Anisotropic network model: systematic evaluation and a new web interface

Integrating computational and experimental approaches in discovery and validation of MmpL3 pore domain inhibitors for specific labelling of Mycobacterium tuberculosis

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), continues to pose a significant global health threat. Identifying new druggable targets is crucial for the advancement of drug development. Equally critical is the development of precise methods for monitoring Mtb to effectively combat this disease. Addressing these needs, our study pinpointed the pore domain (PD) of MtbMmpL3 as a new binding site for virtual screening, which led to the discovery of the small molecule ZY27. To confirm the binding site and action mode of ZY27, we employed cosolvent molecular dynamics (CMD), steered molecular dynamics (SMD), and long timescale molecular dynamics (MD) simulations of 5 μs. These in silico studies verified that ZY27 binds to the PD of MtbMmpL3. In antimicrobial activity tests, ZY27 exhibited potent anti-Mtb activity and high selectivity among mycobacterial species. Whole-genome sequencing of spontaneous ZY27-resistant Mtb variants, complemented by acid-fast staining experiments, confirmed that ZY27 specifically targets MtbMmpL3. Utilizing the ligand-protein binding data, we designed and synthesized two solvatochromic fluorescent probes, 27FP1 and 27FP2, based on ZY27. Further investigations through flow cytometry and confocal microscopy confirmed that these probes specifically label Mtb cells via the MtbMmpL3 binding mechanism.

Knot data analysis using multiscale Gauss link integral

In the past decade, topological data analysis has emerged as a powerful algebraic topology approach in data science. Although knot theory and related subjects are a focus of study in mathematics, their success in practical applications is quite limited due to the lack of localization and quantization. We address these challenges by introducing knot data analysis (KDA), a paradigm that incorporates curve segmentation and multiscale analysis into the Gauss link integral. The resulting multiscale Gauss link integral (mGLI) recovers the global topological properties of knots and links at an appropriate scale and offers a multiscale geometric topology approach to capture the local structures and connectivities in data. By integration with machine learning or deep learning, the proposed mGLI significantly outperforms other state-of-the-art methods across various benchmark problems in 13 intricately complex biological datasets, including protein flexibility analysis, protein-ligand interactions, human Ether-à-go-go-Related Gene potassium channel blockade screening, and quantitative toxicity assessment. Our KDA opens a research area-knot deep learning-in data science.

Characterization of hyperactive mutations in the renal potassium channel ROMK uncovers unique effects on channel biogenesis and ion conductance

Hypertension affects one billion people worldwide and is the most common risk factor for cardiovascular disease, yet a comprehensive picture of its underlying genetic factors is incomplete. Amongst regulators of blood pressure is the renal outer medullary potassium (ROMK) channel. While select ROMK mutants are prone to premature degradation and lead to disease, heterozygous carriers of some of these same alleles are protected from hypertension. Therefore, we hypothesized that gain-of-function (GoF) ROMK variants which increase potassium flux may predispose people to hypertension. To begin to test this hypothesis, we employed genetic screens and a candidate-based approach to identify six GoF variants in yeast. Subsequent functional assays in higher cells revealed two variant classes. The first group exhibited greater stability in the endoplasmic reticulum, enhanced channel assembly, and/or increased protein at the cell surface. The second group of variants resided in the PIP2-binding pocket, and computational modeling coupled with patch-clamp studies demonstrated lower free energy for channel opening and slowed current rundown, consistent with an acquired PIP2-activated state. Together, these findings advance our understanding of ROMK structure-function, suggest the existence of hyperactive ROMK alleles in humans, and establish a system to facilitate the development of ROMK-targeted antihypertensives.

MinActionPath2: path generation between different conformations of large macromolecular assemblies by action minimization

Recent progress in solving macromolecular structures and assemblies by cryogenic electron microscopy techniques enables sampling of their conformations in different states that are relevant to their biological function. Knowing the transition path between these conformations would provide new avenues for drug discovery. While the experimental study of transition paths is intrinsically difficult, in-silico methods can be used to generate an initial guess for those paths. The Elastic Network Model (ENM), along with a coarse-grained representation (CG) of the structures are among the most popular models to explore such possible paths. Here we propose an update to our software platform MinActionPath that generates non-linear transition paths based on ENM and CG models, using action minimization to solve the equations of motion. The new website enables the study of large structures such as ribosomes or entire virus envelopes. It provides direct visualization of the trajectories along with quantitative analyses of their behaviors at http://dynstr.pasteur.fr/servers/minactionpath/minactionpath2_submission.

Anisotropic Network Analysis of Open/Closed HCN4 Channel Advocates Asymmetric Subunit Cooperativity in cAMP Modulation of Gating

Hyperpolarization-activated cyclic nucleotide-modulated (HCN) channels are opened in an allosteric manner by membrane hyperpolarization and cyclic nucleotides such as cAMP. Because of conflicting reports from experimental studies on whether cAMP binding to the four available binding sites in the channel tetramer operates cooperatively in gating, we employ here a computational approach as a promising route to examine ligand-induced conformational changes after binding to individual sites. By combining an elastic network model (ENM) with linear response theory (LRT) for modeling the apo-holo transition of the cyclic nucleotide-binding domain (CNBD) in HCN channels, we observe a distinct pattern of cooperativity matching the "positive-negative-positive" cooperativity reported from functional studies. This cooperativity pattern is highly conserved among HCN subtypes (HCN4, HCN1), but only to a lesser extent visible in structurally related channels, which are only gated by voltage (KAT1) or cyclic nucleotides (TAX4). This suggests an inherent cooperativity between subunits in HCN channels as part of a ligand-triggered gating mechanism in these channels.

Exploiting cyclodextrins as artificial chaperones to enhance enzyme protection through supramolecular engineering

We report a method of enzyme stabilisation exploiting the artificial protein chaperone properties of β-cyclodextrin (β-CD) covalently embedded in an ultrathin organosilica layer. Putative interaction points of this artificial chaperone system with the surface of the selected enzyme were studied in silico using a protein energy landscape exploration simulation algorithm. We show that this enzyme shielding method allows for drastic enhancement of enzyme stability under thermal and chemical stress conditions, along with broadening the optimal temperature range of the biocatalyst. The presence of the β-CD macrocycle within the protective layer supports protein refolding after treatment with a surfactant.

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.

Fragment-based Drug Discovery Strategy and its Application to the Design of SARS-CoV-2 Main Protease Inhibitor

Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV-2) emerged at the end of 2019, causing a highly infectious and pathogenic disease known as 2019 coronavirus disease. This disease poses a serious threat to human health and public safety. The SARS-CoV-2 main protease (Mpro) is a highly sought-after target for developing drugs against COVID-19 due to its exceptional specificity. Its crystal structure has been extensively documented. Numerous strategies have been employed in the investigation of Mpro inhibitors. This paper is primarily concerned with Fragment-based Drug Discovery (FBDD), which has emerged as an effective approach to drug design in recent times. Here, we summarize the research on the approach of FBDD and its application in developing inhibitors for SARS-CoV-2 Mpro.

