首页|University of Florence Reports Findings in Machine Learning (Machine Learning-En hanced Quantum Chemistry-Assisted Refinement of the Active Site Structure of Met alloproteins)
University of Florence Reports Findings in Machine Learning (Machine Learning-En hanced Quantum Chemistry-Assisted Refinement of the Active Site Structure of Met alloproteins)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Sesto Fiorenti no, Italy, by NewsRx journalists, research stated, “Understanding the fine struc tural details of inhibitor binding at the active site of metalloenzymes can have a profound impact on the rational drug design targeted to this broad class of b iomolecules. Structural techniques such as NMR, cryo-EM, and X-ray crystallograp hy can provide bond lengths and angles, but the uncertainties in these measureme nts can be as large as the range of values that have been observed for these qua ntities in all the published structures.” The news reporters obtained a quote from the research from the University of Flo rence, “This uncertainty is far too large to allow for reliable calculations at the quantum chemical (QC) levels for developing precise structure-activity relat ionships or for improving the energetic considerations in protein-inhibitor stud ies. Therefore, the need arises to rely upon computational methods to refine the active site structures well beyond the resolution obtained with routine applica tion of structural methods. In a recent paper, we have shown that it is possible to refine the active site of cobalt(II)-substituted MMP12, a metalloprotein tha t is a relevant drug target, by matching to the experimental pseudocontact shift s (PCS) those calculated using multireference ab initio QC methods. The computat ional cost of this methodology becomes a significant bottleneck when the startin g structure is not sufficiently close to the final one, which is often the case with biomolecular structures. To tackle this problem, we have developed an appro ach based on a neural network (NN) and a support vector regression (SVR) and app lied it to the refinement of the active site structure of oxalate-inhibited huma n carbonic anhydrase 2 (hCAII), another prototypical metalloprotein target. The refined structure gives a remarkably good agreement between the QC-calculated an d the experimental PCS.”
Sesto FiorentinoItalyEuropeChemist ryCyborgsEmerging TechnologiesMachine LearningMetalloproteinsPeptides and ProteinsQuantum Chemistry
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