Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.     

Improving the detection of transmembrane β-barrel chains with N-to-1 extreme learning machines

MOTIVATION: Transmembrane β-barrels (TMBBs) are extremely important proteins that play key roles in several cell functions. They cross the lipid bilayer with β-barrel structures. TMBBs are presently found in the outer membranes of Gram-negative bacteria and of mitochondria and chloroplasts. Loop exposure outside the bacterial cell membranes makes TMBBs important targets for vaccine or drug therapies. In genomes, they are not highly represented and are difficult to identify with experimental approaches. Several computational methods have been developed to discriminate TMBBs from other types of proteins. However, the best performing approaches have a high fraction of false positive predictions. RESULTS: In this article, we introduce a new machine learning approach for TMBB detection based on N-to-1 Extreme Learning Machines that significantly outperforms previous methods achieving a Matthews correlation coefficient of 0.82, a probability of correct prediction of 0.92 and a sensitivity of 0.73.     

Grammatical-Restrained Hidden Conditional Random Fields for Bioinformatics applications

Background  

Discriminative models are designed to naturally address classification tasks. However, some applications require the inclusion of grammar rules, and in these cases generative models, such as Hidden Markov Models (HMMs) and Stochastic Grammars, are routinely applied.     

Draft genomes and genomic divergence of two Lepidurus tadpole shrimp species (Crustacea,Branchiopoda, Notostraca)

Crustaceans of the order Notostraca (Branchiopoda) are distributed worldwide and are known for the remarkable morphological stasis between their extant and Permian fossil species. Moreover, these crustaceans show relevant ecological traits and a wide range of reproductive strategies. However, genomic studies on notostracans are fairly limited. Here, we present the genome sequences of two notostracan taxa, Lepidurus arcticus and Lepidurus apus lubbocki. Taking advantage of the small genome sizes (~0.11 pg) of these taxa, genomes were sequenced for one individual per species with one run on the Illumina HiSeq X platform. We finally assembled 73.2 Mbp (L. arcticus) and 90.3 Mbp (L. apus lubbocki) long genomes. Assemblies cover up to 84% of the estimated genome size, with a gene completeness >97% for both genomes. In total, 13%–16% of the assembled genomes consist of repeats, and based on read mapping, L. apus lubbocki shows a significantly lower transposable element content than L. arcticus. The analysis of 2,376 orthologous genes indicates an ~7% divergence between the two Lepidurus taxa, with a nucleotide substitution rate significantly lower than that of Daphnia taxa. K_a/K_s analysis suggests purifying selection in both branchiopod lineages, raising the question of whether the low substitution rate of Lepidurus is correlated with morphological conservation or is linked to specific biological traits. Our analysis demonstrates that, in these organisms, it is possible to obtain high‐quality draft genomes from single individuals with a relatively low sequencing effort. This result makes Lepidurus and Notostraca interesting models for genomic studies at taxonomic, ecological and evolutionary levels.