Predicting Mutation-Induced Allosteric Changes in Structures and Conformational Ensembles of the ABL Kinase Using AlphaFold2 Adaptations with Alanine Sequence Scanning.

Despite the success of AlphaFold2 approaches in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and have been challenged to accurately capture the effects of single point mutations that induced significant structural changes. We examined several implementations of AlphaFold2 methods to predict conformational ensembles for state-switching mutants of the ABL kinase. The results revealed that a combination of randomized alanine sequence masking with shallow multiple sequence alignment subsampling can significantly expand the conformational diversity of the predicted structural ensembles and capture shifts in populations of the active and inactive ABL states. Consistent with the NMR experiments, the predicted conformational ensembles for M309L/L320I and M309L/H415P ABL mutants that perturb the regulatory spine networks featured the increased population of the fully closed inactive state. The proposed adaptation of AlphaFold can reproduce the experimentally observed mutation-induced redistributions in the relative populations of the active and inactive ABL states and capture the effects of regulatory mutations on allosteric structural rearrangements of the kinase domain. The ensemble-based network analysis complemented AlphaFold predictions by revealing allosteric hotspots that correspond to state-switching mutational sites which may explain the global effect of regulatory mutations on structural changes between the ABL states. This study suggested that attention-based learning of long-range dependencies between sequence positions in homologous folds and deciphering patterns of allosteric interactions may further augment the predictive abilities of AlphaFold methods for modeling of alternative protein sates, conformational ensembles and mutation-induced structural transformations.

Exploring Binding Pockets in the Conformational States of the SARS-CoV-2 Spike Trimers for the Screening of Allosteric Inhibitors Using Molecular Simulations and Ensemble-Based Ligand Docking.

Understanding mechanisms of allosteric regulation remains elusive for the SARS-CoV-2 spike protein, despite the increasing interest and effort in discovering allosteric inhibitors of the viral activity and interactions with the host receptor ACE2. The challenges of discovering allosteric modulators of the SARS-CoV-2 spike proteins are associated with the diversity of cryptic allosteric sites and complex molecular mechanisms that can be employed by allosteric ligands, including the alteration of the conformational equilibrium of spike protein and preferential stabilization of specific functional states. In the current study, we combine conformational dynamics analysis of distinct forms of the full-length spike protein trimers and machine-learning-based binding pocket detection with the ensemble-based ligand docking and binding free energy analysis to characterize the potential allosteric binding sites and determine structural and energetic determinants of allosteric inhibition for a series of experimentally validated allosteric molecules. The results demonstrate a good agreement between computational and experimental binding affinities, providing support to the predicted binding modes and suggesting key interactions formed by the allosteric ligands to elicit the experimentally observed inhibition. We establish structural and energetic determinants of allosteric binding for the experimentally known allosteric molecules, indicating a potential mechanism of allosteric modulation by targeting the hinges of the inter-protomer movements and blocking conformational changes between the closed and open spike trimer forms. The results of this study demonstrate that combining ensemble-based ligand docking with conformational states of spike protein and rigorous binding energy analysis enables robust characterization of the ligand binding modes, the identification of allosteric binding hotspots, and the prediction of binding affinities for validated allosteric modulators, which is consistent with the experimental data. This study suggested that the conformational adaptability of the protein allosteric sites and the diversity of ligand bound conformations are both in play to enable efficient targeting of allosteric binding sites and interfere with the conformational changes.

DFT-Aided Vibrational Circular Dichroism Spectroscopy Study of (-)-S-cotinine.

The implementation of a strategy comprising the use of vibrational circular dichroism spectroscopy and DFT calculations allows determination of the solution-state conformational distribution in (-)-S-cotinine, giving further proof of the extra conformer-discriminating potential of this experimental technique, which may offer unique molecular fingerprints of subtly dissimilar molecular conformers of chiral samples. Natural bond orbital electronic structure calculations of the rotational barrier height between the two main conformers of the species indicate that hyperconjugative effects are the key force governing the conformational equilibrium. The negligible effect of the solvent's polarity over both structure and conformational energy profile supports this result.

MDSPACE: Extracting Continuous Conformational Landscapes from Cryo-EM Single Particle Datasets Using 3D-to-2D Flexible Fitting based on Molecular Dynamics Simulation.

