Biology subjects

Madani, A.

Publications and source records attributed to Madani, A..

12 recordsLinked to original sources

bioRxiv2026

Mafb-lineage activation of a short polyalanine (+5) PHOX2B mutation produces severe respiratory dysfunction with preserved postnatal weight gain among survivors in a mouse model of congenital central hypoventilation syndrome

Rationale: Congenital central hypoventilation syndrome (CCHS) is most commonly caused by polyalanine repeat mutations in PHOX2B. The in vivo consequences of the short five-alanine expansion and the contribution of specific hindbrain lineages to the resulting respiratory phenotype remain poorly understood. Objectives: To characterize the neonatal phenotype caused by the Phox2b25Ala/+ mutation and determine the contribution of the MafB lineage associated with the r5-r6 hindbrain territory. Methods: We generated a conditional Phox2b25Ala allele and studied mice with constitutive or MafB-lineage activation. Neonatal survival, growth, gastric milk content, ventilation, and hypercapnic responses were assessed in vivo. Respiratory-network activity and responses to extracellular acidification were recorded in E18.5 isolated brainstem-spinal cord preparations, and retrotrapezoid nucleus (RTN) development was examined histologically. Measurements and Main Results: Constitutive Phox2b25Ala/+ pups exhibited high neonatal mortality, markedly impaired weight gain, reduced gastric milk scores, hypoventilation, increased apnea time, and blunted ventilatory responses to CO2. Embryonic preparations showed a slower respiratory rhythm, an impaired response to acidification, and severe RTN dysgenesis. Activation of the mutant allele in the MafB lineage was also associated with severe respiratory dysfunction, RTN dysgenesis, and early neonatal mortality. In contrast, postnatal weight gain was preserved among surviving MafB-lineage mutants, and the reduction in gastric milk scores was significantly attenuated compared with constitutive mutants. Conclusions: A short PHOX2B polyalanine expansion reproduces major respiratory features of CCHS in mice. Activating the mutant allele in the MafB lineage is sufficient to cause severe respiratory dysfunction and neonatal mortality, whereas postnatal weight gain is preserved among survivors.

neuroscience↗

bioRxiv2025

E1: Retrieval-Augmented Protein Encoder Models

Large language models trained on natural proteins learn powerful representations of protein sequences that are useful for downstream understanding and prediction tasks. Because they are only exposed to individual protein sequences during pretraining without any additional contextual information, conventional protein language models suffer from parameter inefficiencies in learning, baked-in phylogenetic biases, and functional performance issues at larger scales. To address these challenges, we have built Profluent-E1, a family of retrieval-augmented protein language models that explicitly condition on homologous sequences. By integrating retrieved evolutionary context through block-causal multi-sequence attention, E1 captures both general and family-specific constraints without fine-tuning. We train E1 models on four trillion tokens from the Profluent Protein Atlas and achieve state-of-the-art performance across zero-shot fitness and unsupervised contact-map prediction benchmarks - surpassing alternative sequence-only models. Performance scales with model size from 150M to 600M parameters, and E1 can be used flexibly in single-sequence or retrieval-augmented inference mode for fitness prediction, variant ranking, and embeddings for structural tasks. To encourage open science and further development in retrieval-augmented protein language models, we release three models for free research and commercial use at https://github.com/Profluent-AI/E1.

synthetic biology↗

bioRxiv2025

Scaling unlocks broader generation and deeper functional understanding of proteins

Generative protein language models (PLMs) are powerful tools for designing proteins purpose-built to solve problems in medicine, agriculture, and industrial processes. Recent work has trained ever larger language models, but there has been little systematic study of the optimal training distributions and the influence of model scale on the sequences generated by PLMs. We introduce the ProGen3 family of sparse generative PLMs, and we develop compute-optimal scaling laws to scale up to a 46B-parameter model pre-trained on 1.5T amino acid tokens. Pro-Gen3s pre-training data is sampled from an optimized data distribution over the Profluent Protein Atlas v1, a carefully curated dataset of 3.4B full-length proteins. We evaluate for the first time in the wet lab the influence of model scale on the sequences generated by PLMs, and we find that larger models generate viable proteins for a much wider diversity of protein families. Finally, we find both computationally and experimentally that larger models are more responsive to alignment with laboratory data, resulting in improved protein fitness prediction and sequence generation capabilities. These results indicate that larger PLMs like ProGen3-46B trained on larger, well-curated datasets are powerful foundation models that push the frontier of protein design.1

synthetic biology↗

bioRxiv2025

Engineering of CRISPR-Cas PAM recognition using deep learning of vast evolutionary data

