Algorithms for the design of maximum hydropathic complementarity molecules

Area of Science:

• Computational biology
• Bioinformatics
• Drug design

Background:

• Designing sequences with optimal complementarity is crucial in applications like drug design.
• Complementarity between protein sequences is often assessed using hydropathic coefficients of amino acids.

Purpose of the Study:

• To develop and evaluate algorithms for designing strings with optimal complementarity properties.
• To compare the efficacy of mathematical programming methods against heuristic approaches for sequence optimization.

Main Methods:

• Presentation of heuristic and exact optimization algorithms.
• Computational experiments on amino peptides from Semaphorin and human Interleukin-1β.
• Application of mathematical programming techniques.

Main Results:

• Proved optimality of a known solution for Semaphorin-3A and discovered new optimal/near-optimal solutions.
• Found an optimal solution for human interleukin-1β with significantly improved complementarity.
• Demonstrated the superiority of mathematical programming over heuristic methods like simulated annealing.

Conclusions:

• Mathematical programming offers a more successful approach than heuristics for optimizing sequence complementarity.
• The developed algorithm provides a ranking of optimal solutions, aiding further biological research and experimentation.
• Source code is available for non-commercial use, facilitating further research.