IntFOLD: an integrated web resource for high performance protein structure and function prediction

Vaccine design and development: Exploring the interface with computational biology and AI

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

Glyphosate-Induced Phosphonatase Operons in Soil Bacteria of the Genus Achromobacter

Achromobacter insolitus and Achromobacter aegrifaciens, bacterial degraders of the herbicide glyphosate, were found to induce phosphonatase (phosphonoacetaldehyde hydrolase, EC 3.11.1.1) when grown on minimal media with glyphosate as the sole source of phosphorus. The phosphonatases of the strains were purified to an electrophoretically homogeneous state and characterized. The enzymes differed in their kinetic characteristics and some other parameters from the previously described phosphonatases. The phosphonatase of A. insolitus was first revealed to separate into two stable forms, which had similar kinetic characteristics but interacted differently with affinity and ion-exchange resins. The genomes of the investigated bacteria were sequenced. The phosphonatase genes were identified, and their context was determined: the bacteria were shown to have gene clusters, which, besides the phosphonatase operon, included genes for LysR-type transcription activator (substrate sensor) and putative iron-containing oxygenase PhnHD homologous to monooxygenases PhnY and TmpB of marine organophosphonate degraders. Genes of 2-aminoethylphosphonate aminotransferase (PhnW, EC 2.6.1.37) were absent in the achromobacterial phosphonatase operons; instead, we revealed the presence of genes encoding the putative flavin oxidase HpnW. In silico simulation showed 1-hydroxy-2-aminoethylphosphonate to be the most likely substrate of the new monooxygenase, and a number of glycine derivatives structurally similar to glyphosate to be substrates of flavin oxidase.

Comprehensive CCM3 Mutational Analysis in Two Patients with Syndromic Cerebral Cavernous Malformation

Cerebral cavernous malformation (CCM) is a vascular disease that affects the central nervous system, which familial form is due to autosomal dominant mutations in the genes KRIT1(CCM1), MGC4607(CCM2), and PDCD10(CCM3). Patients affected by the PDCD10 mutations usually have the onset of symptoms at an early age and a more aggressive phenotype. The aim of this study is to investigate the molecular mechanism involved with CCM3 disease pathogenesis. Herein, we report two typical cases of CCM3 phenotype and compare the clinical and neuroradiological findings with five patients with a familial form of KRIT1 or CCM2 mutations and six patients with a sporadic form. In addition, we evaluated the PDCD10 gene expression by qPCR and developed a bioinformatic pipeline to understand the structural changes of mutations. The two CCM3 patients had an early onset of symptoms and a high lesion burden. Furthermore, the sequencing showed that Patient 1 had a frameshift mutation in c.222delT; p.(Asn75Thrfs*14) that leads to lacking the last 124 C-terminal amino acids on its primary structure and Patient 2 had a variant on the splicing site region c.475-2A > G. The mRNA expression was fourfold lower in both patients with PDCD10 mutation. Using in silico analysis, we identify that the frameshift mutation transcript lacks the C-terminal FAT-homology domain compared to the wild-type PDCD10 and preserves the N-terminal dimerization domain. The two patients studied here allow estimating the potential impact of mutations in clinical interpretation as well as support to better understand the mechanism and pathogenesis of CCM3.

Embracing the future-is artificial intelligence already better? A comparative study of artificial intelligence performance in diagnostic accuracy and decision-making

BACKGROUND AND PURPOSE

The integration of artificial intelligence (AI) in healthcare has the potential to revolutionize patient care and clinical decision-making. This study aimed to explore the reliability of large language models in neurology by comparing the performance of an AI chatbot with neurologists in diagnostic accuracy and decision-making.

METHODS

A cross-sectional observational study was conducted. A pool of clinical cases from the American Academy of Neurology's Question of the Day application was used as the basis for the study. The AI chatbot used was ChatGPT, based on GPT-3.5. The results were then compared to neurology peers who also answered the questions-a mean of 1500 neurologists/neurology residents.

RESULTS

The study included 188 questions across 22 different categories. The AI chatbot demonstrated a mean success rate of 71.3% in providing correct answers, with varying levels of proficiency across different neurology categories. Compared to neurology peers, the AI chatbot performed at a similar level, with a mean success rate of 69.2% amongst peers. Additionally, the AI chatbot achieved a correct diagnosis in 85.0% of cases and it provided an adequate justification for its correct responses in 96.1%.

CONCLUSIONS

The study highlights the potential of AI, particularly large language models, in assisting with clinical reasoning and decision-making in neurology and emphasizes the importance of AI as a complementary tool to human expertise. Future advancements and refinements are needed to enhance the AI chatbot's performance and broaden its application across various medical specialties.

Structural and Functional Characterization of Obesumbacterium proteus Phytase: A Comprehensive In-Silico Study

Phytate, also known as myoinositol hexakisphosphate, exhibits anti-nutritional properties and possesses a negative environmental impact. Phytase enzymes break down phytate, showing potential in various industries, necessitating thorough biochemical and computational characterizations. The present study focuses on Obesumbacterium proteus phytase (OPP), indicating its similarities with known phytases and its potential through computational analyses. Structure, functional, and docking results shed light on OPP's features, structural stability, strong and stable interaction, and dynamic conformation, with flexible sidechains that could adapt to different temperatures or specific functions. Root Mean Square fluctuation (RMSF) highlighted fluctuating regions in OPP, indicating potential sites for stability enhancement through mutagenesis. The systematic approach developed here could aid in enhancing enzyme properties via a rational engineering approach. Computational analysis expedites enzyme discovery and engineering, complementing the traditional biochemical methods to accelerate the quest for superior enzymes for industrial applications.

Characterization of HMGA2 variants expands the spectrum of Silver-Russell syndrome

Silver-Russell syndrome (SRS) is a heterogeneous disorder characterized by intrauterine and postnatal growth retardation. HMGA2 variants are a rare cause of SRS and its functional role in human linear growth is unclear. Patients with suspected SRS negative for 11p15LOM/mUPD7 underwent whole-exome and/or targeted-genome sequencing. Mutant HMGA2 protein expression and nuclear localization were assessed. Two Hmga2-knockin mouse models were generated. Five clinical SRS patients harbored HMGA2 variants with differing functional impacts: 2 stop-gain nonsense variants (c.49G>T, c.52C>T), c.166A>G missense variant, and 2 frameshift variants (c.144delC, c.145delA) leading to an identical, extended-length protein. Phenotypic features were highly variable. Nuclear localization was reduced/absent for all variants except c.166A>G. Homozygous knockin mice recapitulating the c.166A>G variant (Hmga2K56E) exhibited a growth-restricted phenotype. An Hmga2Ter76-knockin mouse model lacked detectable full-length Hmga2 protein, similarly to patient 3 and 5 variants. These mice were infertile, with a pygmy phenotype. We report a heterogeneous group of individuals with SRS harboring variants in HMGA2 and describe the first Hmga2 missense knockin mouse model (Hmga2K56E) to our knowledge causing a growth-restricted phenotype. In patients with clinical features of SRS but negative genetic screening, HMGA2 should be included in next-generation sequencing testing approaches.

Deep learning applications in protein crystallography

Deep learning techniques can recognize complex patterns in noisy, multidimensional data. In recent years, researchers have started to explore the potential of deep learning in the field of structural biology, including protein crystallography. This field has some significant challenges, in particular producing high-quality and well ordered protein crystals. Additionally, collecting diffraction data with high completeness and quality, and determining and refining protein structures can be problematic. Protein crystallographic data are often high-dimensional, noisy and incomplete. Deep learning algorithms can extract relevant features from these data and learn to recognize patterns, which can improve the success rate of crystallization and the quality of crystal structures. This paper reviews progress in this field.

