Biology subjects

Hosseinzadeh, P.

Publications and source records attributed to Hosseinzadeh, P..

5 recordsLinked to original sources

bioRxiv2026

CyclicMPNN: Stable Cyclic Peptide Sequence Generation

AO_SCPLOWBSTRACTC_SCPLOWCyclic peptides are a promising class of therapeutics due to their attractive drug qualities such as increased structural stability, cell permeability, and resistance to proteolytic degradation. With recent advancements in cyclic peptide backbone generation models like CyclicCAE and RFPeptide, generating cyclic peptide backbones can be done more rapidly compared to traditional algorithm or physics based approaches. However, designing energetically favorable cyclic peptide sequences to fit generated backbones using only canonical amino acids is nontrivial. We fine-tuned the state-of-the-art deep learning model for protein sequence design, ProteinMPNN, using a combination of X-ray crystal structures from the Protein Data Bank and in silico generated cyclic peptides. Our approach surpasses ProteinMPNN in cyclic peptide sequence design, producing energetically stable sequences with a higher success rate of folding into the generated cyclic peptide backbones. We show that CyclicMPNN can be used as a motif-inpainting strategy and in de novo sequence design tasks. We propose that CyclicMPNN will enable the rapid design of energetically stable cyclic peptide sequences, increasing the success rate of therapeutic cyclic peptide development.

biochemistry↗

bioRxiv2025

The open-source Masala software suite: Facilitating rapid methods development for synthetic heteropolymer design

Although canonical protein design has benefited from machine learning methods trained on databases of protein sequences and structures, synthetic heteropolymer design still relies heavily on physics-based methods. The Rosetta software, which provides diverse physics-based methods for designing sequences, exploring conformations, docking molecules, and performing analysis, has proven invaluable to this field. Nevertheless, Rosettas aging architecture, monolithic structure, non-open source code, and steep development learning curve are beginning to hinder new methods development. Here, we introduce the Masala software suite, a free, open-source set of C++ libraries intended to extend Rosetta and other software, and ultimately to be a successor to Rosetta. Masala is structured for modern computing hardware, and its build system automates the creation of application programming interface (API) layers, permitting Masalas use as an extension library for existing software, including Rosetta. Masala features modular architecture in which it is easy for novice developers to add new plugin modules, which can be independently compiled and loaded at runtime, extending functionality of software linking Masala without source code alteration. Here, we describe implementation of Masala modules that accelerate protein and synthetic peptide design. We describe the implementation of Masala real-valued local optimizers and cost function network optimizers that can be used as drop-in replacements for Rosettas minimizer and packer when designing heteropolymers. We explore design-centric guidance terms for promoting desirable features, such as hydrogen bond networks, or discouraging undesirable features, such as unsatisfied buried hydrogen bond donors and acceptors, which we have re-implemented far more efficiently in Masala, providing up to two orders of magnitude of speedup in benchmarks. Finally, we discuss development goals for future versions of Masala.

biochemistry↗

bioRxiv2025

De novo design of miniprotein agonists and antagonists targeting G protein-coupled receptors

G protein-coupled receptors (GPCRs) play key roles in physiology and are central targets for drug discovery and development, but the design of protein agonists and antagonists has been challenging as GPCRs are integral membrane proteins and conformationally dynamic. Here we describe computational de novo design methods and a high throughput "receptor diversion" microscopy-based screen for generating GPCR binding miniproteins with high affinity, potency and selectivity, and the use of these methods to generate agonists for MRGPRX1, NK1R and CCR5, as well as antagonists for CXCR4, CCR5, OXTR, GLP1R, GIPR, GCGR, PTH1R and CGRPR.. Cryo-electron microscopy data reveals atomic-level agreement between designed and experimentally determined structures for CGRPR- and CXCR4-bound antagonists and MRGPRX1-bound agonists. Our de novo design and screening approach opens new frontiers in GPCR drug discovery and development.

bioengineering↗

bioRxiv2025

CyclicCAE: A Conformational Autoencoder for Efficient Heterochiral Macrocyclic Backbone Sampling

Peptide macrocycles are a promising therapeutic class. The inclusion of heterochiral and non-natural amino acids allows far more folds and functions to be accessed, but creates challenges for rational design -- particularly for sampling plausible mainchain conformations. We developed a conformational autoencoder called CyclicCAE to rapidly generate energetically favourable macrocycle scaffolds for heterochiral design and structure prediction. Given the absence of large, available macrocycle datasets, we created a custom dataset in silico using physics-based simulation methods. Trained on this, CyclicCAE produces energetically stable mainchain conformations and designable scaffolds more rapidly than the current state-of-the-art method, the Rosetta software suite's Generalized Kinematic Closure (GeneralizedKIC) method. We show that, despite being trained exclusively on synthetic data, CyclicCAE accurately captures the conformations accessible to peptide macrocycles found in the Protein Data Bank, with considerable speed advantages over GeneralizedKIC. We also demonstrate that CyclicCAE enables users to perform energy minimization in isolation or in a target-bound context, to generate structurally similar or diverse outputs by Markov Chain Monte Carlo sampling, and to conduct inpainting with fixed motifs. This method, which we release under a free and open source licence, will accelerate macrocycle design pipelines, speeding the development of peptide therapeutics.

bioengineering↗

bioRxiv2024

Applying computational protein design to engineer affibodies for affinity-controlled delivery of vascular endothelial growth factor and platelet-derived growth factor

Effective angiogenesis requires the coordinated presentation of vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF), which play competing roles in stimulating vascular outgrowth and stabilization. Dysregulation in VEGF and PDGF secretion are implicated in abnormal vascular structures, poor vessel stability, and inadequate tissue repair. Current biomaterial delivery vehicles for these proteins have a limited ability to precisely control the kinetics of protein release, preventing systematic exploration of their temporal effects. Here, we combined yeast surface display and computational protein design to engineer eight VEGF-specific and PDGF-specific protein binders called affibodies with a broad range of affinities (dissociation constants = 2.23-9260 nM) for affinity-controlled protein release. Starting from one VEGF- and one PDGF-specific affibody discovered from yeast surface display, computational modeling was used to select disruptive mutations to create VEGF-specific affibodies with lower affinities for VEGF and expand the affinity range of PDGF-specific affibodies. In both cases, the specificity of the affibodies for their target protein was either maintained or enhanced. Soluble protein-specific affibodies modulated protein bioactivity as evidenced by changes in VEGF-induced endothelial cell proliferation and luminescent output of a PDGF-responsive fibroblast cell line. Affibody-conjugated polyethylene glycol maleimide (PEG-mal) hydrogels sustained VEGF and PDGF release compared to hydrogels without affibodies, enabling tunability of protein release over 7 days. VEGF and PDGF released from affibody-conjugated hydrogels exhibited higher bioactivity compared to proteins released from hydrogels without affibodies, highlighting the potential of these engineered affinity interactions to prolong protein bioactivity. This work underscores the power of computational protein design to enhance biomaterial functionality, creating a platform for tunable protein delivery to support angiogenesis and tissue repair.

bioengineering↗