Agent-based models for detecting the driving forces of biomolecular interactions

Next-generation metabolic models informed by biomolecular simulations

Metabolic modeling is essential for understanding the mechanistic bases of cellular metabolism in various organisms, from microbes to humans, and the design of fitter microbial strains. Metabolic networks focus on the overall fluxes through biochemical reactions that implicitly rely on several biochemical processes, such as active or diffusive uptake (or export) of nutrients (or metabolites), enzymatic turnover of metabolites, and metal-cofactor enzyme interactions. Despite independent progress in biomolecular simulations, they have yet to be integrated to inform metabolic models. We explore the evolution of computational metabolic modeling approaches, starting with flux balance analysis, dynamic, kinetic delineations of metabolic shifts in single organisms within cells and across tissues, and mutually informing, community-level modeling frameworks and provide a narrative to tie in biomolecular simulations and machine learning predictions to usher the new phase of structure-guided synthetic biology applications. These additions and prospective novel ones are likely to open hitherto untapped paradigms for optimizing/understanding metabolic pathways toward improving bioproduction of protein and small molecule products with downstream applications in health, environment, energy, and sustainability.

Exploiting the Role of Features for Antigens-Antibodies Interaction Site Prediction

Antibodies are a class of proteins that recognize and neutralize pathogens by binding to their antigens. They are the most significant category of biopharmaceuticals for both diagnostic and therapeutic applications. Understanding how antibodies interact with their antigens plays a fundamental role in drug and vaccine design and helps to comprise the complex antigen binding mechanisms. Computational methods for predicting interaction sites of antibody-antigen are of great value due to the overall cost of experimental methods. Machine learning methods and deep learning techniques obtained promising results.In this work, we predict antibody interaction interface sites by applying HSS-PPI, a hybrid method defined to predict the interface sites of general proteins. The approach abstracts the proteins in terms of hierarchical representation and uses a graph convolutional network to classify the amino acids between interface and non-interface. Moreover, we also equipped the amino acids with different sets of physicochemical features together with structural ones to describe the residues. Analyzing the results, we observe that the structural features play a fundamental role in the amino acid descriptions. We compare the obtained performances, evaluated using standard metrics, with the ones obtained with SVM with 3D Zernike descriptors, Parapred, Paratome, and Antibody i-Patch.