Dissecting the innate immune recognition of morphine and its metabolites by TLR4/MD2: an in silico simulation study

A toll-like receptor 4/myeloid differentiation factor 2 complex (TLR4/MD2) has been identified as a non-classical opioid receptor capable of recognizing morphine isomers and activating microglia in a non-enantioselective manner. Additionally, morphine-3-glucuronide (M3G) and morphine-6-glucuronide (M6G), the major metabolites of morphine, possess similar chemical structures but exhibit distinct effects on TLR4 signaling. However, the specific mechanisms by which morphine isomers and morphine metabolites are recognized by the innate immune receptor TLR4/MD2 are not well understood. Herein, molecular dynamics simulations were performed to dissect the molecular recognition of TLR4/MD2 with morphine isomers, M3G and M6G. Morphine and its (+)-enantiomer, dextro-morphine ((+)-morphine), were found to have comparable binding free energies as well as similar interaction modes when interacting with (TLR4/MD2)2. Binding with morphine and (+)-morphine caused the motion of the F126 loop towards the inside of the MD2 cavity, which stabilizes (TLR4/MD2)2 with similar dimerization interfaces. The binding free energies of M3G and M6G with (TLR4/MD2)2, while lower than those of morphine isomers, were comparable to each other. However, the binding behaviors of M3G and M6G exhibited contrasting patterns when interacting with (TLR4/MD2)2. The glucuronide group of M3G bound to the gating loop of MD2 and formed strong interactions with TLR4*, which stabilizes the active heterotetrameric complex. In contrast, M6G was situated in cavity A of MD2, where the critical interactions between M6G and the residues of TLR4* were lost, resulting in fluctuation of (TLR4/MD2)2 away from the active conformation. These results indicate that the pivotal interactions at the dimerization interface between MD2 and TLR4* in M6G-bound (TLR4/MD2)2 were considerably weaker than those in M3G-bound (TLR4/MD2)2, which partially explains why M6G fails to activate TLR4 signaling. The discoveries from this study will offer valuable insights for the advancement of next-generation TLR4 small molecule modulators based on opioids.

Revealing Allosteric Mechanism of Amino Acid Binding Proteins from Open to Closed State

Amino acid binding proteins (AABPs) undergo significant conformational closure in the periplasmic space of Gram-negative bacteria, tightly binding specific amino acid substrates and then initiating transmembrane transport of nutrients. Nevertheless, the possible closure mechanisms after substrate binding, especially long-range signaling, remain unknown. Taking three typical AABPs-glutamine binding protein (GlnBP), histidine binding protein (HisJ) and lysine/arginine/ornithine binding protein (LAOBP) in Escherichia coli (E. coli)-as research subjects, a series of theoretical studies including sequence alignment, Gaussian network model (GNM), anisotropic network model (ANM), conventional molecular dynamics (cMD) and neural relational inference molecular dynamics (NRI-MD) simulations were carried out. Sequence alignment showed that GlnBP, HisJ and LAOBP have high structural similarity. According to the results of the GNM and ANM, AABPs' Index Finger and Thumb domains exhibit closed motion tendencies that contribute to substrate capture and stable binding. Based on cMD trajectories, the Index Finger domain, especially the I-Loop region, exhibits high molecular flexibility, with residues 11 and 117 both being potentially key residues for receptor-ligand recognition and initiation of receptor allostery. Finally, the signaling pathway of AABPs' conformational closure was revealed by NRI-MD training and trajectory reconstruction. This work not only provides a complete picture of AABPs' recognition mechanism and possible conformational closure, but also aids subsequent structure-based design of small-molecule oncology drugs.

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.

Study on the allosteric activation mechanism of SHP2 via elastic network models and neural relational inference molecular dynamics simulation

As a ubiquitous protein tyrosine phosphatase, SHP2 is involved in PD-1/PD-L1 mediated tumor immune escape and undergoes substantial conformational changes. Therefore, it is considered an ideal target for tumor intervention. However, the allosteric mechanisms of SHP2 binding PD-1 intracellular ITIM/ITSM phosphopeptides remain unclear, which greatly hinders the development of novel structure-based anticancer allosteric inhibitors. In this work, the open and closed structural models of SHP2 are first constructed based on this knowledge; next their motion modes are investigated via elastic network models such as the Gaussian network model (GNM), anisotropic network model (ANM) and adaptive anisotropic network model (aANM); and finally, a possible allosteric signaling pathway is proposed using a neural relational inference molecular dynamics (NRI-MD) simulation embedded with an artificial intelligence (AI) strategy. In GNM and ANM, the N-SH2, C-SH2 and PTP domains all exhibit distinct dynamics partitions, and the N-SH2/C-SH2 regions show a rigid rotation relative to PTP. According to a series of intermediate snapshots given by aANM, N-SH2 is first identified with pY223 specifically, inducing a D'E-loop to change from β-sheets to random coils, and then, C-SH2 serves as a fulcrum to drive N-SH2 to rotate 110° completely away from the original active sites of PTP. Finally, a possible allosteric signaling-transfer path for SHP2, namely R220-R138-T108-R32, is proposed based on NRI-MD sampling. This work provides a possible allosteric mechanism of SHP2, which is helpful for the following design of novel allosteric inhibitors and is expected to be used in clinical synergies with PD-1 monoclonal antibody.

Modeling diffuse scattering with simple, physically interpretable models

Diffuse scattering has long been proposed to probe protein dynamics relevant for biological function, and more recently, as a tool to aid structure determination. Despite recent advances in measuring and modeling this signal, the field has not been able to routinely use experimental diffuse scattering for either application. A persistent challenge has been to devise models that are sophisticated enough to robustly reproduce experimental diffuse features but remain readily interpretable from the standpoint of structural biology. This chapter presents eryx, a suite of computational tools to evaluate the primary models of disorder that have been used to analyze protein diffuse scattering. By facilitating comparative modeling, eryx aims to provide insights into the physical origins of this signal and help identify the sources of disorder that are critical for reproducing experimental features. This framework also lays the groundwork for the development of more advanced models that integrate different types of disorder without loss of interpretability.

Identification of the Intrinsic Motions and Related Key Residues Responsible for the Twofold Channel Opening of Poliovirus Capsid by Using an Elastic Network Model Combined with an Internal Coordinate

Poliovirus (PV) is an infectious virus that causes poliomyelitis, which seriously threatens the health of children. The release of viral RNA is a key step of PV in host cell infection, and multiple lines of evidence have demonstrated that RNA release is initiated by the opening of the twofold channels of the PV capsid. However, the mechanism that controls the twofold channel opening is still not well understood. In addition, the channel opening motion of the recombinant PV capsid leads to the destruction of predominant neutralizing epitopes and thus hinders the capsid as a vaccine immunogen. Therefore, it is important to identify the intrinsic motions and the related key residues controlling the twofold channel opening for understanding the virus infection mechanism and developing capsid-based vaccines. In the present work, the width of the channel was selected as an internal coordinate directly related to the channel opening, and then the elastic network model (ENM) combined with the group theory were employed to extract the intrinsic motion modes that mostly contribute to the opening of the twofold channels. Our results show that the channel opening predominately induced by the breathing motion and the overall rotation of each protomer in the capsid. Then, an internal coordinate-based perturbation method was used to identify the key residues regulating the twofold channel opening of PV. The calculation results showed that the predicted key residues are mainly located at the twofold axes, the bottom of the canyons and the quasi threefold axes. Our study is helpful for better understanding the twofold channel opening mechanism and provides a potential target for preventing the opening of the channels, which is of great significance for PV vaccine design. The source code of this study is available at https://github.com/SJGLAB/CapsidKeyRes.git.

