Deepak Bandyopadhyay

Mining protein family specific residue packing patterns from protein structure graphs

Finding recurring residue packing patterns, or spatial motifs, that characterize protein structural families is an important problem in bioinformatics. We apply a novel frequent subgraph mining algorithm to three graph representations of protein three-dimensional (3D) structure. In each protein graph, a vertex represents an amino acid. Vertex-residues are connected by edges using three approaches: first, based on simple distance threshold between contact residues; second using the Delaunay tessellation from computational geometry, and third using the recently developed almost-Delaunay tessellation approach.Applying a frequent subgraph mining algorithm to a set of graphs representing a protein family from the Structural Classification of Proteins (SCOP) database, we typically identify several hundred common subgraphs equivalent to common packing motifs found in the majority of proteins in the family. We also use the counts of motifs extracted from proteins in two different SCOP families as input variables in a binary classification experiment. The resulting models are capable of predicting the protein family association with the accuracy exceeding 90 percent. Our results indicate that graphs based on both almost-Delaunay and Delaunay tessellations are sparser than the contact distance graphs; yet they are robust and efficient for mining protein spatial motif.

Comparing Graph Representations of Protein Structure for Mining Family-Specific Residue-Based Packing Motifs

We find recurring amino-acid residue packing patterns, or spatial motifs, that are characteristic of protein structural families, by applying a novel frequent subgraph mining algorithm to graph representations of protein three-dimensional structure. Graph nodes represent amino acids, and edges are chosen in one of three ways: first, using a threshold for contact distance between residues; second, using Delaunay tessellation; and third, using the recently developed almost-Delaunay edges. For a set of graphs representing a protein family from the Structural Classification of Proteins (SCOP) database, subgraph mining typically identifies several hundred common subgraphs corresponding to spatial motifs that are frequently found in proteins in the family but rarely found outside of it. We find that some of the large motifs map onto known functional regions in two protein families explored in this study, i.e., serine proteases and kinases. We find that graphs based on almost-Delaunay edges significantly reduce the number of edges in the graph representation and hence present computational advantage, yet the patterns extracted from such graphs have a biological interpretation approximately equivalent to that of those extracted from distance based graphs.

Discovery of Small Molecule RIP1 Kinase Inhibitors for the Treatment of Pathologies Associated with Necroptosis.

Potent inhibitors of RIP1 kinase from three distinct series, 1-aminoisoquinolines, pyrrolo[2,3-b]pyridines, and furo[2,3-d]pyrimidines, all of the type II class recognizing a DLG-out inactive conformation, were identified from screening of our in-house kinase focused sets. An exemplar from the furo[2,3-d]pyrimidine series showed a dose proportional response in protection from hypothermia in a mouse model of TNFα induced lethal shock.

Discovery of a First-in-Class Receptor Interacting Protein 1 (RIP1) Kinase Specific Clinical Candidate (GSK2982772) for the Treatment of Inflammatory Diseases

RIP1 regulates necroptosis and inflammation and may play an important role in contributing to a variety of human pathologies, including immune-mediated inflammatory diseases. Small-molecule inhibitors of RIP1 kinase that are suitable for advancement into the clinic have yet to be described. Herein, we report our lead optimization of a benzoxazepinone hit from a DNA-encoded library and the discovery and profile of clinical candidate GSK2982772 (compound 5), currently in phase 2a clinical studies for psoriasis, rheumatoid arthritis, and ulcerative colitis. Compound 5 potently binds to RIP1 with exquisite kinase specificity and has excellent activity in blocking many TNF-dependent cellular responses. Highlighting its potential as a novel anti-inflammatory agent, the inhibitor was also able to reduce spontaneous production of cytokines from human ulcerative colitis explants. The highly favorable physicochemical and ADMET properties of 5, combined with high potency, led to a predicted low oral dose in humans.

Structure-based function inference using protein family-specific fingerprints

We describe a method to assign a protein structure to a functional family using family‐specific fingerprints. Fingerprints represent amino acid packing patterns that occur in most members of a family but are rare in the background, a nonredundant subset of PDB; their information is additional to sequence alignments, sequence patterns, structural superposition, and active‐site templates. Fingerprints were derived for 120 families in SCOP using Frequent Subgraph Mining. For a new structure, all occurrences of these family‐specific fingerprints may be found by a fast algorithm for subgraph isomorphism; the structure can then be assigned to a family with a confidence value derived from the number of fingerprints found and their distribution in background proteins. In validation experiments, we infer the function of new members added to SCOP families and we discriminate between structurally similar, but functionally divergent TIM barrel families. We then apply our method to predict function for several structural genomics proteins, including orphan structures. Some predictions have been corroborated by other computational methods and some validated by subsequent functional characterization.

