scholarly journals Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design

2014 ◽  
Author(s):  
Amir Shahmoradi ◽  
Dariya K. Sydykova ◽  
Stephanie J. Spielman ◽  
Eleisha L. Jackson ◽  
Eric T. Dawson ◽  
...  
Keyword(s):  
Structural Properties ◽  
Packing Density ◽  
Sequence Variation ◽  
Solvent Accessibility ◽  
Protein Structures ◽  
Structural Flexibility ◽  
Relative Solvent Accessibility ◽  
Dynamic Simulations ◽  
Evolutionary Variation ◽  
Flexibility Measures

Several recent works have shown that protein structure can predict site-specific evolutionary sequence variation. In particular, sites that are buried and/or have many contacts with other sites in a structure have been shown to evolve more slowly, on average, than surface sites with few contacts. Here, we present a comprehensive study of the extent to which numerous structural properties can predict sequence variation. The quantities we considered include buriedness (as measured by relative solvent accessibility), packing density (as measured by contact number), structural flexibility (as measured by B factors, root-mean-square fluctuations, and variation in dihedral angles), and variability in designed structures. We obtained structural flexibility measures both from molecular dynamics simulations performed on 9 non-homologous viral protein structures and from variation in homologous variants of those proteins, where available. We obtained measures of variability in designed structures from flexible-backbone design in the Rosetta software. We found that most of the structural properties correlate with site variation in the majority of structures, though the correlations are generally weak (correlation coefficients of 0.1 to 0.4). Moreover, we found that buriedness and packing density were better predictors of evolutionary variation than was structural flexibility. Finally, variability in designed structures was a weaker predictor of evolutionary variability than was buriedness or packing density, but it was comparable in its predictive power to the best structural flexibility measures. We conclude that simple measures of buriedness and packing density are better predictors of evolutionary variation than are more complicated predictors obtained from dynamic simulations, ensembles of homologous structures, or computational protein design.

Brewery: deep learning and deeper profiles for the prediction of 1D protein structure annotations

2020 ◽  
Vol 36 (12) ◽  
pp. 3897-3898
Author(s):  
Mirko Torrisi ◽  
Gianluca Pollastri
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Solvent Accessibility ◽  
Protein Structures ◽  
Evolutionary Information ◽  
Relative Solvent Accessibility ◽  
Multiple Sources ◽  
Contact Density ◽  
And Training ◽  
The Web

Abstract Motivation Protein structural annotations (PSAs) are essential abstractions to deal with the prediction of protein structures. Many increasingly sophisticated PSAs have been devised in the last few decades. However, the need for annotations that are easy to compute, process and predict has not diminished. This is especially true for protein structures that are hardest to predict, such as novel folds. Results We propose Brewery, a suite of ab initio predictors of 1D PSAs. Brewery uses multiple sources of evolutionary information to achieve state-of-the-art predictions of secondary structure, structural motifs, relative solvent accessibility and contact density. Availability and implementation The web server, standalone program, Docker image and training sets of Brewery are available at http://distilldeep.ucd.ie/brewery/. Contact [email protected]

scholarly journals The utility of protein structure as a predictor of site-wise d N /d S varies widely among HIV-1 proteins

2015 ◽  
Vol 12 (111) ◽  
pp. 20150579 ◽  
Author(s):  
Austin G. Meyer ◽  
Claus O. Wilke
Keyword(s):  
Protein Structure ◽  
Reference Site ◽  
Solvent Accessibility ◽  
Relative Solvent Accessibility ◽  
Apical Surface ◽  
Structural Constraints ◽  
Functional Sites ◽  
Protease Enzyme ◽  
Evolutionary Variation ◽  
Hiv 1

