abYsis: Integrated Antibody Sequence and Structure-Management, Analysis, and Prediction.

abYsis is a web-based antibody research system that includes an integrated database of antibody sequence and structure data. The system can be interrogated in numerous ways-from simple text and sequence searches to sophisticated queries that apply 3D structural constraints. The publicly available version includes pre-analyzed sequence data from the European Molecular Biology Laboratory European Nucleotide Archive (EMBL-ENA) and Kabat as well as structure data from the Protein Data Bank. A researcher's own sequences can also be analyzed through the web interface. A defining characteristic of abYsis is that the sequences are automatically numbered with a series of popular schemes such as Kabat and Chothia and then annotated with key information such as complementarity-determining regions and potential post-translational modifications. A unique aspect of abYsis is a set of residue frequency tables for each position in an antibody, allowing "unusual residues" (those rarely seen at a particular position) to be highlighted and decisions to be made on which mutations may be acceptable. This is especially useful when comparing antibodies from different species. abYsis is useful for any researcher specializing in antibody engineering, especially those developing antibodies as drugs. abYsis is available at www.abysis.org.

Cascaded walks in protein sequence space: use of artificial sequences in remote homology detection between natural proteins.

Over the past two decades, many ingenious efforts have been made in protein remote homology detection. Because homologous proteins often diversify extensively in sequence, it is challenging to demonstrate such relatedness through entirely sequence-driven searches. Here, we describe a computational method for the generation of 'protein-like' sequences that serves to bridge gaps in protein sequence space. Sequence profile information, as embodied in a position-specific scoring matrix of multiply aligned sequences of bona fide family members, serves as the starting point in this algorithm. The observed amino acid propensity and the selection of a random number dictate the selection of a residue for each position in the sequence. In a systematic manner, and by applying a 'roulette-wheel' selection approach at each position, we generate parent family-like sequences and thus facilitate an enlargement of sequence space around the family. When generated for a large number of families, we demonstrate that they expand the utility of natural intermediately related sequences in linking distant proteins. In 91% of the assessed examples, inclusion of designed sequences improved fold coverage by 5-10% over searches made in their absence. Furthermore, with several examples from proteins adopting folds such as TIM, globin, lipocalin and others, we demonstrate that the success of including designed sequences in a database positively sensitized methods such as PSI-BLAST and Cascade PSI-BLAST and is a promising opportunity for enormously improved remote homology recognition using sequence information alone.

Analysis and prediction of VH/VL packing in antibodies.

The packing of V(H) and V(L) domains in antibodies can vary, influencing the topography of the antigen-combining site. However, until recently, this has largely been ignored in modelling antibody structure. We present an analysis of the degree of variability observed in known structures together with a machine-learning approach to predict the packing angle. A neural network was trained on sets of interface residues and a genetic algorithm designed to perform 'feature selection' to define which sets of interface residues could be used most successfully to perform the prediction. While this training procedure was very computationally intensive, prediction is performed in a matter of seconds. Thus, not only do we provide a rapid method for predicting the packing angle, but also we define a set of residues that may be important in antibody humanization in order to obtain the correct binding site topography.

Classification of nonenzymatic homologues of protein kinases.

Protein Kinase-Like Non-kinases (PKLNKs), which are closely related to protein kinases, lack the crucial catalytic aspartate in the catalytic loop, and hence cannot function as protein kinase, have been analysed. Using various sensitive sequence analysis methods, we have recognized 82 PKLNKs from four higher eukaryotic organisms, namely, Homo sapiens, Mus musculus, Rattus norvegicus, and Drosophila melanogaster. On the basis of their domain combination and function, PKLNKs have been classified mainly into four categories: (1) Ligand binding PKLNKs, (2) PKLNKs with extracellular protein-protein interaction domain, (3) PKLNKs involved in dimerization, and (4) PKLNKs with cytoplasmic protein-protein interaction module. While members of the first two classes of PKLNKs have transmembrane domain tethered to the PKLNK domain, members of the other two classes of PKLNKs are cytoplasmic in nature. The current classification scheme hopes to provide a convenient framework to classify the PKLNKs from other eukaryotes which would be helpful in deciphering their roles in cellular processes.

Analysis and improvements to Kabat and structurally correct numbering of antibody variable domains.

