Energy landscape of a small peptide revealed by dihedral angle principal component analysis

Computational Design of a Bicyclic Peptide Inhibitor Targeting the ICOS/ICOS-L Protein-Protein Interaction

The interaction between the inducible T-cell costimulatory molecule (ICOS) and its ligand (ICOS-L) is a critical pathway in T-cell activation and immune regulation. We computationally designed a bicyclic peptide (CP5) that inhibits the ICOS/ICOS-L protein-protein interaction (PPI). Using the structural insights derived from the ICOS/ICOS-L co-crystal structure (PDB: 6X4G) and bias-exchange metadynamics simulations (BE-META), we first designed monocyclic peptide candidates containing the β-strand (residues 51-55 51YVYWQ55) of ICOS-L that interact with ICOS. Using Rosetta's flex ddG calculations and further disulfide-bond restraint, we arrived at CP5 (cyclo-RVY[CQPGWC]WVLpG) as a potential ICOS/ICOS-L inhibitor. Using dynamic light scattering (DLS), we examined the interaction between CP5 and ICOS. Importantly, we validated the ICOS/ICOS-L inhibitory activity of CP5 using both TR-FRET assay and ELISA. Notably, CP5 demonstrated satisfactory in vitro pharmacokinetic properties, such as metabolic stability and lipophilicity, positioning it as a promising candidate for further drug development. Our findings provide a foundation for future drug discovery efforts aiming to develop cyclic peptides that specifically target the ICOS/ICOS-L interaction.

Molecular mechanism of the effect of ZnCl2 and MgCl2 solution on the conformation of the tau267-312 monomer

Alzheimer's disease is generally believed to be caused by abnormal aggregation of tau protein; however, there remains a lack of understanding about the aggregation process of tau protein in a solution environment. To explore the conformational properties of the tau protein monomer (tau267-312) in the presence of zinc and magnesium ions, we performed all-atom molecular dynamics simulations of tau267-312 in solutions of zinc chloride and magnesium chloride at different concentrations and compared these results with those obtained in pure water. The calculation results show that the β-sheet content increases significantly in the presence of zinc and magnesium ions, which causes a more compact structure for the tau protein monomers. Furthermore, it was found that stronger interactions between residues, as well as alterations in hydrophilic and hydrophobic interactions, are molecular mechanisms driving structural changes within the tau protein monomers. These findings suggest that zinc and magnesium ions facilitate a more stable conformation and promote the aggregation of tau protein monomers, which is important for understanding the aggregation and folding process of tau protein in the environment of saline solution.

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning

Proteins are inherently dynamic, and their conformational ensembles play a crucial role in biological function. Large-scale motions may govern the protein structure-function relationship, and numerous transient but stable conformations of intrinsically disordered proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease-related aggregations of IDPs is challenging, both experimentally and computationally. In this paper, we first introduce a deep learning-based model, termed Internal Coordinate Net (ICoN), which learns the physical principles of conformational changes from molecular dynamics simulation data. Second, we selected data points through interpolation in the learned latent space to rapidly identify novel synthetic conformations with sophisticated and large-scale side chains and backbone arrangements. Third, with the highly dynamic amyloid-β1-42 (Aβ42) monomer, our deep learning model provided a comprehensive sampling of Aβ42's conformational landscape. Analysis of these synthetic conformations revealed conformational clusters that could be used to rationalize experimental findings. Additionally, the method can identify novel conformations with important interactions in atomistic details that are not included in the training data. New synthetic conformations showed distinct side chain rearrangements that are probed by our electron paramagnetic resonance and amino acid substitution studies. This approach is highly transferable and can be used for any available data for training. The work also demonstrated the ability of deep learning to utilize natural atomistic motions in protein conformation sampling.

Unveiling the Hidden: Dissecting Liraglutide Oligomerization Dual Pathways via Direct Mass Technology, Electron-Capture Dissociation, and Molecular Dynamics

Peptide therapeutics have revolutionized drug design strategies, yet the inherent structural flexibility and conjugated moieties of drug molecules present challenges in discovery, rational design, and manufacturing. Liraglutide, a GLP-1 receptor agonist conjugated with palmitic acid at its lysine residue, exemplifies these challenges by forming oligomers, which may compromise efficacy through progressive formation of aggregates. Here, we incorporate native mass spectrometry platforms including electron-capture dissociation (ECD), direct mass technology (DMT), and molecular dynamics (MD) to capture the early oligomerization process of liraglutide. Our findings reveal a restricted C-terminal region upon oligomer formation, as indicated by the reduced release of z-ions in ECD analysis. Additionally, we identified the formation of higher-order oligomers (n=25-62) by DMT, primarily stabilized by hydrophilic interactions involving preformed stable oligomers (n=14). Together, these integrative mass spectrometry results delineate a dual-pathway oligomerization process for liraglutide, demonstrating the power of mass spectrometry in uncovering hidden pathways of self-association. This approach underscores the potential of mass spectrometry as a key tool in the rational design and optimization of peptide-based therapeutics.

Impact of Amidation on Aβ25-35 Aggregation

Toxic oligomeric species are suspected in the etiology of Alzheimer's disease. The full-length Aβ42 can be studied by the fragment Aβ25-35 as it retains neurotoxicity. According to experimental studies, amidation of the Aβ25-35 carboxyl terminal decreases fibrillation activity while retaining its neurotoxic properties. Our molecular dynamics simulation studied the aggregation of the Aβ25-35 trimer from two initial structures (fibril and randomized helical structures) in their amidated and nonamidated forms. Comparing the amidated and nonamidated systems, the results suggest that antiparallel chains are dominant in nonamidated systems, while the amide group leads to parallel chains. In terms of secondary structures, a higher helix content with a corresponding decrease in β-sheet content is observed as a consequence of amidation. Despite the variation in secondary structures, the chain-chain contacts are still mediated by the Gly motif (GxxxG) and Ile residues in both amidated and nonamidated systems. As neurotoxicity does not change upon amidation, our results imply that clumping of peptides sustained by the Gly motif is a greater contributing factor to toxicity than secondary and quaternary structures.

In Silico Analysis of Temperature-Induced Structural, Stability, and Flexibility Modulations in Camel Cytochrome c

Cytochrome c is a critical protein in energy metabolism, and its structural adaptations to different temperatures play a key role in enabling species like the wild Bactrian camel (Camelus ferus) and the Arabian camel (Camelus dromedarius) to thrive in their respective cold and hot environments. This study investigates the structural, thermodynamic, and dynamic properties of cytochrome c at different temperatures. Thermal Titration Molecular Dynamics (TTMD) simulations, which involve analyzing protein behaviour across a range of temperatures, were carried out using GROMACS, with each simulation running for 100 nanoseconds, at 245 K, 280 K, 303 K, 308 K, and 320 K, to evaluate stability and flexibility. Structural alterations were indicated by an increase in root mean square deviations (RMSDs) to 0.4 nm at 320 K, as opposed to lower RMSD values (0.1-0.2 nm) at 245 K and 280 K. Root mean square fluctuation (RMSF) analyses revealed modest flexibility at 245 K and 280 K (0.1-0.2 nm) but considerable flexibility (0.3-0.4 nm) at 303 K and 320 K. Principal component analysis (PCA) found that the formational space was constrained at lower temperatures but expanded at higher temperatures. Entropy peaked at 280 K (13,816 J/mol) and then fell substantially at 320 K (451.765 J/mol), indicating diminished stability. These findings highlight cytochrome c adaptations for cold stability in Camelus ferus and thermal resilience in Camelus dromedarius, showing evolutionary strategies for harsh conditions.

