Computational biochemistry: a link between base and applied research

Computational biochemistry is mainly based on molecular modelling and informatics tools, combined to try addressing structural, dynamics and functional features of biomolecules, with focus on biopolymers. Its applications include comparative modelling, molecular dynamics simulations, and several other techniques, such as ab initio calculations based on the density functional theory. Computations and simulations are frequently used to manage biochemical problems not easily addressed by wet experimental approaches, such as deciphering the three-dimensional structure of a biopolymer, inferring its in vivo activity, characterizing at a molecular level its catalytic function or its signal transduction mechanism, or studying the impact of mutations on the structure-function relationship in proteins and eventually their effects on carriers~@~Y phenotypes. Besides these purposes, mainly focused on basic research, computational biochemistry is becoming one of the most relevant tools of the drug discovery pipeline. It is useful for identifying putative targets, for solving their structure via computational methods, for better understanding their pathophysiological functions, and for identifying and deploying pharmacological strategies, primarily based on the development of novel compounds with specific target-modifying activities. Not only pharmacology, but also toxicology is taking advantage from computational biochemistry to clarify the mechanism of action of xenobiotics or to prioritize large datasets of compounds in risk evaluation tasks. In my talk, I am going to report some typical applications of computational biochemistry: an application to pharmacology towards the development of novel GPCR agonists for demyelinating neurodegenerative diseases, an investigation on the transport mechanism of an amino acid transporters connected with cancer, and an example of toxicological prioritization among environmental xenobiotics involved either in teratogenic or in endocrine disrupting outcomes.