Is protein folding symmetric with respect to reversing the sequence order?

Is protein folding symmetric with respect to reversing the sequence order?

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Suppose that I have two proteins, protein A and protein B, and suppose that the sequence of amino acids of protein B is exactly the reverse of the sequence of protein A.

For example (these are made-up proteins):

protein A = [G,A,L,G,M,F,R] protein B = [R,F,M,G,L,A,G]

Will the 3D structure of protein B be somehow identical, or perhaps the mirror image, of the 3D structure of protein A?

No! Although there is a relationship, the protein would not fold properly since the C and N terminals are reversed consider the following:


as appose to :


These are completely different molecules!

Co-transcriptional folding is encoded within RNA genes

Most of the existing RNA structure prediction programs fold a completely synthesized RNA molecule. However, within the cell, RNA molecules emerge sequentially during the directed process of transcription. Dedicated experiments with individual RNA molecules have shown that RNA folds while it is being transcribed and that its correct folding can also depend on the proper speed of transcription.


The main aim of this work is to study if and how co-transcriptional folding is encoded within the primary and secondary structure of RNA genes. In order to achieve this, we study the known primary and secondary structures of a comprehensive data set of 361 RNA genes as well as a set of 48 RNA sequences that are known to differ from the originally transcribed sequence units. We detect co-transcriptional folding by defining two measures of directedness which quantify the extend of asymmetry between alternative helices that lie 5' and those that lie 3' of the known helices with which they compete.


We show with statistical significance that co-transcriptional folding strongly influences RNA sequences in two ways: (1) alternative helices that would compete with the formation of the functional structure during co-transcriptional folding are suppressed and (2) the formation of transient structures which may serve as guidelines for the co-transcriptional folding pathway is encouraged.


These findings have a number of implications for RNA secondary structure prediction methods and the detection of RNA genes.


Much of our understanding of protein structure and mechanistic function has been derived from static high-resolution structures. As structural biology has continued to evolve it has become clear that high-resolution structures alone are unable to fully capture the mechanistic basis for protein structure and function in solution. Recently Hydrogen/Deuterium-exchange Mass Spectrometry (HDX-MS) has developed into a powerful and versatile tool for structural biologists that provides novel insights into protein structure and function. HDX-MS enables direct monitoring of a protein's structural fluctuations and conformational changes under native conditions in solution even as it is carrying out its functions. In this review, we focus on the use of HDX-MS to monitor these dynamic changes in proteins. We examine how HDX-MS has been applied to study protein structure and function in systems ranging from large, complex assemblies to intrinsically disordered proteins, and we discuss its use in probing conformational changes during protein folding and catalytic function.

Statement for a Broad Audience

The biophysical and structural characterization of proteins provides novel insight into their functionalities. Protein motions, ranging from small scale local fluctuations to larger concerted structural rearrangements, often determine protein function. Hydrogen/Deuterium-exchange Mass Spectrometry (HDX-MS) has proven a powerful biophysical tool capable of probing changes in protein structure and dynamic protein motions that are often invisible to most other techniques.

2. Theory

2.1. Theory behind the general strategy

A number of ideas and principles, borrowed in established and recent design of synthetic vaccines and petidomimetics, were used (see Ref. [ 3 ] for discussion and e.g. Refs. [ [63] , [64] , [65] , [66] , [67] , [68] , [69] ]), as well as some of the ideas that lie behind the popular ZINC data base [ 70 ]. As discussed in Refs. [ 3 , 4 ], the present investigation started as a use case for the Hyperbolic Dirac Net (HDN) and particularly the associated Q-UEL language for automated inference [ [34] , [35] , [36] , [37] ]. The theory has been discussed elsewhere, e.g. in Refs. [ [34] , [35] , [36] , [37] ], which relate more to the practical and general uses of Q-UEL. These considerations are less important here because present studies can be reproduced by standard bioinformatics and molecular modeling means. Nonetheless, it is doubtful that the research for refs [ 3 , 4 ] could have been done and written up so rapidly without the aid of Q-UEL to interact with websites of the World Wide Web, gather knowledge, and facilitate use of the publically available bioinformatics tools [ 3 ].