Surveying nonvisual arrestins reveals allosteric interactions between functional sites

Arrestins are important scaffolding proteins that are expressed in all vertebrate animals. They regulate cell-signaling events upon binding to active G-protein coupled receptors (GPCR) and trigger endocytosis of active GPCRs. While many of the functional sites on arrestins have been characterized, the question of how these sites interact is unanswered. We used anisotropic network modeling (ANM) together with our covariance compliment techniques to survey all the available structures of the nonvisual arrestins to map how structural changes and protein-binding affect their structural dynamics. We found that activation and clathrin binding have a marked effect on arrestin dynamics, and that these dynamics changes are localized to a small number of distant functional sites. These sites include α-helix 1, the lariat loop, nuclear localization domain, and the C-domain β-sheets on the C-loop side. Our techniques suggest that clathrin binding and/or GPCR activation of arrestin perturb the dynamics of these sites independent of structural changes.

Time Series Analysis of SARS-CoV-2 Genomes and Correlations among Highly Prevalent Mutations

The efforts of the scientific community to tame the recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seem to have been diluted by the emergence of new viral strains. Therefore, it is imperative to understand the effect of mutations on viral evolution. We performed a time series analysis on 59,541 SARS-CoV-2 genomic sequences from around the world to gain insights into the kinetics of the mutations arising in the viral genomes. These 59,541 genomes were grouped according to month (January 2020 to March 2021) based on the collection date. Meta-analysis of these data led us to identify significant mutations in viral genomes. Pearson correlation of these mutations led us to the identification of 16 comutations. Among these comutations, some of the individual mutations have been shown to contribute to viral replication and fitness, suggesting a possible role of other unexplored mutations in viral evolution. We observed that the mutations 241C>T in the 5' untranslated region (UTR), 3037C>T in nsp3, 14408C>T in the RNA-dependent RNA polymerase (RdRp), and 23403A>G in spike are correlated with each other and were grouped in a single cluster by hierarchical clustering. These mutations have replaced the wild-type nucleotides in SARS-CoV-2 sequences. Additionally, we employed a suite of computational tools to investigate the effects of T85I (1059C>T), P323L (14408C>T), and Q57H (25563G>T) mutations in nsp2, RdRp, and the ORF3a protein of SARS-CoV-2, respectively. We observed that the mutations T85I and Q57H tend to be deleterious and destabilize the respective wild-type protein, whereas P323L in RdRp tends to be neutral and has a stabilizing effect. IMPORTANCE We performed a meta-analysis on SARS-CoV-2 genomes categorized by collection month and identified several significant mutations. Pearson correlation analysis of these significant mutations identified 16 comutations having absolute correlation coefficients of >0.4 and a frequency of >30% in the genomes used in this study. The correlation results were further validated by another statistical tool called hierarchical clustering, where mutations were grouped in clusters on the basis of their similarity. We identified several positive and negative correlations among comutations in SARS-CoV-2 isolates from around the world which might contribute to viral pathogenesis. The negative correlations among some of the mutations in SARS-CoV-2 identified in this study warrant further investigations. Further analysis of mutations such as T85I in nsp2 and Q57H in ORF3a protein revealed that these mutations tend to destabilize the protein relative to the wild type, whereas P323L in RdRp is neutral and has a stabilizing effect. Thus, we have identified several comutations which can be further characterized to gain insights into SARS-CoV-2 evolution.

Structural and Dynamic Effects of PTEN C-Terminal Tail Phosphorylation

The phosphatase and tensin homologue deleted on chromosome 10 (PTEN) tumor suppressor gene encodes a tightly regulated dual-specificity phosphatase that serves as the master regulator of PI3K/AKT/mTOR signaling. The carboxy-terminal tail (CTT) is key to regulation and harbors multiple phosphorylation sites (Ser/Thr residues 380-385). CTT phosphorylation suppresses the phosphatase activity by inducing a stable, closed conformation. However, little is known about the mechanisms of phosphorylation-induced CTT-deactivation dynamics. Using explicit solvent microsecond molecular dynamics simulations, we show that CTT phosphorylation leads to a partially collapsed conformation, which alters the secondary structure of PTEN and induces long-range conformational rearrangements that encompass the active site. The active site rearrangements prevent localization of PTEN to the membrane, precluding lipid phosphatase activity. Notably, we have identified phosphorylation-induced allosteric coupling between the interdomain region and a hydrophobic site neighboring the active site in the phosphatase domain. Collectively, the results provide a mechanistic understanding of CTT phosphorylation dynamics and reveal potential druggable allosteric sites in a previously believed clinically undruggable protein.

Fragment-based inhibitor design for SARS-CoV2 main protease

COVID-19 disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV2) has resulted in tremendous loss of lives across the world and is continuing to do so. Extensive work is under progress to develop inhibitors which can prevent the disease by arresting the virus in its life cycle. One such way is by targeting the main protease of the virus which is crucial for the cleavage and conversion of polyproteins into functional units of polypeptides. In this endeavor, our effort was to identify hit molecule inhibitors for SARS-CoV2 main protease using fragment-based drug discovery (FBDD), based on the available crystal structure of chromene-based inhibitor (PDB_ID: 6M2N). The designed molecules were validated by molecular docking and molecular dynamics simulations. The stability of the complexes was further assessed by calculating their binding free energies, normal mode analysis, mechanical stiffness, and principal component analysis.

The effect of dimerization and ligand binding on the dynamics of Kaposi's sarcoma-associated herpesvirus protease

The Kaposi's sarcoma-associated herpesvirus protease is essential for virus maturation. This protease functions under allosteric regulation that establishes its enzymatic activity upon dimerization. It exists in equilibrium between an inactive monomeric state and an active, weakly associating, dimeric state that is stabilized upon ligand binding. The dynamics of the protease dimer and its monomer were studied using the Gaussian network model and the anisotropic network model , and its role in mediating the allosteric regulation is demonstrated. We show that the dimer is composed of five dynamical domains. The central domain is formed upon dimerization and composed of helix five of each monomer, in addition to proximal and distal domains of each monomer. Dimerization reduces the mobility of the central domains and increases the mobility of the distal domains, in particular the binding site within them. The three slowest ANM modes of the dimer assist the protease in ligand binding, motion of the conserved Arg142 and Arg143 toward the oxyanion, and reducing the activation barrier for the tetrahedral transition state by stretching the bond that is cleaved by the protease. In addition, we show that ligand binding reduces the motion of helices α1 and α5 at the interface and explain how ligand binding can stabilize the dimer.

Biophysical Insight into the SARS-CoV2 Spike-ACE2 Interaction and Its Modulation by Hepcidin through a Multifaceted Computational Approach

At the center of the SARS-CoV2 infection, the spike protein and its interaction with the human receptor ACE2 play a central role in the molecular machinery of SARS-CoV2 infection of human cells. Vaccine therapies are a valuable barrier to the worst effects of the virus and to its diffusion, but the need of purposed drugs is emerging as a core target of the fight against COVID19. In this respect, the repurposing of drugs has already led to discovery of drugs thought to reduce the effects of the cytokine storm, but still a drug targeting the spike protein, in the infection stage, is missing. In this work, we present a multifaceted computational approach strongly grounded on a biophysical modeling of biological systems, so to disclose the interaction of the SARS-CoV2 spike protein with ACE2 with a special focus to an allosteric regulation of the spike-ACE2 interaction. Our approach includes the following methodologies: Protein Contact Networks and Network Clustering, Targeted Molecular Dynamics, Elastic Network Modeling, Perturbation Response Scanning, and a computational analysis of energy flow and SEPAS as a protein-softness and monomer-based affinity predictor. We applied this approach to free (closed and open) states of spike protein and spike-ACE2 complexes. Eventually, we analyzed the interactions of free and bound forms of spike with hepcidin (HPC), the major hormone in iron regulation, recently addressed as a central player in the COVID19 pathogenesis, with a special emphasis to the most severe outcomes. Our results demonstrate that, compared with closed and open states, the spike protein in the ACE2-bound state shows higher allosteric potential. The correspondence between hinge sites and the Allosteric Modulation Region (AMR) in the S-ACE complex suggests a molecular basis for hepcidin involvement in COVID19 pathogenesis. We verify the importance of AMR in different states of spike and then study its interactions with HPC and the consequence of the HPC-AMR interaction on spike dynamics and its affinity for ACE2. We propose two complementary mechanisms for HPC effects on spike of SARS-CoV-2; (a) HPC acts as a competitive inhibitor when spike is in a preinfection state (open and with no ACE2), (b) the HPC-AMR interaction pushes the spike structure into the safer closed state. These findings need clear molecular in vivo verification beside clinical observations.

