effective homologous sequences, in particular, will be useful for aiding RNA structure prediction. Availability and implementation RNAcmap is available as a web server at https://sparks-lab.org/server/rnacmap/ and as a standalone application along with the datasets at https://github.com/sparks-lab-org/RNAcmap%5fstandalone. A platform independent and fully configured docker image of RNAcmap is also provided at https://hub.docker.com/r/jaswindersingh2/rnacmap. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Protein structure prediction remains as one of the most important problems in computational biology and biophysics. In the past few years, protein residue–residue contact prediction has undergone substantial improvement, which has made it a critical driving force for successful protein structure prediction. Boosting the accuracy of contact predictions has, therefore, become the forefront of protein structure prediction. Results We show a novel contact map refinement method, ContactGAN, which uses Generative Adversarial Networks (GAN). ContactGAN was able to make a significant improvement over predictions made by recent contact prediction methods when tested on three datasets including protein structure modeling targets in CASP13 and CASP14. We show improvement of precision in contact prediction, which translated into improvement in the accuracy of protein tertiary structure models. On the other hand, observed improvement over trRosetta was relatively small, reasons for which are discussed. ContactGAN will be a valuable addition in the structure prediction pipeline to achieve an extra gain in contact prediction accuracy. Availability and implementation https://github.com/kiharalab/ContactGAN. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation The recent discovery of numerous non-coding RNAs (long non-coding RNAs, in particular) has transformed our perception about the roles of RNAs in living organisms. Our ability to understand them, however, is hampered by our inability to solve their secondary and tertiary structures in high resolution efficiently by existing experimental techniques. Computational prediction of RNA secondary structure, on the other hand, has received much-needed improvement, recently, through deep learning of a large approximate data, followed by transfer learning with gold-standard base-pairing structures from high-resolution 3-D structures. Here, we expand this single-sequence-based learning to the use of evolutionary profiles and mutational coupling. Results The new method allows large improvement not only in canonical base-pairs (RNA secondary structures) but more so in base-pairing associated with tertiary interactions such as pseudoknots, non-canonical and lone base-pairs. In particular, it is highly accurate for those RNAs of more than 1000 homologous sequences by achieving >0.8 F1-score (harmonic mean of sensitivity and precision) for 14/16 RNAs tested. The method can also significantly improve base-pairing prediction by incorporating artificial but functional homologous sequences generated from deep mutational scanning without any modification. The fully automatic method (publicly available as server and standalone software) should provide the scientific community a new powerful tool to capture not only the secondary structure but also tertiary base-pairing information for building three-dimensional models. It also highlights the future of accurately solving the base-pairing structure by using a large number of natural and/or artificial homologous sequences. Availability and implementation Standalone-version of SPOT-RNA2 is available at https://github.com/jaswindersingh2/SPOT-RNA2. Direct prediction can also be made at https://sparks-lab.org/server/spot-rna2/. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The majority of the genome is transcribed to RNA in living organisms. RNA transcripts can form astonishing arrays of secondary and tertiary structures via Watson–Crick, Hoogsteen, or wobble base pairing. In vivo , RNA folding is not a simple thermodynamic event of minimizing free energy. Instead, the process is constrained by transcription, RNA-binding proteins, steric factors, and the microenvironment. RNA secondary structure (RSS) plays myriad roles in numerous biological processes, such as RNA processing, stability, transportation, and translation in prokaryotes and eukaryotes. Emerging evidence has also implicated RSS in RNA trafficking, liquid–liquid phase separation, and plant responses to environmental variations such as temperature and salinity. At molecular level, RSS is correlated with splicing, polyadenylation, protein synthesis, and miRNA biogenesis and functions. In this review, we summarize newly reported methods for probing RSS in vivo and functions and mechanisms of RSS in plant physiology. [ABSTRACT FROM AUTHOR]

