Molecular Dynamics Simulations in Protein-Protein Docking.

Concerted interactions between all the cell components form the basis of biological processes. Protein-protein interactions (PPIs) constitute a tremendous part of this interaction network. Deeper insight into PPIs can help us better understand numerous diseases and lead to the development of new diagnostic and therapeutic strategies. PPI interfaces, until recently, were considered undruggable. However, it is now believed that the interfaces contain "hot spots," which could be targeted by small molecules. Such a strategy would require high-quality structural data of PPIs, which are difficult to obtain experimentally. Therefore, in silico modeling can complement or be an alternative to in vitro approaches. There are several computational methods for analyzing the structural data of the binding partners and modeling of the protein-protein dimer/oligomer structure. The major problem with in silico structure prediction of protein assemblies is obtaining sufficient sampling of protein dynamics. One of the methods that can take protein flexibility and the effects of the environment into account is Molecular Dynamics (MD). While sampling of the whole protein-protein association process with plain MD would be computationally expensive, there are several strategies to harness the method to PPI studies while maintaining reasonable use of resources. This chapter reviews known applications of MD in the PPI investigation workflows.

Leveraging binding pose metadynamics to optimise target fishing predictions for three diverse ligands and their true targets.

Computational target fishing plays an important role in target identification, particularly in drug discovery campaigns utilizing phenotypic screening. Numerous approaches exist to predict potential targets for a given ligand, but true targets may be inconsistently ranked. More advanced simulation methods may provide benefit in such cases by reranking these initial predictions. We evaluated the ability of binding pose metadynamics to improve the predicted rankings for three diverse ligands and their six true targets. Initial predictions using pharmacophore mapping showed no true targets ranked in the top 50 and two targets each ranked within the 50-100, 100-150, and 250-300 ranges respectively. Following binding pose metadynamics, ranking of true targets improved for four out of the six targets and included the highest ranked predictions overall, while rankings deteriorated for two targets. The revised rankings predicted two true targets ranked within the top 50, and one target each within the 50-100, 100-150, 150-200, and 200-250 ranges respectively. The findings of this study demonstrate that binding pose metadynamics may be of benefit in refining initial predictions from structure-based target fishing algorithms, thereby improving the efficiency of the target identification process in drug discovery efforts.

Computational screening combined with well-tempered metadynamics simulations identifies potential TMPRSS2 inhibitors.

Type-II transmembrane serine proteases are effective pharmacological targets for host defence against viral entry and in certain cancer cell progressions. These serine proteases cleave viral spike proteins to expose the fusion peptide for cell entry, which is essential to the life cycle of the virus. TMPRSS2 inhibitors can also fight against respiratory viruses that employ them for cell entry. Our study combining virtual screening, all-atom molecular dynamics, and well-tempered metadynamics simulation identifies vicenin-2, neohesperidin, naringin, and rhoifolin as promising TMPRSS2 antagonists. The binding energies obtained are - 16.3, - 15.4, - 13.6, and - 13.8 kcal/mol for vicenin-2, neohesperidin, naringin, and rhoifolin respectively. The RMSD, RMSF, PCA, DCCM, and binding free energy profiles also correlate with the stable binding of these ligands at the active site of TMPRSS2. The study reveals that these molecules could be promising lead molecules for combating future outbreaks of coronavirus and other respiratory viruses.

Computational Insights into Papaveroline as an In Silico Drug Candidate for Alzheimer's Disease via Fyn Tyrosine Kinase Inhibition.

Alzheimer's disease (AD) poses a significant global health challenge, necessitating the exploration of novel therapeutic strategies. Fyn Tyrosine Kinase has emerged as a key player in AD pathogenesis, making it an attractive target for drug development. This study focuses on investigating the potential of Papaveroline as a drug candidate for AD by targeting Fyn Tyrosine Kinase. The research employed high-throughput virtual screening and QSAR analysis were conducted to identify compounds with optimal drug-like properties, emphasizing adherence to ADMET parameters for further evaluation. Molecular dynamics simulations to analyze the binding interactions between Papaveroline and Staurosporine with Fyn Tyrosine Kinase over a 200-ns period. The study revealed detailed insights into the binding mechanisms and stability of the Papaveroline-Fyn complex, showcasing the compound's potential as an inhibitor of Fyn Tyrosine Kinase. Comparative analysis with natural compounds and a reference compound highlighted Papaveroline's unique characteristics and promising therapeutic implications for AD treatment. Overall, the findings underscore Papaveroline's potential as a valuable drug candidate for targeting Fyn Tyrosine Kinase in AD therapy, offering new avenues for drug discovery in neurodegenerative diseases. This study contributes to advancing our understanding of molecular interactions in AD pathogenesis and paves the way for further research and development in this critical area.

