Analysis of veering during gait in blind individuals.

BACKGROUND: Veering occurs during blind walking, which causes individuals to deviate from crosswalks or fall off platforms. Despite being linked to static postural control, the relationship between veering and gait function (gait variability and plantar pressure), which is presumed to be related to dynamic postural control, has yet to be fully understood. RESEARCH QUESTION: How do gait variability and plantar pressure affect veering? METHODS: This experiment involved a walking task in which 11 blind adults were instructed to walk along a straight path using a white cane. We measured the veering values and analyzed the relationship between gait variability and plantar pressure. RESULTS: One participant with a particularly severe veering tendency was excluded from the analysis. Based on the veering characteristics of the participants, the walking trajectories were classified as veering to the left (14 trials) or the right (14 trials) sides. Correlation analysis showed no significant correlation between the veering value and gait variability (vertical, lateral, and anterior-posterior). Plantar pressure (the ball of the fifth toe and the total) was significantly negatively correlate with the veering value. In contrast, the plantar pressure results for the participant who was excluded showed a different characteristic. SIGNIFICANCE: We hypothesized that blind individuals would exhibit dynamic postural control to stay on a straight path by increasing the plantar pressure on the ball of the fifth toe and the total pressure on the opposite foot when veering occurs. However, this adaptation was not observed in a blind individual with severe veering tendencies.

Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients.

Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients.

Importance: Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. Objectives: To test if different statins differ in their ability to exert protective effects based on molecular computational predictions and electronic medical record analysis. Main Outcomes and Measures: A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2, with a total of 2,436 drugs investigated. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Results: Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Conclusions and Relevance: Different statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review.

The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs. Despite technological advances, large-scale experimental determination of DTIs is still expensive and laborious. Effective and low-cost computational alternatives remain in strong need. Meanwhile, open-access resources have been rapidly growing with massive amount of bioactivity data becoming available, creating unprecedented opportunities for the development of novel in silico models for large-scale DTI prediction. In this work, we review the state-of-the-art computational approaches for identifying DTIs from a data-centric perspective: what the underlying data are and how they are utilized in each study. We also summarize popular public data resources and online tools for DTI prediction. It is found that various types of data were employed including properties of chemical structures, drug therapeutic effects and side effects, drug-target binding, drug-drug interactions, bioactivity data of drug molecules across multiple biological targets, and drug-induced gene expressions. More often, the heterogeneous data were integrated to offer better performance. However, challenges remain such as handling data imbalance, incorporating negative samples and quantitative bioactivity data, as well as maintaining cross-links among different data sources, which are essential for large-scale and automated information integration.

Predicting drug-drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge.

Drug-drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our assumption was that a query drug (Dq) and a drug to be examined (De) likely have DDI if the drugs in the interaction network of De are structurally similar to Dq. A network of De describes the associations between the drugs and the proteins relating to PK and PD for De. These include target proteins, proteins interacting with target proteins, enzymes, and transporters for De. We constructed logistic regression models for DDI prediction using only 2D structural similarities between each Dq and the drugs in the network of De. The results indicated that our models could effectively predict DDIs. It was found that integrating structural similarity scores of the drugs relating to both PK and PD of De was crucial for model performance. In particular, the combination of the target- and enzyme-related scores provided the largest increase of the predictive power.Graphical abstract.

Structural insights of a PI3K/mTOR dual inhibitor with the morpholino-triazine scaffold.

Stimulation of the PI3K/Akt/mTOR pathway, which controls cell proliferation and growth, is often observed in cancer cell. Inhibiting both PI3K and mTOR in this pathway can switch off Akt activation and hence, plays a powerful role for modulating this pathway. PKI-587, a drug containing the structure of morpholino-triazines, shows a dual and nano-molar inhibition activity and is currently in clinical trial. To provide an insight into the mechanism of this dual inhibition, pharmacophore and QSAR models were developed in this work using compounds based on the morpholino-triazines scaffold, followed by a docking study. Pharmacophore model suggested the mechanism of the inhibition of PI3Kα and mTOR by the compounds were mostly the same, which was supported by the docking study showing similar docking modes. The analysis also suggested the importance of the flat plane shape of the ligands, the space surrounding the ligands in the binding pocket, and the slight difference in the shape of the binding sites between PI3Kα and mTOR.

A knowledge-based orientation potential for transcription factor-DNA docking.