Residue coevolution estimations coupled to machine learning methods are revolutionizing the ability of protein structure prediction approaches to model proteins that lack clear homologous templates in the Protein Data Bank (PDB). This has been patent in the last round of the Critical Assessment of Structure Prediction (CASP), which presented several very good models for the hardest targets. Unfortunately, literature reporting on these advances often lacks digests tailored to lay end users; moreover, some of the top-ranking predictors do not provide webservers that can be used by nonexperts. How can then end users benefit from these advances and correctly interpret the predicted models? Here we review the web resources that biologists can use today to take advantage of these state-of-the-art methods in their research, including not only the best de novo modeling servers but also datasets of models precomputed by experts for structurally uncharacterized protein families. We highlight their features, advantages and pitfalls for predicting structures of proteins without clear templates. We present a broad number of applications that span from driving forward biochemical investigations that lack experimental structures to actually assisting experimental structure determination in X-ray diffraction, cryo-EM and other forms of integrative modeling. We also discuss issues that must be considered by users yet still require further developments, such as global and residue-wise model quality estimates and sources of residue coevolution other than monomeric tertiary structure. [ABSTRACT FROM AUTHOR]

Motivation The full description of nucleic acid conformation involves eight torsion angles per nucleotide. To simplify this description, we previously developed a representation of the nucleic acid backbone that assigns each nucleotide a pair of pseudo-torsion angles (eta and theta defined by P and C4ʹ atoms; or etaʹ and thetaʹ defined by P and C1ʹ atoms). A Java program, AMIGOS II, is currently available for calculating eta and theta angles for RNA and for performing motif searches based on eta and theta angles. However, AMIGOS II lacks the ability to parse DNA structures and to calculate etaʹ and thetaʹ angles. It also has little visualization capacity for 3D structure, making it difficult for users to interpret the computational results. Results We present AMIGOS III, a PyMOL plugin that calculates the pseudo-torsion angles eta, theta, etaʹ and thetaʹ for both DNA and RNA structures and performs motif searching based on these angles. Compared to AMIGOS II, AMIGOS III offers improved pseudo-torsion angle visualization for RNA and faster nucleic acid worm database generation; it also introduces pseudo-torsion angle visualization for DNA and nucleic acid worm visualization. Its integration into PyMOL enables easy preparation of tertiary structure inputs and intuitive visualization of involved structures. Availability and implementation https://github.com/pylelab/AMIGOSIII. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Sugar alcohols (polyols) have important roles as nutrients, anti-freezing agents and scavengers of free radicals in cold-adapted bacteria, but the characteristics of polyol dehydrogenases in cold-adapted bacteria remain largely unknown. In this study, based on the observation that a cold-adapted bacterium Pseudomonas mandelii JR-1 predominantly utilized d - sorbitol as its carbon source, among the four polyols examined (d -galactitol, d - mannitol, d -sorbitol and d -xylitol), we cloned and characterized a sorbitol dehydrogenase (SDH, EC 1.1.1.14) belonging to the short-chain dehydrogenase/reductase family from this bacterium (the SDH hereafter referred to as PmSDH). PmSDH contained Asn111, Ser140, Tyr153 and Lys157 as catalytic active site residues and existed as an ∼67-kDa dimer in size-exclusion chromatography. PmSDH converted d -sorbitol to d -fructose using nicotinamide adenine dinucleotide (NAD+) as a cofactor and, vice versa, d -fructose to d -sorbitol using nicotinamide adenine dinucleotide reduced (NADH) as a cofactor. PmSDH maintained its conformational flexibility, secondary and tertiary structures, and thermal stability at 4–25°C. These results indicate that PmSDH, which has a flexible structure and a high catalytic activity at colder temperatures, is well suited to sorbitol utilization in the cold-adapted bacterium P. mandelii JR-1. [ABSTRACT FROM AUTHOR]

To date only a handful of duplicated genes have been described in RNA viruses. This shortage can be attributed to different factors, including the RNA viruses with high mutation rate that would make a large genome more prone to acquire deleterious mutations. This may explain why sequence-based approaches have only found duplications in their most recent evolutionary history. To detect earlier duplications, we performed protein tertiary structure comparisons for every RNA virus family represented in the Protein Data Bank. We present a list of thirty pairs of possible paralogs with <30 per cent sequence identity. It is argued that these pairs are the outcome of six duplication events. These include the α and β subunits of the fungal toxin KP6 present in the dsRNA Ustilago maydis virus (family Totiviridae), the SARS-CoV (Coronaviridae) nsp3 domains SUD-N, SUD-M and X-domain, the Picornavirales (families Picornaviridae , Dicistroviridae, Iflaviridae and Secoviridae) capsid proteins VP1, VP2 and VP3, and the Enterovirus (family Picornaviridae) 3C and 2A cysteine-proteases. Protein tertiary structure comparisons may reveal more duplication events as more three-dimensional protein structures are determined and suggests that, although still rare, gene duplications may be more frequent in RNA viruses than previously thought. Keywords : gene duplications; RNA viruses. [ABSTRACT FROM AUTHOR]

