Venomous animals have evolved a vast array of peptide toxins for prey capture and defense. Nature has evolved the venoms into a huge library of active molecules with high selectivity and affinity, which could be explored as therapeutics or serve as a template for drug design. The individual components of venom i.e. toxins are used in ion channel and receptor studies, drug discovery, and formulation of insecticides. ‘Venominformatics is a systematic bioinformatics approach in which classified, consolidated and cleaned venom data are stored into repositories and integrated with advanced bioinformatics tools and computational biology for the analysis of structure and function of toxins.’ Conus peptides (conopeptides), the main components of Conus venom, represent a unique arsenal of neuropharmacologically active molecules that have been evolutionarily tailored to afford unprecedented and exquisite selectivity for a wide variety of ion-channel subtypes and neuronal receptors. Ziconotide (ω-conotoxin MVIIa from Conus magus (Magician's cone snail)), is proven as an intrathecally administered N-type calcium channel antagonist for the treatment of chronic pain (U.S. Food and Drug Administration. Center for Drug Evaluation and Research) attesting to the pharmaceutical importance of Conus peptides. From the point of view of protein sequence and structure analysis, conopeptides can serve as attractive systems for the studies in sequence comparison, pattern extraction, structure–function correlations, protein–protein interactions and evolutionary analysis. Despite their importance and extensive experimental investigations on them, they have been hardly explored through in silico methods. The present thesis is perhaps the first attempt at deploying a multi-pronged bioinformatics approaches for studies in the burgeoning field of conopeptides. In the process of sequence-structure-function studies of conopeptides, we have created several sequence patterns of different conopeptide families and these have been accepted for inclusion in international databases such as PROSITE, the first pattern database to have been developed (http://www.expasy.org/prosite) and INTERPRO (http://www.ebi.ac.uk/interpro). More importantly, we have carried out extensive literature survey on the peptides for which we have defined the patterns to create PROSITE compatible documentation files (PDOC6004, PDOC60025 and PDOC60027). We have also created a series of sequence patterns and associated documentation filesof pharmaceutically promising peptides from plants and venomous animals (including O-conotoxin and P-conotoxin superfamily members) with knottin scaffold. Knottins provide appealing scaffolds for protein engineering and drug design due to their small size, high structural stability, strong sequence tolerance and easy access to chemical synthesis. The sequence patterns and associated documentation files created by us should be useful in protein family classification and functional annotation. Even though patterns might be useful at the family level, they may not always be adequate at the superfamily level due to hypervariability of mature toxins. In order to overcome this problem, we have demonstrated the applicationos of multi-class support vector machines (MC-SVMs) for the successful in silico classification of the mature conotoxins into their superfamilies. TheI- and J-conotoxin-superfamily members were analyzed in greater detail. On the basis of in silico analysis, we have divided the 28 entries previously grouped as I-conotoxin superfamily in UniProtKB/Swiss-Prot (release 49.0) into I1 and I2 superfamilies inview of their having two different types of signal peptides and exhibiting distinct functions. A comparative study of the theoretically modeled structure of ViTx from Conus virgo, a typical member of I2-conotoxin superfamily, reveals the crucial role of C-terminal region of ViTx in blocking therapeutically important voltage-gated potassium channels. Putative complexes created by us of very recently characterized J-superfamily conotoxin p11-4a with Kv1.6 suggest that the peptide interacts with negatively charged extracellular loops and pore-mouth of the potassium channel and blocks the channel by covering the pore as a lid, akin to previously proposed blocking mechanism of kM-conotoxin RIIIK from Conus radiatus to Tsha1 potassium channel. This finding provides a pointer to experimental work to validate the observations made here. Based on differences in the number and distribution of the positively charged residues in other conopeptides from the J-superfamily, we hypothesize different selectivity profile against subtypes of the potassium channels for these conopeptides. Furthermore, the present thesis reports the application of order-statistic filters and hydrophobicity profiles for predicting the location of membrane-spanning helices. The Proposed method is in particular effective for the class of helical membrane proteins, namely the therapeutically important voltage-gated ion channels, which are natural targets of several conotoxins. Our suggested ab initio approach is comparatively better than other spatial