Computational models in protein science have become the golden standard for the quantitative assessment of protein structure and dynamics. In this field, growing relevance has been attributed to models capable of meeting the increasing demand for single-residue descriptions of their role in protein stability and function. Precision medicine is today a central pillar of translational medicine, holding great promise for tailoring diagnosis and therapies to individual patients through knowledge of their unique genetic fingerprints, expressed as specific protein composition and function. This personalization approach requires reliable and streamlined experimental and modeling tools to derive descriptions for tailoring therapies in a rapid and cost-effective manner. In this perspective, computational models can provide many opportunities to achieve this ambitious goal. On the other hand, industrial biotechnological applications require similar tools for the continuous improvement of both processes and resource efficiency, as in the case of engineered plant strains designed to improve resilience towards climate change impacts, or genetically selected microorganisms aimed at enhancing the efficiency of waste conversion processes. In this framework, protein computational models exploiting the network paradigm have been attracting growing interest and finding increasing applications over the past few years. The network modeling of protein molecular structure has provided a general basis for understanding how structural and functional signals are transmitted in crucial protein processes such as folding and dynamics. Network-based modeling has become a general framework for various methods that have opened new perspectives in the analysis and prediction of allosteric regulation. Protein Contact Networks, Energy Exchange Networks and Elastic Network Models are three prominent examples of how it is possible to describe protein allostery in terms of signal transduction along a network that encodes the functional topology of protein molecular structure. These methods all share common quantitative features, such as residue ranking in terms of their role in allosteric signal propagation. Thanks to this approach, it is possible to boost protein engineering, for instance in biotechnological applications, and lay the groundwork for the development of allosteric drugs \u2014 an innovative class of therapeutic agents with high efficacy and low side effects. The emergence of network pharmacology also enables a multiscale approach to therapeutics, integrating metabolomics, genomics and proteomics, a promising strategy for achieving a truly applied precision medicine and pharmacology.<\/p>\n
Luisa Di Paola <\/strong>is Associate Professor of Chemical and Biochemical Engineering Fundamentals at Universit\u00e0 Campus Bio-Medico di Roma. She holds PhD in Industrial Chemical Processes from the University of Rome “La Sapienza.” Her research bridges computational biochemistry and complex systems analysis, with a special focus on Protein Contact Networks (PCNs) as a framework for decoding protein structure, allosteric regulation, and drug target identification. Her work \u2014 published in leading journals including Chemical Reviews and Biophysical Chemistry and cited<\/p>\n","protected":false},"featured_media":5175,"template":"","meta":{"_acf_changed":false},"schedule":[5],"speaker-category":[6],"class_list":["post-5174","speaker","type-speaker","status-publish","has-post-thumbnail","hentry","schedule-day-1","speaker-category-speakers"],"acf":[],"yoast_head":"\n