Discovering Influenza Virus Neuraminidase Inhibitors via Computational and Experimental Studies

Influenza A and B viruses spread out worldwide, causing several global concerns. Discovering neuraminidase inhibitors to prevent influenza A and B viruses is thus of great interest. In this work, a machine learning model was trained and tested to evaluate the ligand-binding affinity to neuraminidase. The model was then used to predict the binding affinity of compounds from the CHEMBL database, which is a manually curated database of bioactive molecules with drug-like properties. The physical insights into the binding process of ligands to neuraminidase were clarified via molecular docking and molecular dynamics simulations. Experimental investigation on enzymatic activity validated our computational results and suggested that 2 compounds were potential inhibitors of neuraminidase of the influenza A and B viruses.

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning

Proteins are inherently dynamic, and their conformational ensembles are functionally important in biology. Large-scale motions may govern protein structure-function relationship, and numerous transient but stable conformations of Intrinsically Disordered Proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease-related aggregations of IDPs is challenging both experimentally and computationally. In this paper we first introduce a deep learning-based model, termed Internal Coordinate Net (ICoN), which learns the physical principles of conformational changes from Molecular Dynamics (MD) simulation data. Second, we selected interpolating data points in the learned latent space that rapidly identify novel synthetic conformations with sophisticated and large-scale sidechains and backbone arrangements. Third, with the highly dynamic amyloid-β 1-42 (Aβ42) monomer, our deep learning model provided a comprehensive sampling of Aβ42's conformational landscape. Analysis of these synthetic conformations revealed conformational clusters that can be used to rationalize experimental findings. Additionally, the method can identify novel conformations with important interactions in atomistic details that are not included in the training data. New synthetic conformations showed distinct sidechain rearrangements that are probed by our EPR and amino acid substitution studies. This approach is highly transferable and can be used for any available data for training. The work also demonstrated the ability of deep learning to utilize learned natural atomistic motions in protein conformation sampling.

Investigating the effects of the ARG258HIS mutation on RAD51C in inherited Fanconi Anemia and cancer disease

Fanconi anemia is a rare chromosomal instability disorder associated with developmental abnormalities, bone marrow failure, and a heightened susceptibility to leukemia and other cancers. It is an autosomal recessive genetic disorder, necessitating both parents to carry the faulty gene. Diagnostic methods include blood tests, chromosome breakage assessments, and genetic testing. While there is no cure, treatments encompass blood transfusions, bone marrow transplants, and gene therapy, with patients requiring regular check-ups, supportive care, and cancer screening to enhance their quality of life. In this study, we identify a specific substitution (R258H) targeting the crucial binding site of the alpha-helix region in RAD51C. This substitution induces structural disorder in distinct regions, as indicated by the near absence of electron density for multiple amino acids. Intriguingly, these disordered regions do not follow a continuous sequence from the mutation site and extend across domain boundaries. We utilized computational prediction algorithms and Molecular Dynamics (MD) simulations to model RAD51C and its mutation (R258H) structurally. These simulations highlighted alterations in conformational dynamics, the Free Energy Landscape (FEL), and intrinsic molecular motions induced by the mutation, suggesting structural destabilization that could disrupt its function. This observed destabilization in RAD51C due to mutations offers valuable insights that may serve as diagnostic markers for individuals carrying these mutations, particularly in Fanconi anemia.

A review on description dynamics and conformational changes of proteins using combination of principal component analysis and molecular dynamics simulation

Understanding how proteins behave dynamically and undergo conformational changes is essential to comprehending their biological roles. This review article examines the potent tool of using Molecular Dynamics simulations in conjunction with Principal Component Analysis (PCA) to explore protein dynamics. Molecular dynamics data can be made easier to read by removing prominent patterns through the use of PCA, a sophisticated dimensionality reduction approach. Researchers can obtain critical insights into the fundamental principles governing protein function by using PCA on MD simulation data. We provide a systematic approach to PCA that includes data collection, input coordinate selection, and result interpretation. Protein collective movements and fundamental dynamics are made visible by PCA, which makes it possible to identify conformational substates that are crucial to function. By means of principal component analysis, scientists are able to observe and measure large-scale movements, like hinge bending and domain motions, as well as pinpoint areas of protein structural stiffness and flexibility. Moreover, PCA allows temporal separation, distinguishing slower global motions from faster local changes. A strong foundation for researching protein dynamics is provided by the combination of PCA and Molecular Dynamics simulations, which have applications in drug development and enhance our comprehension of intricate biological systems.

Molecular similarity: Theory, applications, and perspectives

Molecular similarity pervades much of our understanding and rationalization of chemistry. This has become particularly evident in the current data-intensive era of chemical research, with similarity measures serving as the backbone of many Machine Learning (ML) supervised and unsupervised procedures. Here, we present a discussion on the role of molecular similarity in drug design, chemical space exploration, chemical "art" generation, molecular representations, and many more. We also discuss more recent topics in molecular similarity, like the ability to efficiently compare large molecular libraries.

Dimerization of the Aβ42 under the Influence of the Gold Nanoparticle: A REMD Study

Advances in Alzheimer's disease (AD) are related to the oligomerization of Amyloid β (Aβ) peptides. Therefore, alteration of the process can prevent AD. We investigated the Aβ42 dimerization under the effects of gold nanoparticles using temperature replica-exchange molecular dynamics (REMD) simulations. The structural change of dimers in the presence and absence of the gold nanoparticle, Au55, was monitored over stable intervals. Physical insights into the oligomerization of Aβ were thus clarified. The computed metrics indicate that Au55 affects the progress of oligomerization. Specifically, the presence of the gold nanoparticle significantly modifies the structure of dimeric Aβ42. The β-content experienced a substantial decrease with the induction of Au55. The turn and coil-contents are also decreased under the effects of the gold nanoparticle. However, the α-content of the dimer exhibited a rigid increase. The influence of gold nanoparticles on the dimeric Aβ42 differs significantly from that of silver nanoparticles, which reduce β-content but increase coil-, turn-, and α-contents. The nature of inhibition will be discussed, in which the vdW interaction plays a driving force for the interaction between the Aβ42 dimer and the gold nanoparticle.

Tackling APOE's structural challenges via in silico modeling in the era of neural networks: Can AlphaFold II help circumvent the problem of lacking full-length protein structure?

The APOE gene, encoding apolipoprotein E, is a predictor of longevity and age-related diseases. Despite numerous genetic studies, the data on molecular mechanisms by which apolipoprotein E affects the human phenotype remain incomplete due to the structural properties of the protein. Recently, a number of studies have used in silico drug discovery techniques based on protein-ligand docking, further highlighting the issue of lacking 3D structure of apolipoprotein E. Using molecular dynamics simulation, we found that AlphaFold II models of apolipoprotein E conformationally significantly differ both from the only available NMR structure, 2L7B, and structures obtained through circular dichroism spectroscopy: the ε4 isoform lacks the salt bridge between R61 and E255, while the ε2 and ε3 isoforms have extensive networks of interdomain interactions. Our findings challenge the benefits of using AlphaFold II for obtaining starting conformations for molecular docking.

