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Abstract Cardiomyopathies are a severe and chronic cardiovascular burden worldwide, affecting a large cohort in the general population. Cysteine and glycine-rich protein 3 (CSRP3) is one of key proteins implicated in dominant dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM). In this study, we device a rapid in silico screening protocol that creates a mutational landscape map for all possible allowed and disallowed substitutions in the protein of interest. This map provides the structural and functional insights on the stability of LIM domains of CSRP3. Further, the sequence analysis delineates the eukaryotic CSRP3 protein orthologs which complements the mutational map, but provide limited information of amino acid exchanges. Next, we also evaluated the effect of HCM/DCM mutations on these domains. One of highly destabilising mutations—L44P (also disease causing) and a neutral mutation—L44M were further subjected to molecular dynamics (MD) simulations. The results establish that L44P substitution affects the LIM domain structure by altering secondary structure and due to loss of hydrophobic interaction with Phenylananine 35. The present study provides a useful perspective to our understanding of the role of mutations in the CSRP3 LIM domains and their evolution. This study provides a novel computational screening method for quick identification of key mutation sites for specific protein structures that can reduce the burden on experimental research.

Interactions between proteins are vital in almost all biological processes. The characterization of protein–protein interactions helps us understand the mechanistic basis of biological processes, thereby enabling the manipulation of proteins for biotechnological and clinical purposes. The interface residues of a protein–protein complex are assumed to have the following two properties: (a) they always interact with a residue of a partner protein, which forms the basis for distance‐based interface residue identification methods, and (b) they are solvent‐exposed in the isolated form of the protein and become buried in the complex form, which forms the basis for Accessible Surface Area (ASA)‐based methods. The study interrogates this popular assumption by recognizing interface residues in protein–protein complexes through these two methods. The results show that a few residues are identified uniquely by each method, and the extent of conservation, propensities, and their contribution to the stability of protein–protein interaction varies substantially between these residues. The case study analyses showed that interface residues, unique to distance, participate in crucial interactions that hold the proteins together, whereas the interface residues unique to the ASA method have a potential role in the recognition, dynamics, and specificity of the complex and can also be a hotspot. Overall, the study recommends applying both distance and ASA methods so that some interface residues missed by either method but crucial to the stability, recognition, dynamics, and function of protein–protein complexes are identified in a complementary manner. [ABSTRACT FROM AUTHOR]

During 11-12 August 2014, a Protein Bioinformatics and Community Resources Retreat was held at the Wellcome Trust Genome Campus in Hinxton, UK. This meeting brought together the principal investigators of several specialized protein resources (such as CAZy, TCDB and MEROPS) as well as those from protein databases from the large Bioinformatics centres (including UniProt and RefSeq). The retreat was divided into five sessions: (1) key challenges, (2) the databases represented, (3) best practices for maintenance and curation, (4) information flow to and from large data centers and (5) communication and funding. An important outcome of this meeting was the creation of a Specialist Protein Resource Network that we believe will improve coordination of the activities of its member resources. We invite further protein database resources to join the network and continue the dialogue.

Insect Olfactory Receptors (ORs) are diverse family of membrane protein receptors responsible for most of the insect olfactory perception and communication, and hence they are of utmost importance for developing repellents or pesticides. Accurate gene prediction of insect ORs from newly sequenced genomes is an important but challenging task. We have developed a dedicated webserver, 'insectOR', to predict and validate insect OR genes using multiple gene prediction algorithms, accompanied by relevant validations. It is possible to employ this server nearly automatically and perform rapid prediction of the OR gene loci from thousands of OR-protein-to-genome alignments, resolve gene boundaries for tandem OR genes and refine them further to provide more complete OR gene models. InsectOR outperformed the popular genome annotation pipelines (MAKER and NCBI eukaryotic genome annotation) in terms of overall sensitivity at base, exon and locus level, when tested on two distantly related insect genomes. It displayed more than 95% nucleotide level precision in both tests. Finally, given the same input data and parameters, InsectOR missed less than 2% gene loci, in contrast to 55% loci missed by MAKER for Drosophila melanogaster. The webserver is freely available on the web at http://caps.ncbs.res.in/insectOR/ and the basic package can be downloaded from https://github.com/sdk15/insectOR for local use. This tool will allow biologists to perform quick preliminary identification of insect olfactory receptor genes from newly sequenced genomes and also assist in their further detailed annotation. Its usage can also be extended to other divergent gene families.