Objectives: Persistent antigen exposure leads to the accumulation of lymphocytes and subsequent tertiary lymphoid structures (TLS). We investigated the relationship of tumor microenvironment (TME) with respect to programmed death ligand 1 (PD-L1), its receptor programmed death 1 (PD-1), and TLS in upper tract urothelial carcinoma (UTUC) cases and compared them with UTUC associated with urothelial bladder carcinoma (UTUC-BCa).Methods: We retrospectively identified 72 patients with UTUC. Representative slides were reviewed, and TLS were counted. Immunohistochemical stains for PD-1 and PD-L1 were performed. PD-1-positive lymphocytes were counted and H-score for PD-L1-positive membranous staining was determined.Results: PD-L1 expression in the tumor was present in 55.1% of the UTUC cases. Higher stage was associated with increased PD-L1 expression (P = .035). TLS were present in 33.3% and their presence was significantly associated with PD-L1 positivity (P = .024). This association remained significant after adjustment for UTUC-BCa. TLS were also associated with a greater number of infiltrating PD-1-positive lymphocytes (P = .013).Conclusions: This study is one of the first comparative studies of the TME in UTUC and UTUC-BCa. PD-L1 is expressed in a subset of UTUC and is associated with TLS. The presence of TLS is an inherent characteristic of UTUC and not secondary to the presence of BCa. [ABSTRACT FROM AUTHOR]

Esophageal squamous cell carcinoma (ESCA) exhibits high intratumoral molecular heterogeneity posing a challenge to cancer therapy. Immune checkpoint blockade therapy has been approved for this disease, but with modest results. RNA‐Seq data from paired tumor and surrounding nonmalignant tissue from 14 patients diagnosed with ESCA without previous treatment and from The Cancer Genome Atlas‐ESCA cohort were analyzed. Herein, we investigated ESCA immune landscape including mutation‐derived neoantigens and immune cell subpopulations. Tumor‐associated antigen expression was determined by in silico analyses and confirmed by immunohistochemistry showing that PRAME, CEACAM4, and MAGEA11 proteins are expressed on tumors. Immune checkpoint molecules gene expression was higher in the tumor compared with surrounding nonmalignant tissue, but its expression varies greatly among patients. TCR repertoire and BCR transcripts analysis evidenced low clonal diversity with one TCR clone predicted to be specific for a MAGEA11‐derived peptide. A high number of B‐cell clones infiltrating the tumors and the abundance of these cells in tertiary lymphoid structures observed in ESCA tumors support B cells as a potential immune modulator in this tumor. [ABSTRACT FROM AUTHOR]

Many phenolic compounds, derived from lignin during the pretreatment of lignocellulosic biomass, could obviously inhibit the activity of cellulolytic and hemicellulolytic enzymes. Acetosyringone (AS) is one of the phenolic compounds produced from lignin degradation. In this study, we investigated the inhibitory effects of AS on xylanase activity through kinetic experiments. The results showed that AS could obviously inhibit the activity of xylanase in a reversible and noncompetitive binding manner (up to 50% activity loss). Inhibitory kinetics and constants of xylanase on AS were conducted by the HCH-1 model (β = 0.0090 ± 0.0009 mM−1). Furthermore, intrinsic and 8-anilino-1-naphthalenesulfonic (ANS)-binding fluorescence results showed that the tertiary structure of AS-mediated xylanase was altered. These findings provide new insights into the role of AS in xylanase activity. Our results also suggest that AS was an inhibitor of xylanase and targeting AS was a potential strategy to increase xylose production. Acetosyringone inhibited xylanase activity in a reversible and non-competitive binding manner, which might be related to the disruption of tertiary structure of xylanase. [ABSTRACT FROM AUTHOR]

