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At least 91 records · Page 5

Direct Observation of Cooperative Protein Structural Dynamics of Homodimeric Hemoglobin from 100 ps to 10 ms with Pump-Probe X-ray Solution Scattering

Proteins serve as molecular machines in performing their biological functions, but the detailed structural transitions are difficult to observe in their native aqueous environments in real time. For example, despite extensive studies, the solution-phase structures of the intermediates along the allosteric pathways for the transitions between the relaxed (R) and tense (T) forms have been elusive. In this work, we employed picosecond X-ray solution scattering and novel structural analysis to track the details of the structural dynamics of wild-type homodimeric hemoglobin (HbI) from the clam Scapharca inaequivalvis and its F97Y mutant over a wide time range from 100 ps to 56.2 ms. From kinetic analysis of the measured time-resolved X-ray solution scattering data, we identified three structurally distinct intermediates (I 1 , I 2 , and I 3 ) and their kinetic pathways common for both the wild type and the mutant. The data revealed that the singly liganded and unliganded forms of each intermediate share the same structure, providing direct evidence that the ligand photolysis of only a single subunit induces the same structural change as the complete photolysis of both subunits does. In addition, by applying novel structural analysis to the scattering data, we elucidated the detailed structural changes in the protein, including changes in the heme-heme distance, the quaternary rotation angle of subunits, and interfacial water gain/loss. The earliest, R-like I 1 intermediate is generated within 100 ps and transforms to the R-like I 2 intermediate with a time constant of 3.2 ± 0.2 ns. Subsequently, the late, T-like I 3 intermediate is formed via subunit rotation, a decrease in the heme-heme distance, and substantial gain of interfacial water and exhibits ligation-dependent formation kinetics with time constants of 730 ± 120 ns for the fully photolyzed form and 5.6 ± 0.8 μs for the partially photolyzed form. For the mutant, the overall kinetics are accelerated, and the formation of the T-like I 3 intermediate involves interfacial water loss (instead of water entry) and lacks the contraction of the heme-heme distance, thus underscoring the dramatic effect of the F97Y mutation. The ability to keep track of the detailed movements of the protein in aqueous solution in real time provides new insights into the protein structural dynamics.

59 BASIC BIOLOGICAL SCIENCES↗

Deep Green Unannotated Protein Structures

The Deep Green list is based on the identification and curation of conserved unannotated proteins in three green lineage (Viridiplantae) model organisms; Arabidopsis thaliana, Chlamydomonas reinhardtii, and Setaria viridis. Preliminary characterization of Deep Green proteins and genes was done using various informatics tools and published data sets and is presented in Knoshaug, Sun, et al., 2023, submitted. The structures of these unannotated proteins were also predicted using AlphaFold (Jumper et al., 2021). The data deposited here are the AlphaFold structural predictions having the highest pLDDT score and thus identified as the best folded structure (ranked_0). These data enable others to do in-depth structural characterizations to aid in functional characterization leading to deeper understanding of plant biology. References: Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P. and Hassabis, D. (2021) Highly accurate protein structure prediction with AlphaFold. Nature, 596:583-589. Knoshaug, E. P., Sun, P., Nag, A., Nguyen, H., Mattoon, E. M., Zhang, N., Liu, J., Chen, C., Cheng, J., Zhang, R., St. John, P., and Umen, J. (submitted) Identification and preliminary characterization of conserved uncharacterized proteins from Chlamydomonas reinhardtii, Arabidopsis thaliana, and Setaria viridis.

09 BIOMASS FUELS↗

A traditional evolutionary history of foot-and-mouth disease viruses in Southeast Asia challenged by analyses of non-structural protein coding sequences

Recombination of rapidly evolving RNA-viruses provides an important mechanism for diversifcation, spread, and emergence of new variants with enhanced ftness. Foot-and-mouth disease virus (FMDV) causes an important transboundary disease of livestock that is endemic to most countries in Asia and Africa. Maintenance and spread of FMDV are driven by periods of dominance of specifc viral lineages. Current understanding of the molecular epidemiology of FMDV lineages is generally based on the phylogenetic relationship of the capsid-encoding genes, with less attention to the process of recombination and evolution of non-structural proteins. In this study, the putative recombination breakpoints of FMDVs endemic to Southeast Asia were determined using full-open reading frame sequences. Subsequently, the lineages’ divergence times of recombination-free genome regions were estimated. These analyses revealed a close relationship between two of the earliest endemic viral lineages that appear unrelated when only considering the phylogeny of their capsid proteins. Contrastingly, one lineage, named O/CATHAY, known for having a particular host predilection (pigs) has evolved independently. Additionally, intra-lineage recombination occurred at diferent breakpoints compared to the inter-lineage process. These results provide new insights about FMDV recombination patterns and the evolutionary interdependence of FMDV serotypes and lineages.

