12 research outputs found
PREreview of "Defining amino acid pairs as structural units suggests mutation sensitivity to adjacent residues"
No full textThis Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/8237332.
Note: We reviewed an updated version of this preprint for a journal. The comments are posted with this preprint in the hopes the authors post the updated version that we commented on here as the journal we are reviewing for does not place limits on updating preprints during the peer review process.
Ashraya Ravikumar and James Fraser
Summary:
The traditional Ramachandan plot uses the ϕ and ψ torsion angles about the N-C bond and C-C bond respectively to represent aspects of the three dimensional protein backbone structure in two dimensions. Some of the atoms involved in the calculation of ϕ and ψ torsion angles of a residue come from the adjacent residues in the protein chain. In this work, the authors consider the ψ angle of residue i and ϕ angle of residue i+1 as an entity and analyze the distribution of these amino acid pairs. Their approach has the advantage of the torsion angle pair being fully contained in an amino acid pair and the ease of representation of these pairs in the familiar Ramachandran like plot. The authors show that their cross peptide bond plot covers more area than the traditional ϕ,ψ plot and identifies certain structural elements that are "recurring outliers" using the traditional plot. They also show some differences in conformational preference between thermophilic and mesophilic proteins. There is an initial attempt at experimental validation, with small stability changes (measured by melting temperature) upon point mutations to amino acids more favored for that specific region of the cross plot; however, this validation is limited and would benefit from examples intended to be neutral and destabilizing. The major strength of the paper is a new concept that is very simple yet also powerful for identifying regions of conformational space that should be considered "valid", not outliers. In doing so, their method provides a lot of scope for some interesting future work and new ways of validating protein structures, refinement procedures, and structure predictions. The major recurring issue in the manuscript however has been lack of clarity and lack of attention to detail, which can be improved in a future (third?) iteration of the manuscript. The major and minor points of concern are expanded below:
Major points:
1. In Figure 1, the authors claim that "Standard secondary structures (such as left and right -helices, -strands and turns) are clearly recognizable" from the gray stick plots. While the -helices are recognizable especially in clusters 12 and 19, it is not possible to identify -strands and turns from these images. More amino acids may have to be added to the visualization on either side of the pair to make this clear.
2. Their analysis on the correlation of cross bond angles using MD simulations need more details and discussion. The text points to Figure 2 and Table S1 to show the correlation but Figure 2 simply shows the five structures which were simulated along with the amino acid pairs and the cross peptide bond plot. It is unclear how to interpret the correlation between ϕ(k) and ψ(k+1) from this figure. Also, their choice of proteins for this analysis seems arbitrary. What do they mean by "small" protein? What is the dataset of structures they started out with before randomly picking these five structures?
3. How do the authors claim that cluster 9 and 10 shown in Figure 4 represent a transition into helix? The ϕ(k+1),ψ(k+1) distribution is not indicative of being part of a helix.
4. The authors refer to Figure 5 in their discussion about the cluster 15 representing the Type II turn. The representatives of the cluster do appear in different structural contexts. But, are they all indeed turns? Do they satisfy other criteria to be called a beta turn - either distance between C atoms of i and i+3 residue or H bond between carbonyl oxygen of i and amide hydrogen of i+3. This information is needed to support the statement that type II turns are also common in random coil regions. Also, Figure 5 caption says the representatives are from cluster 6, but we are assuming the authors mean cluster 15.
5. The authors have not mentioned what is the color scale used to color the nodes in Figure 6. We are assuming that warmer color means higher probability of the cluster being occupied by thermophiles. The clusters that are part of the most prominent transitions are very close to each other. Maybe many of the amino acid pairs from thermophiles that are classified into cluster 12 could be part of cluster 19 with some minor change in ψ or ϕ angle. Are the alphafold predicted structures accurate enough to distinguish between such close ψ,ϕ angles? These observations could also be a result of inherent biases in alphafold. Perhaps the authors could also analyze experimental structures of mesophiles and thermophiles to see if these trends hold.
6. The statement "Visual inspection of the most prominent cases suggests that the preferred clusters in thermophiles, presumably the more thermostable ones, are those which appear more ordered" is not well supported. If this statement is being made purely based on the cartoon representation of the two cluster transitions shown as inset in Figure 6, then it does not look convincing, especially 1 to 11. Perhaps the authors could analyze the extent of disorder in a more systematic way by comparing preferred clusters of thermophiles and mesophiles and quantitatively look at the difference in disorder, if any. The authors can also show specific examples of transition matrix along with pictorial representations of differences in angles/planes so that the reader can understand them better.