In analysing protein sequence and structure, standardized numbering schemes allow comparison of features without explicit alignment. This has proved particularly valuable in the case of antibodies. The most widely used schemes (Kabat: sequence-based; Chothia: structure-based) differ only in the numbering of the complementarity determining regions (CDRs). We have analyzed the numbered annotations in the widely used Kabat database and found that approximately 10% of entries contain errors or inconsistencies. Further analysis of sequence alignments in the context of structure suggest that the sites of the insertions in some framework regions in the Kabat and Chothia schemes are incorrect. We therefore propose a corrected version of the Chothia scheme which is structurally correct throughout the CDRs and frameworks. To perform this analysis, we have developed, and made available, a tool for the automatic application of Kabat, Chothia and modified-Chothia numbering schemes and have carefully benchmarked the performance of this tool.

Analyzing the "degree of humanness" of antibody sequences.

Genetically engineered mouse antibodies are now commonly in clinical use. However, their development is limited because the human immune system tends to regard them as foreign and this triggers an immune response. The solution is to make engineered antibodies appear more human. Here, we propose a method to assess the "degree of humanness" of antibody sequences providing a tool that may contribute to predictions of antigenicity. We analyzed sequences of antibodies belonging to various chains/classes in human and mouse. Our analysis of metrics based on percentage sequence identity between antibody sequences shows distinct differences between human and mouse sequences. Based on mean sequence identity and standard deviation, we calculated Z-scores for data sets of antibody sequences extracted from the Kabat database. We applied the analysis to a set of humanized and chimeric antibodies and to human germline sequences. We conclude that this approach may aid in the selection of more suitable mouse variable domains for antibody engineering to render them more human but in general, we find that typicality of a sequence compared with the expressed human repertoire is not well correlated with antigenicity. We have provided a Web server allowing humanness to be assigned for a sequence.

Cascade PSI-BLAST web server: a remote homology search tool for relating protein domains.

Owing to high evolutionary divergence, it is not always possible to identify distantly related protein domains by sequence search techniques. Intermediate sequences possess sequence features of more than one protein and facilitate detection of remotely related proteins. We have demonstrated recently the employment of Cascade PSI-BLAST where we perform PSI-BLAST for many 'generations', initiating searches from new homologues as well. Such a rigorous propagation through generations of PSI-BLAST employs effectively the role of intermediates in detecting distant similarities between proteins. This approach has been tested on a large number of folds and its performance in detecting superfamily level relationships is approximately 35% better than simple PSI-BLAST searches. We present a web server for this search method that permits users to perform Cascade PSI-BLAST searches against the Pfam, SCOP and SwissProt databases. The URL for this server is http://crick.mbu.iisc.ernet.in/~CASCADE/CascadeBlast.html.

Assessment of a rigorous transitive profile based search method to detect remotely similar proteins.

Profile-based sequence search procedures are commonly employed to detect remote relationships between proteins. We provide an assessment of a Cascade PSI-BLAST protocol that rigorously employs intermediate sequences in detecting remote relationships between proteins. In this approach we detect using PSI-BLAST, which involves multiple rounds of iteration, an initial set of homologues for a protein in a 'first generation' search by querying a database. We propagate a 'second generation' search in the database, involving multiple runs of PSI-BLAST using each of the homologues identified in the previous generation as queries to recognize homologues not detected earlier. This non-directed search process can be viewed as an iteration of iterations that is continued to detect further homologues until no new hits are detectable. We present an assessment of the coverage of this 'cascaded' intermediate sequence search on diverse folds and find that searches for up to three generations detect most known homologues of a query. Our assessments show that this approach appears to perform better than the traditional use of PSI-BLAST by detecting 15% more relationships within a family and 35% more relationships within a superfamily. We show that such searches can be performed on generalized sequence databases and non-trivial relationships between proteins can be detected effectively. Such a propagation of searches maximizes the chances of detecting distant homologies by effectively scanning protein "fold space".

KinG: a database of protein kinases in genomes.

The KinG database is a comprehensive collection of serine/threonine/tyrosine-specific kinases and their homologues identified in various completed genomes using sequence and profile search methods. The database hosted at http://hodgkin. mbu.iisc.ernet.in/ approximately king provides the amino acid sequences, functional domain assignments and classification of gene products containing protein kinase domains. A search tool enabling the retrieval of protein kinases with specified subfamily and domain combinations is one of the key features of the resource. Identification of a kinase catalytic domain in the user's query sequence is possible using another search tool. The occurrence and location of critical catalytic residues if the query has a catalytic kinase domain, recognition of non-kinase domains in the sequence and subfamily classification of the kinase in the query will help in deciphering the biological role of the kinase. This online compilation can also be used to compare the protein kinases of a given subfamily and domain combinations across various genomes. Another exclusive feature of the database is the collection of the Ser/Thr/Tyr protein kinases and similar sequences encoded in the genomes of archaea and bacteria.