In communication networks, the existence of fractional factors can be characterized as the feasibility of data transmission. When some nonadjacent nodes with each other are damaged and a special ...channel is assigned, the possibility of data transmission in a communication network is equivalent to the existence of fractional ID-factor-critical covered graph. As a consequence, the existence of fractional ID-a,b-factor-critical covered graph plays a key role in studying data transmissions of communication networks. Neighborhood and minimum degree of a graph or a network are often used to measure the vulnerability and robustness of a graph or a network, which are two important parameters considered in network design. In this article, we mainly investigate the relationship between neighborhood of independent set, minimum degree and the fractional ID-a,b-factor-critical covered graph, and acquire a neighborhood of independent set and minimum degree condition for a graph being fractional ID-a,b-factor-critical covered, which is a generalization of the previous results.
Let a≤b be two positive integers. We say that a graph G has all a,b-factors if it has an h-factor for every function h:V(G)→Z+ such that a≤h(v)≤b for all v∈V(G) and ∑v∈V(G)h(v)≡0(mod2), and has all ...fractional a,b-factors if it has a fractional p-factor for every p:V(G)→Z+ such that a≤p(v)≤b for all v∈V(G). In this paper, we provide tight spectral radius conditions for graphs having all a,b-factors (3≤a<b) and all fractional a,b-factors (1≤a<b), respectively.
Marc van Regenmortel was the Editor‐in‐Chief of the Journal of Molecular Recognition for the last 25 years. Without attempting to summarize Marc's exceptional career and achievements, we would like ...to tell the story of the tortuous and contingent path to the unravelling of a key molecular recognition process in antigenicity. Life is indeed full of contingencies and scientific life, full of meetings and random encounters, is prone to contingencies, a key element in discovery and innovation.
In communication networks, the binding numbers of graphs (or networks) are often used to measure the vulnerability and robustness of graphs (or networks). Furthermore, the fractional factors of ...graphs and the fractional ID-a, b-factor-critical covered graphs have a great deal of important applications in the data transmission networks. In this paper, we investigate the relationship between the binding numbers of graphs and the fractional ID-a, b-factor-critical covered graphs, and derive a binding number condition for a graph to be fractional ID-a, b-factor-critical covered, which is an extension of Zhou’s previous result S. Zhou, Binding numbers for fractional ID-k-factor-critical graphs, Acta Mathematica Sinica, English Series 30(1)(2014)181–186.
High‐redox potential laccases (HRPLs) from white‐rot fungi are versatile biocatalysts whose practical use is highly dependent on their thermostability. In this work, an evolved HRPL variant was ...subjected to structure‐guided evolution to improve its thermostability. We first selected several surface flexible loops in the laccase structure by inspecting them through molecular dynamics and an analysis of B‐factors. The resulting segments were grouped into three MORPHING (Mutagenic Organized Recombination Process by Homologous In vivo Grouping) blocks, which were constructed in Saccharomyces cerevisiae and explored at high temperatures. This evolution process gave rise to a double mutant that showed a half‐life at 70°C enhanced by 31 min with an optimum temperature for activity of 75°C and similar kinetic parameters. The Ser264Lys and Ser356Asn mutations modified the contacts established between these residues and those that surround them, altering the surface loops and thereby the enzyme properties.
The flexibility of protein structure is related to various biological processes, such as molecular recognition, allosteric regulation, catalytic activity, and protein stability. At the molecular ...level, protein dynamics and flexibility are important factors to understand protein function. DNA‐binding proteins and Coronavirus proteins are of great concern and relatively unique proteins. However, exploring the flexibility of DNA‐binding proteins and Coronavirus proteins through experiments or calculations is a difficult process. Since protein dihedral rotational motion can be used to predict protein structural changes, it provides key information about protein local conformation. Therefore, this paper introduces a method to improve the accuracy of protein flexibility prediction, DihProFle (Prediction of DNA‐binding proteins and Coronavirus proteins flexibility introduces the calculated dihedral Angle information). Based on protein dihedral Angle information, protein evolution information, and amino acid physical and chemical properties, DihProFle realizes the prediction of protein flexibility in two cases on DNA‐binding proteins and Coronavirus proteins, and assigns flexibility class to each protein sequence position. In this study, compared with the flexible prediction using sequence evolution information, and physicochemical properties of amino acids, the flexible prediction accuracy based on protein dihedral Angle information, sequence evolution information and physicochemical properties of amino acids improved by 2.2% and 3.1% in the nonstrict and strict conditions, respectively. And DihProFle achieves better performance than previous methods for protein flexibility analysis. In addition, we further analyzed the correlation of amino acid properties and protein dihedral angles with residues flexibility. The results show that the charged hydrophilic residues have higher proportion in the flexible region, and the rigid region tends to be in the angular range of the protein dihedral angle (such as the ψ angle of amino acid residues is more flexible than rigid in the range of 91°–120°). Therefore, the results indicate that hydrophilic residues and protein dihedral angle information play an important role in protein flexibility.