7. In the context specific mutation analysis, the differences in ΔTm observed are very small and the raw DSF data is not shown in the supplemental figure. Are these changes significant enough to conclude about the effect of these context specific mutations on protein stability? Additional experiments are likely needed to place these ΔTm changes in context. What is the typical change in ΔTm for a predicted neutral or deleterious mutation? Can anything be inferred based on prior deep mutational scans for GFP or another protein to help give this analysis more power?
8. Over the years, Ramachandran angle restraints have become part of structure refinement protocols, which when applied inappropriately, could lead to over-optimization of ϕ-ψ angles. For such cases a simple Ramachandran validation will fail to identify issues in the structure and needs a more global approach such as the Ramachandran Z-score (https://www.sciencedirect.c.... From the way the authors' approach is designed, it could have potential for a similar application. They have shown in Figure 3 how outliers of Ramachandran plot fall into acceptable regions in their cross-peptide bond plot. Along these lines, the authors should discuss about global validation metrics derivable from their method (perhaps related to the normalized marginal distribution distance shown in Figure 7)
Minor points:
1. In the introduction section, the authors mention the disadvantage of the (ϕ,ψ)2 method. But apart from mentioning the protein blocks (PB) method, they don't explain how their approach improves over the PB method. It's important to place this work in context to PB since PB's have been used for several structure related applications. Another related work is TERMs (https://www.sciencedirect.c... where the protein structure is broken down into smaller structural entities which are then used to assess model quality, sequence/structure compatibility, conformational transitions, etc. The authors could discuss about the relationship of between their work and TERMs
2. The authors should add labels to panels within figures instead of addressing them as top, middle, etc.
3. What is the correct resolution cut off used to extract structures from PDB? Results and discussion says 1.8Å but methods say 1.5Å. Also, the authors need to provide the list of PDB structures used.
4. The authors have probably cited the wrong reference for the proteomes of mesophile/thermophile bacterial pairs in materials and methods
5. Page 5, para 2, text says Figure 7 while it's actually referring to Figure 6.
6. Page 5, para 3, Fig S3 should be changed to Fig S4
7. Page 6, para 1, unclear if authors are referring to Fig S2 or Figure 7 since Figure S2 does not have the FH and YH distribution. Same para, reference to Figure 6 instead of Figure 7.
8. In Materials and Methods, page 7, under "Calculation of correlation coefficients between dihedral angles", it should be Figure 2
Ashraya Ravikumar and James Fraser
Competing interests
The author declares that they have no competing interests
PREreview of "Approximating conformational Boltzmann distributions with AlphaFold2 predictions"
No full text<p><strong>This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at <a href="https://prereview.org/reviews/10048116">https://prereview.org/reviews/10048116</a>.</strong></p>
<p>In this manuscript the authors have tested the hypothesis that the MSA constructed by AlphaFold2 (AF2) contains information about the distribution of different conformational states of a protein. Whereas the current way of thinking about AF2's MSA-predicted Cβ–Cβ distance maps focuses on their power to provide binary classifications of inter-residue contacts, the authors propose that Cβ–Cβ distances should instead be thought of as a set of collective variables that approximate a Boltzmann distribution. This is a novel hypothesis that lends AF2 the ability to decipher the conformational Boltzmann distributions of proteins. The authors test this in the contexts of protein dynamics, mutation impacts, and protein-protein interactions. They start with analyzing the correlation between AF2 contact distance and spin label distance distributions obtained from EPR spectroscopy using T4 lysozyme as a model, finding a general agreement despite broader AF2 distributions. Following this, they explore if AF2 can approximate free energy changes in systems that contain multiple biologically important minima, using EGFR KD studies for this purpose. AF2 accurately identifies altered contact distance distributions corresponding to active or inactive conformations in several mutations, indicating a sensitivity to alterations that stabilize particular conformational states. Next, they assess sensitivity to thermodynamically destabilizing mutations. AF2 was able to predict different contact distance probabilities for disruptive mutations like L198R in UBA1, but was less sensitive for milder mutations like L198A. Lastly, AF2's sensitivity to protein-protein interactions was explored using the μ-opioid receptor (μOR). Although the helix displacement distances observed in the predicted structure of isolated and complexed μOR do not exactly match with expected values, AF2 did successfully predict differences in select contact distance distributions of active/inactive-state μOR. Demonstrating that Cβ–Cβ distance probabilities from the same AF2-learned distribution reflect distances observed in differentially behaving domains of a protein lends strong support to the hypothesis that AF2 contact distance distributions can approximate conformational distributions. </p><p>The manuscript explores the correlations and sensitivities of AF2 predicted Cβ–Cβ distances across a variety of protein contexts, giving a