Protein Conformational Space at the Edge of Allostery: Turning a Non-allosteric Malate Dehydrogenase into an "Allosterized" Enzyme using Evolution Guided Punctual Mutations

Biochemical, structural and dynamical characterizations of the lactate dehydrogenase from Selenomonas ruminantium provide information about an intermediate evolutionary step prior to complete allosteric regulation acquisition in the super family of lactate and malate dehydrogenases

In this work, we investigated the lactate dehydrogenase (LDH) from Selenomonas ruminantium (S. rum), an enzyme that differs at key amino acid positions from canonical allosteric LDHs. The wild type (Wt) of this enzyme recognises pyuvate as all LDHs. However, introducing a single point mutation in the active site loop (I85R) allows S. Rum LDH to recognize the oxaloacetate substrate as a typical malate dehydrogenase (MalDH), whilst maintaining homotropic activation as an LDH. We report the tertiary structure of the Wt and I85RLDH mutant. The Wt S. rum enzyme structure binds NADH and malonate, whilst also resembling the typical compact R-active state of canonical LDHs. The structure of the mutant with I85R was solved in the Apo State (without ligand), and shows no large conformational reorganization such as that observed with canonical allosteric LDHs in Apo state. This is due to a local structural feature typical of S. rum LDH that prevents large-scale conformational reorganization. The S. rum LDH was also studied using Molecular Dynamics simulations, probing specific local deformations of the active site that allow the S. rum LDH to sample the T-inactive state. We propose that, with respect to the LDH/MalDH superfamily, the S. rum enzyme possesses a specificstructural and dynamical way to ensure homotropic activation.

Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches

Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.

Allostery Frustrates the Experimentalist

Proteins interact with other proteins, with nucleic acids, lipids, carbohydrates and various small molecules in the living cell. These interactions have been quantified and structurally characterized in numerous studies such that we today have a comprehensive picture of protein structure and function. However, proteins are dynamic and even folded proteins are likely more heterogeneous than they appear in most descriptions. One property of proteins that relies on dynamics and heterogeneity is allostery, the ability of a protein to change structure and function upon ligand binding to an allosteric site. Over the last decades the concept of allostery was broadened to embrace all types of long-range interactions across a protein including purely entropic changes without a conformational change in single protein domains. But with this re-definition came a problem: How do we measure allostery? In this opinion, we discuss some caveats arising from the quantitative description of single-domain allostery from an experimental perspective and how the limitations cannot be separated from the definition of allostery per se. Furthermore, we attempt to tie together allostery with the concept of frustration in an effort to investigate the links between these two complex, and yet general, properties of proteins. We arrive at the conclusion that the sensitivity to perturbation of allosteric networks in single protein domains is too large for the networks to be of significant biological relevance.