Reading the three-dimensional structure of a protein from its amino acid sequence

While all the information required for the folding of a protein is contained in its amino acid sequence, one has not yet learnt how to extract this information so as to predict the detailed, biological active, three-dimensional structure of a protein whose sequence is known. This situation is not particularly satisfactory, in keeping with the fact that while linear sequencing of the amino acids specifying a protein is relatively simple to carry out, the determination of the folded-native-conformation can only be done by an elaborate X-ray diffraction analysis performed on crystals of the protein or, if the protein is very small, by nuclear magnetic resonance techniques. Using insight obtained from lattice model simulations of the folding of small proteins (fewer than 100 residues), in particular of the fact that this phenomenon is essentially controlled by conserved contacts among strongly interacting amino acids, which also stabilize local elementary structures formed early in the folding process and leading to the (post-critical) folding core when they assemble together, we have worked out a successful strategy for reading the three-dimensional structure of a notional protein from its amino acid sequence.Comment: misprints eliminated and small mistakes correcte

The looping probability of random heteropolymers helps to understand the scaling properties of biopolymers

Random heteropolymers are a minimal description of biopolymers and can provide a theoretical framework to the investigate the formation of loops in biophysical experiments. A two--state model provides a consistent and robust way to study the scaling properties of loop formation in polymers of the size of typical biological systems. Combining it with self--adjusting simulated--tempering simulations, we can calculate numerically the looping properties of several realizations of the random interactions within the chain. Differently from homopolymers, random heteropolymers display at different temperatures a continuous set of scaling exponents. The necessity of using self--averaging quantities makes finite--size effects dominant at low temperatures even for long polymers, shadowing the length--independent character of looping probability expected in analogy with homopolymeric globules. This could provide a simple explanation for the small scaling exponents found in experiments, for example in chromosome folding

Properties of low-dimensional collective variables in the molecular dynamics of biopolymers

The description of the dynamics of a complex, high-dimensional system in terms of a low-dimensional set of collective variables Y can be fruitful if the low dimensional representation satisfies a Langevin equation with drift and diffusion coefficients which depend only on Y. We present a computational scheme to evaluate whether a given collective variable provides a faithful low-dimensional representation of the dynamics of a high-dimensional system. The scheme is based on the framework of finite-difference Langevin-equation, similar to that used for molecular-dynamics simulations. This allows one to calculate the drift and diffusion coefficients in any point of the full-dimensional system. The width of the distribution of drift and diffusion coefficients in an ensemble of microscopic points at the same value of Y indicates to which extent the dynamics of Y is described by a simple Langevin equation. Using a simple protein model we show that collective variables often used to describe biopolymers display a non-negligible width both in the drift and in the diffusion coefficients. We also show that the associated effective force is compatible with the equilibrium free--energy calculated from a microscopic sampling, but results in markedly different dynamical properties

Time delay as a key to Apoptosis Induction in the p53 Network

A feedback mechanism that involves the proteins p53 and mdm2, induces cell death as a controled response to severe DNA damage. A minimal model for this mechanism demonstrates that the respone may be dynamic and connected with the time needed to translate the mdm2 protein. The response takes place if the dissociation constant k between p53 and mdm2 varies from its normal value. Although it is widely believed that it is an increase in k that triggers the response, we show that the experimental behaviour is better described by a decrease in the dissociation constant. The response is quite robust upon changes in the parameters of the system, as required by any control mechanism, except for few weak points, which could be connected with the onset of cancer

Protein folding: Can high-performance computing improve our understanding?

Proteins are complex physical systems of great biological and pharmaceutical interest. Computer simulations can be useful to understand how they fold to their biologically active conformation, but have to face two problems, namely the roughness of the energy landscape and the wide range of time scales associated with the folding process. Models at atomic detail are able to describe the protein with a high degree of realism, but are computationally very demanding and their results usually are difficult to analyse. Models with simplified degrees of freedom are less accurate but are good at highlighting the basic physical mechanism which controls protein dynamics. A combination of the two can be the right solution to the protein folding problem

Hiking in the energy landscape in sequence space: a bumpy road to good folders

With the help of a simple 20 letters, lattice model of heteropolymers, we investigate the energy landscape in the space of designed good-folder sequences. Low-energy sequences form clusters, interconnected via neutral networks, in the space of sequences. Residues which play a key role in the foldability of the chain and in the stability of the native state are highly conserved, even among the chains belonging to different clusters. If, according to the interaction matrix, some strong attractive interactions are almost degenerate (i.e. they can be realized by more than one type of aminoacid contacts) sequence clusters group into a few super-clusters. Sequences belonging to different super-clusters are dissimilar, displaying very small ($\approx 10$) similarity, and residues in key-sites are, as a rule, not conserved. Similar behavior is observed in the analysis of real protein sequences.Comment: 17 pages 5 figures Corrected typos added auxiliary informatio