The cytokinin receptor OHK4/OsHK4 regulates inflorescence architecture in rice via an IDEAL PLANT ARCHITECTURE1/WEALTHY FARMER'S PANICLE-mediated positive feedback circuit

Inflorescence architecture is important for rice (Oryza sativa) grain yield. The phytohormone cytokinin (CK) has been shown to regulate rice inflorescence development; however, the underlying mechanism mediated by CK perception is still unclear. Employing a forward genetic approach, we isolated an inactive variant of the CK receptor OHK4/OsHK4 gene named panicle length1, which shows decreased panicle size due to reduced inflorescence meristem (IM) activity. A 2-amino acid deletion in the long α-helix stalk of the sensory module of OHK4 impairs the homodimerization and ligand-binding capacity of the receptor, even though the residues do not touch the ligand-binding domain or the dimerization interface. This deletion impairs CK signaling that occurs through the type-B response regulator OsRR21, which acts downstream of OHK4 in controlling inflorescence size. Meanwhile, we found that IDEAL PLANT ARCHITECTURE1(IPA1)/WEALTHY FARMER'S PANICLE (WFP), encoding a positive regulator of IM development, acts downstream of CK signaling and is directly activated by OsRR21. Additionally, we revealed that IPA1/WFP directly binds to the OHK4 promoter and upregulates its expression through interactions with 2 TCP transcription factors, forming a positive feedback circuit. Altogether, we identified the OHK4-OsRR21-IPA1 regulatory module, providing important insights into the role of CK signaling in regulating rice inflorescence architecture.

Mechanisms of Fibroblast Growth Factor 21 Adsorption on Macroion Layers: Molecular Dynamics Modeling and Kinetic Measurements

Molecular dynamic modeling and various experimental techniques, including multi-angle dynamic light scattering (MADLS), streaming potential, optical waveguide light spectroscopy (OWLS), quartz crystal microbalance with dissipation (QCM), and atomic force microscopy (AFM), were applied to determine the basic physicochemical parameters of fibroblast growth factor 21 in electrolyte solutions. The protein size and shape, cross-section area, dependence of the nominal charge on pH, and isoelectric point of 5.3 were acquired. These data enabled the interpretation of the adsorption kinetics of FGF 21 on bare and macrocation-covered silica investigated by OWLS and QCM. It was confirmed that the protein molecules irreversibly adsorbed on the latter substrate, forming layers with controlled coverage up to 0.8 mg m-2, while their adsorption on bare silica was much smaller. The viability of two cell lines, CHO-K1 and L-929, on both bare and macrocation/FGF 21-covered substrates was also determined. It is postulated that the acquired results can serve as useful reference systems for designing complexes that can extend the half-life of FGF 21 in its active state.

Protein quality assessment with a loss function designed for high-quality decoys

Motivation: The prediction of a protein 3D structure is essential for understanding protein function, drug discovery, and disease mechanisms; with the advent of methods like AlphaFold that are capable of producing very high-quality decoys, ensuring the quality of those decoys can provide further confidence in the accuracy of their predictions. Results: In this work, we describe Qϵ, a graph convolutional network (GCN) that utilizes a minimal set of atom and residue features as inputs to predict the global distance test total score (GDTTS) and local distance difference test (lDDT) score of a decoy. To improve the model's performance, we introduce a novel loss function based on the ϵ-insensitive loss function used for SVM regression. This loss function is specifically designed for evaluating the characteristics of the quality assessment problem and provides predictions with improved accuracy over standard loss functions used for this task. Despite using only a minimal set of features, it matches the performance of recent state-of-the-art methods like DeepUMQA. Availability: The code for Qϵ is available at https://github.com/soumyadip1997/qepsilon.

Production of recombinant proteins including the B-cell epitopes of autolysin A of Staphylococcus aureus isolated from clinical sheep mastitis and their potential for vaccine development

Staphylococcus aureus is the most common clinical mastitis-associated pathogen in sheep which contributes to reduced welfare of affected animals and, therefore, compromises the quality and quantity of milk production. To prevent mastitis and its spread, it is essential to guarantee adequate breeding conditions and animal health, through the adoption of good farm management practices and the application of suitable biosecurity measures. Vaccination can play a strategic role in prevention, control, and eradication of diseases. The identification of secreted and cellular antigens of the predominant sheep-CC130/ST700/t1773 lineage would assist in the design of effective vaccine against mammary infections caused by S. aureus. In the current study, we carried out a 3D structural prediction analysis with the identification of the best B cell epitopes of the whole and secreted portion of S. aureus AtlA. Fragments of atlA, containing the main predicted epitopes, were amplified, cloned, and expressed in Escherichia coli for recombinant protein production. Two selected clones produced recombinant proteins (rAtl4 and rAtl8) showing strong reactivity with a hyperimmune serum against the native AtlA and with blood sera collected from sheep with clinical S. aureus mastitis. These may represent potential candidate protein-based vaccines able to elicit a protective immune response to be evaluated by vaccination and subsequent challenge of the vaccinated sheep.

In Silico Characterisation of the Aedes aegypti Gustatory Receptors

Aedes aegypti, also known as the dengue mosquito or the yellow fewer mosquito, is the vector of dengue, chikungunya, Zika, Mayaro and yellow fever viruses. The A. aegypti genome contains an array of gustatory receptor (GR) proteins that are related to the recognition of taste. In this study, we performed in silico molecular characterization of all 72 A. aegypti GRs reported in the latest version of A. aegypti genome AaegL5. Phylogenetic analysis classified the receptors into three major clads. Multiple GRs were found to encode multiple transcripts. Physicochemical attributes such as the aliphatic index, hydropathicity index and isoelectric point indicated that A. aegypti gustatory receptors are highly stable and are tailored to perform under a variety of cellular environments. Analysis for subcellular localization indicated that all the GRs are located either in the extracellular matrix or the plasma membrane. Results also indicated that the GRs are distributed mainly on chromosomes 2 and 3, which house 22 and 49 GRs, respectively, whereas chromosome 1 houses only one GR. NCBI-CDD analysis showed the presence of a highly conserved 7tm_7 chemosensory receptor protein superfamily that includes gustatory and odorant receptors from insect species Anopheles gambiae and Drosophila melanogaster. Further, three significantly enriched ungapped motifs in the protein sequence of all 72 A. aegypti gustatory receptors were found. High-quality 3D models for the tertiary structures were predicted with significantly higher confidence, along with ligand-binding residues. Prediction of S-nitrosylation sites indicated the presence of target cysteines in all the GRs with close proximity to the ligand-bindings sites within the 3D structure of the receptors. In addition, two highly conserved motifs inside the GR proteins were discovered that house a tyrosine (Y) and a cysteine (C) residue which may serve as targets for NO-mediated tyrosine nitration and S-nitrosylation, respectively. This study will help devise strategies for functional genomic studies of these important receptor molecules in A. aegypti and other mosquito species through in vitro and in vivo studies.