Dynamic Interplay of Loop Motions Governs the Molecular Level Regulatory Dynamics in Spleen Tyrosine Kinase: Insights from Molecular Dynamics Simulations

The spleen tyrosine kinase (Syk) is a key regulator in immune cell signaling and is linked to various mechanisms in cancer and neurodegenerative diseases. Although most computational research on Syk focuses on novel drug design, the molecular-level regulatory dynamics remain unexplored. In this study, we utilized 5 × 1 μs all-atom molecular dynamics simulations of the Syk kinase domain, examining it in combinations of activation segment phosphorylated/unphosphorylated (at Tyr525, Tyr526) and the "DFG"-Asp protonated/deprotonated (at Asp512) states to investigate conformational variations and regulatory dynamics of various loops and motifs within the kinase domain. Our findings revealed that the formation and disruption of several electrostatic interactions among residues within and near the activation segment likely influenced its dynamics. The protein structure network analysis indicated that the N-terminal and C-terminal anchors were stabilized by connections with the nearby stable helical regions. The P-loop showed conformational variation characterized by movements toward and away from the conserved "HRD"-motif. Additionally, there was a significant correlation between the movement of the β3-αC loop and the P-loop, which controls the dimensions of the adenine-binding cavity of the C-spine region. Overall, understanding these significant motions of the Syk kinase domain enhances our knowledge of its functional regulatory mechanism and can guide future research.

Structural Insight into Melatonin's Influence on the Conformation of Aβ42 Dimer Studied by Molecular Dynamics Simulation

The accumulation of amyloid-beta (Aβ) oligomers is recognized as a potential culprit in Alzheimer's disease (AD). Experimental studies show that melatonin, a hormone that mainly regulates circadian rhythm and sleep, can interact with Aβ peptides and disrupt the formation of oligomers. However, how melatonin inhibits the oligomerization of soluble Aβ is unclear. Here, by computational simulations, we investigate the effect of different levels of melatonin on the conformation of the Aβ42 dimer. We find that the conformation of the Aβ42 dimer is dependent on melatonin levels. When melatonin is absent, the dimer mainly forms a parallel β-sheet in the CHC region. When one melatonin molecule is present, the overall conformation of the dimer does not change much, but the N-terminal of the dimer tends to adopt antiparallel β-sheets. When two melatoinin molecules are present, the Aβ42 dimer exhibits significant structural change, especially in its central region, resulting in a more compact conformation, and forms parallel β-sheets in the C-terminal. This conformational difference induced by different levels of melatoinin can shed light on the protective role of melatonin.

The Curious Case of A31P, a Topology-Switching Mutant of the Repressor of Primer Protein: A Molecular Dynamics Study of Its Folding and Misfolding

The effect of mutations on protein structures is usually rather localized and minor. Finding a mutation that can single-handedly change the fold and/or topology of a protein structure is a rare exception. The A31P mutant of the homodimeric Repressor of primer (Rop) protein is one such exception: This single mutation ─and as demonstrated by two independent crystal structure determinations─ can convert the canonical (left-handed/all-antiparallel) 4-α-helical bundle of Rop to a new form (right-handed/mixed parallel and antiparallel bundle) displaying a previously unobserved "bisecting U" topology. The main problem with understanding the dramatic effect of this mutation on the folding of Rop is to understand its very existence: Most computational methods appear to agree that the mutation should have had no appreciable effect, with the majority of energy minimization methods and protein structure prediction protocols indicating that this mutation is fully consistent with the native Rop structure, requiring only a local and minor change at the mutation site. Here we use two long (10 μs each) molecular dynamics simulations to compare the stability and dynamics of the native Rop versus a hypothetical structure that is identical with the native Rop but is carrying this single Alanine31 to Proline mutation. Comparative analysis of the two trajectories convincingly shows that, in contrast to the indications from energy minimization ─but in agreement with the experimental data─, this hypothetical native-like A31P structure is unstable, with its turn regions almost completely unfolding, even under the relatively mild 320 K NpT simulations that we have used for this study. We discuss the implication of these findings for the folding of the A31P mutant, especially with respect to the proposed model of a double-funneled energy landscape.

A computational study to assess the pathogenicity of single or combinations of missense variants on respiratory complex I

Variants found in the respiratory complex I (CI) subunit genes encoded by mitochondrial DNA can cause severe genetic diseases. However, it is difficult to establish a priori whether a single or a combination of CI variants may impact oxidative phosphorylation. Here we propose a computational approach based on coarse-grained molecular dynamics simulations aimed at investigating new CI variants. One of the primary CI variants associated with the Leber hereditary optic neuropathy (m.14484T>C/MT-ND6) was used as a test case and was investigated alone or in combination with two additional rare CI variants whose role remains uncertain. We found that the primary variant positioned in the E-channel region, which is fundamental for CI function, stiffens the enzyme dynamics. Moreover, a new mechanism for the transition between π- and α-conformation in the helix carrying the primary variant is proposed. This may have implications for the E-channel opening/closing mechanism. Finally, our findings show that one of the rare variants, located next to the primary one, further worsens the stiffening, while the other rare variant does not affect CI function. This approach may be extended to other variants candidate to exert a pathogenic impact on CI dynamics, or to investigate the interaction of multiple variants.

Differential Effects of Post-translational Modifications on the Membrane Interaction of Huntingtin Protein

Huntington's disease is a neurodegenerative disorder caused by an expanded polyglutamine stretch near the N-terminus of the huntingtin (HTT) protein, rendering the protein more prone to aggregate. The first 17 residues in HTT (Nt17) interact with lipid membranes and harbor multiple post-translational modifications (PTMs) that can modulate HTT conformation and aggregation. In this study, we used a combination of biophysical studies and molecular simulations to investigate the effect of PTMs on the helicity of Nt17 in the presence of various lipid membranes. We demonstrate that anionic lipids such as PI4P, PI(4,5)P2, and GM1 significantly enhance the helical structure of unmodified Nt17. This effect is attenuated by single acetylation events at K6, K9, or K15, whereas tri-acetylation at these sites abolishes Nt17-membrane interaction. Similarly, single phosphorylation at S13 and S16 decreased but did not abolish the POPG and PIP2-induced helicity, while dual phosphorylation at these sites markedly diminished Nt17 helicity, regardless of lipid composition. The helicity of Nt17 with phosphorylation at T3 is insensitive to the membrane environment. Oxidation at M8 variably affects membrane-induced helicity, highlighting a lipid-dependent modulation of the Nt17 structure. Altogether, our findings reveal differential effects of PTMs and crosstalks between PTMs on membrane interaction and conformation of HTT. Intriguingly, the effects of phosphorylation at T3 or single acetylation at K6, K9, and K15 on Nt17 conformation in the presence of certain membranes do not mirror that observed in the absence of membranes. Our studies provide novel insights into the complex relationship between Nt17 structure, PTMs, and membrane binding.