Stochastic Proximity Embedding: Methods and Applications

Since its inception in 1996, the stochastic proximity embedding (SPE) algorithm and its variants have been applied to a wide range of problems in computational chemistry and biology with notable success. At its core, SPE attempts to generate Euclidean coordinates for a set of points so that they satisfy a prescribed set of geometric constraints. The algorithm’s appeal rests on three factors: 1) its conceptual and programmatic simplicity; 2) its superior speed and scaling properties; and 3) its broad applicability. Here, we review some of the key applications, outline known limitations and ways to circumvent them, and highlight additional problem domains where the use of this technique could lead to significant breakthroughs.

Perspectives on the Discovery of Small-Molecule Modulators for Epigenetic Processes

Epigenetic gene regulation is a critical process controlling differentiation and development, the malfunction of which may underpin a variety of diseases. In this article, we review the current landscape of small-molecule epigenetic modulators including drugs on the market, key compounds in clinical trials, and chemical probes being used in epigenetic mechanistic studies. Hit identification strategies for the discovery of small-molecule epigenetic modulators are summarized with respect to writers, erasers, and readers of histone marks. Perspectives are provided on opportunities for new hit discovery approaches, some of which may define the next generation of therapeutic intervention strategies for epigenetic processes.

Distance-based identification of structure motifs in proteins using constrained frequent subgraph mining.

Structure motifs are amino acid packing patterns that occur frequently within a set of protein structures. We define a labeled graph representation of protein structure in which vertices correspond to amino acid residues and edges connect pairs of residues and are labeled by (1) the Euclidian distance between the C(alpha) atoms of the two residues and (2) a boolean indicating whether the two residues are in physical/chemical contact. Using this representation, a structure motif corresponds to a labeled clique that occurs frequently among the graphs representing the protein structures. The pairwise distance constraints on each edge in a clique serve to limit the variation in geometry among different occurrences of a structure motif. We present an efficient constrained subgraph mining algorithm to discover structure motifs in this setting. Compared with contact graph representations, the number of spurious structure motifs is greatly reduced. Using this algorithm, structure motifs were located for several SCOP families including the Eukaryotic Serine Proteases, Nuclear Binding Domains, Papain-like Cysteine Proteases, and FAD/NAD-linked Reductases. For each family, we typically obtain a handful of motifs within seconds of processing time. The occurrences of these motifs throughout the PDB were strongly associated with the original SCOP family, as measured using a hyper-geometric distribution. The motifs were found to cover functionally important sites like the catalytic triad for Serine Proteases and co-factor binding sites for Nuclear Binding Domains. The fact that many motifs are highly family-specific can be used to classify new proteins or to provide functional annotation in Structural Genomics Projects.

Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development

Protein function prediction is one of the central problems in computational biology. We present a novel automated protein structure-based function prediction method using libraries of local residue packing patterns that are common to most proteins in a known functional family. Critical to this approach is the representation of a protein structure as a graph where residue vertices (residue name used as a vertex label) are connected by geometrical proximity edges. The approach employs two steps. First, it uses a fast subgraph mining algorithm to find all occurrences of family-specific labeled subgraphs for all well characterized protein structural and functional families. Second, it queries a new structure for occurrences of a set of motifs characteristic of a known family, using a graph index to speed up Ullman’s subgraph isomorphism algorithm. The confidence of function inference from structure depends on the number of family-specific motifs found in the query structure compared with their distribution in a large non-redundant database of proteins. This method can assign a new structure to a specific functional family in cases where sequence alignments, sequence patterns, structural superposition and active site templates fail to provide accurate annotation.

Almost-Delaunay simplices: Robust neighbor relations for imprecise 3D points using CGAL

This paper describes a new computational geometry technique, almost-Delaunay simplices, that was implemented for 3D points using CGAL. Almost-Delaunay simplices capture possible sets of Delaunay neighbors in the presence of a bounded perturbation, and give a framework for nearest neighbor analysis in imprecise point sets such as protein structures. The use of CGAL helps us tune our implementation so that it is reasonably fast and also performs robust computation for all inputs, and also lets us distribute our technique to potential users in a portable, reusable and extensible form. The implementation, available on http://www.cs.unc.edu/~debug/software is faster and more memory efficient than our prototype MATLAB implementation, and enables us to scale our neighbor analysis to large sets of protein structures, each with 100-3000 residues.