Supervisors info:
Οικονομίδου Βασιλική, Επίκουρη Καθηγήτρια, Τομέας Βιολογίας Κυττάρου και Βιοφυσικής, Τμήμα Βιολογίας, Πανεπιστήμιο Αθηνών
Summary:
Monoclonal antibodies (mAbs) represent the most promising and rapidly growing class of therapeutic compounds, as efficient implements for treating a wide variety of human chronic and acute diseases. Despite their benefits, human-derived mAbs or murine-derived mAbs do have deficiencies, such as short in vivo life span and low stability. A major drawback in the exploitation of antibodies is their tendency to form aggregates, a process by which protein molecules assemble into stable and often insoluble complexes. In this study our purpose was to design and develop an automatic computational method, called ANTISOMA, for the detection of these small peptides, the substitution of certain residues of these peptides and finally the optimization of the aggregation propensity of these regions. In order to detect the ‘aggregation-prone’ regions we used AMYLPRED2, a consensus method, developed in our lab, for the prediction of these ‘aggregation-prone’ peptides on amino acid sequence. Taking into account the position of each residue in the protein and their accessibility to the solvent, “hotspot” residues are substituted, utilizing additional experimental aggregation propensities of amino acids. New optimized models as well as comparison between the “aggregation-prone” surface before and after substitutions are provided. For the evaluation of our method we used five (5) therapeutic monoclonal antibodies, derived from the Protein Data Bank. Our method performed efficiently during optimization. In particular, a decrease in the “aggregation-prone” surface was observed for all mAbs, which indicates that targeted substitutions may modify the aggregation profile on exposed surfaces of mAbs. Rational design of mAbs, in combination with additional experimental approaches, will enable improvements in the efficacy and safety of protein therapeutics.
Keywords:
monoclonal antibodies, ‘aggregation-prone’ regions, automatic computational method, residues substitution
