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The authors acknowledge financial and programmatic support from the Howard Hughes Medical Institute. Program and institutional staff who assisted with data collection and program operation are acknowledged in the supplementary materials

The short cytoplasmic tails of the α- and β-chains of integrin adhesion receptors regulate integrin activation and cell signaling. Significantly less is known about proteins that bind to α-integrin cytoplasmic tails (CTs) as opposed to β-CTs to regulate integrins. Calcium and integrin binding protein 1 (CIB1) was previously identified as an αIIb binding partner that inhibits agonist-induced activation of the platelet-specific integrin, αIIbβ3. A sequence alignment of all α-integrin CTs revealed that key residues in the CIB1 binding site of αIIb are well-conserved, and was used to delineate a consensus binding site (I/L-x-x-x-L/M-W/Y-K-x-G-F-F). Because the CIB1 binding site of αIIb is conserved in all α-integrins and CIB1 expression is ubiquitous, we asked if CIB1 could interact with other α-integrin CTs. We predicted that multiple α-integrin CTs were capable of binding to the same hydrophobic binding pocket on CIB1 with docking models generated by all-atom replica exchange discrete molecular dynamics. Af...

Calcium- and integrin-binding protein 1 (CIB1) is a small, ubiquitously expressed protein that was first identified as an intracellular binding partner of a platelet-specific α-integrin cytoplasmic tail. Although early studies revealed a role for CIB1 in regulating platelet integrin activity, recent studies have indicated a more diverse role for CIB1 in many different cell types and processes, including calcium signaling, migration, adhesion, proliferation, and survival. Increasing evidence also points to a novel role for CIB1 in cancer and cardiovascular disease. In addition, an array of CIB1 binding partners has been identified that provide important insight into how CIB1 may regulate these processes. Some of these binding partners include the serine/threonine kinases, p21-activated kinase 1 (PAK1), apoptosis signal-regulating kinase 1 (ASK1), and polo-like kinase 3 (PLK3). Structural and mutational studies indicate that CIB1 binds most or all of its partners via a well-defined hydrophobic cleft. Although CIB1 itself lacks known enzymatic activity, it supports the PI3K/AKT and MEK/ERK oncogenic signaling pathways, in part, by directly modulating enzymes in these pathways. In this review, we discuss our current understanding of CIB1 and key questions regarding structure and function and how this seemingly diminutive protein impacts important signaling pathways and cellular processes in human health and disease.-Leisner, T. M., Freeman, T. C., Black, J. L., Parise, L. V. CIB1: a small protein with big ambitions.

Motivation: We previously reported the development of a highly accurate statistical algorithm for identifying β-barrel outer membrane proteins or transmembrane β-barrels (TMBBs), from genomic sequence data of Gram-negative bacteria (Freeman,T.C. and Wimley,W.C. (2010) Bioinformatics, 26, 1965–1974). We have now applied this identification algorithm to all available Gram-negative bacterial genomes (over 600 chromosomes) and have constructed a publicly available, searchable, up-to-date, database of all proteins in these genomes. Results: For each protein in the database, there is information on (i) β-barrel membrane protein probability for identification of β-barrels, (ii) β-strand and β-hairpin propensity for structure and topology prediction, (iii) signal sequence score because most TMBBs are secreted through the inner membrane translocon and, thus, have a signal sequence, and (iv) transmembrane α-helix predictions, for reducing false positive predictions. This information is sufficient for the accurate identification of most β-barrel membrane proteins in these genomes. In the database there are nearly 50 000 predicted TMBBs (out of 1.9 million total putative proteins). Of those, more than 15 000 are ‘hypothetical’ or ‘putative’ proteins, not previously identified as TMBBs. This wealth of genomic information is not available anywhere else. Availability: The TMBB genomic database is available at http://beta-barrel.tulane.edu/. Contact: wwimley@tulane.edu

We have developed an effective pathway for the prediction and characterization of novel transmembrane β-barrel proteins. The Freeman–Wimley algorithm, which is a highly accurate prediction method based on the physicochemical properties of experimentally characterized transmembrane β barrel (TMBB) structures, was used to predict TMBBs in the genome of Salmonella typhimurium LT2. The previously uncharacterized product of gene yshA was tested as a model for validating the algorithm. YshA is a highly conserved 230-residue protein that is predicted to have 10 transmembrane β-strands and an N-terminal signal sequence. All of the physicochemical and spectroscopic properties exhibited by YshA are consistent with the prediction that it is a TMBB. Specifically, recombinant YshA localizes to the outer membrane when expressed in Escherichia coli ; YshA has a β-sheet-rich secondary structure with stable tertiary contacts in the presence of detergent micelles or when reconstituted into a lipid bilayer. When in a lipid bilayer, YshA forms a membrane-spanning pore with an effective radius of ~ 0.7 nm. Taken together, these data substantiate the predictions made by the Freeman–Wimley algorithm by showing that YshA is a TMBB protein.

