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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. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

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. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS‐CoV‐2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride toward achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation. [ABSTRACT FROM AUTHOR]

Carbohydrates and glycoproteins modulate key biological functions. However, experimental structure determination of sugar polymers is notoriously difficult. Computational approaches can aid in carbohydrate structure prediction, structure determination, and design. In this work, we developed a glycan-modeling algorithm, GlycanTreeModeler, that computationally builds glycans layer-by-layer, using adaptive kernel density estimates (KDE) of common glycan conformations derived from data in the Protein Data Bank (PDB) and from quantum mechanics (QM) calculations. GlycanTreeModeler was benchmarked on a test set of glycan structures of varying lengths, or "trees". Structures predicted by GlycanTreeModeler agreed with native structures at high accuracy for both de novo modeling and experimental density-guided building. We employed these tools to design de novo glycan trees into a protein nanoparticle vaccine to shield regions of the scaffold from antibody recognition, and experimentally verified shielding. This work will inform glycoprotein model prediction, glycan masking, and further aid computational methods in experimental structure determination and refinement. Author summary: Many biological proteins are chemically modified to induce specific structure and function. Carbohydrates (glycans) are one such modification that play an important role in signaling, stability, solubility, aggregation, and the immune system. In this work, we have developed and extensively benchmarked a computational protocol for predicting the structures of these glycans, while improving the Rosetta Software Suite through the development of new general analysis frameworks, such as the SimpleMetric system and extensive glycan tools for modeling and design. We describe the benchmarking, optimization, and use of a novel computational method for three dimensional modeling of glycans and glyco-conjugates called the GlycanTreeModeler. This method is unique in that it grows glycans layer-by-layer using extensive data-driven methods. We thoroughly benchmark the method and detail iterative improvement of the GlycanTreeModeler through scoring and kinematic improvements, and show how these methods can be useful in glycan-related computational tasks and glycan masking of a novel vaccine scaffold in-vitro and in-vivo. [ABSTRACT FROM AUTHOR]

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. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities. [ABSTRACT FROM AUTHOR]

The RosettaCarbohydrate framework is a new tool for modeling a wide variety of saccharide and glycoconjugate structures. This report describes the development of the framework and highlights its applications. The framework integrates with established protocols within the Rosetta modeling and design suite, and it handles the vast complexity and variety of carbohydrate molecules, including branching and sugar modifications. To address challenges of sampling and scoring, RosettaCarbohydrate can sample glycosidic bonds, side-chain conformations, and ring forms, and it utilizes a glycan-specific term within its scoring function. Rosetta can work with standard PDB, GLYCAM, and GlycoWorkbench ( .gws) file formats. Saccharide residue-specific chemical information is stored internally, permitting glycoengineering and design. Carbohydrate-specific applications described herein include virtual glycosylation, loop-modeling of carbohydrates, and docking of glyco-ligands to antibodies. Benchmarking data are presented and compared to other studies, demonstrating Rosetta's ability to predict glyco-ligand binding. The framework expands the tools available to glycoscientists and engineers. © 2016 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS-CoV-2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride toward achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation., (© 2024 The Protein Society.)

Neuronal synaptic functional deficits are linked to impaired learning and memory in Alzheimer's disease (AD). We recently demonstrated that O-GlcNAc, a novel cytosolic and nuclear carbohydrate post-translational modification, is enriched at neuronal synapses and positively regulates synaptic plasticity linked to learning and memory in mice. Reduced levels of O-GlcNAc have been observed in AD, suggesting a possible link to deficits in synaptic plasticity. Using lectin enrichment and mass spectrometry, we mapped several human cortical synaptic O-GlcNAc modification sites. Overlap in patterns of O-GlcNAcation between mouse and human appears to be high, as previously mapped mouse synaptic O-GlcNAc sites in Bassoon, Piccolo, and tubulin polymerization promoting protein p25 were identified in human. Novel O-GlcNAc modification sites were identified on Mek2 and RPN13/ADRM1. Mek2 is a signaling component of the Erk 1/2 pathway involved in synaptic plasticity. RPN13 is a component of the proteasomal degradation pathway. The potential interplay of phosphorylation with mapped O-GlcNAc sites, and possible implication of those sites in synaptic plasticity in normal versus AD states is discussed. iTRAQ is a powerful differential isotopic quantitative approach in proteomics. Pulsed Q dissociation (PQD) is a recently introduced fragmentation strategy that enables detection of low mass iTRAQ reporter ions in ion trap mass spectrometry. We optimized LTQ ion trap settings for PQD-based iTRAQ quantitation and demonstrated its utility in O-GlcNAc site mapping. Using iTRAQ, abnormal synaptic expression levels of several proteins previously implicated in AD pathology were observed in addition to novel changes in synaptic specific protein expression including Synapsin II.