Flavio Seno

Geometry and symmetry presculpt the free-energy landscape of proteins

We present a simple physical model that demonstrates that the native-state folds of proteins can emerge on the basis of considerations of geometry and symmetry. We show that the inherent anisotropy of a chain molecule, the geometrical and energetic constraints placed by the hydrogen bonds and sterics, and hydrophobicity are sufficient to yield a free-energy landscape with broad minima even for a homopolymer. These minima correspond to marginally compact structures comprising the menu of folds that proteins choose from to house their native states in. Our results provide a general framework for understanding the common characteristics of globular proteins.

Protein Structures and Optimal Folding from a Geometrical Variational Principle

A novel approach, validated by an analysis of barnase and chymotrypsin inhibitor, is introduced to elucidate the paramount role played by the geometry of the protein backbone in steering the folding to the native state. It is found that native states of proteins, compared with compact artificial backbones, have an exceedingly large number of conformations with a given amount of structural overlap with them; moreover, the density of overlapping conformations, at a given overlap, of unrelated proteins of the same length are nearly equal. These results suggest an extremality principle underlying protein evolution, which, in turn, is shown to be possibly associated with the emergence of secondary structures.

PASTA 2.0: an improved server for protein aggregation prediction

The formation of amyloid aggregates upon protein misfolding is related to several devastating degenerative diseases. The propensities of different protein sequences to aggregate into amyloids, how they are enhanced by pathogenic mutations, the presence of aggregation hot spots stabilizing pathological interactions, the establishing of cross-amyloid interactions between co-aggregating proteins, all rely at the molecular level on the stability of the amyloid cross-beta structure. Our redesigned server, PASTA 2.0, provides a versatile platform where all of these different features can be easily predicted on a genomic scale given input sequences. The server provides other pieces of information, such as intrinsic disorder and secondary structure predictions, that complement the aggregation data. The PASTA 2.0 energy function evaluates the stability of putative cross-beta pairings between different sequence stretches. It was re-derived on a larger dataset of globular protein domains. The resulting algorithm was benchmarked on comprehensive peptide and protein test sets, leading to improved, state-of-the-art results with more amyloid forming regions correctly detected at high specificity. The PASTA 2.0 server can be accessed at http://protein.bio.unipd.it/pasta2/.

Recurrent oligomers in proteins: An optimal scheme reconciling accurate and concise backbone representations in automated folding and design studies

A novel scheme is introduced to capture the spatial correlations of consecutive amino acids in naturally occurring proteins. This knowledge‐based strategy is able to carry out optimally automated subdivisions of protein fragments into classes of similarity. The goal is to provide the minimal set of protein oligomers (termed “oligons” for brevity) that is able to represent any other fragment. At variance with previous studies in which recurrent local motifs were classified, our concern is to provide simplified protein representations that have been optimised for use in automated folding and/or design attempts. In such contexts, it is paramount to limit the number of degrees of freedom per amino acid without incurring loss of accuracy of structural representations. The suggested method finds, by construction, the optimal compromise between these needs. Several possible oligon lengths are considered. It is shown that meaningful classifications cannot be done for lengths greater than six or smaller than four. Different contexts are considered for which oligons of length five or six are recommendable. With only a few dozen oligons of such length, virtually any protein can be reproduced within typical experimental uncertainties. Structural data for the oligons are made publicly available. Proteins 2000;40:662–674.

Dynamical scaling of the DNA unzipping transition.

We report studies of the dynamics of a set of exactly solvable lattice models for the force-induced DNA unzipping transition. Besides yielding the whole equilibrium phase diagram, which reveals a reentrance, these models enable us to characterize the dynamics of the process starting from a nonequilibrium initial condition. The thermal melting of DNA displays a model dependent time evolution. On the contrary, the dynamical mechanism for the unzipping by force is very robust and the scaling behavior is independent of the details of the description and, hence, superuniversal.

