Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) has become a cornerstone technology in clinical microbiology, enabling rapid and accurate microbial identification. However, the development of data-driven diagnostic models remains limited by the lack of sufficiently large, balanced, and standardized spectral datasets. This study investigates the use of deep generative models to synthesize realistic MALDI-TOF MS spectra, aiming to overcome data scarcity and support the development of robust machine learning tools in microbiology. We adapt and evaluate three generative models, Variational Autoencoders (MALDIVAEs), Generative Adversarial Networks (MALDIGANs), and Denoising Diffusion Probabilistic Model (MALDIffusion), for the conditional generation of microbial spectra guided by species labels. Generation is conditioned on species labels, and spectral fidelity and diversity are assessed using diverse metrics. Our experiments show that synthetic data generated by MALDIVAE, MALDIGAN, and MALDIffusion are statistically and diagnostically comparable to real measurements, enabling classifiers trained exclusively on synthetic samples to reach performance levels similar to those trained on real data. While all models faithfully reproduce the peak structure and variability of MALDI-TOF spectra, MALDIffusion obtains this fidelity at a substantially higher computational cost, and MALDIGAN shows competitive but slightly less stable behaviour. In contrast, MALDIVAE offers the most favorable balance between realism, stability, and efficiency. Furthermore, augmenting minority species with synthetic spectra markedly improves classification accuracy, effectively mitigating class imbalance and domain mismatch without compromising the authenticity of the generated data.

The evolution of drug-resistant microbial species is one of the major challenges to global health. The development of new antimicrobial treatments such as antimicrobial peptides needs to be accelerated to combat this threat. However, the discovery of novel antimicrobial peptides is hampered by low-throughput biochemical assays. Computational techniques can be used for rapid screening of promising antimicrobial peptide candidates prior to testing in the wet lab. The vast majority of existing antimicrobial peptide predictors are non-targeted in nature, i.e., they can predict whether a given peptide sequence is antimicrobial, but they are unable to predict whether the sequence can target a particular microbial species. In this work, we have developed a targeted antimicrobial peptide activity predictor that can predict whether a peptide is effective against a given microbial species or not. This has been made possible through zero-shot and few-shot machine learning. The proposed predictor called AMP0 takes in the peptide amino acid sequence and any N/C-termini modifications together with the genomic sequence of a target microbial species to generate targeted predictions. It is important to note that the proposed method can generate predictions for species that are not part of its training set. The accuracy of predictions for novel test species can be further improved by providing a few example peptides for that species. Our computational cross-validation results show that the pro-posed scheme is particularly effective for targeted antimicrobial prediction in comparison to existing approaches and can be used for screening potential antimicrobial peptides in a targeted manner especially for cases in which the number of training examples is small. The webserver of the method is available at http://ampzero.pythonanywhere.com.

The plethora of bacteria and other tiny organisms that live in your gut, often referred to as the gut microbiome, don't just help you digest food and fight disease. As detailed in a new study, they also provide a very accurate biological clock that shows your physical age--a fact that may open up wide-ranging possibilities for health and longevity studies. The link between the gut biome and age is described by longevity researcher Alex Zhavoronkov and a team of his colleagues at Insilico Medicine, an artificial intelligence startup focused on drug discovery, biomarker development, and aging research. Relatively little is known about how our gut biomes transition from one stage to another as we age, or about links between our age and the state of our gut biomes. In their paper, which is awaiting peer review but can be found on the preprint server bioRxiv, the team describes how they examined 3,663 curated samples of gut bacteria from 1,165 healthy people, aged 20-90, from countries in Europe, Asia, and North America.