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Biological homeostasis is a dynamic and elastic equilibrium of countless interlinked biochemical reactions. A key goal of life sciences is to understand these dynamics; bioengineers seek to reconfigure such networks. Both goals require the ability to monitor the concentration of individual intracellular metabolites with sufficient spatiotemporal resolution. To achieve this, a range of protein or protein/DNA signalling circuits with optical readouts have been constructed. Protein biosensors can provide quantitative information at subsecond temporal and suborganelle spatial resolution. However, their construction is fraught with difficulties related to integrating the affinity- and selectivity-endowing components with the signal reporters. We argue that development of efficient approaches for construction of chemically induced dimerisation systems and reporter domains with large dynamic ranges will solve these problems. Biosensors enable quantification of analytes of choice inside living cells.Protein biosensors can transform the way we monitor and engineer metabolism.The key challenges are to develop biosensors with large dynamic range and tuneable specificity.Ultimately, we seek to democratise this technology so that it is readily available to the scientific community.

Thrombin is the key enzyme of the entire hemostatic process since it is able to exert both procoagulant and anticoagulant functions; therefore, it represents an attractive target for the developments of biomolecules with therapeutic potential. Thrombin can perform its many functional activities because of its ability to recognize a wide variety of substrates, inhibitors, and cofactors. These molecules frequently are bound to positively charged regions on the surface of protein called exosites. In this review, we carried out extensive analyses of the structural determinants of thrombin partnerships by surveying literature data as well as the structural content of the Protein Data Bank (PDB). In particular, we used the information collected on functional, natural, and synthetic molecular ligands to define the anatomy of the exosites and to quantify the interface area between thrombin and exosite ligands. In this framework, we reviewed in detail the specificity of thrombin binding to aptamers, a class of compounds with intriguing pharmaceutical properties. Although these compounds anchor to protein using conservative patterns on its surface, the present analysis highlights some interesting peculiarities. Moreover, the impact of thrombin binding aptamers in the elucidation of the cross-talk between the two distant exosites is illustrated. Collectively, the data and the work here reviewed may provide insights into the design of novel thrombin inhibitors.

We present an idea of protein molecules that challenges the traditional view of proteins as simple molecular machines and suggests instead that they exhibit a basic form of “intelligence”. The idea stems from suggestions coming from Integrated Information Theory (IIT), network theory, and allostery to explore how proteins process information, adapt to their environment, and even show memory-like behaviors. We define protein intelligence using IIT and focus on how proteins integrate information (in terms of the parameter Φ coming from IIT) and balance their core (stable, ordered regions) and periphery (flexible, disordered regions). This balance allows proteins to remain stable while adapting to changes and operating in a critical state where order and disorder coexist. We summarize recent findings on conformational memory, allosteric regulation, protein intrinsic disorder, liquid-liquid phase separation, and critical transitions, and compare protein behavior to other complex systems like ecosystems and neural networks. While our perspective offers a unified framework to understand proteins, it also raises questions about applying intelligence concepts to molecular systems. We discuss how this understanding could advance protein engineering, drug design, and synthetic biology, while at the same time acknowledging the challenges of creating adaptive, “intelligent” proteins. This concept bridges the gap between mechanistic and systems-level views of proteins and offers a comprehensive understanding of their dynamic and adaptive nature. We have tried to redefine the traditionally metaphorical concept of “intelligence” in biochemistry as a measurable property while simultaneously establishing the material foundation of protein intelligence through the identification of fundamental elements such as memory and learning in molecular systems.

Most proteins comprise two or more domains from a limited suite of protein families. These domains are often rearranged in various combinations through gene fusion events to evolve new protein functions, including the acquisition of protein allostery through the incorporation of regulatory domains. The enzyme 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase (DAH7PS) is the first enzyme of aromatic amino acid biosynthesis and displays a diverse range of allosteric mechanisms. DAH7PSs adopt a common architecture with a shared (β/α)₈ catalytic domain which can be attached to an ACT-like or a chorismate mutase regulatory domain that operates via distinct mechanisms. These respective domains confer allosteric regulation by controlling DAH7PS function in response to ligand Tyr or prephenate. Starting with contemporary DAH7PS proteins, two protein chimeras were created, with interchanged regulatory domains. Both engineered proteins were catalytically active and delivered new functional allostery with switched ligand specificity and allosteric mechanisms delivered by their nonhomologous regulatory domains. This interchangeability of protein domains represents an efficient method not only to engineer allostery in multidomain proteins but to create a new bifunctional enzyme.