Assessing the Performance of Peptide Force Fields for Modeling the Solution Structural Ensembles of Cyclic Peptides

Molecular dynamics simulation is a powerful tool for characterizing the solution structural ensembles of cyclic peptides. However, the ability of simulation to recapitulate experimental results and make accurate predictions largely depends on the force fields used. In our work here, we evaluate the performance of seven state-of-the-art force fields in recapitulating the experimental NMR results in water of 12 benchmark cyclic peptides, consisting of 6 cyclic pentapeptides, 4 cyclic hexapeptides, and 2 cyclic heptapeptides. The results show that RSFF2+TIP3P, RSFF2C+TIP3P, and Amber14SB+TIP3P exhibit similar and the best performance, all recapitulating the NMR-derived structure information on 10 cyclic peptides. Amber19SB+OPC successfully recapitulates the NMR-derived structure information on 8 cyclic peptides. In contrast, OPLS-AA/M+TIP4P, Amber03+TIP3P, and Amber14SBonlysc+GB-neck2 could only recapitulate the NMR-derived structure information on 5 cyclic peptides, the majority of which are not well-structured.

Identification of the inhibition mechanism of carbonic anhydrase II by fructooligosaccharides

Polygonatum sibiricum (P. sibiricum), recognized as a precious nourishing Chinese traditional medicine, exhibits the pharmacological effect of anti-aging. In this work, we proposed a novel mechanism underlying this effect related to the less studied bioactive compounds fructooligosaccharides in P. sibiricum (PFOS) to identify the inhibition effect of the small glycosyl molecules on the age-related zinc metalloprotease carbonic anhydrase II (CA II). Molecular docking and molecular dynamics simulation were used to investigate the structural and energetic properties of the complex systems consisting of the CA II enzyme and two possible structures of PFOS molecules (PFOS-A and PFOS-B). The binding affinity of PFOS-A (-7.27 ± 1.02 kcal/mol) and PFOS-B (-8.09 ± 1.75 kcal/mol) shows the spontaneity of the binding process and the stability of the combination in the solvent. Based on the residue energy decomposition and nonbonded interactions analysis, the C-, D- and G-sheet fragments of the CA II were found to be crucial in binding process. Van der Waals interactions form on the hydrophobic surface of CAII mainly with 131PHE and 135VAL, while hydrogen bonds form on the hydrophilic surface mainly with 67ASN and 92GLN. The binding of PFOS results in the blocking of the zinc ions pocket and then inhibiting its catalytic activity, the stability of which has been further demonstrated by free energy landscape. These findings provide evidence of the effective inhibition of PFOS to CA II enzyme, which leads to a novel direction for exploring the mechanism of traditional Chinese medicine focused on small molecule fructooligosaccharides.

The use of collective variables and enhanced sampling in the simulations of existing and emerging microporous materials

Microporous materials, including zeolites, metal-organic frameworks, and cage compounds, offer diverse functionalities due to their unique dynamics and guest confinement properties. These materials play a significant role in separation, catalysis, and sensing, but their complexity hinders exploration using traditional atomistic simulations. This review explores collective variables (CVs) paired with enhanced sampling as a powerful approach to enable efficient investigation of key features in microporous materials. We highlight successful applications of CVs in studying adsorption, diffusion, phase transitions, and mechanical properties, demonstrating their crucial role in guiding material design and optimisation. The future of CVs lies in integration with techniques like machine learning, allowing for enhanced efficiency and accuracy. By tailoring CVs to specific materials and developing multi-scale approaches we can further unlock the intricacies of these fascinating materials. Simulations are a cornerstone in unravelling the complexities of microporous materials and are crucial for our future understanding.

Silver nanoparticles alter the dimerization of Aβ42 studied by REMD simulations

The aggregation of amyloid beta (Aβ) peptides is associated with the development of Alzheimer's disease (AD). However, there has been a growing belief that the oligomerization of Aβ species in different environments has a neurotoxic effect on the patient's brain, causing damage. It is necessary to comprehend the compositions of Aβ oligomers in order to develop medications that may effectively inhibit these neurotoxic forms that affect the nervous system of AD patients. Thus, dissociation or inhibition of Aβ aggregation may be able to prevent AD. To date, the search for traditional agents and biomolecules has largely been unsuccessful. In this context, nanoparticles have emerged as potential candidates to directly inhibit the formation of Aβ oligomers. The oligomerization of the dimeric Aβ peptides with or without the influence of a silver nanoparticle was thus investigated using temperature replica-exchange molecular dynamics (REMD) simulations. The physical insights into the dimeric Aβ oligomerization were clarified by analyzing intermolecular contact maps, the free energy landscape of the dimeric oligomer, secondary structure terms, etc. The difference in obtained metrics between Aβ with or without a silver nanoparticle provides a picture of the influence of silver nanoparticles on the oligomerization process. The underlying mechanisms that are involved in altering Aβ oligomerization will be discussed. The obtained results may play an important role in searching for Aβ inhibitor pathways.

SARS-CoV-2 omicron RBD forms a weaker binding affinity to hACE2 compared to Delta RBD in in-silico studies

The COVID-19 pandemic sparked an unprecedented race in biotechnology in a search for effective therapies and a preventive vaccine. The continued appearance of SARS-CoV-2 variants of concern (VoCs) further swept the world. The entry of SARS-CoV-2 into cells is mediated by binding the receptor-binding domain (RBD) of the S protein to the cell-surface receptor, human angiotensin-converting enzyme 2 (hACE2). In this study, using a coarse-grained force field to parameterize the system, we employed steered-molecular dynamics (SMD) simulations to reveal the binding of SARS-CoV-2 Delta/Omicron RBD to hACE2. Our benchmarked results demonstrate a good correlation between computed rupture force and experimental binding free energy for known protein-protein systems. Moreover, our findings show that the Omicron RBD has a weaker binding affinity to hACE2, consistent with the respective experimental results. This indicates that our method can effectively be applied to other emerging SARS-CoV-2 strains.Communicated by Ramaswamy H. Sarma.

Substituted Oligosaccharides as Protein Mimics: Deep Learning Free Energy Landscapes

Protein-protein complexes power the majority of cellular processes. Interfering with the formation of such complexes using well-designed mimics is a difficult, yet actively pursued, research endeavor. Due to the limited availability of results on the conformational preferences of oligosaccharides compared to polypeptides, the former have been much less explored than the latter as protein mimics, despite interesting ADMET characteristics. In this work, the conformational landscapes of a series of 956 substituted glucopyranose oligomers of lengths 3 to 12 designed as protein interface mimics are revealed using microsecond-time-scale, enhanced-sampling molecular dynamics simulations. Deep convolutional networks are trained on these large conformational ensembles, to predict the stability of longer oligosaccharide structures from those of their constituent trimer motifs. Deep generative adversarial networks are then designed to suggest plausible conformations for oligosaccharide mimics of arbitrary length and substituent sequences that can subsequently be used as input to docking simulations. Analyzing the performance of the neural networks also yields insights into the intricate collective effects that dominate oligosaccharide conformational dynamics.

