1,720,959 research outputs found
A minimal model of three-state folding dynamics of helical proteins
Full text linkA diffusion-collision-like model is proposed for helical proteins with three-state folding dynamics. The model generalizes a previous scheme based on the dynamics of putatively essential parts of the protein (foldons) that was successfully tested on proteins with two-state folding. We show that the extended model, unlike the original one, allows satisfactory calculation of the folding rate and reconstruction of the salient steps of the folding pathway of two proteins with three-state folding (Im7 and p16). The dramatic reduction of variables achieved by focusing on the foldons makes our model a good candidate for a minimal description of the folding process also for three-state folders, Finally, the applicability of the foldon diffusion-collision model to two-state and three-state folders suggests that different folding mechanisms are amenable to conceptually homogeneous descriptions. The implications for a unification of the variety of folding theories so far proposed for helical proteins are discussed in the final discussion. © 2005 American Chemical Society
Computational and Theoretical Methods for Protein Folding
Full text linkA computational approach is essential whenever the complexity of the process under study is such that direct theoretical or experimental approaches are not viable. This is the case for protein folding, for which a significant amount of data are being collected. This paper reports on the essential role of in silico methods and the unprecedented interplay of computational and theoretical approaches, which is a defining point of the interdisciplinary investigations of the protein folding process. Besides giving an overview of the available computational methods and tools, we argue that computation plays not merely an ancillary role but has a more constructive function in that computational work may precede theory and experiments. More precisely, computation can provide the primary conceptual clues to inspire subsequent theoretical and experimental work even in a case where no preexisting evidence or theoretical frameworks are available. This is cogently manifested in the application of machine learning methods to come to grips with the folding dynamics. These close relationships suggested complementing the review of computational methods within the appropriate theoretical context to provide a self-contained outlook of the basic concepts that have converged into a unified description of folding and have grown in a synergic relationship with their computational counterpart. Finally, the advantages and limitations of current computational methodologies are discussed to show how the smart analysis of large amounts of data and the development of more effective algorithms can improve our understanding of protein folding
A Minimal Model of Three-State Folding Dynamics of Helical Proteins
No full textA diffusion-collision-like model is proposed for helical proteins with three-state folding dynamics. The model
generalizes a previous scheme based on the dynamics of putatively essential parts of the protein (foldons)
that was successfully tested on proteins with two-state folding. We show that the extended model, unlike the
original one, allows satisfactory calculation of the folding rate and reconstruction of the salient steps of the
folding pathway of two proteins with three-state folding (Im7 and p16). The dramatic reduction of variables
achieved by focusing on the foldons makes our model a good candidate for a minimal description of the
folding process also for three-state folders. Finally, the applicability of the foldon diffusion-collision model
to two-state and three-state folders suggests that different folding mechanisms are amenable to conceptually
homogeneous descriptions. The implications for a unification of the variety of folding theories so far proposed
for helical proteins are discussed in the final discussion
Diffusion-collision of foldons elucidates the kinetic effects of point mutations and suggests control strategies of the folding process of helical proteins
Full text linkIn this article we use mutation
studies as a benchmark for a minimal model of the
folding process of helical proteins. The model ascribes
a pivotal role to the collisional dynamics of a
few crucial residues (foldons) and predicts the folding
rates by exploiting information drawn from the
protein sequence. We show that our model rationalizes
the effects of point mutations on the kinetics of
folding. The folding times of two proteins and their
mutants are predicted. Stability and location of
foldons have a critical role as the determinants of
protein folding. This allows us to elucidate two main
mechanisms for the kinetic effects of mutations.
First, it turns out that the mutations eliciting the
most notable effects alter protein stability through
stabilization or destabilization of the foldons. Secondly,
the folding rate is affected via a modification
of the foldon topology by those mutations that lead
to the birth or death of foldons. The few mispredicted
folding rates of some mutants hint at the
limits of the current version of the folding model
proposed in the present article. The performance of
our folding model declines in case the mutated
residues are subject to strong long-range forces.
That foldons are the critical targets of mutation
studies has notable implications for design strategies
and is of particular interest to address the issue
of the kinetic regulation of single proteins in the
general context of the overall dynamics of the interactome
Dynamics of the Minimally Frustrated Helices Determine the Hierarchical Folding of Small Helical Proteins
No full textIn this paper we aim at determining the key residues of small helical proteins in order to build up reduced
models of the folding dynamics. We start by arguing that the folding process can be dissected into concurrent
fast and slow dynamics. The fast events are the quasiautonomous coil-to-helix transitions occurring in the
minimally frustrated initiation sites of folding in the early stages of the process. The slow processes consist in
the docking of the fluctuating helices formed in these critical sites. We show that a neural network devised to
predict native secondary structures from sequence can be used to estimate the probabilities of formation of
these helical traits as they are embedded in the protein. The resulting probabilities are shown to correlate well
with the experimental helicities measured in the same isolated peptides. The relevance of this finding to the
hierarchical character of folding is confirmed within the framework of a diffusion-collision-like mechanism.
We demonstrate that thermodynamic and topological features of these critical helices allow accurate estimation
of the folding times of five proteins that have been kinetically studied. This suggests that these critical helices
determine the fundamental events of the whole folding process. A remarkable feature of our model is that not
all of the native helices are eligible as critical helices, whereas the whole set of the native helices has been used
so far in other reconstructions of the folding mechanism. This stresses that the minimally frustrated helices of
these helical proteins comprise the minimal set of determinants of the folding process
Neuronal current fMRI: pushing the limits of MR-based functional neuroimaging
No full textPascarella Stefano Compiani Mario Staderini Enrico Mari
Noise and randomlike behaviour of perceptrons: theory and applications to protein structure prediction
No full textIn the first part of this paper we study the performance of a single-layer perceptron that is expected to classify patterns into classes in the case where the mapping to be learned is corrupted by noise. Extending previous results concerning the statistical behavior of perceptrons, we distinguish two mutually exclusive kinds of noise (I noise and R noise) and study their effect on the statistical information that can be drawn from the output. In the presence of I noise, the learning stage results in the convergence of the output to the probabilities that the input occurs in each class. R noise, on the contrary, perturbs the learning of probabilities to the extent that the performance of the perceptron deteriorates and the network becomes equivalent to a random predictor. We derive an analytical expression for the efficiency of classification of inputs affected by strong R noise. We argue that, from the standpoint of the efficiency score, the network is equivalent to a device performing biased random flights in the space of the weights, which are ruled by the statistical information stored by the network during the learning stage. The second part of the paper is devoted to the application of our model to the prediction of protein secondary structures where one has to deal with the effects of R noise. Our results are shown to be consistent with data drawn from experiments and simulations of the folding process. In particular, the existence of coding and noncoding traits of the protein is properly rationalized in terms of R-noise intensity. In addition, our model provides a justification of the seeming existence of a relationship between the prediction efficiency and the amount of R noise in the sequence-to-structure mapping. Finally, we define an entropylike parameter that is useful as a measure of R noise
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
- …
