Structure Prediction Programs Research Articles

532 Three-dimensional structure of a microbial nanomachine and its role in acne vulgaris

Treating acne with antibiotics has led to the emergence of multidrug resistant (MDR) bacteria. MDR bacterial infections incur high healthcare costs and result in higher patient mortality. New antibiotics that target specific bacteria to avoid collateral microbiota damage are needed. Antibiotics used to treat acne reduce levels of Cutibacterium acnes. Acne-associated C. acnes genomes include putative, non-ribosomal peptide synthetase (NRPS) genes. Microbes use NRPS proteins to assemble unique metabolites. Some of these metabolites are virulence factors (e.g., Escherichia coli siderophores). Inhibiting bacterial virulence factors has been proposed as a strategy to minimize antibiotic collateral damage. No C. acnes NRPS has yet been characterized. One of the putative, acne-associated NRPS C. acnes genes (PPA-RS12630) is found almost exclusively within the Propionibacterium genus, which includes C. acnes. Based on sequence homology, we hypothesize that PPA-RS12630 is an iterative NRPS with a tri-domain structure consisting of adenylation, peptidyl carrier, and condensation domains. Microbes synthesize the siderophore vicibactin using this type of NRPS. Our group used structure prediction programs to design a PPA-RS12630 E. coli expression construct. Recombinant PPA-RS12630 was expressed, purified, and used in biochemical and structural investigations. Negative-stain electron microscopy of recombinant PPA-RS12630 demonstrated a flexible, tri-domain structure consistent with an iterative NRPS. Cryogenic electron microscopy (cryoEM) data was collected on PPA-RS12630 to determine its three-dimensional structure. The high-resolution structure of this protein will provide a road map to guide the biochemical characterization of PPA-RS12630 and other related C. acnes proteins. These investigations will help us to understand the role of C. acnes in human diseases, develop precision C. acnes therapeutics, and decrease the burden of MDR bacterial infections.

Present Impact of AlphaFold2 Revolution on Structural Biology, and an Illustration With the Structure Prediction of the Bacteriophage J-1 Host Adhesion Device.

In 2021, the release of AlphaFold2 - the DeepMind’s machine-learning protein structure prediction program - revolutionized structural biology. Results of the CASP14 contest were an immense surprise as AlphaFold2 successfully predicted 3D structures of nearly all submitted protein sequences. The AlphaFold2 craze has rapidly spread the life science community since structural biologists as well as untrained biologists have now the possibility to obtain high-confidence protein structures. This revolution is opening new avenues to address challenging biological questions. Moreover, AlphaFold2 is imposing itself as an essential step of any structural biology project, and requires us to revisit our structural biology workflows. On one hand, AlphaFold2 synergizes with experimental methods including X-ray crystallography and cryo-electron microscopy. On the other hand, it is, to date, the only method enabling structural analyses of large and flexible assemblies resistant to experimental approaches. We illustrate this valuable application of AlphaFold2 with the structure prediction of the whole host adhesion device from the Lactobacillus casei bacteriophage J-1. With the ongoing improvement of AlphaFold2 algorithms and notebooks, there is no doubt that AlphaFold2-driven biological stories will increasingly be reported, which questions the future directions of experimental structural biology.

In silico design of short hairpin RNA (shRNA)Molecules for DNA pol gene of Contagious Ecthyma virus (ORFV)

Contagious ecthyma is an infectious skin disease of ruminants caused by the ORF virus (ORFV) that is a member of genus Parapoxvirus of the Poxviridae family. In addition to the significant effects on lambs and human, ORFVs have been recently shown to infect other hosts. The disease causes significant economic damages to the sheep industry, so attempts to eliminate it must be taken into.RNA interference (RNAi) is an evolutionarily conserved mechanism in which the expression of homologous target genesis is suppressed by means of double-stranded RNA molecules. Since RNAi can be considered as a therapeutic method for viral gene silencing, we tend to make the most of this capability. The present study aims to design potential shRNAs to knockdown the DNA-polymerase gene coded by ORF025.A significant number of computational methods such as clustal omega website to target alignment, BLAST-NCBI to similarity search, CLC software to secondary structure prediction, BLOCK-iTRNAi Designer and WI siRNA Selection Program and Software to design of shRNA molecules and scoring have been applied for shRNA molecules designing against the ORF025-DNA pol gene of ORFV. Then three shRNA molecules were logically designed against the ORF025-DNA pol gene. In conclusion the present study provides a strong and superior approach for achieving a validated strategy to design an antiviral shRNA molecule that meets many sequence features for efficient ORFV knockdown and treatment at the mRNA level. The efficiency of these anti ORFV shRNAs need to be tested

Concordance of X-ray and AlphaFold2 Models of SARS-CoV-2 Main Protease with Residual Dipolar Couplings Measured in Solution.

