ClustENMD: Efficient sampling of biomolecular conformational space at atomic resolution

Identifying a non-conserved site for achieving allosteric covalent inhibition of CECR2

The bromodomain (BRD) represents a highly conserved structural module that provides BRD proteins with fundamental functionality in modulating protein-protein interactions involved in diverse biological processes such as chromatin-mediated gene transcription, DNA recombination, replication and repair. Consequently, dysregulation of BRD proteins has been implicated in the pathogenesis of numerous human diseases. In recent years, considerable scientific endeavors have focused on unraveling the molecular mechanisms underlying BRDs and developing inhibitors that target these domains. While these inhibitors compete for binding with the acetylated lysine binding site of BRDs, achieving inhibition of BRD proteins via competitive pocket binding has proven challenging due to the conserved nature of these pockets. To address this limitation, the present study employed dynamic simulations for a comprehensive analysis, leading to the identification of a non-conserved pocket in CECR2 for achieving BRD family inhibition through allosteric modulation. Subsequently, the compound BAY 11-7085 was proven capable of covalently binding to C494 of this pocket after covalent docking and biological verification in vitro. The allosteric inhibition strategy of CECR2 was further verified by the structurally optimized compound LC-CE-7, which is an allosteric covalent CECR2 inhibitor with anti-cancer effects in MDA-MB-231 cells.

InSty: A ProDy Module for Evaluating Protein Interactions and Stability

ProDy is a widely used application programming interface for analyzing the collective dynamics of proteins and their complexes, offering enhanced capabilities to address the growing needs of the computational biology community to bridge structure and function. Here, we introduce InSty, a new module integrated into ProDy to identify and quantify intra- and intermolecular interactions critical to protein stability and structural dynamics. InSty analyzes the non-covalent interactions using conformational ensemble data from both experiments and computational predictions, assesses their time evolution and persistence during molecular dynamics simulations as well as their conservation across homologs. It provides insights into the significance of these interactions in achieving function and/or supporting stability. InSty outputs lend themselves to statistical evaluation, visualization, and automated ensemble analysis for interpreting the significance of the interactions in the context of protein dynamics, sequence evolution, and allostery. Consolidation of InSty with various ProDy modules enables its efficient usage as a versatile tool that supports mutagenesis studies and identifies critical spots for functional interactions. The InSty module is available as part of the ProDy package at https://github.com/prody/ProDy.

Extended Sampling of Macromolecular Conformations from Uniformly Distributed Points on Multidimensional Normal Mode Hyperspheres

Proteins are dynamic entities that adopt diverse conformations, which play a pivotal role in their function. Understanding these conformations is essential, and protein collective motions, particularly those captured by normal mode (NM) and their linear combinations, provide a robust means for conformational sampling. This work introduces a novel approach to obtaining a uniformly oriented set of a given number of lowest frequency NM combined vectors and generating harmonically equidistant restrained structures along them. They are all thus uniformly located on concentric hyperspheres, systematically covering the defined NM space fully. The generated structures are further relaxed with standard molecular dynamics (MD) simulations to explore the conformational space. The efficiency of the approach we termed "distributed points Molecular Dynamics using Normal Modes" (dpMDNM) was assessed by applying it to hen egg-white lysozyme and human cytochrome P450 3A4 (CYP3A4). To this purpose, we compared our new approach with other methods and analyzed the sampling of existing experimental structures. Our results demonstrate the efficacy of dpMDNM in extensive conformational sampling, particularly as more NMs are considered. Ensembles generated by dpMDNM exhibited a broad coverage of the experimental structures, providing valuable insights into the functional aspects of lysozyme and CYP3A4. Furthermore, dpMDNM also covered lysozyme structures with relatively elevated energies corresponding to transient states not easily obtained by standard MD simulations, in conformity with nuclear magnetic resonance structural indications. This method offers an efficient and rational framework for comprehensive protein conformational sampling, contributing significantly to our understanding of protein dynamics and function.