Interface-driven structural evolution on diltiazem as novel uPAR inhibitors: from in silico design to in vitro evaluation.

The urokinase-type plasminogen activator receptor (uPAR) emerges as a key target for anti-metastasis owing to its pivotal role in facilitating the invasive and migratory processes of cancer cells. Recently, we identified the uPAR-targeting anti-metastatic ability of diltiazem (22), a commonly used antihypertensive agent. Fine-tuning the chemical structures of known hits represents a vital branch of drug development. To develop novel anti-metastatic drugs, we performed an interface-driven structural evolution strategy on 22. The uPAR-targeting and anti-cancer abilities of this antihypertensive drug wereidentified by us recently. Based on in silico strategy, including extensive molecular dynamics (MD) simulations, hierarchical binding free energy predictions, and ADMET profilings, we designed, synthesized, and identified three new diltiazem derivatives (221-8, 221-57, and 221-68) as uPAR inhibitors. Indeed, all of these three derivatives exhibited uPAR-depending inhibitory activity against PC-3 cell line invasion at micromolar level. Particularly, derivatives 221-68 and 221-8 showed enhanced uPAR-dependent inhibitory activity against the tumor cell invasion compared to the original compound. Microsecond timesclae MD simulations demonstrated the optimized moiety of 221-68 and 221-8 forming more comprehensive interactions with the uPAR, highlighting the reasonability of our strategy. This work introduces three novel uPAR inhibitors, which not only pave the way for the development of effective anti-metastatic therapeutics, but also emphasize the efficacy and robustness of an in silico-based lead compound optimization strategy in drug design.

Application of Funnel Metadynamics to the Platelet Integrin αIIbß3 in Complex with an RGD Peptide.

Integrin αIIbß3 mediates platelet aggregation by binding the Arginyl-Glycyl-Aspartic acid (RGD) sequence of fibrinogen. RGD binding occurs at a site topographically proximal to the αIIb and ß3 subunits, promoting the conformational activation of the receptor from bent to extended states. While several experimental approaches have characterized RGD binding to αIIbß3 integrin, applying computational methods has been significantly more challenging due to limited sampling and the need for a priori information regarding the interactions between the RGD peptide and integrin. In this study, we employed all-atom simulations using funnel metadynamics (FM) to evaluate the interactions of an RGD peptide with the αIIb and ß3 subunits of integrin. FM incorporates an external history-dependent potential on selected degrees of freedom while applying a funnel-shaped restraint potential to limit RGD exploration of the unbound state. Furthermore, it does not require a priori information about the interactions, enhancing the sampling at a low computational cost. Our FM simulations reveal significant molecular changes in the ß3 subunit of integrin upon RGD binding and provide a free-energy landscape with a low-energy binding mode surrounded by higher-energy prebinding states. The strong agreement between previous experimental and computational data and our results highlights the reliability of FM as a method for studying dynamic interactions of complex systems such as integrin.

Engineered nanoparticles as Selinexor drug delivery systems across the cell membrane and related signaling pathways in cancer cells.

In the present work, molecular dynamics simulation is applied to evaluate the drug carrier efficiency of graphene oxide nanoflake (GONF) for loading of Selinexor (SXR) drug as well as the drug delivery by 2D material through the membrane in aqueous solution. In addition, to investigate the adsorption and penetration of drug-nanocarrier complex into the cell membrane, well-tempered metadynamics simulations and steered molecular dynamics (SMD) simulations were performed. Based on the obtained results, it is evident that intermolecular hydrogen bonds (HBs) and π-π interactions play a significant role in expediting the interaction between drug molecules and the graphene oxide (GO) nanosheet, ultimately resulting in the formation of a stable SXR-GO complex. The Lennard-Jones (L-J) energy value for the interaction of SXR with GONF is calculated to be approximately -98.85 kJ/mol. In the SXR-GONF complex system, the dominant interaction between SXR and GONF is attributed to the L-J term, resulting from the formation of a strong π-π interaction between the drug molecules and the substrate surface. Moreover, our simulations show by decreasing the distance of GONF with respect to cell membrane, the interaction energy of GONF-membrane significantly decrease to -1500 kJ/mol resulting in fast diffusion of SXR-GONF complex toward the bilayer surface that is favored opening the way to natural drug nanocapsule.