LoopGrafter: a web tool for transplanting dynamical loops for protein engineering

The transplantation of loops between structurally related proteins is a compelling method to improve the activity, specificity and stability of enzymes. However, despite the interest of loop regions in protein engineering, the available methods of loop-based rational protein design are scarce. One particular difficulty related to loop engineering is the unique dynamism that enables them to exert allosteric control over the catalytic function of enzymes. Thus, when engaging in a transplantation effort, such dynamics in the context of protein structure need consideration. A second practical challenge is identifying successful excision points for the transplantation or grafting. Here, we present LoopGrafter (https://loschmidt.chemi.muni.cz/loopgrafter/), a web server that specifically guides in the loop grafting process between structurally related proteins. The server provides a step-by-step interactive procedure in which the user can successively identify loops in the two input proteins, calculate their geometries, assess their similarities and dynamics, and select a number of loops to be transplanted. All possible different chimeric proteins derived from any existing recombination point are calculated, and 3D models for each of them are constructed and energetically evaluated. The obtained results can be interactively visualized in a user-friendly graphical interface and downloaded for detailed structural analyses.

Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0

Cryo-electron microscopy (cryoEM) has become a well established technique with the potential to produce structures of large and dynamic supramolecular complexes that are not amenable to traditional approaches for studying structure and dynamics. The size and low resolution of such molecular systems often make structural modelling and molecular dynamics simulations challenging and computationally expensive. This, together with the growing wealth of structural data arising from cryoEM and other structural biology methods, has driven a trend in the computational biophysics community towards the development of new pipelines for analysing global dynamics using coarse-grained models and methods. At the centre of this trend has been a return to elastic network models, normal mode analysis (NMA) and ensemble analyses such as principal component analysis, and the growth of hybrid simulation methodologies that make use of them. Here, this field is reviewed with a focus on ProDy, the Python application programming interface for protein dynamics, which has been developed over the last decade. Two key developments in this area are highlighted: (i) ensemble NMA towards extracting and comparing the signature dynamics of homologous structures, aided by the recent SignDy pipeline, and (ii) pseudoatom fitting for more efficient global dynamics analyses of large and low-resolution supramolecular assemblies from cryoEM, revisited in the CryoDy pipeline. It is believed that such a renewal and extension of old models and methods in new pipelines will be critical for driving the field forward into the next cryoEM revolution.

Impact of new variants on SARS-CoV-2 infectivity and neutralization: A molecular assessment of the alterations in the spike-host protein interactions

The emergence of SARS-CoV-2 variants necessitates rational assessment of their impact on the recognition and neutralization of the virus by the host cell. We present a comparative analysis of the interactions of Alpha, Beta, Gamma, and Delta variants with cognate molecules (ACE2 and/or furin), neutralizing nanobodies (Nbs), and monoclonal antibodies (mAbs) using in silico methods, in addition to Nb-binding assays. Our study elucidates the molecular origin of the ability of Beta and Delta variants to evade selected antibodies, such as REGN10933, LY-CoV555, B38, C105, or H11-H4, while being insensitive to others including REGN10987. Experiments confirm that nanobody Nb20 retains neutralizing activity against the Delta variant. The substitutions T478K and L452R in the Delta variant enhance associations with ACE2, whereas P681R promotes recognition by proteases, thus facilitating viral entry. The Ab-specific responses of variants highlight how full-atomic structure and dynamics analyses are required for assessing the response to newly emerging variants.

Protein Dynamics to Define and Refine Disordered Protein Ensembles

Intrinsically disordered proteins and unfolded proteins have fluctuating conformational ensembles that are fundamental to their biological function and impact protein folding, stability, and misfolding. Despite the importance of protein dynamics and conformational sampling, time-dependent data types are not fully exploited when defining and refining disordered protein ensembles. Here we introduce a computational framework using an elastic network model and normal-mode displacements to generate a dynamic disordered ensemble consistent with NMR-derived dynamics parameters, including transverse R₂ relaxation rates and Lipari-Szabo order parameters (S² values). We illustrate our approach using the unfolded state of the drkN SH3 domain to show that the dynamical ensembles give better agreement than a static ensemble for a wide range of experimental validation data including NMR chemical shifts, J-couplings, nuclear Overhauser effects, paramagnetic relaxation enhancements, residual dipolar couplings, hydrodynamic radii, single-molecule fluorescence Förster resonance energy transfer, and small-angle X-ray scattering.

Protein Fluctuations in Response to Random External Forces

Elastic network models (ENMs) have been widely used in the last decades to investigate protein motions and dynamics. There the intrinsic fluctuations based on the isolated structures are obtained from the normal modes of these elastic networks, and they generally show good agreement with the B-factors extracted from X-ray crystallographic experiments, which are commonly considered to be indicators of protein flexibility. In this paper, we propose a new approach to analyze protein fluctuations and flexibility, which has a more appropriate physical basis. It is based on the application of random forces to the protein ENM to simulate the effects of collisions of solvent on a protein structure. For this purpose, we consider both the Cα-atom coarse-grained anisotropic network model (ANM) and an elastic network augmented with points included for the crystallized waters. We apply random forces to these protein networks everywhere, as well as only on the protein surface alone. Despite the randomness of the directions of the applied perturbations, the computed average displacements of the protein network show a remarkably good agreement with the experimental B-factors. In particular, for our set of 919 protein structures, we find that the highest correlation with the B-factors is obtained when applying forces to the external surface of the water-augmented ANM (an overall gain of 3% in the Pearson’s coefficient for the entire dataset, with improvements up to 30% for individual proteins), rather than when evaluating the fluctuations obtained from the normal modes of a standard Cα-atom coarse-grained ANM. It follows that protein fluctuations should be considered not just as the intrinsic fluctuations of the internal dynamics, but also equally well as responses to external solvent forces, or as a combination of both.

The Inherent Coupling of Intrinsically Disordered Regions in the Multidomain Receptor Tyrosine Kinase KIT

RTK KIT regulates a variety of crucial cellular processes via its cytoplasmic domain (CD), which is composed of the tyrosine kinase domain, crowned by the highly flexible domains—the juxtamembrane region, kinase insertion domain, and C-tail, which are key recruitment regions for downstream signalling proteins. To prepare a structural basis for the characterization of the interactions of KIT with its signalling proteins (KIT INTERACTOME), we generated the 3D model of the full-length CD attached to the transmembrane helix. This generic model of KIT in inactive state was studied by molecular dynamics simulation under conditions mimicking the natural environment of KIT. With the accurate atomistic description of the multidomain KIT dynamics, we explained its intrinsic (intra-domain) and extrinsic (inter-domain) disorder and represented the conformational assemble of KIT through free energy landscapes. Strongly coupled movements within each domain and between distant domains of KIT prove the functional interdependence of these regions, described as allosteric regulation, a phenomenon widely observed in many proteins. We suggested that KIT, in its inactive state, encodes all properties of the active protein and its post-transduction events.

