A simple protein folding algorithm using a binary code and secondary structure constraints

Abstract

We describe an algorithm to predict tertiary structures of small proteins. In contrast to most current folding algorithms, it uses very few energy parameters. Given the secondary structural elements in the sequence--alpha-helices and beta-strands--the algorithm searches the remaining conformational space of a simplified real-space representation of chains to find a minimum energy of an exceedingly simple potential function. The potential is based only on a single type of favorable interaction between hydrophobic residues, an unfavorable excluded volume term of spatial overlaps and, for sheet proteins, an interstrand hydrogen bond interaction. Where appropriate, the known disulfide bonds are constrained by a square-law potential. Conformations are searched by a genetic algorithm. The model predicts reasonably well the known tertiary folds of seven out of the 10 small proteins we consider. We draw two conclusions. First, for the proteins we tested, this exceedingly simple potential function is no worse than others having hundreds of energy parameters in finding the right general tertiary structures. Second, despite its simplicity, the potential function is not the weak link in this algorithm. Differences between our predicted structures and the correct targets can be ascribed to shortcomings in our search strategy. This potential function may be useful for testing other conformational search strategies.

Factors affecting the ability of energy functions to discriminate correct from incorrect folds

“…Such an effective energy function is the common prerequisite for all theoretical approaches to protein folding. Energy functions are often used to drive the conformational changes of a polypeptide chain as it folds through phase space (Wilson & Doniach, 1989;Covell, 1992Covell, , 1994Sun, 1993;Bowie & Eisenberg, 1994;Dandekar & Argos, 1994Wallqvist & Ullner, 1994;Vieth et al, 1994Vieth et al, , 1995Monge et al, 1995;Mumenthaler & Braun, 1995;Srinivasan & Rose, 1995;Sun et al, 1995). Alternatively, they are employed to discriminate amongst candidate (or`d ecoy'') folds generated by methods that are independent (or semi-independent) of the energy function.…”

Distance geometry generates native‐like folds for small helical proteins using the consensus distances of predicted protein structures

“…An alternative approach is first to sample conformational space as exhaustively as possible, given computational limits, then to apply a scoring function to assess the fitness of each candidate structure. In either case, reduction of the available conformational space is achieved by discretization on a lattice (Covell, 1992(Covell, , 1994Hinds & Levitt, 1992, 1994Vieth et al, 1994) or sampling in torsion space (Wilson & Doniach, 1989;Bowie & Eisenberg, 1994;Dandekar & Argos, 1994Monge et al, 1995;Mumenthaler & Braun, 199.5;Srinivasan & Rose, 1995;Sun et al, 1995, Yue & Dill, 1996Simons et al, 1997). However, reduction of the search space also decreases the fidelity with which the native fold can be represented.…”

Protein fold recognition without Boltzmann statistics or explicit physical basis

“…A certain number of parameters is necessary to fit the general features of the data, but the number of parameters should be kept small to avoid over-fitting. In the case of fitting parameters for fold recognition force fields, literature estimates of the approximate number of parameters span the range from less than ten (Sun et al, 1995;Thomas & Dill, 1996) to tens of thousands (Hendlich et al, 1990;Jones & Thornton, 1993). We tried to explore the lower limit of the number of parameters in our fold recognition force field.…”