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Background  Leptospirosis is one of the most widespread zoonoses in the world and with over 260 pathogenic serovars there is an urgent need for a molecular system of classification. The development of multilocus sequence typing (MLST) schemes for  Leptospira  spp. is addressing this issue. The aim of this study was to identify loci with potential to enhance  Leptospira  strain discrimination by sequencing-based methods.    Methodology and Principal Findings  We used bioinformatics to evaluate pre-existing loci with the potential to increase the discrimination of outbreak strains. Previously deposited sequence data were evaluated by phylogenetic analyses using either single or concatenated sequences. We identified and evaluated the applicability of the  ligB ,  secY ,  rpoB  and  lipL41  loci, individually and in combination, to discriminate between 38 pathogenic  Leptospira  strains and to cluster them according to the species they belonged to. Pairwise identity among the loci ranged from 82.0–92.0%, while interspecies identity was 97.7–98.5%. Using the  ligB-secY-rpoB-lipL41  superlocus it was possible to discriminate 34/38 strains, which belong to six pathogenic  Leptospira  species. In addition, the sequences were concatenated with the superloci from 16 sequence types from a previous MLST scheme employed to study the association of a leptospiral clone with an outbreak of human leptospirosis in Thailand. Their use enhanced the discriminative power of the existing scheme. The  lipL41  and  rpoB  loci raised the resolution from 81.0–100%, but the enhanced scheme still remains limited to the  L. interrogans  and  L. kirschneri  species.    Conclusions  As the first aim of our study, the  ligB-secY-rpoB-lipL41  superlocus demonstrated a satisfactory level of discrimination among the strains evaluated. Second, the inclusion of the  rpoB  and  lipL41  loci to a MLST scheme provided high resolution for discrimination of strains within  L. interrogans  and  L. kirschneri  and might be useful in future epidemiological studies.

Bioinformatics Describes Novel Loci for High Resolution Discrimination of Leptospira Isolates