broad view of its capabilities and limitations. Transitions between the various sections flowed well, and overall the writing was well worded and easily comprehensible. In addition, the presentation was balanced. It doesn't just focus on the success of AF2, but also highlights where its sensitivities might vary or fall short, providing a balanced view of its capabilities. Given limited computational resources, the conformational space explored by MD and MCMC simulations is limited by their initial states. AI methods are instead limited by how informative their system definitions (MSAs and pre-set theoretical or experimental contact distance distributions) are, allowing AI methods, such as the AF2 method outlined by the authors, to more effectively sample conformational space. This is a very fascinating implication of their work which the authors have briefly mentioned in the discussion. This (and the connection to Figure 7 in the paper) warrants a deeper discussion, but the main conclusions the authors come to are within the scope of the manuscript, and are backed up by the evidence presented. </p><p>There are a few points we would like to bring to the attention of the authors to strengthen the manuscript further.</p><p><b>Major points:</b></p><ol><li><p>There are some difficulties interpreting Figure 2. </p><p>(a) It is important to mark the distances between the two chosen pairs of atoms in the active and inactive state. Without this information, the purpose of Figure 2D is unclear and Figure 2D, F and G are difficult to understand. </p><p>(b) Also, what is the threshold distance to classify a state as active or inactive?</p><p>(c) Figure 2E seems confusing with different axis and ranges.</p></li><li><p>In case of DDR1, does the MD simulations reflect the peak distances (between 7.5 and 10.0 Å for DFG-in and between 16.0 and 18.0 Å for DFG-out) observed for AF2 distance distributions? Also, the probability distribution shift towards shorter distances for Y755A does not seem particularly strong at first glance. Is this why the double alanine mutant was included? Are there also MD simulations of the double mutant that show a reduced preference for the DFG-out conformation?</p></li><li><p>The overall results on EGFR mutants are striking. Many of these mutants (most notably L858R have structures deposited in the PDB (ID:2ITT and many others) that are potentially part of the overall training of AF2/OpenFold. Can you comment on how this might affect the results? </p></li></ol><p><b>Minor Points:</b></p><ol><li><p>There is some ambiguity in the statement, "The central hypothesis of this manuscript is that the collective contact distance distributions predicted by AF2 contain relevant information that can approximate Boltzmann distributions provided the relevant conformational states can be adequately described by these contact distances." We suggest adding to this such that a stronger connection is formed between the theory section and the remainder of the paper. For example, the authors could explain that the contact distances specified in each section are the set of CVs you describe earlier, "we identify a set of CVs, <b>ξ</b> = (ξ1, ξ2, …, ξ<i>m</i>)...". It would also be helpful to clarify that the distributions predicted by AF2 represent the ensemble averaged observable, as described by equation 4. Lastly, the authors mention that these distributions can approximate Boltzmann distributions, but this is somewhat vague. This could be reworded to say that AF2 distributions can approximate experimentally derived Boltzmann distributions of the same distance.</p></li><li><p>The authors are comparing Cβ–Cβ distances determined by AF2 to spin label distances from EPR. This is explained in the methods section, but the procedure for adjusting the spin label distances to facilitate a meaningful comparison between them and the AF2 distances is somewhat unclear. To make a stronger justification for why these are comparable, the authors could clarify the procedure. For example, some context from the authors' previous paper, <i>De Novo High-Resolution Protein Structure Determination from Sparse Spin labeling EPR Data</i>: "[distance from spin label] d<sub>SL</sub> is a starting point for the upper estimate of d<sub>Cβ</sub>, and subtracting the effective distance of 6Å twice from d<sub>SL</sub> gives a starting point for the lower estimate of d<sub>Cβ</sub>" could be beneficial. Including a rank correlation coefficient, as hinted above, could also help emphasize that the results demonstrate "similar <i>relative</i> probabilities among the contact distances for AF2 and EPR"</p></li><li><p>In the comparison of distance distributions between AF2 predictions and EPR measures, the major peaks of the two distributions are similar but in certain cases (127CB - 154CB, 120CB - 131CB), some additional peaks are found beyond 10A. A statistical comparison of the distributions, perhaps using a KS test, will help in evaluating the significance of the similarities.</p></li><li><p>Typo in Hamiltonian Equation 1 (should be momentum squared)</p></li><li><p>In the T4 Lysozyme example, how were the six contacts between the 12 unique residues found?</p></li><li><p>In Figure 5, the fourth row could have more discussion/explanation. What does the colorbar represent? There is no label.</p></li><li><p>As mentioned earlier, the connection between the Discussion and Figure 7 is not well established. The authors could expand on their writing and/or make the figure more simplified to match the discussion better.</p></li></ol><p>Reviewed by:</p><p>Jessica Flowers, Angelica Lam, Ashraya Ravikumar, James Fraser</p>
<h2>Competing interests</h2>
<p>
The author declares that they have no competing interests.