Structural compliance: A new metric for protein flexibility Scaramozzino, Domenico; Khade, Pranav M.; Jernigan, Robert L. ...
Proteins, structure, function, and bioinformatics,
November 2020, 2020-11-00, 20201101, Letnik:
88, Številka:
11
Journal Article
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Proteins are the active players in performing essential molecular activities throughout biology, and their dynamics has been broadly demonstrated to relate to their mechanisms. The intrinsic ...fluctuations have often been used to represent their dynamics and then compared to the experimental B‐factors. However, proteins do not move in a vacuum and their motions are modulated by solvent that can impose forces on the structure. In this paper, we introduce a new structural concept, which has been called the structural compliance, for the evaluation of the global and local deformability of the protein structure in response to intramolecular and solvent forces. Based on the application of pairwise pulling forces to a protein elastic network, this structural quantity has been computed and sometimes is even found to yield an improved correlation with the experimental B‐factors, meaning that it may serve as a better metric for protein flexibility. The inverse of structural compliance, namely the structural stiffness, has also been defined, which shows a clear anticorrelation with the experimental data. Although the present applications are made to proteins, this approach can also be applied to other biomolecular structures such as RNA. This present study considers only elastic network models, but the approach could be applied further to conventional atomic molecular dynamics. Compliance is found to have a slightly better agreement with the experimental B‐factors, perhaps reflecting its bias toward the effects of local perturbations, in contrast to mean square fluctuations. The code for calculating protein compliance and stiffness is freely accessible at https://jerniganlab.github.io/Software/PACKMAN/Tutorials/compliance.
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•Protein flexibility plays a crucial role in biological function.•MEDUSA is a web-server for prediction of protein flexibility from sequence.•It uses a deep convolutional network to ...assign flexibility class for each residue.•MEDUSA provides binary, three-class and five-class predictions.•It provides important insights about the protein function mechanism.
Information on the protein flexibility is essential to understand crucial molecular mechanisms such as protein stability, interactions with other molecules and protein functions in general. B-factor obtained in the X-ray crystallography experiments is the most common flexibility descriptor available for the majority of the resolved protein structures. Since the gap between the number of the resolved protein structures and available protein sequences is continuously growing, it is important to provide computational tools for protein flexibility prediction from amino acid sequence. In the current study, we report a Deep Learning based protein flexibility prediction tool MEDUSA (https://www.dsimb.inserm.fr/MEDUSA). MEDUSA uses evolutionary information extracted from protein homologous sequences and amino acid physico-chemical properties as input for a convolutional neural network to assign a flexibility class to each protein sequence position. Trained on a non-redundant dataset of X-ray structures, MEDUSA provides flexibility prediction in two, three and five classes. MEDUSA is freely available as a web-server providing a clear visualization of the prediction results as well as a standalone utility (https://github.com/DSIMB/medusa). Analysis of the MEDUSA output allows a user to identify the potentially highly deformable protein regions and general dynamic properties of the protein.
An odd1,b-factor of a graph G is a spanning subgraph F of G with dF(x)∈{1,3,⋯,b} for every x∈V(G), where b is a positive odd integer. Let |E(G)| be the size of G, and let ρ(G) be the spectral radius ...of G. In this article, we first verify three lower bounds for |E(G)| in a graph G of even order n to guarantee the existence of an odd 1,b-factor in G. Then we prove two lower bounds for ρ(G) in a graph G of even order n to guarantee the existence of an odd 1,b-factor in G. Furthermore, we create some extremal graphs to show all the lower bounds derived in this article are sharp.
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