Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity in -regulatory elements.

Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging.

SBILib: a handle for protein modeling and engineering.

Summary: The SBILib Python library provides an integrated platform for the analysis of macromolecular structures and interactions. It combines simple 3D file parsing and workup methods with more advanced analytical tools. SBILib includes modules for macromolecular interactions, loops, super-secondary structures, and biological sequences, as well as wrappers for external tools with which to integrate their results and facilitate the comparative analysis of protein structures and their complexes.

A PD-L1/EGFR bispecific antibody combines immune checkpoint blockade and direct anti-cancer action for an enhanced anti-tumor response.

Immune checkpoint blockade (ICB) with antibodies has shown durable clinical responses in a wide range of cancer types, but the overall response rate is still limited. Other effective therapeutic modalities to increase the ICB response rates are urgently needed. New bispecific antibody (bsAb) formats combining the ICB effect and a direct action on cancer cells could improve the efficacy of current immunotherapies.

A generic framework for hierarchical de novo protein design.

De novo protein design enables the exploration of novel sequences and structures absent from the natural protein universe. De novo design also stands as a stringent test for our understanding of the underlying physical principles of protein folding and may lead to the development of proteins with unmatched functional characteristics. The first fundamental challenge of de novo design is to devise "designable" structural templates leading to sequences that will adopt the predicted fold.

RosettaSurf-A surface-centric computational design approach.

Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules.

Mutational Hotspot in the SARS-CoV-2 Spike Protein N-Terminal Domain Conferring Immune Escape Potential.

Global efforts are being made to monitor the evolution of SARS-CoV-2, aiming for early identification of genotypes providing increased infectivity or virulence. However, viral lineage-focused tracking might fail in early detection of advantageous mutations emerging independently across phylogenies. Here, the emergence patterns of Spike mutations were investigated in sequences deposited in local and global databases to identify mutational hotspots across phylogenies and we evaluated their impact on SARS-CoV-2 evolution.

On the use of direct-coupling analysis with a reduced alphabet of amino acids combined with super-secondary structure motifs for protein fold prediction.

Direct-coupling analysis (DCA) for studying the coevolution of residues in proteins has been widely used to predict the three-dimensional structure of a protein from its sequence. We present RADI/raDIMod, a variation of the original DCA algorithm that groups chemically equivalent residues combined with super-secondary structure motifs to model protein structures. Interestingly, the simplification produced by grouping amino acids into only two groups (polar and non-polar) is still representative of the physicochemical nature that characterizes the protein structure and it is in line with the role of hydrophobic forces in protein-folding funneling.

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.

On the prediction of DNA-binding preferences of C2H2-ZF domains using structural models: application on human CTCF.

Cis2-His2 zinc finger (C2H2-ZF) proteins are the largest family of transcription factors in human and higher metazoans. To date, the DNA-binding preferences of many members of this family remain unknown. We have developed a computational method to predict their DNA-binding preferences.

SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions.

Background: Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein-protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities.

Bottom-up de novo design of functional proteins with complex structural features.

De novo protein design has enabled the creation of new protein structures. However, the design of functional proteins has proved challenging, in part due to the difficulty of transplanting structurally complex functional sites to available protein structures. Here, we used a bottom-up approach to build de novo proteins tailored to accommodate structurally complex functional motifs.

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.

De novo protein design enables the precise induction of RSV-neutralizing antibodies.

De novo protein design has been successful in expanding the natural protein repertoire. However, most de novo proteins lack biological function, presenting a major methodological challenge. In vaccinology, the induction of precise antibody responses remains a cornerstone for next-generation vaccines.

Author Correction: A computationally designed chimeric antigen receptor provides a small-molecule safety switch for T-cell therapy.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A computationally designed chimeric antigen receptor provides a small-molecule safety switch for T-cell therapy.

Approaches to increase the activity of chimeric antigen receptor (CAR)-T cells against solid tumors may also increase the risk of toxicity and other side effects. To improve the safety of CAR-T-cell therapy, we computationally designed a chemically disruptable heterodimer (CDH) based on the binding of two human proteins. The CDH self-assembles, can be disrupted by a small-molecule drug and has a high-affinity protein interface with minimal amino acid deviation from wild-type human proteins.

