tertiary structures
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2022 ◽  
Vol 12 (1) ◽  
Kathleen L. Vergunst ◽  
David N. Langelaan

AbstractHydrophobins are small proteins that are secreted by fungi, accumulate at interfaces, modify surface hydrophobicity, and self-assemble into large amyloid-like structures. These unusual properties make hydrophobins an attractive target for commercial applications as green emulsifiers and surface modifying agents. Hydrophobins have diverse sequences and tertiary structures, and depending on the hydrophobin, different regions of their structure have been proposed to be required for self-assembly. To provide insight into the assembly process, we determined the first crystal structure of a class I hydrophobin, SC16. Based on the crystal structure, we identified a putative intermolecular contact that may be important for rodlet assembly and was formed in part by the N-terminal tail of SC16. Surprisingly, removal of the N-terminal tail did not influence the self-assembly kinetics of SC16 or the morphology of its rodlets. These results suggest that other regions of this hydrophobin class are required for rodlet formation and indicate that the N-terminal tail of SC16 is amenable to modification so that functionalized hydrophobin assemblies can be created.

2021 ◽  
Vol 8 (1) ◽  
pp. 37
Zili Song ◽  
Maoqiang He ◽  
Ruilin Zhao ◽  
Landa Qi ◽  
Guocan Chen ◽  

As an indispensable essential amino acid in the human body, lysine is extremely rich in edible mushrooms. The α-aminoadipic acid (AAA) pathway is regarded as the biosynthetic pathway of lysine in higher fungal species in Agaricomycetes. However, there is no deep understanding about the molecular evolutionary relationship between lysine biosynthesis and species in Agaricomycetes. Herein, we analyzed the molecular evolution of lysine biosynthesis in Agaricomycetes. The phylogenetic relationships of 93 species in 34 families and nine orders in Agaricomycetes were constructed with six sequences of LSU, SSU, ITS (5.8 S), RPB1, RPB2, and EF1-α datasets, and then the phylogeny of enzymes involved in the AAA pathway were analyzed, especially homocitrate synthase (HCS), α-aminoadipate reductase (AAR), and saccharopine dehydrogenase (SDH). We found that the evolution of the AAA pathway of lysine biosynthesis is consistent with the evolution of species at the order level in Agaricomycetes. The conservation of primary, secondary, predicted tertiary structures, and substrate-binding sites of the enzymes of HCS, AAR, and SDH further exhibited the evolutionary conservation of lysine biosynthesis in Agaricomycetes. Our results provide a better understanding of the evolutionary conservation of the AAA pathway of lysine biosynthesis in Agaricomycetes.

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 46
Yuliya Dantsu ◽  
Ying Zhang ◽  
Wen Zhang

Nucleic-acid-based small molecule and oligonucleotide therapies are attractive topics due to their potential for effective target of disease-related modules and specific control of disease gene expression. As the non-naturally occurring biomolecules, modified DNA/RNA nucleoside and oligonucleotide analogues composed of L-(deoxy)riboses, have been designed and applied as innovative therapeutics with superior plasma stability, weakened cytotoxicity, and inexistent immunogenicity. Although all the chiral centers in the backbone are mirror converted from the natural D-nucleic acids, L-nucleic acids are equipped with the same nucleobases (A, G, C and U or T), which are critical to maintain the programmability and form adaptable tertiary structures for target binding. The types of L-nucleic acid drugs are increasingly varied, from chemically modified nucleoside analogues that interact with pathogenic polymerases to nanoparticles containing hundreds of repeating L-nucleotides that circulate durably in vivo. This article mainly reviews three different aspects of L-nucleic acid therapies, including pharmacological L-nucleosides, Spiegelmers as specific target-binding aptamers, and L-nanostructures as effective drug-delivery devices.