Protein structure acts as a general constraint on the evolution of viral proteins. One widely recognized structural constraint explaining evolutionary variation among sites is the relative solvent accessibility (RSA) of residues in the folded protein. In influenza virus, the distance from functional sites has been found to explain an additional portion of the evolutionary variation in the external antigenic proteins. However, to what extent RSA and distance from a reference site in the protein can be used more generally to explain protein adaptation in other viruses and in the different proteins of any given virus remains an open question. To address this question, we have carried out an analysis of the distribution and structural predictors of site-wise d N /d S in HIV-1. Our results indicate that the distribution of d N /d S in HIV follows a smooth gamma distribution, with no special enrichment or depletion of sites with d N /d S at or above one. The variation in d N /d S can be partially explained by RSA and distance from a reference site in the protein, but these structural constraints do not act uniformly among the different HIV-1 proteins. Structural constraints are highly predictive in just one of the three enzymes and one of three structural proteins in HIV-1. For these two proteins, the protease enzyme and the gp120 structural protein, structure explains between 30 and 40% of the variation in d N /d S . Finally, for the gp120 protein of the receptor-binding complex, we also find that glycosylation sites explain just 2% of the variation in d N /d S and do not explain gp120 evolution independently of either RSA or distance from the apical surface.

scholarly journals SPOT-1D-LM: Reaching Alignment-profile-based Accuracy in Predicting Protein Secondary and Tertiary Structural Properties without Alignment.

2021 ◽  
Author(s):  
Jaspreet Singh ◽  
Kuldip Paliwal ◽  
Jaswinder Singh ◽  
Yaoqi Zhou
Keyword(s):  
Structural Properties ◽  
Solvent Accessibility ◽  
Protein Structures ◽  
Language Models ◽  
Sequence Information ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Structural And Functional Properties ◽  
Sequence Profiles

Protein language models have emerged as an alternative to multiple sequence alignment for enriching sequence information and improving downstream prediction tasks such as biophysical, structural, and functional properties. Here we show that a combination of traditional one-hot encoding with the embeddings from two different language models (ProtTrans and ESM-1b) allows a leap in accuracy over single-sequence based techniques in predicting protein 1D secondary and tertiary structural properties, including backbone torsion angles, solvent accessibility and contact numbers. This large improvement leads to an accuracy comparable to or better than the current state-of-the-art techniques for predicting these 1D structural properties based on sequence profiles generated from multiple sequence alignments. The high-accuracy prediction in both secondary and tertiary structural properties indicates that it is possible to make highly accurate prediction of protein structures without homologous sequences, the remaining obstacle in the post AlphaFold2 era.

scholarly journals Multi-task deep learning for concurrent prediction of protein structural properties

2021 ◽  
Author(s):  
Buzhong Zhang ◽  
Jinyan Li ◽  
Lijun Quan ◽  
Qiang Lyu
Keyword(s):  
Deep Learning ◽  
Structural Properties ◽  
Solvent Accessibility ◽  
Close Association ◽  
Protein Structures ◽  
Secondary Structures ◽  
Structural Features ◽  
New Method ◽  
Machine Learning Techniques ◽  
Protein Secondary Structures

AbstractProtein structural properties are diverse and have the characteristics of spatial hierarchy, such as secondary structures, solvent accessibility and backbone angles. Protein tertiary structures are formed in close association with these features. Separate prediction of these structural properties has been improved with the increasing number of samples of protein structures and with advances in machine learning techniques, but concurrent prediction of these tightly related structural features is more useful to understand the overall protein structure and functions. We introduce a multi-task deep learning method for concurrent prediction of protein secondary structures, solvent accessibility and backbone angles (ϕ, ψ). The new method has main two deep network modules: the first one is designed as a DenseNet architecture a using bidirectional simplified GRU (GRU2) network, and the second module is designed as an updated Google Inception network. The new method is named CRRNN2.CRRNN2 is trained on 14,100 protein sequences and its prediction performance is evaluated by testing on public benchmark datasets: CB513, CASP10, CASP11, CASP12 and TS1199. Compared with state-of-the-art methods, CRRNN2 achieves similar, or better performance on the prediction of 3- and 8-state secondary structures, solvent accessibility and backbone angles (ϕ, ψ). Online CRRN-N2 applications, datasets and standalone software are available at http://qianglab.scst.suda.edu.cn/crrnn2/.

