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Author(s):  
Daniel Varela ◽  
José Santos

AbstractProtein folding is the dynamic process by which a protein folds into its final native structure. This is different to the traditional problem of the prediction of the final protein structure, since it requires a modeling of how protein components interact over time to obtain the final folded structure. In this study we test whether a model of the folding process can be obtained exclusively through machine learning. To this end, protein folding is considered as an emergent process and the cellular automata tool is used to model the folding process. A neural cellular automaton is defined, using a connectionist model that acts as a cellular automaton through the protein chain to define the dynamic folding. Differential evolution is used to automatically obtain the optimized neural cellular automata that provide protein folding. We tested the methods with the Rosetta coarse-grained atomic model of protein representation, using different proteins to analyze the modeling of folding and the structure refinement that the modeling can provide, showing the potential advantages that such methods offer, but also difficulties that arise.


Author(s):  
Xun Chen ◽  
Wei Lu ◽  
Min-Yeh Tsai ◽  
Shikai Jin ◽  
Peter G. Wolynes

AbstractHeme 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.


2021 ◽  
Author(s):  
Pratik Mullick ◽  
Antonio Trovato

Several proteins which are responsible for neuro-degenrerative disorders (Alzheimers, Parkinsons etc) are shown to undergo a mechanism known as liquid liquid phase separation (LLPS). We in this research build a predictor which would answer whether a protein molecule would undergo LLPS or not. For this we used some protein sequences for which we already knew the answer. The ones who undergo LLPS were considered as the positive set and the ones who do not, were taken as the negative set. Depending on the knowledge of amino-acid sequences we identified some relevant variables in the context of LLPS e.g. number of amino acids, length of the best pairings, average register shifts. Using these variables we built a number of scoring functions which were basically analytic functions involving these variables and we also combined some scores already existing in the literature. We considered a total of 43636 protein sequences, among them only 121 were positive. We applied logistic regression and performed cross validation, where 25% of the data were used as the training set and the performance of the obtained results were tested on the remaining 75% of the data. In the training process, we used Simplex algorithm to maximize area under the curve (AUC) in receiver operator characteristics (ROC) space for each of the scores we defined. The optimised parameters were then used to evaluate AUC on the test set to check the accuracy. The best performing score was identified as the predicting model to answer the question whether a protein chain would undergo phase separating behavior or not.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Oliviero Carugo

AbstractA novel and simple procedure (RaSPDB) for Protein Data Bank mining is described. 10 PDB subsets, each containing 7000 randomly selected protein chains, are built and used to make 10 estimations of the average value of a generic feature F—the length of the protein chain, the amino acid composition, the crystallographic resolution, and the secondary structure composition. These 10 estimations are then used to compute an average estimation of F together with its standard error. It is heuristically verified that the dimension of these 10 subsets—7000 protein chains—is sufficiently small to avoid redundancy within each subset and sufficiently large to guarantee stable estimations amongst different subsets. RaSPDB has two major advantages over classical procedures aimed to build a single, non-redundant PDB subset: a larger fraction of the information stored in the PDB is used and an estimation of the standard error of F is possible.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Valeria Guzman-Luna ◽  
Andrew M. Fuchs ◽  
Anna J. Allen ◽  
Alexios Staikos ◽  
Silvia Cavagnero

AbstractThe influence of the ribosome on nascent chains is poorly understood, especially in the case of proteins devoid of signal or arrest sequences. Here, we provide explicit evidence for the interaction of specific ribosomal proteins with ribosome-bound nascent chains (RNCs). We target RNCs pertaining to the intrinsically disordered protein PIR and a number of mutants bearing a variable net charge. All the constructs analyzed in this work lack N-terminal signal sequences. By a combination chemical crosslinking and Western-blotting, we find that all RNCs interact with ribosomal protein L23 and that longer nascent chains also weakly interact with L29. The interacting proteins are spatially clustered on a specific region of the large ribosomal subunit, close to the exit tunnel. Based on chain-length-dependence and mutational studies, we find that the interactions with L23 persist despite drastic variations in RNC sequence. Importantly, we also find that the interactions are highly Mg+2-concentration-dependent. This work is significant because it unravels a novel role of the ribosome, which is shown to engage with the nascent protein chain even in the absence of signal or arrest sequences.


Plants ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2220
Author(s):  
Kenny K. Y. So ◽  
Robert W. Duncan

Interest in canola (Brassica napus L.) ­In response to this interest, scientists have been tasked with altering and optimizing the protein production chain to ensure canola proteins are safe for consumption and economical to produce. Specifically, the role of plant breeders in developing suitable varieties with the necessary protein profiles is crucial to this interdisciplinary endeavour. In this article, we aim to provide an overarching review of the canola protein chain from the perspective of a plant breeder, spanning from the genetic regulation of seed storage proteins in the crop to advancements of novel breeding technologies and their application in improving protein quality in canola. A review on the current uses of canola meal in animal husbandry is presented to underscore potential limitations for the consumption of canola meal in mammals. General discussions on the allergenic potential of canola proteins and the regulation of novel food products are provided to highlight some of the challenges that will be encountered on the road to commercialization and general acceptance of canola protein as a dietary protein source.