Nascent polypeptides are synthesized on ribosomes starting at the N-terminus and simultaneously begin to fold during translation. We constructed N-terminal fragments of prosubtilisin E containing an intramolecular chaperone (IMC) at N-terminus to mimic cotranslational folding intermediates of prosubtilisin. The IMC-fragments of prosubtilisin exhibited progressive enhancement of their secondary structures and thermostabilities with increasing polypeptide length. However, even the largest IMC-fragment with 72 residues truncated from the C-terminus behaved as a molten globule, indicating the requirement of the C-terminal region to have a stable tertiary structure. Furthermore, truncation of the IMC in the IMC-fragments resulted in aggregation, suggesting that the IMC plays a crucial role to prevent misfolding and aggregation of cotranslational folding intermediates during translation of prosubtilisin polypeptide. [ABSTRACT FROM AUTHOR]

Summary: Tertiary lymphoid structure (TLS) provides a local and critical microenvironment for both cellular and humoral immunity and supports effective antigen presentation and lymphocyte activation. However, the gene expression profile and prognostic significance of TLS in oral cancer remain largely unrevealed. In this study, we found the presence of both intratumoral and peritumoral TLSs in a series of 65 patients with oral cancer treated by surgical resection, with positive detection rates of 33.8 and 75.4%, respectively. The presence of intratumoral TLSs, but not peritumoral TLSs, was significantly associated with decreased P53 and Ki67 scores (P = 0·027 and 0·047, respectively). The survival analyses revealed that oral cancer patients with higher grades of TLSs was associated with improved disease‐free survival (DFS) and overall survival (OS) (P = 0·037 and 0·031, respectively). Gene expression profiling analysis of the cytokines and chemokines responsible for lymph‐node neogenesis identified a three‐up‐regulated‐gene set, i.e. IL7, LTB and CXCL13, which was shown to be correlated with human oral cancer‐associated TLSs. This study provides a framework for better understanding of oral cancer‐associated TLSs and for delineating future innovative prognostic biomarkers and immune therapeutic strategies for oral cancer. [ABSTRACT FROM AUTHOR]

The glutathione S-transferase (GST) family plays an important role in the adaptation of herbivorous insects to new host plants and other environmental constrains. The family codes for enzymes that neutralize reactive oxygen species and phytotoxins through the conjugation of reduced glutathione. Here, we studied the molecular evolution of the GST family in Bemisia tabaci , a complex of >35 sibling species, differing in their geographic and host ranges. We tested if some enzymes evolved different functionality, by comparing their sequences in six species, representing five of the six major genetic clades in the complex. Comparisons of the nonsynonymous to synonymous substitution ratios detected positive selection events in 11 codons of 5 cytosolic GSTs. Ten of them are located in the periphery of the GST dimer, suggesting a putative involvement in interactions with other proteins. Modeling the tertiary structure of orthologous enzymes, identified additional 19 mutations in 9 GSTs, likely affecting the enzymes' functionality. Most of the mutation events were found in the environmentally responsive classes Delta and Sigma, indicating a slightly different delta/sigma tool box in each species. At a broader genomic perspective, our analyses indicated a significant expansion of the Delta GST class in B. tabaci and a general association between the diet breadth of hemipteran species and their total number of GST genes. We raise the possibility that at least some of the identified changes improve the fitness of the B. tabaci species carrying them, leading to their better adaptation to specific environments. [ABSTRACT FROM AUTHOR]

Motivation Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful if good templates can be found. Although modern homology detection methods can find remote homologs with high sensitivity, the accuracy of template-based models generated from homology-detection-based alignments is often lower than that from ideal alignments. Results In this study, we propose a new method that generates pairwise sequence alignments for more accurate template-based modeling. The proposed method trains a machine learning model using the structural alignment of known homologs. It is difficult to directly predict sequence alignments using machine learning. Thus, when calculating sequence alignments, instead of a fixed substitution matrix, this method dynamically predicts a substitution score from the trained model. We evaluate our method by carefully splitting the training and test datasets and comparing the predicted structure's accuracy with that of state-of-the-art methods. Our method generates more accurate tertiary structure models than those produced from alignments obtained by other methods. Availability and implementation https://github.com/shuichiro-makigaki/exmachina. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Glycosyltransferases (GTs), a large class of carbohydrate-active enzymes, adds glycosyl moieties to various substrates to generate multiple bioactive compounds, including natural products with pharmaceutical or agrochemical values. Here, we first collected comprehensive information on GTs, including amino acid sequences, coding region sequences, available tertiary structures, protein classification families, catalytic reactions and metabolic pathways. Then, we developed sequence search and molecular docking processes for GTs, resulting in a GTs database (GTDB). In the present study, 520 179 GTs from approximately 21 647 species that involved in 394 kinds of different reactions were deposited in GTDB. GTDB has the following useful features: (i) text search is provided for retrieving the complete details of a query by combining multiple identifiers and data sources; (ii) a convenient browser allows users to browse data by different classifications and download data in batches; (iii) BLAST is offered for searching against pre-defined sequences, which can facilitate the annotation of the biological functions of query GTs; and lastly, (iv) GTdock using AutoDock Vina performs docking simulations of several GTs with the same single acceptor and displays the results based on 3Dmol.js allowing easy view of models. [ABSTRACT FROM AUTHOR]

