Integration of multi-omics data with molecular interaction networks enables elucidation of the pathophysiology of Alzheimer's disease (AD). Using the latest genome-wide association studies (GWAS) including proxy cases and the STRING interactome, we identified an AD network of 142 risk genes and 646 network-proximal genes, many of which were linked to synaptic functions annotated by mouse knockout data. The proximal genes were confirmed to be enriched in a replication GWAS of autopsy-documented cases. By integrating the AD gene network with transcriptomic data of AD and healthy temporal cortices, we identified 17 gene clusters of pathways, such as up-regulated complement activation and lipid metabolism, down-regulated cholinergic activity, and dysregulated RNA metabolism and proteostasis. The relationships among these pathways were further organized by a hierarchy of the AD network pinpointing major parent nodes in graph structure including endocytosis and immune reaction. Control analyses were performed using transcriptomics from cerebellum and a brain-specific interactome. Further integration with cell-specific RNA sequencing data demonstrated genes in our clusters of immunoregulation and complement activation were highly expressed in microglia.

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ('polypharmacology'). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

SUMMARY: SimiCon is a web server designed for an automated identification of equivalent protein-ligand atomic contacts in different conformational models of a complex. The contacts are computed with internal coordinate mechanics (ICM) software with respect to molecular symmetry and the results are shown in the browser as text, tables and interactive 3D graphics. The web server can be executed remotely without a browser to allow users to automate multiple calculations. AVAILABILITY: SimiCon is freely available at http://abagyan.ucsd.edu/SimiCon

MOTIVATION: Prediction of protein-protein complexes from the coordinates of their unbound components usually starts by generating many potential predictions from a rigid-body 6D search followed by a second stage that aims to refine such predictions. Here, we present and evaluate a new method to effectively address the complexity and sampling requirements of the initial exhaustive search. In this approach we combine the projection of the interaction terms into 3D grid-based potentials with the efficiency of spherical harmonics approximations to accelerate the search. The binding energy upon complex formation is approximated as a correlation function composed of van der Waals, electrostatics and desolvation potential terms. The interaction-energy minima are identified by a novel, fast and exhaustive rotational docking search combined with a simple translational scanning. Results obtained on standard protein-protein benchmarks demonstrate its general applicability and robustness. The accuracy is comparable to that of existing state-of-the-art initial exhaustive rigid-body docking tools, but achieving superior efficiency. Moreover, a parallel version of the method performs the docking search in just a few minutes, opening new application opportunities in the current 'omics' world. AVAILABILITY: http://sbg.cib.csic.es/Software/FRODOCK/

MOTIVATION: Efficient fitting tools are needed to take advantage of a fast growth of atomic models of protein domains from crystallography or comparative modeling, and low-resolution density maps of larger molecular assemblies. Here, we report a novel fitting algorithm for the exhaustive and fast overlay of partial high-resolution models into a low-resolution density map. The method incorporates a fast rotational search based on spherical harmonics (SH) combined with a simple translational scanning. RESULTS: This novel combination makes it possible to accurately dock atomic structures into low-resolution electron-density maps in times ranging from seconds to a few minutes. The high-efficiency achieved with simulated and experimental test cases preserves the exhaustiveness needed in these heterogeneous-resolution merging tools. The results demonstrate its efficiency, robustness and high-throughput coverage. AVAILABILITY: http://sbg.cib.csic.es/Software/ADP_EM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: DFprot is a web-based server for predicting main-chain deformability from a single protein conformation. The server automatically performs a normal-mode analysis (NMA) of the uploaded structure and calculates its capability to deform at each of its residues. Non-specialists can easily and rapidly obtain a quantitative first approximation of the flexibility of their structures with a simple and efficient interface. AVAILABILITY: http://sbg.cib.csic.es/Software/DFprot.

MOTIVATION: Machine learning methods such as neural networks, support vector machines, and other classification and regression methods rely on iterative optimization of the model quality in the space of the parameters of the method. Model quality measures (accuracies, correlations, etc.) are frequently overly optimistic because the training sets are dominated by particular families and subfamilies. To overcome the bias, the dataset is usually reduced by filtering out closely related objects. However, such filtering uses fixed similarity thresholds and ignores a part of the training information. RESULTS: We suggested a novel approach to calculate prediction model quality based on assigning to each data point inverse density weights derived from the postulated distance metric. We demonstrated that our new weighted measures estimate the model generalization better and are consistent with the machine learning theory. The Vapnik-Chervonenkis theorem was reformulated and applied to derive the space-uniform error estimates. Two examples were used to illustrate the advantages of the inverse density weighting. First, we demonstrated on a set with a built-in bias that the unweighted cross-validation procedure leads to an overly optimistic quality estimate, while the density-weighted quality estimates are more realistic. Second, an analytical equation for weighted quality estimates was used to derive an SVM model for signal peptide prediction using a full set of known signal peptides, instead of the usual filtered subset.

MOTIVATION: Evolutionary conservation estimated from a multiple sequence alignment is a powerful indicator of the functional significance of a residue and helps to predict active sites, ligand binding sites, and protein interaction interfaces. Many algorithms that calculate conservation work well, provided an accurate and balanced alignment is used. However, such a strong dependence on the alignment makes the results highly variable. We attempted to improve the conservation prediction algorithm by making it more robust and less sensitive to (1) local alignment errors, (2) overrepresentation of sequences in some branches and (3) occasional presence of unrelated sequences. RESULTS: A novel method is presented for robust constrained Bayesian estimation of evolutionary rates that avoids overfitting independent rates and satisfies the above requirements. The method is evaluated and compared with an entropy-based conservation measure on a set of 1494 protein interfaces. We demonstrated that approximately 62% of the analyzed protein interfaces are more conserved than the remaining surface at the 5% significance level. A consistent method to incorporate alignment reliability is proposed and demonstrated to reduce arbitrary variation of calculated rates upon inclusion of distantly related or unrelated sequences into the alignment.