Ferroelasticity in Two-Dimensional Hybrid Ruddlesden-Popper Perovskites Mediated by Cross-Plane Intermolecular Coupling and Metastable Funnel-like Phases.

Ferroelasticity is a phenomenon in which a material exhibits two or more equally stable orientation variants and can be switched from one form to another under an applied stress. Recent works have demonstrated that two-dimensional layered organic-inorganic hybrid Ruddlesden-Popper perovskites can serve as ideal platforms for realizing ferroelasticity, however, the ferroelastic (FE) behavior of structures with a single octahedra layer such as (BA)2PbI4 [BA = CH3(CH2)3NH3+] has remained elusive. Herein, by using a combined first-principles and metadynamics approach, the FE behavior of (BA)2PbI4 under mechanical and thermal stresses is uncovered. FE switching is mediated by cross-plane intermolecular coupling, which could occur through multiple rotational modes, rendering the formation of FE domains and several metastable paraelastic (PE) phases. Such metastable phases are akin to wrinkled structures in other layered materials and can act as a "funnel" of hole carriers. Thermal excitation tends to flatten the kinetic barriers of the transition pathways between orientation variants, suggesting an enhanced concentration of metastable PE states at high temperatures, while halogen mixing with Br raises these barriers and conversely lowers the concentration of PE states. These findings reveal the rich structural diversity of (BA)2PbI4 domains, which can play a vital role in enhancing the optoelectronic properties of the perovskite and raise exciting prospects for mechanical switching, shape memory, and information processing.

Unlocking the specificity of antimicrobial peptide interactions for membrane-targeted therapies.

Antimicrobial peptides (AMPs) are increasingly recognized as potent therapeutic agents, with their selective affinity for pathological membranes, low toxicity profile, and minimal resistance development making them particularly attractive in the pharmaceutical landscape. This study offers a comprehensive analysis of the interaction between specific AMPs, including magainin-2, pleurocidin, CM15, LL37, and clavanin, with lipid bilayer models of very different compositions that have been ordinarily used as biological membrane models of healthy mammal, cancerous, and bacterial cells. Employing unbiased molecular dynamics simulations and metadynamics techniques, we have deciphered the intricate mechanisms by which these peptides recognize pathogenic and pathologic lipid patterns and integrate into lipid assemblies. Our findings reveal that the transverse component of the peptide's hydrophobic dipole moment is critical for membrane interaction, decisively influencing the molecule's orientation and expected therapeutic efficacy. Our approach also provides insight on the kinetic and dynamic dependence on the peptide orientation in the axial and azimuthal angles when coming close to the membrane. The aim is to establish a robust framework for the rational design of peptide-based, membrane-targeted therapies, as well as effective quantitative descriptors that can facilitate the automated design of novel AMPs for these therapies using machine learning methods.

Variations in proton transfer pathways and energetics on pristine and defect-rich quartz surfaces in water: Insights into the bimodal acidities of quartz.

HYPOTHESIS: Understanding the mechanisms of proton transfer on quartz surfaces in water is critical for a range of processes in geochemical, environmental, and materials sciences. The wide range of surface acidities (>9 pKa units) found on the ubiquitous mineral quartz is caused by the structural variations of surface silanol groups. Molecular scale simulations provide essential tools for elucidating the origin of site-specific surface acidities. SIMULATIONS: We used density-functional tight-binding-based molecular dynamics combined with rare-event metadynamics simulations to probe the mechanisms of deprotonation reactions from ten representative surface silanol groups found on both pristine and defect-rich quartz (101) surfaces with Si vacancies. FINDINGS: The results show that deprotonation is a highly dynamic process where both the surface hydroxyls and bridging oxygen atoms serve as the proton acceptors, in addition to water. Deprotonation of embedded silanols through intrasurface proton transfer exhibited lower pKa values with less H-bond participation and higher energy barriers, suggesting a new mechanism to explain the bimodal acidity observed on quartz surface. Defect sites, recently shown to comprise a significant portion of the quartz (101) surface, diversify the coordination and local H-bonding environments of the surface silanols, changing both the deprotonation pathways and energetics, leading to a wider range of pKa values (2.4 to 11.5) than that observed on pristine quartz surface (10.4 and 12.1).

Mechanistic insights into G-protein activation via phosphorylation mediated non-canonical pathway.