Structural Insights into the Giardia lamblia Target of Rapamycin Homolog: A Bioinformatics Approach

TOR proteins, also known as targets of rapamycin, are serine/threonine kinases involved in various signaling pathways that regulate cell growth. The protozoan parasite Giardia lamblia is the causative agent of giardiasis, a neglected infectious disease in humans. In this study, we used a bioinformatics approach to examine the structural features of GTOR, a G. lamblia TOR-like protein, and predict functional associations. Our findings confirmed that it shares significant similarities with functional TOR kinases, including a binding domain for the FKBP-rapamycin complex and a kinase domain resembling that of phosphatidylinositol 3-kinase-related kinases. In addition, it can form multiprotein complexes such as TORC1 and TORC2. These results provide valuable insights into the structure-function relationship of GTOR, highlighting its potential as a molecular target for controlling G. lamblia cell proliferation. Furthermore, our study represents a step toward rational drug design for specific anti-giardiasis therapeutic agents.

Canonical and Alternative Auxin Signaling Systems in Mono-, Di-, and Tetraploid Potatoes

It has long been known that the phytohormone auxin plays a promoting role in tuber formation and stress tolerance in potatoes. Our study aimed to identify and characterize the complete sets of auxin-related genes that presumably constitute the entire auxin signaling system in potato (Solanum tuberosum L.). The corresponding genes were retrieved from sequenced genomes of the doubled monoploid S. tuberosum DM1-3-516-R44 (DM) of the Phureja group, the heterozygous diploid line RH89-039-16 (RH), and the autotetraploid cultivar Otava. Both canonical and noncanonical auxin signaling pathways were considered. Phylogenetic and domain analyses of deduced proteins were supplemented by expression profiling and 3D molecular modeling. The canonical and ABP1-mediated pathways of auxin signaling appeared to be well conserved. The total number of potato genes/proteins presumably involved in canonical auxin signaling is 46 and 108 in monoploid DM and tetraploid Otava, respectively. Among the studied potatoes, spectra of expressed genes obviously associated with auxin signaling were partly cultivar-specific and quite different from analogous spectrum in Arabidopsis. Most of the noncanonical pathways found in Arabidopsis appeared to have low probability in potato. This was equally true for all cultivars used irrespective of their ploidy. Thus, some important features of the (noncanonical) auxin signaling pathways may be variable and species-specific.

Molecular docking in organic, inorganic, and hybrid systems: a tutorial review

Molecular docking simulation is a very popular and well-established computational approach and has been extensively used to understand molecular interactions between a natural organic molecule (ideally taken as a receptor) such as an enzyme, protein, DNA, RNA and a natural or synthetic organic/inorganic molecule (considered as a ligand). But the implementation of docking ideas to synthetic organic, inorganic, or hybrid systems is very limited with respect to their use as a receptor despite their huge popularity in different experimental systems. In this context, molecular docking can be an efficient computational tool for understanding the role of intermolecular interactions in hybrid systems that can help in designing materials on mesoscale for different applications. The current review focuses on the implementation of the docking method in organic, inorganic, and hybrid systems along with examples from different case studies. We describe different resources, including databases and tools required in the docking study and applications. The concept of docking techniques, types of docking models, and the role of different intermolecular interactions involved in the docking process to understand the binding mechanisms are explained. Finally, the challenges and limitations of dockings are also discussed in this review.

Graphical abstract

Bioinformatics characterization of BcsA-like orphan proteins suggest they form a novel family of pseudomonad cyclic-β-glucan synthases

Bacteria produce a variety of polysaccharides with functional roles in cell surface coating, surface and host interactions, and biofilms. We have identified an 'Orphan' bacterial cellulose synthase catalytic subunit (BcsA)-like protein found in four model pseudomonads, P. aeruginosa PA01, P. fluorescens SBW25, P. putida KT2440 and P. syringae pv. tomato DC3000. Pairwise alignments indicated that the Orphan and BcsA proteins shared less than 41% sequence identity suggesting they may not have the same structural folds or function. We identified 112 Orphans among soil and plant-associated pseudomonads as well as in phytopathogenic and human opportunistic pathogenic strains. The wide distribution of these highly conserved proteins suggest they form a novel family of synthases producing a different polysaccharide. In silico analysis, including sequence comparisons, secondary structure and topology predictions, and protein structural modelling, revealed a two-domain transmembrane ovoid-like structure for the Orphan protein with a periplasmic glycosyl hydrolase family GH17 domain linked via a transmembrane region to a cytoplasmic glycosyltransferase family GT2 domain. We suggest the GT2 domain synthesises β-(1,3)-glucan that is transferred to the GH17 domain where it is cleaved and cyclised to produce cyclic-β-(1,3)-glucan (CβG). Our structural models are consistent with enzymatic characterisation and recent molecular simulations of the PaPA01 and PpKT2440 GH17 domains. It also provides a functional explanation linking PaPAK and PaPA14 Orphan (also known as NdvB) transposon mutants with CβG production and biofilm-associated antibiotic resistance. Importantly, cyclic glucans are also involved in osmoregulation, plant infection and induced systemic suppression, and our findings suggest this novel family of CβG synthases may provide similar range of adaptive responses for pseudomonads.

Subfunctionalization of a monolignol to a phytoalexin glucosyltransferase is accompanied by substrate inhibition

Uridine diphosphate-dependent glycosyltransferases (UGTs) mediate the glycosylation of plant metabolites, thereby altering their physicochemical properties and bioactivities. Plants possess numerous UGT genes, with the encoded enzymes often glycosylating multiple substrates and some exhibiting substrate inhibition kinetics, but the biological function and molecular basis of these phenomena are not fully understood. The promiscuous monolignol/phytoalexin glycosyltransferase NbUGT72AY1 exhibits substrate inhibition (K_i) at 4 μM scopoletin, whereas the highly homologous monolignol StUGT72AY2 is inhibited at 190 μM. We therefore used hydrogen/deuterium exchange mass spectrometry and structure-based mutational analyses of both proteins and introduced NbUGT72AY1 residues into StUGT72AY2 and vice versa to study promiscuity and substrate inhibition of UGTs. A single F87I and chimeric mutant of NbUGT72AY1 showed significantly reduced scopoletin substrate inhibition, whereas its monolignol glycosylation activity was almost unaffected. Reverse mutations in StUGT72AY2 resulted in increased scopoletin glycosylation, leading to enhanced promiscuity, which was accompanied by substrate inhibition. Studies of 3D structures identified open and closed UGT conformers, allowing visualization of the dynamics of conformational changes that occur during catalysis. Previously postulated substrate access tunnels likely serve as drainage channels. The results suggest a two-site model in which the second substrate molecule binds near the catalytic site and blocks product release. Mutational studies showed that minor changes in amino acid sequence can enhance the promiscuity of the enzyme and add new capabilities such as substrate inhibition without affecting existing functions. The proposed subfunctionalization mechanism of expanded promiscuity may play a role in enzyme evolution and highlights the importance of promiscuous enzymes in providing new functions.

Two pathogen loci determine Blumeria graminis f. sp. tritici virulence to wheat resistance gene Pm1a

Blumeria graminis f. sp. tritici (Bgt) is a globally important fungal pathogen of wheat that can rapidly evolve to defeat wheat powdery mildew (Pm) resistance genes. Despite periodic regional deployment of the Pm1a resistance gene in US wheat production, Bgt strains that overcome Pm1a have been notably nonpersistent in the United States, while on other continents, they are more widely established. A genome-wide association study (GWAS) was conducted to map sequence variants associated with Pm1a virulence in 216 Bgt isolates from six countries, including the United States. A virulence variant apparently unique to Bgt isolates from the United States was detected in the previously mapped gene AvrPm1a (BgtE-5612) on Bgt chromosome 6; an in vitro growth assay suggested no fitness reduction associated with this variant. A gene on Bgt chromosome 8, Bgt-51526, was shown to function as a second determinant of Pm1a virulence, and despite < 30% amino acid identity, BGT-51526 and BGTE-5612 were predicted to share > 85% of their secondary structure. A co-expression study in Nicotiana benthamiana showed that BGTE-5612 and BGT-51526 each produce a PM1A-dependent hypersensitive response. More than one member of a B. graminis effector family can be recognized by a single wheat immune receptor, and a two-gene model is necessary to explain virulence to Pm1a.

Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology

Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones' rational design, also with the treatment of rare diseases in mind.

In Silico Examination of Single Nucleotide Missense Mutations in NHLH2, a Gene Linked to Infertility and Obesity

Continual advances in our understanding of the human genome have led to exponential increases in known single nucleotide variants. The characterization of each of the variants lags behind. For researchers needing to study a single gene, or multiple genes in a pathway, there must be ways to narrow down pathogenic variants from those that are silent or pose less pathogenicity. In this study, we use the NHLH2 gene which encodes the nescient helix-loop-helix 2 (Nhlh2) transcription factor in a systematic analysis of all missense mutations to date in the gene. The NHLH2 gene was first described in 1992. Knockout mice created in 1997 indicated a role for this protein in body weight control, puberty, and fertility, as well as the motivation for sex and exercise. Only recently have human carriers of NHLH2 missense variants been characterized. Over 300 missense variants for the NHLH2 gene are listed in the NCBI single nucleotide polymorphism database (dbSNP). Using in silico tools, predicted pathogenicity of the variants narrowed the missense variants to 37 which were predicted to affect NHLH2 function. These 37 variants cluster around the basic-helix-loop-helix and DNA binding domains of the transcription factor, and further analysis using in silico tools provided 21 SNV resulting in 22 amino acid changes for future wet lab analysis. The tools used, findings, and predictions for the variants are discussed considering the known function of the NHLH2 transcription factor. Overall use of these in silico tools and analysis of these data contribute to our knowledge of a protein which is both involved in the human genetic syndrome, Prader-Willi syndrome, and in controlling genes involved in body weight control, fertility, puberty, and behavior in the general population, and may provide a systematic methodology for others to characterize variants for their gene of interest.

Quality Estimates for 3D Protein Models

Protein structure modeling is one of the most advanced and complex processes in computational biology. One of the major problems for the protein structure prediction field has been how to estimate the accuracy of the predicted 3D models, on both a local and global level, in the absence of known structures. We must be able to accurately measure the confidence that we have in the quality predicted 3D models of proteins for them to become widely adopted by the general bioscience community. To address this major issue, it was necessary to develop new model quality assessment (MQA) methods and integrate them into our pipelines for building 3D protein models. Our MQA method, called ModFOLD, has been ranked as one of the most accurate MQA tools in independent blind evaluations. This chapter discusses model quality assessment in the protein modeling field, demonstrating both its strengths and limitations. We also present some of the best methods according to independent benchmarking data, which has been gathered in recent years.

Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling

Homology modeling was long considered a method of choice in tertiary protein structure prediction. However, it used to provide models of acceptable quality only when templates with appreciable sequence identity with a target could be found. The threshold value was long assumed to be around 20-30%. Below this level, obtained sequence identity was getting dangerously close to values that can be obtained by chance, after aligning any random, unrelated sequences. In these cases, other approaches, including ab initio folding simulations or fragment assembly, were usually employed. The most recent editions of the CASP and CAMEO community-wide modeling methods assessment have brought some surprising outcomes, proving that much more clues can be inferred from protein sequence analyses than previously thought. In this chapter, we focus on recent advances in the field of difficult protein modeling, pushing the threshold deep into the "twilight zone", with particular attention devoted to improvements in applications of machine learning and model evaluation.

Predicting the Assembly of the Transmembrane Domains of Viral Channel Forming Proteins and Peptide Drug Screening Using a Docking Approach

A de novo assembly algorithm is provided to propose the assembly of bitopic transmembrane domains (TMDs) of membrane proteins. The algorithm is probed using, in particular, viral channel forming proteins (VCPs) such as M2 of influenza A virus, E protein of severe acute respiratory syndrome corona virus (SARS-CoV), 6K of Chikungunya virus (CHIKV), SH of human respiratory syncytial virus (hRSV), and Vpu of human immunodeficiency virus type 2 (HIV-2). The generation of the structures is based on screening a 7-dimensional space. Assembly of the TMDs can be achieved either by simultaneously docking the individual TMDs or via a sequential docking. Scoring based on estimated binding energies (EBEs) of the oligomeric structures is obtained by the tilt to decipher the handedness of the bundles. The bundles match especially well for all-atom models of M2 referring to an experimentally reported tetrameric bundle. Docking of helical poly-peptides to experimental structures of M2 and E protein identifies improving EBEs for positively charged (K,R,H) and aromatic amino acids (F,Y,W). Data are improved when using polypeptides for which the coordinates of the amino acids are adapted to the Cα coordinates of the respective experimentally derived structures of the TMDs of the target proteins.

In silico study predicts a key role of RNA-binding domains 3 and 4 in nucleolin-miRNA interactions

RNA binding proteins (RBPs) regulate many important cellular processes through their interactions with RNA molecules. RBPs are critical for posttranscriptional mechanisms keeping gene regulation in a fine equilibrium. Conversely, dysregulation of RBPs and RNA metabolism pathways is an established hallmark of tumorigenesis. Human nucleolin (NCL) is a multifunctional RBP that interacts with different types of RNA molecules, in part through its four RNA binding domains (RBDs). Particularly, NCL interacts directly with microRNAs (miRNAs) and is involved in their aberrant processing linked with many cancers, including breast cancer. Nonetheless, molecular details of the NCL-miRNA interaction remain obscure. In this study, we used an in silico approach to characterize how NCL targets miRNAs and whether this specificity is imposed by a definite RBD-interface. Here, we present structural models of NCL-RBDs and miRNAs, as well as predict scenarios of NCL-miRNA interactions generated using docking algorithms. Our study suggests a predominant role of NCL RBDs 3 and 4 (RBD3-4) in miRNA binding. We provide detailed analyses of specific motifs/residues at the NCL-substrate interface in both these RBDs and miRNAs. Finally, we propose that the evolutionary emergence of more than two RBDs in NCL in higher organisms coincides with its additional role/s in miRNA processing. Our study shows that RBD3-4 display sequence/structural determinants to specifically recognize miRNA precursor molecules. Moreover, the insights from this study can ultimately support the design of novel antineoplastic drugs aimed at regulating NCL-dependent biological pathways with a causal role in tumorigenesis.

In Silico Structural Analysis of Serine Carboxypeptidase Nf314, a Potential Drug Target in Naegleria fowleri Infections

Naegleria fowleri, also known as the “brain-eating” amoeba, is a free-living protozoan that resides in freshwater bodies. This pathogenic amoeba infects humans as a casual event when swimming in contaminated water. Upon inhalation, N. fowleri invades the central nervous system and causes primary amoebic meningoencephalitis (PAM), a rapidly progressive and often fatal disease. Although PAM is considered rare, reducing its case fatality rate compels the search for pathogen-specific proteins with a structure–function relationship that favors their application as targets for discovering new or improved drugs against N. fowleri infections. Herein, we report a computational approach to study the structural features of Nf314 (a serine carboxypeptidase that is a virulence-related protein in N. fowleri infections) and assess its potential as a drug target, using bioinformatics tools and in silico molecular docking experiments. Our findings suggest that Nf314 has a ligand binding site suitable for the structure-based design of specific inhibitors. This study represents a further step toward postulating a reliable therapeutic target to treat PAM with drugs specifically aimed at blocking the pathogen proliferation by inhibiting protein function.

