Analysis of Rate-limiting Long-range Contacts in the Folding Rate of Three-state and Two-state Proteins

Abstract

In the past decade, when compared to models describing the folding rates of two-state proteins, models describing the folding mechanism of three-state proteins remain quite limited due to the complexity present in the folding mechanism and lack in their experimental data. In the present work, rate-limiting long-range contacts were classified into various bins based on sequence separation distance between the contacting residues and the role of these bins were analyzed for their importance in a data set of 35 three-state proteins. Predicting the folding rates of these proteins have been carried out by relating experimental folding rates and long-range contacts obtained from various sequence separation bins. For comparison, using the present model, prediction of the folding rates of 45 two-state proteins also resulted with good accuracy. Our method shows that long-range contacts observed in the final 3-D structure of proteins at various sequence separation bins are found to be an important descriptor in explaining the folding rates of three-state proteins and suggest that formation of contacts between residues present at these sequence separation distance may be a crucial factor in deciding structure formation and folding rates of these proteins. The aim of our present work is not to construct a new descriptor for the folding rates of three-state proteins, nor is to provide improved means of folding-rate prediction for these proteins. We tend to elucidate that how long-range contacts play a crucial role in the folding mechanism of three state proteins belonging to three major structural classes and implication of these observations due to rate-limiting long-range contacts has been discussed in the light of other experimental studies of protein folding.

The Molten Globule, and Two-State vs. Non-Two-State Folding of Globular Proteins

“…Similar correlations of ln(kf) with ACO were observed for two-state and non-two-state proteins, but the correlation with L was worse for two-state proteins than for non-two-state proteins [77,79]; see also [80]. Among the above-mentioned structurebased properties for two-state proteins, three properties, LRO, Qd and the cliquishness, were also reported to exhibit significant correlations for non-two-state proteins [75,79,81]; LRO and Qd are closely related as LRO ≈ Qd/L. More sophisticated structure-based properties, well correlating with ln(kf) for both two-state and non-two-state proteins, have also been proposed [82][83][84][85][86][87][88][89][90].…”

“…Such similarities between two-state and non-two-state proteins were also found rather generally in the relationships between ln(kf) and structure-based properties (see Fig. 5(B)), which include ACO [77][78][79], LRO [81], the cliquishness [75], the n-order contact distance [83], the geometric contact number [84], the inter-residue interaction parameter [87], the average topological information [89], the entanglement of the native backbone structure [88], and the other structure-based properties [82,85,86,90]. As to the golden triangle for scaling of protein folding rates, proposed by Garbuzynskiy et al [95], we also find similar distributions of ln(kf) between the two-state and the non-two-state proteins.…”

The Molten Globule, and Two-State vs. Non-Two-State Folding of Globular Proteins

“…Similar correlations of ln(k f ) with ACO were observed for two-state and non-two-state proteins, but the correlation with L was worse for two-state proteins than for non-two-state proteins [87,89]; see also [90]. Among the above-mentioned structure-based properties for two-state proteins, three properties, LRO, Q d and the cliquishness, were also reported to exhibit significant correlations for non-two-state proteins [85,89,91]; LRO and Q d are closely related as LRO ≈ Q d /L. More sophisticated structure-based properties, well correlating with ln(k f ) for both two-state and non-two-state proteins, have also been proposed [92][93][94][95][96][97][98][99][100].…”

PFP-RFSM: Protein fold prediction by using random forests and sequence motifs

“…This study utilizes both sequence and predicted structure descriptors as inputs. The sequence representation includes a comprehensive list of features that was previously used for prediction of protein structural class [11,33,34], protein fold types [17] and protein folding rates [35], and protein sub-cellular locations [36]. As suggested by Chou, the feature vector of protein sequences can be seen as a general form of pseudo amino acid composition [37], which can be formulated as…”