Combining computational modeling with sparse and low-resolution data

Structural biology is moving into a new era by shifting its focus from static structures of single proteins and protein domains to large and often fragile multi-component complexes. Over the past decade, structural genomics initiatives aimed to fill the voids in fold space and to provide a census of all protein structures. Completion of such an atlas of protein structures is still ongoing, but not sufficient for a mechanistic understanding of how living cells function. One of the great challenges is to bridge the gap between atomic resolution detail and the more fuzzy description of the molecular complexes that govern cellular processes or host–pathogen interactions. We want to move from cartoon-like representations of multi-component complexes to atomic resolution structures. To characterize the structures of the increasingly large and often flexible complexes, high resolution structure determination (as was possible for example for the ribosome) will likely stay the exception. Rather, data from many different methods providing information on the shape (X-ray crystallography, electron microscopy, SAXS, AFM, etc.) or on contacts between components (mass spectrometry, co-purification, or spectroscopic methods) need to be integrated with prior structural knowledge to build a consistent model of the complex. A particular difficulty is that the ratio between the number of conformational degrees of freedom and the number of measurements becomes unfavorable as we work with large complexes: data become increasingly sparse. Structural characterization of large molecular assemblies often involves a loss in resolution as well as in number and quality of data. We are good at solving structures of single proteins, but classical high-resolution structure determination by X-ray crystallography and NMR spectroscopy is often facing its limits as we move to higher molecular mass and increased flexibility. Therefore, structural studies on large complexes rely on new experimental techniques that complement the classical high resolution methods. But also computational approaches are becoming more important when it comes to integrating and analyzing structural information of often heterogeneous nature. Cryoelectron microscopy may serve as an example of how experimental methods can benefit from computation. Low-resolution data from cryo-EM show their true power when combined with modeling and bioinformatics methods such rigid docking and secondary structure hunting. Even in high resolution structure determination, molecular modeling is always necessary to calculate structures from data, to complement the missing information and to evaluate and score the obtained structures. With sparse data, all these three aspects become increasingly difficult, and the quality of the modeling approach becomes more important. With data alone, algorithms may not converge any more; scoring against data becomes meaningless; and the potential energy function becomes central not only as a help in making algorithms converge but also to score and evaluate the structures. In addition to the sparsity of the data, hybrid approaches bring the additional difficulty that the different sources of data may have rather different quality, and may be in the extreme case incompatible with each other. In addition to scoring the structures, modeling should also score in some way the data going into the calculation. This special issue brings together some of the numerous efforts to solve the problems that come from sparsity of data and from integrating data from different sources in hybrid approaches. The methods range from predominantly force-field based to mostly data based. Systems of very different sizes, ranging from single domains to multi-component complexes, are treated. We hope that you will enjoy reading the issue and find it a useful and inspiring resource