Seven Amino Acid Types Suffice to Create the Core Fold of RNA Polymerase.

The extant complex proteins must have evolved from ancient short and simple ancestors. The double-ψ ß-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its plausible evolutionary pathway started by "interlacing homodimerization" of a half-size peptide, followed by gene duplication and fusion. Furthermore, by simplifying the amino acid repertoire of the peptide, we successfully created the DPBB fold with only seven amino acid types (Ala, Asp, Glu, Gly, Lys, Arg, and Val), which can be coded by only GNN and ARR (R = A or G) codons in the modern translation system. Thus, the DPBB fold could have been materialized by the early translation system and genetic code.

Positive multistate protein design.

MOTIVATION: Structure-based computational protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. The usual approach considers a single rigid backbone as a target, which ignores backbone flexibility. Multistate design (MSD) allows instead to consider several backbone states simultaneously, defining challenging computational problems. RESULTS: We introduce efficient reductions of positive MSD problems to Cost Function Networks with two different fitness definitions and implement them in the Pompd (Positive Multistate Protein design) software. Pompd is able to identify guaranteed optimal sequences of positive multistate full protein redesign problems and exhaustively enumerate suboptimal sequences close to the MSD optimum. Applied to nuclear magnetic resonance and back-rubbed X-ray structures, we observe that the average energy fitness provides the best sequence recovery. Our method outperforms state-of-the-art guaranteed computational design approaches by orders of magnitudes and can solve MSD problems with sizes previously unreachable with guaranteed algorithms. AVAILABILITY AND IMPLEMENTATION: https://forgemia.inra.fr/thomas.schiex/pompd as documented Open Source. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Comparative Study to Decipher the Structural and Dynamics Determinants Underlying the Activity and Thermal Stability of GH-11 Xylanases.

With the growing need for renewable sources of energy, the interest for enzymes capable of biomass degradation has been increasing. In this paper, we consider two different xylanases from the GH-11 family: the particularly active GH-11 xylanase from Neocallimastix patriciarum, NpXyn11A, and the hyper-thermostable mutant of the environmentally isolated GH-11 xylanase, EvXyn11TS. Our aim is to identify the molecular determinants underlying the enhanced capacities of these two enzymes to ultimately graft the abilities of one on the other. Molecular dynamics simulations of the respective free-enzymes and enzyme-xylohexaose complexes were carried out at temperatures of 300, 340, and 500 K. An in-depth analysis of these MD simulations showed how differences in dynamics influence the activity and stability of these two enzymes and allowed us to study and understand in greater depth the molecular and structural basis of these two systems. In light of the results presented in this paper, the thumb region and the larger substrate binding cleft of NpXyn11A seem to play a major role on the activity of this enzyme. Its lower thermal stability may instead be caused by the higher flexibility of certain regions located further from the active site. Regions such as the N-ter, the loops located in the fingers region, the palm loop, and the helix loop seem to be less stable than in the hyper-thermostable EvXyn11TS. By identifying molecular regions that are critical for the stability of these enzymes, this study allowed us to identify promising targets for engineering GH-11 xylanases. Eventually, we identify NpXyn11A as the ideal host for grafting the thermostabilizing traits of EvXyn11TS.

The association between pro-arrhythmic agents and aortic stenosis in young adults: is it sufficient to clarify the sudden unexpected deaths?

Most young patients with mild-to-moderate aortic stenosis show no symptoms, and sudden death appears only occasionally. We hypothesised that malignant ventricular arrhythmias could be responsible for the high incidence of sudden death in such patients. If multiple factors such as asymptomatic aortic stenosis in association with arrhythmia-provoking agents are involved, could it be sufficient to account for sudden unexpected death? In this study, eight cases of sudden death in young adults, with ages ranging from 22 to 36 years, who had never reported any symptoms that could be related to aortic stenosis, were investigated. Full autopsies were performed, and congenital aortic stenosis in all eight cases was confirmed. DNA testing for channelopathies was negative. Comprehensive toxicological analyses found an electrolyte imbalance, or non-toxic concentrations of amitriptyline, terfenadine, caffeine, and ethanol. Collectively, these results suggest that congenital asymptomatic aortic stenosis without cardiac hypertrophy in young adults is not sufficient to cause sudden death merely on its own; rather, an additional provoking factor is necessary. According to our findings, the provoking factor may be a state of physical or emotional stress, a state of electrolyte imbalance, or even taking a therapeutic dose of a particular drug.

Complete combinatorial mutational enumeration of a protein functional site enables sequence-landscape mapping and identifies highly-mutated variants that retain activity.

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.

Correlation between stromal Th and Tc lymphocytes and PD-L1 expression in early breast cancer tumors.

INTRODUCTION: Prognostic and predictive value of PD-L1 as a biomarker in breast cancer remains controversial. While some studies suggest its association with negative prognostic parameters, others reported a highly significant association between PD-L1 expression and tumor-infiltrating lymphocytes, which are known to be an independent favorable prognostic factor. The aim of present study is to examine the relationship between immune response markers and PD-L1 expression in early breast cancer. MATERIAL AND METHODS: Immunohistochemical expression of PD-L1, along with density and composition of stromal lymphocytic infiltrate and peritumoral lymphoid aggregates was analyzed in 95 samples of invasive breast cancer. RESULTS: A strong positive correlation between PD-L1 expression and the density of stromal lymphocytic infiltrate and peritumoral lymphoid aggregates was identified and a cut-off value of 53% coverage of tumor stroma by lymphocytes, with which PD-L1 positivity can be predicted with excellent diagnostic accuracy, was determined for the first time using statistical methods. Additionally, PD-L1 positivity was observed significantly more often in tumors with higher absolute number of both CD4 and CD8 T-lymphocytes in the stromal infiltrate. No significant correlation with molecular subtype of breast cancer was found. CONCLUSIONS: Our results indicate that the density of stromal lymphocytic infiltrate might be a better predictor of PD-L1 positivity in early breast cancer than the molecular subtype and that the key to the optimization of PD-L1 as a biomarker in breast cancer lies in its interpretation in the context of other immune response markers.

Complete Combinatorial Mutational Enumeration of a protein functional site enables sequence-landscape mapping and identifies highly-mutated variants that retain activity.

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 towards achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation.

Time course and expression pattern of the neuronal markers in the developing human spinal cord.

The aim of this study was to examine the spatio-temporal appearance of different neuronal cell subtypes by analyzing expression patterns of several neuronal markers (calretinin, neurofilament 200 (NF200), vanilloid receptor 1(VR1) and calcitonin gene-related peptide (CGRP)) of the embryonic human spinal cord (SC). Developing human SCs from 11 human conceptuses beetwen 5-10 developmental weeks (DW) were examined by light and electron microscopy and immunofluorescence. Light and electron microscopy revealed different embryonic stages of recognizable structure of the SC. NF200, CGRP and VR1 positive cells were observed in SCs during 5th-6th DW. NF200 was predominantly expressed in the ventral part, indicating presence of motoneurons. As development advanced, NF200 was mainly expressed in the marginal zone. Expression of CGRP was intense during all of the investigated periods, predominantly during the 5th-6th DW pointing to neural sensory differentiation, as opposed to the last DW when reduced expression of CGRP in the marginal layer indicated the terminations of the sensory afferents. Expression of VR1 was highest in the intermediate zone, at the beginning and at the end of the investigated periods, pointing to VR1 spatial pattern in the visceral afferents in the grey matter, while the first signs of calretinin were found in the 9th-10th DW ventrally. Delineating the relationships between factors involved in processes of neuronal differentiation as well as spatial and temporal arrangement of SC interrelated neurons can provide a useful information about normal SC development as well as the insight in possible causes of anomalies and disorders during embryonic life.

Mathematical model in post-mortem estimation of brain edema using morphometric parameters.

Current autopsy principles for evaluating the existence of brain edema are based on a macroscopic subjective assessment performed by pathologists. The gold standard is a time-consuming histological verification of the presence of the edema. By measuring the diameters of the cranial cavity, as individually determined morphometric parameters, a mathematical model for rapid evaluation of brain edema was created, based on the brain weight measured during the autopsy. A cohort study was performed on 110 subjects, divided into two groups according to the histological presence or absence of (the - deleted from the text) brain edema. In all subjects, the following measures were determined: the volume and the diameters of the cranial cavity (longitudinal and transverse distance and height), the brain volume, and the brain weight. The complex mathematical algorithm revealed a formula for the coefficient ε, which is useful to conclude whether a brain edema is present or not. The average density of non-edematous brain is 0.967 g/ml, while the average density of edematous brain is 1.148 g/ml. The resulting formula for the coefficient ε is (5.79 x longitudinal distance x transverse distance)/brain weight. Coefficient ε can be calculated using measurements of the diameters of the cranial cavity and the brain weight, performed during the autopsy. If the resulting ε is less than 0.9484, it could be stated that there is cerebral edema with a reliability of 98.5%. The method discussed in this paper aims to eliminate the burden of relying on subjective assessments when determining the presence of a brain edema.