2.2. Basic principles of epitope prediction for design of synthetic vaccines

The challenge is ultimately one of molecular recognition but in practice many key principles for hapten design relate to distinguishing types of naturally occurring epitope. By the term 𠇎pitope” in this paper is meant 𠇌ontinuous epitope”, though several smaller epitopes may be joined to represent a discontinuous epitope in which conformation and relative position in space can sometimes be important. While a synthetic construct implies the use of synthetic chemistry typically combined with a judicious carrier protein to which the peptide is linked chemically, constructs can also be obtained by cloning, using protein engineering principles [ 12 ]. The terms B-epitope and T-epitope relate to the traditional picture of a bone marrow B or thymus T response. B cell epitopes occur at the surface of the protein against which an immune system response is required. They are recognized by B cell receptors or antibodies in their native structure, and are concerned with the bone marrow response and antibody production. T epitopes may be buried inside protein structures and released by proteolysis, and are traditionally considered as concerned with a cellular response and immune system memory, i.e. active immunity. Continuous B cell epitope prediction is very similar to T cell epitope prediction. The focus is on B-epitopes here, though a B-epitope can also be (or overlap with) a T-epitope especially if it has a significant content of hydrophobic residues. Prediction of these has traditionally been based and has mainly been based on the amino acid properties such as hydrophilicity, charge, exposed surface area and secondary structure. There are many predictive algorithms available, but the present author prefers a more 𠇎xpert system” kind of approach that incudes experimental data, though the above biophysical considerations certainly still play a strong role (see below).

2.3. Some theoretical issues related to design of antagonists of COVID-19 infection

The previous paper [ 3 ] focused primarily on design of synthetic peptides as infection antagonists. However, partly for the reason of greater conformational flexibility discussed below, smaller less flexible organic molecules (i.e. with fewer rotatable bonds) are the traditional province of the synthetic chemist rather than use of an automated peptide synthesizer, and are preferred for pharmaceutical application. Consideration of peptides is more often considered as merely a useful intermediate step in more traditional pharmaceutical compound design. Biodegradability per se of peptides is not the main concern, since including d -amino acids in the design prevents proteolysis. In preliminary docking and simulation studies, the peptides do bind to 11β-hydroxysteroid dehydrogenase type 1, but less strongly and with several binding modes [ 3 ]. This weaker binding is not in itself a contraindication of the idea that these peptides bind at the same site as the more rigid non-peptide molecules, because it is an expected consequence of the much greater flexibility of peptides compared with molecules with, for example, multiple aromatic ring scaffolds. Conventional wisdom (e.g. Ref. [ 12 ]) frequently uses the rule-of-thumb that the total change in intramolecular (bond rotational) entropy of a peptide ligand is roughly TΔSTotal =ਁ.5 kcal mol 𢄡 per residue at 300 K, corresponding approximately to a 12-fold reduction in conformational freedom per residue on binding. Because van der Wall's and hydrogen bonding tend to be very roughly equivalent for peptides in water and in well bound forms, the water entropy effects known as hydrophobic effects (along with electrostatic forces) play an important role in determining the balance of energies and final outcome. KRSFIEDLLFNKV would thus cost about +19.5 kcal/mol entropic contribution to bind rigidly, primarily compensated by hydrophobic contacts at up to about 𢄡.7 kcal/mol in going from an aqueous to a non-polar environment, i.e. �.1 kcal/mol for a 13 residue peptide or analogue of KRSFIEDLLFNKV. That example would not favor binding, but the proper calculation is in the details which should show balance that favors good binding if that is found to be the case experimentally. Despite the above comments, the flexibility of peptides does provide more opportunities to fit a specific binding site, i.e. they can show some accommodation and they are more tolerant to imperfections in the design process. However, this is also an argument for their importance as an intermediate step in the design of more conventional pharmaceutical agents.

Is protein folding symmetric with respect to reversing the sequence order? - Biology

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Is protein folding symmetric with respect to reversing the sequence order? - Biology

We thank Professor T. Parac-Vogt for allowing us to use the CD equipment, and the beamline scientists at the I04 beamline at Diamond Light Source and the NW12A beamline at Photon Factory Advanced Ring for their assistance.

