Detailed Images of Deep Brain Stimulation Leads Using Micro-CT.

Introduction: One of the challenges in directional deep brain stimulation (DBS) is to determine the orientation of implanted electrodes relative to targeted regions. Post-operative images must be aligned with a model of the implanted lead, usually a computer-based model provided by the manufacturer. This paper shows that models can alternatively be obtained by capturing images of individual leads using micro-CT, a high-resolution CT technique.

Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning.

Throughout evolution, protein families undergo substantial sequence divergence while preserving structure and function. Although most mutations are deleterious, evolution can explore sequence space via epistatic networks of intramolecular interactions that alleviate the harmful mutations. However, comprehensive analysis of such epistatic networks across protein families remains limited.

Emergent time scales of epistasis in protein evolution.

We introduce a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutations to diverged homologs. Our in silico evolution encompasses random nucleotide mutations, insertions and deletions, and models selection using a fitness landscape, which is inferred via a generative probabilistic model for protein families. We show that the proposed framework accurately reproduces the sequence statistics of both short-time (experimental) and long-time (natural) protein evolution, suggesting applicability also to relatively data-poor intermediate evolutionary time scales, which are currently inaccessible to evolution experiments.

Generating Artificial Ribozymes Using Sparse Coevolutionary Models.

RNA ribozyme (Walter Engelke, Biologist (London, England) 49:199-203, 2002) datasets typically contain from a few hundred to a few thousand naturally occurring sequences. However, the potential sequence space of RNA is huge. For example, the number of possible RNA sequences of length 150 nucleotides is approximately , a figure that far surpasses the estimated number of atoms in the known universe, which is around .

Towards parsimonious generative modeling of RNA families.

Generative probabilistic models emerge as a new paradigm in data-driven, evolution-informed design of biomolecular sequences. This paper introduces a novel approach, called Edge Activation Direct Coupling Analysis (eaDCA), tailored to the characteristics of RNA sequences, with a strong emphasis on simplicity, efficiency, and interpretability. eaDCA explicitly constructs sparse coevolutionary models for RNA families, achieving performance levels comparable to more complex methods while utilizing a significantly lower number of parameters.

In Vivo Metabolic Imaging of [1- C]Pyruvate-d Hyperpolarized By Reversible Exchange With Parahydrogen.

Metabolic magnetic resonance imaging (MRI) using hyperpolarized (HP) pyruvate is becoming a non-invasive technique for diagnosing, staging, and monitoring response to treatment in cancer and other diseases. The clinically established method for producing HP pyruvate, dissolution dynamic nuclear polarization, however, is rather complex and slow. Signal Amplification By Reversible Exchange (SABRE) is an ultra-fast and low-cost method based on fast chemical exchange.

Generating interacting protein sequences using domain-to-domain translation.

Motivation: Being able to artificially design novel proteins of desired function is pivotal in many biological and biomedical applications. Generative statistical modeling has recently emerged as a new paradigm for designing amino acid sequences, including in particular models and embedding methods borrowed from natural language processing (NLP). However, most approaches target single proteins or protein domains, and do not take into account any functional specificity or interaction with the context.

Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins.

Predicting protein-protein interactions from sequences is an important goal of computational biology. Various sources of information can be used to this end. Starting from the sequences of two interacting protein families, one can use phylogeny or residue coevolution to infer which paralogs are specific interaction partners within each species.

Structure and Function of a Dehydrating Condensation Domain in Nonribosomal Peptide Biosynthesis.

Dehydroamino acids are important structural motifs and biosynthetic intermediates for natural products. Many bioactive natural products of nonribosomal origin contain dehydroamino acids; however, the biosynthesis of dehydroamino acids in most nonribosomal peptides is not well understood. Here, we provide biochemical and bioinformatic evidence in support of the role of a unique class of condensation domains in dehydration (C).

Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes.

Characterizing the effect of mutations is key to understand the evolution of protein sequences and to separate neutral amino-acid changes from deleterious ones. Epistatic interactions between residues can lead to a context dependence of mutation effects. Context dependence constrains the amino-acid changes that can contribute to polymorphism in the short term, and the ones that can accumulate between species in the long term.

Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes.

The emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major concern given their potential impact on the transmissibility and pathogenicity of the virus as well as the efficacy of therapeutic interventions. Here, we predict the mutability of all positions in SARS-CoV-2 protein domains to forecast the appearance of unseen variants. Using sequence data from other coronaviruses, preexisting to SARS-CoV-2, we build statistical models that not only capture amino acid conservation but also more complex patterns resulting from epistasis.

Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution.

During their evolution, proteins explore sequence space via an interplay between random mutations and phenotypic selection. Here, we build upon recent progress in reconstructing data-driven fitness landscapes for families of homologous proteins, to propose stochastic models of experimental protein evolution. These models predict quantitatively important features of experimentally evolved sequence libraries, like fitness distributions and position-specific mutational spectra.

adabmDCA: adaptive Boltzmann machine learning for biological sequences.

Background: Boltzmann machines are energy-based models that have been shown to provide an accurate statistical description of domains of evolutionary-related protein and RNA families. They are parametrized in terms of local biases accounting for residue conservation, and pairwise terms to model epistatic coevolution between residues. From the model parameters, it is possible to extract an accurate prediction of the three-dimensional contact map of the target domain.

Efficient generative modeling of protein sequences using simple autoregressive models.

Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly accurate but computationally efficient generative sequence models. We show that they perform similarly to existing approaches based on Boltzmann machines or deep generative models, but at a substantially lower computational cost (by a factor between 10 and 10).

Sparse generative modeling via parameter reduction of Boltzmann machines: Application to protein-sequence families.

Boltzmann machines (BMs) are widely used as generative models. For example, pairwise Potts models (PMs), which are instances of the BM class, provide accurate statistical models of families of evolutionarily related protein sequences. Their parameters are the local fields, which describe site-specific patterns of amino acid conservation, and the two-site couplings, which mirror the coevolution between pairs of sites.

On the effect of phylogenetic correlations in coevolution-based contact prediction in proteins.

Coevolution-based contact prediction, either directly by coevolutionary couplings resulting from global statistical sequence models or using structural supervision and deep learning, has found widespread application in protein-structure prediction from sequence. However, one of the basic assumptions in global statistical modeling is that sequences form an at least approximately independent sample of an unknown probability distribution, which is to be learned from data. In the case of protein families, this assumption is obviously violated by phylogenetic relations between protein sequences.

Food sources for camptandriid crabs in an arid mangrove ecosystem of the Persian Gulf: a stable isotope approach.

Crabs of the family Camptandriidae are the most dominant burrowing crabs inhabiting arid mangrove forests of the Persian Gulf. They play important roles in the structuring and functioning of mangrove ecosystems by modulating biogeochemical processes and cycling of nutrients, serving as important ecosystem engineers. We analysed stable carbon (C) and nitrogen (N) isotope values of three camptandriid crabs (, and ) and their potential food sources in the Hara Biosphere Reserve, northern Persian Gulf.

Rbm10 facilitates heterochromatin assembly via the Clr6 HDAC complex.

Splicing factors have recently been shown to be involved in heterochromatin formation, but their role in controlling heterochromatin structure and function remains poorly understood. In this study, we identified a fission yeast homologue of human splicing factor RBM10, which has been linked to TARP syndrome. Overexpression of Rbm10 in fission yeast leads to strong global intron retention.

Aligning biological sequences by exploiting residue conservation and coevolution.

Sequences of nucleotides (for DNA and RNA) or amino acids (for proteins) are central objects in biology. Among the most important computational problems is that of sequence alignment, i.e.

FilterDCA: Interpretable supervised contact prediction using inter-domain coevolution.

Predicting three-dimensional protein structure and assembling protein complexes using sequence information belongs to the most prominent tasks in computational biology. Recently substantial progress has been obtained in the case of single proteins using a combination of unsupervised coevolutionary sequence analysis with structurally supervised deep learning. While reaching impressive accuracies in predicting residue-residue contacts, deep learning has a number of disadvantages.

An evolution-based model for designing chorismate mutase enzymes.

The rational design of enzymes is an important goal for both fundamental and practical reasons. Here, we describe a process to learn the constraints for specifying proteins purely from evolutionary sequence data, design and build libraries of synthetic genes, and test them for activity in vivo using a quantitative complementation assay. For chorismate mutase, a key enzyme in the biosynthesis of aromatic amino acids, we demonstrate the design of natural-like catalytic function with substantial sequence diversity.