RosettaHDX: Predicting antibody-antigen interaction from hydrogen-deuterium exchange mass spectrometry data.

High-throughput characterization of antibody-antigen complexes at the atomic level is critical for understanding antibody function enabling therapeutic development. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) enables rapid epitope mapping, but its data are too sparse for independent structure determination. In this study, we introduce RosettaHDX, a hybrid method that combines computational docking with differential HDX-MS data to enhance the accuracy of antibody-antigen complex models beyond what either method can achieve individually.

WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking.

While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practices. We posit that without a sound model evaluation framework, the AI community's efforts cannot reach their full potential, thereby slowing the progress and transfer of innovation into real-world drug discovery. Thus, in this paper, we seek to establish a new gold standard for small molecule drug discovery benchmarking, .

CACHE Challenge #1: Targeting the WDR Domain of LRRK2, A Parkinson's Disease Associated Protein.

The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinson's disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods.

Posttranslationally modified self-peptides promote hypertension in mouse models.

Posttranslational modifications can enhance immunogenicity of self-proteins. In several conditions, including hypertension, systemic lupus erythematosus, and heart failure, isolevuglandins (IsoLGs) are formed by lipid peroxidation and covalently bond with protein lysine residues. Here, we show that the murine class I major histocompatibility complex (MHC-I) variant H-2Db uniquely presents isoLG-modified peptides and developed a computational pipeline that identifies structural features for MHC-I accommodation of such peptides.

REvoLd: Ultra-Large Library Screening with an Evolutionary Algorithm in Rosetta.

Ultra-large make-on-demand compound libraries now contain billions of readily available compounds. This represents a golden opportunity for in-silico drug discovery. One challenge, however, is the time and computational cost of an exhaustive screen of such large libraries when receptor flexibility is taken into account.

Recent Advances in Automated Structure-Based De Novo Drug Design.

As the number of determined and predicted protein structures and the size of druglike 'make-on-demand' libraries soar, the time-consuming nature of structure-based computer-aided drug design calls for innovative computational algorithms. drug design introduces heuristics to accelerate searching in the vast chemical space. This review focuses on recent advances in structure-based drug design, ranging from conventional fragment-based methods, evolutionary algorithms, and Metropolis Monte Carlo methods to deep generative models.

Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors.

The fusion of traditional chemical descriptors with Graph Neural Networks (GNNs) offers a compelling strategy for enhancing ligand-based virtual screening methodologies. A comprehensive evaluation revealed that the benefits derived from this integrative strategy vary significantly among different GNNs. Specifically, while GCN and SchNet demonstrate pronounced improvements by incorporating descriptors, SphereNet exhibits only marginal enhancement.

Rosetta FlexPepDock to predict peptide-MHC binding: An approach for non-canonical amino acids.

Computation methods that predict the binding of peptides to MHC-I are important tools for screening and identifying immunogenic antigens and have the potential to accelerate vaccine and drug development. However, most available tools are sequence-based and optimized only for peptides containing the twenty canonical amino acids. This omits a large number of peptides containing non-canonical amino acids (NCAA), or residues that undergo varied post-translational modifications such as glycosylation or phosphorylation.

Integrative model of the FSH receptor reveals the structural role of the flexible hinge region.

The follicle-stimulating hormone receptor (FSHR) belongs to the glycoprotein hormone receptors, a subfamily of G-protein-coupled receptors (GPCRs). FSHR is involved in reproductive processes such as gonadal development and maturation. Structurally, the extensive extracellular domain, which contains the hormone-binding site and is linked to the transmembrane domain by the hinge region (HR), is characteristic for these receptors.

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks.

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met.

Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1.

The rise of antibiotic resistance calls for new therapeutics targeting resistance factors such as the New Delhi metallo-β-lactamase 1 (NDM-1), a bacterial enzyme that degrades β-lactam antibiotics. We present structure-guided computational methods for designing peptide macrocycles built from mixtures of l- and d-amino acids that are able to bind to and inhibit targets of therapeutic interest. Our methods explicitly consider the propensity of a peptide to favor a binding-competent conformation, which we found to predict rank order of experimentally observed IC values across seven designed NDM-1- inhibiting peptides.

Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design.

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University.

Efficient Sampling of Protein Loop Regions Using Conformational Hashing Complemented with Random Coordinate Descent.

construction of loop regions is an important problem in computational structural biology. Compared to regions with well-defined secondary structure, loops tend to exhibit significant conformational heterogeneity. As a result, their structures are often ambiguous when determined using experimental data obtained by crystallography, cryo-EM, or NMR.

Macromolecular modeling and design in Rosetta: recent methods and frameworks.

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities.