Funding information

A. R. D. Voet and L. Van Meervelt acknowledge the FWO (G0F9316N, G051917N and G0E4717N) for funding. J. R. H. Tame acknowledges support from OpenEye Scientific Software and the JSPS for funding. B. Mylemans and S. Wouters acknowledge the FWO for a PhD fellowship (ASP/17). T. Schiex and D. Simoncini acknowledge Agreenium for postdoctoral funding their work has been partially funded by the French Agence Nationale de la Recherche, Grant ANR-16-C40-0028 (to TS). K. Y. J. Zhang thanks JSPS for funding..


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There is active research ongoing to uncover the mechanisms by which disease-associated proteins misfold, aggregate, and cause cellular toxicity. Continued progress in our ability to interrogate amyloid-forming proteins and their interactions with other cellular proteins provide confidence that novel therapies will be identified for multiple disease states. Therapeutic options now being explored include targeting misfolded protein-chaperone interactions at various points in the proteostatic pathway, promoting protein clearance, and large-scale rebalancing of proteostatic network. However, the identification and in vivo validation of new therapeutic compounds is impeded by the shortage of known disease drivers and the lack of reliable biomarkers for monitoring therapeutic responses in relevant animal models. However, the increase in cooperative research and collaboration among the drug discovery community (pharmaceutical companies, foundations, academia, contract research organizations, clinicians, regulatory agencies, advocacy groups and patients) is a positive shift that can help accelerate the identification of novel therapeutic modalities.

Supporting information

S1 Fig. Architecture of the deep 3D convolutional neural network model and hyperparameter search.

(A) The overall organization of the model begins with the input tensor and ends with a final layer that outputs ΔΔG prediction. The numbers in parentheses before the Flatten layer represent the dimensionality of the output from each layer in the format (width, height, depth, property channels). The number in parentheses starting from the Flatten layer represent the number of output features from each of the densely connected layers. This optimized architecture was determined through cross-validation. (B) Results from cross validating the sizes of the convolutional layers while keeping the size of the densely connected layer at 32 neurons. (C) Results from cross validating the size of the densely connected layer while keeping the sizes of the convolutional layers at (16, 24, 32). (D) Results from cross validating the dimensions of the input grid.

S2 Fig. Pairwise percent sequence identity of the proteins in the S2648 and VariBench data sets.

(A) A heatmap representation of the pairwise percent sequence identity matrix of the proteins in the S2648 data set. (B) A heatmap representation of the pairwise percent sequence identity matrix of the proteins in the VariBench data set. It is obvious from these two heatmaps that there is substantial pairwise homology (percent identity > 25%) in both S2648 and VariBench. The pairwise identity matrices were obtained using the Clustal Omega multiple sequence alignment program [73].

S3 Fig. CNNs trained using only direct mutations show a large prediction bias.

(A) Performance of an ensemble of ten networks trained using only the set of 1,744 direct mutations on predicting the ΔΔGs of the direct mutations in the blind test set The Pearson correlation coefficient (r) between predicted values and experimentally determined values is 0.47, and the root-mean-square deviation (σ) of predicted values from experimentally determined values is 1.38 kcal/mol. (B) Performance of the same ensemble of ten networks on predicting the ΔΔGs of the reverse mutations in the blind test set The Pearson correlation coefficient (r) between predicted values and experimentally determined values is -0.06, and the root-mean-square deviation (σ) of predicted values from experimentally determined values is 2.40 kcal/mol. (C) Direct versus reverse ΔΔG values of all the mutations in the blind test set predicted by the same ensemble of networks. (B) and (C) highlight that the models trained with only direct mutations have a large bias and, when compared to the models trained using the balanced data set, the necessity of adding reverse mutations to correct the bias. The dots are colored in gradient from blue to red such that blue represents the most accurate prediction and red represents the least accurate.

Figure 2. Design of acidic-(HhH)2. Symmetric-(HhH)2 is a fully symmetric protein constructed in a previous study to understand the properties of ancient protein forms.(27) Derived from a family of dsDNA binding proteins, it is positively charged at neutral pH. To generate a hyperacidic model protein for this study, all lysine residues in symmetric-(HhH)2 were substituted with glutamate (see Results for more details). The conserved loop residues between the two helices of the HhH motif (PGIGP) are underlined, and the linker residues (GSVE) between the two HhH motifs are rendered in italics in the sequence and colored magenta in the structural model. The C-terminus of acidic-(HhH)2 bears a 6xHis tag connected by a two-residue Leu-Glu linker (not shown).


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