Exploring the folding energy landscapes of heme proteins using a hybrid AWSEM-heme model

Heme is an active center in many proteins. Here we explore computationally the role of heme in protein folding and protein structure. We model heme proteins using a hybrid model employing the AWSEM Hamiltonian, a coarse-grained forcefield for the protein chain along with AMBER, an all-atom forcefield for the heme. We carefully designed transferable force fields that model the interactions between the protein and the heme. The types of protein–ligand interactions in the hybrid model include thioester covalent bonds, coordinated covalent bonds, hydrogen bonds, and electrostatics. We explore the influence of different types of hemes (heme b and heme c) on folding and structure prediction. Including both types of heme improves the quality of protein structure predictions. The free energy landscape shows that both types of heme can act as nucleation sites for protein folding and stabilize the protein folded state. In binding the heme, coordinated covalent bonds and thioester covalent bonds for heme c drive the heme toward the native pocket. The electrostatics also facilitates the search for the binding site.

Structural Modelling of KCNQ1 and KCNH2 Double Mutant Proteins, Identified in Two Severe Long QT Syndrome Cases, Reveals New Insights into Cardiac Channelopathies

Congenital long QT syndrome (LQTS) is a cardiac channelopathy characterized by a prolongation of the QT interval and T-wave abnormalities, caused, in most cases, by mutations in KCNQ1, KCNH2, and SCN5A. Although the predominant pattern of LQTS inheritance is autosomal dominant, compound heterozygous mutations in genes encoding potassium channels have been reported, often with early disease onset and more severe phenotypes. Since the molecular mechanisms underlying severe phenotypes in carriers of compound heterozygous mutations are unknown, it is possible that these compound mutations lead to synergistic or additive alterations to channel structure and function. In this study, all-atom molecular dynamic simulations of KCNQ1 and hERG channels were carried out, including wild-type and channels with compound mutations found in two patients with severe LQTS phenotypes and limited family history of the disease. Because channels can likely incorporate different subunit combinations from different alleles, there are multiple possible configurations of ion channels in LQTS patients. This analysis allowed us to establish the structural impact of different configurations of mutant channels in the activated/open state. Our data suggest that channels with these mutations show moderate changes in folding energy (in most cases of stabilizing character) and changes in channel mobility and volume, differentiating them from each other and from WT. This would indicate possible alterations in K+ ion flow. Hetero-tetrameric mutant channels showed intermediate structural and volume alterations vis-à-vis homo-tetrameric channels. These findings support the hypothesis that hetero-tetrameric channels in patients with compound heterozygous mutations do not necessarily lead to synergistic structural alterations.

Potential treatment for chronic myeloid leukemia using microRNA: in silico comparison between plants and human microRNAs in targeting BCR-ABL1 gene

Background Chronic myeloid leukemia (CML) is a myeloproliferative disorder characterized by the expression of the BCR-ABL1 fusion gene. Tyrosine kinase inhibitors (TKI) are used to treat CML, but mutations in the tyrosine kinase domain contribute to CML chemo-resistance. Therefore, finding alternative molecular-targeted therapy is important for the comprehensive treatment of CML. MicroRNAs (miRNA) are small non-coding regulatory RNAs which suppress the expression of their target genes by binding to the 3′ untranslated region (3′UTR) of the target mRNA. Hypothetically, the miRNA-mRNA interaction would suppress BCR-ABL1 expression and consequently reduce and inhibit CML cell proliferation. Thus, our objective was to determine the target interaction of human and plant miRNAs targeting the 3′UTR region of BCR-ABL1 in terms of miRNA binding conformity, protein interaction network, and pathways using in silico analysis. The 3′UTR sequence of BCR-ABL1 is obtained from Ensembl Genome Browser while the binding conformity was determined using the PsRNATarget Analysis Server, RNA22, Target Rank Server, and DIANA TOOLS. Protein-protein interaction network and pathway analysis are determined using STRING, Cytoscape, and KEGG pathway analysis. Results Five plants and five human miRNAs show strong binding conformity with 3′UTR of BCR-ABL1. The strongest binding conformity was shown by Oryza sativa’s Osa-miR1858a and osa-miR1858b with −24.4 kcal/mol folding energy and a p value of 0.0077. Meanwhile, in human miRNA, the hsa-miR-891a-3p shows the highest miTG score of 0.99 with −12 kcal/mol folding energy and a p value of 0.037. Apart from ABL1, osa-miR1858a/osa-miR1858b and hsa-miR891a-3p also target other 720 and 645 genes, respectively. The interaction network of Osa-miR1858a/osa-miR1858b and hsa-miR891a-3p identifies nineteen and twelve ABL1’s immediate neighboring proteins, respectively. The pathways analysis focuses on the RAS, MAPK, CML, and hematopoietic cell lineage pathway. Conclusion Both plant and human miRNAs tested in this study could be a potential therapeutic prospect in CML treatment, but thermodynamically, osa-miR1858a/osa-miR1858b binding to ABL1 is more favorable. However, it is important to carry out more research in vitro and in vivo and clinical studies to assess its efficacy as a targeted therapy for CML. Graphical abstract

A Mathematical Analysis of RNA Structural Motifs in Viruses

RNA stem-loop structures play an important role in almost every step of the viral replication cycle. In this contribution, a mathematical analysis is performed on a large dataset of RNA secondary structure elements in the coding regions of viruses by using topological indices that capture the Laplacian eigenvalues of the associated RNA graph representations and thereby enable structural classification, supplemented by folding energy and mutational robustness. The application of such an analysis for viral RNA structural motifs is described, being able to extract structural categories such as stem-loop structures of different sizes according to the tree-graph representation of the RNA structure, in our attempt to find novel functional motifs. While the analysis is carried on a large dataset of viral RNA structures, it can be applied more generally to other data that involve RNA secondary structures in biological agents.

