Conformational Clustering Research Articles

Unveiling molecular mechanism underlying inhibition of human islet amyloid polypeptide fibrillation by benzene carboxylic acid-peptide conjugate

Type 2 diabetes (T2D) is linked to the apoptosis of insulin-producing β-cells due to the aberrant fibrillation of the human islet amyloid polypeptide (hIAPP or amylin) to cytotoxic aggregates. Profit et al. generated conjugates (C1–C7) by appending benzene carboxylic acids of varying charges to the N-terminal of hIAPP22-29 fragment peptide NFGAILSS to modulate hIAPP fibrillation and cytotoxicity. C5 (4 µM) derived by conjugating low-cost, commercially available benzene-1,2,4,5-tetracarboxylic acid known as pyromellitic acid to NFGAILSS completely abolishes the hIAPP (40 µM) self-assembly as noted in the thioflavin T (ThT) fluorescence assay. The circular dichroism (CD) spectra highlighted that C5 stabilized hIAPP in a distinctive conformation and blocked its conformational switching to amyloidogenic β-sheet structure. C5 possessing a charge-dense pyromellitic acid moiety appended on the N-terminal region of self-recognition hIAPP fragment sequence NFGAILSS created significant interest as it effectively inhibited hIAPP fibrillation and possessed lower molecular mass, smaller size, and charge as compared to hIAPP aggregation inhibitor EEEENFGAILSS (P10). However, it remains unclear how C5 traps hIAPP into a unique conformation that abolishes its self-aggregation propensity. Thus, molecular dynamics (MD) simulations have been employed to illuminate the conformational transitions and structural changes in hIAPP on the inclusion of C5. C5 displayed high-affinity binding interactions to hIAPP (ΔGbinding = −37.11 ± 3.72 kcal/mol) with major contributions from the van der Waals and electrostatic interactions. Furthermore, residue-specific binding free energy analysis depicted high-affinity binding interactions of C5 with Phe15, His18, Asp21, and Tyr37 of hIAPP that play a crucial role in hIAPP fibrillation. The negatively charged carboxylate groups of pyromellitic acid moiety of C5 displayed interactions with the key residues of hIAPP as noted in the conformational clustering analysis. Notably higher sampling of helix from 24.00 ± 0.84 % in hIAPP to 34.20 ± 1.92 % in hIAPP–C5 is consistent with the CD studies, which depicted that C5 trapped hIAPP monomer in a conformation that blocked its self-association. MD simulations illuminated the molecular mechanism and atomistic details of the C5 interactions with hIAPP, which are responsible for its inhibitory activity against hIAPP fibrillation and alleviating hIAPP aggregates-induced cytotoxicity.

Exploration of small molecule compounds targeting abdominal aortic aneurysm based on CMap database and molecular dynamics simulation.

The mitigation of abdominal aortic aneurysm (AAA) growth through pharmaceutical intervention offers the potential to avert the perils associated with AAA rupture and the subsequent need for surgical intervention. Nevertheless, the existing effective drugs for AAA treatment are limited, necessitating a pressing exploration for novel therapeutic medications. AAA-related transcriptome data were downloaded from GEO, and differentially expressed genes (DEGs) in AAA tissue were screened for GO and KEGG enrichment analyses. Small molecule compounds and their target proteins with negative connectivity to the AAA expression profile were predicted in the Connectivity Map (CMap) database. Molecular docking and molecular dynamics simulation were performed to predict the binding of the target protein to the small molecule compound, and the MM/GBSA method was used to calculate the binding free energy. Cluster analysis was performed using the cluster tool in the GROMACS package. An AAA cell-free model was built, and CETSA experiments were used to demonstrate the binding ability of small molecules to the target protein in cells. A total of 2244 DEGs in AAA were obtained through differential analysis, and the DEGs were mainly enriched in the tubulin binding biological function and cell cycle pathway. The CMap results showed that Apicidin had a potential therapeutic effect on AAA with a connectivity score of -97.74, and HDAC4 was the target protein of Apicidin. Based on literature, HDAC4-Apicidin was selected as the subsequent research object. The lowest affinity of Apicidin-HDAC4 molecular docking was -8.218kcal/mol. Molecular dynamics simulation results indicated that Apicidin-HDAC4 could form a stable complex. MM/GBSA analysis showed a total binding free energy of -55.40 ± 0.79kcal/mol, and cluster analysis showed that there were two main conformational clusters during the binding process, accounting for 22.4% and 57.8%, respectively. Apicidin could form hydrogen bonds with surrounding residues for stable binding. CETSA experiment proved the stable binding ability of Apicidin and HDAC4. Apicidin inhibited HDAC4 in AAA and exhibited favorable protein-ligand interactions and stability, making it a potential candidate drug for treating AAA.

