The PyRosetta Toolkit: a graphical user interface for the Rosetta software suite

An interactive visualization tool for educational outreach in protein contact map overlap analysis

Recent advancements in contact map-based protein three-dimensional (3D) structure prediction have been driven by the evolution of deep learning algorithms. However, the gap in accessible software tools for novices in this domain remains a significant challenge. This study introduces GoFold, a novel, standalone graphical user interface (GUI) designed for beginners to perform contact map overlap (CMO) problems for better template selection. Unlike existing tools that cater more to research needs or assume foundational knowledge, GoFold offers an intuitive, user-friendly platform with comprehensive tutorials. It stands out in its ability to visually represent the CMO problem, allowing users to input proteins in various formats and explore the CMO problem. The educational value of GoFold is demonstrated through benchmarking against the state-of-the-art contact map overlap method, map_align, using two datasets: PSICOV and CAMEO. GoFold exhibits superior performance in terms of TM-score and Z-score metrics across diverse qualities of contact maps and target difficulties. Notably, GoFold runs efficiently on personal computers without any third-party dependencies, thereby making it accessible to the general public for promoting citizen science. The tool is freely available for download for macOS, Linux, and Windows.

Neuroinflammation, autoinflammation, splenomegaly and anemia caused by bi-allelic mutations in IRAK4

We describe a novel, severe autoinflammatory syndrome characterized by neuroinflammation, systemic autoinflammation, splenomegaly, and anemia (NASA) caused by bi-allelic mutations in IRAK4. IRAK-4 is a serine/threonine kinase with a pivotal role in innate immune signaling from toll-like receptors and production of pro-inflammatory cytokines. In humans, bi-allelic mutations in IRAK4 result in IRAK-4 deficiency and increased susceptibility to pyogenic bacterial infections, but autoinflammation has never been described. We describe 5 affected patients from 2 unrelated families with compound heterozygous mutations in IRAK4 (c.C877T (p.Q293*)/c.G958T (p.D320Y); and c.A86C (p.Q29P)/c.161 + 1G>A) resulting in severe systemic autoinflammation, massive splenomegaly and severe transfusion dependent anemia and, in 3/5 cases, severe neuroinflammation and seizures. IRAK-4 protein expression was reduced in peripheral blood mononuclear cells (PBMC) in affected patients. Immunological analysis demonstrated elevated serum tumor necrosis factor (TNF), interleukin (IL) 1 beta (IL-1β), IL-6, IL-8, interferon α2a (IFN-α2a), and interferon β (IFN-β); and elevated cerebrospinal fluid (CSF) IL-6 without elevation of CSF IFN-α despite perturbed interferon gene signature. Mutations were located within the death domain (DD; p.Q29P and splice site mutation c.161 + 1G>A) and kinase domain (p.Q293*/p.D320Y) of IRAK-4. Structure-based modeling of the DD mutation p.Q29P showed alteration in the alignment of a loop within the DD with loss of contact distance and hydrogen bond interactions with IRAK-1/2 within the myddosome complex. The kinase domain mutation p.D320Y was predicted to stabilize interactions within the kinase active site. While precise mechanisms of autoinflammation in NASA remain uncertain, we speculate that loss of negative regulation of IRAK-4 and IRAK-1; dysregulation of myddosome assembly and disassembly; or kinase active site instability may drive dysregulated IL-6 and TNF production. Blockade of IL-6 resulted in immediate and complete amelioration of systemic autoinflammation and anemia in all 5 patients treated; however, neuroinflammation has, so far proven recalcitrant to IL-6 blockade and the janus kinase (JAK) inhibitor baricitinib, likely due to lack of central nervous system penetration of both drugs. We therefore highlight that bi-allelic mutation in IRAK4 may be associated with a severe and complex autoinflammatory and neuroinflammatory phenotype that we have called NASA (neuroinflammation, autoinflammation, splenomegaly and anemia), in addition to immunodeficiency in humans.

