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On the nature of the protein folding code.

ABSTRACT: This paper investigates quantitatively the characteristics of the local folding code. The overlapping four-residue fragments which make up the amino acid sequences of 114 proteins are divided into classes on the basis of the physical properties of their constituent amino acids. The distribution of structural types associated with each class of sequence fragment is determined and compared with an ensemble of random structural distributions of the same size selected from the actual protein structures. A criterion is proposed, based on the relative entropies of the two types of distribution, and on a hypothesis as to the characteristics of fragments which code for local structure, that makes it possible to identify those four-residue sequence elements which encode specific time-averaged structure. It is determined that, by this criterion, only 60-70% of the four-residue fragments encode specific structures. It is suggested that the remaining sequence fragments intrinsically encode susceptibility to conformational alteration under the influence of long-range interactions and that this susceptibility is required for correct folding of the molecule. This feature introduces an inherent indeterminacy into the local folding code. The implications of this observation for the prediction of protein structure by various methods are briefly discussed.

SUBMITTER: Rackovsky S

PROVIDER: S-EPMC45720 | biostudies-other | 1993 Jan

REPOSITORIES: biostudies-other

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On the nature of the protein folding code.

Rackovsky S S

Proceedings of the National Academy of Sciences of the United States of America 19930101 2

This paper investigates quantitatively the characteristics of the local folding code. The overlapping four-residue fragments which make up the amino acid sequences of 114 proteins are divided into classes on the basis of the physical properties of their constituent amino acids. The distribution of structural types associated with each class of sequence fragment is determined and compared with an ensemble of random structural distributions of the same size selected from the actual protein structu ...[more]

PMID: 8421700

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Project description:BACKGROUND:RNA folding is an ongoing compute-intensive task of bioinformatics. Parallelization and improving code locality for this kind of algorithms is one of the most relevant areas in computational biology. Fortunately, RNA secondary structure approaches, such as Nussinov's recurrence, involve mathematical operations over affine control loops whose iteration space can be represented by the polyhedral model. This allows us to apply powerful polyhedral compilation techniques based on the transitive closure of dependence graphs to generate parallel tiled code implementing Nussinov's RNA folding. Such techniques are within the iteration space slicing framework - the transitive dependences are applied to the statement instances of interest to produce valid tiles. The main problem at generating parallel tiled code is defining a proper tile size and tile dimension which impact parallelism degree and code locality. RESULTS:To choose the best tile size and tile dimension, we first construct parallel parametric tiled code (parameters are variables defining tile size). With this purpose, we first generate two nonparametric tiled codes with different fixed tile sizes but with the same code structure and then derive a general affine model, which describes all integer factors available in expressions of those codes. Using this model and known integer factors present in the mentioned expressions (they define the left-hand side of the model), we find unknown integers in this model for each integer factor available in the same fixed tiled code position and replace in this code expressions, including integer factors, with those including parameters. Then we use this parallel parametric tiled code to implement the well-known tile size selection (TSS) technique, which allows us to discover in a given search space the best tile size and tile dimension maximizing target code performance. CONCLUSIONS:For a given search space, the presented approach allows us to choose the best tile size and tile dimension in parallel tiled code implementing Nussinov's RNA folding. Experimental results, received on modern Intel multi-core processors, demonstrate that this code outperforms known closely related implementations when the length of RNA strands is bigger than 2500.

Project description:Background: Proteins fold robustly and reproducibly in vivo, but many cannot fold in vitro in isolation from cellular components. Despite the remarkable progress that has been achieved by the artificial intelligence approaches in predicting the protein native conformations, the pathways that lead to such conformations, either in vitro or in vivo, remain largely unknown. The slow progress in recapitulating protein folding pathways in silico may be an indication of the fundamental deficiencies in our understanding of folding as it occurs in nature. Here we consider the possibility that protein folding in living cells may not be driven solely by the decrease in Gibbs free energy and propose that protein folding in vivo should be modeled as an active energy-dependent process. The mechanism of action of such a protein folding machine might include direct manipulation of the peptide backbone. Methods: To show the feasibility of a protein folding machine, we conducted molecular dynamics simulations that were augmented by the application of mechanical force to rotate the C-terminal amino acid while simultaneously limiting the N-terminal amino acid movements. Results: Remarkably, the addition of this simple manipulation of peptide backbones to the standard molecular dynamics simulation indeed facilitated the formation of native structures in five diverse alpha-helical peptides. Steric clashes that arise in the peptides due to the forced directional rotation resulted in the behavior of the peptide backbone no longer resembling a freely jointed chain. Conclusions: These simulations show the feasibility of a protein folding machine operating under the conditions when the movements of the polypeptide backbone are restricted by applying external forces and constraints. Further investigation is needed to see whether such an effect may play a role during co-translational protein folding in vivo and how it can be utilized to facilitate folding of proteins in artificial environments.

Dataset Information

On the nature of the protein folding code.

Publications

On the nature of the protein folding code.

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