Long-Range Changes in Neurolysin Dynamics Upon Inhibitor Binding

One Descriptor to Fold Them All: Harnessing Intuition and Machine Learning to Identify Transferable Lasso Peptide Reaction Coordinates

Identifying optimal reaction coordinates for complex conformational changes and protein folding remains an outstanding challenge. This study combines collective variable (CV) discovery based on chemical intuition and machine learning with enhanced sampling to converge the folding free energy landscape of lasso peptides, a unique class of natural products with knot-like tertiary structures. This knotted scaffold imparts remarkable stability, making lasso peptides resistant to proteolytic degradation, thermal denaturation, and extreme pH conditions. Although their direct synthesis would enable therapeutic design, it has not yet been possible due to the improbable occurrence of spontaneous lasso folding. Thus, simulations characterizing the folding propensity are needed to identify strategies for increasing access to the lasso architecture by stabilizing the pre-lasso ensemble before isopeptide bond formation. Herein, harmonic linear discriminant analysis (HLDA) is combined with metadynamics-enhanced sampling to discover CVs capable of distinguishing the pre-lasso fold and converging the folding propensity. Intuitive CVs are compared to iterative rounds of HLDA to identify CVs that not only accomplish these goals for one lasso peptide but also seem to be transferable to others, establishing a protocol for the identification of folding reaction coordinates for lasso peptides.

From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field.

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.

Quantitative Analysis of Dynamic Allostery

Dynamic allostery refers to one important class of allosteric regulation that does not involve noticeable conformational changes upon effector binding. In recent years, many "quasi"-dynamic allosteric proteins have been found to only experience subtle conformational changes during allosteric regulation. However, as enthalpic and entropic contributions are coupled to each other and even tiny conformational changes could bring in noticeable free energy changes, a quantitative description is essential to understand the contribution of pure dynamic allostery. Here, by developing a unified anisotropic elastic network model (uANM) considering both side-chain information and ligand heavy atoms, we quantitatively estimated the contribution of pure dynamic allostery in a dataset of known allosteric proteins by excluding the conformational changes upon ligand binding. We found that the contribution of pure dynamic allostery is generally small (much weaker than previously expected) and robustly exhibits an allosteric activation effect, which exponentially decays with the distance between the substrate and the allosteric ligand. We further constructed toy models to study the determinant factors of dynamic allostery in monomeric and oligomeric proteins using the uANM. Analysis of the toy models revealed that a short distance, a small angle between the two ligands, strong protein-ligand interactions, and weak protein internal interactions lead to strong dynamic allostery. Our study provides a quantitative estimation of pure dynamic allostery and facilitates the understanding of dynamic-allostery-controlled biological processes and the design of allosteric drugs and proteins.

Oncological drug discovery: AI meets structure-based computational research

The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.

Creating Maps of the Ligand Binding Landscape for Kinetics-Based Drug Discovery

Simulations of ligand-protein interactions can be very useful for drug design and to gain biological insight. Full pathways of ligand-protein binding can be used to get information about ligand binding transition states, which form the rate-limiting step of the binding and release processes. However, these simulations are typically limited by the presence of large energy barriers that separate stable poses of interest. Here we describe a simulation protocol for exploring and analyzing landscapes of ligand-protein interactions that makes use of molecular docking, enhanced molecular simulation with the weighted ensemble algorithm, and network analysis. It can be accomplished using a modest cluster of graphics processing units and freely accessible software. This protocol focuses on the construction and analysis of a network model of ligand binding poses and provides links to resources that describe the other steps in more detail. The end result of this protocol is a map of the ligand-protein binding landscape that identifies transition states of the ligand binding pathway, as well as alternative bound poses that could be stabilized with modifications to the ligand.

Identification and Characterization of Two Structurally Related Dipeptides that Enhance Catalytic Efficiency of Neurolysin

Neurolysin (Nln) is a recently recognized endogenous mechanism functioning to preserve the brain from ischemic injury. To further understand the pathophysiological function of this peptidase in stroke and other neurologic disorders, the present study was designed to identify small molecule activators of Nln. Using a computational approach, the structure of Nln was explored, which was followed by docking and in silico screening of ∼140,000 molecules from the National Cancer Institute Developmental Therapeutics Program database. Top ranking compounds were evaluated in an Nln enzymatic assay, and two hit histidine-dipeptides were further studied in detail. The identified dipeptides enhanced the rate of synthetic substrate hydrolysis by recombinant (human and rat) and mouse brain-purified Nln in a concentration-dependent manner (micromolar A50 and Amax ≥ 300%) but had negligible effect on activity of closely related peptidases. Both dipeptides also enhanced hydrolysis of Nln endogenous substrates neurotensin, angiotensin I, and bradykinin and increased efficiency of the synthetic substrate hydrolysis (Vmax/Km ratio) in a concentration-dependent manner. The dipeptides and competitive inhibitor dynorphin A (1-13) did not affect each other's affinity for Nln, suggesting differing nature of their respective binding sites. Lastly, drug affinity responsive target stability (DARTS) and differential scanning fluorimetry (DSF) assays confirmed concentration-dependent interaction of Nln with the activator molecule. This is the first study demonstrating that Nln activity can be enhanced by small molecules, although the peptidic nature and low potency of the activators limit their application. The identified dipeptides provide a chemical scaffold to develop high-potency, drug-like molecules as research tools and potential drug leads. SIGNIFICANCE STATEMENT: This study describes discovery of two molecules that selectively enhance activity of peptidase Nln-a newly recognized cerebroprotective mechanism in the poststroke brain. The identified molecules will serve as a chemical scaffold for development of drug-like molecules to further study Nln and may become lead structures for a new class of drugs. In addition, our conceptual and methodological framework and research findings might be used for other peptidases and enzymes, the activation of which bears therapeutic potential.