</p>
Stereochemical studies on peptide and protein structures: Implications for validation, flexibility, and dynamics
No full textAccuracy of 3-D structures of proteins is crucial, both in terms of its agreement with the experimental data used to determine the structure and stereochemistry, especially when they are utilized for applications like drug design. Hence, stereochemical validation of 3-D structures is of fundamental importance. In this thesis, we have performed stereochemical studies on various systems, starting from peptide structures to high-resolution protein structures and large multi-protein assemblies. In peptide structures, we noted that subtle deviations in backbone bond lengths and angles from ideal bond geometry significantly alters the allowed Ramachandran (ϕ,ψ) space, which is visualized using bond geometry-specific steric maps. We utilized these bond geometry-specific steric maps for stereochemical validation of residues in protein structures where we distinguish between genuine (ϕ,ψ) angle outliers and outliers due to modeling errors. This was done by analyzing if the observed (ϕ,ψ) value of a residue occurs in disallowed or allowed regions of their own bond geometry-specific steric map. We showed that these maps are significantly affected through variations in backbone bond geometry during atomic vibrations. Thus, disallowed (ϕ,ψ) regions at one timepoint can become allowed at another timepoint. We also suggested how high energy barriers in the (ϕ,ψ) space are potentially crossed during conformational transitions. Our analysis of the relationship between conformational strain in protein structures due to unfavorable (ϕ,ψ) angles and flexibility in local regions of proteins showed that they are only weakly related. This is likely due to the variable nature of allowed (ϕ,ψ) space itself, where adjustments to bond lengths and angles could lower the supposedly high-energy associated with an unfavorable (ϕ,ψ) conformation. With the emergence of an increasing number of cryoEM structures, we assessed their stereochemical quality and highlighted areas requiring improvement. We also showed that global resolution of cryo-EM structures is not a robust indicator of their quality. Our comparison of atomic packing in cryo-EM and crystal structures revealed that cryo-EM structures are less tightly packed than crystal structures, which is likely due to the nature of samples used in structure determination. We also suggest that the level of atomic packing seen in cryo-EM structures resembles the native state better. Overall, in this thesis, our studies on stereochemistry of peptides and proteins have generated a new framework for Ramachandran angle validation. We have also explored the implications of these studies on the flexibility and dynamics of proteins. Stereochemical studies on cryo-EM structures, which are predominantly multi-protein assemblies, have highlighted the red flags in these structures that their users should be aware of
Stereochemical Assessment of (phi,psi) Outliers in Protein Structures Using Bond Geometry-Specific Ramachandran Steric-Maps
No full textRamachandran validation of protein structures is commonly performed using developments, such as MolProbity. We suggest tailoring such analyses by position-wise, geometry-specific steric-maps, which show (phi,psi) regions with steric-clash at every residue position. These maps are different from the classical steric-map because they are highly sensitive to bond length and angle values that are used, in our steric-maps, as observed in the residue positions in super-high-resolution peptide and protein structures. (phi,psi) outliers observed in such structures seldom have steric-clash. Therefore, we propose that a (phi,psi) outlier is unacceptable if it is located within the steric-clash region of a bond geometry-specific steric-map for a residue position. These steric-maps also suggest position-specific accessible (phi,psi) space. The PARAMA web resource performs in-depth position-wise analysis of protein structures using bond geometry-specific steric-maps
PREreview of "Decoding the Cure-all Effects of Ginseng"
No full textThis Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/8102883.
