Computationally Designed Molecules Modulate ALS-Related Amyloidogenic TDP-43307-319 Aggregation.

Abnormal cytosolic aggregation of TAR DNA-binding protein of 43 kDa (TDP-43) is observed in multiple diseases, including amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration, and Alzheimer's disease. Previous studies have shown that TDP-43307-319 located at the C-terminal of TDP-43 can form higher-order oligomers and fibrils. Of particular interest are the hexamers that adopt a cylindrin structure that has been strongly correlated to neurotoxicity. In this study, we use the joint pharmacophore space (JPS) model to identify and generate potential TDP-43 inhibitors. Five JPS-designed molecules are evaluated using both experimental and computational methods: ion mobility mass spectrometry, thioflavin T fluorescence assay, circular dichroism spectroscopy, atomic force microscopy, and molecular dynamics simulations. We found that all five molecules can prevent the amyloid fibril formation of TDP-43307-319, but their efficacy varies significantly. Furthermore, among the five molecules, [AC0101] is the most efficient in preventing the formation of higher-order oligomers and dissociating preformed higher-order oligomers. Molecular dynamics simulations show that [AC0101] both is the most flexible and forms the most hydrogen bonds with the TDP-43307-319 monomer. The JPS-designed molecules can insert themselves between the ß-strands in the hexameric cylindrin structure of TDP-43307-319 and can open its structure. Possible mechanisms for JPS-designed molecules to inhibit and dissociate TDP-43307-319 oligomers on an atomistic scale are proposed.

Predictive models for human-AI nexus in group decision making.

Machine learning (ML) and artificial intelligence (AI) have had a profound impact on our lives. Domains like health and learning are naturally helped by human-AI interactions and decision making. In these areas, as ML algorithms prove their value in making important decisions, humans add their distinctive expertise and judgment on social and interpersonal issues that need to be considered in tandem with algorithmic inputs of information. Some questions naturally arise. What rules and regulations should be invoked on the employment of AI, and what protocols should be in place to evaluate available AI resources? What are the forms of effective communication and coordination with AI that best promote effective human-AI teamwork? In this review, we highlight factors that we believe are especially important in assembling and managing human-AI decision making in a group setting.

Modulating ALS-Related Amyloidogenic TDP-43307-319 Oligomeric Aggregates with Computationally Derived Therapeutic Molecules.

TDP-43 aggregates are a salient feature of amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and a variety of other neurodegenerative diseases, including Alzheimer's disease (AD). With an anticipated growth in the most susceptible demographic, projections predict neurodegenerative diseases will potentially affect 15 million people in the United States by 2050. Currently, there are no cures for ALS, FTD, or AD. Previous studies of the amyloidogenic core of TDP-43 have demonstrated that oligomers greater than a trimer are associated with toxicity. Utilizing a joint pharmacophore space (JPS) method, potential drugs have been designed specifically for amyloid-related diseases. These molecules were generated on the basis of key chemical features necessary for blood-brain barrier permeability, low adverse side effects, and target selectivity. Combining ion-mobility mass spectrometry and atomic force microscopy with the JPS computational method allows us to more efficiently evaluate a potential drug's efficacy in disrupting the development of putative toxic species. Our results demonstrate the dissociation of higher-order oligomers in the presence of these novel JPS-generated inhibitors into smaller oligomer species. Additionally, drugs approved by the Food and Drug Administration for the treatment of ALS were also evaluated and demonstrated to maintain higher-order oligomeric assemblies. Possible mechanisms for the observed action of the JPS molecules are discussed.

Structural balance emerges and explains performance in risky decision-making.

Polarization affects many forms of social organization. A key issue focuses on which affective relationships are prone to change and how their change relates to performance. In this study, we analyze a financial institutional over a two-year period that employed 66 day traders, focusing on links between changes in affective relations and trading performance. Traders' affective relations were inferred from their IMs (>2 million messages) and trading performance was measured from profit and loss statements (>1 million trades). Here, we find that triads of relationships, the building blocks of larger social structures, have a propensity towards affective balance, but one unbalanced configuration resists change. Further, balance is positively related to performance. Traders with balanced networks have the "hot hand", showing streaks of high performance. Research implications focus on how changes in polarization relate to performance and polarized states can depolarize.

Dynamics of collective performance in collaboration networks.