Exploring Protein Conformational Changes Using a Large-Scale Biophysical Sampling Augmented Deep Learning Strategy

Inspired by the success of deep learning in predicting static protein structures, researchers are now actively exploring other deep learning algorithms aimed at predicting the conformational changes of proteins. Currently, a major challenge in the development of such models lies in the limited training data characterizing different conformational transitions. To address this issue, molecular dynamics simulations is combined with enhanced sampling methods to create a large-scale database. To this end, the study simulates the conformational changes of 2635 proteins featuring two known stable states, and collects the structural information along each transition pathway. Utilizing this database, a general deep learning model capable of predicting the transition pathway for a given protein is developed. The model exhibits general robustness across proteins with varying sequence lengths (ranging from 44 to 704 amino acids) and accommodates different types of conformational changes. Great agreement is shown between predictions and experimental data in several systems and successfully apply this model to identify a novel allosteric regulation in an important biological system, the human β-cardiac myosin. These results demonstrate the effectiveness of the model in revealing the nature of protein conformational changes.

On the quaternary structure of human D-3-phosphoglycerate dehydrogenase

D-3-phosphoglycerate dehydrogenase (PHGDH) catalyzes the NAD+-dependent conversion of D-3-phospho-glycerate to 3-phosphohydroxypyruvate, the first step in the phosphorylated pathway for L-serine (L-Ser) biosynthesis. L-Ser plays different relevant metabolic roles in eukaryotic cells: alterations in L-Ser metabolism have been linked to serious neurological disorders. The human PHGDH (hPHGDH), showing a homotetrameric state in solution, is made of four domains, among which there are two regulatory domains at the C-terminus: the aspartate kinase-chorismate mutase-tyrA prephenate dehydrogenase (ACT) and allosteric substrate-binding (ASB) domains. The structure of hPHGDH was solved only for a truncated, dimeric form harboring the N-terminal end containing the substrate and the cofactor binding domains. A model ensemble of the tetrameric hPHGDH was generated using AlphaFold coupled with molecular dynamics refinement. By analyzing the inter-subunit interactions at the tetrameric interface, the residues F418, L478, P479, R454, and Y495 were selected and their role was studied by the alanine-scanning mutagenesis approach. The F418A variant modifies the putative ASB, slightly alters the activity, the fraction of protein in the tetrameric state, and the protein stability; it seems relevant in dimers' recognition to yield the tetrameric oligomer. On the contrary, the R454A, L478A, P479A, and Y495A variants (ACT domain) determine a loss of the tetrameric assembly, resulting in low stability and misfolding, triggering the aggregation and hampering the activity. The predicted tetrameric interface seems mediated by residues at the ACT domain, and the tetramer formation seems crucial for proper folding of hPHGDH, which, in turn, is essential for both stability and functionality.

pyCapsid: identifying dominant dynamics and quasi-rigid mechanical units in protein shells

SUMMARY

pyCapsid is a Python package developed to facilitate the characterization of the dynamics and quasi-rigid mechanical units of protein shells and other protein complexes. The package was developed in response to the rapid increase of high-resolution structures, particularly capsids of viruses, requiring multiscale biophysical analyses. Given a protein shell, pyCapsid generates the collective vibrations of its amino-acid residues, identifies quasi-rigid mechanical regions associated with the disassembly of the structure, and maps the results back to the input proteins for interpretation. pyCapsid summarizes the main results in a report that includes publication-quality figures.

AVAILABILITY AND IMPLEMENTATION

pyCapsid's source code is available under MIT License on GitHub. It is compatible with Python 3.8-3.10 and has been deployed in two leading Python package-management systems, PIP and Conda. Installation instructions and tutorials are available in the online documentation and in the pyCapsid's YouTube playlist. In addition, a cloud-based implementation of pyCapsid is available as a Google Colab notebook. pyCapsid Colab does not require installation and generates the same report and outputs as the installable version. Users can post issues regarding pyCapsid in the repository's issues section.

