Algorithms for the design of maximum hydropathic complementarity molecules.
ABSTRACT In this article, we address the problem of designing a string with optimal complementarity properties with respect to another given string according to a given criterion. The motivation comes from a drug design application, in which the complementarity between two sequences (proteins) is measured according to the values of the hydropathic coefficients associated with the sequence elements (amino acids). We present heuristic and exact optimization algorithms, and we report on some computational experiments on amino peptides taken from Semaphorin and human Interleukin-1β, which have already been investigated in the literature using heuristic algorithms. With our techniques, we proved the optimality of a known solution for Semaphorin-3A, and we discovered several other optimal and near-optimal solutions in a short computing time; we also found in fractions of a second an optimal solution for human interleukin-1β, whose complementary value is one order of magnitude better than previously known ones. The source code of a prototype C++ implementation of our algorithms is freely available for noncommercial use on the web. As a main result, we showed that in this context mathematical programming methods are more successful than heuristics, such as simulated annealing. Our algorithm unfolds its potential, especially when different measures could be used for scoring peptides, and is able to provide not only a single optimal solution, but a ranking of provable good ones; this ranking can then be used by biologists as a starting basis for further refinements, simulations, or in vitro experiments.
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ABSTRACT: A computer program that progressively evaluates the hydrophilicity and hydrophobicity of a protein along its amino acid sequence has been devised. For this purpose, a hydropathy scale has been composed wherein the hydrophilic and hydrophobic properties of each of the 20 amino acid side-chains is taken into consideration. The scale is based on an amalgam of experimental observations derived from the literature. The program uses a moving-segment approach that continuously determines the average hydropathy within a segment of predetermined length as it advances through the sequence. The consecutive scores are plotted from the amino to the carboxy terminus. At the same time, a midpoint line is printed that corresponds to the grand average of the hydropathy of the amino acid compositions found in most of the sequenced proteins. In the case of soluble, globular proteins there is a remarkable correspondence between the interior portions of their sequence and the regions appearing on the hydrophobic side of the midpoint line, as well as the exterior portions and the regions on the hydrophilic side. The correlation was demonstrated by comparisons between the plotted values and known structures determined by crystallography. In the case of membrane-bound proteins, the portions of their sequences that are located within the lipid bilayer are also clearly delineated by large uninterrupted areas on the hydrophobic side of the midpoint line. As such, the membrane-spanning segments of these proteins can be identified by this procedure. Although the method is not unique and embodies principles that have long been appreciated, its simplicity and its graphic nature make it a very useful tool for the evaluation of protein structures.Journal of Molecular Biology 06/1982; 157(1):105-32. · 3.91 Impact Factor
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ABSTRACT: A computer-designed hydropathically complementary peptide to human interleukin 1 beta (IL1 beta) precursor sequence 204-215 recognized the 204-215 peptide as well the entire IL1 beta protein with binding affinities in the micromolar range. Interaction between the complementary pair was characterized by analytical high-performance liquid affinity chromatography on columns derivatized with the computer-generated peptide. Recognition selectivity was clearly shown by the ability of the computer-generated complementary peptide columns to purify the IL1 beta-(204-215)-peptide from complex synthetic mixtures with high yields, independently of the type of solid support used. Recognition specificity was demonstrated by the inability of the IL1 beta-(204-215)-peptide and IL1 beta molecules to interact with blank columns or columns derivatized with other non-related peptides. Furthermore, scrambling the sequence of the computer-generated peptide or the IL1 beta-(204-215)-peptide in such a way as to alter their hydropathic profiles had the effect of abolishing binding. The complementary pair failed to interact in the presence of competing peptide, thus providing further evidence of specificity. Computer-generated complementary peptide affinity columns also proved useful for purification of recombinant human IL1 beta protein directly from crude Escherichia coli lysates.Biochemical Journal 04/1992; 282 ( Pt 3):773-9. · 4.65 Impact Factor
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ABSTRACT: We describe the tripeptide neutrophil chemoattractant N-acetyl Pro-Gly-Pro (PGP), derived from the breakdown of extracellular matrix (ECM), which shares sequence and structural homology with an important domain on alpha chemokines. PGP caused chemotaxis and production of superoxide through CXC receptors, and administration of peptide caused recruitment of neutrophils (PMNs) into lungs of control, but not CXCR2-deficient mice. PGP was generated in mouse lung after exposure to lipopolysaccharide, and in vivo and in vitro blockade of PGP with monoclonal antibody suppressed PMN responses as much as chemokine-specific monoclonal antibody. Extended PGP treatment caused alveolar enlargement and right ventricular hypertrophy in mice. PGP was detectable in substantial concentrations in a majority of bronchoalveolar lavage samples from individuals with chronic obstructive pulmonary disease, but not control individuals. Thus, PGP's activity links degradation of ECM with neutrophil recruitment in airway inflammation, and PGP may be a biomarker and therapeutic target for neutrophilic inflammatory diseases.Nature Medicine 04/2006; 12(3):317-23. · 22.86 Impact Factor