Novel approach for efficient pharmacophore-based virtual screening: method and applications.

Virtual screening is emerging as a productive and cost-effective technology in rational drug design for the identification of novel lead compounds. An important model for virtual screening is the pharmacophore. Pharmacophore is the spatial configuration of essential features that enable a ligand molecule to interact with a specific target receptor. In the absence of a known receptor structure, a pharmacophore can be identified from a set of ligands that have been observed to interact with the target receptor. Here, we present a novel computational method for pharmacophore detection and virtual screening. The pharmacophore detection module is able to (i) align multiple flexible ligands in a deterministic manner without exhaustive enumeration of the conformational space, (ii) detect subsets of input ligands that may bind to different binding sites or have different binding modes, (iii) address cases where the input ligands have different affinities by defining weighted pharmacophores based on the number of ligands that share them, and (iv) automatically select the most appropriate pharmacophore candidates for virtual screening. The algorithm is highly efficient, allowing a fast exploration of the chemical space by virtual screening of huge compound databases. The performance of PharmaGist was successfully evaluated on a commonly used data set of G-Protein Coupled Receptor alpha1A. Additionally, a large-scale evaluation using the DUD (directory of useful decoys) data set was performed. DUD contains 2950 active ligands for 40 different receptors, with 36 decoy compounds for each active ligand. PharmaGist enrichment rates are comparable with other state-of-the-art tools for virtual screening.

Analysis and classification of RNA tertiary structures.

There is a fast growing interest in noncoding RNA transcripts. These transcripts are not translated into proteins, but play essential roles in many cellular and pathological processes. Recent efforts toward comprehension of their function has led to a substantial increase in both the number and the size of solved RNA structures. With the aim of addressing questions relating to RNA structural diversity, we examined RNA conservation at three structural levels: primary, secondary, and tertiary structure. Additionally, we developed an automated method for classifying RNA structures based on spatial (three-dimensional [3D]) similarity. Applying the method to all solved RNA structures resulted in a classified database of RNA tertiary structures (DARTS). DARTS embodies 1333 solved RNA structures classified into 94 clusters. The classification is hierarchical, reflecting the structural relationship between and within clusters. We also developed an application for searching DARTS with a new structure. The search is fast and its performance was successfully tested on all solved RNA structures since the creation of DARTS. A user-friendly interface for both the database and the search application is available online. We show intracluster and intercluster similarities in DARTS and demonstrate the usefulness of the search application. The analysis reveals the current structural repertoire of RNA and exposes common global folds and local tertiary motifs. Further study of these conserved substructures may suggest possible RNA domains and building blocks. This should be beneficial for structure prediction and for gaining insights into structure-function relationships.

Structural similarity of genetically interacting proteins.

BACKGROUND: The study of gene mutants and their interactions is fundamental to understanding gene function and backup mechanisms within the cell. The recent availability of large scale genetic interaction networks in yeast and worm allows the investigation of the biological mechanisms underlying these interactions at a global scale. To date, less than 2% of the known genetic interactions in yeast or worm can be accounted for by sequence similarity. RESULTS: Here, we perform a genome-scale structural comparison among protein pairs in the two species. We show that significant fractions of genetic interactions involve structurally similar proteins, spanning 7-10% and 14% of all known interactions in yeast and worm, respectively. We identify several structural features that are predictive of genetic interactions and show their superiority over sequence-based features. CONCLUSION: Structural similarity is an important property that can explain and predict genetic interactions. According to the available data, the most abundant mechanism for genetic interactions among structurally similar proteins is a common interacting partner shared by two genetically interacting proteins.

Deterministic pharmacophore detection via multiple flexible alignment of drug-like molecules.

We present a novel highly efficient method for the detection of a pharmacophore from a set of drug-like ligands that interact with a target receptor. A pharmacophore is a spatial arrangement of physico-chemical features in a ligand that is essential for the interaction with a specific receptor. In the absence of a known three-dimensional (3D) receptor structure, a pharmacophore can be identified from a multiple structural alignment of ligand molecules. The key advantages of the presented algorithm are: (a) its ability to multiply align flexible ligands in a deterministic manner, (b) its ability to focus on subsets of the input ligands, which may share a large common substructure, resulting in the detection of both outlier molecules and alternative binding modes, and (c) its computational efficiency, which allows to detect pharmacophores shared by a large number of molecules on a standard PC. The algorithm was extensively tested on a dataset of almost 80 ligands acting on 12 different receptors. The results, which were achieved using a set of standard default parameters, were consistent with reference pharmacophores that were derived from the bound ligand-receptor complexes. The pharmacophores detected by the algorithm are expected to be a key component in the discovery of new leads by screening large databases of drug-like molecules. A user-friendly web interface is available at http://bioinfo3d.cs.tau.ac.il/pharma. Supplementary material can be found at http://bioinfo3d.cs.tau.ac.il/pharma/reduction/.