Chiral carbon dots - a functional domain for tyrosinase Cu active site modulation via remote target interaction

The nano-hybrid enzyme is an ideal catalytic system that integrates various advantages from biocatalysis and nanocatalysis into homogeneous and heterogeneous catalysis. However, great efforts are still needed to fully understand the interactions between nanoparticles and enzymes. Here, we show chiral carbon dots (CDs) as a new functional domain for tyrosinase Cu active site modulation via remote target interaction. Three kinds of chiral CDs (LCDs-1/-2/-3; DCDs-1/-2/-3) were fabricated by thermal treatment of citric acid and L/D-aspartic acid. Then a series of CDs/tyrosinase composites (namely, nano-hybrid-enzymes) were prepared, demonstrating good regulation of enzyme catalytic kinetics. Especially, we find that LCDs-1 is an irreversible inhibitor with great inhibition effect while the others are all reversible inhibitors. Furthermore, it is suggested by both experiments and all-atom molecular dynamics simulations that the joint effect of LCDs-1 and tyrosinase makes LCDs-1 serve as a new functional domain, which has a distinguished ability to control the conformational changes of the key sites of the active center of the tyrosinase (e.g., H60) and thus the escaping behavior of copper ions and the catalytic activity. This work opens a new route for nano-hybrid-enzyme design and enzyme activity regulation with chiral carbon materials as functional domains via remote target interaction.

Allosteric and transport modulation of human concentrative nucleoside transporter 3 at the atomic scale

Nucleosides are important precursors of nucleotide synthesis in cells, and nucleoside transporters play an important role in many physiological processes by mediating transmembrane transport and absorption. During nucleoside transport, such proteins undergo a significant conformational transition between the outward- and inward-facing states, which leads to alternating access of the substrate-binding site to either side of the membrane. In this work, a variety of molecular simulation methods have been applied to comparatively investigate the motion modes of human concentrative nucleoside transporter 3 (hCNT3) in three states, as well as global and local cavity conformational changes; and finally, a possible elevator-like transport mechanism consistent with experimental data was proposed. The results of the Gaussian network model (GNM) and anisotropic network model (ANM) show that hCNT3 as a whole tends to contract inwards and shift towards a membrane inside, exhibiting an allosteric process that is more energetically favorable than the rigid conversion. To reveal the complete allosteric process of hCNT3 in detail, a series of intermediate conformations were obtained by an adaptive anisotropic network model (aANM). One of the simulated intermediate states is similar to that of a crystal structure, which indicates that the allosteric process is reliable; the state with lower energy is slightly inclined to the inward-facing structure rather than the expected intermediate crystal structure. The final HOLE analysis showed that except for the outward-facing state, the transport channels were gradually enlarged, which was conductive to the directional transport of nucleosides. Our work provides a theoretical basis for the multistep elevator-like transportation mechanism of nucleosides, which helps to further understand the dynamic recognition between nucleoside substrates and hCNT3 as well as the design of nucleoside anticancer drugs.

Modeling coronavirus spike protein dynamics: implications for immunogenicity and immune escape

The ongoing COVID-19 pandemic is a global public health emergency requiring urgent development of efficacious vaccines. While concentrated research efforts have focused primarily on antibody-based vaccines that neutralize SARS-CoV-2, and several first-generation vaccines have either been approved or received emergency use authorization, it is forecasted that COVID-19 will become an endemic disease requiring updated second-generation vaccines. The SARS-CoV-2 surface spike (S) glycoprotein represents a prime target for vaccine development because antibodies that block viral attachment and entry, i.e., neutralizing antibodies, bind almost exclusively to the receptor-binding domain. Here, we develop computational models for a large subset of S proteins associated with SARS-CoV-2, implemented through coarse-grained elastic network models and normal mode analysis. We then analyze local protein domain dynamics of the S protein systems and their thermal stability to characterize structural and dynamical variability among them. These results are compared against existing experimental data and used to elucidate the impact and mechanisms of SARS-CoV-2 S protein mutations and their associated antibody binding behavior. We construct a SARS-CoV-2 antigenic map and offer predictions about the neutralization capabilities of antibody and S mutant combinations based on protein dynamic signatures. We then compare SARS-CoV-2 S protein dynamics to SARS-CoV and MERS-CoV S proteins to investigate differing antibody binding and cellular fusion mechanisms that may explain the high transmissibility of SARS-CoV-2. The outbreaks associated with SARS-CoV, MERS-CoV, and SARS-CoV-2 over the last two decades suggest that the threat presented by coronaviruses is ever-changing and long term. Our results provide insights into the dynamics-driven mechanisms of immunogenicity associated with coronavirus S proteins and present a new, to our knowledge, approach to characterize and screen potential mutant candidates for immunogen design, as well as to characterize emerging natural variants that may escape vaccine-induced antibody responses.

Approximating deformation fields for the analysis of continuous heterogeneity of biological macromolecules by 3D Zernike polynomials

Structural biology has evolved greatly due to the advances introduced in fields like electron microscopy. This image-capturing technique, combined with improved algorithms and current data processing software, allows the recovery of different conformational states of a macromolecule, opening new possibilities for the study of its flexibility and dynamic events. However, the ensemble analysis of these different conformations, and in particular their placement into a common variable space in which the differences and similarities can be easily recognized, is not an easy matter. To simplify the analysis of continuous heterogeneity data, this work proposes a new automatic algorithm that relies on a mathematical basis defined over the sphere to estimate the deformation fields describing conformational transitions among different structures. Thanks to the approximation of these deformation fields, it is possible to describe the forces acting on the molecules due to the presence of different motions. It is also possible to represent and compare several structures in a low-dimensional mapping, which summarizes the structural characteristics of different states. All these analyses are integrated into a common framework, providing the user with the ability to combine them seamlessly. In addition, this new approach is a significant step forward compared with principal component analysis and normal mode analysis of cryo-electron microscopy maps, avoiding the need to select components or modes and producing localized analysis.

Low-Frequency Harmonic Perturbations Drive Protein Conformational Changes

Protein dynamics has been investigated since almost half a century, as it is believed to constitute the fundamental connection between structure and function. Elastic network models (ENMs) have been widely used to predict protein dynamics, flexibility and the biological mechanism, from which remarkable results have been found regarding the prediction of protein conformational changes. Starting from the knowledge of the reference structure only, these conformational changes have been usually predicted either by looking at the individual mode shapes of vibrations (i.e., by considering the free vibrations of the ENM) or by applying static perturbations to the protein network (i.e., by considering a linear response theory). In this paper, we put together the two previous approaches and evaluate the complete protein response under the application of dynamic perturbations. Harmonic forces with random directions are applied to the protein ENM, which are meant to simulate the single frequency-dependent components of the collisions of the surrounding particles, and the protein response is computed by solving the dynamic equations in the underdamped regime, where mass, viscous damping and elastic stiffness contributions are explicitly taken into account. The obtained motion is investigated both in the coordinate space and in the sub-space of principal components (PCs). The results show that the application of perturbations in the low-frequency range is able to drive the protein conformational change, leading to remarkably high values of direction similarity. Eventually, this suggests that protein conformational change might be triggered by external collisions and favored by the inherent low-frequency dynamics of the protein structure.