Molecular Dynamics‐Based Conformational Simulation Method for Analysis of Arrival Time Distributions in Ion Mobility Mass Spectrometry

AbstractIon mobility‐mass spectrometry (IM‐MS) has recently contributed to the structural analysis of molecules, including supramolecules and proteins, by determining the ion arrival time distributions correlated with the collision cross sections (CCSs), as well as the mass‐to‐charge ratios. However, its application range is still limited owing to the lack of general CCSs simulation methods based on possible molecular conformations. Here, a molecular dynamics‐based conformational search method for simulating CCS distributions using projection approximation is reported. As a case study, the gas‐phase conformations of the sodium adducts of conformationally flexible polyketones with 3,3‐dimethylpentane‐2,4‐dione as the monomer are analyzed. The sodium adduct of the hexamer (m/z 781.4 for [1 + Na]+) showed a monomodal arrival time distribution, but that of the octamer sodium adduct (m/z 1033.5 for [2 + Na]+) is multimodal. The conformational analysis indicated an unimodal CCS distribution of simulated [1 + Na]+ conformations in which the sodium cation is mainly bound at the chain terminal. Conversely, four clusters of conformations are obtained for [2 + Na]+ based on the Na+‐coordination sites, which qualitatively reproduced the observed CCS distribution. This approach will extend the utility of IM‐MS for the conformational analysis of flexible molecules in the gas phase.

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 introduced an unsupervised 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 for deep learning to utilize learned natural atomistic motions in protein conformation sampling.

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 first an unsupervised deep learning-based model, termed Internal Coordinate Net (ICoN), is developed that learns the physical principles of conformational changes from molecular dynamics (MD) simulation data. Second, interpolating data points in the learned latent space are selected that rapidly identify novel synthetic conformations with sophisticated and large-scale sidechains 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 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. The proposed approach is highly transferable and can be used for any available data for training. The work also demonstrated the ability for deep learning to utilize learned natural atomistic motions in protein conformation sampling.

Hydrophobic Mismatch in the Thylakoid Membrane Regulates Photosynthetic Light Harvesting.

The ability to harvest light effectively in a changing environment is necessary to ensure efficient photosynthesis and crop growth. One mechanism, known as qE, protects photosystem II (PSII) and regulates electron transfer through the harmless dissipation of excess absorbed photons as heat. This process involves reversible clustering of the major light-harvesting complexes of PSII (LHCII) in the thylakoid membrane and relies upon the ΔpH gradient and the allosteric modulator protein PsbS. To date, the exact role of PsbS in the qE mechanism has remained elusive. Here, we show that PsbS induces hydrophobic mismatch in the thylakoid membrane through dynamic rearrangement of lipids around LHCII leading to observed membrane thinning. We found that upon illumination, the thylakoid membrane reversibly shrinks from around 4.3 to 3.2 nm, without PsbS, this response is eliminated. Furthermore, we show that the lipid digalactosyldiacylglycerol (DGDG) is repelled from the LHCII-PsbS complex due to an increase in both the pKa of lumenal residues and in the dipole moment of LHCII, which allows for further conformational change and clustering in the membrane. Our results suggest a mechanistic role for PsbS as a facilitator of a hydrophobic mismatch-mediated phase transition between LHCII-PsbS and its environment. This could act as the driving force to sort LHCII into photoprotective nanodomains in the thylakoid membrane. This work shows an example of the key role of the hydrophobic mismatch process in regulating membrane protein function in plants.

