Biology subjects

Meda, R. S.

Publications and source records attributed to Meda, R. S..

4 recordsLinked to original sources

bioRxiv2026

Protein solubility depends on centrifugation: Aiki-Sol, a per-regime predictor for E. coli

MotivationSequence-based predictors of recombinant protein solubility in Escherichia coli have plateaued (NESG independent-test AUC 0.760 [->]~ 0.80 over eight years of protein-language-model variants). The plateau hides a latent confound: the centrifugation regime used to separate the soluble from the insoluble fraction is a hidden variable collapsed into a single binary "soluble" label. The proteins biochemistry does not change between regimes; what changes is which fraction of the lysate is recovered as soluble. Existing predictors treat the regime as label noise rather than a feature, and sequence overlap between training and test partitions masks the resulting failure mode. ResultsWe release the Aiki-Sol Dataset, a tiered E. coli solubility corpus: a ~ 85K stringency-annotated benchmark, an Apache-licensed ~ 147K extension adding binary-only-labelled proteins, and a ~ 229K research-tier pool incorporating non-commercially-licensed sources. On the ~ 85K benchmark, scored on sequence-cluster-disjoint partitions, the strongest published binary comparator falls below chance on the 32,000 x g stratum (AUC 0.491 {+/-} 0.020); a fine-tuned ESM-2 650M backbone with five protocol-matched out-puts lifts pooled AUC by +0.108 (paired-bootstrap CI lower bound +0.090). The gain is curation, not architecture: structure-aware predictors given ESMFold structures do not outperform the sequence-only frame, and capacity scaled to 3B parameters does not exceed the conditioned 650M backbone. The released model, Aiki-Sol, jointly supervises five per-stringency outputs alongside a marginal output for stringency-unknown proteins; on five external cohorts it lifts cohort-mean AUC from 0.69-0.70 to 0.825, with a [≥] +0.10-0.16 lift on the three cohorts at measurably-zero training-pool overlap. Availability and implementationAiki-Sol model weights (Apache 2.0), the 147K-row license-clean training pool of the deployment checkpoint (CC BY 4.0), the cluster-disjoint per-stringency 5-fold partition assignments, per-cohort prediction CSVs, and source code for training, inference, and figure reproduction are available at https://github.com/aikium-public/aiki-sol and archived at Zenodo 10.5281/zenodo.20151817. The research-tier 229K checkpoint is released under CC-BY-NC-ND 4.0 (inheriting the most-restrictive upstream-source tier); its training CSV and the 84,809-protein stringency-annotated bench-mark of [§]2.1 mix non-commercial-tier upstream sources and are not redistributed verbatim. Upstream sources are documented in Data availability and SI [§]S1. The deployment artefact is distributed as a Python package (pip install aikisol) with a predict(seq) entry point. Contactvenkatesh@aikium.com. Supplementary informationSupplementary text, figures, and tables are available at Bioinformatics online.

bioinformatics↗

bioRxiv2026

Aiki-GeNano: Multi-Stage Preference Optimization for Generative Design of Developable Nanobodies

Therapeutic nanobodies must combine target binding with biophysical and chemical properties that determine manufacturability, stability, and clinical viability, collectively termed developability, yet most computational design pipelines still treat developability as a post-hoc filter rather than an integrated training objective. We present Aiki-GeNano, a three-stage language-model alignment pipeline for epitope-conditioned nanobody generation that integrates multiple developability signals directly into training, using only sequence information and previously published predictors. Across 65 target epitopes and relative to the supervised baseline, the combined pipeline raised predicted mean melting temperature by 6.6 {degrees}C, halved isomerization-motif severity, reduced deamidation, N-glycosylation sequons and CDR methionine-oxidation motifs, and preserved predicted humanness and solubility. On a shared 10-target GPCR benchmark, Aiki-GeNano achieved the highest predicted melting temperature and the lowest isomerization severity among five contemporary VHH generators. Starting from ProtGPT2 and a 1.35-million-pair binder dataset generated on an mRNA-display platform, the pipeline applies supervised fine-tuning, Direct Preference Optimization on 522,800 pairs ranked by a composite of selectivity, predicted thermal stability, solubility, and humanness, and Group Reward-Decoupled Policy Optimization against six sequence-based rewards (FR2 hydrophobicity, hydrophobic-patch coverage, chemical-liability motifs, Wilkinson-Harrison expression probability, VHH hallmark residues, scaffold integrity). Generated sequences differ from the nearest training sequence by a mean of 8.1-9.0 amino acids out of 126, and two alternative training trajectories converge to distinct amino-acid-composition strategies with similar liability outcomes but different thermal-stability gains, indicating initialization-dependent convergence of the reward-optimized policy. Predicted humanness was preserved at the level of the camelid VHH scaffold of the training library -- a data-side limitation rather than a methodological one, since the framework was effectively constant across all preference pairs. Applicability to the drug discovery and development pipeline, limitations of predicted-property evaluation, and future work are discussed.

bioinformatics↗

bioRxiv2026

Aiki-XP: leakage-controlled multimodal prediction of within-species relative protein expression at pan-bacterial scale

Generalizable protein-expression prediction can accelerate protein engineering, inform disease mechanisms, and help optimize heterologous recombinant protein production. Protein expression is governed by many interacting parameters that no single omics view captures. We develop Aiki-XP, a multimodal platform integrating four biological scales (genome, operon, coding sequence, protein) plus biophysical features across 492,026 genes from 385 bacterial species. Aiki-XP predicts within-species relative abundance (per-species z-score rank), not absolute copies per cell. Under a leakage-controlled gene-operon split Aiki-XP reaches Spearman{rho} nc = 0.592 versus 0.509 for ESM-C 600M alone, and each tier of a monotone protein[->]operon[->]genome deployment ladder yields a statistically significant gain; a five-recipe rank-average ensemble adds a further +0.016. All recipes were locked before external evaluation; transfer to heterologous, cross-species, and novel-phylum benchmarks demonstrates utility and limits. Ablations and scaling experiments identify operon-scale genomic context, not protein-language-model capacity, as the rate-limiting input at this scale; one foundation model per biological scale suffices, with same-scale stacking adding little.

bioinformatics↗

bioRxiv2025

Large Language Model Agent for Modular Task Execution in Drug Discovery

We present a modular framework powered by large language models (LLMs) that automates and streamlines key tasks across the early-stage computational drug discovery pipeline. By combining LLM reasoning with domain-specific tools, the framework performs biomedical data retrieval, domain-specific question answering, molecular generation, property prediction, property-aware molecular refinement, and 3D protein-ligand structure generation. In a case study targeting BCL-2 in lymphocytic leukemia, the agent autonomously retrieved relevant biomolecular information--including FASTA sequences, SMILES representations, and literature--and answered mechanistic questions with improved contextual accuracy over standard LLMs. It then generated chemically diverse seed molecules and predicted 67 ADMET-related properties, which guided iterative molecular refinement. Across two refinement rounds, the number of molecules with QED > 0.6 increased from 34 to 55, and those passing at least four out of five empirical drug-likeness rules rose from 29 to 52, within a pool of 194 molecules. The framework also employed Boltz-2 to generate 3D protein-ligand complexes and provide rapid binding affinity estimates for candidate compounds. These results demonstrate that the approach effectively supports molecular screening, prioritization, and structure evaluation. Its modular design enables flexible integration of evolving tools and models, providing a scalable foundation for AI-assisted therapeutic discovery.

pharmacology and toxicology↗