Acinetobacter baumannii satellite phage Aci01-2-Phanie depends on a helper myophage for its multiplication.

We report the discovery of a satellite-helper phage system with a novel type of dependence on a tail donor. The Acinetobacter baumannii satellite podovirus Aci01-2-Phanie (short name Phanie) uses a phage phi29-like DNA replication and packaging mode. Its linear 11,885 bp dsDNA genome bears 171 bp inverted terminal repeats (ITR). Phanie is related to phage DU-PP-III from Pectobacterium and to members of the Astrithrvirus from Salmonella enterica. Together, they form a new clade of phages with 27% to 30% identity over the whole genome. Detailed 3D protein structure prediction and mass spectrometry analyses demonstrate that Phanie encodes its capsid structural genes and genes necessary to form a short tail. However, our study reveals that Phanie virions are non-infectious unless they associate with the contractile tail of an unrelated phage, Aci01-1, to produce chimeric myoviruses. Following the coinfection of Phanie with myovirus Aci01-1, hybrid viral particles composed of Phanie capsids and Aci01-1 contractile tails are assembled together with Phanie and Aci01-1 particles.IMPORTANCEThere are few reported cases of satellite-helper phage interactions but many more may be yet undiscovered. Here we describe a new mode of satellite phage dependence on a helper phage. Phanie, like phage phi29, replicates its linear dsDNA by a protein primed-mechanism and protects it inside podovirus-like particles. However, these particles are defective, requiring the acquisition of the tail from a myovirus helper for production of infectious virions. The formation of chimeras between a phi29-like podovirus and a helper contractile tail reveals an unexpected association between very different bacterial viruses.

Evaluation of the stereochemical quality of predicted RNA 3D models in the RNA-Puzzles submissions.

In silico prediction is a well-established approach to derive a general shape of an RNA molecule based on its sequence or secondary structure. This paper reports an analysis of the stereochemical quality of the RNA three-dimensional models predicted using dedicated computer programs. The stereochemistry of 1052 RNA 3D structures, including 1030 models predicted by fully automated and human-guided approaches within 22 RNA-Puzzles challenges and reference structures, is analyzed. The evaluation is based on standards of RNA stereochemistry that the Protein Data Bank requires from deposited experimental structures. Deviations from standard bond lengths and angles, planarity, or chirality are quantified. A reduction in the number of such deviations should help in the improvement of RNA 3D structure modeling approaches.

New prediction categories in CASP15.

Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In CASP15, four new prediction categories were introduced: RNA structure, ligand-protein complexes, accuracy of oligomeric structures and their interfaces, and ensembles of alternative conformations. This paper lists technical specifications for these categories and describes their integration in the CASP data management system.

Automated benchmarking of combined protein structure and ligand conformation prediction.

The prediction of protein-ligand complexes (PLC), using both experimental and predicted structures, is an active and important area of research, underscored by the inclusion of the Protein-Ligand Interaction category in the latest round of the Critical Assessment of Protein Structure Prediction experiment CASP15. The prediction task in CASP15 consisted of predicting both the three-dimensional structure of the receptor protein as well as the position and conformation of the ligand. This paper addresses the challenges and proposed solutions for devising automated benchmarking techniques for PLC prediction. The reliability of experimentally solved PLC as ground truth reference structures is assessed using various validation criteria. Similarity of PLC to previously released complexes are employed to judge PLC diversity and the difficulty of a PLC as a prediction target. We show that the commonly used PDBBind time-split test-set is inappropriate for comprehensive PLC evaluation, with state-of-the-art tools showing conflicting results on a more representative and high quality dataset constructed for benchmarking purposes. We also show that redocking on crystal structures is a much simpler task than docking into predicted protein models, demonstrated by the two PLC-prediction-specific scoring metrics created. Finally, we introduce a fully automated pipeline that predicts PLC and evaluates the accuracy of the protein structure, ligand pose, and protein-ligand interactions.

