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We proposed a probabilistic algorithm to solve the Multiple SequenceAlignment problem. The algorithm is a Simulated Annealing (SA) that exploitsthe representation of the Multiple Alignment between $D$ sequences as adirected polymer in $D$ dimensions. Within this representation we can easilytrack the evolution in the configuration space of the alignment through localmoves of low computational cost. At variance with other probabilisticalgorithms proposed to solve this problem, our approach allows for the creationand deletion of gaps without extra computational cost. The algorithm was testedaligning proteins from the kinases family. When D=3 the results are consistentwith those obtained using a complete algorithm. For $D>3$ where the completealgorithm fails, we show that our algorithm still converges to reasonablealignments. Moreover, we study the space of solutions obtained and show thatdepending on the number of sequences aligned the solutions are organized indifferent ways, suggesting a possible source of errors for progressivealgorithms.Comment: 7 pages and 11 figure

Simulated annealing algorithm for the multiple sequence alignment problem: The approach of polymers in a random medium