Without magic bullets: the biological basis for public health interventions against protein folding disorders

Protein folding disorders of aging like Alzheimer's and Parkinson's diseases currently present intractable medical challenges. 'Small molecule' interventions - drug treatments - often have, at best, palliative impact, failing to alter disease course. The design of individual or population level interventions will likely require a deeper understanding of protein folding and its regulation than currently provided by contemporary 'physics' or culture-bound medical magic bullet models. Here, a topological rate distortion analysis is applied to the problem of protein folding and regulation that is similar in spirit to Tlusty's (2010a) elegant exploration of the genetic code. The formalism produces large-scale, quasi-equilibrium 'resilience' states representing normal and pathological protein folding regulation under a cellular-level cognitive paradigm similar to that proposed by Atlan and Cohen (1998) for the immune system. Generalization to long times produces diffusion models of protein folding disorders in which epigenetic or life history factors determine the rate of onset of regulatory failure, in essence, a premature aging driven by familiar synergisms between disjunctions of resource allocation and need in the context of socially or physiologically toxic exposures and chronic powerlessness at individual and group scales. Application of an HPA axis model is made to recent observed differences in Alzheimer's onset rates in White and African American subpopulations as a function of an index of distress-proneness

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. Our approach demonstrates efficiency in generating artificial RNA sequences that closely resemble their natural counterparts in both statistical analyses and SHAPE-MaP experiments, and in predicting the effect of mutations. Notably, eaDCA provides a unique feature: estimating the number of potential functional sequences within a given RNA family. For example, in the case of cyclic di-AMP riboswitches (RF00379), our analysis suggests the existence of approximately $\mathbf{10^{39}}$ functional nucleotide sequences. While huge compared to the known $< \mathbf{4,000}$ natural sequences, this number represents only a tiny fraction of the vast pool of nearly $\mathbf{10^{82}}$ possible nucleotide sequences of the same length (136 nucleotides). These results underscore the promise of sparse and interpretable generative models, such as eaDCA, in enhancing our understanding of the expansive RNA sequence space.Comment: 33 pages (including SI

Taking into account nucleosomes for predicting gene expression

The eukaryotic genome is organized in a chain of nucleosomes that consist of 145-147. bp of DNA wrapped around a histone octamer protein core. Binding of transcription factors (TF) to nucleosomal DNA is frequently impeded, which makes it a challenging task to calculate TF occupancy at a given regulatory genomic site for predicting gene expression. Here, we review methods to calculate TF binding to DNA in the presence of nucleosomes. The main theoretical problems are (i) the computation speed that is becoming a bottleneck when partial unwrapping of DNA from the nucleosome is considered, (ii) the perturbation of the binding equilibrium by the activity of ATP-dependent chromatin remodelers, which translocate nucleosomes along the DNA, and (iii) the model parameterization from high-throughput sequencing data and fluorescence microscopy experiments in living cells. We discuss strategies that address these issues to efficiently compute transcription factor binding in chromatin. © 2013 Elsevier Inc

Pattern statistics on Markov chains and sensitivity to parameter estimation

BACKGROUND: In order to compute pattern statistics in computational biology a Markov model is commonly used to take into account the sequence composition. Usually its parameter must be estimated. The aim of this paper is to determine how sensitive these statistics are to parameter estimation, and what are the consequences of this variability on pattern studies (finding the most over-represented words in a genome, the most significant common words to a set of sequences,...). RESULTS: In the particular case where pattern statistics (overlap counting only) computed through binomial approximations we use the delta-method to give an explicit expression of σ, the standard deviation of a pattern statistic. This result is validated using simulations and a simple pattern study is also considered. CONCLUSION: We establish that the use of high order Markov model could easily lead to major mistakes due to the high sensitivity of pattern statistics to parameter estimation

Frustration in Biomolecules

Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of "frustration" in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding, how a large part of the biological functions of proteins are related to subtle local frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology.Comment: 97 pages, 30 figure

Development and application of software and algorithms for network approaches to proteomics data analysis

