iFace: A Bioinformatics Tool for the Analysis of Protein-Protein Interface

Detailed knowledge of protein-protein interaction is essential to understand various biochemical and biological functions. In this paper, we present a bioinformatics tool to analyze the protein-protein interfaces using three-dimensional structural information. iFace identifies protein-protein interaction sites and various interactions that contribute  to the specificity and strength of the protein complex

DIAL: a web-based server for the automatic identification of structural domains in proteins

DIAL is a web server for the automatic identification of structural domains given the 3D coordinates of a protein. Delineation of the structural domains and their exact boundaries are the starting points for the better realization of distantly related members of the domain families, for the rational design of the experiments and for clearer understanding of the biological function. The current server can examine crystallographic multiple chains and provide structural domain solutions that can also describe domain swapping events. The server can be accessed from . The Supplementary data can be accessed from

GenDiS: Genomic Distribution of protein structural domain Superfamilies

Several proteins that have substantially diverged during evolution retain similar three-dimensional structures and biological function inspite of poor sequence identity. The database on Genomic Distribution of protein structural domain Superfamilies (GenDiS) provides record for the distribution of 4001 protein domains organized as 1194 structural superfamilies across 18 997 genomes at various levels of hierarchy in taxonomy. GenDiS database provides a survey of protein domains enlisted in sequence databases employing a 3-fold sequence search approach. Lineage-specific literature is obtained from the taxonomy database for individual protein members to provide a platform for performing genomic and phyletic studies across organisms. The database documents residual properties and provides alignments for the various superfamily members in genomes, offering insights into the rational design of experiments and for the better understanding of a superfamily. GenDiS database can be accessed at http://www.ncbs.res.in/~faculty/mini/gendis/home.html

PASS2: an automated database of protein alignments organised as structural superfamilies

BACKGROUND: The functional selection and three-dimensional structural constraints of proteins in nature often relates to the retention of significant sequence similarity between proteins of similar fold and function despite poor sequence identity. Organization of structure-based sequence alignments for distantly related proteins, provides a map of the conserved and critical regions of the protein universe that is useful for the analysis of folding principles, for the evolutionary unification of protein families and for maximizing the information return from experimental structure determination. The Protein Alignment organised as Structural Superfamily (PASS2) database represents continuously updated, structural alignments for evolutionary related, sequentially distant proteins. DESCRIPTION: An automated and updated version of PASS2 is, in direct correspondence with SCOP 1.63, consisting of sequences having identity below 40% among themselves. Protein domains have been grouped into 628 multi-member superfamilies and 566 single member superfamilies. Structure-based sequence alignments for the superfamilies have been obtained using COMPARER, while initial equivalencies have been derived from a preliminary superposition using LSQMAN or STAMP 4.0. The final sequence alignments have been annotated for structural features using JOY4.0. The database is supplemented with sequence relatives belonging to different genomes, conserved spatially interacting and structural motifs, probabilistic hidden markov models of superfamilies based on the alignments and useful links to other databases. Probabilistic models and sensitive position specific profiles obtained from reliable superfamily alignments aid annotation of remote homologues and are useful tools in structural and functional genomics. PASS2 presents the phylogeny of its members both based on sequence and structural dissimilarities. Clustering of members allows us to understand diversification of the family members. The search engine has been improved for simpler browsing of the database. CONCLUSIONS: The database resolves alignments among the structural domains consisting of evolutionarily diverged set of sequences. Availability of reliable sequence alignments of distantly related proteins despite poor sequence identity and single-member superfamilies permit better sampling of structures in libraries for fold recognition of new sequences and for the understanding of protein structure-function relationships of individual superfamilies. PASS2 is accessible a

iMOTdb—a comprehensive collection of spatially interacting motifs in proteins

Realization of conserved residues that represent a protein family is crucial for clearer understanding of biological function as well as for the better recognition of additional members in sequence databases. Functionally important residues are recognized well due to their high degree of conservation in closely related sequences and are annotated in functional motif databases. Structural motifs are central to the integrity of the fold and require careful analysis for their identification. We report the availability of a database of spatially interacting motifs in single protein structures as well as those among distantly related protein structures that belong to a superfamily. Spatial interactions amongst conserved motifs are automatically measured using sequence similarity scores and distance calculations. Interactions between pairs of conserved motifs are described in the form of pseudoenergies. iMOTdb database provides information for 854 488 motifs corresponding to 60 849 protein structural domains and 22 648 protein structural entries