CRISPR-Cas enzymes must recognize a protospacer-adjacent motif (PAM) to edit a genomic site, significantly limiting the range of targetable sequences in a genome. Machine learning-based protein engineering provides a powerful solution to efficiently generate Cas protein variants tailored to recognize specific PAMs. Here, we present Protein2PAM, an evolution-informed deep learning model trained on a dataset of over 45,000 CRISPR-Cas PAMs. Protein2PAM rapidly and accurately predicts PAM specificity directly from Cas proteins across Type I, II, and V CRISPR-Cas systems. Using in silico deep mutational scanning, we demonstrate that the model can identify residues critical for PAM recognition in Cas9 without utilizing structural information. As a proof of concept for protein engineering, we employ Protein2PAM to computationally evolve Nme1Cas9, generating variants with broadened PAM recognition and up to a 50-fold increase in PAM cleavage rates compared to the wild-type under in vitro conditions. This work represents the first successful application of machine learning to achieve customization of Cas enzymes for alternate PAM recognition, paving the way for personalized genome editing.

synthetic biology↗

bioRxiv2024

Adapting protein language models for structure-conditioned design

Generative models for protein design trained on experimentally determined structures have proven useful for a variety of design tasks. However, such methods are limited by the quantity and diversity of structures used for training, which represent a small, biased fraction of protein space. Here, we describe proseLM, a method for protein sequence design based on adaptation of protein language models to incorporate structural and functional context. We show that proseLM benefits from the scaling trends of underlying language models, and that the addition of non-protein context - nucleic acids, ligands, and ions - improves recovery of native residues during design by 4-5% across model scales. These improvements are most pronounced for residues that directly interface with non-protein context, which are faithfully recovered at rates >70% by the most capable proseLM models. We experimentally validated proseLM by optimizing the editing efficiency of genome editors in human cells, achieving a 50% increase in base editing activity, and by redesigning therapeutic antibodies, resulting in a PD-1 binder with 2.2 nM affinity.

bioengineering↗

bioRxiv2024

Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences

Gene editing has the potential to solve fundamental challenges in agriculture, biotechnology, and human health. CRISPR-based gene editors derived from microbes, while powerful, often show significant functional tradeoffs when ported into non-native environments, such as human cells. Artificial intelligence (AI) enabled design provides a powerful alternative with potential to bypass evolutionary constraints and generate editors with optimal properties. Here, using large language models (LLMs) trained on biological diversity at scale, we demonstrate the first successful precision editing of the human genome with a programmable gene editor designed with AI. To achieve this goal, we curated a dataset of over one million CRISPR operons through systematic mining of 26 terabases of assembled genomes and meta-genomes. We demonstrate the capacity of our models by generating 4.8x the number of protein clusters across CRISPR-Cas families found in nature and tailoring single-guide RNA sequences for Cas9-like effector proteins. Several of the generated gene editors show comparable or improved activity and specificity relative to SpCas9, the prototypical gene editing effector, while being 400 mutations away in sequence. Finally, we demonstrate an AI-generated gene editor, denoted as OpenCRISPR-1, exhibits compatibility with base editing. We release OpenCRISPR-1 publicly to facilitate broad, ethical usage across research and commercial applications.

synthetic biology↗

bioRxiv2022

Activity of Tricyclic Pyrrolopyrimidine Gyrase B Inhibitor against Mycobacterium abscessus

Tricyclic pyrrolopyrimidines (TPPs) are a new class of antibacterials inhibiting the ATPase of DNA gyrase. TPP8, a representative of this class, is active against Mycobacterium abscessus in vitro. Spontaneous TPP8 resistance mutations mapped to the ATPase domain of M. abscessus DNA gyrase and the compound inhibited DNA supercoiling activity of recombinant M. abscessus enzyme. Further profiling of TPP8 in macrophage and mouse infection studies demonstrated proof-of-concept activity against M. abscessus ex vivo and in vivo.

microbiology↗

bioRxiv2021

FLIP: Benchmark tasks in fitness landscape inference for proteins

Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications. Critical to its use in designing proteins with desired properties, machine learning models must capture the protein sequence-function relationship, often termed fitness landscape. Existing bench-marks like CASP or CAFA assess structure and function predictions of proteins, respectively, yet they do not target metrics relevant for protein engineering. In this work, we introduce Fitness Landscape Inference for Proteins (FLIP), a benchmark for function prediction to encourage rapid scoring of representation learning for protein engineering. Our curated tasks, baselines, and metrics probe model generalization in settings relevant for protein engineering, e.g. low-resource and extrapolative. Currently, FLIP encompasses experimental data across adeno-associated virus stability for gene therapy, protein domain B1 stability and immunoglobulin binding, and thermostability from multiple protein families. In order to enable ease of use and future expansion to new tasks, all data are presented in a standard format. FLIP scripts and data are freely accessible at https://benchmark.protein.properties.

