Constructing templates for protein structure prediction by simulation of protein folding pathways

Abstract

How a one-dimensional protein sequence folds into a specific 3D structure remains a difficult challenge in structural biology. Many computational methods have been developed in an attempt to predict the tertiary structure of the protein; most of these employ approaches that are based on the accumulated knowledge of solved protein structures. Here we introduce a novel and fully automated approach for predicting the 3-dimensional structure of a protein that is based on the well accepted notion that protein folding is a hierarchical process. Our algorithm follows the hierarchical model by employing two stages: the first aims to find a match between the sequences of short independently-folding structural entities and parts of the target sequence and assigns the respective structures. The second assembles these local structural parts into a complete 3D structure, allowing for long-range interactions between them. We present the results of applying our method to a subset of the targets from CASP6 and CASP7. Our results indicate that for targets with a significant sequence similarity to known structures we are often able to provide predictions that are better than those achieved by two leading servers, and that the most significant improvements in comparison with these methods occur in regions of a gapped structural alignment between the native structure and the closest available structural template. We conclude that in addition to performing well for targets with known homologous structures, our method shows great promise for addressing the more general category of comparative modeling targets, which is our next goal.

Protein structure prediction using a docking‐based hierarchical folding scheme

“…The basic frame of our algorithm is extensively described in a previous article 14. There, we show that our method can handle easy homology modeling cases (high accuracy targets) quite well.…”

“…The subscripts designate three tested versions of FOBIA. FOBIA 0 is the previous version of FOBIA presented in,14 intended for constructing templates for TBM_HA targets. As FOBIA 0 constructs templates and not models, a GDT_TS calculation is not possible, and thus, we present only a Z score value.…”

“…We define a forward edge from a node v 1 in the graph that is assigned to a region [ s 1 , e 1 ] of the target sequence, to every node v 2 that is assigned to region [ s 2 , e 2 ], for which s 1 < s 2 and e 1 < e 2 . An edge between two nodes v 1 and v 2 with num sep residues separating the regions assigned to the nodes is weighted according to the following scheme: If there is an overlap of at least three residues between the regions assigned v 1 and v 2 (meaning num sep ≤ −3), then similar to,14 the edge weight is given by: If −2 ≤ num sep ≤ 2, the edge is given a fixed weight of 2. If num sep ≥ 3 or it is an edge from node S or an edge to node E then the weight of the edge is decided according to the other hits: we search for an intermediate node, v t , that covers a sufficient region between v 1 and v 2 .…”

“…In,14 we deduce BB orientation based on a structure alignment between the structures from which the BBs originated. Here, such a scheme is not suitable, because the BBs do not necessarily originate from closely related structures.…”

“…According to the BB model, if one cuts out a BB fragment from a protein chain and places it in solution, the most highly populated conformation of the fragment is very likely to be similar to the BB when embedded in the native protein. The rational and more specific description of these BBs is given in Ref 14…”