HARMONY: a server for the assessment of protein structures

Protein structure validation is an important step in computational modeling and structure determination. Stereochemical assessment of protein structures examine internal parameters such as bond lengths and Ramachandran (φ,ψ) angles. Gross structure prediction methods such as inverse folding procedure and structure determination especially at low resolution can sometimes give rise to models that are incorrect due to assignment of misfolds or mistracing of electron density maps. Such errors are not reflected as strain in internal parameters. HARMONY is a procedure that examines the compatibility between the sequence and the structure of a protein by assigning scores to individual residues and their amino acid exchange patterns after considering their local environments. Local environments are described by the backbone conformation, solvent accessibility and hydrogen bonding patterns. We are now providing HARMONY through a web server such that users can submit their protein structure files and, if required, the alignment of homologous sequences. Scores are mapped on the structure for subsequent examination that is useful to also recognize regions of possible local errors in protein structures. HARMONY server is located a

Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble

Motivation: So far various bioinformatics and machine learning techniques applied for identification of sequence and functionally conserved residues in proteins. Although few computational methods are available for the prediction of structurally conserved residues from protein structure, almost all methods require homologous structural information and structure-based alignments, which still prove to be a bottleneck in protein structure comparison studies. In this work, we developed a neural network approach for identification of structurally important residues from a single protein structure without using homologous structural information and structural alignment

A machine learning approach for the identification of odorant binding proteins from sequence-derived properties

Background: Odorant binding proteins (OBPs) are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as odorant presenters. Although several sequence based search methods have been exploited for protein family prediction, less effort has been devoted to the prediction of OBPs from sequence data and this area is more challenging due to poor sequence identity between these proteins. Results: In this paper, we propose a new algorithm that uses Regularized Least Squares Classifier (RLSC) in conjunction with multiple physicochemical properties of amino acids to predict odorantbinding proteins. The algorithm was applied to the dataset derived from Pfam and GenDiS database and we obtained overall prediction accuracy of 97.7% (94.5% and 98.4% for positive and negative classes respectively). Conclusion: Our study suggests that RLSC is potentially useful for predicting the odorant binding proteins from sequence-derived properties irrespective of sequence similarity. Our method predicts 92.8% of 56 odorant binding proteins non-homologous to any protein in the swissprot database and 97.1% of the 414 independent dataset proteins, suggesting the usefulness of RLSC method for facilitating the prediction of odorant binding proteins from sequence information

AI for Search and Rescue - Locating a Missing Person

Building on the work done initially as a SURP 2021 project and continued through 2021-23, the focus for this summer project will be on the use of computer technology for locating a missing person. Over the last year, we developed the digital equivalents of about 30 paper-based S&R forms and the infrastructure to collect the respective information. In their current use, these paper forms are filled out by search teams, collected in a command post, and reviewed by search coordinators. This process is time-consuming, prone to errors and loss of information, and relies heavily on the experience, skills, and mental acuity of the search coordinators. At the core of this process is the Lost Person Questionnaire, a lengthy and complex form that collects relevant information about the subject of the S&R mission. For this SURP effort, we will explore the use of Artificial Intelligence and Machine Learning to combine information about the ongoing search effort, past missions with similar profiles, and general knowledge such as terrain and travel routes to identify areas of high priority for the search

SCANMOT: searching for similar sequences using a simultaneous scan of multiple sequence motifs

Establishment of similarities between proteins is very important for the study of the relationship between sequence, structure and function and for the analysis of evolutionary relationships. Motif-based search methods play a crucial role in establishing the connections between proteins that are particularly useful for distant relationships. This paper reports SCANMOT, a web-based server that searches for similarities between proteins by simultaneous matching of multiple motifs. SCANMOT searches for similar sequences in entire sequence databases using multiple conserved regions and utilizes inter-motif spacing as restraints. The SCANMOT server is available via