This review arose out of a course for graduate students in the life sciences at UCSF, "Peer Review in the Life Sciences," which aims to introduce junior scientists to peer review in a critical yet constructive way. The students selected preprints to review, led discussions of them, drafted reviews, and revised them based on feedback from peers and instructors.
Summary
The major goal of this paper is to functionally characterize ginsentides, a recently discovered class of cysteine-rich pseudocyclic peptides isolated from the herb ginseng. The authors point out broad reported beneficial effects of ginseng and highlight the knowledge gap regarding mechanisms, and posit that bioactive ginsentides could provide an explanation. This paper aims to close this knowledge gap by linking a previously structurally characterized ginsentide, TP1 (Tam et al. 2018 Sci. Rep.), to a broad variety of beneficial physiological effects such as vasorelaxation and potentially reduced inflammation and stress. The major strength of this paper is that the authors perform experiments probing the relationship between ginsentides and a variety of pathways linked to positive physiological effects. The major weakness of this paper is that, despite the individual experiments, there is a lack of a final coherent mechanistic model for ginsentide action. For example: Fig. 2 demonstrates TP1 cell entry, but does not show intracellular persistence or colocalization with the potential targets suggested in Fig. 3. In Fig. 4 and 7, where TP1 would be acting (intra- or extracellularly) in these pathways is unclear. Fig. 6, however, strongly suggests extracellular binding activity of TP1. The authors do not suggest a model for TP1 activity that suitably integrates the variety and types of effects observed and reconciles potentially contradictory details. Overall, this paper provides a promising roadmap for how phytochemicals may impact medicine, especially cardiovascular health, but requires further elaboration to conclusively demonstrate ginsentide's role in the entirety of the purported effects.
Major Points
We found it difficult to synthesize the data into an overall consistent model of ginsentide action.
The paper begins with data emphasizing the constrained, cysteine-rich cyclic nature of ginsentides in their entrance into cells. However, how structure contributes to this effect is not examined. Indeed, because much of the other data presented suggests extracellular activity, we are unclear how the purported cell penetrating properties play a role. Such a claim would require a structural control (eg. a reduced or denatured TP1 peptide) to demonstrate structure-based cell entry in Fig 2. Could the authors speculate on the persistence of the unique disulfide-rich structure during uptake and inside the cell, as well? Are these data consistent with observed putative extracellular effects in Fig. 5-7?
The interpretation of NO production data (Fig. 4) could be clearer by demonstrating or speculating as to whether TP1 needs to enter cells to activate this pathway or if extracellular binding is sufficient.
We question the conclusions about ginsentide selectivity for the adrenergic receptor (Fig. 5) based on data that show ginsentides reduce aortic contractions. It is difficult to see a mechanistic link between the two, and no speculation is provided.
Fig. 6-7 suggest TP1 is binding extracellular targets, which is difficult to reconcile with the internalization mechanism of the earlier figures. Is this on a faster timescale than the uptake? The overall model suggested is that TP1 is a promiscuous binding peptide that inhibits various protein functions but it is unclear how and whether this depends on the native, disulfide-rich structure of the protein.
We are unable to properly evaluate the proteomics results from the material presented. In the proteomic interaction experiment (Fig. 3), how were background proteins determined? This is a key aspect that is not explained. What exactly were the "control" experimental conditions? The lack of sufficient detail here renders the meaning of these results impossible to determine.
The provided data do not directly support the textual claim that the level of TP1-induced NO production by endothelial cells is sufficient to cause vasorelaxation. It is unclear how much of the vasorelaxation in Fig. 5 is attributable specifically to the produced NO in Fig 4. To establish a causal link, one would need to demonstrate that TP1 does not induce vasorelaxation in a NO-producing-deficient background. We suggest altering the claim (lines 21-22) to allow for a correlative interpretation given that Fig. 4 and Fig. 5 are different sets of experiments. We also suggest adding context that discusses if observed effect sizes have been observed elsewhere (eg. Wang et al. Exp. Ther. Med. 2016).