Characterization and Structural Prediction of Proteins in SARS-CoV-2 Bangladeshi Variant Through Bioinformatics

The renowned respiratory disease induced by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has become a global epidemic in just less than a year by the first half of 2020. The subsequent efficient human-to-human transmission of this virus eventually affected millions of people worldwide. The most devastating thing is that the infection rate is continuously uprising and resulting in significant mortality especially among the older age population and those with health co-morbidities. This enveloped, positive-sense RNA virus is chiefly responsible for the infection of the upper respiratory system. The virulence of the SARS-CoV-2 is mostly regulated by its proteins such as entry to the host cell through fusion mechanism, fusion of infected cells with neighboring uninfected cells to spread virus, inhibition of host gene expression, cellular differentiation, apoptosis, mitochondrial biogenesis, etc. But very little is known about the protein structures and functionalities. Therefore, the main purpose of this study is to learn more about these proteins through bioinformatics approaches. In this study, ORF10, ORF7b, ORF7a, ORF6, membrane glycoprotein, and envelope protein have been selected from a Bangladeshi Corona-virus strain G039392 and a number of bioinformatics tools (MEGA-X-V10.1.7, PONDR, ProtScale, ProtParam, SCRIBER, NetSurfP v2.0, IntFOLD, UCSF Chimera, and PyMol) and strategies were implemented for multiple sequence alignment and phylogeny analysis with 9 different variants, predicting hydropathicity, amino acid compositions, protein-binding propensity, protein disorders, and 2D and 3D protein modeling. Selected proteins were characterized as highly flexible, structurally and electrostatically extremely stable, ordered, biologically active, hydrophobic, and closely related to proteins of different variants. This detailed information regarding the characterization and structure of proteins of SARS-CoV-2 Bangladeshi variant was performed for the first time ever to unveil the deep mechanism behind the virulence features. And this robust appraisal also paves the future way for molecular docking, vaccine development targeting these characterized proteins.

Antibiofilm and staphyloxanthin inhibitory potential of terbinafine against Staphylococcus aureus: in vitro and in vivo studies

Background

Antimicrobial resistance is growing substantially, which necessitates the search for novel therapeutic options. Terbinafine, an allylamine antifungal agent that exhibits a broad spectrum of activity and is used in the treatment of dermatophytosis, could be a possible option to disarm S. aureus virulence.

Methods

Terbinafine inhibitory effect on staphyloxanthin was characterized by quantitative measurement of staphyloxanthin intermediates and molecular docking. The effect of terbinafine on S. aureus stress survival was characterized by viable counting. The anti-biofilm activity of terbinafine on S. aureus was assessed by the crystal violet assay and microscopy. Changes in S. aureus membrane following treatment with terbinafine were determined using Fourier transform infrared (FTIR) analysis. The synergistic action of terbinafine in combination with conventional antibiotics was characterized using the checkerboard assay. qRT-PCR was used to evaluate the impact of terbinafine on S. aureus gene expression. The influence of terbinafine on S. aureus pathogenesis was investigated in mice infection model.

Results

Terbinafine inhibits staphyloxanthin biosynthesis through targeting dehydrosqualene desaturase (CrtN). Docking analysis of terbinafine against the predicted active site of CrtN reveals a binding energy of − 9.579 kcal/mol exemplified by the formation of H-bonds, H-arene bonds, and hydrophobic/hydrophilic interactions with the conserved amino acids of the receptor pocket. Terbinafine treated S. aureus was more susceptible to both oxidative and acid stress as well as human blood killing as compared to untreated cells. Targeting staphyloxanthin by terbinafine rendered S. aureus more sensitive to membrane acting antibiotics. Terbinafine interfered with S. aureus biofilm formation through targeting cell autoaggregation, hydrophobicity, and exopolysaccharide production. Moreover, terbinafine demonstrated a synergistic interaction against S. aureus when combined with conventional antibiotics. Importantly, terbinafine attenuated S. aureus pathogenesis using mice infection model. qRT-PCR revealed that terbinafine repressed expression of the transcriptional regulators sigB, sarA, and msaB, as well as icaA in S. aureus.

Conclusions

Present findings strongly suggest that terbinafine could be used safely and efficiently as an anti-virulent agent to combat S. aureus infections.

DeepUMQA: ultrafast shape recognition-based protein model quality assessment using deep learning

MOTIVATION

Protein model quality assessment is a key component of protein structure prediction. In recent research, the voxelization feature was used to characterize the local structural information of residues, but it may be insufficient for describing residue-level topological information. Design features that can further reflect residue-level topology when combined with deep learning methods are therefore crucial to improve the performance of model quality assessment.

RESULTS

We developed a deep-learning method, DeepUMQA, based on Ultrafast Shape Recognition (USR) for the residue-level single-model quality assessment. In the framework of the deep residual neural network, the residue-level USR feature was introduced to describe the topological relationship between the residue and overall structure by calculating the first moment of a set of residue distance sets and then combined with 1D, 2D and voxelization features to assess the quality of the model. Experimental results on the CASP13, CASP14 test datasets and CAMEO blind test show that USR could supplement the voxelization features to comprehensively characterize residue structure information and significantly improve model assessment accuracy. The performance of DeepUMQA ranks among the top during the state-of-the-art single-model quality assessment methods, including ProQ2, ProQ3, ProQ3D, Ornate, VoroMQA, ProteinGCN, ResNetQA, QDeep, GraphQA, ModFOLD6, ModFOLD7, ModFOLD8, QMEAN3, QMEANDisCo3 and DeepAccNet.

AVAILABILITY AND IMPLEMENTATION

The DeepUMQA server is freely available at http://zhanglab-bioinf.com/DeepUMQA/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evaluation of Hydroxycarboxylic Acid Receptor 1 (HCAR1) as a Building Block for Genetically Encoded Extracellular Lactate Biosensors

The status of lactate has evolved from being considered a waste product of cellular metabolism to a useful metabolic substrate and, more recently, to a signaling molecule. The fluctuations of lactate levels within biological tissues, in particular in the interstitial space, are crucial to assess with high spatial and temporal resolution, and this is best achieved using cellular imaging approaches. In this study, we evaluated the suitability of the lactate receptor, hydroxycarboxylic acid receptor 1 (HCAR1, formerly named GPR81), as a basis for the development of a genetically encoded fluorescent lactate biosensor. We used a biosensor strategy that was successfully applied to molecules such as dopamine, serotonin, and norepinephrine, based on their respective G-protein-coupled receptors. In this study, a set of intensiometric sensors was constructed and expressed in living cells. They showed selective expression at the plasma membrane and responded to physiological concentrations of lactate. However, these sensors lost the original ability of HCAR1 to selectively respond to lactate versus other related small carboxylic acid molecules. Therefore, while representing a promising building block for a lactate biosensor, HCAR1 was found to be sensitive to perturbations of its structure, affecting its ability to distinguish between related carboxylic molecules.