Today, many complex tasks are assigned to teams, rather than individuals. One reason for teaming up is expansion of the skill coverage of each individual to the joint team skill set. However, numerous empirical studies of human groups suggest that the performance of equally skilled teams can widely differ. Two natural question arise: What are the factors defining team performance? and How can we best predict the performance of a given team on a specific task? While the team members' task-related capabilities constrain the potential for the team's success, the key to understanding team performance is in the analysis of the team process, encompassing the behaviors of the team members during task completion. In this study, we extend the existing body of research on team process and prediction models of team performance. Specifically, we analyze the dynamics of historical team performance over a series of tasks as well as the fine-grained patterns of collaboration between team members, and formally connect these dynamics to the team performance in the predictive models. Our major qualitative finding is that higher performing teams have well-connected collaboration networks-as indicated by the topological and spectral properties of the latter-which are more robust to perturbations, and where network processes spread more efficiently. Our major quantitative finding is that our predictive models deliver accurate team performance predictions-with a prediction error of 15-25%-on a variety of simple tasks, outperforming baseline models that do not capture the micro-level dynamics of team member behaviors. We also show how to use our models in an application, for optimal online planning of workload distribution in an organization. Our findings emphasize the importance of studying the dynamics of team collaboration as the major driver of high performance in teams.

Spatial coherence of oriented white matter microstructure: Applications to white matter regions associated with genetic similarity.

We present a method to discover differences between populations with respect to the spatial coherence of their oriented white matter microstructure in arbitrarily shaped white matter regions. This method is applied to diffusion MRI scans of a subset of the Human Connectome Project dataset: 57 pairs of monozygotic and 52 pairs of dizygotic twins. After controlling for morphological similarity between twins, we identify 3.7% of all white matter as being associated with genetic similarity (35.1 k voxels, p<10-4, false discovery rate 1.5%), 75% of which spatially clusters into twenty-two contiguous white matter regions. Furthermore, we show that the orientation similarity within these regions generalizes to a subset of 47 pairs of non-twin siblings, and show that these siblings are on average as similar as dizygotic twins. The regions are located in deep white matter including the superior longitudinal fasciculus, the optic radiations, the middle cerebellar peduncle, the corticospinal tract, and within the anterior temporal lobe, as well as the cerebellum, brain stem, and amygdalae. These results extend previous work using undirected fractional anisotrophy for measuring putative heritable influences in white matter. Our multidirectional extension better accounts for crossing fiber connections within voxels. This bottom up approach has at its basis a novel measurement of coherence within neighboring voxel dyads between subjects, and avoids some of the fundamental ambiguities encountered with tractographic approaches to white matter analysis that estimate global connectivity.

Learning about learning: Mining human brain sub-network biomarkers from fMRI data.

Modeling the brain as a functional network can reveal the relationship between distributed neurophysiological processes and functional interactions between brain structures. Existing literature on functional brain networks focuses mainly on a battery of network properties in "resting state" employing, for example, modularity, clustering, or path length among regions. In contrast, we seek to uncover functionally connected subnetworks that predict or correlate with cohort differences and are conserved within the subjects within a cohort. We focus on differences in both the rate of learning as well as overall performance in a sensorimotor task across subjects and develop a principled approach for the discovery of discriminative subgraphs of functional connectivity based on imaging acquired during practice. We discover two statistically significant subgraph regions: one involving multiple regions in the visual cortex and another involving the parietal operculum and planum temporale. High functional coherence in the former characterizes sessions in which subjects take longer to perform the task, while high coherence in the latter is associated with high learning rate (performance improvement across trials). Our proposed methodology is general, in that it can be applied to other cognitive tasks, to study learning or to differentiate between healthy patients and patients with neurological disorders, by revealing the salient interactions among brain regions associated with the observed global state. The discovery of such significant discriminative subgraphs promises a better data-driven understanding of the dynamic brain processes associated with high-level cognitive functions.

Discovery of LRRK2 inhibitors using sequential in silico joint pharmacophore space (JPS) and ensemble docking.

Joint pharmacophore space (JPS), ensemble docking and sequential JPS-ensemble docking were used to select three panels of compounds (10 per panel) for evaluation as LRRK2 inhibitors. These computational methods identified four LRRK2 inhibitors with IC50 values <12µM. The sequential JPS-ensemble docking predicted the majority of active hits. One of the inhibitors (Z-8205) identified using this method was also found to inhibit the G2019S mutant of LRRK2 25-fold better than wild-type enzyme. This bias for the G2019S mutant is proposed to arise from an interaction with S2019 in this form of the enzyme. In addition, Z-8205 was found to only inhibit one other kinase when profiled against a panel of 97 kinases at 10µM.