The cells making up all living organisms integrate external and internal signals to carry out the functions of life. Dysregulation of signaling can lead to a variety of grave diseases, including cancer [Slamon et al., 1987]. In order to understand signal transduction, one has to identify and characterize the main constituents of cellular signaling cascades. Proteins are involved in most cellular processes and form the major class of biomolecules responsible for signal transduction. Post-translational modifications (PTMs) of proteins can modulate their enzymatic activity and their protein-protein interactions (PPIs) which in turn can ultimately lead to changes in protein expression. Classical biochemistry has approached the study of proteins, PTMs and interaction from a reductionist view. The abundance, stability and localization of proteins was studied one protein at a time, following the one gene-one protein-one function paradigm [Beadle and Tatum, 1941]. Pathways were considered to be linear, where signals would be transmitted from a gene to proteins, eventually resulting in a specific phenotype. Establishing the crucial link between genotype and phenotype remains challenging despite great advances in omics technologies, such as liquid chromatography (LC)-mass spectrometry (MS) that allow for the system-wide interrogation of proteins. Systems and network biology [Barabási and Oltvai, 2004, Bensimon et al., 2012, Jørgensen and Locard-Paulet, 2012, Choudhary and Mann, 2010] aims to transform modern biology by utilizing omics technologies to understand and uncover the various complex networks that govern the cell. The first detected large-scale biological networks have been found to be highly structured and non-random [Albert and Barabási, 2002]. Furthermore, these are assembled from functional and topological modules. The smallest topological modules are formed by the direct physical interactions within protein-protein and protein-RNA complexes. These molecular machines are able to perform a diverse array of cellular functions, such as transcription and degradation [Alberts, 1998]. Members of functional modules are not required to have a direct physical interaction. Instead, such modules also include proteins with temporal co-regulation throughout the cell cycle [Olsen et al., 2010], or following the circadian day-night rhythm [Robles et al., 2014]. The signaling pathways that make up the cellular network [Jordan et al., 2000] are assembled from a hierarchy of these smaller modules [Barabási and Oltvai, 2004]. The regulation of these modules through dynamic rewiring enables the cell to respond to internal an external stimuli. The main challenge in network biology is to develop techniques to probe the topology of various biological networks, to identify topological and functional modules, and to understand their assembly and dynamic rewiring. LC-MS has become a powerful experimental platform that addresses all these challenges directly [Bensimon et al., 2012], and has long been used to study a wide range of biomolecules that participate in the cellular network. The field of proteomics in particular, which is concerned with the identification and characterization of the proteins in the cell, has been revolutionized by recent technological advances in MS. Proteomics experiments are used not only to quantify peptides and proteins, but also to uncover the edges of the cellular network, by screening for physical PPIs in a global [Hein et al., 2015] or condition specific manner [Kloet et al., 2016]. Crucial for the interpretation of the large-scale data generated by MS experiments is the development of software tools that aid researchers in translating raw measurements into biological insights. The MaxQuant and Perseus platforms were designed for this exact purpose. The aim of this thesis was to develop software tools for the analysis of MS-based proteomics data with a focus on network biology and apply the developed tools to study cellular signaling. The first step was the extension of the Perseus software with network data structures and activities. The new network module allows for the sideby-side analysis of matrices and networks inside an interactive workflow and is described in article 1. We subsequently apply the newly developed software to study the circadian phosphoproteome of cortical synapses (see article 2). In parallel we aimed to improve the analysis of large datasets by adapting the previously Windows-only MaxQuant software to the Linux operating system, which is more prevalent in high performance computing environments (see article 3)

The GDR : a novel approach to detect large-scale genomic sequence patterns

Utvikling av ny sekvenseringsteknologi de to siste tiårene har tillatt dypere dykk ned i de biomolekylære aspektene ved menneskets oppskrift. Hel-genom data fra flere hundre tusen mennesker er allerede tilgjengelig, men hvordan den økende mengden informasjon kan settes sammen til meningsfull funksjonell tolkning er komplisert og krever nye metoder. MikroRNA - mRNA interaksjoner utgjør et enormt genreguleringsnettverk som er vanskelig å predikere, selv for dagens beste maskinlæringsalgoritmer(1). Disse ikke-kodende elementene er involvert i omtrent alle cellulære prosesser i mennesket, primært via delvis komplementær baseparing mellom mikroRNA og mRNA, men det er mye vi ikke forstår av dette nettverkets betydning i vår biologi (2-4). Nye metoder er nødvendige for å kunne utforske genetisk variasjon i dette nettverket, som kan gi nye innblikk i hvordan genene våre reguleres. Her presenteres «The Group Diversity Ratio» (GDR) som en ny målenhet til å møte denne utfordringen. GDR kan kvantifisere evolusjonær struktur av variasjon i store mengder genomisk sekvensdata, med et resultat som kan statistisk valideres. Metoden baserer seg på å måle gruppe-struktur i et distanse-basert fylogenetisk tre av sekvensdata, for forhåndsdefinerte grupper av «blader» i treet. Gruppene representerer en egenskap som kan relateres til sekvensdataen, og det undersøkes til hvilken grad det finnes en sammenheng mellom de to. Metoden kan primært brukes til å raskt skaffe overblikk over store mengder genomisk sekvensdata, som kan gi verdifulle innblikk til videre etterforskning. For å teste metoden ble GDR brukt til å identifisere variasjon assosiert med etniske populasjoner i 3’UTR data fra «The 1000 Genomes Project» (1KGP). 1KGP var det første store prosjektet som adresserte den etniske skjevheten som nå finnes i genom-databaser, og som utgjør en god grunn til å utforske etnisk genetisk variasjon (5). I tillegg til identifikasjon av mer enn 1000 3’UTR sekvenser som inneholder signifikant etnisitet-spesifikk variasjon, viser dette studiet GDR-metodens høye potensial til å undersøke genetisk variasjon i stor skala.The emergence of new sequencing technologies over the past two decades has enabled us to dive deeper into the biomolecular aspect of the human recipe. Entire genomes from several hundred thousand people are already accessible, but how to interpretate the connections between the blueprints and the phenotypes are complicated, even for the best developed machine learning algorithms. Prediction of the microRNA-mRNA targeting network is a classic example, which is involved with gene regulation of all living cell processes. These non-coding features make up complex networks of interactions, where microRNAs primarily target 3’UTRs through partial complementary base-pairing. Thus, the challenge to investigate patterns in such large-scaled genomic sequence data requires new approaches. The Group Diversity Ratio (GDR) metric is presented here as a novel approach to aid in this challenge. The GDR quantifies genome-wide structure in large-scale sequence data with a statistically testable result. Patterns are measured for a group feature that may be related to variation in sequence samples, based on phylogenetic distance estimations. It opens opportunities to quickly gain insights into genomic regions of interests and used to guide further research. To demonstrate the use of the GDR metric, ethnicity-associated variation patterns in more than 1000 human 3’UTRs was identified with the GDR. The study set was from 1000 Genomes project, which was the first major effort to address the problem of ethnic bias in genetic studies and contained more than 2500 whole-genome sequences from 26 ethnic lineages. In addition to detecting significantly distinct 3’UTR elements for ethnic populations, the key finding of this study was the high potentials of the GDR to facilitate more high-throughput characterization of genomic sequence data.M-BIA