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Transferable coarse-grained potential for de novo protein folding and design.
No full textProtein folding and design are major biophysical problems, the solution of which would lead to important applications especially in medicine. Here we provide evidence of how a novel parametrization of the Caterpillar model may be used for both quantitative protein design and folding. With computer simulations it is shown that, for a large set of real protein structures, the model produces designed sequences with similar physical properties to the corresponding natural occurring sequences. The designed sequences require further experimental testing. For an independent set of proteins, previously used as benchmark, the correct folded structure of both the designed and the natural sequences is also demonstrated. The equilibrium folding properties are characterized by free energy calculations. The resulting free energy profiles not only are consistent among natural and designed proteins, but also show a remarkable precision when the folded structures are compared to the experimentally determined ones. Ultimately, the updated Caterpillar model is unique in the combination of its fundamental three features: its simplicity, its ability to produce natural foldable designed sequences, and its structure prediction precision. It is also remarkable that low frustration sequences can be obtained with such a simple and universal design procedure, and that the folding of natural proteins shows funnelled free energy landscapes without the need of any potentials based on the native structure
Folding free energy landscape <i>F</i>(DRMSD)/<i>k<sub>B</sub>T</i><sub>Ref</sub> as a function of DRMSD of the four designed proteins (PDB ids 2l09, 3mx7, chain A of 3obh, and 1qyp).
No full text<p>All profiles have a global minimum around 1.5 and 2 Å DRMSD with a smooth funnelled shape. Due the approximations present in the model and to thermal fluctuations is shifted with respect to DRMSD = 0 (note that to the value DRMSD = 0 of each profile will correspond a different native structure). Because of the definition of DRMSD, the smaller the value the fewer are the possible structures that can have this value of DRMSD. Ultimately, DRMSD = 0 is possible only for the target structure itself. The funnelled profiles with single minimum implies that both an ensemble of arrested structures and a single alternative fold are less stable compared to the desired configuration. In the bottom right inset we plot the folding free energy landscape for 3mx7 as a function of both the DRMSD and the number of hydrogen bonds , to give a visual example of the funnel nature of the folding landscapes. On the left we compare the experimentally determined structures (in yellow) with a typical folded conformation selected as the sampled configurations with the lowest energy at the free energy minimum (in red). The RMSD value is indicated in the middle. The structures were aligned using the <i>RMSD calculator</i> tool in VMD <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112852#pone.0112852-Humphrey1" target="_blank">[52]</a>, while the secondary structure elments where identified with STRIDE <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112852#pone.0112852-Frishman1" target="_blank">[53]</a>.</p
Schematic representation of the MEP algorithm.
No full text<p>For a trial set of the , <i>E</i><sub>HOH</sub>, and parameters and for each protein in the training set a large number (10<sup>5</sup>) of sequences with composition fixed to the natural one are generated following the design scheme in the SM. The scoring function <i>F</i><sub>score</sub> (Eq.S16 in the SM) is then evaluated and the trial parameters are accepted or reject according to a Metropolis like scheme. New parameter sets are generated at each iteration, and the sequences of the proteins in the training set are re-designed by 10<sup>5</sup> simple pair residue swapping moves, which are accepted or rejected according to a standard Metropolis algorithm with the energy defined in Eq.S4 (see SM). During each design iteration, the HP and energy profiles (Eq.S16 in the SM) are averaged over the observed sequences weighted by their Boltzmann weight. The averaging guarantees that the profiles are calculated over the most probable sequences that, as we showed previously <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112852#pone.0112852-Coluzza6" target="_blank">[43]</a>, are robust against mutations and are more thermally stable. After ∼10<sup>8</sup> iterations the interaction parameters converged to their final values: and <i>E</i><sub>HOH</sub>=0.015±0.001, and the residue-residue interaction parameters which are listed in Tab. S1 of the SM.</p
Comparison between the total residue energy <<i>E</i><sup>Tot</sup>/<i>k<sub>B</sub>T</i><sub>Ref</sub>> (Eq.S4 in SM) averaged over all the 10<sup>5</sup> designed sequences per target(abscissa) and the same energy calculated over the native sequence of same target (ordinate).