MOTIVATION: Computational modeling of protein-DNA complexes remains a challenging problem in structural bioinformatics. One of the key factors for a successful protein-DNA docking is a potential function that can accurately discriminate the near-native structures from decoy complexes and at the same time make conformational sampling more efficient. Here, we developed a novel orientation-dependent, knowledge-based, residue-level potential for improving transcription factor (TF)-DNA docking. RESULTS: We demonstrated the performance of this new potential in TF-DNA binding affinity prediction, discrimination of native protein-DNA complex from decoy structures, and most importantly in rigid TF-DNA docking. The rigid TF-DNA docking with the new orientation potential, on a benchmark of 38 complexes, successfully predicts 42% of the cases with root mean square deviations lower than 1 Å and 55% of the cases with root mean square deviations lower than 3 Å. The results suggest that docking with this new orientation-dependent, coarse-grained statistical potential can achieve high-docking accuracy and can serve as a crucial first step in multi-stage flexible protein-DNA docking. AVAILABILITY AND IMPLEMENTATION: The new potential is available at http://bioinfozen.uncc.edu/Protein_DNA_orientation_potential.tar.

High performance transcription factor-DNA docking with GPU computing.

BACKGROUND: Protein-DNA docking is a very challenging problem in structural bioinformatics and has important implications in a number of applications, such as structure-based prediction of transcription factor binding sites and rational drug design. Protein-DNA docking is very computational demanding due to the high cost of energy calculation and the statistical nature of conformational sampling algorithms. More importantly, experiments show that the docking quality depends on the coverage of the conformational sampling space. It is therefore desirable to accelerate the computation of the docking algorithm, not only to reduce computing time, but also to improve docking quality. METHODS: In an attempt to accelerate the sampling process and to improve the docking performance, we developed a graphics processing unit (GPU)-based protein-DNA docking algorithm. The algorithm employs a potential-based energy function to describe the binding affinity of a protein-DNA pair, and integrates Monte-Carlo simulation and a simulated annealing method to search through the conformational space. Algorithmic techniques were developed to improve the computation efficiency and scalability on GPU-based high performance computing systems. RESULTS: The effectiveness of our approach is tested on a non-redundant set of 75 TF-DNA complexes and a newly developed TF-DNA docking benchmark. We demonstrated that the GPU-based docking algorithm can significantly accelerate the simulation process and thereby improving the chance of finding near-native TF-DNA complex structures. This study also suggests that further improvement in protein-DNA docking research would require efforts from two integral aspects: improvement in computation efficiency and energy function design. CONCLUSIONS: We present a high performance computing approach for improving the prediction accuracy of protein-DNA docking. The GPU-based docking algorithm accelerates the search of the conformational space and thus increases the chance of finding more near-native structures. To the best of our knowledge, this is the first ad hoc effort of applying GPU or GPU clusters to the protein-DNA docking problem.

Nonsteroidal anti-inflammatory drug naproxen destabilizes Aß amyloid fibrils: a molecular dynamics investigation.

Using implicit solvent model and replica exchange molecular dynamics, we examine the propensity of a nonsteroidal anti-inflammatory drug, naproxen, to interfere with Aß fibril growth. We also compare the antiaggregation propensity of naproxen with that of ibuprofen. Naproxen's antiaggregation effect is influenced by two factors. Similar to ibuprofen, naproxen destabilizes binding of incoming Aß peptides to the fibril due to direct competition between the ligands and the peptides for the same binding location on the fibril surface (the edge). However, in contrast to ibuprofen, naproxen binding also alters the conformational ensemble of Aß monomers by promoting ß-structure. The second factor weakens naproxen's antiaggregation effect. These findings appear to explain the experimental observations, in which naproxen binds to the Aß fibril with higher affinity than ibuprofen, yet produces weaker antiaggregation action.

Mapping conformational ensembles of aß oligomers in molecular dynamics simulations.

Although the oligomers formed by Aß peptides appear to be the primary cytotoxic species in Alzheimer's disease, detailed information about their structures appears to be lacking. In this article, we use exhaustive replica exchange molecular dynamics and an implicit solvent united-atom model to study the structural properties of Aß monomers, dimers, and tetramers. Our analysis suggests that the conformational ensembles of Aß dimers and tetramers are very similar, but sharply distinct from those sampled by the monomers. The key conformational difference between monomers and oligomers is the formation of ß-structure in the oligomers occurring together with the loss of intrapeptide interactions and helix structure. Our simulations indicate that, independent of oligomer order, the Aß aggregation interface is largely confined to the sequence region 10-23, which forms the bulk of interpeptide interactions. We show that the fractions of ß structure computed in our simulations and measured experimentally are in good agreement.

Binding of nonsteroidal anti-inflammatory drugs to Abeta fibril.

