A geometric recognition algorithm was developed to identify molecular surface complementarity. It is based on a purely geometric approach and takes advantage of techniques applied in the field ofpattern recognition. The algorithm involves an automated procedure including (i) a digital representation of the molecules (derived from atomic coordinates) by three-dimensional discrete functions that distinguishes between the surface and the interior; (ii) the calculation, using Fourier transformation, of a correlation function that assesses the degree of molecular surface overlap and penetration upon relative shifts of the molecules in three dimensions; and (iii) a scan of the relative orientations of the molecules in three dimensions. The algorithm provides a list of correlation values indicating the extent of geometric match between the surfaces of the molecules; each of these values is associated with six numbers describing the relative position (translation and rotation) of the molecules. The procedure is thus equivalent to a six-dimensional search but much faster by design, and the computation time is only moderately dependent on molecular size. The procedure was tested and validated by using five known complexes for which the correct relative position of the molecules in the respective adducts was successfully predicted. The molecular pairs were deoxyhemoglobin and methemoglobin, tRNA synthetase-tyrosinyl adenylate, aspartic proteinase-peptide inhibitor, and trypsin-trypsin inhibitor. A more realistic test was performed with the last two pairs by using the structures of uncomplexed aspartic proteinase and trypsin inhibitor, respectively. The results are indicative of the extent of conformational changes in the molecules tolerated by the algorithm.The association of proteins with their ligands involves intricate inter-and intramolecular interactions, solvation effects, and conformational changes. In view of such complexity, a comprehensive and efficient approach for predicting the formation of protein-ligand complexes from the structure of their free components is not yet available. However, with some assumptions, such predictions become feasible, and several attempts based on energy minimization have been partially successful (1-6). Another simplifying approach that could alleviate some of these difficulties is based on geometric considerations.The three-dimensional (3D) structures of most protein complexes reveal a close geometric match between those parts of the respective surfaces of the protein and the ligand that are in contact. Indeed, the shape and other physical characteristics of the surfaces largely determine the nature of the specific molecular interactions in the complex. Furthermore, in many cases the 3D structure of the components in the complex closely resembles that of the molecules in their free, native state. Geometric matching thus seems to play an important role in determining the structure of a complex.Several investigators have exploited a geometric approach to find shape complement...

In the classical procedures for predicting the structure of protein complexes two molecules are brought in contact at multiple relative positions, the extent of complementarity (geometric and/or energy) at the surface of contact is assessed at each position, and the best fits are retrieved. In view of the higher occurrence of hydrophobic groups at contact sites, their contribution results in more intermolecular atom-atom contacts per unit area for correct matches than for false positive fits. The hydrophobic groups are also potentially less flexible at the surface. Thus, from a practical point of view, a partial representation of the molecules based on hydrophobic groups should improve the quality of the results in finding molecular recognition sites, as compared to full representation. We tested this proposal by applying the idea to an existing geometric fit procedure and compared the results obtained with full vs. hydrophobic representations of molecules in known molecular complexes. The hydrophobic docking yielded distinctly higher signal-to-noise ratio so that the correct match is discriminated better from false positive fits. It appears that nonhydrophobic groups contribute more to false matches. The results are discussed in terms of their relevance to molecular recognition techniques as compared to energy calculations.

Haematococcus pluvialis under stress conditions overproduces the valuable red ketocarotenoid astaxanthin. Two proposed strategies for commercial production are under current analysis. One separates in time the production of biomass (optimal growth, green stage) and pigment (permanent stress, red stage), while the other uses an approach based on continuous culture under limiting stress at steady state. The productivities, efficiencies and yields for the pigment accumulation in each case have been compared and analyzed in terms of the algal basic physiology. The two-stage system indoors yields a richer astaxanthin product (4% of dry biomass) with a final astaxanthin productivity of 11.5 mg L(-1) day(-1), is more readily upscalable and amenable to outdoors production. Furthermore, each stage can be optimized for green biomass growth and red pigment accumulation by adjusting independently the respective ratio of effective irradiance to cell density. We conclude that the two-stage system performs better (by a factor of 2.5-5) than the one-stage system, and the former is best fit in an efficient mass production setup.

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