Molecular dynamics simulations reveal phosphorylation-induced conformational dynamics of the fibroblast growth factor receptor 1 kinase

The Fibroblast Growth Factor Receptor1 (FGFR1) kinase wields exquisite control on cell fate, proliferation, differentiation, and homeostasis. An imbalance of FGFR1 signaling leads to several pathogeneses of diseases ranging from multiple cancers to allergic and neurodegenerative disorders. In this study, we investigated the phosphorylation-induced conformational dynamics of FGFR1 in apo and ATP-bound states via all-atom molecular dynamics simulations. All simulations were performed for 2 × 2 µs. We have also investigated the energetics of the binding of ATP to FGFR1 using the molecular mechanics Poisson-Boltzmann scheme. Our study reveals that the FGFR1 kinase can reach a fully active configuration through phosphorylation and ATP binding. A 3-10 helix formation in the activation loop signifies its rearrangement leading to stability upon ATP binding. The interaction of phosphorylated tyrosine (pTyr654) with positively charged residues forms strong salt-bridge interactions, driving the compactness of the structure. The dynamic cross-correlation map reveals phosphorylation enhances correlated motions and reduces anti-correlated motions between different domains. We believe that the mechanistic understanding of large-conformational changes upon the activation of the FGFR1 kinase will aid the development of novel targeted therapeutics.Communicated by Ramaswamy H. Sarma.

Predicted and Experimental NMR Chemical Shifts at Variable Temperatures: The Effect of Protein Conformational Dynamics

NMR chemical shifts provide a sensitive probe of protein structure and dynamics but remain challenging to predict and interpret. We examine the effect of protein conformational distributions on 15N chemical shifts for dihydrofolate reductase (DHFR), comparing QM/MM predicted shifts with experimental shifts in solution as well as frozen distributions. Representative snapshots from MD trajectories exhibit variation in predicted 15N chemical shifts of up to 25 ppm. The average over the fluctuations is in significantly better agreement with room temperature solution experimental values than the prediction for any single optimal conformations. Meanwhile, solid-state NMR (SSNMR) measurements of frozen solutions at 105 K exhibit broad lines whose widths agree well with the widths of distributions of predicted shifts for samples from the trajectory. The backbone torsion angle ψi-1 varies over 60° on the picosecond time scale, compensated by φi. These fluctuations can explain much of the shift variation.

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.

Analyzing Molecular Dynamics Trajectories Thermodynamically through Artificial Intelligence

Molecular dynamics simulations produce trajectories that correspond to vast amounts of structure when exploring biochemical processes. Extracting valuable information, e.g., important intermediate states and collective variables (CVs) that describe the major movement modes, from molecular trajectories to understand the underlying mechanisms of biological processes presents a significant challenge. To achieve this goal, we introduce a deep learning approach, coined DIKI (deep identification of key intermediates), to determine low-dimensional CVs distinguishing key intermediate conformations without a-priori assumptions. DIKI dynamically plans the distribution of latent space and groups together similar conformations within the same cluster. Moreover, by incorporating two user-defined parameters, namely, coarse focus knob and fine focus knob, to help identify conformations with low free energy and differentiate the subtle distinctions among these conformations, resolution-tunable clustering was achieved. Furthermore, the integration of DIKI with a path-finding algorithm contributes to the identification of crucial intermediates along the lowest free-energy pathway. We postulate that DIKI is a robust and flexible tool that can find widespread applications in the analysis of complex biochemical processes.

Dramatic Differences between the Structural Susceptibility of the S1 Pre- and S2 Postfusion States of the SARS-CoV-2 Spike Protein to External Electric Fields Revealed by Molecular Dynamics Simulations

In its prefusion state, the SARS-CoV-2 spike protein (similarly to other class I viral fusion proteins) is metastable, which is considered to be an important feature for optimizing or regulating its functions. After the binding process of its S1 subunit (S1) with ACE2, the spike protein (S) undergoes a dramatic conformational change where S1 splits from the S2 subunit, which then penetrates the membrane of the host cell, promoting the fusion of the viral and cell membranes. This results in the infection of the host cell. In a previous work, we showed-using large-scale molecular dynamics simulations-that the application of external electric fields (EFs) induces drastic changes and damage in the receptor-binding domain (RBD) of the wild-type spike protein, as well of the Alpha, Beta, and Gamma variants, leaving a structure which cannot be recognized anymore by ACE2. In this work, we first extend the study to the Delta and Omicron variants and confirm the high sensitivity and extreme vulnerability of the RBD of the prefusion state of S to moderate EF (as weak as 104 V/m), but, more importantly, we also show that, in contrast, the S2 subunit of the postfusion state of the spike protein does not suffer structural damage even if electric field intensities four orders of magnitude higher are applied. These results provide a solid scientific basis to confirm the connection between the prefusion-state metastability of the SARS-CoV-2 spike protein and its susceptibility to be damaged by EF. After the virus docks to the ACE2 receptor, the stable and robust postfusion conformation develops, which exhibits a similar resistance to EF (damage threshold higher than 108 V/m) like most globular proteins.

Exploring the viral protease inhibitor space driven by consensus scoring-based virtual screening

Dengue fever presents a major health concern, and the lack of an effective vaccine or definite therapeutic regimen has led the research community to identify safe-by-design potential targets for drug discovery. Since the association of the NS2B co-factor with the protease domain of NS3 is imperative for the catalytic activity of the enzyme complex, inhibitors blocking their interaction could provide an alternative strategy to combat the dengue virus. In this context, the present study is aimed at exploring computer-assisted modeling of significant physicochemical features required for the inhibition of the dengue virus protease complex. First of all, alanine scanning was utilized to map hot spot residues critical for the association of the two subunits, NS2B and NS3pro, by studying their energy profiles. Then, consensus score-based virtual screening was performed to search through the commercially available chemical datasets. After screening, 1,575 small molecules were moved forward into docking studies to investigate their interactions with crucial interfacial residues (i.e., Tyr23, Lys26, Phe46, and Leu58), with only 233 molecules passing that stage. The top 30 molecules were selected based on a detailed profile of intermolecular interactions. After that, the top five molecules were selected for detailed mechanistic studies via molecular dynamics simulations followed by subsequent binding free energy calculations, principal component analysis in conjunction with free energy landscape. To the best of our knowledge, this is the first systematic and comprehensive investigation to identify protein-protein interaction blockers against the target protein at such a large scale, using integrated computational tools. Our results highlight the enhanced stability and good binding affinities towards the target protein of these compounds, which might act as new scaffolds for NS2B-NS3 protease inhibition. Future studies will be directed to explore the detailed atomistic-based structural and energetic framework of the mutation-induced affinity change between the protease domain of the DENV-2 NS3 protein and its cofactor NS2B. The detailed insight in turn might suggest precise and focused targeted points for the structure-based drug design but the computational cost may be a challenge.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00174-0.

Computational Prediction of Cyclic Peptide Structural Ensembles and Application to the Design of Keap1 Binders

The Nrf2 transcription factor is a master regulator of the cellular response to oxidative stress, and Keap1 is its primary negative regulator. Activating Nrf2 by inhibiting the Nrf2-Keap1 protein-protein interaction has shown promise for treating cancer and inflammatory diseases. A loop derived from Nrf2 has been shown to inhibit Keap1 selectively, especially when cyclized, but there are no reliable design methods for predicting an optimal macrocyclization strategy. In this work, we employed all-atom, explicit-solvent molecular dynamics simulations with enhanced sampling methods to predict the relative degree of preorganization for a series of peptides cyclized with a set of bis-thioether "staples". We then correlated these predictions to experimentally measured binding affinities for Keap1 and crystal structures of the cyclic peptides bound to Keap1. This work showcases a computational method for designing cyclic peptides by simulating and comparing their entire solution-phase ensembles, providing key insights into designing cyclic peptides as selective inhibitors of protein-protein interactions.

