Assessing AF2's ability to predict structural ensembles of proteins.

Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined. Additionally, molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors. Recently emerging approaches have focused on expanding the capability of AlphaFold2 (AF2) to predict conformational substates of protein. Here, we benchmark the performance of various workflows that have adapted AF2 for ensemble prediction and compare the obtained structures with ensembles obtained from MD simulations and NMR. We provide an overview of the levels of performance and accessible timescales that can currently be achieved with machine learning (ML) based ensemble generation. Significant minima of the free energy surfaces remain undetected.

Predicting protein conformational motions using energetic frustration analysis and AlphaFold2.

Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deep-learning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins.

Assessing RNA atomistic force fields via energy landscape explorations in implicit solvent.

Predicting the structure and dynamics of RNA molecules still proves challenging because of the relative scarcity of experimental RNA structures on which to train models and the very sensitive nature of RNA towards its environment. In the last decade, several atomistic force fields specifically designed for RNA have been proposed and are commonly used for simulations. However, it is not necessarily clear which force field is the most suitable for a given RNA molecule. In this contribution, we propose the use of the computational energy landscape framework to explore the energy landscape of RNA systems as it can bring complementary information to the more standard approaches of enhanced sampling simulations based on molecular dynamics. We apply the EL framework to the study of a small RNA pseudoknot, the Aquifex aeolicus tmRNA pseudoknot PK1, and we compare the results of five different RNA force fields currently available in the AMBER simulation software, in implicit solvent. With this computational approach, we can not only compare the predicted 'native' states for the different force fields, but the method enables us to study metastable states as well. As a result, our comparison not only looks at structural features of low energy folded structures, but provides insight into folding pathways and higher energy excited states, opening to the possibility of assessing the validity of force fields also based on kinetics and experiments providing information on metastable and unfolded states. Supplementary Information: The online version contains supplementary material available at 10.1007/s12551-024-01202-9.

Evaluation of the efficacy of marine natural products against PARP-1/2 proteins in high-grade serous ovarian cancer: insights into MD and SMD simulations.

High-grade serous ovarian cancer (HGSOC) is the most malignant and ubiquitous phenotype of epithelial ovarian cancer. Originating in the fallopian tubes and rapidly spreading to the ovaries, this highly heterogeneous disease is a result of serous tubal intraepithelial carcinoma. The proteins known as poly(ADP-ribose) polymerase (PARP) aid in the development of HGSOC by repairing the cancer cells that proliferate and spread metastatically. By using molecular docking to screen 1100 marine natural products (MNPs) from different marine environments against PARP-1/2 proteins, prominent PARP inhibitors (PARPi) were identified. Four compounds, alisiaquinone A, alisiaquinone C, ascomindone D and (+)-zampanolide referred to as MNP-1, MNP-2, MNP-3 and MNP-4, respectively, were chosen based on their binding affinity towards PARP-1/2 proteins, and their bioavailability and drug-like qualities were accessed using ADMET analysis. To investigate the structural stability and dynamics of these complexes, molecular dynamics simulations were performed for 200 ns. These results were compared with the complexes of olaparib (OLA), a PARPi that has been approved by the FDA for the treatment of advanced ovarian cancer. We determined that MNP-4 exhibited stronger binding energies with PARP-1/2 proteins than OLA by using MM/PBSA calculations. Hotspot residues from PARP-1 (E883, M890, Y896, D899 and Y907) and PARP-2 (Y449, F450, A451, S457 and Y460) showed strong interactions with the compounds. To comprehend the unbinding mechanism of MNP-4 complexed with PARP-1/2, steered molecular dynamics (SMD) simulations were performed. We concluded from the free energy landscape (FEL) map that PARP-1/2 are well-stabilised when the compound MNP-4 is bound rather than being pulled away from its binding pockets. This finding provides significant evidence regarding PARPi, which could potentially be employed in the therapeutic treatment of HGSOC.Communicated by Ramaswamy H. Sarma.

