Biology subjects

Alamdari, S.

Publications and source records attributed to Alamdari, S..

6 recordsLinked to original sources

bioRxiv2026

FLIP2: Expanding Protein Fitness Landscape Benchmarks for Real-World Machine Learning Applications

Machine learning methods that predict protein fitness from sequence remain sensitive to changes in data distributions, limiting generalization across common conditions encountered in protein engineering. Practically, protein engineers are thus left wondering about the effective utility of ML tools. The FLIP benchmark established protocols for testing generalization under some domain shifts, but it was limited to measurements of thermostability, binding, and viral capsid viability. We introduce FLIP2, a protein fitness benchmark spanning seven new datasets, including enzymes, protein-protein interactions, and light-sensitive proteins, as well as splits that measure generalization relevant to real-world protein engineering campaigns. Evaluating a suite of benchmark models across these datasets and splits reveals that simpler models often matched or outperformed fine-tuned protein language models on FLIP2, challenging the utility of existing transfer learning techniques. Provenance for all datasets has been recorded and we redistribute all data CC-BY 4.0 to facilitate continued progress.

bioengineering↗

bioRxiv2025

The Dayhoff Atlas: scaling sequence diversity for improved protein generation

Organized information powers modern biology, a framework pioneered by Margaret Dayhoffs Atlas of Protein Sequence and Structure and advanced by todays databases and computational methods. Here, we extend this paradigm for the AI era, presenting the Dayhoff Atlas of protein sequence data and generative models to accelerate protein biology and design. The Atlas introduces GigaRef, the largest open dataset of natural proteins, spanning 3.34B genomic and metagenomic sequences across 1.70B clusters, and BackboneRef, which distills structural information from 240,811 synthetic backbones into 46M synthetic sequences. Leveraging these datasets, we trained the Dayhoff protein language models, which can predict mutation effects, scaffold structural motifs, and generate novel proteins within families. Training on metagenomic and structure-based synthetic sequences increased the expression rates of generated proteins, demonstrating the value of data diversity and scale. We release the Dayhoff Atlas code, datasets, and models under a permissive license to empower computation in protein design.

bioengineering↗

bioRxiv2025

Deep learning guided design of protease substrates

Proteases, a class of enzymes that play critical roles in health and disease, exert their function through the cleavage of peptide bonds. Identifying substrates that are efficiently and selectively cleaved by target proteases is essential for studying protease activity and for harnessing their activity in protease-activated diagnostics and therapeutics. However, the vast design space of possible substrates (c.a. 2010 unique amino acid combinations for a 10-mer peptide) and the limited accessibility of high-throughput activity profiling tools hinder the speed and success of substrate design. We present CleaveNet, an end-to-end AI pipeline for the design of protease substrates. Applied to matrix metalloproteinases, CleaveNet enhances the scale, tunability, and efficiency of substrate design. CleaveNet generates peptide substrates that exhibit sound biophysical properties and capture not only well-established but also novel cleavage motifs. To enable precise control over substrate design, CleaveNet incorporates a conditioning tag that enables generation of peptides guided by a target cleavage profile, enabling targeted design of efficient and selective substrates. CleaveNet-generated substrates were validated experimentally through a large-scale in vitro screen, even in the challenging case of designing highly selective substrates for MMP13. We envision that CleaveNet will accelerate our ability to study and capitalize on protease activity, paving the way for new in silico design tools across enzyme classes.

biochemistry↗

bioRxiv2024

ProtNote: a multimodal method for protein-function annotation

Understanding the protein sequence-function relationship is essential for advancing protein biology and engineering. However, fewer than 1% of known protein sequences have human-verified functions. While deep learning methods have demonstrated promise for protein function prediction, current models are limited to predicting only those functions on which they were trained. Here, we introduce ProtNote, a multimodal deep learning model that leverages free-form text to enable both supervised and zero-shot protein function prediction. ProtNote not only maintains near state-of-the-art performance for annotations in its train set, but also generalizes to unseen and novel functions in zero-shot test settings. We envision that ProtNote will enhance protein function discovery by enabling scientists to use free text inputs, without restriction to predefined labels - a necessary capability for navigating the dynamic landscape of protein biology.

bioengineering↗

bioRxiv2023

Protein generation with evolutionary diffusion: sequence is all you need

Deep generative models are increasingly powerful tools for the in silico design of novel proteins. Recently, a family of generative models called diffusion models has demonstrated the ability to generate biologically plausible proteins that are dissimilar to any actual proteins seen in nature, enabling unprecedented capability and control in de novo protein design. However, current state-of-the-art diffusion models generate protein structures, which limits the scope of their training data and restricts generations to a small and biased subset of protein design space. Here, we introduce a general-purpose diffusion framework, EvoDiff, that combines evolutionary-scale data with the distinct conditioning capabilities of diffusion models for controllable protein generation in sequence space. EvoDiff generates high-fidelity, diverse, and structurally-plausible proteins that cover natural sequence and functional space. We show experimentally that EvoDiff generations express, fold, and exhibit expected secondary structure elements. Critically, EvoDiff can generate proteins inaccessible to structure-based models, such as those with disordered regions, while maintaining the ability to design scaffolds for functional structural motifs. We validate the universality of our sequence-based formulation by experimentally characterizing intrinsically-disordered mitochondrial targeting signals, metal-binding proteins, and protein binders designed using EvoDiff. We envision that EvoDiff will expand capabilities in protein engineering beyond the structure-function paradigm toward programmable, sequence-first design.

bioengineering↗

bioRxiv2020

The Ice Nucleating Protein InaZ is Activated by Low Temperature

Ice-nucleation active (INA) bacteria can promote the growth of ice more effectively than any other known material. Utilizing specialized ice-nucleating proteins (INPros), they obtain nutrients from plants by inducing frost damage and, when airborne in the atmosphere, they drive ice nucleation within clouds and may affect global precipitation patterns. Despite their evident environmental importance, the molecular mechanisms behind INPro-induced freezing have remained largely elusive. In the present study, we investigated the folding and the structural basis for interactions between water and the ice-nucleating protein InaZ from the INA bacterium Pseudomonas syringae strain R10.79. Using vibrational sum-frequency generation and two-dimensional infrared spectroscopy, we demonstrate that the ice-active repeats of InaZ adopt a {beta}-helical structure in solution and at water surfaces. In this configuration, hydrogen bonding between INPros and water molecules imposes structural ordering on the adjacent water network. The observed order of water increases as the interface is cooled to temperatures close to the melting point of water. Experimental SFG data combined with spectral calculations and molecular-dynamics simulations shows that the INPro reorients at lower temperatures. We suggest that the reorientation can enhance order-inducing water interactions and, thereby, the effectiveness of ice nucleation by InaZ.

biophysics↗