RNA tertiary structure prediction in CASP15 by the GeneSilico group: Folding simulations based on statistical potentials and spatial restraints.

Ribonucleic acid (RNA) molecules serve as master regulators of cells by encoding their biological function in the ribonucleotide sequence, particularly their ability to interact with other molecules. To understand how RNA molecules perform their biological tasks and to design new sequences with specific functions, it is of great benefit to be able to computationally predict how RNA folds and interacts in the cellular environment. Our workflow for computational modeling of the 3D structures of RNA and its interactions with other molecules uses a set of methods developed in our laboratory, including MeSSPredRNA for predicting canonical and non-canonical base pairs, PARNASSUS for detecting remote homology based on comparisons of sequences and secondary structures, ModeRNA for comparative modeling, the SimRNA family of programs for modeling RNA 3D structure and its complexes with other molecules, and QRNAS for model refinement. In this study, we present the results of testing this workflow in predicting RNA 3D structures in the CASP15 experiment. The overall high score of the computational models predicted by our group demonstrates the robustness of our workflow and its individual components in terms of predicting RNA 3D structures of acceptable quality that are close to the target structures. However, the variance in prediction quality is still quite high, and the results are still too far from the level of protein 3D structure predictions. This exercise led us to consider several improvements, especially to better predict and enforce stacking interactions and non-canonical base pairs.

CASP15 cryo-EM protein and RNA targets: Refinement and analysis using experimental maps.

CASP assessments primarily rely on comparing predicted coordinates with experimental reference structures. However, experimental structures by their nature are only models themselves-their construction involves a certain degree of subjectivity in interpreting density maps and translating them to atomic coordinates. Here, we directly utilized density maps to evaluate the predictions by employing a method for ranking the quality of protein chain predictions based on their fit into the experimental density. The fit-based ranking was found to correlate well with the CASP assessment scores. Overall, the evaluation against the density map indicated that the models are of high accuracy, and occasionally even better than the reference structure in some regions of the model. Local assessment of predicted side chains in a 1.52 Å resolution map showed that side-chains are sometimes poorly positioned. Additionally, the top 118 predictions associated with 9 protein target reference structures were selected for automated refinement, in addition to the top 40 predictions for 11 RNA targets. For both proteins and RNA, the refinement of CASP15 predictions resulted in structures that are close to the reference target structure. This refinement was successful despite large conformational changes often being required, showing that predictions from CASP-assessed methods could serve as a good starting point for building atomic models in cryo-EM maps for both proteins and RNA. Loop modeling continued to pose a challenge for predictors, and together with the lack of consensus amongst models in these regions suggests that modeling, in combination with model-fit to the density, holds the potential for identifying more flexible regions within the structure.

RNA tertiary structure prediction using RNAComposer in CASP15.

As CASP15 participants, in the new category of 3D RNA structure prediction, we applied expert modeling with the support of our proprietary system RNAComposer. Although RNAComposer is primarily known as an automated web server, its features allow it to be used interactively, for example, for homology-based modeling or assembling models from user-provided structural elements. In the paper, we present various scenarios of applying the system to predict the 3D RNA structures that we employed. Their combination with expert input, comparative analysis of models, and routines to select representative resultant structures form a ready-for-reuse workflow. With selected examples, we demonstrate its application for the in silico modeling of natural and synthetic RNA molecules targeted in CASP15.

Deep learning for mining protein data.

The recent emergence of deep learning to characterize complex patterns of protein big data reveals its potential to address the classic challenges in the field of protein data mining. Much research has revealed the promise of deep learning as a powerful tool to transform protein big data into valuable knowledge, leading to scientific discoveries and practical solutions. In this review, we summarize recent publications on deep learning predictive approaches in the field of mining protein data. The application architectures of these methods include multilayer perceptrons, stacked autoencoders, deep belief networks, two- or three-dimensional convolutional neural networks, recurrent neural networks, graph neural networks, and complex neural networks and are described from five perspectives: residue-level prediction, sequence-level prediction, three-dimensional structural analysis, interaction prediction, and mass spectrometry data mining. The advantages and deficiencies of these architectures are presented in relation to various tasks in protein data mining. Additionally, some practical issues and their future directions are discussed, such as robust deep learning for protein noisy data, architecture optimization for specific tasks, efficient deep learning for limited protein data, multimodal deep learning for heterogeneous protein data, and interpretable deep learning for protein understanding. This review provides comprehensive perspectives on general deep learning techniques for protein data analysis.