59 BASIC BIOLOGICAL SCIENCES↗

Foot-and-mouth disease virus non-structural protein 2B negatively regulates the RLR-mediated IFN-β induction

Highlights: • FMDV 2B is a negatively regulator of type I interferon induction. • FMDV 2B can inhibit the phosphorylation of TBK1 and IRF3. • FMDV 2B inhibited the expression of the RIG-I and MDA5. Foot-and-mouth disease virus (FMDV) is the causative agent of Foot-and-mouth disease (FMD), which is an acute and highly contagious disease affecting pigs, cattle and other cloven-hoofed animals. Several studies have shown that FMDV has evolved multiple strategies to evade the host innate immune response, but the underlying mechanisms for immune evasion are still not fully understood. In the current research, we have demonstrated that FMDV utilizes its non-structural protein 2B to sabotage the host immune response. Over-expression of the FMDV 2B inhibited Poly(I:C)-induced or SeV-triggered up-regulation of IFN-β, IL-6 as well as ISG15. When HEK293T cells were transfected with FMDV 2B, the phosphorylation of TBK1 and IRF3 was inhibited. Co-immunoprecipitation and pull-down experiments indicated that FMDV 2B protein could interact with host RIG-I and MDA5. Moreover, FMDV 2B also inhibited the expression of the RIG-I and MDA5. Thus, FMDV 2B negatively regulates the RLR-mediated IFN-β induction by targeting RIG-I and MDA5.

60 APPLIED LIFE SCIENCES↗

Decoy selection for protein structure prediction via extreme gradient boosting and ranking

Background: Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of balance in positive and negative samples (native and non-native decoys) in a decoy set makes the problem even more complicated. Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity. Recent investigations into energy landscape-based decoy selection approaches show promises. However, lack of generalization over varied test cases remains a bottleneck for these methods. Results: We propose a novel decoy selection method, ML-Select, a machine learning framework that exploits the energy landscape associated with the structure space probed through a template-free decoy generation. The proposed method outperforms both clustering and energy ranking-based methods, all the while consistently offering better performance on varied test-cases. Moreover, ML-Select shows promising results even for the decoy sets consisting of mostly low-quality decoys. Conclusions: ML-Select is a useful method for decoy selection. This work suggests further research in finding more effective ways to adopt machine learning frameworks in achieving robust performance for decoy selection in template-free protein structure prediction.

59 BASIC BIOLOGICAL SCIENCES↗

BCL::MP-fold: Membrane protein structure prediction guided by EPR restraints

For many membrane proteins, the determination of their topology remains a challenge for methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Electron paramagnetic resonance (EPR) spectroscopy has evolved as an alternative technique to study structure and dynamics of membrane proteins. The present study demonstrates the feasibility of membrane protein topology determination using limited EPR distance and accessibility measurements. The BCL::MP-Fold (BioChemical Library membrane protein fold) algorithm assembles secondary structure elements (SSEs) in the membrane using a Monte Carlo Metropolis (MCM) approach. Sampled models are evaluated using knowledge-based potential functions and agreement with the EPR data and a knowledge-based energy function. Twenty-nine membrane proteins of up to 696 residues are used to test the algorithm. The RMSD100 value of the most accurate model is better than 8 Å for 27, better than 6 Å for 22, and better than 4 Å for 15 of the 29 proteins, demonstrating the algorithms' ability to sample the native topology. The average enrichment could be improved from 1.3 to 2.5, showing the improved discrimination power by using EPR data. Proteins 2015; 83:1947–1962. © 2015 Wiley Periodicals, Inc

Biochemistry & Molecular Biology↗

Structural genomics of bacterial drug targets: Application of a high-throughput pipeline to solve 58 protein structures from pathogenic and related bacteria

Antibiotic resistance remains a leading cause of severe infections worldwide. Small changes in protein sequence can impact antibiotic efficacy. Here, we report deposition of 58 X-ray crystal structures of bacterial proteins that are known targets for antibiotics, which expands knowledge of structural variation to support future antibiotic discovery or modifications.