Enhancing Cisplatin Efficacy with Low Toxicity in Solid Breast Cancer Cells Using pH-Charge-Reversal Sericin-Based Nanocarriers: Development, Characterization, and In Vitro Biological Assessment.

Platinum-based chemotherapeutic agents are widely employed in cancer treatment because of their effectiveness in targeting DNA. However, this indiscriminate action often affects both cancerous and normal cells, leading to severe side effects and highlighting the need for innovative approaches in achieving precise drug delivery. Nanotechnology presents a promising avenue for addressing these challenges. Protein-based nanocarriers exhibit promising capabilities in the realm of cancer drug delivery with silk sericin nanoparticles standing out as a leading contender. This investigation focuses on creating a sericin-based nanocarrier (SNC) featuring surface charge reversal designed to effectively transport cisplatin (Cispt-SNC) into MCF-7 breast cancer cells. Utilizing AutoDock4.2, our molecular docking analyses identified key amino acids and revealed distinctive conformational clusters, providing insights into the drug-protein interaction landscape and highlighting the potential of sericin as a carrier for controlled drug release. The careful optimization and fabrication of sericin as the carrier material were achieved through flash nanoprecipitation, a straightforward and reproducible method that is devoid of intricate equipment. The physicochemical properties of SNCs and Cispt-SNCs, particularly concerning size, surface charge, and morphology, were evaluated using dynamic light scattering (DLS) and scanning electron microscopy (SEM). Chemical and conformational analyses of the nanocarriers were conducted using Fourier-transform infrared spectroscopy (FTIR) and circular dichroism (CD), and elemental composition analysis was performed through energy-dispersive X-ray spectroscopy (EDX). This approach aimed to achieve the smallest nanoparticle size for Cispt-SNCs (180 nm) and high drug encapsulation efficiency (84%) at an optimal sericin concentration of 0.1% (w/v), maintaining a negative net charge at a physiological pH (7.4). Cellular uptake and cytotoxicity were investigated in MCF-7 breast cancer cells. SNCs demonstrated stability and exhibited a pH-dependent drug release behavior, aligning with the mildly acidic tumor microenvironment (pH 6.0-7.0). Efficient cellular uptake of Cispt-SNC, along with DNA fragmentation and chromatin condensation, was found at pH 6, leading to cell apoptosis. These results collectively indicate the potential of SNCs for achieving controlled drug release in a tumor-specific context. Our in vitro studies reveal the cytotoxicity of both cisplatin and Cispt-SNCs on MCF-7 cells. Cisplatin significantly reduced cell viability at 10 μM concentration (IC50), and the unique combination of sericin and cisplatin showcased enhanced cell viability compared to cisplatin alone, suggesting that controlled drug release is indicated by a gradient decrease in cell viability and highlighting SNCs as promising carriers. The study underscores the promise of protein-based nanocarriers in advancing targeted drug delivery for cancer therapy.

Unveiling fructose and glucose binding to human serum albumin: fluorescence measurements and docking, molecular dynamics and quantum biochemistry computations

This research examines the interaction between human serum albumin (HSA) and various sugar forms (β-D-fructofuranose (FRC), α-D-glucopyranose (GLC), Keto-D-fructose (FRO), Aldehydo-D-glucose (GLO), and modified Aldehydo-D-glucose (GLOm)) using fluorescent spectroscopy, molecular docking simulations, molecular dynamics, protein conformational clusters (EnGens), molecular fractionation with conjugate caps (MFCC) and quantum biochemistry analysis. We analyze molecular and quantum aspects, uncovering interaction energies between sugar atoms and amino acids. Total interaction energy considers protein fragmentation, energetic decomposition, and interaction energy from a bottom-up perspective. Molecular dynamics reveal that unmodified Aldehydo-D-glucose (GLO) escapes HSA binding sites, explaining gradual glycation. We pioneer studying HSA’s binding mechanism with glucose and fructose in a 1:1 ratio using long molecular dynamics simulations. Results suggest the transitional GLOm form has a higher Sudlow I site propensity than unmodified glucose, crucial for K195 glycation. FRO and GLOm interaction tendencies move toward a deeper FA7 cavity, near its center. This approach effectively elucidates small molecule binding mechanisms, consistent with previous experimental results. Communicated by Ramaswamy H. Sarma