Given that only behavioral data is given for the mice experiments (Fig. 8-9), we suggest altering the claim that ginsentides can reduce stress and depressive behavior (lines 26-27, 302). The behavioral tests used are thought to be measures of depression-like behavior and it is unclear if it is appropriate to conflate stress and depression. To support the current claim about stress, we suggest measuring corticosterone levels in TP1-treated mice exposed to stressful situations. With the limited set of experiments performed, consider alternative or more parsimonious interpretations of the results, such as inducing changes to general mobility, hyperactivity, pain, etc. To support the current claim about depression, mobility should be controlled for as a potential confound to determine if TP1 increases mobility absent stressful situations.
Minor Points
Technical questions:
The authors argue that ginsentides can act as a therapeutic because they would be less prone to degradation and heat sensitivity, and a large concern regarding the field of therapeutics is cell delivery. Given the evidenced contribution of energy-dependent endocytosis in cell uptake of TP1 in Fig. 2, we do not believe "cell-penetrating" is the best phrasing as this invokes diffusion/permeability. We suggest rephrasing for precision, potentially to "Cy3-TP1 is uptaken by cells". We do however also appreciate that endocytosis inhibition does not completely abrogate cell uptake, potentially pointing to other contributing processes.
Consider adding Fig. 2B's experimental rationale to the main text for clarity since it is a main figure. We understood it to be a control for the addition of Trypan blue to cells to quench extracellular Cy3-TP1 fluorescence after reading the methods. Additionally, explaining what the "control" treatments were will improve clarity for Fig. 2 overall.
To support the claim that the inhibitors decrease uptake, statistical tests should be between the treated conditions and the Cy3-TP1 condition instead of between the treated conditions and the control (Fig. 2E).
Are there any explanatory interpretations of the data showing a decrease in upstream activators over time (Fig. 4G, p-eNOS; Fig. S3A, p-Akt)?
We were somewhat confused by Fig. S4-6 because it does not seem that the controls (treatment-, TP1-) are set to 100% which we understood them to be from the caption. The text could be improved by an explicit explanation of what targets Fig. S4-8 show TP1 is not acting on.
Fig. 5-7 include adjacent bar graphs with varying y-axis boundaries. Doing this exaggerates the effects of the ginsentides mentioned and can mislead the reader.
There seems to be some discordance regarding the order of events (initial pre-incubation with treatment of monocytes vs. endothelial cells) between the text and caption for Fig. 6.
We suggest including an explanation for using the lung coefficient as a measurement for pulmonary embolisms (Fig. 7).
We suggest including the ginsentide-related patents of the authors in the competing interests (eg. US patent US20200277343A1: "Preparation and use of ginsentides and ginsentide-like peptides").
Stylistic/typographic suggestions:
Typo in line 78. Should be "adaptogens".
We suggest improving the confocal image in Fig. 1 by nicely cropping one representative cell image, including a colored coded legend of stains, and marking the cell membrane to orient the reader.
The nature of line 434's "Cy3-rT1" is unexplained. This may be a typo.
Competing interests
The author declares that they have no competing interests
Protein stabilization by tuning the steric restraint at the reverse turn
No full textThe incorporation of pseudoallylic strain byN-methylation at the solvent exposed loop in proteins leads to a stark increase in their thermodynamic stability that can be tuned by altering the amino acid composition.</p
Protein stabilization by tuning the steric restraint at the reverse turn
No full textReverse turns are solvent-exposed motifs in proteins that are crucial in nucleating -sheets and drive the protein folding. The solvent-exposed nature makes reverse turns more amenable to chemical modifications than -helices or -sheets towards modulating the stability of re-engineered proteins. Here, we utilize van der Waals repulsive forces in tuning the steric restraint at the reverse turn. The steric restraint induced upon N-methylation of the i+1-i+2 amide bond at the reverse turn results in well-folded and stable -sheets in aqueous solution at room temperature. The developed superactive turn inducing motif is tolerant to a wide variety of functional groups present on coded amino acids making the designed turn fully compatible with bioactive loops in proteins. We demonstrate that the steric restraint and the functional groups at the reverse turn act in synergy to modulate the folding of re-engineered -sheets. Introduction of the turn motifs onto a three-stranded -sheet protein, Pin 1 WW domain, resulted in various analogs showing a cooperative two-state transition with thermal stability (T-M) ranging from 62 degrees C to 82 degrees C. Despite modulating the stability of Pin 1 variants by approximate to 2.8 kcal mol(-1) (G(f)), the native fold in all the protein variants was found to be unperturbed. This structural stability is brought about by conformational preorganization at the engineered reverse turn that results in strong intramolecular hydrogen bonds along the three dimensional structure of the protein. Thus, this simple loop engineering strategy via two amino acid substitution provides us a toolkit to modulate the stability of -sheet containing peptides and proteins in aqueous solution that will greatly expand the scope of de novo protein and foldamer design