Dissecting the Role of N-Glycan at N413 in Toll-like Receptor 3 via Molecular Dynamics Simulations

Toll-like receptor 3 (TLR3) is an endosomal receptor involved in initiating immune responses upon viral infection by directly recognizing double-stranded RNA (dsRNA). As one of the most heavily glycosylated TLR family members, the role of glycan at N413 of TLR3 in ligand recognition has been in debate for decades. Herein, to investigate the role of glycans in TLR3, specifically at amino acid residue N413, molecular dynamic simulations were performed. The loop region of LRR12 (residues 323-355), which protrudes from the dsRNA binding TLR3 lateral surface was found to be vital for interacting with dsRNA via the formation of hydrogen bonds. The glycan at N413 not only prevented dsRNA from being exposed to the bulk water during the binding process but further stabilized dsRNA in the TLR3 binding site. When N413 was in the glycosylated form, the binding free energy of TLR3 interacting with dsRNA was significantly lower than that of TLR3 in the N413 unglycosylated form. Additionally, as the glycan at N413 functioned to alter the dynamics of the dsRNA binding process, its flexibility was meanwhile influenced by dsRNA. In all, these results demonstrate that the size, length, and branch of glycan at N413 affect the thermodynamics and dynamics of TLR3 recognition with dsRNA. This study further extends our understanding of the biological role of glycans in the innate immune recognition of dsRNA by TLR3 and provides a new perspective for modulating TLR3 function.

Parameterizing elastic network models to capture the dynamics of proteins

Coarse-grained normal mode analyses of protein dynamics rely on the idea that the geometry of a protein structure contains enough information for computing its fluctuations around its equilibrium conformation. This geometry is captured in the form of an elastic network (EN), namely a network of edges between its residues. The normal modes of a protein are then identified with the normal modes of its EN. Different approaches have been proposed to construct ENs, focusing on the choice of the edges that they are comprised of, and on their parameterizations by the force constants associated with those edges. Here we propose new tools to guide choices on these two facets of EN. We study first different geometric models for ENs. We compare cutoff-based ENs, whose edges have lengths that are smaller than a cutoff distance, with Delaunay-based ENs and find that the latter provide better representations of the geometry of protein structures. We then derive an analytical method for the parameterization of the EN such that its dynamics leads to atomic fluctuations that agree with experimental B-factors. To limit overfitting, we attach a parameter referred to as flexibility constant to each atom instead of to each edge in the EN. The parameterization is expressed as a non-linear optimization problem whose parameters describe both rigid-body and internal motions. We show that this parameterization leads to improved ENs, whose dynamics mimic MD simulations better than ENs with uniform force constants, and reduces the number of normal modes needed to reproduce functional conformational changes.

Dynamics-Evolution Correspondence in Protein Structures

The genotype-phenotype mapping of proteins is a fundamental question in structural biology. In this Letter, with the analysis of a large dataset of proteins from hundreds of protein families, we quantitatively demonstrate the correlations between the noise-induced protein dynamics and mutation-induced variations of native structures, indicating the dynamics-evolution correspondence of proteins. Based on the investigations of the linear responses of native proteins, the origin of such a correspondence is elucidated. It is essential that the noise- and mutation-induced deformations of the proteins are restricted on a common low-dimensional subspace, as confirmed from the data. These results suggest an evolutionary mechanism of the proteins gaining both dynamical flexibility and evolutionary structural variability.

The functional motions and related key residues behind the uncoating of coxsackievirus A16

The coxsackievirus A16 (CVA16) is a highly contagious virus that causes the hand, foot, and mouth disease, which seriously threatens the health of children. At present, there are still no available antiviral drugs or effective treatments against the infection of CVA16, and thus it is of great significance to develop anti-CVA16 vaccines. However, the intrinsic uncoating property of the capsid may destroy the neutralizing epitopes and influence its immunogenicity, which hinders the vaccine developments. In the present work, the functional-quantity-based elastic network model analysis method developed by our group was extended to combine with group theory to investigate the uncoating motions of the CVA16 capsid, and then the functionally key residues controlling the uncoating motions were identified by our functional-quantity-based perturbation method. Several motion modes encoded in the topological structure of the capsid were revealed to be responsible for the uncoating of CVA16 particle. These modes predominantly contribute to the fluctuation of the gyration radius of the capsid. Then, by using the perturbation method, four clusters of key sites involved in the uncoating motions were identified, whose perturbations induce significant changes in the fluctuation of the gyration radius. These key residues are mainly located at the 2-fold channels, the quasi 3-fold channels, the bottom of the canyons, and the inter-subunit interfaces around the 3-fold axes. Our studies are helpful for better understanding the uncoating mechanism of the CVA16 capsid and provide potential target sites to prevent the uncoating motions, which is valuable for the vaccine design against CVA16.

Modal Analysis of the Lysozyme Protein Considering All-Atom and Coarse-Grained Finite Element Models

Proteins are the fundamental entities of several organic activities. They are essential for a broad range of tasks in a way that their shapes and folding processes are crucial to achieving proper biological functions. Low-frequency modes, generally associated with collective movements at terahertz (THz) and sub-terahertz frequencies, have been appointed as critical for the conformational processes of many proteins. Dynamic simulations, such as molecular dynamics, are vastly applied by biochemical researchers in this field. However, in the last years, proposals that define the protein as a simplified elastic macrostructure have shown appealing results when dealing with this type of problem. In this context, modal analysis based on different modelization techniques, i.e., considering both an all-atom (AA) and coarse-grained (CG) representation, is proposed to analyze the hen egg-white lysozyme. This work presents new considerations and conclusions compared to previous analyses. Experimental values for the B-factor, considering all the heavy atoms or only one representative point per amino acid, are used to evaluate the validity of the numerical solutions. In general terms, this comparison allows the assessment of the regional flexibility of the protein. Besides, the low computational requirements make this approach a quick method to extract the protein’s dynamic properties under scrutiny.

Exploring the intrinsic dynamics of SARS-CoV-2, SARS-CoV and MERS-CoV spike glycoprotein through normal mode analysis using anisotropic network model

COVID-19 caused by SARS-CoV-2 have become a global pandemic with serious rate of fatalities. SARS-CoV and MERS-CoV have also caused serious outbreak previously but the intensity was much lower than the ongoing SARS-CoV-2. The main infectivity factor of all the three viruses is the spike glycoprotein. In this study we have examined the intrinsic dynamics of the prefusion, lying state of trimeric S protein of these viruses through Normal Mode Analysis using Anisotropic Network Model. The dynamic modes of the S proteins of the aforementioned viruses were compared by root mean square inner product (RMSIP), spectral overlap and cosine correlation matrix. S proteins show homogenous correlated or anticorrelated motions among their domains but direction of Cα atom among the spike proteins show less similarity. SARS-CoV-2 spike shows high vertically upward motion of the receptor binding motif implying its propensity for binding with the receptor even in the lying state. MERS-CoV spike shows unique dynamical motion compared to the other two S protein indicated by low RMSIP, spectral overlap and cosine correlation value. This study will guide in developing common potential inhibitor molecules against closed state of spike protein of these viruses to prevent conformational switching from lying to standing state.