Biochemical and cellular studies of three human 3-phosphoglycerate dehydrogenase variants responsible for pathological reduced L-serine levels

In the brain, the non-essential amino acid L-serine is produced through the phosphorylated pathway (PP) starting from the glycolytic intermediate 3-phosphoglycerate: among the different roles played by this amino acid, it can be converted into D-serine and glycine, the two main co-agonists of NMDA receptors. In humans, the enzymes of the PP, namely phosphoglycerate dehydrogenase (hPHGDH, which catalyzes the first and rate-limiting step of this pathway), 3-phosphoserine aminotransferase, and 3-phosphoserine phosphatase are likely organized in the cytosol as a metabolic assembly (a "serinosome"). The hPHGDH deficiency is a pathological condition biochemically characterized by reduced levels of L-serine in plasma and cerebrospinal fluid and clinically identified by severe neurological impairment. Here, three single-point variants responsible for hPHGDH deficiency and Neu-Laxova syndrome have been studied. Their biochemical characterization shows that V261M, V425M, and V490M substitutions alter either the kinetic (both maximal activity and Km for 3-phosphoglycerate in the physiological direction) and the structural properties (secondary, tertiary, and quaternary structure, favoring aggregation) of hPHGDH. All the three variants have been successfully ectopically expressed in U251 cells, thus the pathological effect is not due to hindered expression level. At the cellular level, mistargeting and aggregation phenomena have been observed in cells transiently expressing the pathological protein variants, as well as a reduced L-serine cellular level. Previous studies demonstrated that the pharmacological supplementation of L-serine in hPHGDH deficiencies could ameliorate some of the related symptoms: our results now suggest the use of additional and alternative therapeutic approaches.

Scipion-EM-ProDy: A Graphical Interface for the ProDy Python Package within the Scipion Workflow Engine Enabling Integration of Databases, Simulations and Cryo-Electron Microscopy Image Processing

Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common theme has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows, together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development.

Cooperative mechanics of PR65 scaffold underlies the allosteric regulation of the phosphatase PP2A

PR65, a horseshoe-shaped scaffold composed of 15 HEAT (observed in Huntingtin, elongation factor 3, protein phosphatase 2A, and the yeast kinase TOR1) repeats, forms, together with catalytic and regulatory subunits, the heterotrimeric protein phosphatase PP2A. We examined the role of PR65 in enabling PP2A enzymatic activity with computations at various levels of complexity, including hybrid approaches that combine full-atomic and elastic network models. Our study points to the high flexibility of this scaffold allowing for end-to-end distance fluctuations of 40-50 Å between compact and extended conformations. Notably, the intrinsic dynamics of PR65 facilitates complexation with the catalytic subunit and is retained in the PP2A complex enabling PR65 to engage the two domains of the catalytic subunit and provide the mechanical framework for enzymatic activity, with support from the regulatory subunit. In particular, the intra-repeat coils at the C-terminal arm play an important role in allosterically mediating the collective dynamics of PP2A, pointing to target sites for modulating PR65 function.

A RhoA structure with switch II flipped outward revealed the conformational dynamics of switch II region

Small GTPase RhoA switches from GTP-bound state to GDP-bound state by hydrolyzing GTP, which is accelerated by GTPases activating proteins (GAPs). However, less study of RhoA structural dynamic changes was conducted during this process, which is essential for understanding the molecular mechanism of GAP dissociation. Here, we solved a RhoA structure in GDP-bound state with switch II flipped outward. Because lacking the intermolecular interactions with guanine nucleotide, we proposed this conformation of RhoA could be an intermediate after GAP dissociation. Further molecular dynamics simulations found the conformational changes of switch regions are indeed existing in RhoA and involved in the regulation of GAP dissociation and GEF recognition. Besides, the guanine nucleotide binding pocket extended to switch II region, indicating a potential "druggable" cavity for RhoA. Taken together, our study provides a deeper understanding of the dynamic properties of RhoA switch regions and highlights the direction for future drug development.

Iterative Landmark-Based Umbrella Sampling (ILBUS) Protocol for Sampling of Conformational Space of Biomolecules

Computer simulations of biomolecules such as molecular dynamics often suffer from insufficient sampling. Due to limited computational resources, insufficient sampling prevents obtaining proper equilibrium distributions of observed properties. To deal with this problem, we proposed a simulation protocol for efficient resampling of collected off-equilibrium trajectories. These trajectories are utilized for the initial mapping of the conformational space, which is later properly resampled by the introduced Iterative Landmark-Based Umbrella Sampling (ILBUS) method. Reconstruction of static equilibrium properties is achieved by the multistate Bennett acceptance ratio (MBAR) method, which enables efficient use of simulated data. The ILBUS protocol is geometry-based and does not demand any additional collective variable or a dimensional-reduction technique. The only requirement is a set of suitably spaced reference conformations, which serve as landmarks in the mapped conformational space. Additionally, the ILBUS protocol encompasses an iterative process that optimizes the force constant used in the umbrella sampling simulation. Such tuning is an inherent feature of the protocol and does not need to be performed by the user in advance. Furthermore, even the simulations with suboptimal force constants can be used in estimates by MBAR. We demonstrate the feasibility and the performance of this approach in the study of the conformational landscape of the alanine dipeptide, met-enkephalin, and adenylate kinase.