A single-chain variable fragment (scFv) antibody is a recombinant protein in which a peptide linker connects the variable regions of the heavy chain and light chain. Due to its smaller molecular size, an scFv can be expressed using Escherichia coli. The presence of two disulphide bonds in the molecule often prevents expression of correctly folded scFv in the E. coli cytoplasm, making a refolding process necessary to regenerate scFv activity. The refolding process is time-consuming and requires large amounts of expensive reagents, such as guanidine hydrochloride, l -arginine and glutathione. Here, to conveniently obtain scFv proteins, we devised a simple and systematic method to optimize the co-expression of chaperone proteins and to combine them with specially engineered E. coli strains that permit the formation of stable disulphide bonds within the cytoplasm. Several scFv proteins were successfully obtained in a soluble form from E. coli cytoplasm. Thermal denaturation experiments and/or surface plasmon resonance measurements revealed that the thus-obtained scFvs possessed a stable tertiary structure and antigen-binding activity. The combined use of engineered E. coli with the simplified and systematic chaperone optimization can be useful for the production of scFv proteins. [ABSTRACT FROM AUTHOR]

Motivation Because DNA-binding proteins (DNA-BPs) play a vital role in all aspects of genetic activity, the development of reliable and efficient systems for automatic DNA-BP classification is becoming a crucial proteomic technology. Key to this technology is the discovery of powerful protein representations and feature extraction methods. The goal of this article is to develop experimentally a system for automatic DNA-BP classification by comparing and combining different descriptors taken from different types of protein representations. Results The descriptors we evaluate include those starting from the position-specific scoring matrix (PSSM) of proteins, those derived from the amino-acid sequence (AAS), various matrix representations of proteins and features taken from the three-dimensional tertiary structure of proteins. We also introduce some new variants of protein descriptors. Each descriptor is used to train a separate support vector machine (SVM), and results are combined by sum rule. Our final system obtains state-or-the-art results on three benchmark DNA-BP datasets. Availability and implementation The MATLAB code for replicating the experiments presented in this paper is available at https://github.com/LorisNanni. [ABSTRACT FROM AUTHOR]

A sensitive and selective method for detection and quantitation of the enantiomers of 18 synthetic cathinones with tertiary amine structure using HPLC–UV-VIS has been developed. Two chiral columns, Astec Cellulose DMP and Amylose-based Chiralpak AS-H, have been examined separately. Mobile phase composed of hexane, isopropanol and triethylamine (99.0:1.0:0.1) was used under an isocratic elution mode. Three of these compounds were separated simultaneously after being spiked into urine and plasma samples. 2,3-Methylenedioxy pyrovalerone was used as an internal standard for the purpose of quantitation. The analytical method has been validated in terms of linearity, limits of detection (LOD), limits of quantitation (LOQ), recoveries and reproducibilities in urine and plasma matrices. The calibration curves exhibited correlation coefficients better than 0.99. It was found that the LODs of these cathinone derivatives in urine were in the range of 1.00–1.47 ppm; while in plasma, the LODs were in the range of 0.14–0.67 ppm. The LOQs in urine were in the range of 3.03–4.46 ppm and in plasma they were in the range of 0.42–2.04 ppm. The method recoveries in terms of percent error averaged 2.4% and 3.2% for the spiked plasma and urine samples, respectively; while interday and intraday reproducibilities reported at three different levels, 5, 100 and 200 ppm, in terms of coefficient of variance were in the range of (0.27–5.39)% in plasma and (0.47–3.12)% in urine which lies in the acceptable range. [ABSTRACT FROM AUTHOR]