PharmaGist: a webserver for ligand-based pharmacophore detection.

Predicting molecular interactions is a major goal in rational drug design. Pharmacophore, which is the spatial arrangement of features that is essential for a molecule to interact with a specific target receptor, is an important model for achieving this goal. We present a freely available web server, named PharmaGist, for pharmacophore detection. The employed method is ligand based. Namely, it does not require the structure of the target receptor. Instead, the input is a set of structures of drug-like molecules that are known to bind to the receptor. The output consists of candidate pharmacophores that are computed by multiple flexible alignment of the input ligands. The method handles the flexibility of the input ligands explicitly and in deterministic manner within the alignment process. PharmaGist is also highly efficient, where a typical run with up to 32 drug-like molecules takes seconds to a few minutes on a stardard PC. Another important characteristic is the capability of detecting pharmacophores shared by different subsets of input molecules. This capability is a key advantage when the ligands belong to different binding modes or when the input contains outliers. The webserver has a user-friendly interface available at http://bioinfo3d.cs.tau.ac.il/PharmaGist.

EMatch: discovery of high resolution structural homologues of protein domains in intermediate resolution cryo-EM maps.

Cryo-EM has become an increasingly powerful technique for elucidating the structure, dynamics, and function of large flexible macromolecule assemblies that cannot be determined at atomic resolution. However, due to the relatively low resolution of cryo-EM data, a major challenge is to identify components of complexes appearing in cryo-EM maps. Here, we describe EMatch, a novel integrated approach for recognizing structural homologues of protein domains present in a 6-10 A resolution cryo-EM map and constructing a quasi-atomic structural model of their assembly. The method is highly efficient and has been successfully validated on various simulated data. The strength of the method is demonstrated by a domain assembly of an experimental cryo-EM map of native GroEL at 6 A resolution.

EMatch: an efficient method for aligning atomic resolution subunits into intermediate-resolution cryo-EM maps of large macromolecular assemblies.

Structural analysis of biological machines is essential for inferring their function and mechanism. Nevertheless, owing to their large size and instability, deciphering the atomic structure of macromolecular assemblies is still considered as a challenging task that cannot keep up with the rapid advances in the protein-identification process. In contrast, structural data at lower resolution is becoming more and more available owing to recent advances in cryo-electron microscopy (cryo-EM) techniques. Once a cryo-EM map is acquired, one of the basic questions asked is what are the folds of the components in the assembly and what is their configuration. Here, a novel knowledge-based computational method, named EMatch, towards tackling this task for cryo-EM maps at 6-10 A resolution is presented. The method recognizes and locates possible atomic resolution structural homologues of protein domains in the assembly. The strengths of EMatch are demonstrated on a cryo-EM map of native GroEL at 6 A resolution.

The ARTS web server for aligning RNA tertiary structures.

RNA molecules with common structural features may share similar functional properties. Structural comparison of RNAs and detection of common substructures is, thus, a highly important task. Nevertheless, the current available tools in the RNA community provide only a partial solution, since they either work at the 2D level or are suitable for detecting predefined or local contiguous tertiary motifs only. Here, we describe a web server built around ARTS, a method for aligning tertiary structures of nucleic acids (both RNA and DNA). ARTS receives a pair of 3D nucleic acid structures and searches for a priori unknown common substructures. The search is truly 3D and irrespective of the order of the nucleotides on the chain. The identified common substructures can be large global folds with hundreds and even thousands of nucleotides as well as small local motifs with at least two successive base pairs. The method is highly efficient and has been used to conduct an all-against-all comparison of all the RNA structures in the Protein Data Bank. The web server together with a software package for download are freely accessible at http://bioinfo3d.cs.tau.ac.il/ARTS.

ARTS: alignment of RNA tertiary structures.

MOTIVATION: A fast growing number of non-coding RNAs have recently been discovered to play essential roles in many cellular processes. Similar to proteins, understanding the functions of these active RNAs requires methods for analyzing their tertiary structures. However, in contrast to the wide range of structure-based approaches available for proteins, there is still a lack of methods for studying RNA structures. RESULTS: We present a new computational method named ARTS (alignment of RNA tertiary structures). The method compares two nucleic acid structures (RNAs or DNAs) and detects a-priori unknown common substructures. These substructures can be either large global folds containing hundreds and even thousands of nucleotides or small local tertiary motifs with at least two successive base pairs. To the best of our knowledge, this is the first method of this type. The method is highly-efficient and was used to conduct an all-against-all comparison of all the RNA structures currently available in the Protein Data Bank. AVAILABILITY: The program, a web-server and supplementary information are available on http://bioinfo3d.cs.tau.ac.il/ARTS

From structure to function: methods and applications.

