scholarly journals PLUG (Pruning of Local Unrealistic Geometries) removes restrictions on biophysical modeling for protein design

2018 ◽  
Author(s):  
Mark A. Hallen
Keyword(s):  
Protein Design ◽  
Conformational Space ◽  
Biophysical Model ◽  
Biophysical Modeling ◽  
Backbone Flexibility ◽  
Energy Functions ◽  
Design Algorithm ◽  
Dead End ◽  
Pruning Algorithms ◽  
Design Calculations

AbstractProtein design algorithms must search an enormous conformational space to identify favorable conformations. As a result, those that perform this search with guarantees of accuracy generally start with a conformational pruning step, such as dead-end elimination (DEE). However, the mathematical assumptions of DEE-based pruning algorithms have up to now severely restricted the biophysical model that can feasibly be used in protein design. To lift these restrictions, I propose to prune local unrealistic geometries (PLUG) using a linear programming-based method. PLUG’s biophysical model consists only of well-known lower bounds on interatomic distances. PLUG is intended as pre-processing for energy-based protein design calculations, whose biophysical model need not support DEE pruning. Based on 96 test cases, PLUG is at least as effective at pruning as DEE for larger protein designs—the type that most require pruning. When combined with the LUTE protein design algorithm, PLUG greatly facilitates designs that account for continuous entropy, large multistate designs with continuous flexibility, and designs with extensive continuous backbone flexibility and advanced non-pairwise energy functions. Many of these designs are tractable only with PLUG, either for empirical reasons (LUTE’s machine learning step achieves an accurate fit only after PLUG pruning), or for theoretical reasons (many energy functions are fundamentally incompatible with DEE).

Energy functions for protein design

1999 ◽  
Vol 9 (4) ◽  
pp. 509-513 ◽  
Author(s):  
D GORDON ◽  
S MARSHALL ◽  
S MAYOT
Keyword(s):  
Protein Design ◽  
Energy Functions

scholarly journals Potential for rapid genetic adaptation to warming in a Great Barrier Reef coral

2017 ◽  
Author(s):  
Mikhail V. Matz ◽  
Eric A. Treml ◽  
Galina V. Aglyamova ◽  
Madeleine J. H. van Oppen ◽  
Line K. Bay
Keyword(s):  
Genetic Variation ◽  
Great Barrier Reef ◽  
Coral Cover ◽  
Reef Coral ◽  
Biophysical Model ◽  
Detectable Effect ◽  
Genetic Adaptation ◽  
Barrier Reef ◽  
Biophysical Modeling ◽  
Adaptive Genetic Variation

AbstractCan genetic adaptation in reef-building corals keep pace with the current rate of sea surface warming? Here we combine population genomic, biophysical modeling, and evolutionary simulations to predict future adaptation of the common coralAcropora milleporaon the Great Barrier Reef. Loss of coral cover in recent decades did not yet have detectable effect on genetic diversity in our species. Genomic analysis of migration patterns closely matched the biophysical model of larval dispersal in favoring the spread of existing heat-tolerant alleles from lower to higher latitudes. Given these conditions we find that standing genetic variation could be sufficient to fuel rapid adaptation ofA. milleporato warming for the next 100-200 years, although random thermal anomalies would drive increasingly severe mortality episodes. However, this adaptation will inevitably cease unless the warming is slowed down, since no realistic mutation rate could replenish adaptive genetic variation fast enough.

scholarly journals Lobster Fishery Connectivity and Management in Indonesia Waters

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Waluyo Waluyo ◽  
Taslim Arifin
Keyword(s):  
Genetic Structure ◽  
Fishery Management ◽  
Marine Organism ◽  
Molecular Genetic ◽  
Three Dimensional ◽  
Geographical Information ◽  
Biophysical Model ◽  
Complex Life Cycle ◽  
Biophysical Modeling ◽  
The Individual

The distribution of lobsters in Indonesia waters is very wide, even lobster species in Indonesia are also scattered in the tropical waters of the western Pacific Ocean, the Indian Ocean, Africa to Japanese waters.Indonesia waters are divided into 11 (eleven) Fishery Management Zone (FMZ). Lobsters in Indonesia may come from various water areas, both national and regional waters zone, it’s called the sink population, its spread is influenced by the movement of the current. Lobster seed is nurtured by nature through oceancurrents from Australia, East Indonesia, Japan, then back to Australia. Lobsters have a complex life cycle,where adult lobsters inhabit coral reefs as a place to lay eggs, then hatch into planktonic larvae, and grow up in open seas and carry out diurnal and ontogenetic vertical migrations before returning to nurseries in shallow coastal areas and reefs. coral, as well as habitat by the type of species. Literature research had used at leasttwo methodologies to estimate the distribution and connection sensitivity matrices of marine organism larvae.The two most common approaches are using genetic markers and numerical biophysical modeling. Thus, this research uses molecular genetic techniques to explain the genetic structure of lobster populations using a biophysical model approach that can explain the genetic structure of lobsters, as well as the distribution base on regional oceanographic synthesis data and lobster biology known in Indonesia waters. This model has four components, namely: 1) a benthic module based on a Geographical Information System (GIS) which is a lobster habitat in the spawning and recruitment process, 2) a physical oceanography module containing daily velocity in the form of a three-dimensional hydrodynamic model, 3) a larva biology module that describes larval life history characteristics, and 4) a Lagrangian Stochastic module that tracks the individual trajectories of larvae.