The 68-kDa homodimeric 3C-like protease of SARS-CoV-2, Mpro (3CLpro/Nsp5), is a promising antiviral drug target. We evaluate the concordance of models generated by the newly introduced AlphaFold2 structure prediction program with residual dipolar couplings (RDCs) measured in solution for 15N–1HN and 13C′–1HN atom pairs. The latter were measured using a new, highly precise TROSY-AntiTROSY Encoded RDC (TATER) experiment. Three sets of AlphaFold2 models were evaluated: (1) MproAF, generated using the standard AlphaFold2 input structural database; (2) MproAFD, where the AlphaFold2 implementation was modified to exclude all candidate template X-ray structures deposited after Jan 1, 2020; and (3) MproAFS, which excluded all structures homologous to coronaviral Mpro. Close agreement between all three sets of AlphaFold models and experimental RDC data is found for most of the protein. For residues in well-defined secondary structure, the agreement decreases somewhat upon Amber relaxation. For these regions, MproAF agreement exceeds that of most high-resolution X-ray structures. Residues from domain 2 that comprise elements of both the active site and the homo-dimerization interface fit less well across all structures. These results indicate novel opportunities for combining experimentation with molecular dynamics simulations, where solution RDCs provide highly precise input for QM/MM simulations of substrate binding/reaction trajectories.

Abstract 2444: Do all 4 NCL RNA Binding Domains play a role in mRNA target specificity? An in silico study to decipher NCL-mRNA interactions

Abstract RNA binding proteins (RBPs) are post-transcriptional regulators that associate with RNA molecules through one or more of their RNA-binding domains (RBDs). The dysregulation of RBPs, and the RNA metabolism pathways they intersect, are known drivers of tumorigenesis. Nucleolin (NCL) is a stress responsive RBP with four RBDs that directly impacts gene expression by altering rRNA processing, RNA localization, miRNA biogenesis, mRNA decay, and translation. NCL is known to interact with both the 3' and 5' untranslated regions (UTR) and coding regions of target mRNAs to either modulate their stability or translation via recognition of AU-rich or G-rich elements in the RNA. However, the consequences of NCL-mRNA interactions are target-specific as well as context and location dependent. While NCL interacts with both 3' and 5' UTR of TP53 mRNA and suppresses its translation, interactions of NCL with 5' UTR of an anti-apoptotic gene, BCL2, stabilizes this transcript. NCL-mediated enhanced biogenesis of tumorigenic mir-15a and mir-16, on the other hand, results in the translational repression of BCL2. Because NCL exerts its functions in such a complex manner, elucidation of its roles in tumorigenic pathways is paramount in the development of therapeutical applications targeting NCL-RNA interactions. Mechanisms driving NCL-mRNA interactions are currently unknown and this knowledge gap is further compounded by the incomplete nature of the current structural information for NCL and its mRNA targets. In this study, we aim to elucidate NCL RBD-mRNA interactions using computational tools. We investigated interactions of NCL with a specific subset of mRNAs that are implicated in breast cancer progression. We have previously generated and validated in silico models for all the four NCL-RBDs (1-4) in various combinations. Using several secondary and tertiary structure prediction programs along with structure refinement programs, we have generated 3D models of TP53, BCL2, CSF-1, and GADD45α mRNA UTR molecules. Our docking analyses suggests that RBD1-2 and RBD3-4 collaborate in most NCL-mRNA interactions using RNA binding motifs (RNP1 & RNP2) on NCL. We further identify additional contact points in the bridging linker regions between the RBDs that might be crucial for the structural stability of these complexes. This study provides details previously not reported in the literature and provides a comprehensive map of NCL RBD-mRNA interactions that have direct pathophysiological relevance in breast cancer. Altered levels of NCL expression are linked with poor prognosis in breast cancers and targeting NCL results in a better survival outcome. Understanding how NCL associates with its mRNA targets will reveal binding pockets and hence potential sites for drug interventions in addition to characterizing the complex nature of NCL-mRNA interactions/specificity in multiple cancer types. Citation Format: Avdar San, Anjana Saxena, Shaneen Singh. Do all 4 NCL RNA Binding Domains play a role in mRNA target specificity? An in silico study to decipher NCL-mRNA interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2444.