IUCrJ ◽  
2021 ◽  
Vol 9 (1) ◽  
Ashraya Ravikumar ◽  
Mrugsen Nagsen Gopnarayan ◽  
Sriram Subramaniam ◽  
Narayanaswamy Srinivasan

An evaluation of systematic differences in local structure and conformation in the interior of protein tertiary structures determined by crystallography and by cryo-electron microscopy (cryo-EM) is reported. The expectation is that any consistent differences between the derived atomic models could provide insights into variations in side-chain packing that result from differences in specimens prepared for analysis between these two methods. By computing an atomic packing score, which provides a quantitative measure of clustering of side-chain atoms in the core of the tertiary structures, it is found that, in general, for structures determined by cryo-EM, side chains are more dispersed than in structures determined by X-ray crystallography over a similar resolution range. This trend is also observed in the packing comparison at subunit interfaces. Similar trends were observed in the packing comparison at the core of tertiary structures of the same proteins determined by both X-ray and cryo-EM methods. It is proposed here that the reduced dispersion of side chains in protein crystals could be due to some level of dehydration in 3D crystals prepared for X-ray crystallography and also because the higher rate of freezing of protein samples for cryo-EM may enable preservation of a more native conformation.

2021 ◽  
Justine S Paradis ◽  
Xiang Feng ◽  
Brigitte Murat ◽  
Robert E Jefferson ◽  
Martyna E Szpakowska ◽  

Communication across membranes controls critical cellular processes and is achieved by receptors translating extracellular signals into selective cytoplasmic responses. While receptor tertiary structures can now be readily characterized, receptor associations into quaternary structures are very challenging to study and their implications in signal transduction remain poorly understood. Here, we report a computational approach for predicting membrane receptor self-associations, and designing receptor oligomers with various quaternary structures and signaling properties. Using this approach, we designed chemokine receptor CXCR4 dimers with reprogrammed stabilities, conformations, and abilities to activate distinct intracellular signaling proteins. In agreement with our predictions, the designed CXCR4s dimerized through distinct conformations and displayed different quaternary structural changes upon activation. Consistent with the active state models, all engineered CXCR4 oligomers activated the G protein Gi, but only a few specific dimer structures also recruited β-arrestins. Overall, we demonstrate that quaternary structures represent an important unforeseen mechanism of receptor biased signaling and reveal the existence of a conformational switch at the dimer interface of several G protein-coupled receptors including CXCR4, mu-Opioid and type-2 Vasopressin receptors that selectively control the activation of G proteins vs β-arrestin-mediated pathways. The approach should prove useful for predicting and designing receptor associations to uncover and reprogram selective cellular signaling functions.

2021 ◽  
Raj Shekhor Roy ◽  
Farhan Quadir ◽  
Elham Soltanikazemi ◽  
Jianlin Cheng

Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accuracy of predicting quaternary structures of protein complexes consisting of multiple chains is still relatively low due to lack of advanced deep learning methods in the field. Because interchain residue-residue contacts can be used as distance restraints to guide quaternary structure modeling, here we develop a deep dilated convolutional residual network method (DRCon) to predict interchain residue-residue contacts in homodimers from residue-residue co-evolutionary signals derived from multiple sequence alignments of monomers, intrachain residue-residue contacts of monomers extracted from true/predicted tertiary structures or predicted by deep learning, and other sequence and structural features. Tested on three homodimer test datasets (Homo_std dataset, DeepHomo dataset, and CASP14-CAPRI dataset), the precision of DRCon for top L/5 interchain contact predictions (L: length of monomer in a homodimer) is 43.46%, 47.15%, and 24.81% respectively, which is substantially better than two existing deep learning interchain contact prediction methods. Moreover, our experiments demonstrate that using predicted tertiary structure or intrachain contacts of monomers in the unbound state as input, DRCon still performs reasonably well, even though its accuracy is lower than when true tertiary structures in the bound state are used as input. Finally, our case study shows that good interchain contact predictions can be used to build high-accuracy quaternary structure models of homodimers.

Marco Pinto Corujo ◽  
Vivian Lindo ◽  
Nikola Chmel ◽  
Alison Rodger

Background: Proteins are biomolecules that consist of sequences of amino acids (primary structure) which can further interact and cause the backbone to fold into more complex structures (secondary and tertiary structures). Any chemical alterations of the molecules after the translation of the messenger RNA code into a protein primary sequence are known as post-translational modifications (PTMs). PTMs may affect the protein’s functionality; thus it is necessary to identify them. PTMs of particular interest to the pharmaceutical industry include deamidation, oxidation, deglycosylation and isomerization, which may occur due to environmental stressors. However, they have proved challenging to identify quickly. Electronic and vibrational spectroscopies have proved valuable tools for studying higher-order structure and stability of proteins. Materials & Methods: In this work, circular dichroism (CD), infrared absorbance (IR) and Raman spectroscopies were applied to characterize antibody (mAb NIP 228) PTMs as a result of different stressors. Mass spectrometry was used to confirm the identity of modifications including the targeted ones. Room temperature CD showed that the secondary structure was the same after all treatments, and temperature-controlled CD showed how protein stability was affected by modifications. Both Raman and IR analysis detected small differences between the reference and deglycosylated proteins, and clearly indicated the presence of other PTMs. Conclusion: This work required some novel computational approaches to pre–process Raman and IR spectra and a review of the band assignments for proteins existing in the literature.