Conformational Strain Indicated by Ramachandran Angles for the Protein Backbone Is Only Weakly Related to the Flexibility
No full textExcitedStates/qfit-3.0: 2024v1
No full text<p>qFit version 2024 v1 is associated with the revision of Wankowicz et al 'Uncovering Protein Ensembles: Automated Multiconformer Building in X-ray Crystallography and CryoEM' eLife 2023.</p>
<p>qFit Algorithm Changes:</p>
<ul>
<li>updating chi sampling dof per iteration to 1 by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/365</li>
<li>347 explore smaller rotamer window by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/366</li>
<li>354 re work angle sampling to rotate 360 by @jessicaflowers in https://github.com/ExcitedStates/qfit-3.0/pull/367</li>
<li>Re-implement open source solvers by @blake-riley in https://github.com/ExcitedStates/qfit-3.0/pull/363</li>
<li>Updating BIC hyperparameter by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/369</li>
<li>Correct crystal and scale info by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/370</li>
<li>Combine qFit residue into qFit protein by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/371</li>
<li>Removing the Phenix Aniso refinement option by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/372</li>
<li>change isotropic sampling directions in backbone by @jessicaflowers in https://github.com/ExcitedStates/qfit-3.0/pull/379</li>
<li>Split QP step into two in sample sidechain by @jessicaflowers in https://github.com/ExcitedStates/qfit-3.0/pull/389</li>
<li>Adding Hetatms in output of multiconformer_model2 by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/393</li>
<li>Solver Error Catches by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/383</li>
<li>Collapsing conformers with the same coordinates by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/200</li>
<li>change rmsd cutoff in qFit segment by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/281</li>
<li>Add in B-factor sampling by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/289</li>
<li>adding flag to only run qFit segment by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/298</li>
<li>301 reconfigure qfit segment by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/306</li>
<li>Updating hardcoded alt locs in backbone collapse by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/311</li>
<li>qscore cutoff option for EM structures by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/317</li>
<li>updating OXT issues by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/318</li>
<li>updating write intermediate conformers argument by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/321</li>
<li>Unify map extraction: use --padding option (and default value) in QFitSegment by @blake-riley in https://github.com/ExcitedStates/qfit-3.0/pull/327</li>
<li>177-qFit to work for EM with appropriate SF by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/322</li>
<li>Segment BIC criteria = nconfs by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/341</li>
</ul>
<p>Post qFit refinement/analysis scripts:</p>
<ul>
<li>move where we add hydrogens to after while loop by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/296</li>
<li>adding reduce failure flag by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/297</li>
<li>adding order solvent picking in refinement by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/303</li>
<li>301 re create refinement script to group occupancy of segment alt locs by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/304</li>
<li>adding target weight params to final refine by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/310</li>
<li>create all rotamers script by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/307</li>
<li>Updating Ligand Occupany script to extract average b-factor by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/387</li>
<li>Add rscc script by @stephaniewankowicz in https://github.com/ExcitedStdStates/qfit-3.0/pull/388</li>
</ul>
<p>Other Maintenence for qFit Repository:</p>
<ul>
<li>Improve the example folder and README file by @ashrayar in https://github.com/ExcitedStates/qfit-3.0/pull/278</li>
<li>Rename TUTORIAL.md to README.md by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/294</li>
<li>Implementing faster qFit Ligand test by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/291</li>
<li>updating numpy and python versions to work on M1 Macs by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/358</li>
<li>Moving M1 change to Main by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/359</li>
<li>Improved Error Message when CPLEX is not installed by @stephaniewankowicz in https://github.com/ExcitedStates/qfit-3.0/pull/362</li>
</ul>
<p><strong>Full Changelog</strong>: https://github.com/ExcitedStates/qfit-3.0/compare/latest...2024v1</p>
ExcitedStates/qfit-3.0: 2024.2
No full text<p>This release implements the move to open-sourced solvers for qFit. It is associated with the revision of Wankowicz et al 'Uncovering Protein Ensembles: Automated Multiconformer Building in X-ray Crystallography and CryoEM' eLife 2023.</p>