In silico Investigation of the Pro-apoptotic Potential of Syringic Acid Analog

Background: Conformational changes in BAX are associated with the activation of its pro-apoptotic potential. Previously, small molecule BAX antagonists have been shown to bring about apoptosis by inducing conformational changes in BAX by direct binding to the serine 184 site of BAX. Methods: In this article, we have proposed that syringic acid analog SA14 can incur apoptosis by directly binding to and inducing conformational changes in BAX. The pro-apoptotic potential of SA14 has been investigated using an in silico structure-based approach, i.e., docking and molecular dynamics computations are employed to study the binding of SA14 to the residues of the active site of BAX. Results: Based on docking results, four BAX-SA14 complexes, each representative of a cluster of conformations, have been selected for molecular dynamics simulations. The root mean square deviation has indicated the formation of stable conformations for two of the complexes. Other parameters, such as root mean square fluctuation, radius of gyration, and solvent accessible surface area, have been used to confirm the results, which have indicated favorable binding between BAX and SA14. Conclusion: Overall, the results have indicated that SA14 can bring about stable conformational changes in BAX and shows merit as a potential BAX-activating pro-apoptotic agent worthy of further experimental studies.

The influence of model building schemes and molecular dynamics sampling on QM-cluster models: the chorismate mutase case study.

Most QM-cluster models of enzymes are constructed based on X-ray crystal structures, which limits comparison to in vivo structure and mechanism. The active site of chorismate mutase from Bacillus subtilis and the enzymatic transformation of chorismate to prephenate is used as a case study to guide construction of QM-cluster models built first from the X-ray crystal structure, then from molecular dynamics (MD) simulation snapshots. The Residue Interaction Network ResidUe Selector (RINRUS) software toolkit, developed by our group to simplify and automate the construction of QM-cluster models, is expanded to handle MD to QM-cluster model workflows. Several options, some employing novel topological clustering from residue interaction network (RIN) information, are evaluated for generating conformational clustering from MD simulation. RINRUS then generates a statistical thermodynamic framework for QM-cluster modeling of the chorismate mutase mechanism via refining 250 MD frames with density functional theory (DFT). The 250 QM-cluster models sampled provide a mean ΔG‡ of 10.3 ± 2.6 kcal mol-1 compared to the experimental value of 15.4 kcal mol-1 at 25 °C. While the difference between theory and experiment is consequential, the level of theory used is modest and therefore "chemical" accuracy is unexpected. More important are the comparisons made between QM-cluster models designed from the X-ray crystal structure versus those from MD frames. The large variations in kinetic and thermodynamic properties arise from geometric changes in the ensemble of QM-cluster models, rather from the composition of the QM-cluster models or from the active site-solvent interface. The findings open the way for further quantitative and reproducible calibration in the field of computational enzymology using the model construction framework afforded with the RINRUS software toolkit.

Early Steps in the O2 Scavenger Process in the Aqueous Phase: Hydrazine vs DEHA.