PDB↗

Role of backbone strain in de novo design of complex α/β protein structures

Abstract We previously elucidated principles for designing ideal proteins with completely consistent local and non-local interactions which have enabled the design of a wide range of new αβ-proteins with four or fewer β-strands. The principles relate local backbone structures to supersecondary-structure packing arrangements of α-helices and β-strands. Here, we test the generality of the principles by employing them to design larger proteins with five- and six- stranded β-sheets flanked by α-helices. The initial designs were monomeric in solution with high thermal stability, and the nuclear magnetic resonance (NMR) structure of one was close to the design model, but for two others the order of strands in the β-sheet was swapped. Investigation into the origins of this strand swapping suggested that the global structures of the design models were more strained than the NMR structures. We incorporated explicit consideration of global backbone strain into the design methodology, and succeeded in designing proteins with the intended unswapped strand arrangements. These results illustrate the value of experimental structure determination in guiding improvement of de novo design, and the importance of consistency between local, supersecondary, and global tertiary interactions in determining protein topology. The augmented set of principles should inform the design of larger functional proteins.

59 BASIC BIOLOGICAL SCIENCES↗

Multistep modeling of protein structure: application to bungarotoxin

Modelling of bungarotoxin in atomic details is presented in this article. The model-building procedure utilizes the low-resolution crystal coordinates of the c-alpha atoms of bungarotoxin, sequence homology within the neurotoxin family, as well as high-resolution x-ray diffraction data of cobratoxin and erabutoxin. Our model-building procedure involves: (a) principles of comparative modelling, (b) embedding procedures of distance geometry, and (c) use of molecular mechanics for optimizing packing. The model is not only consistent with the c-alpha coordinates of crystal structure, but also agrees with solution conformational features of the triple-stranded beta sheet as observed by NOE measurements.

NASA Discipline Exobiology↗

Towards resolving protein structures at the atomic scale using atom probe tomography

In the field of Structural Biology, Atom Probe Tomography (APT) is in the nascent stages of development wherein coarse-grained visuals of proteins have been captured. The characterization of organic samples or biomolecules through the technique is currently limited to the detection of a few dominant signatures. The problem of indecipherable characterization can inherently be traced back to multiple forms of technique-specific responses to organic samples and consequent triggers leading to organic-sample and sample-medium interactions. While it is possible for captured manifestations of protein reconstructions to seemingly appear intact from a basic visual purview, the parameter-protein associative responses throughout the structure as a direct consequence of the inherent workings of the technique (until harmonized with organic sample complexity and behavior) and field evaporation-based factors make non-aberrative atomic associations infeasible. The work focused on identifying and theorizing the (above stated and other) fundamental mechanisms that stronghold the study of intricate atomic to higher order associations in proteins through APT. Attempts at structure elucidation of the cryogenic sample under study, through indirect associations (and methods) based on other imaging techniques, further revealed the distinct and highly distortive nature at the atomic scale deterring structural tunability and thus characterization of APT based cryogenic samples under analysis. As a direct counter to the atomic scale characterization problem, by taking the experiment-specific uncertainties, and probable APT-centric organic sample-based variabilities into account, a basic result is extracted and presented. Through mass-spectrometric and computational analysis, specific individual amino acids (Sulfur-containing protein-bound amino acids) in proteins and aspects of protein structure (probable backbone fragments, partial sequence - partial backbone portions) have been identified and characterized. Under analysis considerations, a few of the simplest known and easily inferable segments that favor structural deteriorations in the reconstructions are stated. Additionally, to overcome technique-specific deterrents to the characterization of biomolecules in cryogenic sample medium, the development of a protein-labeling strategy tailored to APT is suggested.

59 BASIC BIOLOGICAL SCIENCES↗

Improving the chances of successful protein structure determination with a random forest classifier

Using an extended set of protein features calculated separately for protein surface and interior, a new version of XtalPred based on a random forest classifier achieves a significant improvement in predicting the success of structure determination from the primary amino-acid sequence. Obtaining diffraction quality crystals remains one of the major bottlenecks in structural biology. The ability to predict the chances of crystallization from the amino-acid sequence of the protein can, at least partly, address this problem by allowing a crystallographer to select homologs that are more likely to succeed and/or to modify the sequence of the target to avoid features that are detrimental to successful crystallization. In 2007, the now widely used XtalPred algorithm [Slabinski et al. (2007 ▶), Protein Sci.16, 2472–2482] was developed. XtalPred classifies proteins into five ‘crystallization classes’ based on a simple statistical analysis of the physicochemical features of a protein. Here, towards the same goal, advanced machine-learning methods are applied and, in addition, the predictive potential of additional protein features such as predicted surface ruggedness, hydrophobicity, side-chain entropy of surface residues and amino-acid composition of the predicted protein surface are tested. The new XtalPred-RF (random forest) achieves significant improvement of the prediction of crystallization success over the original XtalPred. To illustrate this, XtalPred-RF was tested by revisiting target selection from 271 Pfam families targeted by the Joint Center for Structural Genomics (JCSG) in PSI-2, and it was estimated that the number of targets entered into the protein-production and crystallization pipeline could have been reduced by 30% without lowering the number of families for which the first structures were solved. The prediction improvement depends on the subset of targets used as a testing set and reaches 100% (i.e. twofold) for the top class of predicted targets.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

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. Here, we make the method available to the scientific community to speed biological research.