Screening for cysteine-stabilized scaffolds for developing proteolytic-resistant AMPs

Antimicrobial peptides (AMP) are present in all organisms and can present several activities and potential applications in human and animal health. Screening these molecules scaffolds represents a key point for discovering and developing novel biotechnological products, including antimicrobial, antiviral and anticancer drugs candidates and insecticidal molecules with potential applications in agriculture. Therefore, considering the amount of biological data currently deposited on public databases, computational approaches have been commonly used to predicted and identify novel cysteine-rich peptides scaffolds with known or unknown biological properties. Here, we describe a step-by-step in silico screening for cysteine-rich peptides employing molecular modeling (with a core focus on comparative modeling) and atomistic molecular dynamics simulations. Moreover, we also present the concept of additional tools aiming at the computer-aided screening of new Cs-AMPs based drug candidates. After the computational screening and peptide chemical synthesis, we also provide the reader with a step-by-step in vitro activity evaluation of these candidates, including antibacterial, antifungal, and antiviral assays.

Building Structural Models of a Whole Mycoplasma Cell

Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.

Modeling the MreB-CbtA Interaction to Facilitate the Prediction and Design of Candidate Antibacterial Peptides

Protein-protein interactions (PPIs) have emerged as promising targets for PPI modulators as alternative drugs because they are essential for most biochemical processes in living organisms. In recent years, a spotlight has been put on the development of peptide-based PPI inhibitors as the next-generation therapeutics to combat antimicrobial resistance taking cognizance of protein-based PPI-modulators that interact with target proteins to inhibit function. Although protein-based PPI inhibitors are not effective therapeutic agents because of their high molecular weights, they could serve as sources for peptide-based pharmaceutics if the target-inhibitor complex is accessible and well characterized. The Escherichia coli (E. coli) toxin protein, CbtA, has been identified as a protein-based PPI modulator that binds to the bacterial actin homolog MreB leading to the perturbation of its polymerization dynamics; and consequently has been suggested to have antibacterial properties. Unfortunately, however, the three-dimensional structures of CbtA and the MreB-CbtA complex are currently not available to facilitate the optimization process of the pharmacological properties of CbtA. In this study, computer modeling strategies were used to predict key MreB-CbtA interactions to facilitate the design of antiMreB peptide candidates. A model of the E. coli CbtA was built using the trRosetta software and its stability was assessed through molecular dynamics (MD) simulations. The modeling and simulations data pointed to a model with reasonable quality and stability. Also, the HADDOCK software was used to predict a possible MreB-CbtA complex, which was characterized through MD simulations and compared with MreB-MreB dimmer. The results suggest that CbtA inhibits MreB through the competitive mechanism whereby CbtA competes with MreB monomers for the interprotofilament interface leading to interference with double protofilament formation. Additionally, by using the antiBP software to predict antibacterial peptides in CbtA, and the MreB-CbtA complex as the reference structure to determine important interactions and contacts, candidate antiMreB peptides were suggested. The peptide sequences could be useful in a rational antimicrobial peptide hybridization strategy to design novel antibiotics. All-inclusive, the data reveal the molecular basis of MreB inhibition by CbtA and can be incorporated in the design/development of the next-generation antibacterial peptides targeting MreB.

Repurposing pantoprazole and haloperidol as efflux pump inhibitors in azole resistant clinical Candida albicans and non-albicans isolates

Candida species have a major role in nosocomial infections leading to high morbidity and mortality. Increased resistance to various antifungals, especially azoles is a significant problem. One of the main mechanisms for azole resistance is the up-regulation of efflux pump genes including CDR1 and MDR1. In the current study, clinical Candida isolates were identified to the species level and the antifungal susceptibility (AFS) of different Candida species was determined by disk diffusion method. Furthermore, the main mechanisms of azole resistance were investigated. Finally, haloperidol and pantoprazole were tested for their potential synergistic effect against fluconazole-resistant isolates.

One hundred and twenty-two Candida clinical isolates were used in this study. 70 isolates were Candida albicans (57.4%), the non-albicans Candida species include: C. krusei (20.5%), C. tropicalis (6.6%), C. parapsilosis (5.7%), C. dubliniensis (4.9%) and C. glabrata (4.9%). The AFS testing showed that resistance to fluconazole and voriconazole were 13.1% (n = 16) and 9.8% (n = 12), respectively. Among the 16 resistant isolates, eight isolates (50%) were strong biofilm producers, seven (43.8 %) formed intermediate biofilm and one had no biofilm. All resistant strains overexpressed efflux pumps. Using RT-PCR, the efflux genes CDR1, MDR1 and ABC2 were over-expressed in azole resistant isolates. Haloperidol-fluconazole and pantoprazole-fluconazole combinations reduced the MIC of fluconazole in resistant isolates. The current study showed an increase in azole resistance of Candida species. The majority of resistant isolates form biofilm, and overexpress efflux pumps. Pantoprazole and Haloperidol showed a noteworthy effect as efflux pump inhibitors which oppose the fluconazole resistance in different Candida species.

Cytokinin Perception in Ancient Plants beyond Angiospermae

Cytokinins (CKs) control many plant developmental processes and responses to environmental cues. Although the CK signaling is well understood, we are only beginning to decipher its evolution. Here, we investigated the CK perception apparatus in early-divergent plant species such as bryophyte Physcomitrium patens, lycophyte Selaginella moellendorffii, and gymnosperm Picea abies. Of the eight CHASE-domain containing histidine kinases (CHKs) examined, two CHKs, PpCHK3 and PpCHK4, did not bind CKs. All other CHK receptors showed high-affinity CK binding (K_D of nM range), with a strong preference for isopentenyladenine over other CK nucleobases in the moss and for trans-zeatin over cis-zeatin in the gymnosperm. The pH dependences of CK binding for these six CHKs showed a wide range, which may indicate different subcellular localization of these receptors at either the plasma- or endoplasmic reticulum membrane. Thus, the properties of the whole CK perception apparatuses in early-divergent lineages were demonstrated. Data show that during land plant evolution there was a diversification of the ligand specificity of various CHKs, in particular, the rise in preference for trans-zeatin over cis-zeatin, which indicates a steadily increasing specialization of receptors to various CKs. Finally, this distinct preference of individual receptors to different CK versions culminated in vascular plants, especially angiosperms.

Improved Protein Structure Prediction Using a New Multi-Scale Network and Homologous Templates

The accuracy of de novo protein structure prediction has been improved considerably in recent years, mostly due to the introduction of deep learning techniques. In this work, trRosettaX, an improved version of trRosetta for protein structure prediction is presented. The major improvement over trRosetta consists of two folds. The first is the application of a new multi-scale network, i.e., Res2Net, for improved prediction of inter-residue geometries, including distance and orientations. The second is an attention-based module to exploit multiple homologous templates to increase the accuracy further. Compared with trRosetta, trRosettaX improves the contact precision by 6% and 8% on the free modeling targets of CASP13 and CASP14, respectively. A preliminary version of trRosettaX is ranked as one of the top server groups in CASP14's blind test. Additional benchmark test on 161 targets from CAMEO (between Jun and Sep 2020) shows that trRosettaX achieves an average TM-score ≈0.8, outperforming the top groups in CAMEO. These data suggest the effectiveness of using the multi-scale network and the benefit of incorporating homologous templates into the network. The trRosettaX algorithm is incorporated into the trRosetta server since Nov 2020. The web server, the training and inference codes are available at: https://yanglab.nankai.edu.cn/trRosetta/.

Processing of NODULE INCEPTION controls the transition to nitrogen fixation in root nodules

[Figure: see text].