Toward a Benchmark for Markov State Models: The Folding of HP35

Adopting a 300 μs long MD trajectory of the folding of villin headpiece (HP35) by D. E. Shaw Research, we recently constructed a Markov state model (MSM) based on inter-residue contacts. The model reproduces the folding time and predicts that the native basin and unfolded region consist of metastable substates that are structurally well-characterized. Recognizing the need to establish well-defined benchmark problems, we study to what extent and in what sense this MSM can be employed as a reference model. Hence, we test the robustness of the MSM by comparing it to models that use alternative combinations of features, dimensionality reduction methods, and clustering schemes. The study suggests some main characteristics of the folding of HP35 that should be reproduced by other competitive models. Moreover, the discussion reveals which parts of the MSM workflow matter most for the considered problem and illustrates the promises and pitfalls of state-based models for the interpretation of biomolecular simulations.

Y225A induces long-range conformational changes in human prion protein that are protective in Drosophila

Prion protein (PrP) misfolding is the key trigger in the devastating prion diseases. Yet the sequence and structural determinants of PrP conformation and toxicity are not known in detail. Here, we describe the impact of replacing Y225 in human PrP with A225 from rabbit PrP, an animal highly resistant to prion diseases. We first examined human PrP-Y225A by molecular dynamics simulations. We next introduced human PrP in Drosophila and compared the toxicity of human PrP-WT and Y225A in the eye and in brain neurons. Y225A stabilizes the β2-α2 loop into a 310-helix from six different conformations identified in WT and lowers hydrophobic exposure. Transgenic flies expressing PrP-Y225A exhibit less toxicity in the eye and in brain neurons and less accumulation of insoluble PrP. Overall, we determined that Y225A lowers toxicity in Drosophila assays by promoting a structured loop conformation that increases the stability of the globular domain. These findings are significant because they shed light on the key role of distal α-helix 3 on the dynamics of the loop and the entire globular domain.

MDiGest: A Python package for describing allostery from molecular dynamics simulations

Many biological processes are regulated by allosteric mechanisms that communicate with distant sites in the protein responsible for functionality. The binding of a small molecule at an allosteric site typically induces conformational changes that propagate through the protein along allosteric pathways regulating enzymatic activity. Elucidating those communication pathways from allosteric sites to orthosteric sites is, therefore, essential to gain insights into biochemical processes. Targeting the allosteric pathways by mutagenesis can allow the engineering of proteins with desired functions. Furthermore, binding small molecule modulators along the allosteric pathways is a viable approach to target reactions using allosteric inhibitors/activators with temporal and spatial selectivity. Methods based on network theory can elucidate protein communication networks through the analysis of pairwise correlations observed in molecular dynamics (MD) simulations using molecular descriptors that serve as proxies for allosteric information. Typically, single atomic descriptors such as α-carbon displacements are used as proxies for allosteric information. Therefore, allosteric networks are based on correlations revealed by that descriptor. Here, we introduce a Python software package that provides a comprehensive toolkit for studying allostery from MD simulations of biochemical systems. MDiGest offers the ability to describe protein dynamics by combining different approaches, such as correlations of atomic displacements or dihedral angles, as well as a novel approach based on the correlation of Kabsch-Sander electrostatic couplings. MDiGest allows for comparisons of networks and community structures that capture physical information relevant to allostery. Multiple complementary tools for studying essential dynamics include principal component analysis, root mean square fluctuation, as well as secondary structure-based analyses.

Alpha-cadinol as a potential ACE-inhibitory volatile compound identified from Phaseolus vulgaris L. through in vitro and in silico analysis

Hypertension is a major risk factor of cardiovascular diseases, which is mainly caused due to over activation of renin-angiotensin system. The angiotensin converting enzyme (ACE), which is involved in formation of angiotensin II from angiotensin I, causes the blood vessels to constrict, in turn leading to hypertension. The current study was initiated to understand the role of bioactive volatile compounds from Phaseolus vulgaris L. (common bean), in ACE enzyme inhibition. Beans aqueous extract (BAE) showed maximum ACE inhibition of 88.4 ± 0.8% in comparison to other commonly consumed vegetables like spinach and garlic. The head space gas chromatography-mass spectrometry analysis showed the presence of a number of terpenes and terpenoids, which were present prominently in BAE. In silico molecular docking studies indicated that among the other volatile compounds, alpha-cadinol (-7.27 kcal/mol) and ar-tumerone (-6.44 kcal/mol) have the maximum binding affinity with the active site of ACE, as compared to that of captopril (-6.41 kcal/mol). The molecular dynamic simulation in biological environment, showed that alpha-cadinol forms a stable complex with ACE, with average binding energy of -42 kJ/mol. The ACE:alpha-cadinol complex was found to be stable mainly due to the hydrophobic interactions of alpha-cadinol with active site residues (Tyr523 and Phe457) of ACE. The in silico drug-likeness analysis showed that alpha-cadinol is appropriate for human system with no predicted hepatotoxicity or mutagenicity (AMES toxicity).Communicated by Ramaswamy H. Sarma.

Training Neural Network Models Using Molecular Dynamics Simulation Results to Efficiently Predict Cyclic Hexapeptide Structural Ensembles

Cyclic peptides have emerged as a promising class of therapeutics. However, their de novo design remains challenging, and many cyclic peptide drugs are simply natural products or their derivatives. Most cyclic peptides, including the current cyclic peptide drugs, adopt multiple conformations in water. The ability to characterize cyclic peptide structural ensembles would greatly aid their rational design. In a previous pioneering study, our group demonstrated that using molecular dynamics results to train machine learning models can efficiently predict structural ensembles of cyclic pentapeptides. Using this method, which was termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), linear regression models were able to predict the structural ensembles for an independent test set with R² = 0.94 between the predicted populations for specific structures and the observed populations in molecular dynamics simulations for cyclic pentapeptides. An underlying assumption in these StrEAMM models is that cyclic peptide structural preferences are predominantly influenced by neighboring interactions, namely, interactions between (1,2) and (1,3) residues. Here we demonstrate that for larger cyclic peptides such as cyclic hexapeptides, linear regression models including only (1,2) and (1,3) interactions fail to produce satisfactory predictions (R² = 0.47); further inclusion of (1,4) interactions leads to moderate improvements (R² = 0.75). We show that when using convolutional neural networks and graph neural networks to incorporate complex nonlinear interaction patterns, we can achieve R² = 0.97 and R² = 0.91 for cyclic pentapeptides and hexapeptides, respectively.

