Biology subjects

Cardone, A.

Publications and source records attributed to Cardone, A..

3 recordsLinked to original sources

bioRxiv2026

XAMPGen: An Interpretable SI Pipeline For Novel Antimicrobial Peptide Discovery

Antimicrobial peptides (AMPs) are short peptides with a broad spectrum of activity against bacterial pathogens, representing a promising avenue for combating the growing threat of antibiotic resistance. This study focuses on two main components: 1) generating novel AMPs with desired properties using generative SI (GenSI) and 2) utilizing explainable SI (XSI) methods to validate the GenSI model and derive biological insights. In this work, we fine-tuned ProGen, a decoder-only protein language model, on known AMPs to generate novel peptide sequences with strong antimicrobial properties. To advance the interpretability and reliability of the generative model, we applied Explainable SI (XSI) techniques. Specifically, we used a probing classifier and a regressor to identify cornerstone layers that encode functionally critical information. Self-attention analysis within these layers revealed residue level interdependencies and patterns underlying antimicrobial functions, which we validated against well-characterized AMPs from the literature. Overall, these methods provide mechanistic insight into the model's decision-making process, offering a principled basis for trusting the sequences it generates rather than treating the model as a black box. Candidate sequences were filtered based on predicted Minimum Inhibitory Concentration (MIC) along with human insights into the structural activities of the AMPs in bacterial membranes, and a final set of peptides was selected for experimental testing. Biological assays for determining MIC confirmed antimicrobial activity in all generated and tested peptides, with fluorescence and circular dichroism measurements providing structural insight. With our XSI approach, we are able to reveal correlations between amino acid sequence patterns and binding properties of these sequences to bacterial membrane mimics, that directly correlate with biological activities in cell-based assays. This builds confidence in that the learned amino acid interdependencies as applied to AMPs carries physico-chemical meaning and is relevant for discoveries of novel AMPs with therapeutic potential. This work demonstrates that interpretable generative language models can serve as effective and transparent tools for rational AMP discovery.

bioinformatics↗

bioRxiv2026

Species- and Topic-aware Representation Learning for Antimicrobial Peptide Discovery

Antimicrobial resistance poses a major global health challenge, necessitating efficient strategies to discover potent antimicrobial peptides (AMPs). While recent generative models can produce many candidate sequences, experimentally validating all generated peptides in wet labs is impractical due to the high costs and time involved in such measurements. As a result, there is a strong demand for accurate predictions of peptide efficacy, typically measured as the minimum inhibitory concentration (MIC). We introduce STAMP, a framework for Species- and Topic-aware Representation Learning in AMP Discovery. This unified machine learning framework allows for cross-species predictions of AMP activity. STAMP integrates protein language model embeddings with species conditioning and topic-aware representations that capture sequence-level patterns, enabling generalizable predictions across multiple bacterial species within a single model. We evaluated STAMP on three benchmark datasets, which include two previously published datasets and a newly curated dataset derived from DBAASP, addressing duplicates and inconsistencies systematically. STAMP achieved strong predictive performance across these datasets, demonstrating a Pearson correlation coefficient (PCC) of 0.837 and an R2 of 0.70, outperforming several baseline models. Importantly, we further validated our prediction model using peptides that were experimentally tested for their antimicrobial activity against E.coli. and S.epidermidis bacteria, demonstrating its real-world applicability. Furthermore, residue-level importance analyses provide insights into the sequence determinants governing antimicrobial activity. Together, these results establish STAMP as a scalable framework for MIC prediction and an effective computational tool for accelerating AMP discovery and optimization.

bioinformatics↗

bioRxiv2023

A Meta-model for ADMET Property Prediction Analysis

In drug discovery analysis chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties play a critical role. These properties allow the quantitative evaluation of a designed drugs efficacy. Several machine learning models have been designed for the prediction of ADMET properties. However, no single method seems to enable the accurate prediction of these properties. In this paper, we build a meta-model that learns the best possible way to combine the scores from multiple heterogeneous machine learning models to effectively predict the ADMET properties. We evaluate the performance of our proposed model against the Therapeutics Data Commons (TDC) ADMET benchmark dataset. The proposed meta-model outperforms state-of-the-art methods such as XGBoost in the TDC leaderboard, and it ranks first in five and in the top three positions for fifteen out of twenty-two prediction tasks.

bioinformatics↗