Exploring the activation mechanism of metabotropic glutamate receptor 2.

Homomeric dimerization of metabotropic glutamate receptors (mGlus) is essential for the modulation of their functions and represents a promising avenue for the development of novel therapeutic approaches to address central nervous system diseases. Yet, the scarcity of detailed molecular and energetic data on mGlu2 impedes our in-depth comprehension of their activation process. Here, we employ computational simulation methods to elucidate the activation process and key events associated with the mGlu2, including a detailed analysis of its conformational transitions, the binding of agonists, Gi protein coupling, and the guanosine diphosphate (GDP) release. Our results demonstrate that the activation of mGlu2 is a stepwise process and several energy barriers need to be overcome. Moreover, we also identify the rate-determining step of the mGlu2's transition from the agonist-bound state to its active state. From the perspective of free-energy analysis, we find that the conformational dynamics of mGlu2's subunit follow coupled rather than discrete, independent actions. Asymmetric dimerization is critical for receptor activation. Our calculation results are consistent with the observation of cross-linking and fluorescent-labeled blot experiments, thus illustrating the reliability of our calculations. Besides, we also identify potential key residues in the Gi protein binding position on mGlu2, mGlu2 dimer's TM6-TM6 interface, and Gi α5 helix by the change of energy barriers after mutation. The implications of our findings could lead to a more comprehensive grasp of class C G protein-coupled receptor activation.

The physical and evolutionary energy landscapes of devolved protein sequences corresponding to pseudogenes.

Protein evolution is guided by structural, functional, and dynamical constraints ensuring organismal viability. Pseudogenes are genomic sequences identified in many eukaryotes that lack translational activity due to sequence degradation and thus over time have undergone "devolution." Previously pseudogenized genes sometimes regain their protein-coding function, suggesting they may still encode robust folding energy landscapes despite multiple mutations. We study both the physical folding landscapes of protein sequences corresponding to human pseudogenes using the Associative Memory, Water Mediated, Structure and Energy Model, and the evolutionary energy landscapes obtained using direct coupling analysis (DCA) on their parent protein families. We found that generally mutations that have occurred in pseudogene sequences have disrupted their native global network of stabilizing residue interactions, making it harder for them to fold if they were translated. In some cases, however, energetic frustration has apparently decreased when the functional constraints were removed. We analyzed this unexpected situation for Cyclophilin A, Profilin-1, and Small Ubiquitin-like Modifier 2 Protein. Our analysis reveals that when such mutations in the pseudogene ultimately stabilize folding, at the same time, they likely alter the pseudogenes' former biological activity, as estimated by DCA. We localize most of these stabilizing mutations generally to normally frustrated regions required for binding to other partners.

Insights into the mechanism of peptide fibril growth on gold surface.

Understanding the formation of ß-fibrils over the gold surface is of paramount interest in nano-bio-medicinal Chemistry. The intricate mechanism of self-assembly of neurofibrillogenic peptides and their growth over the gold surface remains elusive, as experiments are limited in unveiling the microscopic dynamic details, in particular, at the early stage of the peptide aggregation. In this work, we carried out equilibrium molecular dynamics and enhanced sampling simulations to elucidate the underlying mechanism of the growth of an amyloid-forming sequence of tau fragments over the gold surface. Our results disclose that the collective intermolecular interactions between the peptide chains and peptides with the gold surface facilitate the peptide adsorption, followed by integration, finally leading to the fibril formation.

Mutations influence the conformational dynamics of the GDP/KRAS complex.

Mutations near allosteric sites can have a significant impact on the function of KRAS. Three specific mutations, K104Q, G12D/K104Q, and G12D/G75A, which are located near allosteric positions, were selected to investigate the molecular mechanisms behind mutation-induced influences on the activity of KRAS. Gaussian accelerated molecular dynamics (GaMD) simulations followed by the principal component analysis (PCA) were performed to improve the sampling of conformational states. The results revealed that these mutations significantly alter the structural flexibility, correlated motions, and dynamic behavior of the switch regions that are essential for KRAS binding to effectors or regulators. Furthermore, the mutations have a significant impact on the hydrogen bonding interactions between GDP and the switch regions, as well as on the electrostatic interactions of magnesium ions (Mg2+) with these regions. Our results verified that these mutations strongly influence the binding of KRAS to its effectors or regulators and allosterically regulate the activity. We believe that this work can provide valuable theoretical insights into a deeper understanding of KRAS function.Communicated by Ramaswamy H. Sarma.

Reassembly of the vesicular structure of niosomes after their destruction in a mechanical field.

This paper presents, for the first time, evidence for vesicle destruction and payload loss at the stage of purification of niosome dispersions by centrifugation, an important operation in the assembly of vesicular materials. The ability of niosomes of different compositions to reassemble, i.e., to restore the vesicular structure after destruction in the field of centrifugal forces, was demonstrated by dynamic light scattering and fluorescence spectroscopy. The kinetics of reassembly of vesicular structures is determined by the strength of the centrifugal field and the composition of niosomes. In contrast to ternary compositions, where particle size and modality are essentially unchanged after redispersion of the precipitate resulting from centrifugation, niosome dispersions containing anionic dicetyl phosphate includes micron-sized particles after redispersion, which vary in size over a wide range throughout the observation period. The reassembly process is complicated by the presence of charge on the surface of the niosomes. Elastic niosomes - ethosomes have been synthesised which, due to the high deformability of the shells, are less susceptible to destruction in the centrifugal field and retain the contents of the aqueous core. Using the "energy landscape" approximation, it is shown that vesicular structures assembled during hydration and reassembled after their centrifugation occupy different positions in the energetic pathway of their preparation. The results obtained should also be taken into account when determining the entrapment efficiency, since this procedure uses centrifugation to separate the load. It is important to note that the physical stability of niosomes, which is usually considered in terms of the functional activity of particles, is manifest and should be considered at the material preparation stage.

Conformational States of the GDP- and GTP-Bound HRAS Affected by A59E and K117R: An Exploration from Gaussian Accelerated Molecular Dynamics.

The HRAS protein is considered a critical target for drug development in cancers. It is vital for effective drug development to understand the effects of mutations on the binding of GTP and GDP to HRAS. We conducted Gaussian accelerated molecular dynamics (GaMD) simulations and free energy landscape (FEL) calculations to investigate the impacts of two mutations (A59E and K117R) on GTP and GDP binding and the conformational states of the switch domain. Our findings demonstrate that these mutations not only modify the flexibility of the switch domains, but also affect the correlated motions of these domains. Furthermore, the mutations significantly disrupt the dynamic behavior of the switch domains, leading to a conformational change in HRAS. Additionally, these mutations significantly impact the switch domain's interactions, including their hydrogen bonding with ligands and electrostatic interactions with magnesium ions. Since the switch domains are crucial for the binding of HRAS to effectors, any alterations in their interactions or conformational states will undoubtedly disrupt the activity of HRAS. This research provides valuable information for the design of drugs targeting HRAS.

Force-induced unzipping of DNA in the presence of solvent molecules.

The melting of double-stranded DNA (dsDNA) in the presence of solvent molecules is a fundamental process with significant implications for understanding the thermal and mechanical behavior of DNA and its interactions with the surrounding environment. The solvents play an essential role in the structural transformation of DNA subjected to a pulling force. In this study, we simulate the thermal and force induced denaturation of dsDNA and elucidate the solvent dependent melting behavior, identifying key factors that influence the stability of DNA melting in presence of solvent molecules. Using a statistical model, we first find the melting profile of short heterogeneous DNA molecules in the presence of solvent molecules in Force ensemble. We also investigate the effect of solvent's strengths on the melting profile of DNA. In the force ensemble, we consider two homogeneous DNA chains and apply the force on different locations along the chain in the presence of solvent molecules. Different pathways manifest the melting of the molecule in both ensembles, and we found several interesting features of melting DNA in a constant force ensemble, such as lower critical force when the chain is pulled from the base pair close to a solvent molecule. The results provide new insights into the force-induced unzipping of DNA and could be used to develop new methods for controlling the unzipping process. By providing a better understanding of melting and unzipping of dsDNA in the presence of solvent molecules, this study provides valuable guidelines for predicting DNA thermodynamic quantities and for designing DNA nanostructures.

Free-energy landscapes of transmembrane homodimers by bias-exchange adaptively biased molecular dynamics.

Membrane proteins play essential roles in various biological functions within the cell. One of the most common functional regulations involves the dimerization of two single-pass transmembrane (TM) helices. Glycophorin A (GpA) and amyloid precursor protein (APP) form TM homodimers in the membrane, which have been investigated both experimentally and computationally. The homodimer structures are well characterized using only four collective variables (CVs) when each TM helix is stable. The CVs are the interhelical distance, the crossing angle, and the Crick angles for two TM helices. However, conformational sampling with multi-dimensional replica-exchange umbrella sampling (REUS) requires too many replicas to sample all the CVs for exploring the conformational landscapes. Here, we show that the bias-exchange adaptively biased molecular dynamics (BE-ABMD) with the four CVs effectively explores the free-energy landscapes of the TM helix dimers of GpA, wild-type APP and its mutants in the IMM1 implicit membrane. Compared to the original ABMD, the bias-exchange algorithm in BE-ABMD can provide a more rapidly converged conformational landscape. The BE-ABMD simulations could also reveal TM packing interfaces of the membrane proteins and the dependence of the free-energy landscapes on the membrane thickness. This approach is valuable for numerous other applications, including those involving explicit solvent and a lipid bilayer in all-atom force fields or Martini coarse-grained models, and enhances our understanding of protein-protein interactions in biological membranes.

Microsecond dynamics of H10N7 influenza neuraminidase reveals the plasticity of loop regions and drug resistance due to the R292K mutation.

At the beginning of the last century, multiple pandemics caused by influenza (flu) viruses severely impacted public health. Despite the development of vaccinations and antiviral medications to prevent and control impending flu outbreaks, unforeseen novel strains and continuously evolving old strains continue to represent a serious threat to human life. Therefore, the recently identified H10N7, for which not much data is available for rational structure-based drug design, needs to be further explored. Here, we investigated the structural dynamics of neuraminidase N7 upon binding of inhibitors, and the drug resistance mechanisms against the oseltamivir (OTV) and laninamivir (LNV) antivirals due to the crucial R292K mutation on the N7 using the computational microscope, molecular dynamics (MD) simulations. In this study, each system underwent long 2 × 1 µs MD simulations to answer the conformational changes and drug resistance mechanisms. These long time-scale dynamics simulations and free energy landscapes demonstrated that the mutant systems showed a high degree of conformational variation compared to their wildtype (WT) counterparts, and the LNV-bound mutant exhibited an extended 150-loop conformation. Further, the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculation and MM/GBSA free energy decomposition were used to characterize the binding of OTV and LNV with WT, and R292K mutated N7, revealing the R292K mutation as drug-resistant, facilitated by a decline in binding interaction and a reduction in the dehydration penalty. Due to the broader binding pocket cavity of the smaller K292 mutant residue relative to the wildtype, the drug carboxylate to K292 hydrogen bonding was lost, and the area surrounding the K292 residue was more accessible to water molecules. This implies that drug resistance could be reduced by strengthening the hydrogen bond contacts between N7 inhibitors and altered N7, creating inhibitors that can form a hydrogen bond to the mutant K292, or preserving the closed cavity conformations.

Dissecting the role of ALK double mutations in drug resistance to lorlatinib with in-depth theoretical modeling and analysis.