Transmembrane beta-barrels (TMBBs) are a special structural class of proteins predominately found in the outer membranes of Gram-negative bacteria, mitochondria, and chloroplasts. It is estimated that 2-3% of a bacterial genome encodes TMBBs, yet less than 40 non-redundant structures have been solved. It would be highly advantageous to have methods to rapidly identify TMBBs from increasingly available genomic databases. A prediction algorithm proposed by Wimley in 2002 was based on the physicochemical properties of TMBBs of known structure. This method used relative amino acid abundances to predict the position of beta-strands and beta-hairpins, which are the major structural subunits of TMBBs, and a mathematical simplification of the topology prediction data called a beta-barrel score. To test the accuracy of this algorithm we scored proteins from a non-redundant database of protein sequences from the Protein Data Bank (NRPDB). The results revealed that the algorithm's ability to discriminate true TMBBs from other proteins, while strong, could be significantly improved. First, we updated the relative amino acid abundances to include the latest structural information. Second, we altered the beta-strand prediction method to account for the fact that certain amino acids have a higher propensity to situate near the lipid/water interface than in the hydrophobic core of the bilayer. Third, we adjusted the calculation of the beta-barrel score to address the lowered beta-hairpin density of larger TMBBs such as BtuB. We reanalyzed the NRPDB and the modifications resulted in a 5-fold decrease in the number of false positives, many of which are either non-bacterial proteins or from Gram-positive organisms. We will use this method to analyze the available genomes of Gram-negative bacteria and the results, along with the signal peptide predictions of SignalP (Bendtsen, et al. 2004) will be deposited into a publicly available database.

The transmembrane β-barrel (TMBB) is one of the two major structural motifs found in membrane-spanning proteins. TMBBs are found exclusively in the outer membranes of Gram-negative bacteria, mitochondria, and chloroplasts. Because TMBBs perform a vast array of functions (e.g. signal transduction and cellular adhesion) and are surface-exposed, they present an exploitable vulnerability in drug-resistant pathogenic Gram-negative bacteria. Technical deficiencies have impeded progress in the structural study of TMBBs, which is unfortunate given the magnitude of their biological significance. Although structural proteomics projects are currently underway to help solve the structures of unknown membrane proteins, the computational prediction of TMBBs has improved rapidly to compensate for the limits of empirical methods. To this end, our lab has developed one of the most accurate TMBB prediction programs available, which was used to build a database of TMBB predictions called TMBB-DB. This is a comprehensive database featuring TMBB prediction data from the proteomes of over 500 species of bacteria with nearly 1.9 million total sequences. Combining the TMBB prediction data with signal peptide prediction data generated using SignalP we predicted that more than 3% of the sequences encoded TMBBs, which on average is more than double the number of known or predicted TMBBs already annotated in the proteomes. Users will have access to an in-depth analysis of each sequence and be allowed to analyze sequences not included in the database using the same prediction tools. This database can be useful in directing the efforts of structural proteomics projects, antimicrobial drug therapy design, or vaccine development.

Transmembrane β-barrels (TMBB) belong to a special structural class of proteins predominately found in the outer membranes of Gram-negative bacteria, mitochondria, and chloroplasts. TMBBs are surface-exposed proteins that perform a variety of functions ranging from iron acquisition to osmotic regulation. These properties suggest that TMBBs have great potential for use in vaccine or drug therapy development. Membrane proteins, such as TMBBs, are notoriously difficult to identify and characterize using traditional experimental approaches due to a variety of technical limitations. However, in silico prediction methods have been considered for handling the task of identifying the enigmatic sequences which fold into TMBBs. A prediction method based on the physicochemical properties of experimentally characterized TMBB structures was developed to predict TMBB-encoding genes from genomic databases. The algorithm's prediction efficiency was tested using a non-redundant set of sequences from proteins of known structure. The algorithm was based on the work of Wimley (2002), but was greatly improved because of its disappointingly high false-positive prediction rate and thusly renamed the Freeman-Wimley algorithm. The improved prediction algorithm developed in this study was shown to be more accurate than previously published prediction methods. Its accuracy is 99% when using the most efficient prediction criteria, i.e. the threshold where the most known TMBBs are correctly predicted and the most non-TMBBs are correctly excluded. The Freeman-Wimley algorithm was used to make predictions in 611 bacterial chromosomes, where an average of 3% of the genes in a given genome encoded TMBBs.