Exploring Nucleic Acid Structure‐Function Relationships in Remote Biochemistry Instruction

The covid-19 pandemic has forced universities around the world to transition to online, remote, or hybrid instruction. This challenge is exacerbated in the laboratory sciences as the limitations on in-person instruction force instructors to conduct curriculum triage and develop new creative avenues for providing meaningful experiential learning opportunities. However, some learning objectives such as nucleic acid structure function relationships can still be pursued with the rigor necessary to train future biochemists and biophysicists using web-based RNA and DNA structural analysis programs. The decades of research in nucleic acid structure prediction algorithms and modeling programs have laid an exceptional foundation for remote teaching and learning in the biosciences. We evaluate the various nucleic acid secondary and tertiary structure prediction programs and their capabilities for remote laboratory activities and research projects. We identify the particular strengths of each program and learning objectives each one can be used to pursue. We also present a series of guided and independent inquiry activities using these prediction programs exploring nucleic acid secondary and tertiary structure suitable for introductory or advanced biochemistry courses using asynchronous or synchronous modalities. Activities include an exploration of nucleic acid secondary structures and relative stabilities, identifying non-canonical interactions within RNA folds, predicting tertiary interactions, comparing predicted structures to published experimentally-determined secondary structures, and investigating how mutations in the nucleic acid sequence can affect secondary and tertiary structure. Tools to increase engagement and discussion in synchronous virtual laboratory sessions are also presented. These activities focus on the SARS-CoV-2 viral RNA structure but can be modified for any particular RNA or DNA. Available biophysical technology resources provide an avenue for continuing high-level instruction despite the challenges of classroom and laboratory de-densification.

Sequence–function correlation of the transmembrane domains in NS4B of HCV using a computational approach

An algorithm is applied to propose a sequence–function correlation of the transmembrane domains (TMDs) of the non-structural protein 4B (NS4B) of hepatitis C virus (HCV). The putative sequence of the TMDs is obtained using 20 available secondary structure prediction programs (SSPPs) with different lengths of the overall amino acid sequence of the protein as input. The results support the notion of four helical TMDs. Whilst the region of the first TMDs leaves room for speculation about an additional TMD, the other three TMDs are consistently predicted. Structural features and the role of each of the TMDs is proposed by applying pairwise sequence alignment using BLAST on the level (i) protein sequence alignment and consequent (ii) function-related alignment. Sequence identity with those TMDs of proteins involved in Ca-homeostasis and generation of replication vesicles, such as Nsp3 of corona viruses, murine coronavirus especially mouse hepatitis virus (MHV), middle east respiratory syndrome coronavirus (MERS), severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2, are suggested. Focusing the search on those proteins in particular and their TMDs playing an active role in their mechanism of function, such as transporters, pumps, viral channel forming protein Vpu of human immunodeficiency virus type 1 (HIV-1) and mediators, suggests TMDs 2 and 4 to have functional roles in NS4B, as well as additionally TMD1 and 3 in case of vesicle formation.

Characterization of the C-terminal tail of the Arc protein.

The activity-regulated cytoskeleton-associate protein Arc (or Arg3.1) is specifically linked to memory formation and a number of cognitive disorders, including Alzheimer’s disease and schizophrenia. Since the discovery of Arc in 1995, extensive research has been conducted on the protein to identify its function and mechanisms of action, with solving the structure of Arc as a major goal. However, the Arc protein tends to self-oligomerize in vitro, and is difficult to crystallize. These properties have hindered efforts to obtain the structure of the full-length, whole protein Arc. As an alternative approach, we and others, have sought to solve the structures of various subdomain proteins of Arc, including the N-lobe, C-lobe, and capsid domain (N-lobe + C-lobe). In this study, we characterized the C-terminal tail of Arc using integrated bioinformatic and structural biology techniques. We compared the sequences of Arc proteins in different mammal species and found that the amino-acid composition in the C-terminal tail region has a significantly higher degree of variation rate than the rest of the protein. Structural prediction programs suggested that the C-terminal tail is structurally disordered. Chemical shift analysis based on solution NMR spectra confirmed that the C-terminal tail has a random coil (disordered) structure, and the tail starts from the residue D357. Furthermore, the NMR spectra showed that the C-terminal tail has minimum (if any) interaction with its neighboring capsid domain in Arc. This study fills gaps in our specific understanding of the structural nature and functional contributions of the Arc C-terminus.

