EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in the number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing representative protein conformational ensembles. In this work, we: (1) provide an overview of existing methods and tools for representative protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples from the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing protein conformational ensembles. In this work we: (1) provide an overview of existing methods and tools for protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples found in the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.

MatchTope: A tool to predict the cross reactivity of peptides complexed with Major Histocompatibility Complex I.

The therapeutic targeting of the immune system, for example in vaccinology and cancer treatment, is a challenging task and the subject of active research. Several in silico tools used for predicting immunogenicity are based on the analysis of peptide sequences binding to the Major Histocompatibility Complex (pMHC). However, few of these bioinformatics tools take into account the pMHC three-dimensional structure. Here, we describe a new bioinformatics tool, MatchTope, developed for predicting peptide similarity, which can trigger cross-reactivity events, by computing and analyzing the electrostatic potentials of pMHC complexes. We validated MatchTope by using previously published data from in vitro assays. We thereby demonstrate the strength of MatchTope for similarity prediction between targets derived from several pathogens as well as for indicating possible cross responses between self and tumor peptides. Our results suggest that MatchTope can enhance and speed up future studies in the fields of vaccinology and cancer immunotherapy.

In silico analysis of potential off-target sites to gene editing for Mucopolysaccharidosis type I using the CRISPR/Cas9 system: Implications for population-specific treatments.

Mucopolysaccharidosis type I (MPS I) is caused by alpha-L-iduronidase deficiency encoded by the IDUA gene. Therapy with CRISPR/Cas9 is being developed for treatment, however a detailed investigation of off-target effects must be performed. This study aims to evaluate possible off-targets for a sgRNA aiming to correct the most common variant found in MPS I patients (p.Trp402*). A total of 272 potential off-target sequences was obtained and 84 polymorphic sites were identified in these sequences with a frequency equal to or greater than 1% in at least one of the populations. In the majority of cases, polymorphic sites decrease the chance of off-target cleavage and a new PAM was created, which indicates the importance of such analysis. This study highlights the importance of screening off-targets in a population-specific context using Mucopolysaccharidosis type I as an example of a problem that concerns all therapeutic treatments. Our results can have broader applications for other targets already clinically in use, as they could affect CRISPR/Cas9 safety and efficiency.

High infiltration of B cells in tertiary lymphoid structures, TCR oligoclonality, and neoantigens are part of esophageal squamous cell carcinoma microenvironment.

Esophageal squamous cell carcinoma (ESCA) exhibits high intratumoral molecular heterogeneity posing a challenge to cancer therapy. Immune checkpoint blockade therapy has been approved for this disease, but with modest results. RNA-Seq data from paired tumor and surrounding nonmalignant tissue from 14 patients diagnosed with ESCA without previous treatment and from The Cancer Genome Atlas-ESCA cohort were analyzed. Herein, we investigated ESCA immune landscape including mutation-derived neoantigens and immune cell subpopulations. Tumor-associated antigen expression was determined by in silico analyses and confirmed by immunohistochemistry showing that PRAME, CEACAM4, and MAGEA11 proteins are expressed on tumors. Immune checkpoint molecules gene expression was higher in the tumor compared with surrounding nonmalignant tissue, but its expression varies greatly among patients. TCR repertoire and BCR transcripts analysis evidenced low clonal diversity with one TCR clone predicted to be specific for a MAGEA11-derived peptide. A high number of B-cell clones infiltrating the tumors and the abundance of these cells in tertiary lymphoid structures observed in ESCA tumors support B cells as a potential immune modulator in this tumor.