Perturbation of ACE2 Structural Ensembles by SARS-CoV-2 Spike Protein Binding

The human ACE2 enzyme serves as a critical first recognition point of coronaviruses, including SARS-CoV-2. In particular, the extracellular domain of ACE2 interacts directly with the S1 tailspike protein of the SARS-CoV-2 virion through a broad protein-protein interface. Although this interaction has been characterized by X-ray crystallography, these structures do not reveal significant differences in the ACE2 structure upon S1 protein binding. In this work, using several all-atom molecular dynamics simulations, we show persistent differences in the ACE2 structure upon binding. These differences are determined with the linear discriminant analysis (LDA) machine learning method and validated using independent training and testing datasets, including long trajectories generated by D. E. Shaw Research on the Anton 2 supercomputer. In addition, long trajectories for 78 potent ACE2-binding compounds, also generated by D. E. Shaw Research, were projected onto the LDA classification vector in order to determine whether the ligand-bound ACE2 structures were compatible with S1 protein binding. This allows us to predict which compounds are "apo-like" versus "complex-like" and to pinpoint long-range ligand-induced allosteric changes in the ACE2 structure.

Comparative Dynamics and Functional Mechanisms of the CYP17A1 Tunnels Regulated by Ligand Binding

As an important member of cytochrome P450 (CYP) enzymes, CYP17A1 is a dual-function monooxygenase with a critical role in the synthesis of many human steroid hormones, making it an attractive therapeutic target. The emerging structural information about CYP17A1 and the growing number of inhibitors for these enzymes call for a systematic strategy to delineate and classify mechanisms of ligand transport through tunnels that control catalytic activity. In this work, we applied an integrated computational strategy to different CYP17A1 systems with a panel of ligands to systematically study at the atomic level the mechanism of ligand-binding and tunneling dynamics. Atomistic simulations and binding free energy computations identify the dynamics of dominant tunnels and characterize energetic properties of critical residues responsible for ligand binding. The common transporting pathways including S, 3, and 2c tunnels were identified in CYP17A1 binding systems, while the 2c tunnel is a newly formed pathway upon ligand binding. We employed and integrated several computational approaches including the analysis of functional motions and sequence conservation, atomistic modeling of dynamic residue interaction networks, and perturbation response scanning analysis to dissect ligand tunneling mechanisms. The results revealed the hinge-binding and sliding motions as main functional modes of the tunnel dynamic, and a group of mediating residues as key regulators of tunnel conformational dynamics and allosteric communications. We have also examined and quantified the mutational effects on the tunnel composition, conformational dynamics, and long-range allosteric behavior. The results of this investigation are fully consistent with the experimental data, providing novel rationale to the experiments and offering valuable insights into the relationships between the structure and function of the channel networks and a robust atomistic model of activation mechanisms and allosteric interactions in CYP enzymes.

Tuning the "violin" of protein kinases: The role of dynamics-based allostery

The intricacies of allosteric regulation of protein kinases continue to engage the research community. Allostery, or control from a distance, is seen as a fundamental biomolecular mechanism for proteins. From the traditional methods of conformational selection and induced fit, the field has grown to include the role of protein motions in defining a dynamics-based allosteric approach. Harnessing of these continuous motions in the protein to exert allosteric effects can be defined by a "violin" model that focuses on distributions of protein vibrations as opposed to concerted pathways. According to this model, binding of an allosteric modifier causes global redistribution of dynamics in the protein kinase domain that leads to changes in its catalytic properties. This model is consistent with the "entropy-driven allostery" mechanism proposed by Cooper and Dryden in 1984 and does not require, but does not exclude, any major structural changes. We provide an overview of practical implementation of the violin model and how it stands amidst the other known models of protein allostery. Protein kinases have been described as the biomolecules of interest. © 2019 IUBMB Life, 71(6):685-696, 2019.

Collective Variables from Local Fluctuations

We introduce a method to obtain one-dimensional collective variables for studying rarely occurring transitions between two metastable states separated by a high free energy barrier. No previous information, not even approximated, on the path followed during the transition is needed. The only requirement is to know the fluctuations of the system while in the two metastable states. With this information in hand, we build the collective variable using a modified version of Fisher's linear discriminant analysis. The usefulness of this approach is tested on the metadynamics simulation of two representative systems. The first is the freezing of silver iodide into the superionic α-phase, and the second is the study of a classical Diels-Alder reaction. The collective variable works very well in these two diverse cases.