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

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

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

Exploration of Cryptic Pockets Using Enhanced Sampling Along Normal Modes: A Case Study of KRAS G12D

Identification of cryptic pockets has the potential to open new therapeutic opportunities by discovering ligand binding sites that remain hidden in static apo structures of a target protein. Moreover, allosteric cryptic pockets can become valuable for designing target-selective ligands when the natural ligand binding sites are conserved in variants of a protein. For example, before an allosteric cryptic pocket was discovered, KRAS was considered undruggable due to its smooth surface and conservation of the GDP/GTP binding pocket across the wild type and oncogenic isoforms. Recent identification of the Switch-II cryptic pocket in the KRASG12C mutant and FDA approval of anticancer drugs targeting this site underscores the importance of cryptic pockets in solving pharmaceutical challenges. Here, we present a newly developed approach for the exploration of cryptic pockets using weighted ensemble molecular dynamics simulations with inherent normal modes as progress coordinates applied to the wild type KRAS and the G12D mutant. We performed extensive all-atomic simulations (>400 μs) with and without several cosolvents (xenon, ethanol, benzene), and analyzed trajectories using three distinct methods to search for potential binding pockets. These methods have been applied as a proof-of-concept to KRAS and have shown they can predict known cryptic binding sites. Furthermore, we performed ligand-binding simulations of a known inhibitor (MRTX1133) to shed light on the nature of cryptic pockets in KRASG12D and the role of conformational selection vs induced-fit mechanism in the formation of these cryptic pockets.

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

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

Toward Reliable Conformational Energies of Amino Acids and Dipeptides─The DipCONFS Benchmark and DipCONL Datasets

Simulating peptides and proteins is becoming increasingly important, leading to a growing need for efficient computational methods. These are typically semiempirical quantum mechanical (SQM) methods, force fields (FFs), or machine-learned interatomic potentials (MLIPs), all of which require a large amount of accurate data for robust training and evaluation. To assess potential reference methods and complement the available data, we introduce two sets, DipCONFL and DipCONFS, which cover large parts of the conformational space of 17 amino acids and their 289 possible dipeptides in aqueous solution. The conformers were selected from the exhaustive PeptideCS dataset by Andris et al. [ J. Phys. Chem. B 2022, 126, 5949-5958]. The structures, originally generated with GFN2-xTB, were reoptimized using the accurate r2SCAN-3c density functional theory (DFT) composite method including the implicit CPCM water solvation model. The DipCONFS benchmark set contains 918 conformers and is one of the largest sets with highly accurate coupled cluster conformational energies so far. It is employed to evaluate various DFT and wave function theory (WFT) methods, especially regarding whether they are accurate enough to be used as reliable reference methods for larger datasets intended for training and testing more approximated SQM, FF, and MLIP methods. The results reveal that the originally provided BP86-D3(BJ)/DGauss-DZVP conformational energies are not sufficiently accurate. Among the DFT methods tested as an alternative reference level, the revDSD-PBEP86-D4 double hybrid performs best with a mean absolute error (MAD) of 0.2 kcal mol-1 compared with the PNO-LCCSD(T)-F12b reference. The very efficient r2SCAN-3c composite method also shows excellent results, with an MAD of 0.3 kcal mol-1, similar to the best-tested hybrid ωB97M-D4. With these findings, we compiled the large DipCONFL set, which includes over 29,000 realistic conformers in solution with reasonably accurate r2SCAN-3c reference conformational energies, gradients, and further properties potentially relevant for training MLIP methods. This set, also in comparison to DipCONFS, is used to assess the performance of various SQM, FF, and MLIP methods robustly and can complement training sets for those.

Structural and quantum chemical basis for OCP-mediated quenching of phycobilisomes

Cyanobacteria use large antenna complexes called phycobilisomes (PBSs) for light harvesting. However, intense light triggers non-photochemical quenching, where the orange carotenoid protein (OCP) binds to PBS, dissipating excess energy as heat. The mechanism of efficiently transferring energy from phycocyanobilins in PBS to canthaxanthin in OCP remains insufficiently understood. Using cryo-electron microscopy, we unveiled the OCP-PBS complex structure at 1.6- to 2.1-angstrom resolution, showcasing its inherent flexibility. Using multiscale quantum chemistry, we disclosed the quenching mechanism. Identifying key protein residues, we clarified how canthaxanthin's transition dipole moment in its lowest-energy dark state becomes large enough for efficient energy transfer from phycocyanobilins. Our energy transfer model offers a detailed understanding of the atomic determinants of light harvesting regulation and antenna architecture in cyanobacteria.

