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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 217 records · Page 12Linked to original sources

bioRxiv2014

A systematic survey of an intragenic epistatic landscape

Mutations are the source of evolutionary variation. The interactions of multiple mutations can have important effects on fitness and evolutionary trajectories. We have recently described the distribution of fitness effects of all single mutations for a nine amino acid region of yeast Hsp90 (Hsp82) implicated in substrate binding. Here, we report and discuss the distribution of intragenic epistatic effects within this region in seven Hsp90 point mutant backgrounds of neutral to slightly deleterious effect, resulting in an analysis of more than 1000 double-mutants. We find negative epistasis between substitutions to be common, and positive epistasis to be rare - resulting in a pattern that indicates a drastic change in the distribution of fitness effects one step away from the wild type. This can be well explained by a concave relationship between phenotype and genotype (i.e., a concave shape of the local fitness landscape), suggesting mutational robustness intrinsic to the local sequence space. Structural analyses indicate that, in this region, epistatic effects are most pronounced when a solvent-inaccessible position is involved in the interaction. In contrast, all 18 observations of positive epistasis involved at least one mutation at a solvent-exposed position. By combining the analysis of evolutionary and biophysical properties of an epistatic landscape, these results contribute to a more detailed understanding of the complexity of protein evolution.

Evolutionary Biology↗

bioRxiv2015

Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing

Functional magnetic resonance imaging (fMRI) research is routinely criticized for being statistically underpowered due to characteristically small sample sizes and much larger sample sizes are being increasingly recommended. Additionally, various sources of artifact inherent in fMRI data can have detrimental impact on effect size estimates and statistical power. Here we show how specific removal of non-BOLD artifacts can improve effect size estimation and statistical power in task-fMRI contexts, with particular application to the social-cognitive domain of mentalizing/theory of mind. Non-BOLD variability identification and removal is achieved in a biophysical and statistically principled manner by combining multi-echo fMRI acquisition and independent components analysis (ME-ICA). Group-level effect size estimates on two different mentalizing tasks were enhanced by ME-ICA at a median rate of 24% in regions canonically associated with mentalizing, while much more substantial boosts (40-149%) were observed in non-canonical cerebellar areas. This effect size boosting is primarily a consequence of reduction of non-BOLD noise at the subject-level, which then translates into consequent reductions in between-subject variance at the group-level. Power simulations demonstrate that enhanced effect size enables highly-powered studies at traditional sample sizes. Cerebellar effects observed after applying ME-ICA may be unobservable with conventional imaging at traditional sample sizes. Thus, ME-ICA allows for principled design-agnostic non-BOLD artifact removal that can substantially improve effect size estimates and statistical power in task-fMRI contexts. ME-ICA could help issues regarding statistical power and non-BOLD noise and enable potential for novel discovery of aspects of brain organization that are currently under-appreciated and not well understood.

Neuroscience↗

bioRxiv2015

Learning quantitative sequence-function relationships from high-throughput biological data

Understanding the transcriptional regulatory code, as well as other types of information encoded within biomolecular sequences, will require learning biophysical models of sequence-function relationships from high-throughput data. Controlling and characterizing the noise in such experiments, however, is notoriously difficult. The unpredictability of such noise creates problems for standard likelihood-based methods in statistical learning, which require that the quantitative form of experimental noise be known precisely. However, when this unpredictability is properly accounted for, important theoretical aspects of statistical learning which remain hidden in standard treatments are revealed. Specifically, one finds a close relationship between the standard inference method, based on likelihood, and an alternative inference method based on mutual information. Here we review and extend this relationship. We also describe its implications for learning sequence-function relationships from real biological data. Finally, we detail an idealized experiment in which these results can be demonstrated analytically.