No full text<p>Each point corresponds to one protein in the data set and shows a strong linear trend verified by the fit (red line) with a correlation coefficient of ∼0.995 and a slope of ∼1.000 indicating that two energies are perfectly correlated. In the insets we show the comparison of the HP profiles (top left) and interaction energy <i>E/k<sub>B</sub></i><i>T</i><sub>Ref</sub> of each residue with all other (bottom right), this time each point corresponds to a single residue of each test protein. In both cases the data follow a remarkable linear trend (fits in green and blue lines respectively), and a positive correlation close to unity. For the HP profiles the correlation coefficient (∼0.98) indicates that when in natural proteins we find an hydrophobic residue also the design procedure will put one and vice versa. While the correlation coefficient (∼0.90) of <i>E/k<sub>B</sub></i><i>T</i><sub>Ref</sub> demonstrates that each natural residue has a very similar contribution to the total energy compared to the designed ones. A perfect match cannot be expected since natural sequences might have experience a selection pressure influenced by interactions not represented in the model, different environmental conditions or simply unknown functional requirements. Nevertheless the accordance is remarkable.</p
Folding free energy landscape <i>F</i>(DRMSD)/<i>k<sub>B</sub>T</i><sub>Ref</sub> of the 15 proteins set selected to test the accuracy of the MEP optimized parameters.
No full text<p>The profiles have a common funnel shape and show a clustering of the free energy minima in the region 1.5 and 2 Å DRMSD consistent with the results obtained for designed sequences. In b) we plot the free energies for proteins with the worst (2ptl) and the best (3nmd-E) distance of the folded structure from the native one. For the latter the free energy profile shows a minimum remarkably close to the native state probably due to the highly simplified structure of protein 3nmd-E. The minimum of 2ptl, on the other hand, is located further away from the low DRMSD values than the other proteins. This apparent discrepancy is due to the definition of the DRMSD which includes the contribution from the atoms located in the long unstructured tail from the residue 1 to 18. Since the probability of observing that particular conformation in solution is very low, it follows that the particular realization of the native structure has a large entropy penalty. However if we measure the overlap ignoring the contribution from the tail we see that the predicted structure of the protein core is again reasonably close to the experimentally determined one (≈5.2 Å RMSD). In the insets we compare the experimental structures (in yellow) super-imposed to the equilibrium configurations (in red), and we show that the proteins refolded with a precision between 2.4 and 4.1 Å RMSD.</p
Real-space representation of the backbone of the Caterpillar model.
No full text<p>The large blue sphere represent the self-avoidance volume of the atoms, while the interaction radius of each residue is represented by the large dashed circle or radius 6 Å (see Eq. S2 in Methods). The H and O atoms interact through a 10–12 Lennard-Jones potential tuned with a quadratic orientation term that selects for alignment of the C, H, O, and N atoms involved in a bond (see top right inset and Eq. S1 in Methods). The backbone fluctuates only around the torsional angles and .</p
Assessment of the MARTINI 3 Performance for Short Peptide Self-Assembly
No full textThe coarse-grained
MARTINI force field, initially developed for
membranes, has proven to be an exceptional tool for investigating
supramolecular peptide assemblies. Over the years, the force field
underwent refinements to enhance accuracy, enabling, for example,
the reproduction of protein–ligand interactions and constant
pH behavior. However, these protein-focused improvements seem to have
compromised its ability to model short peptide self-assembly. In this
study, we assess the performance of MARTINI 3 in reproducing peptide
self-assembly using the well-established diphenylalanine (FF) as our
test case. Unlike its success in version 2.1, FF does not even exhibit
aggregation in version 3. By systematically exploring parameters for
the aromatic side chains and charged backbone beads, we established
a parameter set that effectively reproduces tube formation. Remarkably,
these parameter adjustments also replicate the self-assembly of other
di- and tripeptides and coassemblies. Furthermore, our analysis uncovers
pivotal insights for enhancing the performance of MARTINI in modeling
short peptide self-assembly. Specifically, we identify issues stemming
from overestimated hydrophilicity arising from charged termini
and disruptions in π-stacking interactions due to insufficient
planarity in aromatic groups and a discrepancy in intermolecular distances
between this and backbone–backbone interactions. This investigation
demonstrates that strategic modifications can harness the advancements
offered by MARTINI 3 for the realm of short peptide self-assembly
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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