Comparative protein structure modeling by iterative alignment, model building and model assessment. Nucleic Acids Res

Comparative or homology protein structure model-ing is severely limited by errors in the alignment of a modeled sequence with related proteins of known three-dimensional structure. To ameliorate this problem, we have developed an automated method that optimizes both the alignment and the model implied by it. This task is achieved by a genetic algorithm protocol that starts with a set of initial alignments and then iterates through re-alignment, model building and model assessment to optimize a model assessment score. During this iterative pro-cess: (i) new alignments are constructed by applica-tion of a number of operators, such as alignment mutations and cross-overs; (ii) comparative models corresponding to these alignments are built by satisfaction of spatial restraints, as implemented in our program MODELLER; (iii) the models are assessed by a variety of criteria, partly depending on an atomic statistical potential. When testing the procedure on a very dif®cult set of 19 modeling targets sharing only 4±27 % sequence identity with their template structures, the average ®nal align-ment accuracy increased from 37 to 45 % relative to the initial alignment (the alignment accuracy was measured as the percentage of positions in the tested alignment that were identical to the reference structure-based alignment). Correspondingly, the average model accuracy increased from 43 to 54% (the model accuracy was measured as the percent-age of the Ca atoms of the model that were within 5 AÊ of the corresponding Ca atoms in the super-posed native structure). The present method also compares favorably with two of the most successful previously described methods, PSI-BLAST and SAM. The accuracy of the ®nal models would be increased further if a better method for ranking of the models were available