MOTIVATION: Multiple STructural Alignment (MSTA) provides valuable information for solving problems such as fold recognition. The consistency-based approach tries to find conflict-free subsets of alignments from a pre-computed all-to-all Pairwise Alignment Library (PAL). If large proportions of conflicts exist in the library, consistency can be hard to get. On the other hand, multiple structural superposition has been used in many MSTA methods to refine alignments. However, multiple structural superposition is dependent on alignments, and a superposition generated based on erroneous alignments is not guaranteed to be the optimal superposition. Correcting errors after making errors is not as good as avoiding errors from the beginning. Hence it is important to refine the pairwise library to reduce the number of conflicts before any consistency-based assembly. RESULTS: We present an algorithm, Iterative Refinement of Induced Structural alignment (IRIS), to refine the PAL. A new measurement for the consistency of a library is also proposed. Experiments show that our algorithm can greatly improve T-COFFEE performance for less consistent pairwise alignment libraries. The final multiple alignment outperforms most state-of-the-art MSTA algorithms at assembling 15 transglycosidases. Results on three other benchmarks showed that the algorithm consistently improves multiple alignment performance. AVAILABILITY: The C++ code of the algorithm is available upon request.

One of the approaches to protein structure prediction is to obtain energy functions which can recognize the native conformation of a given sequence among a zoo of conformations. The discriminations can be done by assigning the lowest energy to the native conformation, with the guarantee that the native is in the zoo. Well-adjusted functions, then, can be used in the search for other (near-) natives. Here the aim is the discrimination at relatively high resolution (RMSD difference between the native and the closest nonnative is around 1 Å) by pairwise energy potentials. The results show that the potential can be trained to discriminate between the native conformation of one protein as the (near-) global minimum, and other nonnatives, including energy-minimized ones (or local minima). This potential function is able to identify the native conformation of another protein, too.

Protein structural knowledge is essential to understanding the molecular basis of disease.Drug design and discovery are facilitated by understanding the three dimensional shape of relevant proteins, as will be future modes of disease intervention such as gene therapy.While identification of disease related protein sequences has dramatically increased through genome mapping efforts, the rate of determination of the associated structures remains relatively slow.Despite recent advancements, experimental methods of protein structure determination remain time consuming and labor intensive.Therefore, computational methods which can predict the correct protein structure for a given amino acid sequence would be a tremendous aid in biomedical research.Homology modeling is precluded if the sequence of interest has low similarity to proteins of known structure.For these sequences, structure prediction requires an energy potential which can identify the native structure out of any number of alternative conformations.We have developed a solvation energy potential which is optimized to identify the native structure as the lowest in energy.The potential has few adjustable parameters which are "trained" so the Protein Data Bank (PDB) structure for each protein in a training set is the lowest in energy when compared to a very large set of alternatives -over lo5 alternatives on average.Intramolecular contact definitions are obtained for all possible amino acid pairs from a survey of protein structures and are continuous as a function of interresidue distance.Training with only 25 native proteins produces an energy potential which is quite successful in structure identification tests.Small proteins and those with prosthetic groups pose the greatest challenge.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies arc not made or tlistrihuted for prolit or commercial advantage ;1nJ that copies bear this ntoticc and the full citation on the first page.I'0 copy othrrwise.to rcpuhlish.to post on servers or to redistribute to lists.requires prior specific permission and~or a fee.