Fusion of two unrelated protein domains in a chimera protein and its 3D prediction: Justification of the x-ray reference structures as a prediction benchmark.

Proteins are naturally formed by domains edging their functional and structural properties. A domain out of the context of an entire protein can retain its structure and to some extent also function on its own. These properties rationalize construction of artificial fusion multidomain proteins with unique combination of various functions. Information on the specific functional and structural characteristics of individual domains in the context of new artificial fusion proteins is inevitably encoded in sequential order of composing domains defining their mutual spatial positions. So the challenges in designing new proteins with new domain combinations lie dominantly in structure/function prediction and its context dependency. Despite the enormous body of publications on artificial fusion proteins, the task of their structure/function prediction is complex and nontrivial. The degree of spatial freedom facilitated by a linker between domains and their mutual orientation driven by noncovalent interactions is beyond a simple and straightforward methodology to predict their structure with reasonable accuracy. In the presented manuscript, we tested methodology using available modeling tools and computational methods. We show that the process and methodology of such prediction are not straightforward and must be done with care even when recently introduced AlphaFold II is used. We also addressed a question of benchmarking standards for prediction of multidomain protein structures-x-ray or Nuclear Magnetic Resonance experiments. On the study of six two-domain protein chimeras as well as their composing domains and their x-ray structures selected from PDB, we conclude that the major obstacle for justified prediction is inappropriate sampling of the conformational space by the explored methods. On the other hands, we can still address particular steps of the methodology and improve the process of chimera proteins prediction.

Computational Pipeline for Reference-Free Comparative Analysis of RNA 3D Structures Applied to SARS-CoV-2 UTR Models.

RNA is a unique biomolecule that is involved in a variety of fundamental biological functions, all of which depend solely on its structure and dynamics. Since the experimental determination of crystal RNA structures is laborious, computational 3D structure prediction methods are experiencing an ongoing and thriving development. Such methods can lead to many models; thus, it is necessary to build comparisons and extract common structural motifs for further medical or biological studies. Here, we introduce a computational pipeline dedicated to reference-free high-throughput comparative analysis of 3D RNA structures. We show its application in the RNA-Puzzles challenge, in which five participating groups attempted to predict the three-dimensional structures of 5'- and 3'-untranslated regions (UTRs) of the SARS-CoV-2 genome. We report the results of this puzzle and discuss the structural motifs obtained from the analysis. All simulated models and tools incorporated into the pipeline are open to scientific and academic use.

3dDNA: A Computational Method of Building DNA 3D Structures.

Considerable progress has been made in the prediction methods of 3D structures of RNAs. In contrast, no such methods are available for DNAs. The determination of 3D structures of the latter is also increasingly needed for understanding their functions and designing new DNA molecules. Since the number of experimental structures of DNA is limited at present, here, we propose a computational and template-based method, 3dDNA, which combines DNA and RNA template libraries to predict DNA 3D structures. It was benchmarked on three test sets with different numbers of chains, and the results show that 3dDNA can predict DNA 3D structures with a mean RMSD of about 2.36 Å for those with one or two chains and fewer than 4 Å with three or more chains.

Structure folding of RNA kissing complexes in salt solutions: predicting 3D structure, stability, and folding pathway.