What the protein data bank tells us about the evolutionary conservation of protein conformational diversity

Proteins sample a multitude of different conformations by undergoing small- and large-scale conformational changes that are often intrinsic to their functions. Information about these changes is often captured in the Protein Data Bank by the apparently redundant deposition of independent structural solutions of identical proteins. Here, we mine these data to examine the conservation of large-scale conformational changes between homologous proteins. This is important for both practical reasons, such as predicting alternative conformations of a protein by comparative modeling, and conceptual reasons, such as understanding the extent of conservation of different features in evolution. To study this question, we introduce a novel approach to compare conformational changes between proteins by the comparison of their difference distance maps (DDMs). We found that proteins undergoing similar conformational changes have similar DDMs and that this similarity could be quantified by the correlation between the DDMs. By comparing the DDMs of homologous protein pairs, we found that large-scale conformational changes show a high level of conservation across a broad range of sequence identities. This shows that conformational space is usually conserved between homologs, even relatively distant ones.

A sequence space search engine for computational protein design to modulate molecular functionality

De-novo protein design explores the untapped sequence space that is otherwise less discovered during the evolutionary process. This necessitates an efficient sequence space search engine for effective convergence in computational protein design. We propose a greedy simulated annealing-based Monte-Carlo parallel search algorithm for better sequence-structure compatibility probing in protein design. The guidance provided by the evolutionary profile, the greedy approach, and the cooling schedule adopted in the Monte Carlo simulation ensures sufficient exploration and exploitation of the search space leading to faster convergence. On evaluating the proposed algorithm, we find that a dataset of 76 target scaffolds report an average root-mean-square-deviation (RMSD) of 1.07 Å and an average TM-Score of 0.93 with the modeled designed protein sequences. High sequence recapitulation of 48.7% (59.4%) observed in the design sequences for all (hydrophobic) solvent-inaccessible residues again establish the goodness of the proposed algorithm. A high (93.4%) intra-group recapitulation of hydrophobic residues in the solvent-inaccessible region indicates that the proposed protein design algorithm preserves the core residues in the protein and provides alternative residue combinations in the solvent-accessible regions of the target protein. Furthermore, a COFACTOR-based protein functional analysis shows that the design sequences exhibit altered molecular functionality and introduce new molecular functions compared to the target scaffolds.Communicated by Ramaswamy H. Sarma.

Sampling of Protein Conformational Space Using Hybrid Simulations: A Critical Assessment of Recent Methods

Recent years have seen several hybrid simulation methods for exploring the conformational space of proteins and their complexes or assemblies. These methods often combine fast analytical approaches with computationally expensive full atomic molecular dynamics (MD) simulations with the goal of rapidly sampling large and cooperative conformational changes at full atomic resolution. We present here a systematic comparison of the utility and limits of four such hybrid methods that have been introduced in recent years: MD with excited normal modes (MDeNM), collective modes-driven MD (CoMD), and elastic network model (ENM)-based generation, clustering, and relaxation of conformations (ClustENM) as well as its updated version integrated with MD simulations (ClustENMD). We analyzed the predicted conformational spaces using each of these four hybrid methods, applied to four well-studied proteins, triosephosphate isomerase (TIM), 3-phosphoglycerate kinase (PGK), HIV-1 protease (PR) and HIV-1 reverse transcriptase (RT), which provide extensive ensembles of experimental structures for benchmarking and comparing the methods. We show that a rigorous multi-faceted comparison and multiple metrics are necessary to properly assess the differences between conformational ensembles and provide an optimal protocol for achieving good agreement with experimental data. While all four hybrid methods perform well in general, being especially useful as computationally efficient methods that retain atomic resolution, the systematic analysis of the same systems by these four hybrid methods highlights the strengths and limitations of the methods and provides guidance for parameters and protocols to be adopted in future studies.