Activation of heterotrimeric G-proteins (Gαßγ) downstream to receptor tyrosine kinases (RTKs) is a well-established crosstalk between the signaling pathways mediated by G-protein coupled receptors (GPCRs) and RTKs. While GPCR serves as a guanine exchange factor (GEF) in the canonical activation of Gα that facilitates the exchange of GDP for GTP, the mechanism through which RTK phosphorylations induce Gα activation remains unclear. Recent experimental studies revealed that the epidermal growth factor receptor (EGFR), a well-known RTK, phosphorylates the helical domain tyrosine residues Y154 and Y155 and accelerates the GDP release from the Gαi3, a subtype of Gα-protein. Using well-tempered metadynamics and extensive unbiased molecular dynamics simulations, we captured the GDP release event and identified the intermediates between bound and unbound states through Markov state models. In addition to weakened salt bridges at the domain interface, phosphorylations induced the unfolding of helix αF, which contributed to increased flexibility near the hinge region, facilitating a greater distance between domains in the phosphorylated Gαi3. Although the larger domain separation in the phosphorylated system provided an unobstructed path for the nucleotide, the accelerated release of GDP was attributed to increased fluctuations in several conserved regions like P-loop, switch 1, and switch 2. Overall, this study provides atomistic insights into the activation of G-proteins induced by RTK phosphorylations and identifies the specific structural motifs involved in the process. The knowledge gained from the study could establish a foundation for targeting non-canonical signaling pathways and developing therapeutic strategies against the ailments associated with dysregulated G-protein signaling.

Effect of Anchoring Dynamics on Proton-Coupled Electron Transfer in the Ru(bda) Coordination Oligomer on a Graphitic Surface.

The oligomeric ruthenium-based water oxidation catalyst, Ru(bda), is known to be experimentally anchored on graphitic surfaces through CH-π stacking interactions between the auxiliary bda ([2,2'-bipyridine]-6,6'-dicarboxylate) ligand bonded to ruthenium and the hexagonal rings of the surface. This anchoring provides control over their molecular coverage and enables efficient catalysis of water oxidation to dioxygen. The oligomeric nature of the molecule offers multiple anchoring sites at the surface, greatly enhancing the overall stability of the hybrid catalyst-graphitic surface anode through dynamic bonding. However, the impact of this dynamic anchoring on the overall catalytic mechanism is still a topic of debate. In this study, a crucial proton-coupled electron transfer event in the catalytic cycle is investigated using DFT-based molecular dynamics simulations plus metadynamics. The CH-π stacking anchoring plays a critical role not only in stabilizing this hybrid system but also in facilitating the proton-coupled electron transfer event with possible vibronic couplings between the anchoring bonds motion and charge fluctuations at the catalyst - graphitic surface interface. Furthermore, this computational investigation displays the presence of a quartet spin state intermediate that can lead to the experimentally observed and thermodynamically more stable doublet spin state.

Discovery of Novel Anti-Resistance AR Antagonists Guided by Funnel Metadynamics Simulation.

Androgen receptor (AR) antagonists are widely used for the treatment of prostate cancer (PCa), but their therapeutic efficacy is usually compromised by the rapid emergence of drug resistance. However, the lack of the detailed interaction between AR and its antagonists poses a major obstacle to the design of novel AR antagonists. Here, funnel metadynamics is employed to elucidate the inherent regulation mechanisms of three AR antagonists (hydroxyflutamide, enzalutamide, and darolutamide) on AR. For the first time it is observed that the binding of antagonists significantly disturbed the C-terminus of AR helix-11, thereby disrupting the specific internal hydrophobic contacts of AR-LBD and correspondingly the communication between AR ligand binding pocket (AR-LBP), activation function 2 (AF2), and binding function 3 (BF3). The subsequent bioassays verified the necessity of the hydrophobic contacts for AR function. Furthermore, it is found that darolutamide, a newly approved AR antagonist capable of fighting almost all reported drug resistant AR mutants, can induce antagonistic binding structure. Subsequently, docking-based virtual screening toward the dominant binding conformation of AR for darolutamide is conducted, and three novel AR antagonists with favorable binding affinity and strong capability to combat drug resistance are identified by in vitro bioassays. This work provides a novel rational strategy for the development of anti-resistant AR antagonists.

Cyclodextrins: Establishing building blocks for AI-driven drug design by determining affinity constants in silico.