Data-guided Multi-Map variables for ensemble refinement of molecular movies

Driving molecular dynamics simulations with data-guided collective variables offer a promising strategy to recover thermodynamic information from structure-centric experiments. Here, the three-dimensional electron density of a protein, as it would be determined by cryo-EM or x-ray crystallography, is used to achieve simultaneously free-energy costs of conformational transitions and refined atomic structures. Unlike previous density-driven molecular dynamics methodologies that determine only the best map-model fits, our work employs the recently developed Multi-Map methodology to monitor concerted movements within equilibrium, non-equilibrium, and enhanced sampling simulations. Construction of all-atom ensembles along the chosen values of the Multi-Map variable enables simultaneous estimation of average properties, as well as real-space refinement of the structures contributing to such averages. Using three proteins of increasing size, we demonstrate that biased simulation along the reaction coordinates derived from electron densities can capture conformational transitions between known intermediates. The simulated pathways appear reversible with minimal hysteresis and require only low-resolution density information to guide the transition. The induced transitions also produce estimates for free energy differences that can be directly compared to experimental observables and population distributions. The refined model quality is superior compared to those found in the Protein Data Bank. We find that the best quantitative agreement with experimental free-energy differences is obtained using medium resolution density information coupled to comparatively large structural transitions. Practical considerations for probing the transitions between multiple intermediate density states are also discussed.

Studying the Effects of ACE2 Mutations on the Stability, Dynamics, and Dissociation Process of SARS-CoV-2 S1/hACE2 Complexes

A highly infectious coronavirus, SARS-CoV-2, has spread in many countries. This virus recognizes its receptor, angiotensin-converting enzyme 2 (ACE2), using the receptor binding domain of its spike protein subunit S1. Many missense mutations are reported in various human populations for the ACE2 gene. In the current study, we predict the affinity of many ACE2 variants for binding to S1 protein using different computational approaches. The dissociation process of S1 from some variants of ACE2 is studied in the current work by molecular dynamics approaches. We study the relation between structural dynamics of ACE2 in closed and open states and its affinity for S1 protein of SARS-CoV-2.

The Bio3D packages for structural bioinformatics

Bio3D is a family of R packages for the analysis of biomolecular sequence, structure, and dynamics. Major functionality includes biomolecular database searching and retrieval, sequence and structure conservation analysis, ensemble normal mode analysis, protein structure and correlation network analysis, principal component, and related multivariate analysis methods. Here, we review recent package developments, including a new underlying segregation into separate packages for distinct analysis, and introduce a new method for structure analysis named ensemble difference distance matrix analysis (eDDM). The eDDM approach calculates and compares atomic distance matrices across large sets of homologous atomic structures to help identify the residue wise determinants underlying specific functional processes. An eDDM workflow is detailed along with an example application to a large protein family. As a new member of the Bio3D family, the Bio3D-eddm package supports both experimental and theoretical simulation-generated structures, is integrated with other methods for dissecting sequence-structure-function relationships, and can be used in a highly automated and reproducible manner. Bio3D is distributed as an integrated set of platform independent open source R packages available from: http://thegrantlab.org/bio3d/.

Allosteric Communications between Domains Modulate the Activity of Matrix Metalloprotease-1

An understanding of the structure-dynamics relationship is essential for understanding how a protein works. Prior research has shown that the activity of a protein correlates with intradomain dynamics occurring at picosecond to millisecond timescales. However, the correlation between interdomain dynamics and the function of a protein is poorly understood. Here, we show that communications between the catalytic and hemopexin domains of matrix metalloprotease-1 (MMP1) on type 1 collagen fibrils correlate with its activity. Using single-molecule Förster resonance energy transfer, we identified functionally relevant open conformations in which the two MMP1 domains are well separated, which were significantly absent for catalytically inactive point mutant (E219Q) of MMP1 and could be modulated by an inhibitor or an enhancer of activity. The observed relevance of open conformations resolves the debate about the roles of open and closed MMP1 structures in function. We fitted the histograms of single-molecule Förster resonance energy transfer values to a sum of two Gaussians and the autocorrelations to an exponential and power law. We used a two-state Poisson process to describe the dynamics and calculate the kinetic rates from the fit parameters. All-atom and coarse-grained simulations reproduced some of the experimental features and revealed substrate-dependent MMP1 dynamics. Our results suggest that an interdomain separation facilitates opening up the catalytic pocket so that the collagen chains come closer to the MMP1 active site. Coordination of functional conformations at different parts of MMP1 occurs via allosteric communications that can take place via interactions mediated by collagen even if the linker between the domains is absent. Modeling dynamics as a Poisson process enables connecting the picosecond timescales of molecular dynamics simulations with the millisecond timescales of single-molecule measurements. Water-soluble MMP1 interacting with water-insoluble collagen fibrils poses challenges for biochemical studies that the single-molecule tracking can overcome for other insoluble substrates. Interdomain communications are likely important for multidomain proteins.

Structural compliance: A new metric for protein flexibility

Proteins are the active players in performing essential molecular activities throughout biology, and their dynamics has been broadly demonstrated to relate to their mechanisms. The intrinsic fluctuations have often been used to represent their dynamics and then compared to the experimental B-factors. However, proteins do not move in a vacuum and their motions are modulated by solvent that can impose forces on the structure. In this paper, we introduce a new structural concept, which has been called the structural compliance, for the evaluation of the global and local deformability of the protein structure in response to intramolecular and solvent forces. Based on the application of pairwise pulling forces to a protein elastic network, this structural quantity has been computed and sometimes is even found to yield an improved correlation with the experimental B-factors, meaning that it may serve as a better metric for protein flexibility. The inverse of structural compliance, namely the structural stiffness, has also been defined, which shows a clear anticorrelation with the experimental data. Although the present applications are made to proteins, this approach can also be applied to other biomolecular structures such as RNA. This present study considers only elastic network models, but the approach could be applied further to conventional atomic molecular dynamics. Compliance is found to have a slightly better agreement with the experimental B-factors, perhaps reflecting its bias toward the effects of local perturbations, in contrast to mean square fluctuations. The code for calculating protein compliance and stiffness is freely accessible at https://jerniganlab.github.io/Software/PACKMAN/Tutorials/compliance.

Higher Accuracy Achieved for Protein-Ligand Binding Pose Prediction by Elastic Network Model-Based Ensemble Docking

Molecular docking plays an indispensable role in predicting the receptor-ligand interactions in which the protein receptor is usually kept rigid, whereas the ligand is treated as being flexible. Because of the inherent flexibility of proteins, the binding pocket of apo receptors might undergo significant conformational rearrangement upon ligand binding, which limits the prediction accuracy of docking. Here, we present an iterative anisotropic network model (iterANM)-based ensemble docking approach, which generates multiple holo-like receptor structures starting from the apo receptor and incorporates protein flexibility into docking. In a validation data set consisting of 233 chemically diverse cyclin-dependent kinase 2 (CDK2) inhibitors, the iterANM-based ensemble docking achieves higher capacity to reproduce native-like binding poses compared with those using single apo receptor conformation or conformational ensemble from molecular dynamics simulations. The prediction success rate within the top5-ranked binding poses produced by the iterANM can further be improved through reranking with the molecular mechanics-Poisson-Boltzmann surface area method. In a smaller data set with 58 CDK2 inhibitors, the iterANM-based ensemble shows a higher success rate compared with the flexible receptor-based docking procedure AutoDockFR and other receptor conformation generation approaches. Further, an additional docking test consisting of 10 diverse receptor-ligand combinations shows that the iterANM is robustly applicable for different receptor structures. These results suggest the iterANM-based ensemble docking as an accurate, efficient, and practical framework to predict the binding mode of a ligand for receptors with flexibility.