filters, confirming to the efficacy of including the concept of order or ranking information for prediction of TM helicdes. Such approaches should be of value for improved prediction performance including in large-scale applications. In addition, anlaysis has been carried out of the role of context in the relationship between form and function for the true PDB hits of some nonCys-rich PROSITE patterns. We have found specific examples of true hits of some PROSITE patterns displaying structural plasticity by assuming significantly different local conformation, depending upon the context. The work was carried out as a part of the research interest in our group in studying structural and other features of protein sequence patterns. The Contributions of the candidate to venominormatics include, creation of protein sequence patterns and information highlighting the importance of the patterns as gleaned from the lteratures for family classification: profile HMM and MC-SVMs for conotoxin superfamily classification; in silico characterization of I1 and I2 conotoxin superfamilies; studies of interaction with Kv1 channels of typical members of I2 and 3 conotoxin superfamilies and development of improved methods for detecting membrane-spanning helices. Chapter I starts with a brief account of venominformatics; bioinformatics for venoms and toxins. Chapter 2 presents a regular expression based classification of Conus peptides. Chapter 3 revisits the 28 entries previously grouped as I-conotoxin superfamily in UniProt Swiss-Prot knowledgebase (release 49.0) having four disulfide bonds with Cys arrangement C-C-CC-CC-C-C and they inhibit or modify ion channels of nerve cells. Chapter 4 describes pseudo-amino acid composition and MC-SVMs approach for conotoxin superfamily classification. Chapter 5 describes in silico detection of binding mode with Kv1.6 channel of J-superfamily conotoxin p114a from bermivorouos cone snail, Conus planorbis. Chapter 6 presents a comparative sequence-structure-function analysis of naturally occurring Cys-rich peptides having the Knottin or inhibitor cystine knot(ICK) scaffold, from different plants and venomous animals based on information available in the knottin database(http://knottin.cbs.cnrs.fr/). Chapter 7 describes the application of order-statistic filters and hydrophobicity profiles for detecting membrane-spanning helices. Chapter 8 describes the role of context in the relationship between form and function for the true PDB hits of some non Cys-rich PROSITE patterns. Chapter 9 summaries the important findings of the present studies on naturally occurring bioactive Cys-rich peptides with emphasis on Conus peptides and their interactions with respective target such as voltage-gated ion channels.

This thesis deals with the analysis of natural peptide libraries using mass spectrometry. In the course of the study, both ribosomal and non-ribosomal classes of peptides have been investigated. Microheterogeneity, post-translational modifications (PTM), isobaric amino acids and disulfide crosslinks present critical challenges in routine mass spectral structure determination of natural peptides. These problems form the core of this thesis. Chapter 2 describes an approach where chemical derivatization, in unison with high resolution LC-MSn experiments, resulted in deconvolution of a microheterogenous peptide library of B. subtilis K1. Chapter 3 describes an approach for distinction between isobaric amino acids (Leu/Ile/Hyp), by the use of combined ETD-CID fragmentation, through characteristic side chain losses. Chapters 4-6 address a long standing problem in structure elucidation of peptide toxins; the determination of disulfide connectivity. Through the use of direct mass spectral CID fragmentation, a methodology has been proposed for determination of the S-S pairing schemes in polypeptides. Further, an algorithm DisConnect has been developed for a rapid and robust solution to the problem. This general approach is applicable to both peptides and proteins, irrespective of the size and the number of disulfide bonds present. The method has been successfully applied to a large number of peptide toxins from marine cone snails, conotoxins, synthetic foldamers and proteins. Chapter 7 describes an attempt to integrate next generation sequencing (NGS) data with mass spectrometric analysis of the crude venom. This approach couples rapidly generated cDNA sequences, with high-throughput LC-ESI-MS/MS analysis, which provides mass spectral fragmentation information. An algorithm has been developed that allows the construction of a putative conus peptide database from the NGS data, followed by a protocol that permits rapid annotation of tandem MS data. The approach is exemplified by an analysis of the peptide components present in the venom of Conus amadis, yielding 225 chemically unique sequences, with identification of more than 150 sites of PTMs. In summary, this thesis presents different methodologies that address the existing limitations of de novo mass spectral structure determination of natural peptides and presents new methodologies that permit for rapid and efficient analysis of complex mixtures.