Energetic Selection of Topology in Ferredoxins

Models of early protein evolution posit the existence of short peptides that bound metals and ions and served as transporters, membranes or catalysts. The Cys-X-X-Cys-X-X-Cys heptapeptide located within bacterial ferredoxins, enclosing an Fe4S4 metal center, is an attractive candidate for such an early peptide. Ferredoxins are ancient proteins and the simple α+β fold is found alone or as a domain in larger proteins throughout all three kingdoms of life. Previous analyses of the heptapeptide conformation in experimentally determined ferredoxin structures revealed a pervasive right-handed topology, despite the fact that the Fe4S4 cluster is achiral. Conformational enumeration of a model CGGCGGC heptapeptide bound to a cubane iron-sulfur cluster indicates both left-handed and right-handed folds could exist and have comparable stabilities. However, only the natural ferredoxin topology provides a significant network of backbone-to-cluster hydrogen bonds that would stabilize the metal-peptide complex. The optimal peptide configuration (alternating αL,αR) is that of an α-sheet, providing an additional mechanism where oligomerization could stabilize the peptide and facilitate iron-sulfur cluster binding

Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward