Normal mode analysis of protein dynamics

Molecular recognition between volatile molecules and odorant binding proteins 7 by homology modeling, molecular docking and molecular dynamics simulation

BACKGROUND

Odorant-binding proteins (OBPs) in insects are key to detection and recognition of external chemical signals associated with survival. OBP7 in Spodoptera frugiperda's larval stage (SfruOBP7) may search for host plants by sensing plant volatiles, which are important sources of pest attractants and repellents. However, the atomic-level basis of binding modes remains elusive.

RESULTS

SfruOBP7 structure was constructed through homology modeling, and complex models of six plant volatiles ((E)-2-hexenol, α-pinene, (Z)-3-hexenyl acetate, lauric acid, O-cymene and 1-octanol) and SfruOBP7 were obtained through molecular docking. To study the detailed interactions between the six plant volatile molecules and SfruOBP7, we conducted three 300 ns molecular dynamics simulations for each study object. The correlation coefficients between binding free energy obtained by molecular mechanics/generalized Born surface area together with solvated interaction energy methods and experimental values are 0.90 and 0.88, respectively, showing a good correlation. By comparing binding free energy along with interaction patterns between SfruOBP7 and the six volatile molecules, hotspot residues of SfruOBP7 when binding with different volatile molecules were determined. Hydrophobic interactions stemming from van der Waals interactions play a significant role in SfruOBP7 and these plant volatile systems.

CONCLUSION

The optimized three-dimensional structure of SfruOBP7 and its binding modes with six plant volatiles revealed their interactions, thus providing a means for estimating the binding energies of other plant volatiles. Our study will help to guide the rational design of effective and selective insect attractants. © 2024 Society of Chemical Industry.

Integrating Newton's equations of motion in the reciprocal space

We here present the normal dynamics technique, which recasts the Newton's equations of motion in terms of phonon normal modes by exploiting a proper sampling of the reciprocal space. After introducing the theoretical background, we discuss how the reciprocal space sampling enables us to (i) obtain a computational speedup by selecting which and how many wave vectors of the Brillouin zone will be considered and (ii) account for distortions realized across large atomic distances without the use of large simulation cells. We implemented the approach into an open-source code, which we used to present three case studies: in the first one, we elucidate the general strategy for the sampling of the reciprocal space; in the second one, we illustrate the potential of the approach by studying the stabilization effect of temperature in α-uranium; and in the last one, we investigate the characterization of Raman spectra at different temperatures in MoS2/MX2 transition metal dichalcogenide heterostructures. Finally, we discuss how the procedure is general and can be used to simulate periodic, semiperiodic, and finite systems such as crystals, slabs, nanoclusters, or molecules.

Structural shifts in TolC facilitate Efflux-Mediated β-lactam resistance

Efflux-mediated β-lactam resistance is a major public health concern, reducing the effectiveness of β-lactam antibiotics against many bacteria. Structural analyses show the efflux protein TolC in Gram-negative bacteria acts as a channel for antibiotics, impacting bacterial susceptibility and virulence. This study examines β-lactam drug efflux mediated by TolC using experimental and computational methods. Molecular dynamics simulations of drug-free TolC reveal essential movements and key residues involved in TolC opening. A whole-gene-saturation mutagenesis assay, mutating each TolC residue and measuring fitness effects under β-lactam selection, is performed. Here we show the TolC-mediated efflux of three antibiotics: oxacillin, piperacillin, and carbenicillin. Steered molecular dynamics simulations identify general and drug-specific efflux mechanisms, revealing key positions at TolC's periplasmic entry affecting efflux motions. Our findings provide insights into TolC's structural dynamics, aiding the design of new antibiotics to overcome bacterial efflux mechanisms.

AlphaFold2 Reveals Structural Patterns of Seasonal Haplotype Diversification in SARS-CoV-2 Nucleocapsid Protein Variants

The COVID-19 pandemic saw the emergence of various Variants of Concern (VOCs) that took the world by storm, often replacing the ones that preceded them. The characteristic mutant constellations of these VOCs increased viral transmissibility and infectivity. Their origin and evolution remain puzzling. With the help of data mining efforts and the GISAID database, a chronology of 22 haplotypes described viral evolution up until 23 July 2023. Since the three-dimensional atomic structures of proteins corresponding to the identified haplotypes are not available, ab initio methods were here utilized. Regions of intrinsic disorder proved to be important for viral evolution, as evidenced by the targeted change to the nucleocapsid (N) protein at the sequence, structure, and biochemical levels. The linker region of the N-protein, which binds to the RNA genome and self-oligomerizes for efficient genome packaging, was greatly impacted by mutations throughout the pandemic, followed by changes in structure and intrinsic disorder. Remarkably, VOC constellations acted co-operatively to balance the more extreme effects of individual haplotypes. Our strategy of mapping the dynamic evolutionary landscape of genetically linked mutations to the N-protein structure demonstrates the utility of ab initio modeling and deep learning tools for therapeutic intervention.

Insight into the molecular recognition of human and polar bear pregnane X receptor by three organic pollutants using molecular docking and molecular dynamics simulations

Pregnane X receptor (PXR) is a heterologous biosensor that is involved in the metabolic pathway of environmental pollutants, regulating the transcription of genes involved in biotransformation. There are significant differences in the selectivity and specificity of organic pollutants (OPs) toward polar bear PXR (pbPXR) and human PXR (hPXR), but the detailed dynamical characteristics of their interactions are unclear. Homology Modeling, molecular docking, molecular dynamics simulation, and free energy calculation were used to analyze the recognition of pbPXR and hPXR by three OPs: BPA, chlordane and toxaphene. Comparing interaction patterns along with binding free energy of pbPXR and hPXR with these three OPs revealed that although pbPXR and hPXR interact similar with these three OPs, these OPs have different effects on the internal dynamics of pbPXR and hPXR. This results in significant alterations in the interaction of key residues near Leu209, Met243, Phe288, Met323, and His407 with OPs, thereby influencing their binding energy. Non-polar interactions, especially van der Waals interactions, were found to be the dominating factors in interacting of these OPs with PXRs. The region surrounding these key residues facilitates hydrophobic contacts with PXR, which are crucial for the selective activation of PXRs in different species by these three OPs. These findings are of significant guidance in understanding the impacts of environmental endocrine disruptors on different organisms.

Insight into the Binding Interaction between PEDCs and hERRγ Utilizing Molecular Docking and Molecular Dynamics Simulations

Phenolic environmental endocrine-disrupting chemicals (PEDCs) are persistent EDCs that are widely found in food packaging materials and environmental media and seriously threaten human health and ecological security. Human estrogen-related receptor γ (hERRγ) has been proposed as a mediator for the low-dose effects of many environmental PEDCs; however, the atomic-level descriptions of dynamical structural features and interactions of hERRγ and PEDCs are still unclarified. Herein, how three PEDCs, 4-(1-methylpropyl)phenol (4-sec-butylphenol), 5,6,7,8-tetrahydro-2-naphthol (tetrahydro-2-napthol), and 2,2-bis(4-hydroxy-3,5-dimethoxyphenyl)propane (BP(2,2)(Me)), interact with hERRγ to produce its estrogenic disruption effects was studied. Molecular docking and multiple molecular dynamics (MD) simulations were first conducted to distinguish the detailed interaction pattern of hERRγ with PEDCs. These binding structures revealed that residues around Leu271, Leu309, Leu345, and Phe435 are important when binding with PEDCs. Furthermore, the binding energies of PEDCs with hERRγ were also characterized using the molecular mechanics/Poisson Boltzmann surface area (MM-PBSA) and solvated interaction energy (SIE) methods, and the results showed that the interactions of CH-π, π-π, and hydrogen bonds are the major contributors for hERRγ binding to these three PEDCs. What is striking is that the methoxide groups of BP(2,2)(Me), as hydrophobic groups, can help to reduce the binding energy of PEDCs binding with hERRγ. These results provide important guidance for further understanding the influence of PEDCs on human health problems.

A Practical Guide to All-Atom and Coarse-Grained Molecular Dynamics Simulations Using Amber and Gromacs: A Case Study of Disulfide-Bond Impact on the Intrinsically Disordered Amyloid Beta

Intrinsically disordered proteins (IDPs) pose challenges to conventional experimental techniques due to their large-scale conformational fluctuations and transient structural elements. This work presents computational methods for studying IDPs at various resolutions using the Amber and Gromacs packages with both all-atom (Amber ff19SB with the OPC water model) and coarse-grained (Martini 3 and SIRAH) approaches. The effectiveness of these methodologies is demonstrated by examining the monomeric form of amyloid-β (Aβ42), an IDP, with and without disulfide bonds at different resolutions. Our results clearly show that the addition of a disulfide bond decreases the β-content of Aβ42; however, it increases the tendency of the monomeric Aβ42 to form fibril-like conformations, explaining the various aggregation rates observed in experiments. Moreover, analysis of the monomeric Aβ42 compactness, secondary structure content, and comparison between calculated and experimental chemical shifts demonstrates that all three methods provide a reasonable choice to study IDPs; however, coarse-grained approaches may lack some atomistic details, such as secondary structure recognition, due to the simplifications used. In general, this study not only explains the role of disulfide bonds in Aβ42 but also provides a step-by-step protocol for setting up, conducting, and analyzing molecular dynamics (MD) simulations, which is adaptable for studying other biomacromolecules, including folded and disordered proteins and peptides.

Three-metal ion mechanism of cross-linked and uncross-linked DNA polymerase β: A theoretical study

In our recent publication, we have proposed a revised base excision repair pathway in which DNA polymerase β (Polβ) catalyzes Schiff base formation prior to the gap-filling DNA synthesis followed by β-elimination. In addition, the polymerase activity of Polβ employs the "three-metal ion mechanism" instead of the long-standing "two-metal ion mechanism" to catalyze phosphodiester bond formation based on the fact derived from time-resolved x-ray crystallography that a third Mg2+ was captured in the polymerase active site after the chemical reaction was initiated. In this study, we develop the models of the uncross-linked and cross-linked Polβ complexes and investigate the "three-metal ion mechanism" vs the "two-metal ion mechanism" by using the quantum mechanics/molecular mechanics molecular dynamics simulations. Our results suggest that the presence of the third Mg2+ ion stabilizes the reaction-state structures, strengthens correct nucleotide binding, and accelerates phosphodiester bond formation. The improved understanding of Polβ's catalytic mechanism provides valuable insights into DNA replication and damage repair.

Molecular docking and molecular dynamics simulation decoding molecular mechanism of EDCs binding to hERRγ

CONTEXT

Human estrogen-related receptor γ (hERRγ) is a key protein involved in various endocrines and metabolic signaling. Numerous environmental endocrine-disrupting chemicals (EDCs) can impact related physiological activities through receptor signaling pathways. Focused on hERRγ with 4-isopropylphenol, bisphenol-F (BPF), and BP(2,2)(Un) complexes, we executed molecular docking and multiple molecular dynamics (MD) simulations along with molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) and solvation interaction energy (SIE) calculation to study the detailed dynamical structural characteristics and interactions between them. Molecular docking showed that hydrogen bonds and hydrophobic interactions were the prime interactions to keep the stability of BPF-hERRγ and hERRγ-BP(2,2)(Un) complexes. Through MD simulations, we observed that all complexes reach equilibrium during the initial 50 ns of simulation, but these three EDCs lead to local structure changes in hERRγ. Energy results further identified key residues L268, V313, L345, and F435 around the binding pockets through CH-π, π-π, and hydrogen bonds interactions play an important stabilizing role in the recognition with EDCs. And most noticeable of all, hydrophobic methoxide groups in BP(2,2)(Un) is useful for decreasing the binding ability between EDCs and hERRγ. These results may contribute to evaluate latent diseases associated with EDCs exposure at the micro level and find potential substitutes.

METHOD

Autodock4.2 was used to conduct the molecular docking, sietraj program was performed to calculate the energy, and VMD software was used to visualize the structure. Amber18 was conducted to perform the MD simulation and other analyses.

Molecular dynamics simulations to explore the binding mode between the amyloid-β protein precursor (APP) and adaptor protein Mint2

Alzheimer's disease (AD) presents a significant challenge in neurodegenerative disease management, with limited therapeutic options available for its prevention and treatment. At the heart of AD pathogenesis is the amyloid-β (Aβ) protein precursor (APP), with the interaction between APP and the adaptor protein Mint2 being crucial. Despite previous explorations into the APP-Mint2 interaction, the dynamic regulatory mechanisms by which Mint2 modulates APP binding remain poorly understood. This study undertakes molecular dynamics simulations across four distinct systems-free Mint2, Mint2 bound to APP, a mutant form of Mint2, and the mutant form bound to APP-over an extensive 400 ns timeframe. Our findings reveal that the mutant Mint2 experiences significant secondary structural transformations, notably the formation of an α-helix in residues S55-K65 upon APP binding, within the 400 ns simulation period. Additionally, we observed a reduction in the active pocket size of the mutant Mint2 compared to its wild-type counterpart, enhancing its APP binding affinity. These insights hold promise for guiding the development of novel inhibitors targeting the Mints family, potentially paving the way for new therapeutic strategies in AD prevention and treatment.

Bacterial Chaperone Domain Insertions Convert Human FKBP12 into an Excellent Protein-Folding Catalyst-A Structural and Functional Analysis

Many folding enzymes use separate domains for the binding of substrate proteins and for the catalysis of slow folding reactions such as prolyl isomerization. FKBP12 is a small prolyl isomerase without a chaperone domain. Its folding activity is low, but it could be increased by inserting the chaperone domain from the homolog SlyD of E. coli near the prolyl isomerase active site. We inserted two other chaperone domains into human FKBP12: the chaperone domain of SlpA from E. coli, and the chaperone domain of SlyD from Thermococcus sp. Both stabilized FKBP12 and greatly increased its folding activity. The insertion of these chaperone domains had no influence on the FKBP12 and the chaperone domain structure, as revealed by two crystal structures of the chimeric proteins. The relative domain orientations differ in the two crystal structures, presumably representing snapshots of a more open and a more closed conformation. Together with crystal structures from SlyD-like proteins, they suggest a path for how substrate proteins might be transferred from the chaperone domain to the prolyl isomerase domain.

Using deep learning and molecular dynamics simulations to unravel the regulation mechanism of peptides as noncompetitive inhibitor of xanthine oxidase

Xanthine oxidase (XO) is a crucial enzyme in the development of hyperuricemia and gout. This study focuses on LWM and ALPM, two food-derived inhibitors of XO. We used molecular docking to obtain three systems and then conducted 200 ns molecular dynamics simulations for the Apo, LWM, and ALPM systems. The results reveal a stronger binding affinity of the LWM peptide to XO, potentially due to increased hydrogen bond formation. Notable changes were observed in the XO tunnel upon inhibitor binding, particularly with LWM, which showed a thinner, longer, and more twisted configuration compared to ALPM. The study highlights the importance of residue F914 in the allosteric pathway. Methodologically, we utilized the perturbed response scan (PRS) based on Python, enhancing tools for MD analysis. These findings deepen our understanding of food-derived anti-XO inhibitors and could inform the development of food-based therapeutics for reducing uric acid levels with minimal side effects.

Chimeric vaccine design against the conserved TonB-dependent receptor-like β-barrel domain from the outer membrane tbpA and hpuB proteins of Kingella kingae ATCC 23330

Kingella kingae is a Gram-negative bacterium that primarily causes pediatric infections such as septicemia, endocarditis, and osteoarticular infections. Its virulence is attributed to the outer membrane proteins having implications in bacterial adhesion, invasion, nutrition, and host tissue damage. TonB-dependent receptors (TBDRs) play an important role in nutrition and were previously implicated as vaccine targets in other bacteria. Therefore, we targeted the conserved β-barrel TBDR domain of these proteins for designing a vaccine construct that could elicit humoral and cellular immune responses. We used bioinformatic tools to mine TBDR-containing proteins from K. kingae ATCC 23330 and then predict B- and T-cell epitopes from their conserved β-barrel TDR domain. A chimeric vaccine construct was designed using three antigenic epitopes, covering >98% of the world population and capable of inciting humoral and adaptive immune responses. The final construct elicited a robust immune response. Docking and dynamics simulation showed good binding affinity of the vaccine construct to various receptors of the immune system. Additionally, the vaccine was predicted to be safe and non-allergenic, making it a promising candidate for further development. In conclusion, our study demonstrates the potential of immunoinformatics approaches in designing chimeric vaccines against K. kingae infections. The chimeric vaccine we designed can serve as a blueprint for future experimental studies to develop an effective vaccine against this pathogen, which can serve as a potential strategy to prevent K. kingae infections.

Structural and pKa Estimation of the Amphipathic HR1 in SARS-CoV-2: Insights from Constant pH MD, Linear vs. Nonlinear Normal Mode Analysis

A comprehensive understanding of molecular interactions and functions is imperative for unraveling the intricacies of viral protein behavior and conformational dynamics during cellular entry. Focusing on the SARS-CoV-2 spike protein (SARS-CoV-2 sp), a Principal Component Analysis (PCA) on a subset comprising 131 A-chain structures in presence of various inhibitors was conducted. Our analyses unveiled a compelling correlation between PCA modes and Anisotropic Network Model (ANM) modes, underscoring the reliability and functional significance of low-frequency modes in adapting to diverse inhibitor binding scenarios. The role of HR1 in viral processing, both linear Normal Mode Analysis (NMA) and Nonlinear NMA were implemented. Linear NMA exhibited substantial inter-structure variability, as evident from a higher Root Mean Square Deviation (RMSD) range (7.30 Å), nonlinear NMA show stability throughout the simulations (RMSD 4.85 Å). Frequency analysis further emphasized that the energy requirements for conformational changes in nonlinear modes are notably lower compared to their linear counterparts. Using simulations of molecular dynamics at constant pH (cpH-MD), we successfully predicted the pKa order of the interconnected residues within the HR1 mutations at lower pH values, suggesting a transition to a post-fusion structure. The pKa determination study illustrates the profound effects of pH variations on protein structure. Key results include pKa values of 9.5179 for lys-921 in the D936H mutant, 9.50 for the D950N mutant, and a slightly higher value of 10.49 for the D936Y variant. To further understand the behavior and physicochemical characteristics of the protein in a biologically relevant setting, we also examine hydrophobic regions in the prefused states of the HR1 protein mutants D950N, D936Y, and D936H in our study. This analysis was conducted to ascertain the hydrophobic moment of the protein within a lipid environment, shedding light on its behavior and physicochemical properties in a biologically relevant context.

Conformational heterogeneity and probability distributions from single-particle cryo-electron microscopy

Single-particle cryo-electron microscopy (cryo-EM) is a technique that takes projection images of biomolecules frozen at cryogenic temperatures. A major advantage of this technique is its ability to image single biomolecules in heterogeneous conformations. While this poses a challenge for data analysis, recent algorithmic advances have enabled the recovery of heterogeneous conformations from the noisy imaging data. Here, we review methods for the reconstruction and heterogeneity analysis of cryo-EM images, ranging from linear-transformation-based methods to nonlinear deep generative models. We overview the dimensionality-reduction techniques used in heterogeneous 3D reconstruction methods and specify what information each method can infer from the data. Then, we review the methods that use cryo-EM images to estimate probability distributions over conformations in reduced subspaces or predefined by atomistic simulations. We conclude with the ongoing challenges for the cryo-EM community.

Atomic dynamics in fluids: Normal mode analysis revisited

Developing microscopic understanding of the thermal properties of liquids is challenging due to their strong dynamic disorder, which prevents characterization of the atomic degrees of freedom. There have been significant research interests in the past few decades to extend the normal mode analysis for solids to instantaneous structures of liquids. However, the nature of normal modes that arise from these unstable structures is still elusive. In this paper, we explore the instantaneous eigenmodes of dynamical matrices of various Lennard-Jones argon liquid and gas systems at high temperatures and show that the normal modes can be interpreted as an interpolation of T→∞ (gas) and T=0 (solid) mode descriptions. We find that normal modes become increasingly collisional and translational, recovering atomistic gaslike behavior rather than vibrational with increase in temperature, suggesting that normal modes in liquids may be described by both solidlike and gaslike modes.

A Retrospective on the Development of Methods for the Analysis of Protein Conformational Ensembles

Analysing protein conformational ensembles whether from molecular dynamics (MD) simulation or other sources for functionally relevant conformational changes can be very challenging. In the nineteen nineties dimensional reduction methods were developed primarily for analysing MD trajectories to determine dominant motions with the aim of understanding their relationship to function. Coarse-graining methods were also developed so the conformational change between two structures could be described in terms of the relative motion of a small number of quasi-rigid regions rather than in terms of a large number of atoms. When these methods are combined, they can characterize the large-scale motions inherent in a conformational ensemble providing insight into possible functional mechanism. The dimensional reduction methods first applied to protein conformational ensembles were referred to as Quasi-Harmonic Analysis, Principal Component Analysis and Essential Dynamics Analysis. A retrospective on the origin of these methods is presented, the relationships between them explained, and more recent developments reviewed.

Comprehensive Evaluation of End-Point Free Energy Techniques in Carboxylated-Pillar[6]arene Host–Guest Binding: III. Force-Field Comparison, Three-Trajectory Realization and Further Dielectric Augmentation

Host–guest binding, despite the relatively simple structural and chemical features of individual components, still poses a challenge in computational modelling. The extreme underperformance of standard end-point methods in host–guest binding makes them practically useless. In the current work, we explore a potentially promising modification of the three-trajectory realization. The alteration couples the binding-induced structural reorganization into free energy estimation and suffers from dramatic fluctuations in internal energies in protein–ligand situations. Fortunately, the relatively small size of host–guest systems minimizes the magnitude of internal fluctuations and makes the three-trajectory realization practically suitable. Due to the incorporation of intra-molecular interactions in free energy estimation, a strong dependence on the force field parameters could be incurred. Thus, a term-specific investigation of transferable GAFF derivatives is presented, and noticeable differences in many aspects are identified between commonly applied GAFF and GAFF2. These force-field differences lead to different dynamic behaviors of the macrocyclic host, which ultimately would influence the end-point sampling and binding thermodynamics. Therefore, the three-trajectory end-point free energy calculations are performed with both GAFF versions. Additionally, due to the noticeable differences between host dynamics under GAFF and GAFF2, we add additional benchmarks of the single-trajectory end-point calculations. When only the ranks of binding affinities are pursued, the three-trajectory realization performs very well, comparable to and even better than the regressed PBSA_E scoring function and the dielectric constant-variable regime. With the GAFF parameter set, the TIP3P water in explicit solvent sampling and either PB or GB implicit solvent model in free energy estimation, the predictive power of the three-trajectory realization in ranking calculations surpasses all existing end-point methods on this dataset. We further combine the three-trajectory realization with another promising modified end-point regime of varying the interior dielectric constant. The combined regime does not incur sizable improvements for ranks and deviations from experiment exhibit non-monotonic variations.

Torsion angles to map and visualize the conformational space of a protein

Present understanding of protein structure dynamics trails behind that of static structures. A torsion-angle based approach, called representation of protein entities (RoPE), derives an interpretable conformational space which correlates with data collection temperature, resolution and reaction coordinate. For more complex systems, atomic coordinates fail to separate functional conformational states, which are still preserved by torsion angle-derived space. This indicates that torsion angles are often a more sensitive and biologically relevant descriptor for protein conformational dynamics than atomic coordinates. This article is protected by copyright. All rights reserved.

Structural and energetic basis of interaction between human estrogen-related receptor γ and environmental endocrine disruptors from multiple molecular dynamics simulations and free energy predictions

Environmental endocrine disruptors (EEDs), a class of molecules that are widespread in our environment, may adversely affect the endocrine system. Exploring the interactions between these compounds and their potential targets is important for assessing their role in the organism. Focused on the human estrogen-related receptor γ (hERRγ) with BPA, BPB, HPTE, BPE, BP(2,2)(Et), and BP(2,2)(MeO) complexes, respectively, we groped for the mechanisms of conformational changes and interactions of hERRγ when binding to these six EEDs by combining multiple molecular dynamics (MD) simulations with energy prediction (MM-PBSA and solvated interaction energy (SIE)). Dynamics analysis results revealed these six EEDs have different effects on the internal dynamics of hERRγ, resulting in significant changes in the interaction of key residues around Leu268, Val313, Leu345, and Phe435 with EEDs, and thus affected its binding energy with these EEDs. The energy calculations further demonstrated that van der Waals interactions are critical for these EEDs binding to hERRγ. These results present detailed molecular insight into the interaction features between EEDs and hERRγ and help guide the search for safer alternatives to BPA.

Identifying Protein-Ligand Interactions via a Novel Distance Self-Feedback Biomolecular Interaction Network

Efficient and accurate characterizations of protein-ligand interactions are key to understanding biology at the molecular level. They are particularly useful in pharmaceutical industry applications. They are usually computationally demanding for those widely applied dynamics-based methods in identifying important residues or calculating ligand binding free energy. In this work, we proposed a graph deep learning (DL) framework, namely, the distance self-feedback biomolecular interaction network (DSBIN), in which the relationship between the complex structure and binding affinity can be established by means of a carefully designed distance self-feedback module and interaction layer. Our model can directly provide a quantitative evaluation of inhibitor binding affinities (pK_d). More importantly, the DSBIN model efficiently identifies key interactions for inhibitor binding and thus intrinsically bears the interpretability. Its generalization performance was further verified using 1405 unseen structures. The predicted binding free energies' deviations were calculated to be less than 1.37 kcal/mol for more than 55% structures. Moreover, we also compared the DSBIN model with a commonly used theoretical method in calculating the substrate binding free energy, MM/GBSA. Our results show that the current DL model has generally better performance in predicting the binding free energy. For a specific complex system, mannopentaose/TmCBM27, the DSBIN predicted binding free energy is -8.21 kcal/mol, which is very close to experimentally measured -7.76 kcal/mol and MM/GBSA calculated -7.16 kcal/mol. Meanwhile, all important aromatic residues around the binding pocket can be identified by our DL model. Considering the accuracy and efficiency of the newly developed DL model, it may be very helpful in the field of drug design and molecular recognition.

Molecular basis of ssDNA recognition by RBM45 protein of neurodegenerative disease from multiple molecular dynamics simulations and energy predictions

Neurodegenerative diseases (NDD) are a group of cognitive and behavioral disorders characterized by progressive loss of neuronal structure and function. As the population ages, the incidence is getting higher and higher, but there is currently no effective treatment. The details of RNA/DNA recognition by the RNA-binding protein RBM45 closely related to neurodegenerative diseases through its two tandem RNA-recognition domains at its N-terminus have important implications for structure-based drug discovery against degenerative diseases. To explore the key characteristics of interaction between ssDNA and RBM45, we performed multiple molecular dynamics (MD) simulations along with MM-PBSA energy prediction on the complexes of wild type (WT) and three mutant RBM45s (K100A, F124A/Y165A, and F29A/F70A/F124A/Y165A) with ssDNA, respectively. The findings suggest that these mutated residues of RBM45 modify the interaction of their surrounding residues with ssDNA, thereby affecting RBM45 protein binding to ssDNA. In contrast with WT RBM45 protein, variations in van der Waals and electrostatic interactions with ssDNA caused by these three RBM45 mutants are critical to affect binding between them. In addition, energy analysis showed that RBM45 is a specific ssDNA-binding protein. The results of our work provide valuable theoretical guidance for the design effective drugs of NDD.

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host-guest binding: II. regression and dielectric constant

End-point free energy calculations as a powerful tool have been widely applied in protein-ligand and protein-protein interactions. It is often recognized that these end-point techniques serve as an option of intermediate accuracy and computational cost compared with more rigorous statistical mechanic models (e.g., alchemical transformation) and coarser molecular docking. However, it is observed that this intermediate level of accuracy does not hold in relatively simple and prototypical host-guest systems. Specifically, in our previous work investigating a set of carboxylated-pillar[6]arene host-guest complexes, end-point methods provide free energy estimates deviating significantly from the experimental reference, and the rank of binding affinities is also incorrectly computed. These observations suggest the unsuitability and inapplicability of standard end-point free energy techniques in host-guest systems, and alteration and development are required to make them practically usable. In this work, we consider two ways to improve the performance of end-point techniques. The first one is the PBSA_E regression that varies the weights of different free energy terms in the end-point calculation procedure, while the second one is considering the interior dielectric constant as an additional variable in the end-point equation. By detailed investigation of the calculation procedure and the simulation outcome, we prove that these two treatments (i.e., regression and dielectric constant) are manipulating the end-point equation in a somehow similar way, i.e., weakening the electrostatic contribution and strengthening the non-polar terms, although there are still many detailed differences between these two methods. With the trained end-point scheme, the RMSE of the computed affinities is improved from the standard ~ 12 kcal/mol to ~ 2.4 kcal/mol, which is comparable to another altered end-point method (ELIE) trained with system-specific data. By tuning PBSA_E weighting factors with the host-specific data, it is possible to further decrease the prediction error to ~ 2.1 kcal/mol. These observations along with the extremely efficient optimized-structure computation procedure suggest the regression (i.e., PBSA_E as well as its GBSA_E extension) as a practically applicable solution that brings end-point methods back into the library of usable tools for host-guest binding. However, the dielectric-constant-variable scheme cannot effectively minimize the experiment-calculation discrepancy for absolute binding affinities, but is able to improve the calculation of affinity ranks. This phenomenon is somehow different from the protein-ligand case and suggests the difference between host-guest and biomacromolecular (protein-ligand and protein-protein) systems. Therefore, the spectrum of tools usable for protein-ligand complexes could be unsuitable for host-guest binding, and numerical validations are necessary to screen out really workable solutions in these 'prototypical' situations.

Rational design of stapled helical peptides as antidiabetic PPARγ antagonists to target coactivator site by decreasing unfavorable entropy penalty instead of increasing favorable enthalpy contribution

Peroxisome proliferator-activated receptor γ (PPARγ) is a ligand-activated transcription factor belonging to the nuclear hormone receptor and has been exploited as a well-established druggable target for the treatment of diabetes mellitus (DM). Traditionally, small-molecule compounds have been developed to attack at the ligand site and Ser273 phosphorylation site of PPARγ. In this study, we derived helical peptide segments from the LXXLL motif region of coactivator proteins as antidiabetic PPARγ antagonists, which were expected to competitively disrupt the native interaction between PPARγ and its cognate coactivators by rebinding at PPARγ coactivator site. Structural analysis, dynamics simulation and energetics dissection revealed that these peptides cannot be well folded into active helical structure when splitting from the protein context of their parent coactivators and exhibit a large flexibility and intrinsic disorder in the free state, which would, therefore, incur a considerable entropy penalty upon rebinding to PPARγ. Hydrocarbon stapling strategy was employed to constrain these free coactivator peptides into ordered helical conformation, thus largely minimizing unfavorable entropy penalty but having only a moderate effect on favorable enthalpy contribution. The computational findings were further substantiated by fluorescence-based assays; the binding affinity of three potent SRC1, NCoA6 and p300 coactivator peptides to PPARγ was observed to be improved by 7.2-fold, 4.2-fold and 5.7-fold upon the stapling, which were also measured to have an efficient competitive potency with their unstapled counterparts for PPARγ coactivator site, with CC₅₀ = 0.096, 0.12 and 0.18 μM, respectively.

The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database

The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database (AlphaFold DB), we perform comparative analyses of the proteins of different organisms. The statistics of AlphaFold-predicted structures show that, for organisms with higher complexity, their constituent proteins will have larger radii of gyration, higher coil fractions, and slower vibrations, statistically. By conducting normal mode analysis and scaling analyses, we demonstrate that higher organismal complexity correlates with lower fractal dimensions in both the structure and dynamics of the constituent proteins, suggesting that higher functional specialization is associated with higher organismal complexity. We also uncover the topology and sequence bases of these correlations. As the organismal complexity increases, the residue contact networks of the constituent proteins will be more assortative, and these proteins will have a higher degree of hydrophilic-hydrophobic segregation in the sequences. Furthermore, by comparing the statistical structural proximity across the proteomes with the phylogenetic tree of homologous proteins, we show that, statistical structural proximity across the proteomes may indirectly reflect the phylogenetic proximity, indicating a statistical trend of protein evolution in parallel with organism evolution. This study provides new insights into how the diversity in the functionality of proteins increases and how the dimensionality of the manifold of protein dynamics reduces during evolution, contributing to the understanding of the origin and evolution of lives.

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host-guest binding: I. Standard procedure

Despite the massive application of end-point free energy methods in protein-ligand and protein-protein interactions, computational understandings about their performance in relatively simple and prototypical host-guest systems are limited. In this work, we present a comprehensive benchmark calculation with standard end-point free energy techniques in a recent host-guest dataset containing 13 host-guest pairs involving the carboxylated-pillar[6]arene host. We first assess the charge schemes for solutes by comparing the charge-produced electrostatics with many ab initio references, in order to obtain a preliminary albeit detailed view of the charge quality. Then, we focus on four modelling details of end-point free energy calculations, including the docking procedure for the generation of initial condition, the charge scheme for host and guest molecules, the water model used in explicit-solvent sampling, and the end-point methods for free energy estimation. The binding thermodynamics obtained with different modelling schemes are compared with experimental references, and some practical guidelines on maximizing the performance of end-point methods in practical host-guest systems are summarized. Further, we compare our simulation outcome with predictions in the grand challenge and discuss further developments to improve the prediction quality of end-point free energy methods. Overall, unlike the widely acknowledged applicability in protein-ligand binding, the standard end-point calculations cannot produce useful outcomes in host-guest binding and thus are not recommended unless alterations are performed.

Structure-based analysis and rational design of human peroxiredoxin-1's C-terminus-derived peptides to target sulfiredoxin-1 in pancreatic cancer

Human peroxiredoxin (PRX) family of antioxidant enzymes reduces hydrogen peroxide and alkyl hydroperoxide involved in the redox signaling, among which the widely documented PRX1 is a versatile molecule regulating cell growth, differentiation and apoptosis, and has been implicated in the tumorigensis of pancreatic cancer. In this study, we systematically examined the complex crystal structure of PRX1 with its cognate interacting partner sulfiredoxin-1 (SRX1) at molecular level, and found that the PRX1-SRX1 association is a typical peptide-mediated protein-protein interaction, where a 18-mer C-terminal tail (CTT) segment of PRX1 was identified to be primarily responsible for the interaction, which contributes ~80% and ~ 55% to the total binding potency of SRX1 to PRX1 monomer and homodimer, respectively. We also demonstrated that the SRX1 exhibits a strong global selectivity for PRX1 CTT tail over other PRX family proteins. Next, the intermolecular interaction between PRX1 CTT tail and SRX1 was investigated at structural, energetic and dynamic levels, from which a 9-mer core region of PRX1 CTT tail was defined as the SRX1-binding hotspot. Biophysical assays substantiated that the CTT and CTTc peptides (out of PRX1 protein context) can bind in an independent manner and possess a close affinity to SRX1. Based on the CTTc sketch a computational combinatorial library consisting of 216 designed peptide derivatives was rationally generated, from which the top-5 hits were found to have comparable affinity with CTT peptide and improved affinity relative to CTTc peptide. They can be used as structurally reduced lead molecular entities to further develop new peptidic agents for therapeutic purpose to disrupt the native PRX1-SRX1 interaction by competing with PRX1 CTT tail for the peptide-binding pocket of SRX1.

Comprehensive binary interaction mapping of τ phosphotyrosine sites with SH2 domains in the human genome: Implications for the rational design of self-inhibitory phosphopeptides to target τ hyperphosphorylation signaling in Alzheimer's Disease

Human microtubule-associated protein Tau (τ) is abundant in the axons of neurons where it stabilizes microtubule bundles; abnormally hyperphosphorylated τ is a hallmark of Alzheimer's disease (AD) and related tauopathies. The hyperphosphorylation events can be recognized by phosphotyrosine-recognition domain SH2 (Src homology 2) to elicit downstream τ signaling in AD pathology. In this study, a comprehensive binary interaction map (CBIM) of all the 6 τ phosphotyrosine sites with 120 SH2 domains in the human genome was systematically created at structural level using computational analyses and binding assays, from which we were able to identify those of strong and moderate binding pairs of sites to domains. It is found that the SH2-recognition specificity of different τ phosphotyrosine sites has been evolutionally optimized to become roughly orthogonal to each other, and thus these site phosphorylations would regulate different but probably partially overlapped biological functions in τ signaling. Some SH2 groups such as SRC, RIN, PLCG, SOCS and SH2D were revealed to have effective binding potency as compared to others; they could be regarded as potential τ-associated proteins to transduce the downstream signaling. We further determined the systematic binding affinities of 6 τ-phosphopeptides to the 11 SH2 domains in SRC group, from which the FYN-τ¹⁸ and YES-τ²⁹ pairs were identified as strong binders. Subsequently, rational molecular design was performed on τ¹⁸ and τ²⁹ to derive a number of τ-phosphopeptide mutants with increased affinity; they are self-inhibitory candidates to competitively target τ hyperphosphorylation events in AD. In addition, it is revealed that the primary anchor pY₀ and secondary anchor X₊₃ of τ-phosphopeptides play an important role in SRC-group SH2 recognition, which confer stability and specificity to the SH2-phosphopeptide binding, respectively.

Drug Design by Pharmacophore and Virtual Screening Approach

Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature.

Response of Terahertz Protein Vibrations to Ligand Binding: Calmodulin-Peptide Complexes as a Case Study

Terahertz vibrations are sensitive reporters of the structure and interactions of proteins. Ligand binding alters the nature and distribution of these collective vibrations. The ligand-induced changes in the terahertz protein vibrations contribute to the binding entropy and to the overall thermodynamic stability of the resultant protein-ligand complexes. Here, we have examined the response of the low-frequency (below 6 terahertz) collective vibrations of the calcium-loaded calmodulin (CaM) to binding to five different ligands, both in the presence and absence of water, using normal-mode analysis and molecular dynamics simulations. A comparison of the vibrational spectra of hydrated and dry systems reveals that protein-solvent interactions stiffen the terahertz protein vibrations and that these solvent-coupled collective vibrations contribute significantly to the hydration-sensitive variation in the vibrational entropy of CaM. In the absence of water, the low-frequency vibrations of CaM are stiffened by ligand binding. On the contrary, the number and the cumulative vibrational entropy of low-frequency vibrational modes (ω < 200 cm^-1) of the hydrated CaM are increased noticeably after binding to the peptides, indicating binding-induced softening of collective vibrations of the protein. Although the calculated and experimental binding affinities of the chosen complexes correlated reasonably well, no systematic correlation was observed between the protein vibrational entropy and the binding affinity. The results underscored the importance of the interplay of protein-ligand and solvent interactions in modulating the low-frequency vibrations of proteins.

Computational Study on the Dynamics of Mycobacterium Tuberculosis RNA Polymerase Assembly

Gene regulation is an intricate phenomenon involving precise function of many macromolecular complexes. Molecular basis of this phenomenon is highly complex and cannot be fully understood using a single technique. Computational approaches can play a crucial role in overall understanding of functional and mechanistic features of a protein or an assembly. Large amounts of structural data pertaining to these complexes are publicly available. In this project, we took advantage of the availability of the structural information to unravel functional intricacies of Mycobacterium tuberculosis RNA polymerase upon interaction with RbpA. In this article, we discuss how the knowledge on protein structure and dynamics can be exploited to study function using various computational tools and resources. Overall, this article provides an overview of various computational methods which can be efficiently used to understand the role of any protein. We hope especially the nonexperts in the field could benefit from our article.

Systematic profiling and identification of the peptide-mediated interactions between human Yes-associated protein and its partners in esophageal cancer

Human Yes-associated protein (YAP) is involved in the Hippo signaling pathway and serves as a coactivator to modulate gene expression, which contains a transactivation domain (TD) responsible for binding to the downstream TEA domain family (TEAD) of transcription factors and two WW1/2 domains that recognize the proline-rich motifs (PRMs) present in a variety of upstream protein partners through peptide-mediated interactions (PMIs). The downstream YAP TD-TEAD interactions are closely associated with gastric cancer, and a number of therapeutic agents have been developed to target the interactions. In contrast, the upstream YAP WW1/2-partner interactions are thought to be involved in esophageal cancer but still remain largely unexplored. Here, we attempted to elucidate the complicated PMIs between the YAP WW1/2 domains and various PRMs of YAP-interacting proteins. A total of 106 peptide segments carrying the class I WW-binding motif [P/L]Px[Y/P] were extracted from 22 partner candidates, which are potential recognition sites of YAP WW1/2 domains. Structural and energetic analyses of the intermolecular interactions between the domains and peptides created a systematic domain-peptide binding profile, from which a number of biologically functional PMIs were identified and then substantiated in vitro using fluorescence spectroscopy assays. It is revealed that: (a) The sequence requirement for the partner recognition site binding to YAP WW1/2 domains is a decapeptide segment that contains a core PRM motif as well as two three-residue extensions from each side of the motif; the core motif and extended sections are responsible for the binding stability and recognition specificity of domain-peptide interaction, respectively. (b) There is an exquisite difference in the recognition specificity of the two domains; the LPxP and PPxP appear to more prefer WW1 than WW2, whereas the WW2 can bind more effectively to LPxY and PPxY than WW1. (c) WW2 generally exhibits a higher affinity to the panel of recognition site candidates than WW1. In addition, a number of partner peptides were found as promising recognition sites of the two domains and/or to have a good selectivity between the two domains. For example, the DVL1 peptide was determined to have moderate affinity to WW2 and strong selectivity for WW2 over WW1. Hydrogen bonds play a central role in selectivity.

Multiple Molecular Dynamics Simulations and Free-Energy Predictions Uncover the Susceptibility of Variants of HIV-1 Protease against Inhibitors Darunavir and KNI-1657

HIV-1 protease (PR) is considered to be the main targets of anti-AIDS drug design because of its role in the proteolytic processing of viral polyproteins. However, the emergence of drug-resistant HIV has become a major problem in the therapy of HIV-1-infected patients. Focused on the complexes of wild type (WT) PR and two mutant PRs (V32I/L33F/I54M/V82I and V32I/L33F/I54M/I84 V) with inhibitors Darunavir (DRV) and KNI-1657 (KNI), respectively, we have conducted research on the conformational dynamics and the resistance mechanism caused by residue mutations through multiple molecular dynamics (MD) simulations combined with an energy (MM-PBSA and solvated interaction energy (SIE)) prediction. The results indicate that mutated residues of PR alter the distance between flap regions and catalytic sites, the volume of the inner catalytic site, and the curling degree of the flap tips, thereby affecting DRV and KNI inhibitor binding to PR. These mutated residues reduced the binding affinity of the two mutant PRs to DRV, resulting in drug resistance, whereas the two mutant PRs increase the binding affinity with KNI, indicating they enhance the sensitivity to KNI. Compared with the WT PR, the changes in van der Waals interaction and electrostatic interaction in the two variant PRs play a vital part in the binding of PR with DRV and KNI. These results may supply valuable guidance for the design of anti-AIDS drugs targeting PR.

Low-frequency collective motion of DNA-binding domain defines p53 function

Most mutations in the DNA-binding domain (DBD) of p53 inactivate or rescue the protein function interacting with the minor groove of DNA. However, how the conformation changes propagating from the mutation sites result in distinct molecular recognition is still not well understood. As the protein mobility is an intrinsic property encrypted in its primary structure, we examined if different structures of wild-type and mutant p53 core domains display any unique patterns of intrinsic mobility. Normal mode calculation was employed to characterize the collective dynamics of DBD in p53 monomer and tetramer as well as their mutants. Intriguingly, the low-frequency collective motions of DBD show similar patterns between the wild-type protein and the rescued mutants. The analysis on atomic backbone fluctuations and low-frequency vibration mode statistics does further support the correlation between the intrinsic collective motion of DBD and the p53 protein function. The mutations in the DBD influence the low-frequency vibration of the p53 tetramer via the change of the collective motions among its four monomers. These findings thus provide new insights for understanding the physical mechanism of p53 protein structure-function relationship and help find the small molecule drug to modulate protein dynamic for disease therapy.

Protein conformational transitions explored by a morphing approach based on normal mode analysis in internal coordinates

Large-scale conformational changes are essential for proteins to function properly. Given that these transition events rarely occur, however, it is challenging to comprehend their underlying mechanisms through experimental and theoretical approaches. In this study, we propose a new computational methodology called internal coordinate normal mode-guided elastic network interpolation (ICONGENI) to predict conformational transition pathways in proteins. Its basic approach is to sample intermediate conformations by interpolating the interatomic distance between two end-point conformations with the degrees of freedom constrained by the low-frequency dynamics afforded by normal mode analysis in internal coordinates. For validation of ICONGENI, it is applied to proteins that undergo open-closed transitions, and the simulation results (i.e., simulated transition pathways) are compared with those of another technique, to demonstrate that ICONGENI can explore highly reliable pathways in terms of thermal and chemical stability. Furthermore, we generate an ensemble of transition pathways through ICONGENI and investigate the possibility of using this method to reveal the transition mechanisms even when there are unknown metastable states on rough energy landscapes.

Exploring the molecular basis of UG-rich RNA recognition by the human splicing factor TDP-43 using molecular dynamics simulation and free energy calculation

Transactivation response element RNA/DNA-binding protein 43 (TDP-43) is involved in the regulation of alternative splicing of human neurodegenerative disease-related genes through binding to long UG-rich RNA sequences. Mutations in TDP-43, most in the homeodomain, cause neurological disorders such as amyotrophic lateral sclerosis and fronto temporal lobar degeneration. Several mutants destabilize the structure and disrupt RNA-binding activity. The biological functions of these mutants have been characterized, but the structural basis behind the loss of RNA-binding activity is unclear. Focused on the specific TDP-43-ssRNA complex (PDB code 4BS2), we applied molecular dynamics simulations and the molecular mechanics Poisson-Boltzmann surface area free energy calculation to characterize and explore the structural and dynamic effects between ssRNA and TDP-43. The energetic analysis indicated that the intermolecular van der Waals interaction and nonpolar solvation energy play an important role in the binding process of TDP-43 and ssRNA. Compared with the wild-type TDP-43, the reduction of the polar or non-polar interaction between all the mutants F149A, D105A/S254A, R171A/D174A, F147L/F149L/F229L/F231L and ssRNA is the main reason for the reduction of its binding free energy. Decomposing energies suggested that the extensive interactions between TDP-43 and the nitrogenous bases of ssRNA are responsible for the specific ssRNA recognition by TDP-43. These results elucidated the TDP-43-ssRNA interaction comprehensively and further extended our understanding of the previous experimental data. The uncovering of TDP-43-ssRNA recognition mechanism will provide us useful insights and new chances for the development of anti-neurodegenerative drugs.

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.

Comparative Analysis of Structural and Dynamical Features of Ribosome Upon Association With mRNA Reveals Potential Role of Ribosomal Proteins

Ribosomes play a critical role in maintaining cellular proteostasis. The binding of messenger RNA (mRNA) to the ribosome regulates kinetics of protein synthesis. To generate an understanding of the structural, mechanistic, and dynamical features of mRNA recognition in the ribosome, we have analysed mRNA-protein interactions through a structural comparison of the ribosomal complex in the presence and absence of mRNA. To do so, we compared the 3-Dimensional (3D) structures of components of the two assembly structures and analysed their structural differences because of mRNA binding, using elastic network models and structural network-based analysis. We observe that the head region of 30S ribosomal subunit undergoes structural displacement and subunit rearrangement to accommodate incoming mRNA. We find that these changes are observed in proteins that lie far from the mRNA-protein interface, implying allostery. Further, through perturbation response scanning, we show that the proteins S13, S19, and S20 act as universal sensors that are sensitive to changes in the inter protein network, upon binding of 30S complex with mRNA and other initiation factors. Our study highlights the significance of mRNA binding in the ribosome complex and identifies putative allosteric sites corresponding to alterations in structure and/or dynamics, in regions away from mRNA binding sites in the complex. Overall, our work provides fresh insights into mRNA association with the ribosome, highlighting changes in the interactions and dynamics of the ribosome assembly because of the binding.

Dasatinib-SIK2 Binding Elucidated by Homology Modeling, Molecular Docking, and Dynamics Simulations

Salt-inducible kinases (SIKs) are calcium/calmodulin-dependent protein kinase (CAMK)-like (CAMKL) family members implicated in insulin signal transduction, metabolic regulation, inflammatory response, and other processes. Here, we focused on SIK2, which is a target of the Food and Drug Administration (FDA)-approved pan inhibitor N-(2-chloro-6-methylphenyl)-2-(6-(4-(2-hydroxyethyl)piperazin-1-yl)-2-methylpyrimidin-4-ylamino)thiazole-5-carboxamide (dasatinib), and constructed four representative SIK2 structures by homology modeling. We investigated the interactions between dasatinib and SIK2 via molecular docking, molecular dynamics simulation, and binding free energy calculation and found that dasatinib showed strong binding affinity for SIK2. Binding free energy calculations suggested that the modification of various dasatinib regions may provide useful information for drug design and to guide the discovery of novel dasatinib-based SIK2 inhibitors.

Serine 477 plays a crucial role in the interaction of the SARS-CoV-2 spike protein with the human receptor ACE2

Since the worldwide outbreak of the infectious disease COVID-19, several studies have been published to understand the structural mechanism of the novel coronavirus SARS-CoV-2. During the infection process, the SARS-CoV-2 spike (S) protein plays a crucial role in the receptor recognition and cell membrane fusion process by interacting with the human angiotensin-converting enzyme 2 (hACE2) receptor. However, new variants of these spike proteins emerge as the virus passes through the disease reservoir. This poses a major challenge for designing a potent antigen for an effective immune response against the spike protein. Through a normal mode analysis (NMA) we identified the highly flexible region in the receptor binding domain (RBD) of SARS-CoV-2, starting from residue 475 up to residue 485. Structurally, the position S477 shows the highest flexibility among them. At the same time, S477 is hitherto the most frequently exchanged amino acid residue in the RBDs of SARS-CoV-2 mutants. Therefore, using MD simulations, we have investigated the role of S477 and its two frequent mutations (S477G and S477N) at the RBD during the binding to hACE2. We found that the amino acid exchanges S477G and S477N strengthen the binding of the SARS-COV-2 spike with the hACE2 receptor.

Binding Thermodynamics and Interaction Patterns of Inhibitor-Major Urinary Protein-I Binding from Extensive Free-Energy Calculations: Benchmarking AMBER Force Fields

Mouse major urinary protein (MUP) plays a key role in the pheromone communication system. The one-end-closed β-barrel of MUP-I forms a small, deep, and hydrophobic central cavity, which could accommodate structurally diverse ligands. Previous computational studies employed old protein force fields and short simulation times to determine the binding thermodynamics or investigated only a small number of structurally similar ligands, which resulted in sampled regions far from the experimental structure, nonconverged sampling outcomes, and limited understanding of the possible interaction patterns that the cavity could produce. In this work, extensive end-point and alchemical free-energy calculations with advanced protein force fields were performed to determine the binding thermodynamics of a series of MUP-inhibitor systems and investigate the inter- and intramolecular interaction patterns. Three series of inhibitors with a total of 14 ligands were simulated. We independently simulated the MUP-inhibitor complexes under two advanced AMBER force fields. Our benchmark test showed that the advanced AMBER force fields including AMBER19SB and AMBER14SB provided better descriptions of the system, and the backbone root-mean-square deviation (RMSD) was significantly lowered compared with previous computational studies with old protein force fields. Surprisingly, although the latest AMBER force field AMBER19SB provided better descriptions of various observables, it neither improved the binding thermodynamics nor lowered the backbone RMSD compared with the previously proposed and widely used AMBER14SB. The older but widely used AMBER14SB actually achieved better performance in the prediction of binding affinities from the alchemical and end-point free-energy calculations. We further analyzed the protein-ligand interaction networks to identify important residues stabilizing the bound structure. Six residues including PHE38, LEU40, PHE90, ALA103, LEU105, and TYR120 were found to contribute the most significant part of protein-ligand interactions, and 10 residues were found to provide favorable interactions stabilizing the bound state. The two AMBER force fields gave extremely similar interaction networks, and the secondary structures also showed similar behavior. Thus, the intra- and intermolecular interaction networks described with the two AMBER force fields are similar. Therefore, AMBER14SB could still be the default option in free-energy calculations to achieve highly accurate binding thermodynamics and interaction patterns.

Application and Comprehensive Analysis of Neighbor Approximated Information Theoretic Configurational Entropy Methods to Protein-Ligand Binding Cases

The binding entropy is an important thermodynamic quantity which has numerous applications in studies of the biophysical process, and configurational entropy is often one of the major contributors in it. Therefore, its accurate estimation is important, though it is challenging mostly due to sampling limitations, anharmonicity, and multimodality of atomic fluctuations. The present work reports a Neighbor Approximated Maximum Information Spanning Tree (A-MIST) method for conformational entropy and presents its performance and computational advantage over conventional Mutual Information Expansion (MIE) and Maximum Information Spanning Tree (MIST) for two protein-ligand binding cases: indirubin-5-sulfonate to Plasmodium falciparum Protein Kinase 5 (PfPK5) and P. falciparum RON2-peptide to P. falciparum Apical Membrane Antigen 1 (PfAMA1). Important structural regions considering binding configurational entropy are identified, and physical origins for such are discussed. A thorough performance evaluation is done of a set of four entropy estimators (Maximum Likelihood (ML), Miller-Madow (MM), Chao-Shen (CS), and James and Stein shrinkage (JS)) with known varying degrees of sensitivity of the entropy estimate on the extent of sampling, each with two schemes for discretization of fluctuation data of Degrees of Freedom (DFs) to estimate Probability Density Functions (PDFs). Our comprehensive evaluation of influences of variations of parameters shows Neighbor Approximated MIE (A-MIE) outperforms MIE in terms of convergence and computational efficiency. In the case of A-MIE/MIE, results are sensitive to the choice of root atoms, graph search algorithm used for the Bond-Angle-Torsion (BAT) conversion, and entropy estimator, while A-MIST/MIST are not. A-MIST yields binding entropy within 0.5 kcal/mol of MIST with only 20-30% computation. Moreover, all these methods have been implemented in an OpenMP/MPI hybrid parallel C++11 code, and also a python package for data preprocessing and entropy contribution analysis is developed and made available. A comparative analysis of features of current implementation and existing tools is also presented.

Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors

We apply the hit-to-lead ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and lead-optimization TIES (thermodynamic integration with enhanced sampling) methods to compute the binding free energies of a series of ligands at the A1 and A2A adenosine receptors, members of a subclass of the GPCR (G protein-coupled receptor) superfamily. Our predicted binding free energies, calculated using ESMACS, show a good correlation with previously reported experimental values of the ligands studied. Relative binding free energies, calculated using TIES, accurately predict experimentally determined values within a mean absolute error of approximately 1 kcal mol-1. Our methodology may be applied widely within the GPCR superfamily and to other small molecule-receptor protein systems.

Context contribution to the intermolecular recognition of human ACE2-derived peptides by SARS-CoV-2 spike protein: implications for improving the peptide affinity but not altering the peptide specificity by optimizing indirect readout

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an etiological agent of the current rapidly growing outbreak of coronavirus disease (COVID-19), which is straining health systems around the world. Disrupting the intermolecular association of SARS-CoV-2 spike glycoprotein (S protein) with its cell surface receptor human angiotensin-converting enzyme 2 (hACE2) has been recognized as a promising therapeutic strategy against COVID-19. The association is a typical peptide-mediated interaction, where the hACE adopts an α1-helix, which can form a two-helix bundle with the α2-helix, to pack against a flat pocket on the S protein surface. Here, we demonstrate that the protein context of full-length hACE plays an essential role in supporting the hACE2 α1-helix recognition by viral S protein. Energetic analysis reveals that the α1-helical peptide (αHP) and also the two-helix bundle peptide (tBP) cannot bind effectively to S protein when they are split from the hACE protein. The context contributes moderately and considerably to the direct readout (DR) and indirect readout (IR) of peptide recognition, respectively. Dynamics simulation suggests that the two free peptides exhibit a large intrinsic disorder without the support of protein context, which would incur a considerable entropy penalty upon binding to S protein. To restore the IR effect lost by splitting peptides from hACE, we herein propose employing hydrocarbon stapling and cyclization strategies to constrain the free αHP and tBP peptides into their native ordered conformations, respectively. The stapling and cyclization are carefully designed in order to avoid influencing the peptide DR effect, which has been demonstrated to improve the peptide binding affinity (but not specificity) to S protein. The stapling/cyclization-imposed conformational constraint can effectively minimize the unfavorable IR effect (i) by reducing the peptide flexibility and entropy cost upon their binding to S protein, and (ii) by helping peptide pre-folding into their native state to facilitate the conformational selection by S protein.

SAMPL7 TrimerTrip host-guest binding affinities from extensive alchemical and end-point free energy calculations

The prediction of host-guest binding affinities with computational modelling is still a challenging task. In the 7th statistical assessment of the modeling of proteins and ligands (SAMPL) challenge, a new host named TrimerTrip was synthesized and the thermodynamic parameters of 16 structurally diverse guests binding to the host were characterized. In the TrimerTrip-guest challenge, only structures of the host and the guests are provided, which indicates that the predictions of both the binding poses and the binding affinities are under assessment. In this work, starting from the binding poses obtained from our previous enhanced sampling simulations in the configurational space, we perform extensive alchemical and end-point free energy calculations to calculate the host-guest binding affinities retrospectively. The alchemical predictions with two widely accepted charge schemes (i.e. AM1-BCC and RESP) are in good agreement with the experimental reference, while the end-point estimates perform poorly in reproducing the experimental binding affinities. Aside from the absolute value of the binding affinity, the rank of binding free energies is also crucial in drug design. Surprisingly, the end-point MM/PBSA method seems very powerful in reproducing the experimental rank of binding affinities. Although the length of our simulations is long and the intermediate spacing is dense, the convergence behavior is not very good, which may arise from the flexibility of the host molecule. Enhanced sampling techniques in the configurational space may be required to obtain fully converged sampling. Further, as the length of sampling in alchemical free energy calculations already achieves several hundred ns, performing direct simulations of the binding/unbinding event in the physical space could be more useful and insightful. More details about the binding pathway and mechanism could be obtained in this way. The nonequilibrium method could also be a nice choice if one insists to use the alchemical method, as the intermediate sampling is avoided to some extent.

SAMPL7 TrimerTrip host-guest binding poses and binding affinities from spherical-coordinates-biased simulations

Host-guest binding remains a major challenge in modern computational modelling. The newest 7th statistical assessment of the modeling of proteins and ligands (SAMPL) challenge contains a new series of host-guest systems. The TrimerTrip host binds to 16 structurally diverse guests. Previously, we have successfully employed the spherical coordinates as the collective variables coupled with the enhanced sampling technique metadynamics to enhance the sampling of the binding/unbinding event, search for possible binding poses and calculate the binding affinities in all three host-guest binding cases of the 6th SAMPL challenge. In this work, we report a retrospective study on the TrimerTrip host-guest systems by employing the same protocol to investigate the TrimerTrip host in the SAMPL7 challenge. As no binding pose is provided by the SAMPL7 host, our simulations initiate from randomly selected configurations and are proceeded long enough to obtain converged free energy estimates and search for possible binding poses. The calculated binding affinities are in good agreement with the experimental reference, and the obtained binding poses serve as a nice starting point for end-point or alchemical free energy calculations. Note that as the work is performed after the close of the SAMPL7 challenge, we do not participate in the challenge and the results are not formally submitted to the SAMPL7 challenge.