Investigating Potential Cancer Therapeutics: Insight into Histone Deacetylases (HDACs) Inhibitions

Histone deacetylases (HDACs) are enzymes that remove acetyl groups from ɛ-amino of histone, and their involvement in the development and progression of cancer disorders makes them an interesting therapeutic target. This study seeks to discover new inhibitors that selectively inhibit HDAC enzymes which are linked to deadly disorders like T-cell lymphoma, childhood neuroblastoma, and colon cancer. MOE was used to dock libraries of ZINC database molecules within the catalytic active pocket of target HDACs. The top three hits were submitted to MD simulations ranked on binding affinities and well-occupied interaction mechanisms determined from molecular docking studies. Inside the catalytic active site of HDACs, the two stable inhibitors LIG1 and LIG2 affect the protein flexibility, as evidenced by RMSD, RMSF, Rg, and PCA. MD simulations of HDACs complexes revealed an alteration from extended to bent motional changes within loop regions. The structural deviation following superimposition shows flexibility via a visual inspection of movable loops at different timeframes. According to PCA, the activity of HDACs inhibitors induces structural dynamics that might potentially be utilized to define the nature of protein inhibition. The findings suggest that this study offers solid proof to investigate LIG1 and LIG2 as potential HDAC inhibitors.

Interactions of the male contraceptive target EPPIN with semenogelin-1 and small organic ligands

Novel male contraceptives will promote gender equality in sharing contraceptive responsibility. The sperm-associated protein epididymal protease inhibitor (EPPIN) is a promising target for non-hormonal male contraception. EPPIN interacts with the semen coagulum protein semenogelin-1 (SEMG1) on the sperm surface, leading to transient inhibition of sperm motility after ejaculation. Small organic molecules targeting EPPIN's SEMG1-binding are under development as male contraceptives. Here, we combined computational approaches to uncover key aspects underlying EPPIN binding to SEMG1 and small organic ligands. We generated a human EPPIN model showing a typical arrangement of the WFDC (Whey-acid four disulfide core)-type and Kunitz-type domains, connected by a hinge region. Determining the EPPIN model's intrinsic motion by molecular dynamics simulations and normal mode analysis revealed a conformation, presenting a binding pocket that accommodates SEMG1Glu229-Gln247, EP055, and EP012. EPPIN's residues Phe63 and Lys68 (WFDC domain), Asp71 (hinge region), and Asn113, Asn114, and Asn115 (Kunitz domain) were identified as hot spots for SEMG1, EP055, and EP012 binding. Moreover, hydrophobic and hydrophilic residues in the WFDC and Kunitz domains allow plasma membrane anchoring, orienting the EPPIN binding pocket to the solvent. Targeting EPPIN's essential residues for its biomolecular interactions may improve the rational design of EPPIN ligands as spermiostatic compounds.

AI-Aided Search for New HIV-1 Protease Ligands

The availability of drugs capable of blocking the replication of microorganisms has been one of the greatest triumphs in the history of medicine, but the emergence of an ever-increasing number of resistant strains poses a serious problem for the treatment of infectious diseases. The search for new potential ligands for proteins involved in the life cycle of pathogens is, therefore, an extremely important research field today. In this work, we have considered the HIV-1 protease, one of the main targets for AIDS therapy. Several drugs are used today in clinical practice whose mechanism of action is based on the inhibition of this enzyme, but after years of use, even these molecules are beginning to be interested by resistance phenomena. We used a simple artificial intelligence system for the initial screening of a data set of potential ligands. These results were validated by docking and molecular dynamics, leading to the identification of a potential new ligand of the enzyme which does not belong to any known class of HIV-1 protease inhibitors. The computational protocol used in this work is simple and does not require large computational power. Furthermore, the availability of a large number of structural information on viral proteins and the presence of numerous experimental data on their ligands, with which it is possible to compare the results obtained with computational methods, make this research field the ideal terrain for the application of these new computational techniques.

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

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

MDexciteR: Enhanced Sampling Molecular Dynamics by Excited Normal Modes or Principal Components Obtained from Experiments

Molecular dynamics with excited normal modes (MDeNM) is an enhanced sampling method for exploring conformational changes in proteins with minimal biases. The excitation corresponds to injecting kinetic energy along normal modes describing intrinsic collective motions. Herein, we developed a new automated open-source implementation, MDexciteR (https://github.com/mcosta27/MDexciteR), enabling the integration of MDeNM with two commonly used simulation programs with GPU support. Second, we generalized the method to include the excitation of principal components calculated from experimental ensembles. Finally, we evaluated whether the use of coarse-grained normal modes calculated with elastic network representations preserved the performance and accuracy of the method. The advantages and limitations of these new approaches are discussed based on results obtained for three different protein test cases: two globular and a protein/membrane system.