Predictions and analyses of RNA nearest neighbor parameters for modified nucleotides.

The most popular RNA secondary structure prediction programs utilize free energy (ΔG°37) minimization and rely upon thermodynamic parameters from the nearest neighbor (NN) model. Experimental parameters are derived from a series of optical melting experiments; however, acquiring enough melt data to derive accurate NN parameters with modified base pairs is expensive and time consuming. Given the multitude of known natural modifications and the continuing use and development of unnatural nucleotides, experimentally characterizing all modified NNs is impractical. This dilemma necessitates a computational model that can predict NN thermodynamics where experimental data is scarce or absent. Here, we present a combined molecular dynamics/quantum mechanics protocol that accurately predicts experimental NN ΔG°37 parameters for modified nucleotides with neighboring Watson–Crick base pairs. NN predictions for Watson-Crick and modified base pairs yielded an overall RMSD of 0.32 kcal/mol when compared with experimentally derived parameters. NN predictions involving modified bases without experimental parameters (N6-methyladenosine, 2-aminopurineriboside, and 5-methylcytidine) demonstrated promising agreement with available experimental melt data. This procedure not only yields accurate NN ΔG°37 predictions but also quantifies stacking and hydrogen bonding differences between modified NNs and their canonical counterparts, allowing investigators to identify energetic differences and providing insight into sources of (de)stabilization from nucleotide modifications.

Sequence-Based Prediction of Metamorphic Behavior in Proteins

An increasing number of proteins have been demonstrated in recent years to adopt multiple three-dimensional folds with different functions. These metamorphic proteins are characterized by having two or more folds with significant differences in their secondary structure, in which each fold is stabilized by a distinct local environment. So far, ∼90 metamorphic proteins have been identified in the Protein Databank, but we and others hypothesize that a far greater number of metamorphic proteins remain undiscovered. In this work, we introduce a computational model to predict metamorphic behavior in proteins using only knowledge of the sequence. In this model, secondary structure prediction programs are used to calculate diversity indices, which are measures of uncertainty in predicted secondary structure at each position in the sequence; these are then used to assign protein sequences as likely to be metamorphic versus monomorphic (i.e., having just one fold). We constructed a reference data set to train our classification method, which includes a novel compilation of 136 likely monomorphic proteins and a set of 201 metamorphic protein structures taken from the literature. Our model is able to classify proteins as metamorphic versus monomorphic with a Matthews correlation coefficient of ∼0.36 and true positive/true negative rates of ∼65%/80%, suggesting that it is possible to predict metamorphic behavior in proteins using only sequence information.

Novel structure of the N-terminal helical domain of BibA, a group B streptococcus immunogenic bacterial adhesin.

BibA, a group B streptococcus (GBS) surface protein, has been shown to protect the pathogen from phagocytic killing by sequestering a complement inhibitor: C4b-binding protein (C4BP). Here, the X-ray crystallographic structure of a GBS BibA fragment (BibA126-398) and a low-resolution small-angle X-ray scattering (SAXS) structure of the full-length N-terminal domain (BibA34-400) are described. The BibA126-398 fragment crystal structure displayed a novel and predominantly helical structure. The tertiary arrangement of helices forms four antiparallel three-helix-bundle-motif repeats, with one long helix from a bundle extending into the next. Multiple mutations on recombinant BibA34-400 delayed the degradation of the protein, and circular dichroism spectroscopy of BibA34-400 suggested a similar secondary-structure composition to that observed in the crystallized BibA126-398 fragment. A model was generated for the 92 N-terminal residues (BibA34-125) using structural similarity prediction programs, and a BibA34-400 model was generated by combining the coordinates of BibA34-126 and BibA126-398. The X-ray structure of BibA126-398 and the model of BibA34-400 fitted well into the calculated SAXS envelope. One possible binding site for the BibA N-terminal domain was localized to the N-terminal CCP (complement-control protein) domains of the C4BP α-chain, as indicated by the decreased binding of BibA to a ΔCCP1 C4BP α-chain mutant. In summary, it is suggested that the GBS surface protein BibA, which consists of three antiparallel α-helical-bundle motifs, is unique and belongs to a new class of Gram-positive surface adhesins.