2021 ◽  
Vol 22 (1) ◽  
Lupeng Kong ◽  
Fusong Ju ◽  
Haicang Zhang ◽  
Shiwei Sun ◽  
Dongbo Bu

Abstract Background Accurate prediction of protein tertiary structures is highly desired as the knowledge of protein structures provides invaluable insights into protein functions. We have designed two approaches to protein structure prediction, including a template-based modeling approach (called ProALIGN) and an ab initio prediction approach (called ProFOLD). Briefly speaking, ProALIGN aligns a target protein with templates through exploiting the patterns of context-specific alignment motifs and then builds the final structure with reference to the homologous templates. In contrast, ProFOLD uses an end-to-end neural network to estimate inter-residue distances of target proteins and builds structures that satisfy these distance constraints. These two approaches emphasize different characteristics of target proteins: ProALIGN exploits structure information of homologous templates of target proteins while ProFOLD exploits the co-evolutionary information carried by homologous protein sequences. Recent progress has shown that the combination of template-based modeling and ab initio approaches is promising. Results In the study, we present FALCON2, a web server that integrates ProALIGN and ProFOLD to provide high-quality protein structure prediction service. For a target protein, FALCON2 executes ProALIGN and ProFOLD simultaneously to predict possible structures and selects the most likely one as the final prediction result. We evaluated FALCON2 on widely-used benchmarks, including 104 CASP13 (the 13th Critical Assessment of protein Structure Prediction) targets and 91 CASP14 targets. In-depth examination suggests that when high-quality templates are available, ProALIGN is superior to ProFOLD and in other cases, ProFOLD shows better performance. By integrating these two approaches with different emphasis, FALCON2 server outperforms the two individual approaches and also achieves state-of-the-art performance compared with existing approaches. Conclusions By integrating template-based modeling and ab initio approaches, FALCON2 provides an easy-to-use and high-quality protein structure prediction service for the community and we expect it to enable insights into a deep understanding of protein functions.

2021 ◽  
Vol 118 (33) ◽  
pp. e2109085118
Steve L. Bonilla ◽  
Sarah K. Denny ◽  
John H. Shin ◽  
Aurora Alvarez-Buylla ◽  
William J. Greenleaf ◽  

Despite RNA’s diverse secondary and tertiary structures and its complex conformational changes, nature utilizes a limited set of structural “motifs”—helices, junctions, and tertiary contact modules—to build diverse functional RNAs. Thus, in-depth descriptions of a relatively small universe of RNA motifs may lead to predictive models of RNA tertiary conformational landscapes. Motifs may have different properties depending on sequence and secondary structure, giving rise to subclasses that expand the universe of RNA building blocks. Yet we know very little about motif subclasses, given the challenges in mapping conformational properties in high throughput. Previously, we used “RNA on a massively parallel array” (RNA-MaP), a quantitative, high-throughput technique, to study thousands of helices and two-way junctions. Here, we adapt RNA-MaP to study the thermodynamic and conformational properties of tetraloop/tetraloop receptor (TL/TLR) tertiary contact motifs, analyzing 1,493 TLR sequences from different classes. Clustering analyses revealed variability in TL specificity, stability, and conformational behavior. Nevertheless, natural GAAA/11ntR TL/TLRs, while varying in tertiary stability by ∼2.5 kcal/mol, exhibited conserved TL specificity and conformational properties. Thus, RNAs may tune stability without altering the overall structure of these TL/TLRs. Furthermore, their stability correlated with natural frequency, suggesting thermodynamics as the dominant selection pressure. In contrast, other TL/TLRs displayed heterogenous conformational behavior and appear to not be under strong thermodynamic selection. Our results build toward a generalizable model of RNA-folding thermodynamics based on the properties of isolated motifs, and our characterized TL/TLR library can be used to engineer RNAs with predictable thermodynamic and conformational behavior.

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