scholarly journals Local Packing Density Is the Main Structural Determinant of the Rate of Protein Sequence Evolution at Site Level

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
So-Wei Yeh ◽  
Tsun-Tsao Huang ◽  
Jen-Wei Liu ◽  
Sung-Huan Yu ◽  
Chien-Hua Shih ◽  
...  
Keyword(s):  
Packing Density ◽  
Protein Sequence ◽  
Solvent Accessibility ◽  
Sequence Evolution ◽  
Relative Solvent Accessibility ◽  
Sequence Variability ◽  
Substitution Rates ◽  
Contact Number ◽  
Local Packing Density ◽  
Structural Determinant

Functional and biophysical constraints result in site-dependent patterns of protein sequence variability. It is commonly assumed that the key structural determinant of site-specific rates of evolution is the Relative Solvent Accessibility (RSA). However, a recent study found that amino acid substitution rates correlate better with two Local Packing Density (LPD) measures, the Weighted Contact Number (WCN) and the Contact Number (CN), than with RSA. This work aims at a more thorough assessment. To this end, in addition to substitution rates, we considered four other sequence variability scores, four measures of solvent accessibility (SA), and other CN measures. We compared all properties for each protein of a structurally and functionally diverse representative dataset of monomeric enzymes. We show that the best sequence variability measures take into account phylogenetic tree topology. More importantly, we show that both LPD measures (WCN and CN) correlate better than all of the SA measures, regardless of the sequence variability score used. Moreover, the independent contribution of the best LPD measure is approximately four times larger than that of the best SA measure. This study strongly supports the conclusion that a site’s packing density rather than its solvent accessibility is the main structural determinant of its rate of evolution.

scholarly journals BCrystal: an interpretable sequence-based protein crystallization predictor

2019 ◽  
Vol 36 (5) ◽  
pp. 1429-1438 ◽  
Author(s):  
Abdurrahman Elbasir ◽  
Raghvendra Mall ◽  
Khalid Kunji ◽  
Reda Rawi ◽  
Zeyaul Islam ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Solvent Accessibility ◽  
Protein Crystallization ◽  
Protein Structures ◽  
Attrition Rate ◽  
Supplementary Information ◽  
Gradient Boosting ◽  
Relative Solvent Accessibility ◽  
X Ray Crystallography ◽  
Matthew’S Correlation Coefficient

Abstract Motivation X-ray crystallography has facilitated the majority of protein structures determined to date. Sequence-based predictors that can accurately estimate protein crystallization propensities would be highly beneficial to overcome the high expenditure, large attrition rate, and to reduce the trial-and-error settings required for crystallization. Results In this study, we present a novel model, BCrystal, which uses an optimized gradient boosting machine (XGBoost) on sequence, structural and physio-chemical features extracted from the proteins of interest. BCrystal also provides explanations, highlighting the most important features for the predicted crystallization propensity of an individual protein using the SHAP algorithm. On three independent test sets, BCrystal outperforms state-of-the-art sequence-based methods by more than 12.5% in accuracy, 18% in recall and 0.253 in Matthew’s correlation coefficient, with an average accuracy of 93.7%, recall of 96.63% and Matthew’s correlation coefficient of 0.868. For relative solvent accessibility of exposed residues, we observed higher values to associate positively with protein crystallizability and the number of disordered regions, fraction of coils and tripeptide stretches that contain multiple histidines associate negatively with crystallizability. The higher accuracy of BCrystal enables it to accurately screen for sequence variants with enhanced crystallizability. Availability and implementation Our BCrystal webserver is at https://machinelearning-protein.qcri.org/ and source code is available at https://github.com/raghvendra5688/BCrystal. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Dissecting the roles of local packing density and longer-range effects in protein sequence evolution