Bioinformatics↗

bioRxiv2015

Fundamental Structure and Modulation of Neuronal Excitability: Synaptic Control of Coding, Resonance, and Network Synchronization

Neuronal encoding and collective network activity depend on the precise mechanism for generating action potentials. A dynamic switch in this mechanism could greatly expand the functional repertoire of neurons and circuits. Here we show that changes in neuronal biophysics control a complex, yet fundamental, sequence of dynamic transitions in neuronal excitability in which neurons switch from integrators to resonators near the spike threshold, from simple voltage dynamics to the bistable co-existence of action potentials and quiescence, and from continuous class-I to discontinuous class-II firing rate encoding. Using multiple bifurcation theory, we prove that this transition sequence is universal in conductance-based neurons. Using dynamic-clamp and pharmacology, we show experimentally that an increase in leak conductance or application of the inhibitory agonist GABA can dynamically induce these transitions in hippocampal and brainstem neurons. Our results imply that synaptic activity can flexibly control resonance, excitability and bistability of neurons. In simulated neuronal networks, we show that such synaptically induced transitions provide a mechanism for the dynamic gating of input signals and the targeted synchronization of sub-networks with a tunable number of neurons.\n\nSignificanceNeuronal function depends on the mechanism by which neurons transform synaptic input into action potentials (APs). It is unclear how neurons might control the AP mechanism to systematically modulate their responses to input signals or their collective behavior. Here we identify a complex, but model-independent, universal sequence of transitions in the dynamics of AP generation. Using patch-clamp recordings, we show that synaptic receptor activation can flexibly change the AP dynamics, confirming our theoretical predictions: non-resonant neurons develop a sub-threshold resonance, become bistable, and develop an abrupt jump in onset AP frequency. Our results explain how synapses or neuro-modulators could control neuronal excitability, influence information processing, and processing during collective network dynamics.

Neuroscience↗

bioRxiv2015

Dendrites enable a robust mechanism for neuronal stimulus selectivity

Hearing, vision, touch -- underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Non-linear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of non-preferred stimuli. Using a multi-subunit non-linear model, we demonstrate that selectivity can arise from the spatial distribution of synapses. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. Contrary to an equivalent linear model, our model can maintain stimulus selectivity even when 50% of synapses fail or when more than 50% of dendrites fail. We then use a Layer 2/3 biophysical neuron model to show that our implementation is consistent with recent experimental observations, of a mixture of selectivities in dendrites, that can differ from the somatic selectivity, and of hyperpolarization broadening somatic tuning without affecting dendritic tuning. Our model predicts that an initially non-selective neuron can become selective when depolarized. In addition to motivating new experiments, the model's increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.

Neuroscience↗

bioRxiv2015

Crowdsourcing: Spatial clustering of low-affinity binding sites amplifies in vivo transcription factor occupancy

To predict in vivo occupancy of a transcription factor (TF), current models consider only the immediate genomic context of a putative binding site (BS) - impact of the sites spatial chromatin context is not known. Using clusters of spatially proximal enhancers, or archipelagos, and DNase footprints to quantify TF occupancy, we report for the first time an emergent group-level effect on occupancy, whereby BS within an archipelago experience greater in vivo occupancy than comparable BS outside archipelagos, i.e. BS not in spatial proximity with other homotypic BS. This occupancy boost is tissue-specific and scales robustly with the total number of BS, or enhancers, for the TF in the archipelago. Interestingly, enhancers within an archipelago are non-uniformly impacted by the occupancy boost; specifically, archipelago enhancers that are enriched for BS corresponding to degenerate motifs exhibit the greatest occupancy boost, as well as the highest overall accessibility, evolutionary selection, and expression at neighboring gene loci. Strikingly, archipelago-wide activity scales with expression of TFs with degenerate, but not specific, motifs. We explain these results through biophysical modelling, which suggests that spatially proximal homotypic BS facilitate TF diffusion, and induce boosts in local TF concentration and occupancy. Together, we demonstrate for the first time cooperativity among genomically distal homotypic BS that is contingent upon their spatial proximity, consistent with a TF diffusion model. Through leveraging of three-dimensional chromatin structure and TF availability, weak archipelago binding sites crowdsource their occupancy as well as context specificity, with coordinated switch-like effect on overall archipelago activity.