RNA kissing complexes are essential for genomic RNA dimerization and regulation of gene expression, and their structures and stability are critical to their biological functions. In this work, we used our previously developed coarse-grained model with an implicit structure-based electrostatic potential to predict three-dimensional (3D) structures and stability of RNA kissing complexes in salt solutions. For extensive RNA kissing complexes, our model shows great reliability in predicting 3D structures from their sequences, and our additional predictions indicate that the model can capture the dependence of 3D structures of RNA kissing complexes on monovalent/divalent ion concentrations. Moreover, the comparisons with extensive experimental data show that the model can make reliable predictions on the stability for various RNA kissing complexes over wide ranges of monovalent/divalent ion concentrations. Notably, for RNA kissing complexes, our further analyses show the important contribution of coaxial stacking to the 3D structures and stronger stability than the corresponding kissing-interface duplexes at high salts. Furthermore, our comprehensive analyses for RNA kissing complexes reveal that the thermally folding pathway for a complex sequence is mainly determined by the relative stability of two possible folded states of kissing complex and extended duplex, which can be significantly modulated by its sequence.

A novel pH and thermo-tolerant halophilic alpha-amylase from moderate halophile Nesterenkonia sp. strain F: gene analysis, molecular cloning, heterologous expression and biochemical characterization.

A novel pH and thermo-tolerate halophilic alpha-amylase from moderately halophilic bacterium, Nesterenkonia sp.strain F was cloned and expressed in Escherichia coli. 16S rRNA sequence of the strain shared 99.46% similarities with closely related type species. Also, the genome sequence shared ANI values below 92% and dDDH values below 52% with the closely related type species. Consequently, it is proposed that strain F represents a novel species. The AmyF gene was 1390 bp long and encodes an alpha-amylase of 463 amino acid residues with pI of 4.62. The deduced AmyF shared very low sequence similarity (< 24%) with functionally characterized recombinant halophilic alpha-amylases. The recombinant alpha-amylase was successfully purified from Ni-NTA columns with a molecular mass of about 52 KDa on sodium dodecyl sulfate polyacrylamide gel electrophoresis. The enzyme was active over a wide range of temperature (25-75 °C) and pH (4-9) with optimum activity at 45 °C and 7.5, respectively. Also, although it was active over a various concentrations of NaCl and KCl (0-4 M), increasing activity of the enzyme was observed with increasing concentration of these salts. Low concentrations of Ca2+ ion had no activating effect, but high concentrations of the ion (40-200 mM) enhanced activity of AmyF. The enzyme activity was increased by increasing concentrations of Mg2+, Zn2+, Hg2+ and Fe3+. However, it was inhibited only at very high concentrations of these metal ions. Cu2+ did not decrease the amylase activity and the highest activity was observed at 100 mM of the ion. These properties indicate wide potential applications of this recombinant enzyme in starch processing industries. This is the first isolation, cloning and characterization of a gene encoding alpha-amylase from Nesternkonia genus.

RNA 3D structure prediction guided by independent folding of homologous sequences.

BACKGROUND: The understanding of the importance of RNA has dramatically changed over recent years. As in the case of proteins, the function of an RNA molecule is encoded in its tertiary structure, which in turn is determined by the molecule's sequence. The prediction of tertiary structures of complex RNAs is still a challenging task. RESULTS: Using the observation that RNA sequences from the same RNA family fold into conserved structure, we test herein whether parallel modeling of RNA homologs can improve ab initio RNA structure prediction. EvoClustRNA is a multi-step modeling process, in which homologous sequences for the target sequence are selected using the Rfam database. Subsequently, independent folding simulations using Rosetta FARFAR and SimRNA are carried out. The model of the target sequence is selected based on the most common structural arrangement of the common helical fragments. As a test, on two blind RNA-Puzzles challenges, EvoClustRNA predictions ranked as the first of all submissions for the L-glutamine riboswitch and as the second for the ZMP riboswitch. Moreover, through a benchmark of known structures, we discovered several cases in which particular homologs were unusually amenable to structure recovery in folding simulations compared to the single original target sequence. CONCLUSION: This work, for the first time to our knowledge, demonstrates the importance of the selection of the target sequence from an alignment of an RNA family for the success of RNA 3D structure prediction. These observations prompt investigations into a new direction of research for checking 3D structure "foldability" or "predictability" of related RNA sequences to obtain accurate predictions. To support new research in this area, we provide all relevant scripts in a documented and ready-to-use form. By exploring new ideas and identifying limitations of the current RNA 3D structure prediction methods, this work is bringing us closer to the near-native computational RNA 3D models.