Nonsteroidal anti-inflammatory drugs are considered as potential therapeutic agents against Alzheimer's disease. Using replica exchange molecular dynamics and atomistic implicit solvent model, we studied the mechanisms of binding of naproxen and ibuprofen to the Abeta fibril derived from solid-state NMR measurements. The binding temperature of naproxen is found to be almost 40 K higher than of ibuprofen implicating higher binding affinity of naproxen. The key factor, which enhances naproxen binding, is strong interactions between ligands bound to the surface of the fibril. The naphthalene ring in naproxen appears to provide a dominant contribution to ligand-ligand interactions. In contrast, ligand-fibril interactions cannot explain differences in the binding affinities of naproxen and ibuprofen. The concave fibril edge with the groove is identified as the primary binding location for both ligands. We show that confinement of the ligands to the groove facilitates ligand-ligand interactions that lowers the energy of the ligands bound to the concave edge compared with those bound to the convex edge. Our simulations appear to provide microscopic rationale for the differing binding affinities of naproxen and ibuprofen observed experimentally.

Molecular dynamics simulations of anti-aggregation effect of ibuprofen.

Using implicit solvent molecular dynamics and replica exchange simulations, we study the impact of ibuprofen on the growth of wild-type Abeta fibrils. We show that binding of ibuprofen to Abeta destabilizes the interactions between incoming peptides and the fibril. As a result, ibuprofen interference modifies the free energy landscape of fibril growth and reduces the free energy gain of Abeta peptide binding to the fibril by approximately 2.5 RT at 360 K. Furthermore, ibuprofen interactions shift the thermodynamic equilibrium from fibril-like locked states to disordered docked states. Ibuprofen's anti-aggregation effect is explained by its competition with incoming Abeta peptides for the same binding site located on the fibril edge. Although ibuprofen impedes fibril growth, it does not significantly change the mechanism of fibril elongation or the structure of Abeta peptides bound to the fibril.

Globular state in the oligomers formed by Abeta peptides.

Replica exchange molecular dynamics and implicit solvent model are used to study two oligomeric species of Abeta peptides, dimer and tetramer, which are typically observed in in vitro experiments. Based on the analysis of free energy landscapes, density distributions, and chain flexibility, we propose that the oligomer formation is a continuous transition occurring without metastable states. The density distribution computations suggest that Abeta oligomer consists of two volume regions-the core with fairly flat density profile and the surface layer with rapidly decreasing density. The core is mostly formed by the N-terminal residues, whereas the C-terminal tends to occur in the surface layer. Lowering the temperature results in the redistribution of peptide atoms from the surface layer into the core. Using these findings, we argue that Abeta oligomer resembles polymer globule in poor solvent. Abeta dimers and tetramers are found to be structurally similar suggesting that the conformations of Abeta peptides do not depend on the order of small oligomers.

Computational backbone mutagenesis of Abeta peptides: probing the role of backbone hydrogen bonds in aggregation.

Using replica exchange molecular dynamics (REMD) and united atom implicit solvent model we examine the role of backbone hydrogen bonds (HBs) in Abeta aggregation. The importance of HBs appears to depend on the aggregation stage. The backbone HBs have little effect on the stability of Abeta dimers or on their aggregation interface. The HBs also do not play a critical role in initial binding of Abeta peptides to the amyloid fibril. Their elimination does not change the continuous character of Abeta binding nor its temperature. However, cancellation of HBs forming between incoming Abeta peptides and the fibril disrupts the locked fibril-like states in the bound peptides. Without the support of HBs, bound Abeta peptides form few long beta-strands on the fibril edge. As a result, the deletion of peptide-fibril HBs is expected to impede fibril growth. As for the peptides bound to Abeta fibril the deletion of interpeptide HBs reduces the beta propensity in the dimers making them less competent for amyloid assembly. These simulation findings together with the backbone mutagenesis experiments suggest that a viable strategy for arresting fibril growth is the disruption of interpeptide HBs.

Molecular dynamics simulations of Ibuprofen binding to Abeta peptides.

Using replica exchange molecular dynamics simulations and the implicit solvent model we probed binding of ibuprofen to Abeta(10-40) monomers and amyloid fibrils. We found that the concave (CV) fibril edge has significantly higher binding affinity for ibuprofen than the convex edge. Furthermore, binding of ibuprofen to Abeta monomers, as compared to fibrils, results in a smaller free energy gain. The difference in binding free energies is likely to be related to the presence of the groove on the CV fibril edge, in which ibuprofen tends to accumulate. The confinement effect of the groove promotes the formation of large low-energy ibuprofen clusters, which rarely occur on the surface of Abeta monomers. These observations led us to suggest that the ibuprofen binding mechanism for Abeta fibrils is different from that for monomers. In general, ibuprofen shows a preference to bind to those regions of Abeta monomers (amino terminal) and fibrils (the CV edge) that are also the primary aggregation interfaces. Based on our findings and on available experimental data, we propose a rationale for the ibuprofen antiaggregation effect.

Side chain interactions can impede amyloid fibril growth: replica exchange simulations of Abeta peptide mutant.