59 BASIC BIOLOGICAL SCIENCES↗

Hop/STIP1 depletion alters nuclear structure via depletion of nuclear structural protein emerin

Highlights: • The link between the co-chaperone Hop/STIP1 and nuclear protein emerin is studied. • Hop overexpression or depletion targets emerin for degradation. • Hop and emerin interact in complexes that do not require Hsp90, but accommodate Hsp70. • Nuclear structure is altered in Hop and emerin depleted cells. Hop/STIP1 is a co-chaperone of Hsp70 and Hsp90 that regulates a number of cell biology processes via interactions with cellular proteins. Here we report a new relationship between Hop and the nuclear structural protein emerin in maintenance of nuclear morphology. Depletion or overexpression of Hop resulted in the reduction of emerin protein levels via proteasomal and lysosomal pathways. Co-immunoprecipitation assays confirmed that Hop and emerin are in a common complex, which could accommodate Hsp70 but not Hsp90, and that TPR2AB is required for the association. Loss of Hop or emerin led to a deformation of nuclear structure, a statistically significant decrease in nuclear size, and was associated with changes in the levels of nuclear proteins, lamin A-C and fibrillarin. The nuclear defects upon Hop loss could be rescued by emerin overexpression. Taken together, these data suggest that Hop stabilises emerin and that loss of Hop alters nuclear structure via emerin degradation.

60 APPLIED LIFE SCIENCES↗

Naegleria fowleri: Protein structures to facilitate drug discovery for the deadly, pathogenic free-living amoeba

Naegleria fowleri is a pathogenic, thermophilic, free-living amoeba which causes primary amebic meningoencephalitis (PAM). Penetrating the olfactory mucosa, the brain-eating amoeba travels along the olfactory nerves, burrowing through the cribriform plate to its destination: the brain’s frontal lobes. The amoeba thrives in warm, freshwater environments, with peak infection rates in the summer months and has a mortality rate of approximately 97%. A major contributor to the pathogen’s high mortality is the lack of sensitivity of N . fowleri to current drug therapies, even in the face of combination-drug therapy. To enable rational drug discovery and design efforts we have pursued protein production and crystallography-based structure determination efforts for likely drug targets from N . fowleri . The genes were selected if they had homology to drug targets listed in Drug Bank or were nominated by primary investigators engaged in N . fowleri research. In 2017, 178 N . fowleri protein targets were queued to the Seattle Structural Genomics Center of Infectious Disease (SSGCID) pipeline, and to date 89 soluble recombinant proteins and 19 unique target structures have been produced. Many of the new protein structures are potential drug targets and contain structural differences compared to their human homologs, which could allow for the development of pathogen-specific inhibitors. Five of the structures were analyzed in more detail, and four of five show promise that selective inhibitors of the active site could be found. The 19 solved crystal structures build a foundation for future work in combating this devastating disease by encouraging further investigation to stimulate drug discovery for this neglected pathogen.

59 BASIC BIOLOGICAL SCIENCES↗

RCSB Protein Data Bank: improved annotation, search and visualization of membrane protein structures archived in the PDB

Abstract Motivation Membrane proteins are encoded by approximately one fifth of human genes but account for more than half of all US FDA approved drug targets. Thanks to new technological advances, the number of membrane proteins archived in the PDB is growing rapidly. However, automatic identification of membrane proteins or inference of membrane location is not a trivial task. Results We present recent improvements to the RCSB Protein Data Bank web portal (RCSB PDB, rcsb.org) that provide a wealth of new membrane protein annotations integrated from four external resources: OPM, PDBTM, MemProtMD and mpstruc. We have substantially enhanced the presentation of data on membrane proteins. The number of membrane proteins with annotations available on rcsb.org was increased by ∼80%. Users can search for these annotations, explore corresponding tree hierarchies, display membrane segments at the 1D amino acid sequence level, and visualize the predicted location of the membrane layer in 3D. Availability and implementation Annotations, search, tree data and visualization are available at our rcsb.org web portal. Membrane visualization is supported by the open-source Mol* viewer (molstar.org and github.com/molstar/molstar). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