Revolutionizing enzyme engineering through artificial intelligence and machine learning

The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.

Deep template-based protein structure prediction

MOTIVATION

Protein structure prediction has been greatly improved by deep learning, but most efforts are devoted to template-free modeling. But very few deep learning methods are developed for TBM (template-based modeling), a popular technique for protein structure prediction. TBM has been studied extensively in the past, but its accuracy is not satisfactory when highly similar templates are not available.

RESULTS

This paper presents a new method NDThreader (New Deep-learning Threader) to address the challenges of TBM. NDThreader first employs DRNF (deep convolutional residual neural fields), which is an integration of deep ResNet (convolutional residue neural networks) and CRF (conditional random fields), to align a query protein to templates without using any distance information. Then NDThreader uses ADMM (alternating direction method of multipliers) and DRNF to further improve sequence-template alignments by making use of predicted distance potential. Finally, NDThreader builds 3D models from a sequence-template alignment by feeding it and sequence coevolution information into a deep ResNet to predict inter-atom distance distribution, which is then fed into PyRosetta for 3D model construction. Our experimental results show that NDThreader greatly outperforms existing methods such as CNFpred, HHpred, DeepThreader and CEthreader. NDThreader was blindly tested in CASP14 as a part of RaptorX server, which obtained the best average GDT score among all CASP14 servers on the 58 TBM targets.

PolyFold: An interactive visual simulator for distance-based protein folding

Recent advances in distance-based protein folding have led to a paradigm shift in protein structure prediction. Through sufficiently precise estimation of the inter-residue distance matrix for a protein sequence, it is now feasible to predict the correct folds for new proteins much more accurately than ever before. Despite the exciting progress, a dedicated visualization system that can dynamically capture the distance-based folding process is still lacking. Most molecular visualizers typically provide only a static view of a folded protein conformation, but do not capture the folding process. Even among the selected few graphical interfaces that do adopt a dynamic perspective, none of them are distance-based. Here we present PolyFold, an interactive visual simulator for dynamically capturing the distance-based protein folding process through real-time rendering of a distance matrix and its compatible spatial conformation as it folds in an intuitive and easy-to-use interface. PolyFold integrates highly convergent stochastic optimization algorithms with on-demand customizations and interactive manipulations to maximally satisfy the geometric constraints imposed by a distance matrix. PolyFold is capable of simulating the complex process of protein folding even on modest personal computers, thus making it accessible to the general public for fostering citizen science. Open source code of PolyFold is freely available for download at https://github.com/Bhattacharya-Lab/PolyFold. It is implemented in cross-platform Java and binary executables are available for macOS, Linux, and Windows.

Elfin UI: A Graphical Interface for Protein Design With Modular Building Blocks

Molecular models have enabled understanding of biological structures and functions and allowed design of novel macro-molecules. Graphical user interfaces (GUIs) in molecular modeling are generally focused on atomic representations, but, especially for proteins, do not usually address designs of complex and large architectures, from nanometers to microns. Therefore, we have developed Elfin UI as a Blender add-on for the interactive design of large protein architectures with custom shapes. Elfin UI relies on compatible building blocks to design single- and multiple-chain protein structures. The software can be used: (1) as an interactive environment to explore building blocks combinations; and (2) as a computer aided design (CAD) tool to define target shapes that guide automated design. Elfin UI allows users to rapidly build new protein shapes, without the need to focus on amino acid sequence, and aims to make design of proteins and protein-based materials intuitive and accessible to researchers and members of the general public with limited expertise in protein engineering.

The state-of-the-art strategies of protein engineering for enzyme stabilization

Enzymes generated by natural recruitment and protein engineering have greatly contribute in various sets of applications. However, their insufficient stability is a bottleneck that limit the rapid development of biocatalysis. Novel approaches based on precise and global structural dissection, advanced gene manipulation, and combination with the multidisciplinary techniques open a new horizon to generate stable enzymes efficiently. Here, we comprehensively introduced emerging advances of protein engineering strategies for enzyme stabilization. Then, we highlighted practical cases to show importance of enzyme stabilization in pharmaceutical and industrial applications. Combining computational enzyme design with molecular evolution will hold considerable promise in this field.

