Large-scale evaluation of dynamically important residues in proteins predicted by the perturbation analysis of a coarse-grained elastic model

Backgrounds  

It is increasingly recognized that protein functions often require intricate conformational dynamics, which involves a network of key amino acid residues that couple spatially separated functional sites. Tremendous efforts have been made to identify these key residues by experimental and computational means.     

Fast dynamics perturbation analysis for prediction of protein functional sites

Background  

We present a fast version of the dynamics perturbation analysis (DPA) algorithm to predict functional sites in protein structures. The original DPA algorithm finds regions in proteins where interactions cause a large change in the protein conformational distribution, as measured using the relative entropy D _x . Such regions are associated with functional sites.     

Sequence based residue depth prediction using evolutionary information and predicted secondary structure

Background  

Residue depth allows determining how deeply a given residue is buried, in contrast to the solvent accessibility that differentiates between buried and solvent-exposed residues. When compared with the solvent accessibility, the depth allows studying deep-level structures and functional sites, and formation of the protein folding nucleus. Accurate prediction of residue depth would provide valuable information for fold recognition, prediction of functional sites, and protein design.     

Robust MS quantification method for phospho-peptides using 18O/16O labeling

Background  

Quantitative measurements of specific protein phosphorylation sites, as presented here, can be used to investigate signal transduction pathways, which is an important aspect of cell dynamics. The presented method quantitatively compares peptide abundances from experiments using ¹⁸O/¹⁶O labeling starting from elaborated MS spectra. It was originally developed to study signaling cascades activated by amyloid-β treatment of neurons used as a cellular model system with relevance to Alzheimer's disease, but is generally applicable.     

Subfamily logos: visualization of sequence deviations at alignment positions with high information content

Background  

Recognition of relevant sequence deviations can be valuable for elucidating functional differences between protein subfamilies. Interesting residues at highly conserved positions can then be mutated and experimentally analyzed. However, identification of such sites is tedious because automated approaches are scarce.     

Comparison of molecular dynamics and superfamily spaces of protein domain deformation

Background  

It is well known the strong relationship between protein structure and flexibility, on one hand, and biological protein function, on the other hand. Technically, protein flexibility exploration is an essential task in many applications, such as protein structure prediction and modeling. In this contribution we have compared two different approaches to explore the flexibility space of protein domains: i) molecular dynamics (MD-space), and ii) the study of the structural changes within superfamily (SF-space).     

Variation in structural location and amino acid conservation of functional sites in protein domain families

Background  

The functional sites of a protein present important information for determining its cellular function and are fundamental in drug design. Accordingly, accurate methods for the prediction of functional sites are of immense value. Most available methods are based on a set of homologous sequences and structural or evolutionary information, and assume that functional sites are more conserved than the average. In the analysis presented here, we have investigated the conservation of location and type of amino acids at functional sites, and compared the behaviour of functional sites between different protein domains.     

A protein evolution model with independent sites that reproduces site-specific amino acid distributions from the Protein Data Bank

Background  

Since thermodynamic stability is a global property of proteins that has to be conserved during evolution, the selective pressure at a given site of a protein sequence depends on the amino acids present at other sites. However, models of molecular evolution that aim at reconstructing the evolutionary history of macromolecules become computationally intractable if such correlations between sites are explicitly taken into account.     

A computational survey of candidate exonic splicing enhancer motifs in the model plant <Emphasis Type="Italic">Arabidopsis thaliana</Emphasis>

Background  

Algorithmic approaches to splice site prediction have relied mainly on the consensus patterns found at the boundaries between protein coding and non-coding regions. However exonic splicing enhancers have been shown to enhance the utilization of nearby splice sites.     

High-throughput identification of interacting protein-protein binding sites

Background  

With the advent of increasing sequence and structural data, a number of methods have been proposed to locate putative protein binding sites from protein surfaces. Therefore, methods that are able to identify whether these binding sites interact are needed.     

Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information

Background  

Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.     

Modeling structure and flexibility of <Emphasis Type="Italic">Candida antarctica</Emphasis> lipase B in organic solvents

Background  

The structure and flexibility of Candida antarctica lipase B in water and five different organic solvent models was investigated using multiple molecular dynamics simulations to describe the effect of solvents on structure and dynamics. Interactions of the solvents with the protein and the distribution of water molecules at the protein surface were examined.     

Accelerated search for biomolecular network models to interpret high-throughput experimental data

Background  

The functions of human cells are carried out by biomolecular networks, which include proteins, genes, and regulatory sites within DNA that encode and control protein expression. Models of biomolecular network structure and dynamics can be inferred from high-throughput measurements of gene and protein expression. We build on our previously developed fuzzy logic method for bridging quantitative and qualitative biological data to address the challenges of noisy, low resolution high-throughput measurements, i.e., from gene expression microarrays. We employ an evolutionary search algorithm to accelerate the search for hypothetical fuzzy biomolecular network models consistent with a biological data set. We also develop a method to estimate the probability of a potential network model fitting a set of data by chance. The resulting metric provides an estimate of both model quality and dataset quality, identifying data that are too noisy to identify meaningful correlations between the measured variables.     

Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties

Background  

The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties.     

A global optimization algorithm for protein surface alignment

Background  

A relevant problem in drug design is the comparison and recognition of protein binding sites. Binding sites recognition is generally based on geometry often combined with physico-chemical properties of the site since the conformation, size and chemical composition of the protein surface are all relevant for the interaction with a specific ligand. Several matching strategies have been designed for the recognition of protein-ligand binding sites and of protein-protein interfaces but the problem cannot be considered solved.     

A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction

Background  

Predicting which molecules can bind to a given binding site of a protein with known 3D structure is important to decipher the protein function, and useful in drug design. A classical assumption in structural biology is that proteins with similar 3D structures have related molecular functions, and therefore may bind similar ligands. However, proteins that do not display any overall sequence or structure similarity may also bind similar ligands if they contain similar binding sites. Quantitatively assessing the similarity between binding sites may therefore be useful to propose new ligands for a given pocket, based on those known for similar pockets.     

PocketMatch: A new algorithm to compare binding sites in protein structures

Background  

Recognizing similarities and deriving relationships among protein molecules is a fundamental requirement in present-day biology. Similarities can be present at various levels which can be detected through comparison of protein sequences or their structural folds. In some cases similarities obscure at these levels could be present merely in the substructures at their binding sites. Inferring functional similarities between protein molecules by comparing their binding sites is still largely exploratory and not as yet a routine protocol. One of the main reasons for this is the limitation in the choice of appropriate analytical tools that can compare binding sites with high sensitivity. To benefit from the enormous amount of structural data that is being rapidly accumulated, it is essential to have high throughput tools that enable large scale binding site comparison.     

Functional asymmetry in the lysyl-tRNA synthetase explored by molecular dynamics,free energy calculations and experiment

Background  

Charging of transfer-RNA with cognate amino acid is accomplished by the aminoacyl-tRNA synthetases, and proceeds through an aminoacyl adenylate intermediate. The lysyl-tRNA synthetase has evolved an active site that specifically binds lysine and ATP. Previous molecular dynamics simulations of the heat-inducible Escherichia coli lysyl-tRNA synthetase, LysU, have revealed differences in the binding of ATP and aspects of asymmetry between the nominally equivalent active sites of this dimeric enzyme. The possibility that this asymmetry results in different binding affinities for the ligands is addressed here by a parallel computational and biochemical study.     

Prediction of protein-protein binding site by using core interface residue and support vector machine

Background  

The prediction of protein-protein binding site can provide structural annotation to the protein interaction data from proteomics studies. This is very important for the biological application of the protein interaction data that is increasing rapidly. Moreover, methods for predicting protein interaction sites can also provide crucial information for improving the speed and accuracy of protein docking methods.     

Rates of woody encroachment in African savannas reflect water constraints and fire disturbance

Aim

The aims of this study were to (1) estimate current rates of woody encroachment across African savannas; (2) identify relationships between change in woody cover and potential drivers, including water constraints, fire frequency and livestock density. The found relationships led us to pursue a third goal: (3) use temporal dynamics in woody cover to estimate potential woody cover.

Location

Sub‐Saharan African savannas.

Methods

The study used very high spatial resolution satellite imagery at sites with overlapping older (2002–2006) and newer (2011–2016) imagery to estimate change in woody cover. We sampled 596 sites in 38 separate areas across African savannas. Areas with high anthropogenic impact were avoided in order to more clearly identify the influence of environmental factors. Relationships between woody cover change and potential drivers were identified using linear regression and simultaneous autoregression, where the latter accounts for spatial autocorrelation.

Results

The mean annual change in woody cover across our study areas was 0.25% per year. Although we cannot explain the general trend of encroachment based on our data, we found that change rates were positively correlated with the difference between potential woody cover and actual woody cover (a proxy for water availability; p < .001), and negatively correlated with fire frequency (p < .01). Using the relationship between rates of encroachment and initial cover, we estimated potential woody cover at different rainfall levels.

Main conclusions

The results indicate that woody encroachment is ongoing and widespread across African savannas. The fact that the difference between potential and actual cover was the most significant predictor highlights the central role of water availability and tree–tree competition in controlling change in woody populations, both in water‐limited and mesic savannas. Our approach to derive potential woody cover from the woody cover change trajectories demonstrates that temporal dynamics in woody populations can be used to infer resource limitations.