Dynamic allosteric networks drive adenosine A1 receptor activation and G-protein coupling.

G-protein coupled receptors (GPCRs) present specific activation pathways and signaling among receptor subtypes. Hence, an extensive knowledge of the structural dynamics of the receptor is critical for the development of therapeutics. Here, we target the adenosine A1 receptor (A1R), for which a negligible number of drugs have been approved. We combine molecular dynamics simulations, enhanced sampling techniques, network theory and pocket detection to decipher the activation pathway of A1R, decode the allosteric networks and identify transient pockets. The A1R activation pathway reveal hidden intermediate and pre-active states together with the inactive and fully-active states observed experimentally. The protein energy networks computed throughout these conformational states successfully unravel the extra and intracellular allosteric centers and the communication pathways that couples them. We observe that the allosteric networks are dynamic, being increased along activation and fine-tuned in presence of the trimeric G-proteins. Overlap of transient pockets and energy networks uncover how the allosteric coupling between pockets and distinct functional regions of the receptor is altered along activation. By an in-depth analysis of the bridge between activation pathway, energy networks and transient pockets, we provide a further understanding of A1R. This information can be useful to ease the design of allosteric modulators for A1R.

Time Evolution of the Millisecond Allosteric Activation of Imidazole Glycerol Phosphate Synthase.

Deciphering the molecular mechanisms of enzymatic allosteric regulation requires the structural characterization of functional states and also their time evolution toward the formation of the allosterically activated ternary complex. The transient nature and usually slow millisecond time scale interconversion between these functional states hamper their experimental and computational characterization. Here, we combine extensive molecular dynamics simulations, enhanced sampling techniques, and dynamical networks to describe the allosteric activation of imidazole glycerol phosphate synthase (IGPS) from the substrate-free form to the active ternary complex. IGPS is a heterodimeric bienzyme complex whose HisH subunit is responsible for hydrolyzing glutamine and delivering ammonia for the cyclase activity in HisF. Despite significant advances in understanding the underlying allosteric mechanism, essential molecular details of the long-range millisecond allosteric activation of IGPS remain hidden. Without using a priori information of the active state, our simulations uncover how IGPS, with the allosteric effector bound in HisF, spontaneously captures glutamine in a catalytically inactive HisH conformation, subsequently attains a closed HisF:HisH interface, and finally forms the oxyanion hole in HisH for efficient glutamine hydrolysis. We show that the combined effector and substrate binding dramatically decreases the conformational barrier associated with oxyanion hole formation, in line with the experimentally observed 4500-fold activity increase in glutamine hydrolysis. The allosteric activation is controlled by correlated time-evolving dynamic networks connecting the effector and substrate binding sites. This computational strategy tailored to describe millisecond events can be used to rationalize the effect of mutations on the allosteric regulation and guide IGPS engineering efforts.

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD).

There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.

In Silico Identification and Experimental Validation of Distal Activity-Enhancing Mutations in Tryptophan Synthase.

Allostery is a central mechanism for the regulation of multi-enzyme complexes. The mechanistic basis that drives allosteric regulation is poorly understood but harbors key information for enzyme engineering. In the present study, we focus on the tryptophan synthase complex that is composed of TrpA and TrpB subunits, which allosterically activate each other. Specifically, we develop a rational approach for identifying key amino acid residues of TrpB distal from the active site. Those residues are predicted to be crucial for shifting the inefficient conformational ensemble of the isolated TrpB to a productive ensemble through intra-subunit allosteric effects. The experimental validation of the conformationally driven TrpB design demonstrates its superior stand-alone activity in the absence of TrpA, comparable to those enhancements obtained after multiple rounds of experimental laboratory evolution. Our work evidences that the current challenge of distal active site prediction for enhanced function in computational enzyme design has become within reach.

Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation.

Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena. The innov'SAR approach is interpolative, extrapolative and makes outside-the-box, predictions not found in other state-of-the-art ML or deep learning approaches. Equally significant is the finding that our approach to ML in the present context, flanked by advanced molecular dynamics simulations, uncovers the connection between epistatic mutational interactions and protein robustness.

Deciphering the Allosterically Driven Conformational Ensemble in Tryptophan Synthase Evolution.

Multimeric enzyme complexes are ubiquitous in nature and catalyze a broad range of useful biological transformations. They are often characterized by a tight allosteric coupling between subunits, making them highly inefficient when isolated. A good example is Tryptophan synthase (TrpS), an allosteric heterodimeric enzyme in the form of an αßßα complex that catalyzes the biosynthesis of L-tryptophan. In this study, we decipher the allosteric regulation existing in TrpS from Pyrococcus furiosus (PfTrpS), and how the allosteric conformational ensemble is recovered in laboratory-evolved stand-alone ß-subunit variants. We find that recovering the conformational ensemble of a subdomain of TrpS affecting the relative stabilities of open, partially closed, and closed conformations is a prerequisite for enhancing the catalytic efficiency of the ß-subunit in the absence of its binding partner. The distal mutations resuscitate the allosterically driven conformational regulation and alter the populations and rates of exchange between these multiple conformational states, which are essential for the multistep reaction pathway of the enzyme. Interestingly, these distal mutations can be a priori predicted by careful analysis of the conformational ensemble of the TrpS enzyme through computational methods. Our study provides the enzyme design field with a rational approach for evolving allosteric enzymes toward improved stand-alone function for biosynthetic applications.

Role of conformational dynamics in the evolution of novel enzyme function.

The free energy landscape concept that describes enzymes as an ensemble of differently populated conformational sub-states in dynamic equilibrium is key for evaluating enzyme activity, enantioselectivity, and specificity. Mutations introduced in the enzyme sequence can alter the populations of the pre-existing conformational states, thus strongly modifying the enzyme ability to accommodate alternative substrates, revert its enantiopreferences, and even increase the activity for some residual promiscuous reactions. In this feature article, we present an overview of the current experimental and computational strategies to explore the conformational free energy landscape of enzymes. We provide a series of recent publications that highlight the key role of conformational dynamics for the enzyme evolution towards new functions and substrates, and provide some perspectives on how conformational dynamism should be considered in future computational enzyme design protocols.

Inducing high activity of a thermophilic enzyme at ambient temperatures by directed evolution.

The long-standing problem of achieving high activity of a thermophilic enzyme at low temperatures and short reaction times with little tradeoff in thermostability has been solved by directed evolution, an alcohol dehydrogenase found in hot springs serving as the catalyst in enantioselective ketone reductions.

Exploring the reversal of enantioselectivity on a zinc-dependent alcohol dehydrogenase.

Alcohol Dehydrogenase (ADH) enzymes catalyse the reversible reduction of prochiral ketones to the corresponding alcohols. These enzymes present two differently shaped active site pockets, which dictate their substrate scope and selectivity. In this study, we computationally evaluate the effect of two commonly reported active site mutations (I86A, and W110T) on a secondary alcohol dehydrogenase from Thermoanaerobacter brockii (TbSADH) through Molecular Dynamics simulations. Our results indicate that the introduced mutations induce dramatic changes in the shape of the active site, but most importantly they impact the substrate-enzyme interactions. We demonstrate that the combination of Molecular Dynamics simulations with the tools POVME and NCIplot corresponds to a powerful strategy for rationalising and engineering the stereoselectivity of ADH variants.