MOTIVATION: We present a structural alignment database that is specifically targeted for use in derivation and optimization of sequence-structure alignment algorithms for homology modeling. We have paid attention to ensure that fold-space is properly sampled, that the structures involved in alignments are of significant resolution (better than 2.5 A) and the alignments are accurate and reliable. RESULTS: Alignments have been taken from the HOMSTRAD, BAliBASE and SCOP-based Gerstein databases along with alignments generated by a global structural alignment method described here. In order to discriminate between equivalent alignments from these different sources, we have developed a novel scoring function, Contact Alignment Quality score, which evaluates trial alignments by their statistical significance combined with their ability to reproduce conserved three-dimensional residue contacts. The resulting non-redundant, unbiased database contains 1927 alignments from across fold-space with high-resolution structures and a wide range of sequence identities. AVAILABILITY: The database can be interactively queried either over the web at http://abagyan.scripps.edu/lab/web/sad/show.cgi or by using MySQL, and is also available to download over the web.

Large scale discovery of new human genes, gene families and isozymes creates an exciting biomedical opportunity. Advanced modeling by homology techniques can be employed to generate 3D models for most of the interesting new gene family members. This opens many new opportunities for structural structure based functional annotation and molecular design. Several key computational technologies enable structural protein modeling and rational drug design: sequence-structure relationships, multiple sequence alignments, threading molecular mechanics; global energy optimization; protein modeling by homology; structure based protein functional annotation (e.g. prediction of possible active sites, small molecule binding sites, protein-protein interaction sites, flexibility annotation); protein design and redesign using structure prediction algorithms; structural immunology, e.g. derivation and design of peptide sequence specific for a particular HLA presenting molecule based on the HLA structure; peptide-protein docking; protein-protein docking; small molecule docking and virtual ligand screening; cheminformatics algorithms and virtual chemistry.We have been developing the internal coordinate mechanics approach to structure prediction for the last 16 years. It is aimed at de novo prediction of large structural rearrangements. To achieve that goal an ICM program has been developed and combined accurate energy functions with powerful global optimization algorithm in the internal coordinate space.The most recent developments include on-the-fly calculations of accurate Poisson electrostatics using the efficient boundary element algorithm based on the analytical molecular surface. This allows to predict de novo a solution structure of a 23-residue peptide.New computational technology for virtual ligand screening allows to generate alternative receptor conformations and perform flexible docking of hundreds of thousands of virtual compounds to the binding site. The success of this technology is demonstrated on several benchmarks and in six experimental projects which tested the prediction results. For well defined binding pockets only about the initial compound set actually needs to be synthesized and tested. Using ICM docking and scoring technology we have identified new ligands in a number of cases, including cases in which a model was used instead of a crystallographic structure and a case in which protein-protein interaction has been targeted.The most interesting recent results tested experimentally include: the de novo design of novel antagonists of thyroid hormone receptor, for which no antagonists were known before and, consequently, no antagonist-bound structure was known; de novo design of ligands targeting Ephrin-Ephrin receptor interactions.

BACKGROUND: High-density oligonucleotide arrays have become a valuable tool for high-throughput gene expression profiling. Increasing the array information density and improving the analysis algorithms are two important computational research topics. RESULTS: A new algorithm, Match-Only Integral Distribution (MOID), was developed to analyze high-density oligonucleotide arrays. Using known data from both spiking experiments and no-change experiments performed with Affymetrix GeneChip arrays, MOID and the Affymetrix algorithm implemented in Microarray Suite 4.0 (MAS4) were compared. While MOID gave similar performance to MAS4 in the spiking experiments, better performance was observed in the no-change experiments.MOID also provides a set of alternative statistical analysis tools to MAS4. There are two main features that distinguish MOID from MAS4. First, MOID uses continuous P values for the likelihood of gene presence, while MAS4 resorts to discrete absolute calls. Secondly, MOID uses heuristic confidence intervals for both gene expression levels and fold change values, while MAS4 categorizes the significance of gene expression level changes into discrete fold change calls. CONCLUSIONS: The results show that by using MOID, Affymetrix GeneChip arrays may need as little as ten probes per gene without compromising analysis accuracy.

Article Free Access Share on Derivation of sensitive discrimination potential for virtual ligand screening Authors: Maxim Totrov Skirball Institute, NYU Medical Center, 540 1st Ave., New York, NY Skirball Institute, NYU Medical Center, 540 1st Ave., New York, NYView Profile , Ruben Abagyan Skirball Institute, NYU Medical Center, 540 1st Ave., New York, NY Skirball Institute, NYU Medical Center, 540 1st Ave., New York, NYView Profile Authors Info & Claims RECOMB '99: Proceedings of the third annual international conference on Computational molecular biologyApril 1999 Pages 312–320https://doi.org/10.1145/299432.299509Online:01 April 1999Publication History 22citation353DownloadsMetricsTotal Citations22Total Downloads353Last 12 Months11Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

No abstract available.