Biology subjects

Thiel, M.

Publications and source records attributed to Thiel, M..

3 recordsLinked to original sources

bioRxiv2026

AliceDB database and pipeline for identification of natural protein variants based on mass spectrometry measurement data

The natural variation that distinguishes living organisms within a single species is currently being studied intensively, primarily at the genetic level. Unfortunately, studies of natural variants at the level of protein gene products are not very common, mainly due to the lack of appropriate databases and bioinformatics tools. The main research technique used to study proteomes/peptidomes is mass spectrometry (MS). A classic method for interpreting raw mass spectrometry data in proteomic/peptidomic studies involves the use of databases containing representative (canonical) sequences that define the proteome of the organism under study. In this paper, we present the AliceDB database, which contains information on over 7 million natural variants of protein sequences described in the scientific literature for Homo sapiens. The data contained in the AliceDB database can be utilized using widely available and commonly used software for interpreting proteomic data. Test results regarding the use of the AliceDB database for the interpretation of proteomic data indicate that accounting for the presence of natural variants increases both the number and quality of identified proteins. Furthermore, it is easy to identify protein sequence variants that may, for example, be of significance in medicine.

bioinformatics↗

bioRxiv2026

PlasmidLM: A Promptable DNA Language Model via Verifiable-Reward Post-Training

Generative DNA models are typically next-token completers: they extend a sequence but offer no native interface for telling the model what to make. PlasmidLM is a promptable DNA language model for plasmids. A designer supplies a human-readable component specification, for example a high-copy E. coli vector with kanamycin resistance and an EGFP reporter, and the model generates the corresponding multi-kilobase construct in a single autoregressive pass. Prompts are unordered sets of named-part tokens at the granularity of biological shorthand, not learned latent codes or rigid grammars. We evaluate outputs along two axes: a sequence is viable if structurally plausible as a plasmid, and faithful if its detected components match the prompt. Their conjunction is the useful-plasmid rate, the primary metric we report. On a held-out 1,000-prompt benchmark, the post-trained model achieves a useful-plasmid rate of 48.5% at single-shot decoding and 89.7% under best-of-4 sampling. Verifiable-reward post-training with GRPO against a 660-entry sequence motif registry improves the useful-plasmid rate across all sampling budgets. We release the 19.3M-parameter model, evaluation suite, and a paired benchmark of prompt-sequence pairs.

bioengineering↗

bioRxiv2026

Emergent Biological Realism in RL-Trained DNA Language Models

We compare the efficacy and distributional effects of supervised fine-tuning (SFT) and reinforcement learning (RL) post-training for PlasmidGPT, a foundation model for whole-plasmid generation, using Group Relative Policy Optimization (GRPO) for the RL model. Using a biologically motivated reward function encoding functional annotations, length constraints, and repeat penalties, the RL model achieves a 71.6% quality-control pass rate across 8 prompts on 4,000 sequences, compared to 4.3% for the pretrained baseline and 11.0% for SFT. A five-model reward ablation identifies the cassette arrangement bonus, which rewards correct promoter [->] CDS [->] terminator ordering, as the critical reward component. Rejection-sampling baselines indicate that the gain is not recovered by sampling more heavily from the base model. Beyond directly optimized features, RL-generated sequences converge toward real plasmid distributions in 3-mer composition and minimum free energy density, neither of which is directly optimized by the reward function. Minimum free energy density independently converges to the real-plasmid regime under both SFT and RL despite these being parallel post-training paths. On a small curated hold-out set, RL improves continuation log-likelihood over the pretrained baseline on all 29 held-out sequences (mean {Delta} = +0.83 nats).

bioinformatics↗