Biology subjects

Jamali, K.

Publications and source records attributed to Jamali, K..

6 recordsLinked to original sources

bioRxiv2026

De novo design of flexible protein interactions with GuideFlip

De novo design of protein binders requires a target structure. However, for flexible targets, such as intrinsically disordered proteins, this structure does not exist until the binder has stabilized the interaction. Such targets are therefore difficult for methods that separate structure generation from sequence design. We introduce GuideFlip, which co-designs structure and sequence through guided discrete flow matching: binder residues are assigned progressively while the complex is re-predicted at each step, allowing the evolving interface to affect the design process. GuideFlip reduces the hydrophobic bias of direct AlphaFold optimization and improves in silico success rates over existing approaches. We release a database of binder candidates for 177 human disordered proteins. Experimentally, we obtain de novo binders to the C-terminus of -synuclein and the disordered amino terminus of RBX1 with hit rates of 13.5% and 41.7%, respectively, and we confirm the epitopes of selected binders by NMR and mutagenesis. Applying GuideFlip to flexibility on the binder side, we design a nanobody that binds the agonist-bound {beta}1-adrenergic receptor in the active state, but not the receptor in its inactive state, with a 75% hit rate and cryo-EM structure confirming the design. GuideFlip enables protein design where bound structures emerge only upon binding.

biophysics↗

bioRxiv2026

Cryo-EM image processing of amyloid filaments in RELION-5.1

We present new tools for the structure determination of amyloid filaments from electron cryo-microscopy (cryo-EM) images. We introduce a new algorithm for automated filament picking, based on their characteristic 4.75 [A] repeat signal; we implement the new auto-picker in a fully automated procedure for on-the-fly pre-processing of cryo-EM data sets of amyloid filaments; we present a graphical tool to select filament types based on bi-hierarchical clustering of filaments and 2D class assignments; and we introduce a denoising neural network for Blush regularisation that is re-trained on amyloid reconstructions. The implementation of these tools in release 5.1 of our open-software package RELION ensures broad applicability. We demonstrate their usefulness on two experimental data sets, including a previously described data set on recombinant human islet amyloid protein (hIAPP) with the S20G mutation for which we identify two new filament types.

biophysics↗

bioRxiv2026

A portable orthogonal replication system enables continuous gene evolution near the biological speed limit

Orthogonal DNA replication systems uncouple the mutagenesis of target genes from host viability, enabling target gene hypermutation beyond the genomic critical error threshold and thereby unlocking access to greater sequence space for accelerated evolution. Here we introduce a series of upgrades to the E. coli orthogonal replication system, EcORep. We develop strategies to efficiently establish, engineer, and transform orthogonal replicons. We develop and utilize replicon-REXER to establish a 77 kb replicon, the largest orthogonal replicon reported to date. Directed evolution of the orthogonal DNA polymerase yielded variants with mutation rates of [~]10-4 substitutions per base per generation and best-in-class mutational spectra. These polymerases are three orders of magnitude more mutagenic than the first-generation EcORep system, enable mutagenesis at one million times the genomic levels, and straddle the evolutionary critical error threshold for the mutation of genes tested. Using the highly mutagenic EcORep system, we rapidly evolve an ethanol assimilation pathway for increased performance. Furthermore, we find that the three components sufficient to drive the minimal EcORep system enable O-replication systems to be established in other Gram-negative bacteria. Thus, we establish VinORep in Vibrio natriegens. VinORep combines O-replicon mutation, around the limit for molecular evolution of genes, with the fastest growing organism, to realize gene evolution approaching the biological speed limit. We exemplify the utility of this advance through the rapid evolution of new function - via the accumulation of tens of mutations, and selection - in 16 hours.

synthetic biology↗

bioRxiv2023

Data-driven regularisation lowers the size barrier of cryo-EM structure determination

Macromolecular structure determination by electron cryo-microscopy (cryo-EM) is limited by the alignment of noisy images of individual particles. Because smaller particles have weaker signals, alignment errors impose size limitations on its applicability. Here, we explore how image alignment is improved by the application of deep-learning to exploit prior knowledge about biological macromolecular structures that would otherwise be difficult to express mathematically. We train a denoising convolutional neural network on pairs of half-set reconstructions from the electron microscopy data bank (EMDB) and use this denoiser as an alternative to a commonly used smoothness prior. We demonstrate that this approach, which we call Blush regularisation, yields better reconstructions than existing algorithms, in particular for data with low signal-to-noise ratios. The reconstruction of a protein-nucleic acid complex with a molecular weight of 40 kDa, which was previously intractable, illustrates that regularisation through denoising will expand the applicability of cryo-EM structure determination for a wide range of biological macromolecules.

biophysics↗

bioRxiv2023

Automated model building and protein identification in cryo-EM maps

Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention. We present ModelAngelo, a machine-learning approach for automated atomic model building in cryo-EM maps. By combining information from the cryo-EM map with information from protein sequence and structure in a single graph neural network, ModelAngelo builds atomic models for proteins that are of similar quality as those generated by human experts. For nucleotides, ModelAngelo builds backbones with similar accuracy as humans. By using its predicted amino acid probabilities for each residue in hidden Markov model sequence searches, ModelAngelo outperforms human experts in the identification of proteins with unknown sequences. ModelAngelo will thus remove bottlenecks and increase objectivity in cryo-EM structure determination.

molecular biology↗

bioRxiv2022

Fast protein structure searching using structure graph embeddings

Comparing and searching protein structures independent of primary sequence has proved useful for remote homology detection, function annotation and protein classification. Fast and accurate methods to search with structures will be essential to make use of the vast databases that have recently become available, in the same way that fast protein sequence searching underpins much of bioinformatics. We train a simple graph neural network using supervised contrastive learning to learn a low-dimensional embedding of protein structure. The method, called Progres, is available as software at https://github.com/greener-group/progres and as a web server at https://progres.mrc-lmb.cam.ac.uk. It has accuracy comparable to the best current methods and can search the AlphaFold database TED domains in a tenth of a second per query on CPU.

bioinformatics↗