Existence of HLA-Mismatched Unrelated Donors Closes the Gap in Donor Availability Regardless of Recipient Ancestry.

In patients without a matched sibling donor (MSD) or well-matched unrelated donor (MUD), hematopoietic cell transplantation (HCT) can still be successful when using an HLA-mismatched unrelated donor (MMUD) in combination with post-transplantation cyclophosphamide (PTCy), abatacept, or other novel approaches. This may allow clinicians to choose a suitable donor from a wide range of donor options while optimizing other donor selection characteristics, including donor age. We hypothesized that allowing for a 5/8 HLA match level considering high-resolution matching at HLA-A, -B, -C and -DRB1, there is a potential to close the donor availability gap for all patients regardless of their race/ethnicity. In this work, we estimate the likelihood of matching for all racial/ethnic groups at different HLA match thresholds. Our study aimed to assess the potential for identifying an available MUD or MMUD in the National Marrow Donor Program (NMDP)/Be The Match (BTM) donor registry for 21 detailed and 5 broad racial/ethnic groups, using high-resolution HLA matching for HLA-A, -B, -C, and -DRB1 at various levels (8/8, 7/8, 6/8, and 5/8). We used donor registry population data from the NMDP/BTM in 2020 and redistributed the donor registry data according to existing population ratios, accounting for demonstrated donor availability. Finally, we used a genetic model at the population level to estimate the match likelihood for detailed and broad racial/ethnic groups. Likelihood of 8/8 HLA match ranging from 16% to 74% were obtained for various detailed racial/ethnic groups with available donors age ≤35 years. When considering more mismatches in the HLA loci, registry coverage became >99% with a 5/8 HLA match level for donors of all ages or those age ≤35 years, with HLA-DPB1 T cell epitope permissive matching, or when searching for donors outside of their racial/ethnic group. Our registry models demonstrate the potential for using MMUDs at various HLA match levels to study whether this will expand access to HCT across racial/ethnic groups. Expanded donor options may erase the donor availability gap for all patients while allowing for selection of MMUDs with favorable characteristics, such as younger age.

Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance.

The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptide sequence feature for predicting AVPs. Our Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP) approach achieves a higher accuracy than either the model with all features or current state-of-the-art single classifiers. Understanding the features that are associated with AVP activity is a core need to identify and design new AVPs in novel systems. The FIRM-AVP code and standalone software package are available at https://github.com/pmartR/FIRM-AVP with an accompanying web application at https://msc-viz.emsl.pnnl.gov/AVPR .

PARGT: a software tool for predicting antimicrobial resistance in bacteria.

With the ever-increasing availability of whole-genome sequences, machine-learning approaches can be used as an alternative to traditional alignment-based methods for identifying new antimicrobial-resistance genes. Such approaches are especially helpful when pathogens cannot be cultured in the lab. In previous work, we proposed a game-theory-based feature evaluation algorithm. When using the protein characteristics identified by this algorithm, called 'features' in machine learning, our model accurately identified antimicrobial resistance (AMR) genes in Gram-negative bacteria. Here we extend our study to Gram-positive bacteria showing that coupling game-theory-identified features with machine learning achieved classification accuracies between 87% and 90% for genes encoding resistance to the antibiotics bacitracin and vancomycin. Importantly, we present a standalone software tool that implements the game-theory algorithm and machine-learning model used in these studies.

Publisher Correction: Antimicrobial Resistance Prediction for Gram-Negative Bacteria via Game Theory-Based Feature Evaluation.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Antimicrobial Resistance Prediction for Gram-Negative Bacteria via Game Theory-Based Feature Evaluation.

The increasing prevalence of antimicrobial-resistant bacteria drives the need for advanced methods to identify antimicrobial-resistance (AMR) genes in bacterial pathogens. With the availability of whole genome sequences, best-hit methods can be used to identify AMR genes by differentiating unknown sequences with known AMR sequences in existing online repositories. Nevertheless, these methods may not perform well when identifying resistance genes with sequences having low sequence identity with known sequences. We present a machine learning approach that uses protein sequences, with sequence identity ranging between 10% and 90%, as an alternative to conventional DNA sequence alignment-based approaches to identify putative AMR genes in Gram-negative bacteria. By using game theory to choose which protein characteristics to use in our machine learning model, we can predict AMR protein sequences for Gram-negative bacteria with an accuracy ranging from 93% to 99%. In order to obtain similar classification results, identity thresholds as low as 53% were required when using BLASTp.