Bioengineering Strategies for Protein-Based Nanoparticles

In recent years, the practical application of protein-based nanoparticles (PNPs) has expanded rapidly into areas like drug delivery, vaccine development, and biocatalysis. PNPs possess unique features that make them attractive as potential platforms for a variety of nanobiotechnological applications. They self-assemble from multiple protein subunits into hollow monodisperse structures; they are highly stable, biocompatible, and biodegradable; and their external components and encapsulation properties can be readily manipulated by chemical or genetic strategies. Moreover, their complex and perfect symmetry have motivated researchers to mimic their properties in order to create de novo protein assemblies. This review focuses on recent advances in the bioengineering and bioconjugation of PNPs and the implementation of synthetic biology concepts to exploit and enhance PNP's intrinsic properties and to impart them with novel functionalities.

RosettaAntibodyDesign (RAbD): A general framework for computational antibody design

A structural-bioinformatics-based computational methodology and framework have been developed for the design of antibodies to targets of interest. RosettaAntibodyDesign (RAbD) samples the diverse sequence, structure, and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications. The program samples antibody sequences and structures by grafting structures from a widely accepted set of the canonical clusters of CDRs (North et al., J. Mol. Biol., 406:228-256, 2011). It then performs sequence design according to amino acid sequence profiles of each cluster, and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints. Starting from an existing experimental or computationally modeled antigen-antibody structure, RAbD can be used to redesign a single CDR or multiple CDRs with loops of different length, conformation, and sequence. We rigorously benchmarked RAbD on a set of 60 diverse antibody-antigen complexes, using two design strategies-optimizing total Rosetta energy and optimizing interface energy alone. We utilized two novel metrics for measuring success in computational protein design. The design risk ratio (DRR) is equal to the frequency of recovery of native CDR lengths and clusters divided by the frequency of sampling of those features during the Monte Carlo design procedure. Ratios greater than 1.0 indicate that the design process is picking out the native more frequently than expected from their sampled rate. We achieved DRRs for the non-H3 CDRs of between 2.4 and 4.0. The antigen risk ratio (ARR) is the ratio of frequencies of the native amino acid types, CDR lengths, and clusters in the output decoys for simulations performed in the presence and absence of the antigen. For CDRs, we achieved cluster ARRs as high as 2.5 for L1 and 1.5 for H2. For sequence design simulations without CDR grafting, the overall recovery for the native amino acid types for residues that contact the antigen in the native structures was 72% in simulations performed in the presence of the antigen and 48% in simulations performed without the antigen, for an ARR of 1.5. For the non-contacting residues, the ARR was 1.08. This shows that the sequence profiles are able to maintain the amino acid types of these conserved, buried sites, while recovery of the exposed, contacting residues requires the presence of the antigen-antibody interface. We tested RAbD experimentally on both a lambda and kappa antibody-antigen complex, successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters.

Foldit Standalone: a video game-derived protein structure manipulation interface using Rosetta

Summary

Foldit Standalone is an interactive graphical interface to the Rosetta molecular modeling package. In contrast to most command-line or batch interactions with Rosetta, Foldit Standalone is designed to allow easy, real-time, direct manipulation of protein structures, while also giving access to the extensive power of Rosetta computations. Derived from the user interface of the scientific discovery game Foldit (itself based on Rosetta), Foldit Standalone has added more advanced features and removed the competitive game elements. Foldit Standalone was built from the ground up with a custom rendering and event engine, configurable visualizations and interactions driven by Rosetta. Foldit Standalone contains, among other features: electron density and contact map visualizations, multiple sequence alignment tools for template-based modeling, rigid body transformation controls, RosettaScripts support and an embedded Lua interpreter.