Cyclodextrins (CDs) are cyclic carbohydrate polymers that hold significant promise for drug delivery and industrial applications. Their effectiveness depends on their ability to encapsulate target molecules with strong affinity and specificity, but quantifying affinities in these systems accurately is challenging for a variety of reasons. Computational methods represent an exceptional complement to in vitro assays because they can be employed for existing and hypothetical molecules, providing high resolution structures in addition to a mechanistic, dynamic, kinetic, and thermodynamic characterization. Here, we employ potential of mean force (PMF) calculations obtained from guided metadynamics simulations to characterize the 1:1 inclusion complexes between four different modified ßCDs, with different type, number, and location of substitutions, and two sterol molecules (cholesterol and 7-ketocholesterol). Our methods, validated for reproducibility through four independent repeated simulations per system and different post processing techniques, offer new insights into the formation and stability of CD-sterol inclusion complexes. A systematic distinct orientation preference where the sterol tail projects from the CD's larger face and significant impacts of CD substitutions on binding are observed. Notably, sampling only the CD cavity's wide face during simulations yielded comparable binding energies to full-cavity sampling, but in less time and with reduced statistical uncertainty, suggesting a more efficient approach. Bridging computational methods with complex molecular interactions, our research enables predictive CD designs for diverse applications. Moreover, the high reproducibility, sensitivity, and cost-effectiveness of the studied methods pave the way for extensive studies of massive CD-ligand combinations, enabling AI algorithm training and automated molecular design.

Unraveling the molecular dynamics of sugammadex-rocuronium complexation: A blueprint for cyclodextrin drug design.

Sugammadex, marketed as Bridion™, is an approved cyclodextrin (CD) based drug for the reversal of neuromuscular blockade in adults undergoing surgery. Sugammadex forms an inclusion complex with the neuromuscular blocking agent (NMBA) rocuronium, allowing rapid reversal of muscle paralysis. In silico methods have been developed for studying CD inclusion complexes, aimed at accurately predicting their structural, energetic, dynamic, and kinetic properties, as well as binding constants. Here, a computational study aimed at characterizing the sugammadex-rocuronium system from the perspective of docking calculations, free molecular dynamics (MD) simulations, and biased metadynamics simulations with potential of mean force (PMF) calculations is presented. The aim is to provide detailed information about this system, as well as to use it as a model system for validation of the methods. This method predicts results in line with experimental evidence for both the optimal structure and the quantitative value for the binding constant. Interestingly, there is a less profound preference for the orientation than might be assumed based on electrostatic interactions, suggesting that both orientations may exist in solution. These results show that this technology can efficiently analyze CD inclusion complexes and could be used to facilitate the development and optimization of novel applications for CDs.

Computational study of the mechanism of binding of antifungal icofungipen in the active site of eukaryotic isoleucyl tRNA synthetase from Candida albicans.

The eukaryotic fungal species Candida albicans is a critical infective pathogenic agent. The ß-amino acid, Icofungipen, is an effective inhibitor of Candida albicans. Icofungipen binds at the active site of the isoleucyl tRNA synthetase (IleRS) from Candida albicans (CaIleRS) and halts protein translation in fungus. In the present work, we have investigated the mechanism of binding of Icogungipen (abbreviated as IFP). Molecular dynamics (MD) simulations show that the carboxylic acid group of IFP in the CaIleRS: IFP complex is more oriented towards the Connective Polypeptide (CP) core loop compared to the carboxylic acid group of Ile in the CaIleRS: Ile complex. The Arg 410 of the CP core loop near the substrate is extended towards the IFP. Due to the difference in the conformation of residues of the CP core loop, the KMSKR loop is more proximal to the CP core loop in CaIleRS: IFP. The editing domain which is covalently linked with the CP core loop in the CaIleRS: IFP complex is also oriented in such a way that the active site cavity is narrow and longer. The metadynamics calculation shows that the IFP is trapped in a deeper potential well compared to Ile which is due to the effective closure of the gateway of the active site by KMSKR and CP core loop. The thin, long shape of the active site and the closed gate of the active site in CaIleRS: IFP complex is responsible for the effective capture of IFP relative to Ile in the active site.Communicated by Ramaswamy H. Sarma.

Recovery from desensitization in GluA2 AMPA receptors is affected by a single mutation in the N-terminal domain interface.

