Crystal structures of native cytochrome c6 from Thermosynechococcus elongatus in two different space groups and implications for its oligomerization

Current Metabolic Engineering Strategies for Photosynthetic Bioproduction in Cyanobacteria

Cyanobacteria are photosynthetic microorganisms capable of using solar energy to convert CO₂ and H₂O into O₂ and energy-rich organic compounds, thus enabling sustainable production of a wide range of bio-products. More and more strains of cyanobacteria are identified that show great promise as cell platforms for the generation of bioproducts. However, strain development is still required to optimize their biosynthesis and increase titers for industrial applications. This review describes the most well-known, newest and most promising strains available to the community and gives an overview of current cyanobacterial biotechnology and the latest innovative strategies used for engineering cyanobacteria. We summarize advanced synthetic biology tools for modulating gene expression and their use in metabolic pathway engineering to increase the production of value-added compounds, such as terpenoids, fatty acids and sugars, to provide a go-to source for scientists starting research in cyanobacterial metabolic engineering.

Structure of cyanobacterial photosystem I complexed with ferredoxin at 1.97 Å resolution

Photosystem I (PSI) is a light driven electron pump transferring electrons from Cytochrome c₆ (Cyt c₆) to Ferredoxin (Fd). An understanding of this electron transfer process is hampered by a paucity of structural detail concerning PSI:Fd interface and the possible binding sites of Cyt c₆. Here we describe the high resolution cryo-EM structure of Thermosynechococcus elongatus BP-1 PSI in complex with Fd and a loosely bound Cyt c₆. Side chain interactions at the PSI:Fd interface including bridging water molecules are visualized in detail. The structure explains the properties of mutants of PsaE and PsaC that affect kinetics of Fd binding and suggests a molecular switch for the dissociation of Fd upon reduction. Calorimetry-based thermodynamic analyses confirms a single binding site for Fd and demonstrates that PSI:Fd complexation is purely driven by entropy. A possible reaction cycle for the efficient transfer of electrons from Cyt c₆ to Fd via PSI is proposed.

In order to aid the understanding of the electron transfer process within the cyanobacterial photosystem I, its structure - when complexed with Ferredoxin - is determined at 1.97 Å resolution.

Current directions in combining simulation-based macromolecular modeling approaches with deep learning

Introduction: Structure-guided drug discovery relies on accurate computational methods for modeling macromolecules. Simulations provide means of predicting macromolecular folds, of discovering function from structure, and of designing macromolecules to serve as drugs. Success rates are limited for any of these tasks, however. Recently, deep neural network-based methods have greatly enhanced the accuracy of predictions of protein structure from sequence, generating excitement about the potential impact of deep learning.Areas covered: This review introduces biologists to deep neural network architecture, surveys recent successes of deep learning in structure prediction, and discusses emerging deep learning-based approaches for structure-function analysis and design. Particular focus is given to the interplay between simulation-based and neural network-based approaches.Expert opinion: As deep learning grows integral to macromolecular modeling, simulation- and neural network-based approaches must grow more tightly interconnected. Modular software architecture must emerge allowing both types of tools to be combined with maximal versatility. Open sharing of code under permissive licenses will be essential. Although experiments will remain the gold standard for reliable information to guide drug discovery, we may soon see successful drug development projects based on high-accuracy predictions from algorithms that combine simulation with deep learning - the ultimate validation of this combination's power.