Free energy-based model of CTCF-mediated chromatin looping in the human genome.

In recent years, high-throughput techniques have revealed considerable structural organization of the human genome with diverse regions of the chromatin interacting with each other in the form of loops. Some of these loops are quite complex and may encompass regions comprised of many interacting chain segments around a central locus. Popular techniques for extracting this information are chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and high-throughput chromosome conformation capture (Hi-C). Here, we introduce a physics-based method to predict the three-dimensional structure of chromatin from population-averaged ChIA-PET data. The approach uses experimentally-validated data from human B-lymphoblastoid cells to generate 2D meta-structures of chromatin using a dynamic programming algorithm that explores the chromatin free energy landscape. By generating both optimal and suboptimal meta-structures we can calculate both the free energy and additionally the relative thermodynamic probability. A 3D structure prediction program with applied restraints then can be used to generate the tertiary structures. The main advantage of this approach for population-averaged experimental data is that it provides a way to distinguish between the principal and the spurious contacts. This study also finds that euchromatin appear to have rather precisely regulated 2D meta-structures compared to heterochromatin. The program source-code is available at https://github.com/plewczynski/looper.

A dual apolipoprotein C-II mimetic-apolipoprotein C-III antagonist peptide lowers plasma triglycerides.

Recent genetic studies have established that hypertriglyceridemia (HTG) is causally related to cardiovascular disease, making it an active area for drug development. We describe a strategy for lowering triglycerides (TGs) with an apolipoprotein C-II (apoC-II) mimetic peptide called D6PV that activates lipoprotein lipase (LPL), the main plasma TG-hydrolyzing enzyme, and antagonizes the TG-raising effect of apoC-III. The design of D6PV was motivated by a combination of all-atom molecular dynamics simulation of apoC-II on the Anton 2 supercomputer, structural prediction programs, and biophysical techniques. Efficacy of D6PV was assessed ex vivo in human HTG plasma and was found to be more potent than full-length apoC-II in activating LPL. D6PV markedly lowered TG by more than 80% within a few hours in both apoC-II-deficient mice and hAPOC3-transgenic (Tg) mice. In hAPOC3-Tg mice, D6PV treatment reduced plasma apoC-III by 80% and apoB by 65%. Furthermore, low-density lipoprotein (LDL) cholesterol did not accumulate but rather was decreased by 10% when hAPOC3-Tg mice lacking the LDL-receptor (hAPOC3-Tg × Ldlr-/- ) were treated with the peptide. D6PV lowered TG by 50% in whole-body inducible Lpl knockout (iLpl-/- ) mice, confirming that it can also act independently of LPL. D6PV displayed good subcutaneous bioavailability of about 80% in nonhuman primates. Because it binds to high-density lipoproteins, which serve as a long-term reservoir, it also has an extended terminal half-life (42 to 50 hours) in nonhuman primates. In summary, D6PV decreases plasma TG by acting as a dual apoC-II mimetic and apoC-III antagonist, thereby demonstrating its potential as a treatment for HTG.

RNAknot: A new algorithm for RNA secondary structure prediction based on genetic algorithm and GRASP method.

The prediction of the optimal secondary structure for a given RNA sequence represents a challenging computational problem in bioinformatics. This challenge becomes harder especially with the discovery of different pseudoknot classes, which is a complex topology that plays diverse roles in biological processes. Many recent studies have been proposed to predict RNA secondary structure with some pseudoknot classes, but only a few of them have reached satisfying results in terms of both complexity and accuracy. Here we present RNAknot, a new method for predicting RNA secondary structure that contains the following components: stems, hairpin loops, multi-branched loops or multi-loops, bulge loops, and internal loops, in addition to two types of pseudoknots, H-type pseudoknot and Hairpin kissing. RNAknot is based on a genetic algorithm and Greedy Randomized Adaptive Search Procedure (GRASP), and it uses the free energy as fitness function to evaluate the obtained structures. In order to validate the performance of the presented method 131 tests have been performed using two datasets of 26 and 105 RNA sequences, which have been taken from the two data bases RNAstrand and Pseudobase respectively. The obtained results are compared with those of some RNA secondary structure prediction programs such as Vs_subopt, CyloFold, IPknot, Kinefold, RNAstructure, and Sfold. The results of this comparative study show that the prediction accuracy of our proposed approach is significantly improved compared to those obtained by the other programs. For the first dataset, RNAknot has the highest specificity (SP) (71.23%) and sensitivity (SN) (72.15%) averages compared to the other programs. Concerning the second dataset, the RNA secondary structure predictions obtained by the RNAknot correspond to the highest averages of SP (85.49%) and F-measure (79.97%) compared to the other programs. The program is available as a jar file in the link: www.bachmek.umi.ac.ma/wp-content/uploads/RNAknot.0.0.2.rar.