AMPA-type ionotropic glutamate receptors (AMPARs) are central to various neurological processes, including memory and learning. They assemble as homo- or heterotetramers of GluA1, GluA2, GluA3, and GluA4 subunits, each consisting of an N-terminal domain (NTD), a ligand-binding domain, a transmembrane domain, and a C-terminal domain. While AMPAR gating is primarily controlled by reconfiguration in the ligand-binding domain layer, our study focuses on the NTDs, which also influence gating, yet the underlying mechanism remains enigmatic. In this investigation, we employ molecular dynamics simulations to evaluate the NTD interface strength in GluA1, GluA2, and NTD mutants GluA2-H229N and GluA1-N222H. Our findings reveal that GluA1 has a significantly weaker NTD interface than GluA2. The NTD interface of GluA2 can be weakened by a single point mutation in the NTD dimer-of-dimer interface, namely H229N, which renders GluA2 more GluA1-like. Electrophysiology recordings demonstrate that this mutation also leads to slower recovery from desensitization. Moreover, we observe that lowering the pH induces more splayed NTD states and enhances desensitization in GluA2. We hypothesized that H229 was responsible for this pH sensitivity; however, GluA2-H229N was also affected by pH, meaning that H229 is not solely responsible and that protons exert their effect across multiple domains of the AMPAR. In summary, our work unveils an allosteric connection between the NTD interface strength and AMPAR desensitization.

Thermal isomerization rates in retinal analogues using Ab-Initio molecular dynamics.

For a detailed understanding of chemical processes in nature and industry, we need accurate models of chemical reactions in complex environments. While Eyring transition state theory is commonly used for modeling chemical reactions, it is most accurate for small molecules in the gas phase. A wide range of alternative rate theories exist that can better capture reactions involving complex molecules and environmental effects. However, they require that the chemical reaction is sampled by molecular dynamics simulations. This is a formidable challenge since the accessible simulation timescales are many orders of magnitude smaller than typical timescales of chemical reactions. To overcome these limitations, rare event methods involving enhanced molecular dynamics sampling are employed. In this work, thermal isomerization of retinal is studied using tight-binding density functional theory. Results from transition state theory are compared to those obtained from enhanced sampling. Rates obtained from dynamical reweighting using infrequent metadynamics simulations were in close agreement with those from transition state theory. Meanwhile, rates obtained from application of Kramers' rate equation to a sampled free energy profile along a torsional dihedral reaction coordinate were found to be up to three orders of magnitude higher. This discrepancy raises concerns about applying rate methods to one-dimensional reaction coordinates in chemical reactions.

Unravelling carbohydrate binding module 21 (CBM21) dynamics of interaction with amylose.

The carbohydrate binding module 21 (CBM21) from Rhizopus oryzae is a dual-site CBM proposed to disrupt polysaccharide structures. Additionally, it serves as a purification tag in industry. CBM21 crystal structure features a Glc residue in an unusual 1S3 conformation, whose relevance for the CBM mechanism of action is unclear. In this context, we seek to contribute for the understanding of CBM21 mechanism of action by: i) investigating the role of the 1S3 conformation on carbohydrate recognition, and ii) characterize the protein-carbohydrate binding dynamics using molecular dynamics and metadynamics simulations at MM and QM/MM levels. Results indicate the 1S3 Glc conformation is unlikely to occur under biological conditions, being originated from the crystallographic environment. CBM21 binding to small ligands appears transient and unstable, while protein dimerization and polysaccharide chain size influence complex stability. In interactions with amylose, CBM21 exhibits a repeated unbinding followed by re-binding, while simultaneously alternating between binding sites I and II. These results suggest that CBM21 acts through transient interactions, directing carbohydrates to the catalytic center rather than forming strong and long-lasting bonds with carbohydrates. Accordingly, we expect such atomistic depiction of CBM21 mechanism could aid in CBM design targeting biotechnological applications.

Molecular dynamics-based identification of binding pathways and two distinct high-affinity sites for succinate in succinate receptor 1/GPR91.

SUCNR1 is an auto- and paracrine sensor of the metabolic stress signal succinate. Using unsupervised molecular dynamics (MD) simulations (170.400 ns) and mutagenesis across human, mouse, and rat SUCNR1, we characterize how a five-arginine motif around the extracellular pole of TM-VI determines the initial capture of succinate in the extracellular vestibule (ECV) to either stay or move down to the orthosteric site. Metadynamics demonstrate low-energy succinate binding in both sites, with an energy barrier corresponding to an intermediate stage during which succinate, with an associated water cluster, unlocks the hydrogen-bond-stabilized conformationally constrained extracellular loop (ECL)-2b. Importantly, simultaneous binding of two succinate molecules through either a "sequential" or "bypassing" mode is a frequent endpoint. The mono-carboxylate NF-56-EJ40 antagonist enters SUCNR1 between TM-I and -II and does not unlock ECL-2b. It is proposed that occupancy of both high-affinity sites is required for selective activation of SUCNR1 by high local succinate concentrations.