Quantifying spatial relationships from whole retinal images.

MOTIVATION: Microscopy advances have enabled the acquisition of large-scale biological images that capture whole tissues in situ. This in turn has fostered the study of spatial relationships between cells and various biological structures, which has proved enormously beneficial toward understanding organ and organism function. However, the unique nature of biological images and tissues precludes the application of many existing spatial mining and quantification methods necessary to make inferences about the data. Especially difficult is attempting to quantify the spatial correlation between heterogeneous structures and point objects, which often occurs in many biological tissues. RESULTS: We develop a method to quantify the spatial correlation between a continuous structure and point data in large (17 500 × 17 500 pixel) biological images. We use this method to study the spatial relationship between the vasculature and a type of cell in the retina called astrocytes. We use a geodesic feature space based on vascular structures and embed astrocytes into the space by spatial sampling. We then propose a quantification method in this feature space that enables us to empirically demonstrate that the spatial distribution of astrocytes is often correlated with vascular structure. Additionally, these patterns are conserved in the retina after injury. These results prove the long-assumed patterns of astrocyte spatial distribution and provide a novel methodology for conducting other spatial studies of similar tissue and structures. AVAILABILITY: The Matlab code for the method described in this article can be found at http://www.cs.ucsb.edu/∼dbl/software.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Novel method for pharmacophore analysis by examining the joint pharmacophore space.

We propose a novel method for pharmacophore analysis by examining the Joint Pharmacophore Space of chemical compounds, targets, and chemical/biological properties. The proposed approach is a notable deviation from existing techniques that analyze compounds on a target-by-target basis, aimed at extracting and optimizing a specific pharmacophore. The underlying geometry of the pharmacophores is responsible for binding between compounds and targets as well as properties of compounds such as Blood Brain Barrier permeability. The identification of this joint space enables us to cluster and classify similar pharmacophores based on geometric arrangements, analyze the diversity of this space, ascribe positive/negative properties to the subspaces, and query and mine a database of compounds for presence or absence of activity. Extensive experiments are carried out to validate the presence of subspaces that uniquely identify geometric configurations conforming to certain biological activities. The discriminative potential of these subspaces is also verified by employing them as a molecular descriptor. Empirical results show promising performance in terms of classification quality highlighting the utility of mining the joint pharmacophore space.

Using stochastic causal trees to augment Bayesian networks for modeling eQTL datasets.

BACKGROUND: The combination of genotypic and genome-wide expression data arising from segregating populations offers an unprecedented opportunity to model and dissect complex phenotypes. The immense potential offered by these data derives from the fact that genotypic variation is the sole source of perturbation and can therefore be used to reconcile changes in gene expression programs with the parental genotypes. To date, several methodologies have been developed for modeling eQTL data. These methods generally leverage genotypic data to resolve causal relationships among gene pairs implicated as associates in the expression data. In particular, leading studies have augmented Bayesian networks with genotypic data, providing a powerful framework for learning and modeling causal relationships. While these initial efforts have provided promising results, one major drawback associated with these methods is that they are generally limited to resolving causal orderings for transcripts most proximal to the genomic loci. In this manuscript, we present a probabilistic method capable of learning the causal relationships between transcripts at all levels in the network. We use the information provided by our method as a prior for Bayesian network structure learning, resulting in enhanced performance for gene network reconstruction. RESULTS: Using established protocols to synthesize eQTL networks and corresponding data, we show that our method achieves improved performance over existing leading methods. For the goal of gene network reconstruction, our method achieves improvements in recall ranging from 20% to 90% across a broad range of precision levels and for datasets of varying sample sizes. Additionally, we show that the learned networks can be utilized for expression quantitative trait loci mapping, resulting in upwards of 10-fold increases in recall over traditional univariate mapping. CONCLUSIONS: Using the information from our method as a prior for Bayesian network structure learning yields large improvements in accuracy for the tasks of gene network reconstruction and expression quantitative trait loci mapping. In particular, our method is effective for establishing causal relationships between transcripts located both proximally and distally from genomic loci.

Probabilistic Substructure Mining From Small-Molecule Screens.

Identifying the overrepresented substructures from a set of molecules with similar activity is a common task in chemical informatics. Existing substructure miners are deterministic, requiring the activity of all mined molecules to be known with high confidence. In contrast, we introduce pGraphSig, a probabilistic structure miner, which effectively mines structures from noisy data, where many molecules are labeled with their probability of being active. We benchmark pGraphSig on data from several small-molecule high throughput screens, finding that it can more effectively identify overrepresented structures than a deterministic structure miner.