scholarly journals Live imaging and biophysical modeling support a button-based mechanism of somatic homolog pairing in Drosophila

2020 ◽  
Author(s):  
Myron Child ◽  
Jack R. Bateman ◽  
Amir Jahangiri ◽  
Armando Reimer ◽  
Nicholas C. Lammers ◽  
...  
Keyword(s):  
Spatial Configuration ◽  
Live Imaging ◽  
Biophysical Model ◽  
Structural Basis ◽  
Biophysical Modeling ◽  
Homologous Chromosomes ◽  
3D Genome ◽  
Homolog Pairing ◽  
Rna Labeling ◽  
Regulated Gene Expression

AbstractThe spatial configuration of the eukaryotic genome is organized and dynamic, providing the structural basis for regulated gene expression in living cells. In Drosophila melanogaster, 3D genome organization is characterized by somatic homolog pairing, where homologous chromosomes are intimately paired from end to end; however, the process by which homologs identify one another and pair has remained mysterious. A recent model proposed that specifically interacting “buttons” encoded along the lengths of homologous chromosomes drive somatic homolog pairing. Here, we turned this hypothesis into a precise biophysical model to demonstrate that a button-based mechanism can lead to chromosome-wide pairing. We tested our model and constrained its free parameters using live-imaging measurements of chromosomal loci tagged with the MS2 and PP7 nascent RNA labeling systems. Our analysis showed strong agreement between model predictions and experiments in the separation dynamics of tagged homologous loci as they transition from unpaired to paired states, and in the percentage of nuclei that become paired as embryonic development proceeds. In sum, as a result of this dialogue between theory and experiment, our data strongly support a button-based mechanism of somatic homolog pairing in Drosophila and provide a theoretical framework for revealing the molecular identity and regulation of buttons.

scholarly journals Amino-acid site variability among natural and designed proteins

2013 ◽  
Author(s):  
Eleisha L. Jackson ◽  
Noah Ollikainen ◽  
Arthur W. Covert III ◽  
Tanja Kortemme ◽  
Claus O. Wilke
Keyword(s):  
Amino Acid ◽  
Protein Design ◽  
Protein Sequences ◽  
Structural Constraints ◽  
Scoring Functions ◽  
Solvent Exposure ◽  
Backbone Flexibility ◽  
Hydrophobic Residues ◽  
Designed Proteins ◽  
Site Variability

Computational protein design attempts to create protein sequences that fold stably into pre-specified structures. Here we compare alignments of designed proteins to alignments of natural proteins and assess how closely designed sequences recapitulate patterns of sequence variation found in natural protein sequences. We design proteins using RosettaDesign, and we evaluate both fixed-backbone designs and variable-backbone designs with different amounts of backbone flexibility. We find that proteins designed with a fixed backbone tend to underestimate the amount of site variability observed in natural proteins while proteins designed with an intermediate amount of backbone flexibility result in more realistic site variability. Further, the correlation between solvent exposure and site variability in designed proteins is lower than that in natural proteins. This finding suggests that site variability is too uniform across different solvent exposure states (i.e., buried residues are too variable or exposed residues too conserved). When comparing the amino acid frequencies in the designed proteins with those in natural proteins we find that in the designed proteins hydrophobic residues are underrepresented in the core. From these results we conclude that intermediate backbone flexibility during design results in more accurate protein design and that either scoring functions or backbone sampling methods require further improvement to accurately replicate structural constraints on site variability.

scholarly journals Positive multistate protein design

2019 ◽  
Vol 36 (1) ◽  
pp. 122-130
Author(s):  
Jelena Vucinic ◽  
David Simoncini ◽  
Manon Ruffini ◽  
Sophie Barbe ◽  
Thomas Schiex
Keyword(s):  
Protein Design ◽  
Critical Role ◽  
Average Energy ◽  
Computational Design ◽  
Amino Acid Sequences ◽  
Supplementary Information ◽  
Backbone Flexibility ◽  
Identify Amino Acid ◽  
Design Software ◽  
Protein Redesign

Abstract 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.

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