The rapid increase in experimental data along with recent progress in computational methods has brought modern biology a step closer toward solving one of the most challenging problems: prediction of protein function. Comprehension of protein function at its most basic level requires understanding of molecular interactions. Currently, it is becoming universally accepted that the scale of the accumulated data for analysis and for prediction necessitate highly efficient computational tools with appropriate application capabilities. The review presents the up-to-date advances in computational methods for structural pattern discovery and for prediction of molecular associations. We focus on their applications toward a range of biological problems and highlight the advantages of the combination of these methods and their integration with biological experiments. We provide examples, synergistically merging structural modeling, rigid and flexible structural alignment and detection of conserved structural patterns and docking (rigid and flexible with hinge-bending movements). We hope the review will lead to a broader utilization of computational methods, and their cross-fertilization with experiment.

BioInfo3D: a suite of tools for structural bioinformatics.

Here, we describe BioInfo3D, a suite of freely available web services for protein structural analysis. The FlexProt method performs flexible structural alignment of protein molecules. FlexProt simultaneously detects the hinge regions and aligns the rigid subparts of the molecules. It does not require an a priori knowledge of the flexible hinge regions. MultiProt and MASS perform simultaneous comparison of multiple protein structures. PatchDock performs prediction of protein-protein and protein-small molecule interactions. The input to all services is either protein PDB codes or protein structures uploaded to the server. All the services are available at http://bioinfo3d.cs.tau.ac.il.

Predicting molecular interactions in silico: I. A guide to pharmacophore identification and its applications to drug design.

A major goal in contemporary drug design is to develop new ligands with high affinity of binding toward a given protein receptor. Pharmacophore, which is the three-dimensional arrangement of essential features that enable a molecule to exert a particular biological effect, is a very useful model for achieving this goal. If the three dimensional structure of the receptor is known, pharmacophore is a complementary tool to standard techniques, such as docking. However, frequently the structure of the receptor protein is unknown and only a set of ligands together with their measured binding affinities towards the receptor is available. In such a case, a pharmacophore based strategy is one of the few applicable tools. Here we present a broad, yet concise guide to pharmacophore identification and review a sample of applications for drug design. In particular, we present the framework of the algorithms, classify their modules and point out their advantages and challenges.

Multiple structural alignment by secondary structures: algorithm and applications.

We present MASS (Multiple Alignment by Secondary Structures), a novel highly efficient method for structural alignment of multiple protein molecules and detection of common structural motifs. MASS is based on a two-level alignment, using both secondary structure and atomic representation. Utilizing secondary structure information aids in filtering out noisy solutions and achieves efficiency and robustness. Currently, only a few methods are available for addressing the multiple structural alignment task. In addition to using secondary structure information, the advantage of MASS as compared to these methods is that it is a combination of several important characteristics: (1) While most existing methods are based on series of pairwise comparisons, and thus might miss optimal global solutions, MASS is truly multiple, considering all the molecules simultaneously; (2) MASS is sequence order-independent and thus capable of detecting nontopological structural motifs; (3) MASS is able to detect not only structural motifs, shared by all input molecules, but also motifs shared only by subsets of the molecules. Here, we show the application of MASS to various protein ensembles. We demonstrate its ability to handle a large number (order of tens) of molecules, to detect nontopological motifs and to find biologically meaningful alignments within nonpredefined subsets of the input. In particular, we show how by using conserved structural motifs, one can guide protein-protein docking, which is a notoriously difficult problem. MASS is freely available at http://bioinfo3d.cs.tau.ac.il/MASS/.

Taking geometry to its edge: fast unbound rigid (and hinge-bent) docking.

We present a very efficient rigid "unbound" soft docking methodology, which is based on detection of geometric shape complementarity, allowing liberal steric clash at the interface. The method is based on local shape feature matching, avoiding the exhaustive search of the 6D transformation space. Our experiments at CAPRI rounds 1 and 2 show that although the method does not perform an exhaustive search of the 6D transformation space, the "correct" solution is never lost. However, such a solution might rank low for large proteins, because there are alternatives with significantly larger geometrically compatible interfaces. In many cases this problem can be resolved by successful a priori focusing on the vicinity of potential binding sites as well as the extension of the technique to flexible (hinge-bent) docking. This is demonstrated in the experiments performed as a lesson from our CAPRI experience.