bioengineering↗

bioRxiv2021

Deep neural language modeling enables functional protein generation across families

Bypassing natures evolutionary trajectory, de novo protein generation--defined as creating artificial protein sequences from scratch--could enable breakthrough solutions for biomedical and environmental challenges. Viewing amino acid sequences as a language, we demonstrate that a deep learning-based language model can generate functional artificial protein sequences across families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. Our protein language model is trained by simply learning to predict the next amino acid for over 280 million protein sequences from thousands of protein families, without biophysical or coevolutionary modeling. We experimentally evaluate model-generated artificial proteins on five distinct antibacterial lysozyme families. Artificial proteins show similar activities and catalytic efficiencies as representative natural lysozymes, including hen egg white lysozyme, while reaching as low as 44% identity to any known naturally-evolved protein. The X-ray crystal structure of an enzymatically active artificial protein recapitulates the conserved fold and positioning of active site residues found in natural proteins. We demonstrate our language models ability to be adapted to different protein families by accurately predicting the functionality of artificial chorismate mutase and malate dehydrogenase proteins. These results indicate that neural language models successfully perform de novo protein generation across protein families and may prove to be a tool to shortcut evolution.

synthetic biology↗

bioRxiv2021

OBSTRUCTIVE APNEAS IN A MOUSE MODEL OF CONGENITAL CENTRAL HYPOVENTILATION SYNDROME

RationaleCongenital Central Hypoventilation Syndrome (CCHS) is characterized by life-threatening sleep hypoventilation, and is caused by PHOX2B gene mutations, most frequently the PHOX2B27Ala/+ mutation, with patients requiring lifelong ventilatory support. It is unclear whether obstructive apneas are part of the syndrome. ObjectivesTo determine whether Phox2b27Ala/+ mice, which present the main symptoms of CCHS and die within hours after birth, also express obstructive apneas, and to investigate potential underlying mechanisms. MethodsApneas were classified as central, obstructive or mixed by using a novel system combining pneumotachography and laser detection of abdominal movement immediately after birth. Several respiratory nuclei involved in airway patency were examined by immunohistochemistry and electrophysiology in brainstem-spinal cord preparation. Measurements and Main ResultsThe median (interquartile range) of obstructive apnea frequency was 2.3/min (1.5-3.3) in Phox2b27Ala/+ pups versus 0.6/min (0.4-1.0) in wildtypes (P < 0.0001). Obstructive apnea duration was 2.7s (2.3-3.9) in Phox2b27Ala/+ pups versus 1.7s (1.1-1.9) in wildtypes (P < 0.0001). Central and mixed apneas presented similar, significant differences. In Phox2b27Ala/+ preparations, the hypoglossal nucleus had fewer (P < 0.05) and smaller (P < 0.01) neurons, compared to wildtypes. Importantly, coordination of phrenic and hypoglossal motor activities was disrupted, as evidenced by the longer and variable delay of hypoglossal with respect to phrenic activity onset (P < 0.001). ConclusionsThe Phox2b27Ala/+ mutation predisposed pups not only to hypoventilation and central apneas, but also to obstructive and mixed apneas, likely due to hypoglossal dysgenesis. These results thus demand attention towards obstructive events in infants with CCHS.

physiology↗

bioRxiv2020

BERTology Meets Biology: Interpreting Attention in Protein Language Models

Transformer architectures have proven to learn useful representations for protein classification and generation tasks. However, these representations present challenges in interpretability. Through the lens of attention, we analyze the inner workings of the Transformer and explore how the model discerns structural and functional properties of proteins. We show that attention (1) captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure, (2) targets binding sites, a key functional component of proteins, and (3) focuses on progressively more complex biophysical properties with increasing layer depth. We also present a three-dimensional visualization of the interaction between attention and protein structure. Our findings align with known biological processes and provide a tool to aid discovery in protein engineering and synthetic biology. The code for visualization and analysis is available at https://github.com/salesforce/provis.

bioinformatics↗

bioRxiv2020

ProGen: Language Modeling for Protein Generation

Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. We pose protein engineering as an unsupervised sequence generation problem in order to leverage the exponentially growing set of proteins that lack costly, structural annotations. We train a 1.2B-parameter language model, ProGen, on [~]280M protein sequences conditioned on taxonomic and keyword tags such as molecular function and cellular component. This provides ProGen with an unprecedented range of evolutionary sequence diversity and allows it to generate with fine-grained control as demonstrated by metrics based on primary sequence similarity, secondary structure accuracy, and conformational energy.

synthetic biology↗