Anaplastic lymphoma kinase (ALK) is implicated in the genesis of multiple malignant tumors. Lorlatinib stands out as the most advanced and effective inhibitor currently used in the clinic for the treatment of ALK-positive non-small cell lung cancer. However, resistance to lorlatinib has inevitably manifested over time, with double/triple mutations of G1202, L1196, L1198, C1156 and I1171 frequently observed in clinical practice, and tumors regrow within a short time after treatment with lorlatinib. Therefore, elucidating the mechanism of resistance to lorlatinib is paramount in paving the way for innovative therapeutic strategies and the development of next-generation drugs. In this study, we leveraged multiple computational methodologies to delve into the resistance mechanisms of three specific double mutations of ALKG1202R/L1196M, ALKG1202R/L1198F and ALKI1171N/L1198F to lorlatinib. We analyzed these mechanisms through qualitative (PCA, DCCM) and quantitative (MM/GBSA, US) kinetic analyses. The qualitative analysis shows that these mutations exert minimal perturbations on the conformational dynamics of the structural domains of ALK. The energetic and structural assessments show that the van der Waals interactions, formed by the conserved residue Leu1256 within the ATP-binding site and the residues Glu1197 and Met1199 in the hinge domain with lorlatinib, play integral roles in the occurrence of drug resistance. Furthermore, the US simulation results elucidate that the pathways through which lorlatinib dissociates vary across mutant systems, and the distinct environments during the dissociation process culminate in diverse resistance mechanisms. Collectively, these insights provide important clues for the design of novel inhibitors to combat resistance.

Exploring the molecular mechanism of cold-adaption of an alkaline protease mutant by molecular dynamics simulations and residue interaction network.

Psychrophilic proteases have attracted enormous attention in past decades, due to their high catalytic activity at low temperatures in a wide range of industrial processes, especially in the detergent and leather industries. Among them, H5 is an alkaline protease mutant, which featuring psychrophilic-like behavior, but the reasons that H5 with higher activity at low temperatures are still poorly understood. Herein, the molecular dynamics (MD) simulations combined with residue interaction network (RIN) were utilized to investigate the mechanisms of the cold-adaption of mutant H5. The results demonstrated that two loops involved in the substrate binding G100-S104 and S125-S129 in H5 had higher mobility, and the distance enlargement between the two loops modulated the substrate's accessibility compared with wild type counterpart. Besides, H5 enhanced conformational flexibility by weakening salt bridges and increasing interaction with the solvent. In particular, the absence of Lys251-Asp197-Arg247 salt bridge network may contribute to the structural mobility. Based on the free energy landscape and molecular mechanics Poisson-Boltzmann surface area of the wild type and H5, it was elucidated that H5 possessed a large population of interconvertible conformations, resulting in the weaker substrate binding free energy. The calculated RIN topology parameters such as the average degree, average cluster coefficient, and average path length further verified that the mutant H5 attenuated residue-to-residue interactions. The investigation of the mechanisms by which how the residue mutation affects the stability and activity of enzymes provides a theoretical basis for the development of cold-adapted protease.

The Effect of Pulling and Twisting Forces on Chameleon Sequence Peptides.

Chameleon sequences are amino acid sequences found in several distinct configurations in experiment. They challenge our understanding of the link between sequence and structure, and provide insight into structural competition in proteins. Here, we study the energy landscapes for three such sequences, and interrogate how pulling and twisting forces impact the available structural ensembles. Chameleon sequences do not necessarily exhibit multiple structural ensembles on a multifunnel energy landscape when we consider them in isolation. The application of even small forces leads to drastic changes in the energy landscapes. For pulling forces, we observe transitions from helical to extended structures in a very small span of forces. For twisting forces, the picture is much more complex, and highly dependent on the magnitude and handedness of the applied force as well as the reference angle for the twist. Depending on these parameters, more complex and more simplistic energy landscapes are observed alongside more and less diverse structural ensembles. The impact of even small forces is significant, confirming their likely role in folding events. In addition, small forces exerted by the remaining scaffold of a protein may be sufficient to lead to the adoption of a specific structural ensemble by a chameleon sequence.