2021 ◽  
Author(s):  
Oliviero Carugo

Abstract A novel and simple procedure (RaSPDB) for Protein Data Bank mining is described. 10 PDB subsets, each containing 7000 randomly selected protein chains, are built and used to make 10 estimations of the average value of a generic feature F – the length of the protein chain, the amino acid composition, the crystallographic resolution, and the secondary structure composition. These 10 estimations are then used to compute an average estimation of F together with its standard error. It is heuristically verified that the dimension of these 10 subsets –7000 protein chains – is sufficiently small to avoid redundancy within each subset and sufficiently large to guarantee stable estimations amongst different subsets. RaSPDB has two major advantages over classical procedures aimed to build a single, non-redundant PDB subset: a larger fraction of the information stored in the PDB is used and an estimation of the standard error of F is possible.


2021 ◽  
Author(s):  
Christopher J. Williams ◽  
David C. Richardson ◽  
Jane S. Richardson

AbstractWe have curated a high-quality, “best parts” reference dataset of about 3 million protein residues in about 15,000 PDB-format coordinate files, each containing only residues with good electron density support for a physically acceptable model conformation. The resulting pre-filtered data typically contains the entire core of each chain, in quite long continuous fragments. Each reference file is a single protein chain, and the total set of files were selected for low redundancy, high resolution, good MolProbity score, and other chain-level criteria. Then each residue was critically tested for adequate local map quality to firmly support its conformation, which must also be free of serious clashes or covalent-geometry outliers. The resulting Top2018 pre-filtered datasets have been released on the Zenodo online web service and is freely available for all uses under a Creative Commons license. Currently, one dataset is residue-filtered on mainchain plus Cβ atoms, and a second dataset is full-residue filtered; each is available at 4 different sequence-identity levels. Here, we illustrate both statistics and examples that show the beneficial consequences of residue-level filtering. That process is necessary because even the best of structures contain a few highly disordered local regions with poor density and low-confidence conformations that should not be included in reference data. Therefore the open distribution of these very large, pre-filtered reference datasets constitutes a notable advance for structural bioinformatics and the fields that depend upon it.The Top2018 dataset provides the first representative sample of 3D protein structure for which excellence of experimental data constrains the detailed local conformation to be correct for essentially all 3 million residues included. Earlier generations of residue-filtered datasets were central in developing MolProbity validation used worldwide, and now Zenodo has enabled anyone to use out latest version as a sound basis for structural bioinformatics, protein design, prediction, improving biomedically important structures, or other applications.


2021 ◽  
Vol 22 (16) ◽  
pp. 8714
Author(s):  
Kwang-Eun Choi ◽  
Jeong-Min Kim ◽  
JeeEun Rhee ◽  
Ae Kyung Park ◽  
Eun-Jin Kim ◽  
...  

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affects the COVID-19 pandemic in the world. The spike protein of the various proteins encoded in SARS-CoV-2 binds to human ACE2, fuses, and enters human cells in the respiratory system. Spike protein, however, is highly variable, and many variants were identified continuously. In this study, Korean mutants for spike protein (D614G and D614A-C terminal domain, L455F and F456L-RBD, and Q787H-S2 domain) were investigated in patients. Because RBD in spike protein is related to direct interaction with ACE2, almost all researches were focused on the RBD region or ACE2-free whole domain region. The 3D structure for spike protein complexed with ACE2 was recently released. The stability analysis through RBD distance among each spike protein chain and the binding free energy calculation between spike protein and ACE2 were performed using MD simulation depending on mutant types in 1-, 2-, and 3-open-complex forms. D614G mutant of CT2 domain, showing to be the most prevalent in the global pandemic, showed higher stability in all open-complex forms than the wild type and other mutants. We hope this study will provide an insight into the importance of conformational fluctuation in the whole domain, although RBD is involved in the direct interaction with ACE2.


2021 ◽  
Author(s):  
Alireza Mashaghi ◽  
Fatemeh Moayed ◽  
Eline J Koers ◽  
Guenter Kramer ◽  
Matthias P Mayer ◽  
...  

The chaperone Hsp90 is well known to undergo important conformational changes, which depend on nucleotide, co-chaperones, substrate interactions and post-translational modifications. Conversely, how the conformations of its unstable and disordered substrates are affected by Hsp90 is difficult to address experimentally, yet central to its function. Here, using optical tweezers and luciferase and glucocorticoid receptor substrates, we find that Hsp90 promotes local contractions in unfolded chains that drive their global compaction down to dimensions of folded states. This compaction has a gradual nature while showing small steps, is stimulated by ATP, and performs mechanical work against counteracting forces that expand the chain dimensions. The Hsp90 interactions suppress the formation of larger-scale folded, misfolded and aggregated structures. The observations support a model in which Hsp90 alters client conformations directly by promoting local intra-chain interactions while suppressing distant ones. We conjecture that chain compaction may be central to how Hsp90 protects unstable kinases and receptor clients, regulates their activity, and how Hsp90 cooperates with Hsp70.


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