Plant proteins, particularly from oilseed meals, are promising renewable resources for texturizing ingredients in food and cosmetic matrices. Application of these ingredients would get rid of petroleum-based products in cosmetics, and to strengthen the use of animal proteins in food, responding to major socio-economic challenges. However, they have insufficient performance compared to currently used products. The functional limitations of plant proteins currently represent a major bottleneck to their industrial development, which must be improved to reach the quality standards of animal proteins and synthetic molecules. Enzymatic transformation processes can be implemented on protein isolates to improve their functional properties. Among these processes, proteolysis and enzymatic cross-linking are the most feasible, sustainable and compatible processes for the use of plant proteins in cosmetics and food industries. Nevertheless, the understanding and control of obtaining functional protein products by these two processes are limited by the lack of knowledge of the relationships between the product characteristics and their properties. Moreover, the process implementations result from laborious and empirical experimental approaches, leading to non-optimal production ways with regard to industrial technical, economic (cost, production duration) and environmental criteria. The main objective of PROSPER project is to develop a generic methodology for obtaining tailor-made plant protein ingredients for targeted applications in food and cosmetic, by controlling enzymatic transformation processes. Its purpose is to allow (i) an improvement in the level of understanding of the relationship between product properties and functionalities; (ii) wider use of plant proteins as functional ingredients; (iii) technological innovation to improve protein functionalities and open up new fields of application. To meet this objective, an original strategy of product engineering will be applied, structured around four main work packages. Reliable analytical tools for the characterization of the products obtained and the monitoring of proteolysis and enzymatic crosslinking processes will be developed initially. Then, the methodology aims to associate the characteristics of the products obtained with functional properties of interest using supervised machine learning methods. In a third step, relationships can be established between the characteristics of the products and a kinetic monitoring parameter of the process, as a representative criterion of a targeted functionality. Modeling/simulation tools for enzymatic transformation processes, based on experimental data regressions, will be developed. Obtained models will be coupled with the established correlations to numerically explore the influence of operating conditions sets on the targeted functionalities, and thus to identify in a rational way the original production routes of a product with targeted functionality. Then, in a last step, these models will be associated with multi-criteria optimization and decision-making tools, in order to establish the optimum functioning of these transformation routes on technical and economic criteria and to analyze these routes towards environmental impacts through life cycle analysis studies.