Availability and Implementation

Foldit Standalone is available for download at https://fold.it/standalone, under the Rosetta license, which is free for academic and non-profit users. It is implemented in cross-platform C ++ and binary executables are available for Windows, macOS and Linux.

Antibody Affinity Maturation by Computational Design

The immune systems protect our bodies from foreign molecules or antigens, where antibodies play important roles. Antibodies evolve over time upon antigen encounter by somatically mutating their genome sequences. The end result is a series of antibodies that display higher affinities and specificities to specific antigens. This process is called affinity maturation. Recent improvements in computer hardware and modeling algorithms now enable the rational design of protein structures and functions, and several works on computer-aided antibody design have been published. In this chapter, we briefly describe computational methods for antibody affinity maturation, focusing on methods for sampling antibody conformations and for scoring designed antibody variants. We also discuss lessons learned from the successful computer-aided design of antibodies.

Differential protein structural disturbances and suppression of assembly partners produced by nonsense GABRG2 epilepsy mutations: implications for disease phenotypic heterogeneity

Mutations in GABA_A receptor subunit genes are frequently associated with epilepsy, and nonsense mutations in GABRG2 are associated with several epilepsy syndromes including childhood absence epilepsy, generalized tonic clonic seizures and the epileptic encephalopathy, Dravet syndrome. The molecular basis for the phenotypic heterogeneity of mutations is unclear. Here we focused on three nonsense mutations in GABRG2 (GABRG2(R136*), GABRG2(Q390*) and GABRG2(W429*)) associated with epilepsies of different severities. Structural modeling and structure-based analysis indicated that the surface of the wild-type γ2 subunit was naturally hydrophobic, which is suitable to be buried in the cell membrane. Different mutant γ2 subunits had different stabilities and different interactions with their wild-type subunit binding partners because they adopted different conformations and had different surface hydrophobicities and different tendency to dimerize. We utilized flow cytometry and biochemical approaches in combination with lifted whole cell patch-clamp recordings. We demonstrated that the truncated subunits had no to minimal surface expression and unchanged or reduced surface expression of wild-type partnering subunits. The amplitudes of GABA-evoked currents from the mutant α1β2γ2(R136*), α1β2γ2(Q390*) and α1β2γ2(W429*) receptors were reduced compared to the currents from α1β2γ2 receptors but with differentially reduced levels. This thus suggests differential protein structure disturbances are correlated with disease severity.

Simplifying and enhancing the use of PyMOL with horizontal scripts

Scripts are used in PyMOL to exert precise control over the appearance of the output and to ease remaking similar images at a later time. We developed horizontal scripts to ease script development. A horizontal script makes a complete scene in PyMOL like a traditional vertical script. The commands in a horizontal script are separated by semicolons. These scripts are edited interactively on the command line with no need for an external text editor. This simpler workflow accelerates script development. In using PyMOL, the illustration of a molecular scene requires an 18-element matrix of view port settings. The default format spans several lines and is laborious to manually reformat for one line. This default format prevents the fast assembly of horizontal scripts that can reproduce a molecular scene. We solved this problem by writing a function that displays the settings on one line in a compact format suitable for horizontal scripts. We also demonstrate the mapping of aliases to horizontal scripts. Many aliases can be defined in a single script file, which can be useful for applying costume molecular representations to any structure. We also redefined horizontal scripts as Python functions to enable the use of the help function to print documentation about an alias to the command history window. We discuss how these methods of using horizontal scripts both simplify and enhance the use of PyMOL in research and education.

Protocols for Molecular Modeling with Rosetta3 and RosettaScripts

Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed “RosettaScripts” for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta’s ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.