Fibroblast Growth Factor 2 Conjugated with Monomethyl Auristatin E Inhibits Tumor Growth in a Mouse Model

Worldwide, cancer is the second leading cause of death. Regardless of the continuous progress in medicine, we still do not have a fully effective anti-cancer therapy. Therefore, the search for new targeted anti-cancer drugs is still an unmet need. Here, we present novel protein–drug conjugates that inhibit tumor growth in a mouse model of human breast cancer. We developed conjugates based on fibroblast growth factor (FGF2) with improved biophysical and biological properties for the efficient killing of cancer cells overproducing fibroblast growth factor receptor 1 (FGFR1). We used hydrophilic and biocompatible PEG4 or PEG27 molecules as a spacer between FGF2 and the toxic agent monomethyl auristatin E. All conjugates exhibited a cytotoxic effect on FGFR1-positive cancer cell lines. The conjugate with the highest hydrodynamic size (42 kDa) and cytotoxicity was found to efficiently inhibit tumor growth in a mouse model of human breast cancer.

Detailed characterization of the UMAMIT proteins provides insight into their evolution, amino acid transport properties, and role in the plant

Amino acid transporters play a critical role in distributing amino acids within the cell compartments and between plant organs. Despite this importance, relatively few amino acid transporter genes have been characterized and their role elucidated with certainty. Two main families of proteins encode amino acid transporters in plants: the amino acid-polyamine-organocation superfamily, containing mostly importers, and the UMAMIT (usually multiple acids move in and out transporter) family, apparently encoding exporters, totaling 63 and 44 genes in Arabidopsis, respectively. Knowledge of UMAMITs is scarce, based on six Arabidopsis genes and a handful of genes from other species. To gain insight into the role of the members of this family and provide data to be used for future characterization, we studied the evolution of the UMAMITs in plants, and determined the functional properties, the structure, and localization of the 47 Arabidopsis UMAMITs. Our analysis showed that the AtUMAMITs are essentially localized at the tonoplast or the plasma membrane, and that most of them are able to export amino acids from the cytosol, confirming a role in intra- and intercellular amino acid transport. As an example, this set of data was used to hypothesize the role of a few AtUMAMITs in the plant and the cell.

Proteomic Tools for the Analysis of Cytoskeleton Proteins

Proteomic analyses have become an essential part of the toolkit of the molecular biologist, given the widespread availability of genomic data and open source or freely accessible bioinformatics software. Tools are available for detecting homologous sequences, recognizing functional domains, and modeling the three-dimensional structure for any given protein sequence, as well as for predicting interactions with other proteins or macromolecules. Although a wealth of structural and functional information is available for many cytoskeletal proteins, with representatives spanning all of the major subfamilies, the majority of cytoskeletal proteins remain partially or totally uncharacterized. Moreover, bioinformatics tools provide a means for studying the effects of synthetic mutations or naturally occurring variants of these cytoskeletal proteins. This chapter discusses various freely available proteomic analysis tools, with a focus on in silico prediction of protein structure and function. The selected tools are notable for providing an easily accessible interface for the novice while retaining advanced functionality for more experienced computational biologists.

Glucosylation of (±)-Menthol by Uridine-Diphosphate-Sugar Dependent Glucosyltransferases from Plants

Menthol is a cyclic monoterpene alcohol of the essential oils of plants of the genus Mentha, which is in demand by various industries due to its diverse sensorial and physiological properties. However, its poor water solubility and its toxic effect limit possible applications. Glycosylation offers a solution as the binding of a sugar residue to small molecules increases their water solubility and stability, renders aroma components odorless and modifies bioactivity. In order to identify plant enzymes that catalyze this reaction, a glycosyltransferase library containing 57 uridine diphosphate sugar-dependent enzymes (UGTs) was screened with (±)-menthol. The identity of the products was confirmed by mass spectrometry and nuclear magnetic resonance spectroscopy. Five enzymes were able to form (±)-menthyl-β-d-glucopyranoside in whole-cell biotransformations: UGT93Y1, UGT93Y2, UGT85K11, UGT72B27 and UGT73B24. In vitro enzyme activity assays revealed highest catalytic activity for UGT93Y1 (7.6 nkat/mg) from Camellia sinensis towards menthol and its isomeric forms. Although UGT93Y2 shares 70% sequence identity with UGT93Y1, it was less efficient. Of the five enzymes, UGT93Y1 stood out because of its high in vivo and in vitro biotransformation rate. The identification of novel menthol glycosyltransferases from the tea plant opens new perspectives for the biotechnological production of menthyl glucoside.

Regulation of Ribosomal Protein Synthesis in Mycobacteria: The Autogenous Control of rpsO

The autogenous regulation of ribosomal protein (r-protein) synthesis plays a key role in maintaining the stoichiometry of ribosomal components in bacteria. In this work, taking the rpsO gene as a classic example, we addressed for the first time the in vivo regulation of r-protein synthesis in the mycobacteria M. smegmatis (Msm) and M. tuberculosis (Mtb). We used a strategy based on chromosomally integrated reporters under the control of the rpsO regulatory regions and the ectopic expression of Msm S15 to measure its impact on the reporter expression. Because the use of E. coli as a host appeared inefficient, a fluorescent reporter system was developed by inserting Msm or Mtb rpsO-egfp fusions into the Msm chromosome and expressing Msm S15 or E. coli S15 in trans from a novel replicative shuttle vector, pAMYC. The results of the eGFP expression measurements in Msm cells provided evidence that the rpsO gene in Msm and Mtb was feedback-regulated at the translation level. The mutagenic analysis showed that the folding of Msm rpsO 5'UTR in a pseudoknot appeared crucial for repression by both Msm S15 and E. coli S15, thus indicating a striking resemblance of the rpsO feedback control in mycobacteria and in E. coli.

Accurate prediction of protein structures and interactions using a three-track neural network

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.

Optimization of expression, purification and secretion of functional recombinant human growth hormone in Escherichia coli using modified staphylococcal protein a signal peptide

Background

Human Growth Hormone (hGH) is a glycoprotein released from the pituitary gland. Due to the wide range of effects in humans, any disruption in hGH secretion could have serious consequences. This highlights the clinical importance of hGH production in the treatment of different diseases associated with a deficiency of this hormone. The production of recombinant mature hormone in suitable hosts and secretion of this therapeutic protein into the extracellular space can be considered as one of the best cost-effective approaches not only to obtain the active form of the protein but also endotoxin-free preparation. Since the natural growth hormone signal peptide is of eukaryotic origin and is not detectable by any of the Escherichia coli secretory systems, including Sec and Tat, and is therefore unable to secrete hGH in the prokaryotic systems, designing a new and efficient signal peptide is essential to direct hGh to the extracellular space.

Results

In this study, using a combination of the bioinformatics design and molecular genetics, the protein A signal peptide from Staphylococcus aureus was modified, redesigned and then fused to the mature hGH coding region. The recombinant hGH was then expressed in E. coli and successfully secreted to the medium through the Sec pathway. Secretion of the hGH into the medium was verified using SDS-PAGE and western blot analysis. Recombinant hGH was then expressed in E. coli and successfully secreted into cell culture medium via the Sec pathway. The secretion of hGH into the extracellular medium was confirmed by SDS-PAGE and Western blot analysis. Furthermore, the addition of glycine was shown to improve hGH secretion onto the culture medium. Equations for determining the optimal conditions were also determined. Functional hGH analysis using an ELISA-based method confirmed that the ratio of the active form of secreted hGH to the inactive form in the periplasm is higher than this ratio in the cytoplasm.

Conclusions

Since the native signal protein peptide of S. aureus protein A was not able to deliver hGH to the extracellular space, it was modified using bioinformatics tools and fused to the n-terminal region of hGh to show that the redesigned signal peptide was functional.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12896-021-00701-x.

