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We describe SILIRID (Simple Ligand-Receptor Interaction Descriptor), a novel fixed size descriptor characterizing protein-ligand interactions. SILIRID can be obtained from the binary interaction fingerprints (IFPs) by summing up the bits corresponding to identical amino acids. This results in a vector of 168 integer numbers corresponding to the product of the number of entries (20 amino acids and one cofactor) and 8 interaction types per amino acid (hydrophobic, aromatic face to face, aromatic edge to face, H-bond donated by the protein, H-bond donated by the ligand, ionic bond with protein cation and protein anion, and interaction with metal ion). Efficiency of SILIRID to distinguish different protein binding sites has been examined in similarity search in sc-PDB database, a druggable portion of the Protein Data Bank, using various protein-ligand complexes as queries. The performance of retrieval of structurally and evolutionary related classes of proteins was comparable to that of state-of-the-art approaches (ROC AUC ≈ 0.91). SILIRID can efficiently be used to visualize chemogenomic space covered by sc-PDB using Generative Topographic Mapping (GTM): sc-PDB SILIRID data form clusters corresponding to different protein types.

Penicillin-Binding Proteins are peptidases that play an important role in cell-wall biogenesis in bacteria and thus maintaining bacterial infections. A wide class of β-lactam drugs are known to act on these proteins and inhibit bacterial infections by disrupting the cell-wall biogenesis pathway. Penicillin-Binding proteins have recently gained importance with the increase in the number of multi-drug resistant bacteria. In this work, we have collected a dataset of over 700 Penicillin-Binding and non-Penicillin Binding Proteins and extracted various sequence-related features. We then created models to classify the proteins into Penicillin-Binding and non-binding using supervised machine learning algorithms such as Support Vector Machines and Random Forest. We obtain a good classification performance for both the models using both the methods.

The question of how best to compare and classify the (three-dimensional) structures of proteins is one of the most important unsolved problems in computational biology. To help tackle this problem, we have developed a novel shape-density superposition algorithm called 3D-Blast which represents and superposes the shapes of protein backbone folds using the spherical polar Fourier correlation technique originally developed by us for protein docking. The utility of this approach is compared with several well-known protein structure alignment algorithms using receiver-operator-characteristic plots of queries against the "gold standard" CATH database. Despite being completely independent of protein sequences and using no information about the internal geometry of proteins, our results from searching the CATH database show that 3D-Blast is highly competitive compared to current state-of-the-art protein structure alignment algorithms. A novel and potentially very useful feature of our approach is that it allows an average or "consensus" fold to be calculated easily for a given group of protein structures. We find that using consensus shapes to represent entire fold families also gives very good database query performance. We propose that using the notion of consensus fold shapes could provide a powerful new way to index existing protein structure databases, and that it offers an objective way to cluster and classify all of the currently known folds in the protein universe., (Copyright © 2011 Wiley Periodicals, Inc.)

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.