Better together: Elements of successful scientific software development in a distributed collaborative community.

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments.

Delineating the Molecular Basis of the Calmodulin‒bMunc13-2 Interaction by Cross-Linking/Mass Spectrometry-Evidence for a Novel CaM Binding Motif in bMunc13-2.

Exploring the interactions between the Ca binding protein calmodulin (CaM) and its target proteins remains a challenging task. Members of the Munc13 protein family play an essential role in short-term synaptic plasticity, modulated via the interaction with CaM at the presynaptic compartment. In this study, we focus on the bMunc13-2 isoform expressed in the brain, as strong changes in synaptic transmission were observed upon its mutagenesis or deletion.

Mono(2-ethylhexyl) phthalate (MEHP) and mono(2-ethyl-5-oxohexyl) phthalate (MEOHP) but not di(2-ethylhexyl) phthalate (DEHP) bind productively to the peroxisome proliferator-activated receptor γ.

Rationale: The most frequently occurring phthalate, di(2-ethylhexyl) phthalate (DEHP), causes adverse effects on glucose homeostasis and insulin sensitivity in several cell models and epidemiological studies. However, thus far, there is no information available on the molecular interaction of phthalates and one of the key regulators of the metabolism, the peroxisome proliferator-activated receptor gamma (PPARγ). Since the endogenous ligand of PPARγ, 15-deoxy-delta-12,14-prostaglandin J (15Δ-PGJ ), features structural similarity to DEHP and its main metabolites produced in human hepatic metabolism, mono(2-ethylhexyl) phthalate (MEHP) and mono(2-ethyl-5-oxohexyl) phthalate (MEOHP), we tested the hypothesis of direct interactions between PPARγ and DEHP or its transformation products.

Identification of a ubiquitin-binding interface using Rosetta and DEER.

ExoU is a type III-secreted cytotoxin expressing A phospholipase activity when injected into eukaryotic target cells by the bacterium The enzymatic activity of ExoU is undetectable in vitro unless ubiquitin, a required cofactor, is added to the reaction. The role of ubiquitin in facilitating ExoU enzymatic activity is poorly understood but of significance for designing inhibitors to prevent tissue injury during infections with strains of producing this toxin. Most ubiquitin-binding proteins, including ExoU, demonstrate a low (micromolar) affinity for monoubiquitin (monoUb).

Web-accessible molecular modeling with Rosetta: The Rosetta Online Server that Includes Everyone (ROSIE).

The Rosetta molecular modeling software package provides a large number of experimentally validated tools for modeling and designing proteins, nucleic acids, and other biopolymers, with new protocols being added continually. While freely available to academic users, external usage is limited by the need for expertise in the Unix command line environment. To make Rosetta protocols available to a wider audience, we previously created a web server called Rosetta Online Server that Includes Everyone (ROSIE), which provides a common environment for hosting web-accessible Rosetta protocols.

Conformational Shift of a β-Hairpin Peptide upon Complex Formation with an Oligo-proline Peptide Studied by Mass Spectrometry.

So-called super-secondary structures such as the β-hairpin, studied here, form an intermediate hierarchy between secondary and tertiary structures of proteins. Their sequence-derived 'pure' peptide backbone conformation is combined with 'remote' interstrand or interresidue contacts reminiscent of the 3D-structure of full-length proteins. This renders them ideally suited for studying potential nucleation sites of protein folding reactions as well as intermolecular interactions.

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.

Rearrangement of the Extracellular Domain/Extracellular Loop 1 Interface Is Critical for Thyrotropin Receptor Activation.

The thyroid stimulating hormone receptor (TSHR) is a G protein-coupled receptor (GPCR) with a characteristic large extracellular domain (ECD). TSHR activation is initiated by binding of the hormone ligand TSH to the ECD. How the extracellular binding event triggers the conformational changes in the transmembrane domain (TMD) necessary for intracellular G protein activation is poorly understood.

Rosetta and the Design of Ligand Binding Sites.

Proteins that bind small molecules (ligands) can be used as biosensors, signal modulators, and sequestering agents. When naturally occurring proteins for a particular target ligand are not available, artificial proteins can be computationally designed. We present a protocol based on RosettaLigand to redesign an existing protein pocket to bind a target ligand.

Engineering an allosteric transcription factor to respond to new ligands.

Genetic regulatory proteins inducible by small molecules are useful synthetic biology tools as sensors and switches. Bacterial allosteric transcription factors (aTFs) are a major class of regulatory proteins, but few aTFs have been redesigned to respond to new effectors beyond natural aTF-inducer pairs. Altering inducer specificity in these proteins is difficult because substitutions that affect inducer binding may also disrupt allostery.