Molecular function prediction using neighborhood features.

The recent advent of high-throughput methods has generated large amounts of gene interaction data. This has allowed the construction of genomewide networks. A significant number of genes in such networks remain uncharacterized and predicting the molecular function of these genes remains a major challenge. A number of existing techniques assume that genes with similar functions are topologically close in the network. Our hypothesis is that genes with similar functions observe similar annotation patterns in their neighborhood, regardless of the distance between them in the interaction network. We thus predict molecular functions of uncharacterized genes by comparing their functional neighborhoods to genes of known function. We propose a two-phase approach. First, we extract functional neighborhood features of a gene using Random Walks with Restarts. We then employ a KNN classifier to predict the function of uncharacterized genes based on the computed neighborhood features. We perform leave-one-out validation experiments on two S. cerevisiae interaction networks and show significant improvements over previous techniques. Our technique provides a natural control of the trade-off between accuracy and coverage of prediction. We further propose and evaluate prediction in sparse genomes by exploiting features from well-annotated genomes.

Mining statistically significant molecular substructures for efficient molecular classification.

The increased availability of large repositories of chemical compounds has created new challenges in designing efficient molecular querying and mining systems. Molecular classification is an important problem in drug development where libraries of chemical compounds are screened and molecules with the highest probability of success against a given target are selected. We have developed a technique called GraphSig to mine significantly over-represented molecular substructures in a given class of molecules. GraphSig successfully overcomes the scalability bottleneck of mining patterns at a low frequency. Patterns mined by GraphSig display correlation with biological activities and serve as an excellent platform on which to build molecular analysis tools. The potential of GraphSig as a chemical descriptor is explored, and support vector machines are used to classify molecules described by patterns mined using GraphSig. Furthermore, the over-represented patterns are more informative than features generated exhaustively by traditional fingerprints; this has potential in providing scaffolds and lead generation. Extensive experiments are carried out to evaluate the proposed techniques, and empirical results show promising performance in terms of classification quality. An implementation of the algorithm is available free for academic use at http://www.uweb.ucsb.edu/ approximately sayan/software/GraphSig.tar.

RRW: repeated random walks on genome-scale protein networks for local cluster discovery.

BACKGROUND: We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW) for discovering functional modules, e.g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins. RESULTS: We apply the proposed technique on a functional network of yeast genes and accurately identify statistically significant clusters of proteins. We validate the biological significance of the results using known complexes in the MIPS complex catalogue database and well-characterized biological processes. We find that 90% of the created clusters have the majority of their catalogued proteins belonging to the same MIPS complex, and about 80% have the majority of their proteins involved in the same biological process. We compare our method to various other clustering techniques, such as the Markov Clustering Algorithm (MCL), and find a significant improvement in the RRW clusters' precision and accuracy values. CONCLUSION: RRW, which is a technique that exploits the topology of the network, is more precise and robust in finding local clusters. In addition, it has the added flexibility of being able to find multi-functional proteins by allowing overlapping clusters.

Predicting genetic interactions with random walks on biological networks.

BACKGROUND: Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse biological networks and show that our method outperforms established methods. RESULTS: By applying random walks on biological networks, we were able to predict synthetic lethal interactions at a true positive rate of 95 percent against a false positive rate of 10 percent in S. cerevisiae. Similarly, in C. elegans, we achieved a true positive rate of 95 against a false positive rate of 7 percent. Furthermore, we demonstrate that the inclusion of non-interacting gene pairs results in a considerable performance improvement. CONCLUSION: We presented a method based on random walks that accurately captures aspects of network topology towards the goal of classifying potential genetic interactions as either synthetic lethal or non-interacting. Our method, which is generalizable to all types of biological networks, is likely to perform well with limited information, as estimated by holding out large portions of the synthetic lethal interactions and non-interactions.

Accurate and scalable techniques for the complex/pathway membership problem in protein networks.