Genomics↗

bioRxiv2015

Integrating Gene Synthesis and Microfluidic Protein Analysis for Rapid Protein Engineering

The capability to rapidly design proteins with novel functions will have a significant impact on medicine, biotechnology, and synthetic biology. Synthetic genes are becoming a commodity, but integrated approaches have yet to be developed that take full advantage of gene synthesis. We developed a solid-phase gene synthesis method based on asymmetric primer extension (APE) and coupled this process directly to high-throughput, on-chip protein expression, purification, and characterization (mechanically induced trapping of molecular interactions, MITOMI). By completely circumventing molecular cloning and cell-based steps, APE-MITOMI reduces the time between protein design and quantitative characterization to 3-4 days. With APE-MITOMI we synthesized and characterized over 440 zinc-finger (ZF) transcription factors (TF), showing that although ZF TFs can be readily engineered to recognize a particular DNA sequence, engineering the precise binding energy landscape remains challenging. We also found that it is possible to engineer ZF - DNA affinity precisely and independently of sequence specificity and that in silico modeling can explain some of the observed affinity differences. APE-MITOMI is a generic approach that should facilitate fundamental studies in protein biophysics, and protein design/engineering.

Bioengineering↗

bioRxiv2015

Rolling the Dice Twice: Evolving Reconstructed Ancient Proteins in Extant Organisms

Scientists have access to artifacts of evolutionary history (namely, the fossil record and genomic sequences of living organisms) but they have limited means with which to infer the exact evolutionary events that occurred to produce todays living world. An intriguing question to arise from this historical limitation is whether the evolutionary paths of organisms are dominated by internal or external controlled processes (i.e., Life as a factory) or whether they are inherently random and subject to completely different outcomes if repeated under identical conditions (i.e., Life as a casino parlor). Two experimental approaches, ancestral sequence reconstruction and experimental evolution with microorganisms, can be used to recapitulate ancient adaptive pathways and provide valuable insights into the mutational steps that constitute an organisms genetic heritage. Ancestral sequence reconstruction follows a backwards-from-present-day strategy in which various ancestral forms of a modern gene or protein are reconstructed and then studied mechanistically. Experimental evolution, by contrast, follows a forward-from-present day strategy in which microbial populations are evolved in the laboratory under defined conditions in which their evolutionary paths may be closely monitored. Here I describe a novel hybrid of these two methods, in which synthetic components constructed from inferred ancestral gene or protein sequences are placed into the genomes of modern organisms that are then experimentally evolved. Through this system, we aim to establish the comparative study of ancient phenotypes as a novel, statistically rigorous methodology with which to explore the respective impacts of biophysics and chance in evolution within the scope of the Extended Synthesis.

Evolutionary Biology↗

bioRxiv2015

Blind spots in ecosystem services research and implementation

Ecosystem service research has gained attraction, and the topic is also high on the policy agenda. Projects such as TEEB have generalized results of individual case studies to provide guidelines for policy makers and stakeholders. Seppelt et al. (2011) raised critical questions about four facets that characterize the holistic ideal of ecosystem services research: (i) biophysical realism of ecosystem data and models; (ii) consideration of trade-offs between ecosystem services; (iii) recognition of off-site effects; and (iv) comprehensive but shrewd involvement of stakeholders within assessment studies. An extended and updated analysis of ecosystem service case studies showed that the majority of these facets were still not addressed by the majority of case studies. Whilst most indicators did not improve within the span analyzed (1996-2013), we found a tendency for an increasing geographical spread of the case studies. Moreover, we incorporated an additional facet, namely the relevance and usability of case study results for the operationalization of the ecosystem service concept. Only a minority of studies addressed this facet sufficiently with no significant trend for improvement over time.

Ecology↗

bioRxiv2016

Changes in attractor dynamics predict altered perceptual decision making with dorsolateral prefrontal tDCS