Selecting Features for Markov Modeling: A Case Study on HP35

Markov state models represent a popular means to interpret molecular dynamics trajectories in terms of memoryless transitions between metastable conformational states. To provide a mechanistic understanding of the considered biomolecular process, these states should reflect structurally distinct conformations and ensure a time scale separation between fast intrastate and slow interstate dynamics. Adopting the folding of villin headpiece (HP35) as a well-established model problem, here we discuss the selection of suitable input coordinates or "features", such as backbone dihedral angles and interresidue distances. We show that dihedral angles account accurately for the structure of the native energy basin of HP35, while the unfolded region of the free energy landscape and the folding process are best described by tertiary contacts of the protein. To construct a contact-based model, we consider various ways to define and select contact distances and introduce a low-pass filtering of the feature trajectory as well as a correlation-based characterization of states. Relying on input data that faithfully account for the mechanistic origin of the studied process, the states of the resulting Markov model are clearly discriminated by the features, describe consistently the hierarchical structure of the free energy landscape, and─as a consequence─correctly reproduce the slow time scales of the process.

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.

Intermediate-aided allostery mechanism for α-glucosidase by Xanthene-11v as an inhibitor using residue interaction network analysis

Exploring allosteric inhibition and the discovery of new inhibitor binding sites are important studies in protein regulation mechanisms and drug discovery. Structural and network-based analyses of trajectories resulting from molecular dynamics (MD) simulations have been developed to discover protein dynamics, landscape, functions, and allosteric regions. Here, an experimentally suggested non-competitive inhibitor, xanthene-11v, was considered to explore its allosteric inhibition mechanism in α-glucosidase MAL12. Comparative structural and network analyses were applied to eight 250 ns independent MD simulations, four of which were performed in the free state and four of which were performed in ligand-bound forms. Projected two-dimensional free energy landscapes (FEL) were constructed from the probabilistic distribution of conformations along the first two principal components. The post-simulation analyses of the coordinates, side-chain torsion angles, non-covalent interaction networks, network communities, and their centralities were performed on α-glucosidase conformations and the intermediate sub-states. Important communities of residues have been found that connect the allosteric site to the active site. Some of these residues like Thr307, Arg312, TYR344, ILE345, Phe357, Asp406, Val407, Asp408, and Leu436 are the key messengers in the transition pathway between allosteric and active sites. Evaluating the probability distribution of distances between gate residues including Val407 in one community and Phe158, and Pro65 in another community depicted the closure of this gate due to the inhibitor binding. Six macro states of protein were deduced from the topology of FEL and analysis of conformational preference of free and ligand-bound systems to these macro states shows a combination of lock-and-key, conformational selection, and induced fit mechanisms are effective in ligand binding. All these results reveal structural states, allosteric mechanisms, and key players in the inhibition pathway of α-glucosidase by xanthene-11v.

An Expedited Route to Optical and Electronic Properties at Finite Temperature via Unsupervised Learning

Electronic properties and absorption spectra are the grounds to investigate molecular electronic states and their interactions with the environment. Modeling and computations are required for the molecular understanding and design strategies of photo-active materials and sensors. However, the interpretation of such properties demands expensive computations and dealing with the interplay of electronic excited states with the conformational freedom of the chromophores in complex matrices (i.e., solvents, biomolecules, crystals) at finite temperature. Computational protocols combining time dependent density functional theory and ab initio molecular dynamics (MD) have become very powerful in this field, although they require still a large number of computations for a detailed reproduction of electronic properties, such as band shapes. Besides the ongoing research in more traditional computational chemistry fields, data analysis and machine learning methods have been increasingly employed as complementary approaches for efficient data exploration, prediction and model development, starting from the data resulting from MD simulations and electronic structure calculations. In this work, dataset reduction capabilities by unsupervised clustering techniques applied to MD trajectories are proposed and tested for the ab initio modeling of electronic absorption spectra of two challenging case studies: a non-covalent charge-transfer dimer and a ruthenium complex in solution at room temperature. The K-medoids clustering technique is applied and is proven to be able to reduce by ∼100 times the total cost of excited state calculations on an MD sampling with no loss in the accuracy and it also provides an easier understanding of the representative structures (medoids) to be analyzed on the molecular scale.

Computational design of cyclic peptides to inhibit protein-peptide interactions

Many protein-protein interactions result from the binding of one folded protein with one short peptide segment, such as complexes formed by SH3 or PDZ domains. These transient protein-peptide interactions are notably involved in cellular signaling pathways and generally have low affinities, which opens the possibility to design competitive inhibitors of these complexes. We present and assess here our computational approach, called Des3PI, to design de novo cyclic peptides with potential high affinity for protein surfaces involved in interactions with peptide segments. The results were not conclusive for two receptors, the αVβ3 integrin and the CXCR4 chemokine receptor, but were promising in the case of SH3 and PDZ domains: For the former, Des3PI was able to find at least one cyclic sequence with six hotspots that binds a SH3 domain with a better theoretical affinity to the known proline-rich RLP2 peptide. For the latter, Des3PI could identify at least four cyclic sequences with four or five hotspots that have lower binding free energies computed by the MM-PBSA method than the reference peptide GKAP.

Revealing Free Energy Landscape From MD Data via Conditional Angle Partition Tree

Deciphering the free energy landscape of biomolecular structure space is crucial for understanding many complex molecular processes, such as protein-protein interaction, RNA folding, and protein folding. A major source of current dynamic structure data is Molecular Dynamics (MD) simulations. Several methods have been proposed to investigate the free energy landscape from MD data, but all of them rely on the assumption that kinetic similarity is associated with global geometric similarity, which may lead to unsatisfactory results. In this paper, we proposed a new method called Conditional Angle Partition Tree to reveal the hierarchical free energy landscape by correlating local geometric similarity with kinetic similarity. Its application on the benchmark alanine dipeptide MD data showed a much better performance than existing methods in exploring and understanding the free energy landscape. We also applied it to the MD data of Villin HP35. Our results are more reasonable on various aspects than those from other methods and very informative on the hierarchical structure of its energy landscape.

On the Uses of PCA to Characterise Molecular Dynamics Simulations of Biological Macromolecules: Basics and Tips for an Effective Use

Principal Component Analysis (PCA) is a procedure widely used to examine data collected from molecular dynamics simulations of biological macromolecules. It allows for greatly reducing the dimensionality of their configurational space, facilitating further qualitative and quantitative analysis. Its simplicity and relatively low computational cost explain its extended use. However, a judicious implementation of PCA requires the knowledge of its theoretical grounds as well as its weaknesses and capabilities. In this article, we review these issues and discuss several strategies developed over the last years to mitigate the main PCA flaws and enhance the reproducibility of its results.

Ligands-induced open-close conformational change during DapE catalysis: Insights from molecular dynamics simulations

The microbial enzyme DapE plays a critical role in the lysine biosynthetic pathway and is considered as a potentially safe antibiotic target. In this study, atomistic simulations are employed to identify the modes of essential dynamics that define the conformational response of the enzyme to ligand binding and unbinding. The binding modes and the binding affinities of the products to the DapE enzyme are estimated from the MM-PBSA method, and the residues contributing to the ligand binding are identified. Various structural analyses and the principal component analysis of the molecular dynamics trajectories reveal that the removal of products from the active site causes a significant change in the overall enzyme structure. Both Cartesian and dihedral principal component analyses are used to characterize the structural changes in terms of domain unfolding and domain twisting motions. In the most dominant mode, that is, the domain unfolding motion, the two catalytic domains move away from the two dimerization domains of the dimeric enzyme, representing a closed-to-open conformational change. The conformational changes are initiated by the coordinated movement of three loops (Asp75-Pro82, Gly240-Asn244, and Thr347-Glu353) that trigger a domain-level movement. From multiple short trajectories, the time constant associated with the domain opening motion is estimated as 43.6 ns. Physiologically, this close-to-open conformational change is essential for the regeneration of the initial state of the enzyme for the subsequent cycle of catalytic action and provides the apo enzyme enough flexibility for efficient substrate binding.