A protein network shows physical interactions as well as functional associations. An important usage of such networks is to discover unknown members of partially known complexes and pathways. A number of methods exist for such analyses, and they can be divided into two main categories based on their treatment of highly connected proteins. In this paper, we show that methods that are not affected by the degree (number of linkages) of a protein give more accurate predictions for certain complexes and pathways. We propose a network flow-based technique to compute the association probability of a pair of proteins. We extend the proposed technique using hierarchical clustering in order to scale well with the size of proteome. We also show that top-k queries are not suitable for a large number of cases, and threshold queries are more meaningful in these cases. Network flow technique with clustering is able to optimize meaningful threshold queries and answer them with high efficiency compared to a similar method that uses Monte Carlo simulation.

FTDP-17 mutations in Tau alter the regulation of microtubule dynamics: an "alternative core" model for normal and pathological Tau action.

Mutations affecting either the structure or regulation of the microtubule-associated protein Tau cause neuronal cell death and dementia. However, the molecular mechanisms mediating these deleterious effects remain unclear. Among the most characterized activities of Tau is the ability to regulate microtubule dynamics, known to be essential for proper cell function and viability. Here we have tested the hypothesis that Tau mutations causing neurodegeneration also alter the ability of Tau to regulate the dynamic instability behaviors of microtubules. Using in vitro microtubule dynamics assays to assess average microtubule growth rates, microtubule growth rate distributions, and catastrophe frequencies, we found that all tested mutants possessing amino acid substitutions or deletions mapping to either the repeat or interrepeat regions of Tau do indeed compromise its ability to regulate microtubule dynamics. Further mutational analyses suggest a novel mechanism of Tau regulatory action based on an "alternative core" of microtubule binding and regulatory activities composed of two repeats and the interrepeat between them. In this model, the interrepeat serves as the primary regulator of microtubule dynamics, whereas the flanking repeats serve as tethers to properly position the interrepeat on the microtubule. Importantly, since there are multiple interrepeats on each Tau molecule, there are also multiple cores on each Tau molecule, each with distinct mechanistic capabilities, thereby providing significant regulatory potential. Taken together, the data are consistent with a microtubule misregulation mechanism for Tau-mediated neuronal cell death and provide a novel mechanistic model for normal and pathological Tau action.

A general modeling and visualization tool for comparing different members of a group: application to studying tau-mediated regulation of microtubule dynamics.

BACKGROUND: Innumerable biological investigations require comparing collections of molecules, cells or organisms to one another with respect to one or more of their properties. Almost all of these comparisons are performed manually, which can be susceptible to inadvertent bias as well as miss subtle effects. The development and application of computer-assisted analytical and interpretive tools could help address these issues and thereby dramatically improve these investigations. RESULTS: We have developed novel computer-assisted analytical and interpretive tools and applied them to recent studies examining the ability of 3-repeat and 4-repeat tau to regulate the dynamic behavior of microtubules in vitro. More specifically, we have developed an automated and objective method to define growth, shortening and attenuation events from real time videos of dynamic microtubules, and demonstrated its validity by comparing it to manually assessed data. Additionally, we have used the same data to develop a general strategy of building different models of interest, computing appropriate dissimilarity functions to compare them, and embedding them on a two-dimensional plot for visualization and easy comparison. Application of these methods to assess microtubule growth rates and growth rate distributions established the validity of the embedding procedure and revealed non-linearity in the relationship between the tau:tubulin molar ratio and growth rate distribution. CONCLUSION: This work addresses the need of the biological community for rigorously quantitative and generally applicable computational tools for comparative studies. The two-dimensional embedding method retains the inherent structure of the data, and yet markedly simplifies comparison between models and parameters of different samples. Most notably, even in cases where numerous parameters exist by which to compare the different samples, our embedding procedure provides a generally applicable computational strategy to detect subtle relationships between different molecules or conditions that might otherwise escape manual analyses.

Type II opsins: evolutionary origin by internal domain duplication?

Opsins are a large group of proteins with seven transmembrane segments (TMSs) that are found in all domains of life. There are two types of opsins that are sometimes considered nonhomologous: type I is known from prokaryotes and some eukaryotes, while type II is known only from Eumetazoan animals. Type II opsins are members of the family of G-protein coupled receptors (GPCRs), which facilitate signal transduction across cell membranes. While previous studies have concluded that multiple transmembrane-containing protein families-including type I opsins-originated by internal domain duplication, the origin of type II opsins has been speculated on but never tested. Here we show that type II opsins do not appear to have originated through a similar internal domain duplication event. This provides further evidence that the two types of opsins are nonhomologous, indicating a convergent evolutionary origin, in which both groups of opsins evolved a seven-TM structure and light sensitivity independently. This convergence may indicate an important role for seven-TM protein structure for retinal-based light sensitivity.