The left dorsolateral prefrontal cortex (dlPFC) has been linked to the accumulation and comparison of perceptual evidence for decision making independent of sensory and response modalities. We investigated the possible neural dynamics underlying the role of dlPFC in perceptual decision making, through a combination of noninvasive neurostimulation in humans and computational modeling. First, we used an established and biophysically realistic model of a decision making network that employs competition between neural populations. Simulation of depolarizing noninvasive brain stimulation in this model decreased decision time, while hyperpolarizing stimulation increased it. This behavioral effect was caused by an increase in the rate of neural activity integration via recurrent connections, as well as changes in the susceptibility of the network to noisy background inputs which modulated population firing rate differences prior to the onset of the stimulus. These pre-stimulus differences biased the response to one or the other option, thus speeding or slowing decisions.\n\nWe then tested these model predictions in healthy participants performing a perceptual decision making task while receiving transcranial direct current stimulation (tDCS) over the left dlPFC, analogous to our simulated network stimulation. We found a striking match between model predictions and experimental results: depolarizing (inward) currents reduced and hyperpolarizing (outward) currents increased response times, but accuracy remained unaffected. Our results provide interventional evidence for the role of left dlPFC in perceptual decision making, and suggest that this region integrates and compares sensory evidence through competitive interactions between pyramidal cell populations which are selective for each response option. Mechanistically, our model suggests that stimulation of this region changes the rate at which evidence can be accumulated through recurrent activity and its susceptibility to background noise. More generally, our approach demonstrates that a linkage between computational modeling and noninvasive brain stimulation allows mechanistic accounts of brain function to be causally tested.

Neuroscience↗

bioRxiv2016

Evolutionary inference across eukaryotes identifies specific pressures favoring mitochondrial gene retention

Since their endosymbiotic origin, mitochondria have lost most of their genes. Despite the central cellular importance of mtDNA, selective mechanisms underlying the evolution of its content across eukaryotes remain contentious, with no data-driven exploration of different hypotheses. We developed HyperTraPS, a powerful methodology coupling stochastic modelling with Bayesian inference to identify the ordering and causes of evolutionary events. Using a large dataset from over 2000 complete mitochondrial genomes, we inferred evolutionary trajectories of mtDNA gene loss across the eukaryotic tree of life. We find that proteins fulfilling central energetic roles in the assembly of mitochondrial complexes are prefentially retained in mitochondria across eukary-otes, biophysically supporting colocalization for redox regulation. We also provide the first quantitative evidence that a combination of GC content and protein hydrophobicity is required to explain mtDNA gene retention, and predicts the success of artificial gene transfer experiments. Our results demonstrate that a combination of these three characteristics explains most mtDNA gene variability, addressing the long-debated question of why particular genes are retained in mitochondrial genomes.

Evolutionary Biology↗

bioRxiv2016

A novel polymorphism in nitric oxide synthase interacting protein (NOSIP) modulates nitric oxide synthesis and influences mortality in human sepsis

Nitric oxide, synthesised by three isoforms of Nitric Oxide synthases viz., nNOS by neurons, eNOS by endothelial cells and iNOS by phagocytes, performs a wide variety of biological functions in neurons, vascular endothelial cells and immune cells. Interaction between inducible nitric oxide synthase (iNOS) and Nitric oxide synthase interacting protein (NOSIP) was observed both in human monocytes and mouse macrophages and in cell free systems by biophysical methods. A novel mutation in nitric oxide synthase interacting protein (NOSIP) determined NO levels produced by human monocytes and was associated with disease severity in Sepsis patients. The study reveals NOSIP as an important regulator of inflammation by virtue of its ability to influence nitric oxide production both in mice and in humans and opens up novel avenues for therapeutic strategies against acute inflammation. While the influence of this novel NOSIP polymorphism in cardio-vascular and neuronal functions could be a subject of future investigations, its role in determining disease severity and mortality of the ongoing Covid 19 pandemic will be of immediate relevance.

Immunology↗

bioRxiv2016

Natural selection driven by DNA binding proteins shapes genome-wide motif statistics

Ectopic DNA binding by transcription factors and other DNA binding proteins can be detrimental to cellular functions and ultimately to organismal fitness. The frequency of protein-DNA binding at non-functional sites depends on the global composition of a genome with respect to all possible short motifs, or k-mer words. To determine whether weak yet ubiquitous protein-DNA interactions could exert significant evolutionary pressures on genomes, we correlate in vitro measurements of binding strengths on all 8-mer words from a large collection of transcription factors, in several different species, against their relative genomic frequencies. Our analysis reveals a clear signal of purifying selection to reduce the large number of weak binding sites genome-wide. This evolutionary process, which we call global selection, has a detectable hallmark in that similar words experience similar evolutionary pressure, a consequence of the biophysics of protein-DNA binding. By analyzing a large collection of genomes, we show that global selection exists in all domains of life, and operates through tiny selective steps, maintaining genomic binding landscapes over long evolutionary timescales.