RNA2Drawer: geometrically strict drawing of nucleic acid structures with graphical structure editing and highlighting of complementary subsequences

ABSTRACTRNA structure prediction programs remain imperfect and many substructures are still identified by manual exploration, which is most efficiently conducted within an RNA structure drawing program. However, most nucleic acid structure drawing programs have limited capability for structure modification (i.e., breaking and forming new bonds between bases), often requiring that the structure notation be textually edited. RNA2Drawer was developed to allow for graphical structure editing while maintaining the geometry of a drawing (e.g., ellipsoid loops, stems with evenly stacked base pairs) throughout structural changes and manual adjustments to the layout by the user. In addition, the program allows for annotations such as colouring and circling of bases and drawing of tertiary interactions (e.g., pseudoknots). RNA2Drawer can also draw commonly desired elements such as an optionally flattened outermost loop and assists structure editing by automatically highlighting complementary subsequences, which facilitates the discovery of potentially new and alternative pairings, particularly tertiary pairings over long-distances, which are biologically critical in the genomes of many RNA viruses and cannot be accurately predicted by current structure prediction programs. Additionally, RNA2Drawer outputs drawings either as PNG files, or as PPTX and SVG files, such that every object of a drawing (e.g., bases, bonds) is an individual PPTX or SVG object, allowing for further manipulation in Microsoft PowerPoint or a vector graphics editor such as Adobe Illustrator. PowerPoint is the standard for presentations and is often used to create figures for publications, and RNA2Drawer is the first program to export drawings as PPTX files.

Improved fragment sampling for ab initio protein structure prediction using deep neural networks

A typical approach to predicting unknown native structures of proteins is to assemble the amino acid residues (fragments) extracted from known structures. The quality of these extracted fragments, which are used to build protein-specific fragment libraries, can determine the success or failure of sampling near-native conformations. Here we show how a high-quality fragment library can be built using deep contextual learning techniques. Our algorithm, called DeepFragLib, employs bidirectional long short-term-memory recurrent neural networks with knowledge distillation for initial fragment classification, followed by an aggregated residual transformation network with cyclically dilated convolution for detecting near-native fragments. DeepFragLib improves the position-averaged proportion of near-native fragments by 12.2% over existing methods and, consequently, produces better near-native structures for 72.0% of the free-modelling domain targets tested when integrated with Rosetta. DeepFragLib is fully parallelized and available for use in conjunction with structure prediction programs. An approach to protein structure prediction is to assemble candidate structures from template fragments, which are extracted from known protein structures. Wang et al. demonstrate that combining deep neural network architectures with a relatively small but high-resolution fragment dataset can improve the quality of the sample fragment libraries used for protein structure prediction.

Structure-based rational design of a novel chimeric PD1-NKG2D receptor for natural killer cells

Chimeric antigen receptor (CAR)-engineered natural killer (NK) cells have the potential to provide the potential for the implementation of allogeneic “off-the-shelf” cellular therapy against cancers. Currently, most CARs are not optimized for NK cells, so new NK-tailored CARs are needed. Here, a major activating receptor of NK cells, NKG2D was harnessed to design different chimeric receptors that mediate strong NK cell signaling. In these NKG2D signaling-based chimeric receptors, the extracellular domain of inhibitory receptor PD-1 was employed to reverse the immune escape mediated by PD-1 ligands in the solid tumors. To achieve the rational design of chimeric PD1-NKG2D receptors, we developed a transmembrane protein tertiary structure prediction program (PredMP & I-TASSER) and optimized the conformation of the PD-1 ectodomain by genetically altering the sequences encoding the hinge and intracellular domain. Finally, we identified a chimeric PD1-NKG2D receptor containing NKG2D hinge region and 4-1BB co-stimulatory domain to exhibit stable surface expression and mediate in vitro cytotoxicity of NK92 cells against various tumor cells. This strategy now provides a promising approach for the computer-aided design (CAD) of potent NK cell-tailored chimeric receptors with NKG2D signaling.