tRFTars: predicting the targets of tRNA-derived fragments

Background tRNA-derived fragments (tRFs) are 14–40-nucleotide-long, small non-coding RNAs derived from specific tRNA cleavage events with key regulatory functions in many biological processes. Many studies have shown that tRFs are associated with Argonaute (AGO) complexes and inhibit gene expression in the same manner as miRNAs. However, there are currently no tools for accurately predicting tRF target genes. Methods We used tRF-mRNA pairs identified by crosslinking, ligation, and sequencing of hybrids (CLASH) and covalent ligation of endogenous AGO-bound RNAs (CLEAR)-CLIP to assess features that may participate in tRF targeting, including the sequence context of each site and tRF-mRNA interactions. We applied genetic algorithm (GA) to select key features and support vector machine (SVM) to construct tRF prediction models. Results We first identified features that globally influenced tRF targeting. Among these features, the most significant were the minimum free folding energy (MFE), position 8 match, number of bases paired in the tRF-mRNA duplex, and length of the tRF, which were consistent with previous findings. Our constructed model yielded an area under the receiver operating characteristic (ROC) curve (AUC) = 0.980 (0.977–0.983) in the training process and an AUC = 0.847 (0.83–0.861) in the test process. The model was applied to all the sites with perfect Watson–Crick complementarity to the seed in the 3′ untranslated region (3′-UTR) of the human genome. Seven of nine target/nontarget genes of tRFs confirmed by reporter assay were predicted. We also validated the predictions via quantitative real-time PCR (qRT-PCR). Thirteen potential target genes from the top of the predictions were significantly down-regulated at the mRNA levels by overexpression of the tRFs (tRF-3001a, tRF-3003a or tRF-3009a). Conclusions Predictions can be obtained online, tRFTars, freely available at http://trftars.cmuzhenninglab.org:3838/tar/, which is the first tool to predict targets of tRFs in humans with a user-friendly interface.

Path to Diversity and to Resistant Uniformity: Intracellular Adaptation to Nutrient Environment

Two adaptation strategies are known, which provide variability and resistance of population. We study the laws of adaptation by the example of proteins and changes in their conformations. The data were obtained in the experiments of V.I. Korogodin on yeast cells with mutations, which have demonstrated the effect of the culture medium on the appearance frequency of pseudo-wild type cells. Here, these archived and published data are analyzed by the statistical approach. Statistical analysis shows the emergence of a sequence of independent foci of the pseudo-wild cells induced by intracellular factor and their association with the cytosolic and nuclear-mitochondrial oxidative pathways; the foci dispersions conform the regularities of the folding energy landscape; intracellular imbalances and gene mutations affect their frequency and diversity. We conclude that the paths from diversity to uniformity of protein conformations obeys the laws of the energy landscape. The nuclear-mitochondrial machinery generates new proteins and their homogeneous foci. Variable foci consist mainly of the former conformations remodeled under ROS from several cytosolic sources. Strong gene expression induces oxidative stress, which increases the frequency of homogeneous conformations and reduces variability. Further, stress activates a new focus of new homogeneous conformations.

Prediction of Protein–Protein Binding Interactions in Dimeric Coiled Coils by Information Contained in Folding Energy Landscapes

Coiled coils represent the simplest form of a complex formed between two interacting protein partners. Their extensive study has led to the development of various methods aimed towards the investigation and design of complex forming interactions. Despite the progress that has been made to predict the binding affinities for protein complexes, and specifically those tailored towards coiled coils, many challenges still remain. In this work, we explore whether the information contained in dimeric coiled coil folding energy landscapes can be used to predict binding interactions. Using the published SYNZIP dataset, we start from the amino acid sequence, to simultaneously fold and dock approximately 1000 coiled coil dimers. Assessment of the folding energy landscapes showed that a model based on the calculated number of clusters for the lowest energy structures displayed a signal that correlates with the experimentally determined protein interactions. Although the revealed correlation is weak, we show that such correlation exists; however, more work remains to establish whether further improvements can be made to the presented model.

Computational Protein Stabilization Can Affect Folding Energy Landscapes and Lead to Domain-Swapped Dimers

<p>The functionality of a protein depends on its unique three-dimensional structure, which is a result of the folding process when the nascent polypeptide follows a funnel-like energy landscape to reach a global energy minimum. Computer-encoded algorithms are increasingly employed to stabilize native proteins for use in research and biotechnology applications. Here, we reveal a unique example where the computational stabilization of a monomeric α/β-hydrolase enzyme (<i>T</i>_m = 73.5°C; Δ<i>T</i>_m > 23°C) affected the protein folding energy landscape. Introduction of eleven single-point stabilizing mutations based on force field calculations and evolutionary analysis yielded catalytically active domain-swapped intermediates trapped in local energy minima. Crystallographic structures revealed that these stabilizing mutations target cryptic hinge regions and newly introduced secondary interfaces, where they make extensive non-covalent interactions between the intertwined misfolded protomers. The existence of domain-swapped dimers in a solution is further confirmed experimentally by data obtained from SAXS and crosslinking mass spectrometry. Unfolding experiments showed that the domain-swapped dimers can be irreversibly converted into native-like monomers, suggesting that the domain-swapping occurs exclusively <i>in vivo</i>. Our findings uncovered hidden protein-folding consequences of computational protein design, which need to be taken into account when applying a rational stabilization to proteins of biological and pharmaceutical interest.</p>