We investigate the first direct proton abstraction reactions from reducing agents (RAHs) hydrazine and diethyl hydroxylamine (DEHA), toward dioxygen (O2) in the aqueous phase, spanning ambient to high-temperature conditions. Quantum chemistry methods and molecular dynamics simulations are employed in this study. Quantum chemistry methods are used to analyze the quasi-equilibrium between a reactive conformation and a transition state in the [RAH,O2] cluster. On the other hand, molecular dynamics simulations estimate the probability of observing a reactive conformation of the [RAH,O2] cluster in the solution. In this study, we assume that the energy barrier of the quasi-equilibrium is sufficiently high for the RAH/O2 association process to be at equilibrium. Our findings indicate that the first proton abstraction process from a reactive conformation cluster by DEHA is energetically favored compared to hydrazine. Conversely, the association process of hydrazine and O2 in solution is more favorable than that of DEHA. Consequently, the rate constant for the first proton abstraction process is similar for both hydrazine and DEHA, particularly at high temperatures, with activation energies of approximately 21.5 ± 1.5 kcal mol-1 for both compounds. These results align with recent experiments investigating the complete O2 scavenger process in liquid water with hydrazine and DEHA. Therefore, our findings support the assumption that first proton abstraction reactions are the rate-determining steps in O2 scavenger processes in the aqueous phase.

Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing.

Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure-function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue-specific deuterium labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB-disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Lastly, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.

Investigating the ibrutinib resistance mechanism of L528W mutation on Brutonʼs tyrosine kinase via molecular dynamics simulations

Drug resistance to Bruton's Tyrosine Kinase (BTK) inhibitors presents a challenge in treating B-cell malignancies, and the mechanism behind drug resistance remains unclear. In this study, we focused on the BTK L528W mutation and investigated the underlying mechanisms of resistance to ibrutinib (including prototype and its active metabolite from, PCI-45227) using a combination of bioinformatics analysis, and molecular dynamics (MD) simulations. Protein stability of wild type (WT) BTK and L528W mutant was predicted using DUET, PoPMuSiC, and I-Mutant2.0. We performed MD simulations of six systems, apo-WT, metabolite-WT, prototype-WT and their mutants, to analyze the significant conformational and BTK-inhibitor binding affinity changes induced by the L528W mutation. Results show that the L528W mutation reduces the conformational stability of BTK compared to the WT. Principal component analysis (PCA) based free energy landscape (FEL) analysis shows that the L528W mutant ensemble tends to form more conformation clusters and exhibit higher levels of local minima than the WT counterpart. The interaction analysis reveal that the L528W mutation disrupts the strong hydrogen bond between Cys481 and inhibitors and reduces the number of hydrogen bonds between inhibitors and BTK in the L528W mutant complex structures compared to the WT. Porcupine plot analysis in association with cross-correlation analysis show the high-intensity flexible motion exhibited by the P-loop region. MM/GBSA calculations show that the L528W mutation in metabolite-BTK and prototype-BTK complexes increases binding free energy compared to the WT, with a reduction in binding affinity confirmed by per-residue energy decomposition. Specifically, the binding free energy increases from −57.86 kcal/mol to −48.26 kcal/mol for the metabolite-BTK complex and from −62.04 kcal/mol to −50.55 kcal/mol for the prototype-BTK complex. Overall, our study finds that the L528W mutation reduces BTK stability, decreases binding affinity, and leads to drug resistance and potential disease recurrence.

Uncovering the interactions between PME and PMEI at the gene and protein levels: Implications for the design of specific PMEI.

Pectin methylesterase inhibitor (PMEI) can specifically bind and inhibit the activity of pectin methylesterase (PME), which has been widely used in fruit and vegetable juice processing. However, the limited three-dimensional structure, unclear action mechanism, low thermal stability and biological activity of PMEI severely limited its application. In this work, molecular recognition and conformational changes of PME and PMEI were analyzed by various molecular simulation methods. Then suggestions were proposed for improving thermal stability and affinity maturation of PMEI through semi-rational design. Phylogenetic trees of PME and PMEI were established using the Maximum likelihood (ML) method. The results show that PME and PMEI have good sequence and structure conservation in various plants, and the simulated data can be widely adopted. In this work, MD simulations were performed using AMBER20 package and ff14SB force field. Protein interaction analysis indicates that H-bonds, van der Waals forces, and the salt bridge formed of K224 with ID116 are the main driving forces for mutual molecular recognition of PME and PMEI. According to the analyses of free energy landscape (FEL), conformational cluster, and motion, the association with PMEI greatly disrupts PME's dispersed functional motion mode and biological function. By monitoring the changes of residue contact number and binding free energy, IG35M/ IG35R: IT93F and IT113W/ IT113W: ID116W mutations contribute to thermal stability and affinity maturation of the PME-PMEI complex system, respectively. This work reveals the interaction between PME and PMEI at the gene and protein levels and provides options for modifying specific PMEI.