Is the Triggering of PD-L1 Dimerization a Potential Mechanism for Food-Derived Small Molecules in Cancer Immunotherapy? A Study by Molecular Dynamics

Using small molecules to inhibit the PD-1/PD-L1 pathway is an important approach in cancer immunotherapy. Natural compounds such as capsaicin, zucapsaicin, 6-gingerol and curcumin have been proposed to have anticancer immunologic functions by downregulating the PD-L1 expression. PD-L1 dimerization promoted by small molecules was recently reported to be a potential mechanism to inhibit the PD-1/PD-L1 pathway. To clarify the molecular mechanism of such compounds on PD-L1 dimerization, molecular docking and molecular dynamics simulations were performed. The results evidenced that these compounds could inhibit PD-1/PD-L1 interactions by directly targeting PD-L1 dimerization. Binding free energy calculations showed that capsaicin, zucapsaicin, 6-gingerol and curcumin have strong binding ability with the PD-L1 dimer, where the affinities of them follow the trend of zucapsaicin > capsaicin > 6-gingerol ≈ curcumin. Analysis by residue energy decomposition, contact numbers and nonbonded interactions revealed that these compounds have a tight interaction with the C-sheet, F-sheet and G-sheet fragments of the PD-L1 dimer, which were also involved in the interactions with PD-1. Moreover, non-polar interactions between these compounds and the key residues Ile54, Tyr56, Met115 and Ala121 play a key role in stabilizing the protein−ligand complexes in solution, in which the 4′-hydroxy-3′-methoxyphenyl group and the carbonyl group of zucapsaicin, capsaicin, 6-ginger and curcumin were significant for the complexation of small molecules with the PD-L1 dimer. The conformational variations of these complexes were further analyzed by free energy landscape (FEL) and principal component analysis (PCA) and showed that these small molecules could make the structure of dimers more stable. This work provides a mechanism insight for food-derived small molecules blocking the PD-1/PD-L1 pathway via directly targeting the PD-L1 dimerization and offers theoretical guidance to discover more effective small molecular drugs in cancer immunotherapy.

Beta-rich intermediates in denaturation of lysozyme: accelerated molecular dynamics simulations

Amyloid fibrillar aggregates play a critical role in many neurodegenerative disorders. Conversion of globular proteins into fibrils is associated with global conformational rearrangement and involves the transformation of α-helices to β-sheets. In the present work, the accelerated molecular dynamics technique was applied to study the unfolding of hen egg-white lysozyme at elevated temperatures, and the transformation of the native structure to a disordered one was analyzed. The influence of the disulfide bonds on the conformational dynamics and the energy landscape of denaturation process was considered. Our results show that formation of the metastable β-enriched conformers of individual protein molecules may precede the aggregation process. These β-rich intermediates can play a role of bricks making up fibrils.Communicated by Ramaswamy H. Sarma.

Identifying the Template for Oligomer to Fibril Conversion for Amyloid-β (1-42) Oligomers using Hamiltonian Replica Exchange Molecular Dynamics

The toxicity of amyloid-β (Aβ) oligomers has been known to be higher compared to mature fibrils. Yet the presence of plaques in Alzheimer's disease patients indicates the significance of oligomer to fibril conversion for Aβ aggregates. In this study, we investigate Aβ_13-42 oligomers having two to five peptide chains using extensive all-atom molecular dynamics simulations to identify the on- or off-pathway intermediates in fibril formation pathway. Hamiltonian replica exchange method through solute tempering (REST2) has been employed to explore the different structures attained by these aggregates. Using intra-chain and inter-chain contacts as reaction coordinates, we obtain the free energy surface for the Aβ_13-42 oligomers. Consequently, their stable conformations and structural features have been identified. The found conformations belonging to most probable structures possess both parallel and anti-parallel β-sheets, characteristic of on- and off-pathway intermediates, respectively. Further, we have measured the tendency to form fibril like interactions among the β-sheet forming residues. Our analysis finds that residues 30-36 possess higher tendency to form fibril like contacts among all the residues. While we find stronger interaction among residues 30-36, these amino acids are also found to be more shielded from water compared to others. With previous experimental studies finding these residues to be more crucial for the stability of Aβ42 oligomers, we propose that interactions within this patch could trigger seed formation that leads to conversion of on-pathway oligomers into disease relevant fibrils.

Conformational Dynamics of the Receptor-Binding Domain of the SARS-CoV-2 Spike Protein

Variants of SARS-CoV-2 keep emerging and causing new waves of COVID-19 around the world. Effective new approaches in drug development are based on the binding of agents, such as neutralizing monoclonal antibodies to a receptor-binding domain (RBD) of SARS-CoV-2 spike protein. However, mutations in RBD may lower the affinity of previously developed antibodies. Therefore, rapid analysis of new variants and selection of a binding partner with high affinity is of great therapeutic importance. Here, we explore a computational approach based on molecular dynamics simulations and conformational clusterization techniques for the wild-type and omicron variants of RBD. Biochemical experiments support the hypothesis of the presence of several conformational states within the RBD assembly. The development of such an approach will facilitate the selection of neutralization drugs with higher affinity based on the primary structure of the target antigen.

Lipid Composition Is Critical for Accurate Membrane Permeability Prediction of Cyclic Peptides by Molecular Dynamics Simulations

Cyclic peptides have attracted attention as a promising pharmaceutical modality due to their potential to selectively inhibit previously undruggable targets, such as intracellular protein-protein interactions. Poor membrane permeability is the biggest bottleneck hindering successful drug discovery based on cyclic peptides. Therefore, the development of computational methods that can predict membrane permeability and support elucidation of the membrane permeation mechanism of drug candidate peptides is much sought after. In this study, we developed a protocol to simulate the behavior in membrane permeation steps and estimate the membrane permeability of large cyclic peptides with more than or equal to 10 residues. This protocol requires the use of a more realistic membrane model than a single-lipid phospholipid bilayer. To select a membrane model, we first analyzed the effect of cholesterol concentration in the model membrane on the potential of mean force and hydrogen bonding networks along the direction perpendicular to the membrane surface as predicted by molecular dynamics simulations using cyclosporine A. These results suggest that a membrane model with 40 or 50 mol % cholesterol was suitable for predicting the permeation process. Subsequently, two types of membrane models containing 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine and 40 and 50 mol % cholesterol were used. To validate the efficiency of our protocol, the membrane permeability of 18 ten-residue peptides was predicted. Correlation coefficients of R > 0.8 between the experimental and calculated permeability values were obtained with both model membranes. The results of this study demonstrate that the lipid membrane is not just a medium but also among the main factors determining the membrane permeability of molecules. The computational protocol proposed in this study and the findings obtained on the effect of membrane model composition will contribute to building a schematic view of the membrane permeation process. Furthermore, the results of this study will eventually aid the elucidation of design rules for peptide drugs with high membrane permeability.