DNA motif discovery and, particularly, computational modeling of transcription factor binding motifs, has been a mecca of algorithmic bioinformatics for several decades. Here, we report the results of the largest open community challenge in Inferring BInding Specificities (IBIS), where participants all over the world were invited to construct binding specificity models from multi-assay experimental data for poorly studied human transcription factors. The submissions were rigorously tested against a rich held-out dataset. Benchmarking demonstrated a consistent advantage of properly designed deep learning models over traditional positional weight matrices and other machine learning methods. Yet, the positional weight matrices displayed a surprisingly strong performance out of the box, being only slightly behind the best deep learning models. A post-challenge assessment of a selection of other deep learning methods further solidified this finding. IBIS highlights the power of benchmarking in finding adequate DNA motif representations, emphasizes the pros and cons of various machine learning methods applied to DNA motif modeling, and establishes a rich dataset, benchmarking protocols, and computational framework for a fair cross-platform evaluation of future models of transcription factor binding motifs in DNA sequences.

The human pathogen Neisseria gonorrhoeae uses a homologous recombination to undergo antigenic variation and avoid an immune response. The surface protein pilin (PilE) is one of the targets for antigenic variation that can be regulated by N. gonorrhoeae mismatch repair (MMR) and a G-quadruplex (G4) located upstream of the pilE promoter. Using bioinformatics tools, we found a correlation between pilE variability and deletion of DNA regions encoding ngMutS or ngMutL proteins, the main participants in N. gonorrhoeae methyl-independent MMR. To understand whether the G4 structure could affect the ngMutL-mediated regulation of pilin antigenic variation, we designed several synthetic pilE G4-containing oligonucleotides, differing in length, and related DNA duplexes. Using CD measurements and biochemical approaches, we have showed that (i) ngMutL preferentially binds to pilE G4 compared to DNA duplex, although the latter is a cognate substrate for ngMutL endonuclease, (ii) protein binding affinity decreases with shortening of quadruplex-containing and duplex ligands, (iii) the G4 structure inhibits ngMutL-induced DNA nicking and modulates cleavage positions; the enzyme does not cleave DNA within G4, but is able to bypass this noncanonical structure. Thus, pilE G4 may regulate the efficiency of pilin antigenic variation by quadruplex binding to ngMutL and suppression of homologous recombination.

Neisseria meningitidis (N. meningitidis) serogroup B (MenB) is the leading cause of invasive meningococcal disease worldwide. The pathogen has a wide range of virulence factors, which are potential vaccine components. Studying the genetic variability of antigens within a population, especially their long-term persistence, is necessary to develop new vaccines and predict the effectiveness of existing ones. The multicomponent 4CMenB vaccine (Bexsero), used since 2014, contains three major genome-derived recombinant proteins: factor H-binding protein (fHbp), Neisserial Heparin-Binding Antigen (NHBA) and Neisserial adhesin A (NadA). Here, we assessed the prevalence and sequence variations of these vaccine antigens in a panel of 5667 meningococcal isolates collected worldwide over the past 10 years and deposited in the PubMLST database. Using multiple amino acid sequence alignments and Random Forest Classifier machine learning methods, we estimated the potential strain coverage of fHbp and NHBA vaccine variants (51 and about 25%, respectively); the NadA antigen sequence was found in only 18% of MenB genomes analyzed, but cross-reactive variants were present in less than 1% of isolates. Based on our findings, we proposed various strategies to improve the 4CMenB vaccine and broaden the coverage of N. meningitidis strains.

Neisseria meningitidis (N. meningitidis) serogroup B (MenB) is the leading cause of invasive meningococcal disease worldwide. The pathogen has a wide range of virulence factors, which are potential vaccine components. Studying the genetic variability of antigens within a population, especially their long-term persistence, is necessary to develop new vaccines and predict the effectiveness of existing ones. The multicomponent 4CMenB vaccine (Bexsero), used since 2014, contains three major genome-derived recombinant proteins: factor H-binding protein (fHbp), Neisserial Heparin-Binding Antigen (NHBA) and Neisserial adhesin A (NadA). Here, we assessed the prevalence and sequence variations of these vaccine antigens in a panel of 5667 meningococcal isolates collected worldwide over the past 10 years and deposited in the PubMLST database. Using multiple amino acid sequence alignments and machine learning methods, we estimated the potential strain coverage by fHbp and NHBA vaccine variants (51 and about 30%, respectively); the NadA antigen sequence was found in only 17.8% of MenB genomes analyzed, but cross-reactive variants were present in less than 1% of isolates. Based on our findings, we proposed various strategies to improve the 4CMenB vaccine and broaden coverage of N. meningitidis strains.