Energy landscapes and heat capacity signatures for peptides correlate with phase separation propensity.

Phase separation plays an important role in the formation of membraneless compartments within the cell and intrinsically disordered proteins with low-complexity sequences can drive this compartmentalisation. Various intermolecular forces, such as aromatic-aromatic and cation-aromatic interactions, promote phase separation. However, little is known about how the ability of proteins to phase separate under physiological conditions is encoded in their energy landscapes and this is the focus of the present investigation. Our results provide a first glimpse into how the energy landscapes of minimal peptides that contain - and cation- interactions differ from the peptides that lack amino acids with such interactions. The peaks in the heat capacity () as a function of temperature report on alternative low-lying conformations that differ significantly in terms of their enthalpic and entropic contributions. The analysis and subsequent quantification of frustration of the energy landscape suggest that the interactions that promote phase separation lead to features (peaks or inflection points) at low temperatures in . More features may occur for peptides containing residues with better phase separation propensity and the energy landscape is more frustrated for such peptides. Overall, this work links the features in the underlying single-molecule potential energy landscapes to their collective phase separation behaviour and identifies quantities ( and frustration metric) that can be utilised in soft material design.

Energy Landscapes and Heat Capacity Signatures for Monomers and Dimers of Amyloid-Forming Hexapeptides.

Amyloid formation is a hallmark of various neurodegenerative disorders. In this contribution, energy landscapes are explored for various hexapeptides that are known to form amyloids. Heat capacity (CV) analysis at low temperature for these hexapeptides reveals that the low energy structures contributing to the first heat capacity feature above a threshold temperature exhibit a variety of backbone conformations for amyloid-forming monomers. The corresponding control sequences do not exhibit such structural polymorphism, as diagnosed via end-to-end distance and a dihedral angle defined for the monomer. A similar heat capacity analysis for dimer conformations obtained using basin-hopping global optimisation shows clear features in end-to-end distance versus dihedral correlation plots, where amyloid-forming sequences exhibit a preference for larger end-to-end distances and larger positive dihedrals. These results hold true for sequences taken from tau, amylin, insulin A chain, a de novo designed peptide, and various control sequences. While there is a little overall correlation between the aggregation propensity and the temperature at which the low-temperature CV feature occurs, further analysis suggests that the amyloid-forming sequences exhibit the key CV feature at a lower temperature compared to control sequences derived from the same protein.

Investigating the structural impact of Omicron RBD mutation on antibody escape and receptor management.

The SARS-CoV-2 Variant B.1.1.5291 evolved rapidly in late November 2021 from the existing mutants sparking fear worldwide owing to its infamous immune escape from a varied class of neutralising antibodies. To assess the structural behaviour of Omicron-Receptor Binding Domain (RBD) upon interacting with cross-reactive CR3022 antibody, we investigated the computational approach of structural engagement in B.1.1529 RBD and wild-type RBD with CR3022 antibody. The current study investigates the interacting interface between the RBDs and CR3022 to decipher the key residues accompanying the potential mutational landscape of SARS-CoV-2 variants. We conducted in-silico docking followed by molecular dynamics simulation analysis to examine the dynamic behaviour of protein-protein interactions. Furthermore, the study unleashed possible interactions post energy decomposition analysis via MM-GBSA. Conclusively, the mutational landscape of RBD eases in designing and discovering the effective neutralisation accompanied by development of a universal vaccine.Communicated by Ramaswamy H. Sarma.

Reducing overprediction of molecular crystal structures via threshold clustering.

Crystal structure prediction is becoming an increasingly valuable tool for assessing polymorphism of crystalline molecular compounds, yet invariably, it overpredicts the number of polymorphs. One of the causes for this overprediction is in neglecting the coalescence of potential energy minima, separated by relatively small energy barriers, into a single basin at finite temperature. Considering this, we demonstrate a method underpinned by the threshold algorithm for clustering potential energy minima into basins, thereby identifying kinetically stable polymorphs and reducing overprediction.