Recently, the design of mechanical networks with protein-inspired responses has become increasingly popular. Here, we review contributions which were motivated by studies of protein dynamics employing coarse-grained elastic network models. First, the concept of evolutionary optimization that we developed to design network structures which execute prescribed tasks is explained. We then review what presumably marks the origin of the idea to design complex functional networks which encode protein-inspired behavior, namely the design of an elastic network structure which emulates the cycles of ATP-powered conformational motion in protein machines. Two recent applications are reviewed. First, the construction of a model molecular motor, whose operation incorporates both the tight coupling power stroke as well as the loose coupling Brownian ratchet mechanism, is discussed. Second, the evolutionary design of network structures which encode optimal long-range communication between remote sites and represent mechanical models of allosteric proteins is presented. We discuss the prospects of designed protein-mimicking elastic networks as model systems to elucidate the design principles and functional signatures underlying the operation of complex protein machinery.

Regulatory processes in biology can be re-conceptualized in terms of logic gates, analogous to those in computer science. Frequently, biological systems need to respond to multiple, sometimes conflicting, inputs to provide the correct output. The language of logic gates can then be used to model complex signal transduction and metabolic processes. Advances in synthetic biology in turn can be used to construct new logic gates, which find a variety of biotechnology applications including in the production of high value chemicals, biosensing, and drug delivery. In this review, we focus on advances in the construction of logic gates that take advantage of biological catalysts, including both protein-based and nucleic acid-based enzymes. These catalyst-based biomolecular logic gates can read a variety of molecular inputs and provide chemical, optical, and electrical outputs, allowing them to interface with other types of biomolecular logic gates or even extend to inorganic systems. Continued advances in molecular modeling and engineering will facilitate the construction of new logic gates, further expanding the utility of biomolecular computing.

Background Many receptors function by binding to multiple ligands, each eliciting a distinct biological output. The extracellular domain of the human prolactin receptor (hPRL-R) uses an identical epitope to bind to both prolactin (hPRL) and growth hormone (hGH), yet little is known about how each hormone binding event triggers the appropriate response. Findings Here, we utilized a phage display library to generate synthetic antibodies (sABs) that preferentially modulate hPRL-R function in a hormone-dependent fashion. We determined the crystal structure of a sAB-hPRL-R complex, which revealed a novel allosteric mechanism of antagonism, whereby the sAB traps the receptor in a conformation more suitable for hGH binding than hPRL. This was validated by examining the effect of the sABs on hormone internalization via the hPRL-R and its downstream signaling pathway. Conclusions The findings suggest that subtle structural changes in the extracellular domain of hPRL-R induced by each hormone determine the biological output triggered by hormone binding. We conclude that sABs generated by phage display selection can detect these subtle structural differences, and therefore can be used to dissect the structural basis of receptor-ligand specificity.

Allostery is the phenomenon of changes in the structure and activity of proteins that appear as a consequence of ligand binding at sites other than the active site. Studying mechanistic basis of allostery leading to protein design with predetermined functional endpoints is an important unmet need of synthetic biology. Here, we screened the amino acid sequence landscape in search of sequence-signatures of allostery using Recurrence Quantitative Analysis (RQA) method. A characteristic vector, comprised of 10 features extracted from RQA was defined for amino acid sequences. Using Principal Component Analysis, four factors were found to be important determinants of allosteric behavior. Our sequence–based predictor method shows 82.6% accuracy, 85.7% sensitivity and 77.9% specificity with the current dataset. Further, we show that Laminarity - Mean - hydrophobicity representing repeated hydrophobic patches is the most crucial indicator of allostery. To our best knowledge this is the first report that describes sequence determinants of allostery based on hydrophobicity. As an outcome of these findings, we plan to explore possibility of inducing allostery in proteins.