InteractiveROSETTA: a graphical user interface for the PyRosetta protein modeling suite

Modern biotechnical research is becoming increasingly reliant on computational structural modeling programs to develop novel solutions to scientific questions. Rosetta is one such protein modeling suite that has already demonstrated wide applicability to a number of diverse research projects. Unfortunately, Rosetta is largely a command-line-driven software package which restricts its use among non-computational researchers. Some graphical interfaces for Rosetta exist, but typically are not as sophisticated as commercial software. Here, we present InteractiveROSETTA, a graphical interface for the PyRosetta framework that presents easy-to-use controls for several of the most widely used Rosetta protocols alongside a sophisticated selection system utilizing PyMOL as a visualizer. InteractiveROSETTA is also capable of interacting with remote Rosetta servers, facilitating sophisticated protocols that are not accessible in PyRosetta or which require greater computational resources.

AVAILABILITY AND IMPLEMENTATION

InteractiveROSETTA is freely available at https://github.com/schenc3/InteractiveROSETTA/releases and relies upon a separate download of PyRosetta which is available at http://www.pyrosetta.org after obtaining a license (free for academic use).

Kinase-mediated changes in nucleosome conformation trigger chromatin decondensation via poly(ADP-ribosyl)ation

Dynamically controlled posttranslational modifications of nucleosomal histones alter chromatin condensation to regulate transcriptional activation. We report that a nuclear tandem kinase, JIL-1, controls gene expression by activating poly(ADP-ribose) polymerase-1 (PARP-1). JIL-1 phosphorylates the C terminus of the H2Av histone variant, which stimulates PARP-1 enzymatic activity in the surrounding chromatin, leading to further modification of histones and chromatin loosening. The H2Av nucleosome has a higher surface representation of PARP-1 binding patch, consisting of H3 and H4 epitopes. Phosphorylation of H2Av by JIL-1 restructures this surface patch, leading to activation of PARP-1. Exposure of Val61 and Leu23 of the H4 histone is critical for PARP-1 binding on nucleosome and PARP-1 activation following H2Av phosphorylation. We propose that chromatin loosening and associated initiation of gene expression is activated by phosphorylation of H2Av in a nucleosome positioned in promoter regions of PARP-1-dependent genes.

Computational tools for designing and engineering enzymes

Protein engineering strategies aimed at constructing enzymes with novel or improved activities, specificities, and stabilities greatly benefit from in silico methods. Computational methods can be principally grouped into three main categories: bioinformatics; molecular modelling; and de novo design. Particularly de novo protein design is experiencing rapid development, resulting in more robust and reliable predictions. A recent trend in the field is to combine several computational approaches in an interactive manner and to complement them with structural analysis and directed evolution. A detailed investigation of designed catalysts provides valuable information on the structural basis of molecular recognition, biochemical catalysis, and natural protein evolution.

Serverification of molecular modeling applications: the Rosetta Online Server that Includes Everyone (ROSIE)

The Rosetta molecular modeling software package provides experimentally tested and rapidly evolving tools for the 3D structure prediction and high-resolution design of proteins, nucleic acids, and a growing number of non-natural polymers. Despite its free availability to academic users and improving documentation, use of Rosetta has largely remained confined to developers and their immediate collaborators due to the code's difficulty of use, the requirement for large computational resources, and the unavailability of servers for most of the Rosetta applications. Here, we present a unified web framework for Rosetta applications called ROSIE (Rosetta Online Server that Includes Everyone). ROSIE provides (a) a common user interface for Rosetta protocols, (b) a stable application programming interface for developers to add additional protocols, (c) a flexible back-end to allow leveraging of computer cluster resources shared by RosettaCommons member institutions, and (d) centralized administration by the RosettaCommons to ensure continuous maintenance. This paper describes the ROSIE server infrastructure, a step-by-step 'serverification' protocol for use by Rosetta developers, and the deployment of the first nine ROSIE applications by six separate developer teams: Docking, RNA de novo, ERRASER, Antibody, Sequence Tolerance, Supercharge, Beta peptide design, NCBB design, and VIP redesign. As illustrated by the number and diversity of these applications, ROSIE offers a general and speedy paradigm for serverification of Rosetta applications that incurs negligible cost to developers and lowers barriers to Rosetta use for the broader biological community. ROSIE is available at http://rosie.rosettacommons.org.