Structure-based maximal affinity model predicts small-molecule druggability

Lead generation is a major hurdle in small-molecule drug discovery, with an estimated 60% of projects failing from lack of lead matter or difficulty in optimizing leads for drug-like properties. It would be valuable to identify these less-druggable targets before incurring substantial expenditure and effort. Here we show that a model-based approach using basic biophysical principles yields good prediction of druggability based solely on the crystal structure of the target binding site. We quantitatively estimate the maximal affinity achievable by a drug-like molecule, and we show that these calculated values correlate with drug discovery outcomes. We experimentally test two predictions using high-throughput screening of a diverse compound collection. The collective results highlight the utility of our approach as well as strategies for tackling difficult targets.     

A computational survey of candidate exonic splicing enhancer motifs in the model plant <Emphasis Type="Italic">Arabidopsis thaliana</Emphasis>

Background  

Algorithmic approaches to splice site prediction have relied mainly on the consensus patterns found at the boundaries between protein coding and non-coding regions. However exonic splicing enhancers have been shown to enhance the utilization of nearby splice sites.     

Association of 25-Hydroxyvitamin D status and genetic variation in the vitamin D metabolic pathway with FEV1 in the Framingham Heart Study

Background

Vitamin D is associated with lung function in cross-sectional studies, and vitamin D inadequacy is hypothesized to play a role in the pathogenesis of chronic obstructive pulmonary disease. Further data are needed to clarify the relation between vitamin D status, genetic variation in vitamin D metabolic genes, and cross-sectional and longitudinal changes in lung function in healthy adults.

Methods

We estimated the association between serum 25-hydroxyvitamin D [25(OH)D] and cross-sectional forced expiratory volume in the first second (FEV₁) in Framingham Heart Study (FHS) Offspring and Third Generation participants and the association between serum 25(OH)D and longitudinal change in FEV₁ in Third Generation participants using linear mixed-effects models. Using a gene-based approach, we investigated the association between 241 SNPs in 6 select vitamin D metabolic genes in relation to longitudinal change in FEV₁ in Offspring participants and pursued replication of these findings in a meta-analyzed set of 4 independent cohorts.

Results

We found a positive cross-sectional association between 25(OH)D and FEV₁ in FHS Offspring and Third Generation participants (P = 0.004). There was little or no association between 25(OH)D and longitudinal change in FEV₁ in Third Generation participants (P = 0.97). In Offspring participants, the CYP2R1 gene, hypothesized to influence usual serum 25(OH)D status, was associated with longitudinal change in FEV₁ (gene-based P < 0.05). The most significantly associated SNP from CYP2R1 had a consistent direction of association with FEV₁ in the meta-analyzed set of replication cohorts, but the association did not reach statistical significance thresholds (P = 0.09).

Conclusions

Serum 25(OH)D status was associated with cross-sectional FEV₁, but not longitudinal change in FEV₁. The inconsistent associations may be driven by differences in the groups studied. CYP2R1 demonstrated a gene-based association with longitudinal change in FEV₁ and is a promising candidate gene for further studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-015-0238-y) contains supplementary material, which is available to authorized users.     

Efficient implementation of a generalized pair hidden Markov model for comparative gene finding

MOTIVATION: The increased availability of genome sequences of closely related organisms has generated much interest in utilizing homology to improve the accuracy of gene prediction programs. Generalized pair hidden Markov models (GPHMMs) have been proposed as one means to address this need. However, all GPHMM implementations currently available are either closed-source or the details of their operation are not fully described in the literature, leaving a significant hurdle for others wishing to advance the state of the art in GPHMM design. RESULTS: We have developed an open-source GPHMM gene finder, TWAIN, which performs very well on two related Aspergillus species, A.fumigatus and A.nidulans, finding 89% of the exons and predicting 74% of the gene models exactly correctly in a test set of 147 conserved gene pairs. We describe the implementation of this GPHMM and we explicitly address the assumptions and limitations of the system. We suggest possible ways of relaxing those assumptions to improve the utility of the system without sacrificing efficiency beyond what is practical. AVAILABILITY: Available at http://www.tigr.org/software/pirate/twain/twain.html under the open-source Artistic License.     

Characterization and Control of the Microbial Community Affiliated with Copper or Aluminum Heat Exchangers of HVAC Systems

Microbial growth in heating ventilation and air-conditioning (HVAC) systems with the subsequent contamination of indoor air is of increasing concern. Microbes and the subsequent biofilms grow easily within heat exchangers. A comparative study where heat exchangers fabricated from antimicrobial copper were evaluated for their ability to limit microbial growth was conducted using a full-scale HVAC system under conditions of normal flow rates using single-pass outside air. Resident bacterial and fungal populations were quantitatively assessed by removing triplicate sets of coupons from each exchanger commencing the fourth week after their installation for the next 30 weeks. The intrinsic biofilm associated with each coupon was extracted and characterized using selective and differential media. The predominant organisms isolated from aluminum exchangers were species of Methylobacterium of which at least three colony morphologies and 11 distinct PFGE patterns we found; of the few bacteria isolated from the copper exchangers, the majority were species of Bacillus. The concentrations and type of bacteria recovered from the control, aluminum, exchangers were found to be dependent on the type of plating media used and were 11,411-47,257 CFU cm(-2) per coupon surface. The concentration of fungi was found to average 378 CFU cm(-2). Significantly lower concentrations of bacteria, 3 CFU cm(-2), and fungi, 1 CFU cm(-2), were recovered from copper exchangers regardless of the plating media used. Commonly used aluminum heat exchangers developed stable, mixed, bacterial/fungal biofilms in excess of 47,000 organisms per cm(2) within 4 weeks of operation, whereas the antimicrobial properties of metallic copper were able to limit the microbial load affiliated with the copper heat exchangers to levels 99.97 % lower during the same time period.     

Repetitive DNA and next-generation sequencing: computational challenges and solutions

Repetitive DNA sequences are abundant in a broad range of species, from bacteria to mammals, and they cover nearly half of the human genome. Repeats have always presented technical challenges for sequence alignment and assembly programs. Next-generation sequencing projects, with their short read lengths and high data volumes, have made these challenges more difficult. From a computational perspective, repeats create ambiguities in alignment and assembly, which, in turn, can produce biases and errors when interpreting results. Simply ignoring repeats is not an option, as this creates problems of its own and may mean that important biological phenomena are missed. We discuss the computational problems surrounding repeats and describe strategies used by current bioinformatics systems to solve them.