Evolutionary Biology↗

bioRxiv2016

Temperature-dependent changes in neuronal dynamics in a patient with an SCN1A mutation and hyperthermia induced seizures

Dravet syndrome is the prototype of SCN1A-mutation associated epilepsies. It is characterised by prolonged seizures, typically provoked by fever. We describe the evaluation of an SCN1A mutation in a child with early-onset temperature-sensitive seizures.\n\nThe patient carries a heterozygous miss-sense variant (c3818C>T; pAla1273Val) in the NaV1.1 brain sodium channel. We characterised the functional effects of the variant using patch clamp recordings on Chinese Hamster Ovary Cells at different temperatures (32, 37, and 40{degrees}C).\n\nThe variant channels produced a temperature-dependent destabilization of channel activation and fast inactivation. Implementing these empirical abnormalities in a computational model produces a higher threshold for depolarization block in the variant, particularly at 40{degrees}C, suggesting a failure to autoregulate at high-input states.\n\nThese results reveal direct effects of abnormalities in biophysical NaV1.1 channel properties on neuronal dynamics. They illustrate the value of combining cellular measurements with computational models to integrate different scales (gene/channel to patient) of observations.

Neuroscience↗

bioRxiv2016

Robust Gap Repair in the Contractile Ring Ensures Timely Completion of Cytokinesis

Cytokinesis in animal cells requires the constriction of an actomyosin contractile ring, whose architecture and mechanism remain poorly understood. We use laser microsurgery to explore the biophysical properties of constricting contractile rings in C. elegans embryos. Laser cutting causes rings to snap open, which is a sign of tension release. However, instead of disintegrating, ring topology recovers and constriction proceeds. In response to severing, a finite gap forms that is proportional to ring perimeters before cutting, demonstrating that tension along the ring decreases throughout constriction. Severed rings repair their gaps by recruiting new material and subsequently increase constriction rate and complete cytokinesis with the same timing as uncut rings. Rings repair successive cuts and exhibit substantial constriction when gap repair is prevented. Our analysis suggests that cytokinesis is accomplished by contractile modules that assemble and contract autonomously, enabling local repair of the actomyosin network throughout constriction. Consequently, cytokinesis is a highly robust process impervious to discontinuities in contractile ring structure.

Cell Biology↗

bioRxiv2016

Acute induction of anomalous blood clotting by molecular amplification of highly substoichiometric levels of bacterial lipopolysaccharide (LPS)

It is well known that a variety of inflammatory diseases are accompanied by hypercoagulability, and a number of more-or-less longer-term signalling pathways have been shown to be involved. In recent work, we have suggested a direct and primary role for bacterial lipopolysaccharide in this hypercoagulability, but it seems never to have been tested directly. Here we show that the addition of tiny concentrations (0.2 ng.L-1) of bacterial lipopolysaccharide (LPS) to both whole blood and platelet-poor plasma of normal, healthy donors leads to marked changes in the nature of the fibrin fibres so formed, as observed by ultrastructural and fluorescence microscopy (the latter implying that the fibrin is actually in an amyloid {beta}-sheet-rich form. They resemble those seen in a number of inflammatory (and also amyloid) diseases, consistent with an involvement of LPS in their aetiology. These changes are mirrored by changes in their viscoelastic properties as measured by thromboelastography. Since the terminal stages of coagulation involve the polymerisation of fibrinogen into fibrin fibres, we tested whether LPS would bind to fibrinogen directly. We demonstrated this using isothermal calorimetry. Finally, we show that these changes in fibre structure are mirrored when the experiment is done simply with purified fibrinogen and thrombin ({+/-} 0.2 ng.L-1 LPS). This ratio of concentrations of LPS:fibrinogen in vivo represents a molecular amplification by the LPS of more than 108-fold, a number that is probably unparalleled in biology. The observation of a direct effect of such highly substoichiometric amounts of LPS on both fibrinogen and coagulation can account for the role of very small numbers of dormant bacteria in disease progression, and opens up this process to further mechanistic analysis and possible treatment.\n\nSignificance statementMost chronic diseases (including those classified as cardiovascular, neurodegenerative, or autoimmune) are accompanied by long-term inflammation. Although typically mediated by inflammatory cytokines, the origin of this inflammation is unclear. We have suggested that one explanation is a dormant microbiome that can shed the highly inflammatory lipopolysaccharide LPS. Such inflammatory diseases are also accompanied by a hypercoagulable phenotype. We here show directly (using 6 different methods) that very low concentrations of LPS can affect the terminal stages of the coagulation properties of blood and plasma significantly, and that this may be mediated via a direct binding of LPS to a small fraction of fibrinogen monomers as assessed biophysically. Such amplification methods may be of more general significance.

