Corrigendum:Local and macroscopic electrostatic interactions in single α-helices

The non-covalent forces that stabilise protein structures are not fully understood. One way to address this is to study equilibria between unfolded states and α-helices in peptides. For these, electrostatic forces are believed to contribute, including interactions between: side chains; the backbone and side chains; and side chains and the helix macrodipole. Here we probe these experimentally using designed peptides. We find that both terminal backbone-side chain and certain side chain-side chain interactions (i.e., local effects between proximal charges, or interatomic contacts) contribute much more to helix stability than side chain-helix macrodipole electrostatics, which are believed to operate at larger distances. This has implications for current descriptions of helix stability, understanding protein folding, and the refinement of force fields for biomolecular modelling and simulations. In addition, it sheds light on the stability of rod-like structures formed by single α-helices that are common in natural proteins including non-muscle myosins

Application of the Ps-function method to macromolecular structure determination

The Ps function derived from anomalous-dispersion data [Okaya, Saito, & Pepinsky (1955). Phys. Rev. 98, 1857-1858] has been tested with observed data for an Hg derivative of a small protein, avian pancreatic polypeptide [Glover, Moss, Tickle, Pitts, Haneef, Wood & Blundell (1985). Adv. Biophys. 20, 1-12]. The Ps map was superimposed on the four Hg sites via a sum function and negative densities were eliminated from the resultant map. This map, with appropriate density inserted at Hg sites, closely resembles a map calculated with true phases; the two maps have a correlation coefficient of 0-67. For 2109 reflexions the unweighted mean phase error is 39.9° but with |FoFc| weighting this reduces to 29.5°

On the Application of Phase-relationships To Complex Structures .29. Choosing the Large-es

The set of large Es through which a structure is solved by direct methods is usually chosen by a convergence or convergence-divergence process. This process aims to give a strong phase-extension pathway starting from a small set of Es whose phases are known or allocated in some way. Sometimes sets of reflexions thus obtained are poorly conditioned and under tangent-formula refinement even initially correct phases will degenerate to randomness. A simple new algorithm has been developed which improves the conditioning of the complete set of reflexions and their relationships and is more appropriate to current trends to start refinement from a complete set of random phases. A particular feature of this algorithm is that it maximizes the minimum number of relationships for any reflexion

Proteins and direct methods

Conventional direct methods, which work so well for small structures, are less successful for macromolecules. Where it has been demonstrated that a solution might be found using direct methods it is then found that the usual figures of merit are unable to distinguish the few good sets of phases from the large number of sets generated. The reasons for the difficulties with very large structures are considered from a first-principles approach taking into account both the factors of having a large number of atoms and low resolution data. A proposal is made for trying to recognize good phase sets by taking a large structure as a sum of a number of smaller structures for each of which a conventional figure of merit can be applied