Models of radiative transfer (RT) are important tools for remote sensing of vegetation, allowing for forward simulations of remotely sensed data as well as inverse estimation of biophysical and biochemical traits from vegetation optical properties. Estimation of foliar protein content is the key to monitor the nitrogen cycle in terrestrial ecosystems, in particular to assess the photosynthetic capacity of plants and to improve nitrogen management in agriculture. However, until now physically based leaf RT models have not allowed for proper spectral decomposition and estimation of leaf dry matter as nitrogen-based proteins and other carbon-based constituents (CBC) from optical properties of fresh and dry foliage. Such an achievement is the key for subsequent upscaling to canopy level and for development of new Earth observation applications. Therefore, we developed a new version of the PROSPECT model, named PROSPECT-PRO, which separates the nitrogen-based constituents (proteins) from CBC (including cellulose, lignin, hemicellulose, starch and sugars). PROSPECT-PRO was calibrated and validated on subsets of the LOPEX dataset, accounting for both fresh and dry broadleaf and grass samples. We applied an iterative model inversion optimization algorithm and identified the optimal spectral ranges for retrieval of proteins and CBC. When combining leaf reflectance and transmittance within the selected optimal spectral domains, PROSPECT-PRO inversions revealed similarly accurate CBC estimates of fresh and dry leaf samples (respective validation R2 = 0.96 and 0.95, NRMSE = 9.6% and 13.4%), whereas a better performance was obtained for fresh than for dry leaves when estimating proteins (respective validation R2 = 0.79 and 0.57, NRMSE = 15.1% and 26.1%). The accurate estimation of leaf constituents for fresh samples is attributed to the optimal spectral feature selection procedure. We further tested the ability of PROSPECT-PRO to estimate leaf mass per area (LMA) as the sum of proteins and CBC using independent datasets acquired for numerous plant species. Results showed that both PROSPECT-PRO and PROSPECT-D inversions were able to produce comparable LMA estimates across an independent dataset gathering 1685 leaf samples (validation R2 = 0.90 and NRMSE = 16.5% for PROSPECT-PRO, and R2 = 0.90 and NRMSE = 18.3% for PROSPECT-D). Findings also revealed that PROSPECT-PRO is capable of assessing the carbon-to‑nitrogen ratio based on the retrieved CBC-to-proteins ratio (R2 = 0.87 and NRMSE = 15.7% for fresh leaves, and R2 = 0.65 and NRMSE = 28.1% for dry leaves). The performance assessment of newly designed PROSPECT-PRO demonstrates a promising potential for its involvement in precision agriculture and ecological applications aiming at estimation of leaf carbon and nitrogen contents from observations of current and forthcoming airborne and satellite imaging spectroscopy sensors.