2015 ◽  
Author(s):  
Amir Shahmoradi ◽  
Claus O Wilke
Keyword(s):  
Packing Density ◽  
Solvent Accessibility ◽  
Voronoi Cell ◽  
Sequence Evolution ◽  
Relative Importance ◽  
Site Specific ◽  
Contact Number ◽  
Evolutionary Variation ◽  
Local Packing Density ◽  
Protein Sequence Evolution

What are the structural determinants of protein sequence evolution? A number of site-specific structural characteristics have been proposed, most of which are broadly related to either the density of contacts or the solvent accessibility of individual residues. Most importantly, there has been disagreement in the literature over the relative importance of solvent accessibility and local packing density for explaining site-specific sequence variability in proteins. We show here that this discussion has been confounded by the definition of local packing density. The most commonly used measures of local packing, such as the contact number and the weighted contact number, represent by definition the combined effects of local packing density and longer-range effects. As an alternative, we here propose a truly local measure of packing density around a single residue, based on the Voronoi cell volume. We show that the Voronoi cell volume, when calculated relative to the geometric center of amino-acid side chains, behaves nearly identically to the relative solvent accessibility, and both can explain, on average, approximately 34\% of the site-specific variation in evolutionary rate in a data set of 209 enzymes. An additional 10\% of variation can be explained by non-local effects that are captured in the weighted contact number. Consequently, evolutionary variation at a site is determined by the combined action of the immediate amino-acid neighbors of that site and of effects mediated by more distant amino acids. We conclude that instead of contrasting solvent accessibility and local packing density, future research should emphasize the relative importance of immediate contacts and longer-range effects on evolutionary variation.

Improved protein relative solvent accessibility prediction using deep multi-view feature learning framework

2021 ◽  
Vol 631 ◽  
pp. 114358
Author(s):  
Xue-Qiang Fan ◽  
Jun Hu ◽  
Ning-Xin Jia ◽  
Dong-Jun Yu ◽  
Gui-Jun Zhang
Keyword(s):  
Solvent Accessibility ◽  
Feature Learning ◽  
Relative Solvent Accessibility ◽  
Learning Framework ◽  
Solvent Accessibility Prediction

scholarly journals Solvent Accessibility of Residues Undergoing Pathogenic Variations in Humans: From Protein Structures to Protein Sequences

2021 ◽  
Vol 7 ◽  
Author(s):  
Castrense Savojardo ◽  
Matteo Manfredi ◽  
Pier Luigi Martelli ◽  
Rita Casadio
Keyword(s):  
Solvent Accessibility ◽  
Protein Structures ◽  
Three Dimensional ◽  
Protein Sequences ◽  
Large Data ◽  
Human Protein ◽  
Dimensional Structure ◽  
Wild Type ◽  
Solvent Exposure ◽  
Data Set

Solvent accessibility (SASA) is a key feature of proteins for determining their folding and stability. SASA is computed from protein structures with different algorithms, and from protein sequences with machine-learning based approaches trained on solved structures. Here we ask the question as to which extent solvent exposure of residues can be associated to the pathogenicity of the variation. By this, SASA of the wild-type residue acquires a role in the context of functional annotation of protein single-residue variations (SRVs). By mapping variations on a curated database of human protein structures, we found that residues targeted by disease related SRVs are less accessible to solvent than residues involved in polymorphisms. The disease association is not evenly distributed among the different residue types: SRVs targeting glycine, tryptophan, tyrosine, and cysteine are more frequently disease associated than others. For all residues, the proportion of disease related SRVs largely increases when the wild-type residue is buried and decreases when it is exposed. The extent of the increase depends on the residue type. With the aid of an in house developed predictor, based on a deep learning procedure and performing at the state-of-the-art, we are able to confirm the above tendency by analyzing a large data set of residues subjected to variations and occurring in some 12,494 human protein sequences still lacking three-dimensional structure (derived from HUMSAVAR). Our data support the notion that surface accessible area is a distinguished property of residues that undergo variation and that pathogenicity is more frequently associated to the buried property than to the exposed one.

Sign in / Sign up

Export Citation Format

Share Document