Effect of phosphorylation on the structural dynamics, thermal stability of human dopamine transporter: A simulation study using normal modes, molecular dynamics and Markov State Model

The Human Dopamine Transporter (hDAT) plays an essential role in modulating the Influx/Efflux of dopamine, and it is involved in the mechanism of certain neurodegenerative diseases such as Parkinson's disease. Several studies have reported important states for Dopamine transport: outward-facing open state (OFo), the outward-facing closed state (OFc), the holo-occluded state closed (holo), and the inward-facing open state (IFo). Furthermore, experimental assays have shown that different phosphorylation conditions in hDAT can affect the rate of dopamine absorption. We present a protocol using hybrid simulation methods to study the conformational dynamics and stability of states of hDAT under different phosphorylation sites. With this protocol, we explored the conformational space of hDAT, identified the states, and evaluated the free energy differences and the transition probabilities between them in each of the phosphorylation cases. We also presented the conformational changes and correlated them with those described in the literature. There is a thesis/hypothesis that the phosphorylation condition corresponding to NP-333 system (where all sites Ser/Thr from residue 2 to 62 and 254 to 613 are phosphorylated, except residue 333) would decrease the rate of dopamine transport from the extracellular medium to the intracellular medium by hDAT as previously described in the literature by Lin et al., 2003. Our results corroborated this thesis/hypothesis and the data reported. It is probably due to the affectation/changes/alteration of the conformational dynamics of this system that makes the intermediate states more likely and makes it difficult to initial states associated with the uptake of dopamine in the extracellular medium, corroborating the experimental results. Furthermore, our results showed that just single phosphorylation/dephosphorylation could alter intrinsic protein motions affecting the sampling of one or more states necessary for dopamine transport. In this sense, the modification of phosphorylation influences protein movements and conformational preferences, affecting the stability of states and the transition between them and, therefore, the transport.

Reweighting methods for elucidation of conformation ensembles of proteins

Proteins are inherently dynamic macromolecules that exist in equilibrium among multiple conformational states, and motions of protein backbone and side chains are fundamental to biological function. The ability to characterize the conformational landscape is particularly important for intrinsically disordered proteins, multidomain proteins, and weakly bound complexes, where single-structure representations are inadequate. As the focus of structural biology shifts from relatively rigid macromolecules toward larger and more complex systems and molecular assemblies, there is a need for structural approaches that can paint a more realistic picture of such conformationally heterogeneous systems. Here, we review reweighting methods for elucidation of structural ensembles based on experimental data, with the focus on applications to multidomain proteins.

Cryo-electron microscopy unveils unique structural features of the human Kir2.1 channel

We present the first structure of the human Kir2.1 channel containing both transmembrane domain (TMD) and cytoplasmic domain (CTD). Kir2.1 channels are strongly inward-rectifying potassium channels that play a key role in maintaining resting membrane potential. Their gating is modulated by phosphatidylinositol 4,5-bisphosphate (PIP2). Genetically inherited defects in Kir2.1 channels are responsible for several rare human diseases, including Andersen's syndrome. The structural analysis (cryo-electron microscopy), surface plasmon resonance, and electrophysiological experiments revealed a well-connected network of interactions between the PIP2-binding site and the G-loop through residues R312 and H221. In addition, molecular dynamics simulations and normal mode analysis showed the intrinsic tendency of the CTD to tether to the TMD and a movement of the secondary anionic binding site to the membrane even without PIP2. Our results revealed structural features unique to human Kir2.1 and provided insights into the connection between G-loop and gating and the pathological mechanisms associated with this channel.

ABCG2/BCRP transport mechanism revealed through kinetically excited targeted molecular dynamics simulations

ABCG2/BCRP is an ABC transporter that plays an important role in tissue protection by exporting endogenous substrates and xenobiotics. ABCG2 is of major interest due to its involvement in multidrug resistance (MDR), and understanding its complex efflux mechanism is essential to preventing MDR and drug-drug interactions (DDI). ABCG2 export is characterized by two major conformational transitions between inward- and outward-facing states, the structures of which have been resolved. Yet, the entire transport cycle has not been characterized to date. Our study bridges the gap between the two extreme conformations by studying connecting pathways. We developed an innovative approach to enhance molecular dynamics simulations, ‘kinetically excited targeted molecular dynamics’, and successfully simulated the transitions between inward- and outward-facing states in both directions and the transport of the endogenous substrate estrone 3-sulfate. We discovered an additional pocket between the two substrate-binding cavities and found that the presence of the substrate in the first cavity is essential to couple the movements between the nucleotide-binding and transmembrane domains. Our study shed new light on the complex efflux mechanism, and we provided transition pathways that can help to identify novel substrates and inhibitors of ABCG2 and probe new drug candidates for MDR and DDI.