Structure prediction goals

Rost, B., Sander, C., Schneider, R. Redefining the goals of protein secondary structure prediction. /. Mol. Biol. 

The goal of protein-structure prediction is to derive the tertiary structure of the protein (defined as the manner in which the protein is bent or folded in three dimensions) given the sequence of amino acids (referred to as the primary structure). In between the primary and tertiary structure is the secondary structure which consists of regularly recurring arrangements of the protein chain in one-dimension (i e, a-helices and (3-sheets)... 

Protein structure prediction [253,254] is one of the major goals of computational molecular biology. Up to now, homology based and threading methods have been the most successful [185,226,255-264]. However, due to the increasing ratio of the number of known protein sequences to the number of solved protein structures, the development of de novo (or related) methods would be extremely valuable. To date, only limited, nevertheless encouraging, progress has been achieved in such direct approaches [42,254]. Purely de novo predictions are now possible only for peptides... 

We divide coarse-grained RNA structure prediction models into two categories fragment-based models examined in Section 15.4 and physics-based models examined in Section 15.5 (force fields) and Section 15.6 (model comparison). We then conclude our review and present future goals and challenges for the field in Section 15.7. 

RNA Structure prediction models represent a tractable method to realize this sequence-to-structure goal by balancing accuracy with computational efficiency. Many of the discussed models and metrics such as radius of g5mation [45] are derived from successful protein structure prediction models. As RNA biology is further quantified and the structure prediction field progresses, new techniques to better predict structure may arise. From the models presented, several improvements are evident ... 

Before embarking on a description of the computational methods involved, and how well they perform, we should address the goals of crystal structure prediction. At its most ambitious level, the aim is to start from nothing more than the structural formula of a molecule and to predict, with perfect reliability, the structure of the resulting solid, with no input from experimental observations. (Here, by structure, we mean the space group, unit cell parameters and a fiiU specification of all atomic positions.) This goal is, of course, unrealistic polymorphism in molecular crystals tells us that there is often not just one crystal structure for a molecule and we know that the crystal that is produced in an experiment depends on a variety of factors, from thermodynamic descriptors of the system (temperature and pressure) to the method of crystallization, solvent used and the presence of impurities. Without a detailed description of the crystallization conditions, prediction of the resulting structure cannot be the aim. Furthermore, many of these factors are not sufficiently well understood to be represented in a computational procedure for crystal structure prediction. 

Polymers are often enough used as films, which were prepared from polymer solutions. As it is known [6], a solution change results to the essential variations of film samples of the same pol5mer. Therefore, a film sample structure prediction as a function of solvent characteristics, from which it was prepared, is the goal solution first stage. It is obvious, that the solubility parameter of solvent 8 is its characteristic the best choice [7, 8], The fractal dimension structure <7 was chosen as its characteristic [9], which can be determined according to the Eqs. (1.9) and (2.20). 

---