Preclinical development of a humanized chimeric antigen receptor against B cell maturation antigen for multiple myeloma.

Multiple myeloma is a prevalent and incurable disease, despite the development of new and effective drugs. The recent development of chimeric antigen receptor (CAR)-T cell therapy has shown impressive results in the treatment of patients with relapsed or refractory hematological B cell malignancies. In the recent years, B-cell maturation antigen (BCMA) has appeared as a promising antigen to target using a variety of immuno-therapy treatments including CART cells, for MM patients.

Human Albumin Impairs Amyloid β-peptide Fibrillation Through its C-terminus: From docking Modeling to Protection Against Neurotoxicity in Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative process characterized by the accumulation of extracellular deposits of amyloid β-peptide (Aβ), which induces neuronal death. Monomeric Aβ is not toxic but tends to aggregate into β-sheets that are neurotoxic. Therefore to prevent or delay AD onset and progression one of the main therapeutic approaches would be to impair Aβ assembly into oligomers and fibrils and to promote disaggregation of the preformed aggregate.

rstoolbox - a Python library for large-scale analysis of computational protein design data and structural bioinformatics.

Background: Large-scale datasets of protein structures and sequences are becoming ubiquitous in many domains of biological research. Experimental approaches and computational modelling methods are generating biological data at an unprecedented rate. The detailed analysis of structure-sequence relationships is critical to unveil governing principles of protein folding, stability and function.

Alternative interaction sites in the influenza A virus nucleoprotein mediate viral escape from the importin-α7 mediated nuclear import pathway.

Influenza A viruses are able to adapt to restrictive conditions due to their high mutation rates. Importin-α7 is a component of the nuclear import machinery required for avian-mammalian adaptation and replicative fitness in human cells. Here, we elucidate the mechanisms by which influenza A viruses may escape replicative restriction in the absence of importin-α7.

Boosting subdominant neutralizing antibody responses with a computationally designed epitope-focused immunogen.

Throughout the last several decades, vaccination has been key to prevent and eradicate infectious diseases. However, many pathogens (e.g.

Rosetta FunFolDes - A general framework for the computational design of functional proteins.

The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability. The low success rates of computational design protocols and the extensive in vitro optimization often required, highlight the challenge of designing proteins that perform essential biochemical functions, such as binding or catalysis. One of the most simplistic approaches for the design of function is to adopt functional motifs in naturally occurring proteins and transplant them to computationally designed proteins.

Structure-based immunogen design-leading the way to the new age of precision vaccines.

Vaccines have been one of the most successful interventions in global health. However, traditional vaccine development has proven insufficient to deal with pathogens that elude the immune system through highly variable and non-functional epitopes. Emerging B cell technologies have yielded potent monoclonal antibodies targeting conserved epitopes, and their structural characterization has provided templates for rational immunogen design.

Bispecific light T-cell engagers for gene-based immunotherapy of epidermal growth factor receptor (EGFR)-positive malignancies.

The recruitment of T-cells by bispecific antibodies secreted from adoptively transferred, gene-modified autologous cells has shown satisfactory results in preclinical cancer models. Even so, the approach's translation into the clinic will require incremental improvements to its efficacy and reduction of its toxicity. Here, we characterized a tandem T-cell recruiting bispecific antibody intended to benefit gene-based immunotherapy approaches, which we call the light T-cell engager (LiTE), consisting of an EGFR-specific single-domain V antibody fused to a CD3-specific scFv.

ATTACK, a novel bispecific T cell-recruiting antibody with trivalent EGFR binding and monovalent CD3 binding for cancer immunotherapy.

The redirection of T cell activity using bispecific antibodies is one of the most promising cancer immunotherapy approaches currently in development, but it is limited by cytokine storm-related toxicities, as well as the pharmacokinetics and tumor-penetrating capabilities of current bispecific antibody formats. Here, we have engineered the ATTACK (), a novel T cell-recruiting bispecific antibody which combines three EGFR-binding single-domain antibodies (V; clone EgA1) with a single CD3-binding single-chain variable fragment (scFv; clone OKT3) in an intermediate molecular weight package. The two specificities are oriented in opposite directions in order to simultaneously engage cancer cells and T cell effectors, and thereby promote immunological synapse formation.