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Protein sequence randomness and sequence/structure correlations.

ABSTRACT: We investigated protein sequence/structure correlation by constructing a space of protein sequences, based on methods developed previously for constructing a space of protein structures. The space is constructed by using a representation of the amino acids as vectors of 10 property factors that encode almost all of their physical properties. Each sequence is represented by a distribution of overlapping sequence fragments. A distance between any two sequences can be calculated. By attaching a weight to each factor, intersequence distances can be varied. We optimize the correlation between corresponding distances in the sequence and structure spaces. The optimal correlation between the sequence and structure spaces is significantly better than that which results from correlating randomly generated sequences, having the overall composition of the data base, with the structure space. However, sets of randomly generated sequences, each of which approximates the composition of the real sequence it replaces, produce correlations with the structure space that are as good as that observed for the actual protein sequences. A connection is proposed with previous studies of the protein folding code. It is shown that the most important property factors for the correlation of the sequence and structure spaces are related to helix/bend preference, side chain bulk, and beta-structure preference.

SUBMITTER: Rahman RS

PROVIDER: S-EPMC1282047 | biostudies-other | 1995 Apr

REPOSITORIES: biostudies-other

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Protein sequence randomness and sequence/structure correlations.

Rahman R S RS Rackovsky S S

Biophysical journal 19950401 4

We investigated protein sequence/structure correlation by constructing a space of protein sequences, based on methods developed previously for constructing a space of protein structures. The space is constructed by using a representation of the amino acids as vectors of 10 property factors that encode almost all of their physical properties. Each sequence is represented by a distribution of overlapping sequence fragments. A distance between any two sequences can be calculated. By attaching a wei ...[more]

PMID: 7787038

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Project description:An active segment of the research community designing small molecules ("minimalist mimics" of peptide fragments) to interfere with protein-protein interactions have based their studies on an implicit hypothesis. Here we refer to this as the Secondary Structure Hypothesis, that might be defined as, "If a small molecule can orient amino acid side-chains in directions that resemble side-chains of the parent secondary structure at the interface, then that small molecule is a candidate to perturb the protein-protein interaction". Rigorous tests of this hypothesis require co-crystallization of minimalist mimics with protein receptors, and comparison of the bound conformations with the interface secondary structures they were designed to resemble. Unfortunately, to the best of our knowledge, there is no such analysis in the literature, and it is unlikely that enough examples will emerge in the near future to test the hypothesis. Research described here was designed to challenge this hypothesis from a different perspective. In a previous study, preferred conformations of a series of novel minimalist mimics were simulated then systematically overlaid on >240 000 crystallographically characterized protein-protein interfaces. Select data from that overlay procedure revealed chemotypes that overlay side chains on various PPI interfaces with a relatively high frequency of occurrence. The first aim of this work was to determine if good secondary structure mimics overlay frequently on PPI interfaces. The second aim of this work was to determine if overlays of preferred conformers at interface regions involve secondary structures. Thus situations where these conformations overlaid extremely well on PPI interfaces were analyzed to determine if secondary structures featured the PPI regions where these molecules overlaid in the previous study. Combining conclusions from these two studies enabled us to formulate a hypothesis that is complementary to the Secondary Structure Hypothesis, but, unlike this, is supported by abundant data. We call this the Interface Mimicry Hypothesis.

Project description:BackgroundThe existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI) network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs.ResultsFor each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks.ConclusionConsiderable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree correlations, biological PPI networks do not actually seem to make use of this possibility.

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Protein sequence randomness and sequence/structure correlations.

Publications

Protein sequence randomness and sequence/structure correlations.

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