Biology subjects

Renfrew, P. D.

Publications and source records attributed to Renfrew, P. D..

9 recordsLinked to original sources

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

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

Large protein databases reveal structural complementarity and functional locality

Recent breakthroughs in protein structure prediction have led to an unprecedented surge in high-quality 3D models, highlighting the need for efficient computational solutions to manage and analyze this wealth of structural data. In our work, we comprehensively examine the structural clusters obtained from the AlphaFold Protein Structure Database (AFDB), a high-quality subset of ESMAtlas, and the Microbiome Immunity Project (MIP). We create a single cohesive low-dimensional representation of the resulting protein space. Our results show that, while each database occupies distinct regions within the protein structure space, they collectively exhibit significant overlap in their functional profiles. High-level biological functions tend to cluster in particular regions, revealing a shared functional landscape despite the diverse sources of data. By creating a single, cohesive low-dimensional representation of protein structure space integrating data from diverse sources, localizing functional annotations within this space, and providing an open-access web-server for exploration, this work offers insights for future research concerning protein sequence-structure-function relationships, enabling various biological questions to be asked about taxonomic assignments, environmental factors, or functional specificity. This approach is generalizable to other or future datasets, enabling further discovery beyond findings presented here.

bioinformatics↗

bioRxiv2022

CMA-ES-Rosetta: Blackbox optimization algorithm traverses rugged peptide docking energy landscapes

Energy minimization is necessary for virtually all modeling and design tasks and involves traversing extremely rugged energy landscapes. Although the gradient descent based minimization routines in Rosetta have fast runtimes, due to these rugged landscapes, minimization often converges into high-energy local minima. Alternative numerical optimization techniques, such as evolution strategies, are more robust to rugged landscapes and have been shown to be highly successful on a diverse set of problems. Here we explore the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), a state-of-the-art derivative-free optimization algorithm, as a complementary approach to the default minimizer in Rosetta. We used a benchmark of 26 peptides, from the FlexPepDock Benchmark, to assess the performance of three algorithms in Rosetta, specifically, CMA-ES, Rosettas default minimizer, and a Monte Carlo protocol of small backbone perturbations. We test the algorithms performance on their ability to dock an idealized peptide to a series of hotspots residues (i.e. constraints) along a native peptide. Of the three methods, CMA-ES was able to find the lowest energy conformation for 23 out of 26 benchmark peptides. The application of CMA-ES allows for an alternative optimization method for macromolecular modeling problems with rough energy landscapes.

biophysics↗

bioRxiv2022

Sequence-structure-function relationships in the microbial protein universe

Abstract / SummaryFor the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that dont rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict [~]200,000 structures for diverse protein sequences from 1,003 representative genomes1 across the microbial tree of life, and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for shifting the focus from obtaining structures to putting them into context, to transform all branches of biology, including a shift from sequence-based to sequence-structure-function based meta-omics analyses.

bioinformatics↗

bioRxiv2019

Structure-Based Function Prediction using Graph Convolutional Networks

The large number of available sequences and the diversity of protein functions challenge current experimental and computational approaches to determining and predicting protein function. We present a deep learning Graph Convolutional Network (GCN) for predicting protein functions and concurrently identifying functionally important residues. This model is initially trained using experimentally determined structures from the Protein Data Bank (PDB) but has significant de-noising capability, with only a minor drop in performance observed when structure predictions are used. We take advantage of this denoising property to train the model on > 200,000 protein structures, including many homology-predicted structures, greatly expanding the reach and applications of the method. Our model learns general structure-function relationships by robustly predicting functions of proteins with [≤] 40% sequence identity to the training set. We show that our GCN architecture predicts functions more accurately than Convolutional Neural Networks trained on sequence data alone and previous competing methods. Using class activation mapping, we automatically identify structural regions at the residue-level that lead to each function prediction for every confidently predicted protein, advancing site-specific function prediction. We use our method to annotate PDB and SWISS-MODEL proteins, making several new confident function predictions spanning both fold and function classifications.

bioinformatics↗

bioRxiv2019

Designing Peptides on a Quantum Computer

Although a wide variety of quantum computers are currently being developed, actual computational results have been largely restricted to contrived, artificial tasks. Finding ways to apply quantum computers to useful, real-world computational tasks remains an active research area. Here we describe our mapping of the protein design problem to the D-Wave quantum annealer. We present a system whereby Rosetta, a state-of-the-art protein design software suite, interfaces with the D-Wave quantum processing unit to find amino acid side chain identities and conformations to stabilize a fixed protein backbone. Our approach, which we call the QPacker, uses a large side-chain rotamer library and the full Rosetta energy function, and in no way reduces the design task to a simpler format. We demonstrate that quantum annealer-based design can be applied to complex real-world design tasks, producing designed molecules comparable to those produced by widely adopted classical design approaches. We also show through large-scale classical folding simulations that the results produced on the quantum annealer can inform wet-lab experiments. For design tasks that scale exponentially on classical computers, the QPacker achieves nearly constant runtime performance over the range of problem sizes that could be tested. We anticipate better than classical performance scaling as quantum computers mature.

bioengineering↗

bioRxiv2017

The Rosetta all-atom energy function for macromolecular modeling and design

Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to design of nanomaterials, protein therapeutics, and vaccines. Central to Rosettas success is the energy function: amodel parameterized from small molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, beta_nov15. Applying these concepts,we explain how to use Rosetta energies to identify and analyze the features of biomolecular models.Finally, we discuss the latest advances in the energy function that extend capabilities from soluble proteins to also include membrane proteins, peptides containing non-canonical amino acids, carbohydrates, nucleic acids, and other macromolecules.

biophysics↗

bioRxiv2016

Rotamer libraries for the high-resolution design of beta-amino acid foldamers

{beta}-amino acids offer attractive opportunities to develop biologically active peptidomimetics, either employed alone or in conjunction with natural -amino acids. Owing to their potential for unique conformational preferences that deviate considerably from -peptide geometries, {beta}-amino acids greatly expand the possible chemistries and physical properties available to polyamide foldamers. Complete in silico support for designing new molecules incorporating nonnatural amino acids typically requires representing their side chain conformations as sets of discrete rotamers for model refinement and sequence optimization. Such rotamer libraries are key components of several state of the art design frameworks. Here we report the development, incorporation in to the Rosetta macromolecular modeling suite, and validation of rotamer libraries for {beta}3-amino acids.

bioinformatics↗