REPETITA: detection and discrimination of the periodicity of protein solenoid repeats by discrete Fourier transform

Motivation: Proteins with solenoid repeats evolve more quickly than non-repetitive ones and their periodicity may be rapidly hidden at sequence level, while still evident in structure. In order to identify these repeats, we propose here a novel method based on a metric characterizing amino-acid properties (polarity, secondary structure, molecular volume, codon diversity, electric charge) using five previously derived numerical functions. Results: The five spectra of the candidate sequences coding for structural repeats, obtained by Discrete Fourier Transform (DFT), show common features allowing determination of repeat periodicity with excellent results. Moreover it is possible to introduce a phase space parameterized by two quantities related to the Fourier spectra which allow for a clear distinction between a non-homologous set of globular proteins and proteins with solenoid repeats. The DFT method is shown to be competitive with other state of the art methods in the detection of solenoid structures, while improving its performance especially in the identification of periodicities, since it is able to recognize the actual repeat length in most cases. Moreover it highlights the relevance of local structural propensities in determining solenoid repeats. Availability: A web tool implementing the algorithm presented in the article (REPETITA) is available with additional details on the data sets at the URL: http://protein.bio.unipd.it/repetita/. Contact: silvio.tosatto@unipd.it

Learning effective amino acid interactions through iterative stochastic techniques

The prediction of the three‐dimensional structures of the native states of proteins from the sequences of their amino acids is one of the most important challenges in molecular biology. An essential task for solving this problem within coarse‐grained models is the deduction of effective interaction potentials between the amino acids. Over the years, several techniques have been developed to extract potentials that are able to discriminate satisfactorily between the native and nonnative folds of a preassigned protein sequence. In general, when these potentials are used in actual dynamical folding simulations, they lead to a drift of the native structure outside the quasinative basin. In this article, we present and validate an approach to overcome this difficulty. By exploiting several numerical and analytical tools, we set up a rigorous iterative scheme to extract potentials satisfying a prerequisite of any viable potential: the stabilization of proteins within their native basin (less than 3–4 Å RMSD). The scheme is flexible and is demonstrated to be applicable to a variety of parameterizations of the energy function, and it provides in each case the optimal potentials. Proteins 2001;42:422–431.

Unified perspective on proteins: a physics approach.

We study a physical system which, while devoid of the complexity one usually associates with proteins, nevertheless displays a remarkable array of proteinlike properties. The constructive hypothesis that this striking resemblance is not accidental not only leads to a unified framework for understanding protein folding, amyloid formation, and protein interactions but also has implications for natural selection.

A measure of data collapse for scaling

Data collapse is a way of establishing scaling and extracting associated exponents in problems showing self-similar or self-affine characteristics as, for example, in equilibrium or non-equilibrium phase transitions, in critical phases, in dynamics of complex systems and many others. We propose a measure to quantify the nature of data collapse. Via a minimization of this measure, the exponents and their error-bars can be obtained. The procedure is illustrated by considering finite-size-scaling near phase transitions and quite strikingly recovering the exact exponents.

Exploring the Universe of Protein Structures beyond the Protein Data Bank

It is currently believed that the atlas of existing protein structures is faithfully represented in the Protein Data Bank. However, whether this atlas covers the full universe of all possible protein structures is still a highly debated issue. By using a sophisticated numerical approach, we performed an exhaustive exploration of the conformational space of a 60 amino acid polypeptide chain described with an accurate all-atom interaction potential. We generated a database of around 30,000 compact folds with at least of secondary structure corresponding to local minima of the potential energy. This ensemble plausibly represents the universe of protein folds of similar length; indeed, all the known folds are represented in the set with good accuracy. However, we discover that the known folds form a rather small subset, which cannot be reproduced by choosing random structures in the database. Rather, natural and possible folds differ by the contact order, on average significantly smaller in the former. This suggests the presence of an evolutionary bias, possibly related to kinetic accessibility, towards structures with shorter loops between contacting residues. Beside their conceptual relevance, the new structures open a range of practical applications such as the development of accurate structure prediction strategies, the optimization of force fields, and the identification and design of novel folds.