Current in silico proteomics require the trifecta analysis, namely, prediction, validation, and functional assessment of a modeled protein. The main drawback of this endeavor is the lack of a single protocol that utilizes a proper set of benchmarked open-source tools to predict a protein’s structure and function accurately. The present study rectifies this drawback through the design and development of such a protocol. The protocol begins with the characterization of a novel coding sequence to identify the expressed protein. It then recognizes and isolates evolutionarily conserved sequence motifs through phylogenetics. The next step is to predict the protein’s secondary structure, followed by the prediction, refinement, and validation of its three-dimensional tertiary structure. These steps enable the functional analysis of the macromolecule through protein docking, which facilitates the identification of the protein’s active site. Each of these steps is crucial for the complete characterization of the protein under study. We have dubbed this process the trifecta analysis. In this study, we have proven the effectiveness of our protocol using the cystatin C and AChE proteins. Beginning with just their sequences, we have characterized both proteins’ structures and functions, including identifying the cystatin C protein’s seven-residue active site and the AChE protein’s active-site gorge via protein–protein and protein–ligand docking, respectively. This process will greatly benefit new and experienced scientists alike in obtaining a strong understanding of the trifecta analysis, resulting in a domino effect that could expand drug development.

Modern computational methods using patient Human Immunodeficiency Virus type 1 (HIV-1) genetic sequences can model population-wide viral transmission dynamics. Accurate transmission inferences can play a critical role in the characterization of high-risk transmission clusters important for enhanced epidemiological control. We evaluated a phylogenetics-based analysis pipeline to infer person-to-person (P2P) infection dates and transmission relationships using 139 patient HIV-1 polymerase Sanger sequences curated by the Southern Alberta HIV Clinic. Parameter combinations tailored to HIV-1 transmissions were tuned with respect to inference accuracy. Inference accuracy was assessed using clinically confirmed P2P transmission patient data. The most accurate parameter settings correctly inferred 48.56% of the P2P relationships (95% confidence interval 63.89–33.33%), slightly lower than next-generation-sequencing methods. The infection date was correctly inferred 43.02% (95% confidence interval 49.89–35.63%). Several novel unsuspected transmission clusters of up to twelve patients were identified. An accuracy trade-off between inferring transmission relationships and infection dates was observed. Using clinically confirmed P2P transmission data as benchmark, our phylogenetic methods identified sufficient P2P transmission relationships using readily available low-resolution Sanger sequences. These approaches may give valuable information about HIV infection dynamics within a population and may be easily deployed to guide public health interventions, without a need for next generation sequencing technology.

The COVID-19 pandemic has illustrated the importance of infection tracking. The role of asymptomatic, undiagnosed individuals in driving infections within this pandemic has become increasingly evident. Modern phylogenetic tools that take into account asymptomatic or undiagnosed individuals can help guide public health responses. We finetuned established phylogenetic pipelines using published SARS-CoV-2 genomic data to examine reasonable estimate transmission networks with the inference of unsampled infection sources. The system utilised Bayesian phylogenetics and TransPhylo to capture the evolutionary and infection dynamics of SARS-CoV-2. Our analyses gave insight into the transmissions within a population including unsampled sources of infection and the results aligned with epidemiological observations. We were able to observe the effects of preventive measures in Canada’s “Atlantic bubble” and in populations such as New York State. The tools also inferred the cross-species disease transmission of SARS-CoV-2 transmission from humans to lions and tigers in New York City’s Bronx Zoo. These phylogenetic tools offer a powerful approach in response to both the COVID-19 and other emerging infectious disease outbreaks.

1,3-Dinitrobenzene (mDNB) is a widely used intermediate in commercial products and causes testicular injury. However, genotoxic effects upon low-level exposure are poorly understood. The present study evaluated the effects of very low-chronic doses of mDNB on sperm nuclear integrity. Male hamsters were treated with 1.5 mg/kg/d/4 wks (group A), 1.5 mg/kg/mDNB/d/week/4 weeks (group B), 1.0 mg/kg/mDNB/3 d/wk/4 wks (group C), or polyethylene glycol 600 (control). Nuclear integrity of distal cauda epididymal sperm was determined using the sperm chromatin structure assay and acridine orange staining (AOS). The germ cell nuclear integrity was assessed by the comet assay. Testicular histopathology was conducted to evaluate the sensitive stages. The comet assay revealed denatured nuclear DNA in group A (in diploid and polyploid cells from weeks 2-5); respectively at week 4 and weeks 3 to 4 in groups B and C. According to AOS, only group A animals exhibited denatured sperm DNA (weeks 1 and 3). The effective sperm count declined from weeks 1 to 6. Mean sperm DNA denaturation extent, percentage cells outside the main population, and standard deviation indicated altered sperm nuclear integrity in group A. Same animals exhibited progressive disruption of the Sertoli cells, while groups B and C exhibited damages on germ cells. The results suggest that mDNB affects sperm nuclear integrity at very low chronic doses targeting cell-specific testicular damage.

Linkage disequilibrium (LD) is a fundamental concept in genetics; critical for studying genetic associations and molecular evolution. However, LD measurements are only reliable for common genetic variants, leaving low-frequency variants unanalyzed. In this work, we introduce cumulative LD (cLD), a stable statistic that captures the rare-variant LD between genetic regions, which reflects more biological interactions between variants, in addition to lack of recombination. We derived the theoretical variance of cLD using delta methods to demonstrate its higher stability than LD for rare variants. This property is also verified by bootstrapped simulations using real data. In application, we find cLD reveals an increased genetic association between genes in 3D chromatin interactions, a phenomenon recently reported negatively by calculating standard LD between common variants. Additionally, we show that cLD is higher between gene pairs reported in interaction databases, identifies unreported protein-protein interactions, and reveals interacting genes distinguishing case/control samples in association studies.

Modern sequencing instruments bring unprecedented opportunity to study within-host viral evolution in conjunction with viral transmissions between hosts. However, no computational simulators are available to assist the characterization of within-host dynamics. This limits our ability to interpret epidemiological predictions incorporating within-host evolution and to validate computational inference tools. To fill this need we developed Apollo, a GPU-accelerated, out-of-core tool for within-host simulation of viral evolution and infection dynamics across population, tissue, and cellular levels. Apollo is scalable to hundreds of millions of viral genomes and can handle complex demographic and population genetic models. Apollo can replicate real within-host viral evolution; accurately recapturing observed viral sequences from HIV and SARS-CoV-2 cohorts derived from initial population-genetic configurations. For practical applications, using Apollo-simulated viral genomes and transmission networks, we validated and uncovered the limitations of a widely used viral transmission inference tool.