A new clustering and nomenclature for beta turns derived from high-resolution protein structures.

Protein loops connect regular secondary structures and contain 4-residue beta turns which represent 63% of the residues in loops. The commonly used classification of beta turns (Type I, I’, II, II’, VIa1, VIa2, VIb, and VIII) was developed in the 1970s and 1980s from analysis of a small number of proteins of average resolution, and represents only two thirds of beta turns observed in proteins (with a generic class Type IV representing the rest). We present a new clustering of beta-turn conformations from a set of 13,030 turns from 1074 ultra-high resolution protein structures (≤1.2 Å). Our clustering is derived from applying the DBSCAN and k-medoids algorithms to this data set with a metric commonly used in directional statistics applied to the set of dihedral angles from the second and third residues of each turn. We define 18 turn types compared to the 8 classical turn types in common use. We propose a new 2-letter nomenclature for all 18 beta-turn types using Ramachandran region names for the two central residues (e.g., ‘A’ and ‘D’ for alpha regions on the left side of the Ramachandran map and ‘a’ and ‘d’ for equivalent regions on the right-hand side; classical Type I turns are ‘AD’ turns and Type I’ turns are ‘ad’). We identify 11 new types of beta turn, 5 of which are sub-types of classical beta-turn types. Up-to-date statistics, probability densities of conformations, and sequence profiles of beta turns in loops were collected and analyzed. A library of turn types, BetaTurnLib18, and cross-platform software, BetaTurnTool18, which identifies turns in an input protein structure, are freely available and redistributable from dunbrack.fccc.edu/betaturn and github.com/sh-maxim/BetaTurn18. Given the ubiquitous nature of beta turns, this comprehensive study updates understanding of beta turns and should also provide useful tools for protein structure determination, refinement, and prediction programs.

Aggregation of Influenza A Virus Nuclear Export Protein.

Influenza A virus nuclear export protein (NEP) plays an important role in the viral life cycle. Recombinant NEP proteins containing (His)6-tag at either N- or C-terminus were obtained by heterologous expression in Escherichia coli cells and their high propensity for aggregation was demonstrated. Dynamic light scattering technique was used to study the kinetics and properties of NEP aggregation in solutions under different conditions (pH, ionic strength, presence of low-molecular-weight additives and organic solvents). Using atomic force microscopy, the predominance of spherical aggregates in all examined NEP preparations was shown, with some amyloid-like structures being observed in the case of NEP-C protein. A number of structure prediction programs were used to identify aggregation-prone regions in the NEP structure. All-atom molecular dynamics simulations indicate a high rate of NEP molecule aggregation and reveal the regions preferentially involved in the intermolecular contacts that are located at the edges of the rod-like protein molecule. Our results suggest that NEP aggregation is determined by different types of interactions and represents an intrinsic property of the protein that appears to be necessary for its functioning in vivo.

Prediction of secondary and tertiary structures of human BC200 RNA (BCYRN1) based on experimental and bioinformatic cross-validation.

Based on experimental and bioinformatic approaches, we present the first empirically established complete secondary structure of human BC200 RNA. BC200 RNA is a brain-specific non-messenger RNA with a confirmed regulatory role in dendritic translation in neurons. Although the involvement of human BC200 RNA in various types of tumour and Alzheimer's disease has been repeatedly confirmed, the exact secondary structure remains not fully elucidated. To determine the secondary structure of BC200 RNA in vitro, we performed partial hydrolysis with sequence-specific nucleases and lead-induced cleavage. We also examined the availabilities of putative single-stranded regions and base-pairing interactions via specific DNAzymes and RNase H assay. To determine the complete spatial folding of BC200 RNA, we used experimental data as constraints in structure prediction programs and performed a comparison of results obtained by several algorithms using different criteria. Based on the experimental-derived secondary structure of BC200 RNA, we also predicted the tertiary structure of BC200 RNA. The presented combination of experimental and bioinformatic approaches not only enabled the determination of the most reliable secondary and tertiary structures of human BC200 RNA (largely in agreement with the previous phylogenetic model), but also verified the compatibility and potential disadvantages of utilizing in silico structure prediction programs.