Design of inhibitory peptide targeting Toxoplasma gondii RON4-human ß-tubulin interactions by implementing structural bioinformatics methods.

Toxoplasma gondii an obligate intracellular parasite causes toxoplasmosis in homeothermic animals. Host invasion of this parasite is mediated by the formation of Moving Junction (MJ) complex which encompasses a network of microneme and Rhoptry Neck proteins (RONs) 2/4/5/8. Among these proteins, RON4 is the only cytosolic secretory protein that is considered as a crucial member, as it directly facilitates the motility of MJ complex by interacting with host tubulin. It is also prominently localized at the host-pathogen interface during the invasion, thus projecting it as a potential drug target. The structure of RON4 is yet to be crystallized. Hence, in this study, fold recognition and Free Energy Landscape sampling was performed to predict the plausible 3D structure of RON4. Further, its interacting pattern with the reported crystal structure of human tubulin was analyzed using molecular docking. Subsequently, a ß-tubulin based inhibitory peptides were derived based on its interacting interface observed in RON4-ß-tubulin docked complex. Following which, a stepwise validation of these peptides for various physico-chemical properties and its homology with antimicrobial peptides were also screened. The peptide (RT_pep) surpassing all these validation filters was modeled and its stability was analysed by Molecular Dynamics simulation. To validate further, the stable conformation of the RT_pep was docked to RON4. Finally, essential molecular dynamics simulation was conducted to determine the stability and atomic motions of native RON4 and also to decipher its association with ß-tubulin and RT_pep. All these analyses cumulatively suggest the therapeutic potential of RT_pep in targeting toxoplasmosis.

All-atom 3D structure prediction of transmembrane ß-barrel proteins from sequences.

Transmembrane ß-barrels (TMBs) carry out major functions in substrate transport and protein biogenesis but experimental determination of their 3D structure is challenging. Encouraged by successful de novo 3D structure prediction of globular and α-helical membrane proteins from sequence alignments alone, we developed an approach to predict the 3D structure of TMBs. The approach combines the maximum-entropy evolutionary coupling method for predicting residue contacts (EVfold) with a machine-learning approach (boctopus2) for predicting ß-strands in the barrel. In a blinded test for 19 TMB proteins of known structure that have a sufficient number of diverse homologous sequences available, this combined method (EVfold_bb) predicts hydrogen-bonded residue pairs between adjacent ß-strands at an accuracy of ∼70%. This accuracy is sufficient for the generation of all-atom 3D models. In the transmembrane barrel region, the average 3D structure accuracy [template-modeling (TM) score] of top-ranked models is 0.54 (ranging from 0.36 to 0.85), with a higher (44%) number of residue pairs in correct strand-strand registration than in earlier methods (18%). Although the nonbarrel regions are predicted less accurately overall, the evolutionary couplings identify some highly constrained loop residues and, for FecA protein, the barrel including the structure of a plug domain can be accurately modeled (TM score = 0.68). Lower prediction accuracy tends to be associated with insufficient sequence information and we therefore expect increasing numbers of ß-barrel families to become accessible to accurate 3D structure prediction as the number of available sequences increases.

In silico characterization of broad range proteases produced by Serratia marcescens EGD-HP20.

In the present study, Serratia marcescens EGD-HP20 strain was demonstrated to utilize poultry waste comprising of both white non-melanized and dark/brown melanized poultry feathers. The potential of the isolate to hydrolyze diverse keratinous wastes was further corroborated by comparative genomics which indicated the presence of genes for broad substrate specific proteases including metallo-proteases, serine endoprotease, dipeptidase, oligopeptidase, etc. Multiple gene sequence alignments of above genes showed 99-100% sequence identities with that of closely related strains of S. marcescens. The secondary structure, 3D structures and energy models suggested the stable nature of all the observed enzymes. Comparative genomics and hydrolysis of mixed feather waste indicated that the above potential of the isolate was associated with synergistic action of various types of proteases.