Rapid and Automated Ab Initio Metabolite Collisional Cross Section Prediction from SMILES Input.

We implemented an ab initio CCS prediction workflow which incrementally refines generated structures using molecular mechanics, a deep learning potential, conformational clustering, and quantum mechanics (QM). Automating intermediate steps for a high performance computing (HPC) environment allows users to input the SMILES structure of small organic molecules and obtain a Boltzmann averaged collisional cross section (CCS) value as output. The CCS of a molecular species is a metric measured by ion mobility spectrometry (IMS) which can improve annotation of untargeted metabolomics experiments. We report only a minor drop in accuracy when we expedite the CCS calculation by replacing the QM geometry refinement step with a single-point energy calculation. Even though the workflow involves stochastic steps (i.e., conformation generation and clustering), the final CCS value was highly reproducible for multiple iterations on L-carnosine. Finally, we illustrate that the gas phase ensembles modeled for the workflow are intermediate files which can be used for the prediction of other properties such as aqueous phase nuclear magnetic resonance chemical shift prediction. The software is available at the following link: https://github.com/DasSusanta/snakemake_ccs.

Binding of viral nuclear localization signal peptides to importin-α nuclear transport protein

Using all-atom replica-exchange molecular dynamics simulations, we mapped the mechanisms of binding of the nuclear localization signal (NLS) sequence from Venezuelan equine encephalitis virus (VEEV) capsid protein to importin-α (impα) transport protein. Our objective was to identify the VEEV NLS sequence fragment that confers native, experimentally resolved binding to impα as well as to study associated binding energetics and conformational ensembles. The two selected VEEV NLS peptide fragments, KKPK and KKPKKE, show strikingly different binding mechanisms. The minNLS peptide KKPK binds non-natively and nonspecifically by adopting five diverse conformational clusters with low similarity to the x-ray structure 3VE6 of NLS-impα complex. Despite the prevalence of non-native interactions, the minNLS peptide still largely binds to the impα major NLS binding site. In contrast, the coreNLS peptide KKPKKE binds specifically and natively, adopting a largely homogeneous binding ensemble with a dominant, highly native-like conformational cluster. The coreNLS peptide retains most of native binding interactions, including π-cation contacts and a tryptophan cage. While KKPK binding is governed by a complex multistate free energy landscape featuring transitions between multiple binding poses, the coreNLS peptide free energy map is simple, exhibiting a single dominant native-like bound basin. We argue that the origin of the coreNLS peptide binding specificity is several electrostatic interactions formed by the two C-terminal amino acids, Lys10 and Glu11, with impα. The coreNLS sequence is then sufficient for native binding, but none of the amino acids flanking minNLS, including Lys10 and Glu11, are strictly necessary for the native pose. Our analyses indicate that the VEEV coreNLS sequence is virtually unique among human and viral proteins interacting with impα making it a potential target for VEEV-specific inhibitors.

Clustering Heterogeneous Conformational Ensembles of Intrinsically Disordered Proteins with t-Distributed Stochastic Neighbor Embedding.

Intrinsically disordered proteins (IDPs) populate a range of conformations that are best described by a heterogeneous ensemble. Grouping an IDP ensemble into "structurally similar" clusters for visualization, interpretation, and analysis purposes is a much-desired but formidable task, as the conformational space of IDPs is inherently high-dimensional and reduction techniques often result in ambiguous classifications. Here, we employ the t-distributed stochastic neighbor embedding (t-SNE) technique to generate homogeneous clusters of IDP conformations from the full heterogeneous ensemble. We illustrate the utility of t-SNE by clustering conformations of two disordered proteins, Aβ42, and α-synuclein, in their APO states and when bound to small molecule ligands. Our results shed light on ordered substates within disordered ensembles and provide structural and mechanistic insights into binding modes that confer specificity and affinity in IDP ligand binding. t-SNE projections preserve the local neighborhood information, provide interpretable visualizations of the conformational heterogeneity within each ensemble, and enable the quantification of cluster populations and their relative shifts upon ligand binding. Our approach provides a new framework for detailed investigations of the thermodynamics and kinetics of IDP ligand binding and will aid rational drug design for IDPs.

