A quantum computer (QC) can operate in parallel on all its possible inputs at once, but the amount of information that can be extracted from the result is limited by the phenomenon of wave function collapse. We present a new computational model, which differs from a QC only in that the result of a measurement is the expectation value of the observable, rather than a random eigenvalue thereof. Such an expectation value QC can solve nondeterministic polynomial-time complete problems in polynomial time. This observation is significant precisely because the computational model can be realized, to a certain extent, by NMR spectroscopy on macroscopic ensembles of quantum spins, namely molecules in a test tube. This is made possible by identifying a manifold of statistical spin states, called pseudo-pure states, the mathematical description of which is isomorphic to that of an isolated spin system. The result is a novel NMR computer that can be programmed much like a QC, but in other respects more closely resembles a DNA computer. Most notably, when applied to intractable combinatorial problems, an NMR computer can use an amount of sample, rather than time, which grows exponentially with the size of the problem. Although NMR computers will be limited by current technology to exhaustive searches over only 15 to 20 bits, searches over as much as 50 bits are in principle possible, and more advanced algorithms could greatly extend the range of applicability of such machines. Cancer is still a major cause of death in the world at the beginning of the- 21st century and remains a major focus for ongoing research and development. In recent years a promising approach to the therapeutic intervention of cancer has focused on antiangiogenesis therapies. VEGFR-3 was detected in advanced human malignancies and correlated with poor prognosis. Previous studies show that activation of the VEGF-C/VEGFR-3 axis promotes cancer metastasis and is associated with clinical progression in patients with lung cancer, indicating that VEGFR-3 is a potential target for cancer therapy. Initial screening has identified other promising VEGFR-3 binding peptides as well. For example, a peptide comprising any of the following amino acid sequences: SGYWWDTWF, SCYWRDTWF, KVGWSSPDW, FVGWTKVLG, YSSSMRWRH, RWRGNAYPG, SAVFRGRWL, WFSASLRFR, and conservative substitution-analogs thereof, binds human VEGFR-3. On the other hand a newly introduced binding energy funnel ‘steepness score’ was applied for the evaluation of the protein–peptide-multi-ligand complexes binding affinity. KNIME-based BiogenetoligandorolTM – Pepcrawler simulations predicted high binding affinity for native protein–peptide-hyper-ligand complexes benchmark and low affinity for low-energy decoy complexes. As a result we managed finally to introduce an algorithm for high-resolution refinement and binding affinity estimation of novel designed inhibitors consisting of CGQMCTVWCSSGC conserved peptide substitution mimetic linked pharmacostructures with potential antagonizing VEGFR-3-mediated oncogenic effects.
fast RRT-based algorithm, high-resolution, refinement, binding affinity, estimation, peptide inhibitors, in silico, discovery, performing high resolution, docking refinement, estimation affinity, conserved peptide, substitution, mimetic pharmacostructure, suppressor VEGFR-3 activity, antagonize VEGFR-3-mediated, oncogenic effects, ensemble quantum computing, NMR spectroscopy, high-resolution, refinement, binding affinity, estimation inhibitors, quantum computing, DNA computing, nondeterministic polynomial-time complete,