Cloning, Expression and 3D Structure Prediction of Chitinase from Chitinolyticbacter meiyuanensis SYBC-H1.

Two CHI genes from Chitinolyticbacter meiyuanensis SYBC-H1 encoding chitinases were identified and their protein 3D structures were predicted. According to the amino acid sequence alignment, CHI1 gene encoding 166 aa had a structural domain similar to the GH18 type II chitinase, and CHI2 gene encoding 383 aa had the same catalytic domain as the glycoside hydrolase family 19 chitinase. In this study, CHI2 chitinase were expressed in Escherichia coli BL21 cells, and this protein was purified by ammonium sulfate precipitation, DEAE-cellulose, and Sephadex G-100 chromatography. Optimal activity of CHI2 chitinase occurred at a temperature of 40 °C and a pH of 6.5. The presence of metal ions Fe(3+), Fe(2+), and Zn(2+) inhibited CHI2 chitinase activity, while Na⁺ and K⁺ promoted its activity. Furthermore, the presence of EGTA, EDTA, and ß-mercaptoethanol significantly increased the stability of CHI2 chitinase. The CHI2 chitinase was active with p-NP-GlcNAc, with the Km and Vm values of 23.0 µmol/L and 9.1 mM/min at a temperature of 37 °C, respectively. Additionally, the CHI2 chitinase was characterized as an N-acetyl glucosaminidase based on the hydrolysate from chitin. Overall, our results demonstrated CHI2 chitinase with remarkable biochemical properties is suitable for bioconversion of chitin waste.

RNA 3D Structure Comparison Using RNA-Puzzles Toolkit.

Computational modeling of RNA three-dimensional (3D) structure may help in unrevealing the molecular mechanisms of RNA molecules and in designing molecules with novel functions. An unbiased blind assessment to benchmark the computational modeling is required to understand the achievements and bottlenecks of the prediction, while a standard structure comparison protocol is necessary. RNA-Puzzles is a community-wide effort on the assessment of blind prediction of RNA tertiary structures. And RNA-Puzzles toolkit is a computational resource derived from RNA-Puzzles, which includes (i) decoy sets generated by different RNA 3D structure prediction methods; (ii) 3D structure normalization, analysis, manipulation, and visualization tools; and (iii) 3D structure comparison metric tools. In this chapter, we illustrate a standard RNA 3D structure prediction assessment protocol using the selected tools from RNA-Puzzles toolkit: rna-tools and RNA_assessment.

Three-dimensional modeling of streptomycin binding single-stranded DNA for aptamer-based biosensors, a molecular dynamics simulation approach.

Streptomycin (STR) an aminoglycoside antibiotic which is used against bacteria in human and animal infection, have serious side effects on different parts of human body. Therefore, there is a crucial need to detect trace amount of it in serum and food products. Aptamers are oligonucleotides or peptides, which bind their targets with high affinity and specificity. These properties make aptamers as suitable candidates for biosensing applications. A 79-mer ss-DNA aptamer was applied for the detection of small amount of STR in various aptasensors. But there is no structural information on the STR-binding aptamer and molecular details underlying the aptamer-STR binding remain unexplored. In this study we provided a 3D-structural model for 79-mer ss-DNA aptamer from the sequence. Using docking program and molecular dynamics (MD) simulation we predicted the binding pocket of ss-DNA aptamer. Our results show STR streptose ring is buried within the groove of DNA model and capped by non Watson-Crick bases. STR interacts with aptamer through forming stable hydrogen bonds. Our computational findings are in fair agreement with experimental results. With the atomic structural details, we gained new insight into the Apt-STR binding interaction that can help to further optimize aptamer efficiency in biosensing applications.Communicated by Ramaswamy H. Sarma.