Illuminating the origins of spectral properties of green fluorescent proteins via proteochemometric and molecular modeling

FPredX: Interpretable models for the prediction of spectral maxima, brightness, and oligomeric states of fluorescent proteins

Fluorescent protein (FP) design is among the challenging protein design problems due to the tradeoffs among multiple properties to be optimized. Despite the accumulated efforts in design and characterization, progress has been slow in gaining a full understanding of sequence-property relationships to tackle the multiobjective design problem in FPs. In this study, we approach this problem by developing FPredX, a collection of gradient-boosted decision tree models, which mapped FP sequences to four major design targets of FPs, including excitation maximum, emission maximum, brightness, and oligomeric state. By training using one-hot encoded multiple aligned sequences with hyperparameters optimization in each model, FPredX models showed excellent prediction performance for all target properties compared with existing methods. We further interpreted the FPredX models by comparing the importance of positions along the aligned FP sequence to the predictive performance and suggested positions, which showed differential importance deemed by FPredX models to the prediction of each target property.

The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope

As an extension of the conventional quantitative structure activity relationship models, proteochemometric (PCM) modelling is a computational method that can predict the bioactivity relations between multiple ligands and multiple targets. Traditional PCM modelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross-term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCM modelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors and machine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross-term descriptors not only significantly increased the performance of PCM modelling but also expanded its application scope from traditional protein-ligand interaction to more abundant interactions, including protein-peptide, protein-DNA and even protein-protein interactions. In this review, target descriptors and cross-term descriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target-Catalyst-Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.