Microbiology↗

bioRxiv2016

Dopamine neurons change the type of excitability in response to stimuli

The dynamics of neural excitability determine the neuronal response to stimuli, its synchronization and resonance properties and, ultimately, the computations it performs in the brain. We investigated the dynamical mechanisms underlying the excitability type of dopamine (DA) neurons, using a conductance based biophysical model, and its regulation by intrinsic and synaptic currents. By calibrating the model to reproduce low frequency tonic firing, NMDA excitation is balanced by GABA-mediated inhibition and leads to type I excitable behavior characterized by a continuous decrease in firing frequency in response to hyperpolarizing currents. Furthermore, we analyzed how excitability type of the DA neuron model is influenced by changes in the intrinsic current composition. A subthreshold sodium current is necessary for a continuous frequency decrease during application of a negative current, and the low-frequency balanced state during simultaneous activation of NMDA and GABA receptors. Blocking this current switches the neuron to type II. Enhancing the anomalous rectifier Ih current also switches the excitability to type II. Key characteristics of synaptic conductances that may be observed in vivo also change the type of excitability: a depolarized GABAR reversal potential or co-activation of AMPARs leads to an abrupt frequency drop to zero, which is typical for type II excitability. Coactivation of NMDARs together with AMPARs and GABARs shifts the the type I/II boundary toward more hyperpolarized GABAR reversal potentials. To better understand how altering each of the aforementioned currents leads to changes in excitability profile of DA neuron, we provide a thorough dynamical analysis. Collectively, these results imply that type I excitability in dopamine neurons might be important for low firing rates and fine-tuning basal dopamine levels, while switching excitability to type II during NMDAR and AMPAR activation may facilitate a transient increase in dopamine concentration, as type II neurons are more amenable to synchronization.\n\nAuthor summaryDopamine neurons play a central role in guiding motivated behaviors. However, complete understanding of computations these neurons perform to encode rewarding and salient stimuli is still forthcoming. Network connectivity influences neural responses to stimuli but so do intrinsic excitability properties of individual neurons, as they define their synchronization properties and neural coding strategy. We investigated the excitability type of the DA neuron and found that, depending on the synaptic and intrinsic current composition, DA neurons can switch from type I to type II excitability. In short, without synaptic inputs or under balanced excitatory and inhibitory inputs DA neurons exhibits type I excitability, while excitatory AMPAR inputs can switch the neuron to type II. Type I neurons are best suited for coding the stimulus intensity due to their ability to smoothly decrease the firing rate. Type I excitability might be important for achieving low a basal DA concentration necessary for normal brain functioning. Switching to type II excitability further enables robust transient DA release of heterogeneous DA neuron population in response to correlated inputs, partially due to evoked population synchrony.

Neuroscience↗

bioRxiv2016

Sequence Entropy and the Absolute Rate of Amino Acid Substitutions

The evolution of model proteins under selection for thermodynamic stability suggests parallels between evolutionary behavior and chemical reaction kinetics. We developed a statistical mechanics theory of protein evolution by dividing amino acid interactions into site-specific and bath components, and show that substitutions between two amino acids occur when their site-specific contributions to stability are nearly identical. Fluctuating epistatic interactions drive stabilities into and out of these regions of near neutrality, with the time spent in the neutral region and thus the rate of substitution governed by physicochemical similarities between the amino acids. We derive a theoretical framework for how site-specific stabilities are determined, and demonstrate that substitution rates and the magnitude of the evolutionary Stokes shift can be predicted from biophysics and the effect of sequence entropy alone. Population genetics underlays our analysis, but population size does not determine the absolute rate of amino acid substitutions.

Evolutionary Biology↗