Relationship between protein structure and geometrical constraints

Abstract

We evaluate to what extent the structure of proteins can be deduced from incomplete knowledge of disulfide bridges, surface assignments, secondary structure assignments, and additional distance constraints. A cost function taking such constraints into account was used to obtain protein structures using a simple minimization algorithm. For small proteins, the approximate structure could be obtained using one additional distance constraint for each amino acid in the protein.We also studied the effect of using predicted secondary structure and surface assignments. The constraints used in this approach typically may be obtained from low-resolution experimental data. When using a cost function based on distances, half of the resulting structures will be mirrored, because the resulting structure and its mirror image will have the same cost. The secondary structure assignments were therefore divided into chirality constraints and distance constraints. Here we report that the problem of mirrored structures, in some cases, can be solved by using a chirality term in the cost function.

Recovery of protein structure from contact maps

“…The intermediate regime, (typically R = 5 − 10 and a g (0) = 0.1 − 0.01 for the values a g (1) = −1.0 and T g = 1 given above), may prove to be useful for proteins longer than those tested is the present work. As for the functional form of the cost function, another possible choice, following Bohr et al [5,6], is to smooth the step function that defines a contact with a sigmoid: Again, we did not find this necessary to achieve fast reconstruction.…”

“…In a typical alpha helix, c i = c o = .778 [6]. To refine the chirality of the preliminary chain that was obtained from the map by growth and adaptation as described above, we perform an additional Monte Carlo procedure.…”

“…This particular aim highlights the difference between what we are trying to accomplish and existing methods [2,[4][5][6][7][8], that use various forms of distance geometry [9] to predict a 3D structure of a real protein from its contact map. Rather than being concerned with obtaining a structure that is close to a real crystallographic one, we want mainly to check whether a map S is physically possible or not, and if not -to propose some S ′ which is physical and, at the same time, is not too different from S. The method has to be fast enough to run in parallel with the search routine (that uses H(A, S) to identify candidate maps S of low energy).…”

A Computational Method for NMR-Constrained Protein Threading

“…There have been a number of studies that combine sparse NMR data with computational techniques for protein structure prediction, including (i) 3D (low-resolution) structure prediction by combining ab initio methods and sparse NOE restraints (Aszodi et al, 1995;Lund et al, 1996;Skolnick et al, 1997;Standley et al, 1999), and (ii) fold recognition by combining threading and secondary structure information extracted from chemical shift data (Ayers et al, 1999).…”

Enhanced protein fold recognition using secondary structure information from nmr

“…In this situation, one would like to use the available data, even if it is not suitable for conventional high resolution structure calculations. This has led to a series of approaches that have their roots in structure prediction, but attempt to incorporate very sparse experimental data such as a few intramolecular distance estimates~Smith- Brown et al, 1993;Aszódi et al, 1995;Lund et al, 1996;Skolnick et al, 1997!. Typically, these methods produce low resolution structures and operate with the caveat that answers may sometimes be quite wrong.…”