UNLABELLED: Computational protein docking is a useful technique for gaining insights into protein interactions. We have developed an algorithm M-ZDOCK for predicting the structure of cyclically symmetric (Cn) multimers based on the structure of an unbound (or partially bound) monomer. Using a grid-based Fast Fourier Transform approach, a space of exclusively symmetric multimers is searched for the best structure. This leads to improvements both in accuracy and running time over the alternative, which is to run a binary docking program ZDOCK and filter the results for near-symmetry. The accuracy is improved because fewer false positives are considered in the search, thus hits are not as easily overlooked. By searching four instead of six degrees of freedom, the required amount of computation is reduced. This program has been tested on several known multimer complexes from the Protein DataBank, including four unbound multimers: three trimers and a pentamer. For all of these cases, M-ZDOCK was able to find at least one hit, whereas only two of the four testcases had hits when using ZDOCK and a symmetry filter. In addition, the running times are 30-40% faster for M-ZDOCK. AVAILABILITY: M-ZDOCK is freely available to academic users at http://zlab.bu.edu/m-zdock/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: http://zlab.bu.edu/m-zdock.

MOTIVATION: Various methods have been proposed to predict the binding affinities of peptides to Major Histocompatibility Complex class I (MHC-I) molecules based on experimental binding data. They can be classified into two groups: (1) AIB methods that assume independent contributions of all peptide positions to the binding to MHC-I molecule (e.g. scoring matrices) and (2) general methods which can take into account interactions between different positions (e.g. artificial neural networks). We aim to compare the prediction accuracies of these methods, and quantify the impact of interactions between peptide positions. RESULTS: We compared several previously published and widely used methods and discovered that the best AIB methods gave significantly better predictions than three previously published general methods, possibly due to the lack of a sufficient training data for the general methods. The best results, however, were achieved with our newly developed general method, which combined a matrix describing independent binding with pair coefficients describing pair-wise interactions between peptide positions. The pair coefficients consistently but only slightly improved prediction accuracy, and were much smaller than the matrix entries. This explains why neglecting them-as is done in AIB methods-can still lead to good predictions. AVAILABILITY: The new prediction model is implemented at http://zlab.bu.edu/SMM. The underlying matrix and pair coefficients are also available as supplementary materials.