The relationship between protein sequence and structure arises entirely from amino acid physical properties. An alternative method is therefore proposed to identify homologs in which residue equivalence is based exclusively on the pairwise physical property similarities of sequences. This approach, the property factor method (PFM), is entirely different from those in current use. A comparison is made between our method and PSI BLAST. We demonstrate that traditionally defined sequence similarity can be very low for pairs of sequences (which therefore cannot be identified using PSI BLAST), but similarity of physical property distributions results in almost identical 3D structures. The performance of PFM is shown to be better than that of PSI BLAST when sequence matching is comparable, based on a comparison using targets from CASP10 (89 targets) and CASP11 (51 targets). It is also shown that PFM outperforms PSI BLAST in informatically challenging targets.homology modeling | amino acid physical properties | protein structure prediction T he prediction of protein structure from sequence is a central problem in molecular biology, with important practical applications. The most reliable approach to this problem is homology modeling (1-3), in which the structure of a target sequence is modeled on the known structures of candidate proteins whose sequences are judged to be similar to that of the target. The standard procedure for calculating the degree of similarity between two sequences involves alignment techniques. Sequence alignment, in its usual incarnation, involves two computational features: i) a matrix that establishes a quantitative degree of similarity between any two amino acids; and ii) a procedure for locating equivalent subsequence fragments in the two sequences of interest and assessing numerical penalties for the presence of insertions or deletions in one sequence relative to the other.In previous work (4), we discussed the problems associated with this approach. In standard alignment, equivalence matrices are self-referential, in the sense that they are generally constructed from alignments. Biases and preconceptions inherent in the initial alignment will therefore be incorporated in a nonlinear fashion into the final result. In the present work, we use a residue-equivalence measure based entirely on amino acid physical properties (5) [the property factor-based method (PFM), details of which are given in Methods]. As a result, no preconceptions as to correct matches color our results. Furthermore, a matrix-matching algorithm adapted from image processing is used to provide an extremely efficient sequence search/matching procedure. It is shown that, together, these lead to results that are not attainable by current methods and to the accurate prediction of structures that were poorly predicted in the two most recent CASP (critical assessment of protein structure prediction) exercises. This approach recalibrates what is possible using sequence alignment, and in the process eliminates much of the computatio...