Fast conformational clustering of extensive molecular dynamics simulation data.

We present an unsupervised data processing workflow that is specifically designed to obtain a fast conformational clustering of long molecular dynamics simulation trajectories. In this approach, we combine two dimensionality reduction algorithms (cc_analysis and encodermap) with a density-based spatial clustering algorithm (hierarchical density-based spatial clustering of applications with noise). The proposed scheme benefits from the strengths of the three algorithms while avoiding most of the drawbacks of the individual methods. Here, the cc_analysis algorithm is applied for the first time to molecular simulation data. The encodermap algorithm complements cc_analysis by providing an efficient way to process and assign large amounts of data to clusters. The main goal of the procedure is to maximize the number of assigned frames of a given trajectory while keeping a clear conformational identity of the clusters that are found. In practice, we achieve this by using an iterative clustering approach and a tunable root-mean-square-deviation-based criterion in the final cluster assignment. This allows us to find clusters of different densities and different degrees of structural identity. With the help of four protein systems, we illustrate the capability and performance of this clustering workflow: wild-type and thermostable mutant of the Trp-cage protein (TC5b and TC10b), NTL9, and Protein B. Each of these test systems poses their individual challenges to the scheme, which, in total, give a nice overview of the advantages and potential difficulties that can arise when using the proposed method.

Generating a conformational landscape of ubiquitin chains at atomistic resolution by back-mapping based sampling.

Ubiquitin chains are flexible multidomain proteins that have important biological functions in cellular signalling. Computational studies with all-atom molecular dynamics simulations of the conformational spaces of polyubiquitins can be challenging due to the system size and a multitude of long-lived meta-stable states. Coarse graining is an efficient approach to overcome this problem-at the cost of losing high-resolution details. Recently, we proposed the back-mapping based sampling (BMBS) approach that reintroduces atomistic information into a given coarse grained (CG) sampling based on a two-dimensional (2D) projection of the conformational landscape, produces an atomistic ensemble and allows to systematically compare the ensembles at the two levels of resolution. Here, we apply BMBS to K48-linked tri-ubiquitin, showing its applicability to larger systems than those it was originally introduced on and demonstrating that the algorithm scales very well with system size. In an extension of the original BMBS we test three different seeding strategies, i.e. different approaches from where in the CG landscape atomistic trajectories are initiated. Furthermore, we apply a recently introduced conformational clustering algorithm to the back-mapped atomistic ensemble. Thus, we obtain insight into the structural composition of the 2D landscape and illustrate that the dimensionality reduction algorithm separates different conformational characteristics very well into different regions of the map. This cluster analysis allows us to show how atomistic trajectories sample conformational states, move through the projection space and in sum converge to an atomistic conformational landscape that slightly differs from the original CG map, indicating a correction of flaws in the CG template.

VSTH: a user-friendly web server for structure-based virtual screening on Tianhe-2.

VSTH is a user-friendly web server with the complete workflow for virtual screening. By self-customized visualization software, users can interactively prepare protein files, set docking sites as well as view binding conformers in a target protein in a few clicks. We provide serval purchasable ligand libraries for selection. And, we integrate six open-source docking programs as computing engine, or as conformational sampling tools for DLIGAND2. Users can select various docking methods simultaneously and personalize computing parameters. After docking processing, user can filter docking conformations by ranked scores, or cluster-based molecular similarity to find highly populated clusters of low-energy conformations. The VSTH web server is free and open to all users at https://matgen.nscc-gz.cn/VirtualScreening.html. Supplementary data are available at Bioinformatics online.