Bioinformatic Analysis of Two TOR (Target of Rapamycin)-Like Proteins Encoded by Entamoeba histolytica Revealed Structural Similarities with Functional Homologs

The target of rapamycin (TOR), also known as FKBP-rapamycin associated protein (FRAP), is a protein kinase belonging to the PIKK (phosphatidylinositol 3-kinase (PI3K)-related kinases) family. TOR kinases are involved in several signaling pathways that control cell growth and proliferation. Entamoeba histolytica, the protozoan parasite that causes human amoebiasis, contains two genes encoding TOR-like proteins: EhFRAP and EhTOR2. To assess their potential as drug targets to control the cell proliferation of E. histolytica, we studied the structural features of EhFRAP and EhTOR2 using a biocomputational approach. The overall results confirmed that both TOR amoebic homologs share structural similarities with functional TOR kinases, and show inherent abilities to form TORC complexes and participate in protein-protein interaction networks. To our knowledge, this study represents the first in silico characterization of the structure-function relationships of EhFRAP and EhTOR2.

ModFOLD8: accurate global and local quality estimates for 3D protein models

Methods for estimating the quality of 3D models of proteins are vital tools for driving the acceptance and utility of predicted tertiary structures by the wider bioscience community. Here we describe the significant major updates to ModFOLD, which has maintained its position as a leading server for the prediction of global and local quality of 3D protein models, over the past decade (>20 000 unique external users). ModFOLD8 is the latest version of the server, which combines the strengths of multiple pure-single and quasi-single model methods. Improvements have been made to the web server interface and there has been successive increases in prediction accuracy, which were achieved through integration of newly developed scoring methods and advanced deep learning-based residue contact predictions. Each version of the ModFOLD server has been independently blind tested in the biennial CASP experiments, as well as being continuously evaluated via the CAMEO project. In CASP13 and CASP14, the ModFOLD7 and ModFOLD8 variants ranked among the top 10 quality estimation methods according to almost every official analysis. Prior to CASP14, ModFOLD8 was also applied for the evaluation of SARS-CoV-2 protein models as part of CASP Commons 2020 initiative. The ModFOLD8 server is freely available at: https://www.reading.ac.uk/bioinf/ModFOLD/.

COMTOP: Protein Residue-Residue Contact Prediction through Mixed Integer Linear Optimization

Protein contact prediction helps reconstruct the tertiary structure that greatly determines a protein’s function; therefore, contact prediction from the sequence is an important problem. Recently there has been exciting progress on this problem, but many of the existing methods are still low quality of prediction accuracy. In this paper, we present a new mixed integer linear programming (MILP)-based consensus method: a Consensus scheme based On a Mixed integer linear opTimization method for prOtein contact Prediction (COMTOP). The MILP-based consensus method combines the strengths of seven selected protein contact prediction methods, including CCMpred, EVfold, DeepCov, NNcon, PconsC4, plmDCA, and PSICOV, by optimizing the number of correctly predicted contacts and achieving a better prediction accuracy. The proposed hybrid protein residue–residue contact prediction scheme was tested in four independent test sets. For 239 highly non-redundant proteins, the method showed a prediction accuracy of 59.68%, 70.79%, 78.86%, 89.04%, 94.51%, and 97.35% for top-5L, top-3L, top-2L, top-L, top-L/2, and top-L/5 contacts, respectively. When tested on the CASP13 and CASP14 test sets, the proposed method obtained accuracies of 75.91% and 77.49% for top-L/5 predictions, respectively. COMTOP was further tested on 57 non-redundant α-helical transmembrane proteins and achieved prediction accuracies of 64.34% and 73.91% for top-L/2 and top-L/5 predictions, respectively. For all test datasets, the improvement of COMTOP in accuracy over the seven individual methods increased with the increasing number of predicted contacts. For example, COMTOP performed much better for large number of contact predictions (such as top-5L and top-3L) than for small number of contact predictions such as top-L/2 and top-L/5. The results and analysis demonstrate that COMTOP can significantly improve the performance of the individual methods; therefore, COMTOP is more robust against different types of test sets. COMTOP also showed better/comparable predictions when compared with the state-of-the-art predictors.

MAP4K4 expression in cardiomyocytes: multiple isoforms, multiple phosphorylations and interactions with striatins

The Ser/Thr kinase MAP4K4, like other GCKIV kinases, has N-terminal kinase and C-terminal citron homology (CNH) domains. MAP4K4 can activate c-Jun N-terminal kinases (JNKs), and studies in the heart suggest it links oxidative stress to JNKs and heart failure. In other systems, MAP4K4 is regulated in striatin-interacting phosphatase and kinase (STRIPAK) complexes, in which one of three striatins tethers PP2A adjacent to a kinase to keep it dephosphorylated and inactive. Our aim was to understand how MAP4K4 is regulated in cardiomyocytes. The rat MAP4K4 gene was not properly defined. We identified the first coding exon of the rat gene using 5′-RACE, we cloned the full-length sequence and confirmed alternative-splicing of MAP4K4 in rat cardiomyocytes. We identified an additional α-helix C-terminal to the kinase domain important for kinase activity. In further studies, FLAG-MAP4K4 was expressed in HEK293 cells or cardiomyocytes. The Ser/Thr protein phosphatase inhibitor calyculin A (CalA) induced MAP4K4 hyperphosphorylation, with phosphorylation of the activation loop and extensive phosphorylation of the linker between the kinase and CNH domains. This required kinase activity. MAP4K4 associated with myosin in untreated cardiomyocytes, and this was lost with CalA-treatment. FLAG-MAP4K4 associated with all three striatins in cardiomyocytes, indicative of regulation within STRIPAK complexes and consistent with activation by CalA. Computational analysis suggested the interaction was direct and mediated via coiled-coil domains. Surprisingly, FLAG-MAP4K4 inhibited JNK activation by H₂O₂ in cardiomyocytes and increased myofibrillar organisation. Our data identify MAP4K4 as a STRIPAK-regulated kinase in cardiomyocytes, and suggest it regulates the cytoskeleton rather than activates JNKs.

Computational Design of Oligosaccharide-Producing Levansucrase from Bacillus licheniformis RN-01 to Increase Its Stability at High Temperature

Levan-type fructooligosaccharides (LFOs) and levan can potentially be used as ingredients in prebiotics, skincare products, and antitumor agents. The Y246S mutant of Bacillus licheniformis RN-01 levansucrase (oligosaccharide-producing levansucrase, OPL) was reported to productively synthesize LFOs; however, OPL's thermostability is low at high temperatures. To enhance OPL structural stability, this study employed molecular dynamics (AMBER) to identify a highly flexible region, as measured by its average root-mean-square fluctuation (RMSF) value, on the OPL surface and computational protein design (Rosetta) to rigidify and increase favorable interactions to increase its structural stability. AMBER identified region nine (residues 277-317) as a highly flexible region that was selected for design because it has the highest number of residues and the second-highest average RMSF, and it is farthest from the active site. Rosetta designed 14 mutants with the best ΔΔG value in each position, where three mutants have better ΔG than OPL. To determine whether their flexibilities and stabilities are lower than those of OPL, all 14 designed mutants were simulated at high temperature (500 K), and we found that K296E, G309S, and A310W mutants were predicted to be more stable and could retain their native structures better than OPL. Our results suggest that enhanced structural stabilities of these mutants are caused by increased hydrogen bond strengths of the designed residues and their neighboring residues. This study designed K296E, G309S, and A310W mutants of OPL with high potential for stability improvement, and they could potentially be used for the effective production of LFOs.