Salivary glands (SGs) represent a permissive site for several sialotropic viruses whose persistence is linked to the development of autoimmunity. Natural Killer (NK) cells play a key role in viral clearance but their involvement in viral infection control and in tertiary lymphoid structures (TLS) development within SGs is unknown. By using an inducible model of TLS in the SGs of wild‐type C57BL/6 mice, induced by the local delivery of a replication‐defective adenovirus (AdV), we demonstrated that circulating NK cells are rapidly recruited to SGs and highly enrich the early inflammatory infiltrate prior to TLS development. NK cells migrating to SGs in response to AdV infection up‐regulate NKp46, undergo proliferation, acquire cytotoxic potential, produce Granzyme‐B and IFN‐γ, and reduce viral load in the acute phase of the infection. Nonetheless, the selective depletion of both circulating and infiltrating NK cells in AdV‐infected mice neither affect the development and frequency of TLS nor the onset of autoimmunity. These data demonstrate that, upon local viral delivery of AdV, peripheral NK cells homing to SGs can exert an early control of the viral infection but are dispensable for the formation of TLS and breach of immunologic tolerance. Circulating Natural Killer cells migrate to salivary glands after local viral delivery, control infection but are dispensable for the formation of tertiary lymphoid structures. [ABSTRACT FROM AUTHOR]

Summary The WD40-repeat proteins are a large family of scaffold molecules that assemble complexes in various cellular processes. Obtaining their structures is the key to understanding their interaction details. We present WDSPdb 2.0, a significantly updated resource providing accurately predicted secondary and tertiary structures and featured sites annotations. Based on an optimized pipeline, WDSPdb 2.0 contains about 600 thousand entries, an increase of 10-fold, and integrates more than 37 000 variants from sources of ClinVar, Cosmic, 1000 Genomes, ExAC, IntOGen, cBioPortal and IntAct. In addition, the web site is largely improved for visualization, exploring and data downloading. Availability and implementation http://www.wdspdb.com/wdsp/ or http://wu.scbb.pkusz.edu.cn/wdsp/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The success in protein tertiary-structure prediction is considered to be a function of coverage and similarity/identity of their sequences with suitable templates in the structural databases. However, this measure of modelability of a protein sequence into its structure may be misleading. Addressing this limitation, we propose here a 'structural difficulty (SD)' index, which is derived from secondary structures, homology and physicochemical features of protein sequences. The SD index reflects the capability of predicting accurate structures and helps to assess the potential for developing proteome level structural databases for various organisms with some of the best methodologies available currently. For instance, the plausibility of populating the structural database of human proteome with reliable quality structures under 3 Å root mean square deviation from the corresponding natives is found to be ~37% of a total of 11 084 manually curated soluble proteins and ~64% for all annotated and reviewed unique soluble protein (344 661 sequences) of UniProtKB. Also for 77 human pathogenic viruses comprising 2365 globular viral proteins out of which only 162 structures are solved experimentally, SD index scores 1336 proteins in the modelable zone. Availability of reliable protein structures may prove a crucial aid in developing species-wise structural proteomic databases for accelerating function annotation and for drug development endeavors. [ABSTRACT FROM AUTHOR]

Motivation: During the evolution, functional sites on the surface of the protein as well as the hydrophobic core maintaining the structural integrity are well-conserved. However, available protein structure alignment methods align protein structures based solely on the 3D geometric similarity, limiting their ability to detect functionally relevant correspondences between the residues of the proteins, especially for distantly related homologous proteins. Results: In this article, we propose a new protein pairwise structure alignment algorithm (UniAlign) that incorporates additional evolutionary information captured in the form of sequence similarity, sequence profiles and residue conservation. We define a per-residue score (UniScore) as a weighted sum of these and other features and develop an iterative optimization procedure to search for an alignment with the best overall UniScore. Our extensive experiments on CDD, HOMSTRAD and BAliBASE benchmark datasets show that UniAlign outperforms commonly used structure alignment methods. We further demonstrate UniAlign's ability to develop family-specific models to drastically improve the quality of the alignments. Availability and implementation: UniAlign is available as a web service at: http://sacan.biomed.drexel.edu/unialign [ABSTRACT FROM AUTHOR]