Modulation of BIN2 kinase activity by HY5 controls hypocotyl elongation in the light

ELONGATED HYPOCOTYL 5 (HY5), a basic domain/leucine zipper (bZIP) transcription factor, acts as a master regulator of transcription to promote photomorphogenesis. At present, it's unclear whether HY5 uses additional mechanisms to inhibit hypocotyl elongation. Here, we demonstrate that HY5 enhances the activity of GSK3-like kinase BRASSINOSTEROID-INSENSITIVE 2 (BIN2), a key repressor of brassinosteroid signaling, to repress hypocotyl elongation. We show that HY5 physically interacts with and genetically acts through BIN2 to inhibit hypocotyl elongation. The interaction of HY5 with BIN2 enhances its kinase activity possibly by the promotion of BIN2 Tyr200 autophosphorylation, and subsequently represses the accumulation of the transcription factor BRASSINAZOLE-RESISTANT 1 (BZR1). Leu137 of HY5 is found to be important for the HY5-BIN2 interaction and HY5-mediated regulation of BIN2 activity, without affecting the transcriptional activity of HY5. HY5 levels increase with light intensity, which gradually enhances BIN2 activity. Thus, our work reveals an additional way in which HY5 promotes photomorphogenesis, and provides an insight into the regulation of GSK3 activity.

M2 amphipathic helices facilitate pH-dependent conformational transition in influenza A virus

The matrix-2 (M2) protein from influenza A virus is a tetrameric, integral transmembrane (TM) protein that plays a vital role in viral replication by proton flux into the virus. The His37 tetrad is a pH sensor in the center of the M2 TM helix that activates the channel in response to the low endosomal pH. M2 consists of different regions that are believed to be involved in membrane targeting, packaging, nucleocapsid binding, and proton transport. Although M2 has been the target of many experimental and theoretical studies that have led to significant insights into its structure and function under differing conditions, the main mechanism of proton transport, its conformational dynamics, and the role of the amphipathic helices (AHs) on proton conductance remain elusive. To this end, we have applied explicit solvent constant pH molecular dynamics using the multisite λ-dynamics approach (CpHMD^MSλD) to investigate the buried ionizable residues comprehensively and to elucidate their effect on the conformational transition. Our model recapitulates the pH-dependent conformational transition of M2 from closed to open state when the AH domain is included in the M2 construct, revealing the role of the amphipathic helices on this transition and shedding light on the proton-transport mechanism. This work demonstrates the importance of including the amphipathic helices in future experimental and theoretical studies of ion channels. Finally, our work shows that explicit solvent CpHMD^MSλD provides a realistic pH-dependent model for membrane proteins.

Spectral analysis of molecular dynamics simulations on PDZ: MD sectors

The idea of protein "sectors" posits that sparse subsets of amino acid residues form cooperative networks that are key elements of protein stability, ligand binding, and allosterism. To date, protein sectors have been calculated by the statistical coupling analysis (SCA) method of Ranganathan and co-workers via the spectral analysis of conservation-weighted evolutionary covariance matrices obtained from a multiple sequence alignments of homologous families of proteins. SCA sectors, a knowledge-based protocol, have been indentified with functional properties and allosterism for a number of systems. In this study, we investigate the utility of the sector idea for the analysis of physics-based molecular dynamics (MD) trajectories of proteins. Our test case for this procedure is PSD95- PDZ3, one of the smallest proteins for which allosterism has been observed. It has served previously as a model system for a number of prediction algorithms, and is well characterized by X-ray crystallography, NMR spectroscopy and site specific mutagenisis. All-atom MD simulations were performed for a total of 500 nanoseconds using AMBER, and MD-calculated covariance matrices for the fluctuations of residue displacements and non-bonded interaction energies were subjected to spectral analysis in a manner analogous to that of SCA. The composition of MD sectors was compared with results from SCA, site specific mutagenesis, and allosterism. The concordance indicates that MD sectors are a viable protocol for analyzing MD trajectories and provide insight into the physical origin of the phenomenon.Communicated by Ramaswamy H. Sarma.

Large-Scale Biomolecular Conformational Transitions Explored by a Combined Elastic Network Model and Enhanced Sampling Molecular Dynamics

Biomolecules often undergo large-scale conformational transitions when carrying out their functions. However, it is still challenging for conventional molecular dynamics simulations to provide adequate structural dynamics information to interpret associated mechanisms. Here, we present a combined elastic network model and enhanced sampling-based strategy (iterANM-IaMD) by adopting iterANM to construct initial conformation space and enhanced sampling IaMD to explore the free energy landscape along specific large-scale conformational transitions. We applied this strategy to three functionally and structurally distinct proteins (adenylate kinase, calmodulin, and p38α kinase), which undergo striking conformational change upon ligand binding. The simulation results for both free and ligand-bound proteins show qualitative and quantitative agreement with existing studies, suggesting iterANM-IaMD as an accurate and efficient tool to investigate structural dynamics involved in complicated biological processes. Our work also provides insights into the relationship between the dynamics and functionality of biomolecules.

Analyzing Fluctuation Properties in Protein Elastic Networks with Sequence-Specific and Distance-Dependent Interactions

Simple protein elastic networks which neglect amino-acid information often yield reasonable predictions of conformational dynamics and are broadly used. Recently, model variants which incorporate sequence-specific and distance-dependent interactions of residue pairs have been constructed and demonstrated to improve agreement with experimental data. We have applied the new variants in a systematic study of protein fluctuation properties and compared their predictions with those of conventional anisotropic network models. We find that the quality of predictions is frequently linked to poor estimations in highly flexible protein regions. An analysis of a large set of protein structures shows that fluctuations of very weakly connected network residues are intrinsically prone to be significantly overestimated by all models. This problem persists in the new models and is not resolved by taking into account sequence information. The effect becomes even enhanced in the model variant which takes into account very soft long-ranged residue interactions. Beyond these shortcomings, we find that model predictions are largely insensitive to the integration of chemical information, at least regarding the fluctuation properties of individual residues. One can furthermore conclude that the inherent drawbacks may present a serious hindrance when improvement of elastic network models are attempted.

Soft regions of protein surface are potent for stable dimer formation

By having knowledge about the characteristics of protein interaction interfaces, we will be able to manipulate protein complexes for therapies. Dimer state is considered as the primary alphabet of the most proteins' quaternary structure. The properties of binding interface between subunits and of noninterface region define the specificity and stability of the intended protein complex. Considering some topological properties and amino acids' affinity for binding in interfaces of protein dimers, we construct the interface-specific recurrence plots. The data obtained from recurrence quantitative analysis, and accessibility-related metrics help us to classify the protein dimers into four distinct classes. Some mechanical properties of binding interfaces are computed for each predefined class of the dimers. The computed mechanical characteristics of binding patch region are compared with those of nonbinding region of proteins. Our observations indicate that the mechanical properties of protein binding sites have a decisive impact on determining the dimer stability. We introduce a new concept in analyzing protein structure by considering mechanical properties of protein structure. We conclude that the interface region between subunits of stable dimers is usually mechanically softer than the interface of unstable protein dimers. AbbreviationsAABaverage affinity for bindingANManisotropic network modelAPCaffinity propagation clusteringASAaccessible surface areaCCDinter residues distanceCSCcomplex stability codeDMdistance matrixΔG_dissPISA-computed dissociation free energyGNMGaussian normal mode analysisNMAnormal mode analysisPBPprotein binding patchPISAproteins, interfaces, structures and assembliesrASArelative accessible area in respect to unfolded state of residuesRMrecurrence matrixrPrelative protrusionRPrecurrence plotRQArecurrence quantitative analysisSEMstandard error of meanCommunicated by Ramaswamy H. Sarma.