Using replica exchange molecular dynamics, we study the effect of Asp23Tyr mutation on Abeta(10-40) fibril growth. The effect of this mutation is revealed through the computation of free energy landscapes, the distributions of peptide-fibril interactions, and by comparison with the wild-type Abeta(10-40) peptide. Asp23Tyr mutation has a relatively minor influence on the docking of Abeta peptides to the fibril. However, it has a strong impact on the locking stage due to profound stabilization of the parallel in-registry beta-sheets formed by the peptides on the fibril edge. The enhanced stability of parallel beta-sheets results from the deletion of side chain interactions formed by Asp23, which are incompatible with the fibril-like conformers. Consequently, Asp23Tyr mutation is expected to promote fibril growth. We argue that strong off-registry side chain interactions may slow down fibril assembly as it occurs for the wild-type Abeta peptide. The analysis of experimental data offers support to our in silico conclusions.

Probing energetics of Abeta fibril elongation by molecular dynamics simulations.

Using replica exchange molecular dynamics simulations and an all-atom implicit solvent model, we probed the energetics of Abeta(10-40) fibril growth. The analysis of the interactions between incoming Abeta peptides and the fibril led us to two conclusions. First, considerable variations in fibril binding propensities are observed along the Abeta sequence. The peptides in the fibril and those binding to its edge interact primarily through their N-termini. Therefore, the mutations affecting the Abeta positions 10-23 are expected to have the largest impact on fibril elongation compared with those occurring in the C-terminus and turn. Second, we performed weak perturbations of the binding free energy landscape by scanning partial deletions of side-chain interactions at various Abeta sequence positions. The results imply that strong side-chain interactions--in particular, hydrophobic contacts--impede fibril growth by favoring disordered docking of incoming peptides. Therefore, fibril elongation may be promoted by moderate reduction of Abeta hydrophobicity. The comparison with available experimental data is presented.

Probing the effect of amino-terminal truncation for Abeta1-40 peptides.

We examine the effect of deletion of the amino-terminal (residues 1-9) on the structure and energetics of Abeta1-40 peptides. To this end, we use replica exchange molecular dynamics to compare the conformational ensembles of Abeta1-40 and amino-truncated Abeta10-40 monomers and dimers. Overall, the deletion of the amino-terminal appears to cause minor structural and energetic changes in Abeta monomers and dimers. More specifically, our findings are as follows: (1) there is a small but discernible conversion of beta-strand structure into helix upon amino-terminal deletion, (2) secondary structure changes due to truncation are caused by missing side chain interactions formed by the amino-terminal, and (3) the amino-terminal together with the central sequence region (residues 10-23) represents the primary aggregation interface in Abeta1-40 dimers. The amino-truncated Abeta10-40 retains this aggregation interface, which is reduced to the central sequence region. We argue that the analysis of available experimental data supports our conclusions. Our findings also suggest that amino-truncated Abeta10-40 peptide is an adequate model for studying Abeta1-40 aggregation.

Interpeptide interactions induce helix to strand structural transition in Abeta peptides.

Replica exchange molecular dynamics and all-atom implicit solvent model are used to compute the structural propensities in Abeta monomers, dimers, and Abeta peptides bound to the edge of amyloid fibril. These systems represent, on an approximate level, different stages in Abeta aggregation. Abeta monomers are shown to form helical structure in the N-terminal (residues 13 to 21). Interpeptide interactions in Abeta dimers and, especially, in the peptides bound to the fibril induce a dramatic shift in the secondary structure, from helical states toward beta-strand conformations. The sequence region 10-23 in Abeta peptide is found to form most of interpeptide interactions upon aggregation. Simulation results are tested by comparing the chemical shifts in Abeta monomers computed from simulations and obtained experimentally. Possible implications of our simulations for designing aggregation-resistant variants of Abeta are discussed.

Replica exchange simulations of the thermodynamics of Abeta fibril growth.

Replica exchange molecular dynamics and an all-atom implicit solvent model are used to probe the thermodynamics of deposition of Alzheimer's Abeta monomers on preformed amyloid fibrils. Consistent with the experiments, two deposition stages have been identified. The docking stage occurs over a wide temperature range, starting with the formation of the first peptide-fibril interactions at 500 K. Docking is completed when a peptide fully adsorbs on the fibril edge at the temperature of 380 K. The docking transition appears to be continuous, and occurs without free energy barriers or intermediates. During docking, incoming Abeta monomer adopts a disordered structure on the fibril edge. The locking stage occurs at the temperature of approximately 360 K and is characterized by the rugged free energy landscape. Locking takes place when incoming Abeta peptide forms a parallel beta-sheet structure on the fibril edge. Because the beta-sheets formed by locked Abeta peptides are typically off-registry, the structure of the locked phase differs from the structure of the fibril interior. The study also reports that binding affinities of two distinct fibril edges with respect to incoming Abeta peptides are different. The peptides bound to the concave edge have significantly lower free energy compared to those bound on the convex edge. Comparison with the available experimental data is discussed.