Flexible Structural Neighborhood—a database of protein structural similarities and alignments

Protein structures are flexible, changing their shapes not only upon substrate binding, but also during evolution as a collective effect of mutations, deletions and insertions. A new generation of protein structure comparison algorithms allows for such flexibility; they go beyond identifying the largest common part between two proteins and find hinge regions and patterns of flexibility in protein families. Here we present a Flexible Structural Neighborhood (FSN), a database of structural neighbors of proteins deposited in PDB as seen by a flexible protein structure alignment program FATCAT, developed previously in our group. The database, searchable by a protein PDB code, provides lists of proteins with statistically significant structural similarity and on lower menu levels provides detailed alignments, interactive superposition of structures and positions of hinges that were identified in the comparison. While superficially similar to other structural protein alignment resources, FSN provides a unique resource to study not only protein structural similarity, but also how protein structures change. FSN is available from a server and by direct links from the PDB database

The gain and loss of chromosomal integron systems in the Treponema species

BACKGROUND: Integron systems are now recognized as important agents of bacterial evolution and are prevalent in most environments. One of the human pathogens known to harbor chromosomal integrons, the Treponema spirochetes are the only clade among spirochete species found to carry integrons. With the recent release of many new Treponema genomes, we were able to study the distribution of chromosomal integrons in this genus. RESULTS: We find that the Treponema spirochetes implicated in human periodontal diseases and those isolated from cow and swine intestines contain chromosomal integrons, but not the Treponema species isolated from termite guts. By examining the species tree of selected spirochetes (based on 31 phylogenetic marker genes) and the phylogenetic tree of predicted integron integrases, and assisted by our analysis of predicted integron recombination sites, we found that all integron systems identified in Treponema spirochetes are likely to have evolved from a common ancestor—a horizontal gain into the clade. Subsequent to this event, the integron system was lost in the branch leading to the speciation of T. pallidum and T. phagedenis (the Treponema sps. implicated in sexually transmitted diseases). We also find that the lengths of the integron attC sites shortened through Treponema speciation, and that the integron gene cassettes of T. denticola are highly strain specific. CONCLUSIONS: This is the first comprehensive study to characterize the chromosomal integron systems in Treponema species. By characterizing integron distribution and cassette contents in the Treponema sps., we link the integrons to the speciation of the various species, especially to the pathogens T. pallidum and T. phagedenis

Surprising complexity of the ancestral apoptosis network

A comparative genomics approach revealed that the genes for several components of the apoptosis network with single copies in vertebrates have multiple paralogs in cnidarian-bilaterian ancestors, suggesting a complex evolutionary history for this network

Integrated web service for improving alignment quality based on segments comparison

BACKGROUND: Defining blocks forming the global protein structure on the basis of local structural regularity is a very fruitful idea, extensively used in description, and prediction of structure from only sequence information. Over many years the secondary structure elements were used as available building blocks with great success. Specially prepared sets of possible structural motifs can be used to describe similarity between very distant, non-homologous proteins. The reason for utilizing the structural information in the description of proteins is straightforward. Structural comparison is able to detect approximately twice as many distant relationships as sequence comparison at the same error rate. RESULTS: Here we provide a new fragment library for Local Structure Segment (LSS) prediction called FRAGlib which is integrated with a previously described segment alignment algorithm SEA. A joined FRAGlib/SEA server provides easy access to both algorithms, allowing a one stop alignment service using a novel approach to protein sequence alignment based on a network matching approach. The FRAGlib used as secondary structure prediction achieves only 73% accuracy in Q3 measure, but when combined with the SEA alignment, it achieves a significant improvement in pairwise sequence alignment quality, as compared to previous SEA implementation and other public alignment algorithms. The FRAGlib algorithm takes ~2 min. to search over FRAGlib database for a typical query protein with 500 residues. The SEA service align two typical proteins within circa ~5 min. All supplementary materials (detailed results of all the benchmarks, the list of test proteins and the whole fragments library) are available for download on-line at . CONCLUSIONS: The joined FRAGlib/SEA server will be a valuable tool both for molecular biologists working on protein sequence analysis and for bioinformaticians developing computational methods of structure prediction and alignment of proteins

Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks

As machine learning has been deployed ubiquitously across applications in modern data science, algorithmic fairness has become a great concern and varieties of fairness criteria have been proposed. Among them, imposing fairness constraints during learning, i.e. in-processing fair training, has been a popular type of training method because they don't require accessing sensitive attributes during test time in contrast to post-processing methods. Although imposing fairness constraints have been studied extensively for classical machine learning models, the effect these techniques have on deep neural networks is still unclear. Recent research has shown that adding fairness constraints to the objective function leads to severe over-fitting to fairness criteria in large models, and how to solve this challenge is an important open question. To address this challenge, we leverage the wisdom and power of pre-training and fine-tuning and develop a simple but novel framework to train fair neural networks in an efficient and inexpensive way. We conduct comprehensive experiments on two popular image datasets with state-of-art architectures under different fairness notions to show that last-layer fine-tuning is sufficient for promoting fairness of the deep neural network. Our framework brings new insights into representation learning in training fair neural networks

ABI Sustaining: The National Center for Genome Analysis Support 2019 Annual Report

National Science Foundation DBI-175990

Novel genes dramatically alter regulatory network topology in amphioxus

Domain rearrangements in the innate immune network of amphioxus suggests that domain shuffling has shaped the evolution of immune systems