This article presents an original approach for extracting atomic-resolution landscapes of continuous conformational variability of biomolecular complexes from cryo electron microscopy (cryo-EM) single particle images. This approach is based on a new 3D-to-2D flexible fitting method, which uses molecular dynamics (MD) simulation and is embedded in an iterative conformational-landscape refinement scheme. This new approach is referred to as MDSPACE, which stands for Molecular Dynamics simulation for Single Particle Analysis of Continuous Conformational hEterogeneity. The article describes the MDSPACE approach and shows its performance using synthetic and experimental datasets.

Lessons from a quarter century of being human in protein science.

Over the past quarter century, my engagement with the protein society has allowed me to witness first-hand the evolution of our deepening understanding of the complexity of protein folding landscapes. During my own evolution as a protein scientist, my passion for protein folding has deepened into an obsession with mapping and decoding the thermodynamic and kinetic secrets of protein landscapes-especially those of rebel proteins, whose "nontraditional" behavior has challenged our paradigms and inspired the expansion of our models and methods. It is perhaps not surprising that I see parallels in the evolution of the landscape framework and in the development of our own trajectories as humans in Science, Technology, Engineering and Mathematics (STEM). Just as with proteins, however, we need to recognize that our individual human landscapes are not isolated from our local departmental and institutional communities, and are integrated into the larger networks of our STEM disciplines, academia, industry, and/or government, not to mention society. My experience with hundreds of participants in the Being Human in STEM (HSTEM) initiative that Amherst College undergraduates and I co-founded in 2016 has helped me find hope for STEM and humanity. If we commit to reconciling our identities as scientists with our responsibilities as human beings, together we can accelerate the evolution of individual, community, and societal landscapes to contribute to addressing the dire challenges facing our planet.

The evolution and engineering of enzyme activity through tuning conformational landscapes.

Proteins are dynamic molecules whose structures consist of an ensemble of conformational states. Dynamics contribute to protein function and a link to protein evolution has begun to emerge. This increased appreciation for the evolutionary impact of conformational sampling has grown from our developing structural biology capabilities and the exploration of directed evolution approaches, which have allowed evolutionary trajectories to be mapped. Recent studies have provided empirical examples of how proteins can evolve via conformational landscape alterations. Moreover, minor conformational substates have been shown to be involved in the emergence of new enzyme functions as they can become enriched through evolution. The role of remote mutations in stabilizing new active site geometries has also granted insight into the molecular basis underpinning poorly understood epistatic effects that guide protein evolution. Finally, we discuss how the growth of our understanding of remote mutations is beginning to refine our approach to engineering enzymes.

Ratiometric Single-Molecule FRET Measurements to Probe Conformational Subpopulations of Intrinsically Disordered Proteins.

Over the past few decades, numerous examples have demonstrated that intrinsic disorder in proteins lies at the heart of many vital processes, including transcriptional regulation, stress response, cellular signaling, and most recently protein liquid-liquid phase separation. The so-called intrinsically disordered proteins (IDPs) involved in these processes have presented a challenge to the classic protein "structure-function paradigm," as their functions do not necessarily involve well-defined structures. Understanding the mechanisms of IDP function is likewise challenging because traditional structure determination methods often fail with such proteins or provide little information about the diverse array of structures that can be related to different functions of a single IDP. Single-molecule fluorescence methods can overcome this ensemble-average masking, allowing the resolution of subpopulations and dynamics and thus providing invaluable insights into IDPs and their function. In this protocol, we describe a ratiometric single-molecule Förster resonance energy transfer (smFRET) routine that permits the investigation of IDP conformational subpopulations and dynamics. We note that this is a basic protocol, and we provide brief information and references for more complex analysis schemes available for in-depth characterization. This protocol covers optical setup preparation and protein handling and provides insights into experimental design and outcomes, together with background information about theory and a brief discussion of troubleshooting. © 2020 by John Wiley & Sons, Inc. Basic Protocol: Ratiometric smFRET detection and analysis of IDPs Support Protocol 1: Fluorophore labeling of a protein through maleimide chemistry Support Protocol 2: Sample chamber preparation Support Protocol 3: Determination of direct excitation of acceptor by donor excitation and leakage of donor emission to acceptor emission channel.