Increasing the efficiency and accuracy of the ABACUS protein sequence design method

2019 ◽  
Vol 36 (1) ◽  
pp. 136-144 ◽  
Author(s):  
Peng Xiong ◽  
Xiuhong Hu ◽  
Bin Huang ◽  
Jiahai Zhang ◽  
Quan Chen ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Design Method ◽  
Solvent Accessibility ◽  
Supplementary Information ◽  
Survey Method ◽  
Energy Functions ◽  
Sequence Design ◽  
Feature Spaces ◽  
Empirical Function ◽  
Representative Points

Abstract Motivation The ABACUS (a backbone-based amino acid usage survey) method uses unique statistical energy functions to carry out protein sequence design. Although some of its results have been experimentally verified, its accuracy remains improvable because several important components of the method have not been specifically optimized for sequence design or in contexts of other parts of the method. The computational efficiency also needs to be improved to support interactive online applications or the consideration of a large number of alternative backbone structures. Results We derived a model to measure solvent accessibility with larger mutual information with residue types than previous models, optimized a set of rotamers which can approximate the sidechain atomic positions more accurately, and devised an empirical function to treat inter-atomic packing with parameters fitted to native structures and optimized in consistence with the rotamer set. Energy calculations have been accelerated by interpolation between pre-determined representative points in high-dimensional structural feature spaces. Sidechain repacking tests showed that ABACUS2 can accurately reproduce the conformation of native sidechains. In sequence design tests, the native residue type recovery rate reached 37.7%, exceeding the value of 32.7% for ABACUS1. Applying ABACUS2 to designed sequences on three native backbones produced proteins shown to be well-folded by experiments. Availability and implementation The ABACUS2 sequence design server can be visited at http://biocomp.ustc.edu.cn/servers/abacus-design.php. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals EvoEF2: accurate and fast energy function for computational protein design

2019 ◽  
Vol 36 (4) ◽  
pp. 1135-1142 ◽  
Author(s):  
Xiaoqiang Huang ◽  
Robin Pearce ◽  
Yang Zhang
Keyword(s):  
Protein Design ◽  
Energy Function ◽  
Interaction Design ◽  
Protein Sequence ◽  
De Novo ◽  
Supplementary Information ◽  
Energy Functions ◽  
Sequence Design ◽  
Physical Energy ◽  
Benchmark Datasets

Abstract Motivation The accuracy and success rate of de novo protein design remain limited, mainly due to the parameter over-fitting of current energy functions and their inability to discriminate incorrect designs from correct designs. Results We developed an extended energy function, EvoEF2, for efficient de novo protein sequence design, based on a previously proposed physical energy function, EvoEF. Remarkably, EvoEF2 recovered 32.5%, 47.9% and 22.3% of all, core and surface residues for 148 test monomers, and was generally applicable to protein–protein interaction design, as it recapitulated 30.9%, 42.4%, 31.3% and 21.4% of all, core, interface and surface residues for 88 test dimers, significantly outperforming EvoEF on the native sequence recapitulation. We further used I-TASSER to evaluate the foldability of the 148 designed monomer sequences, where all of them were predicted to fold into structures with high fold- and atomic-level similarity to their corresponding native structures, as demonstrated by the fact that 87.8% of the predicted structures shared a root-mean-square-deviation less than 2 Å to their native counterparts. The study also demonstrated that the usefulness of physical energy functions is highly correlated with the parameter optimization processes, and EvoEF2, with parameters optimized using sequence recapitulation, is more suitable for computational protein sequence design than EvoEF, which was optimized on thermodynamic mutation data. Availability and implementation The source code of EvoEF2 and the benchmark datasets are freely available at https://zhanglab.ccmb.med.umich.edu/EvoEF. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Protein sequence design with a learned potential

2020 ◽  
Author(s):  
Namrata Anand-Achim ◽  
Raphael R. Eguchi ◽  
Alexander Derry ◽  
Russ B. Altman ◽  
Po-Ssu Huang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Amino Acid ◽  
Protein Design ◽  
Protein Sequence ◽  
State Of The Art ◽  
The State ◽  
Energy Functions ◽  
Sequence Design

AbstractThe primary challenge of fixed-backbone protein design is to find a distribution of sequences that fold to the backbone of interest. This task is central to nearly all protein engineering problems, as achieving a particular backbone conformation is often a prerequisite for hosting specific functions. In this study, we investigate the capability of a deep neural network to learn the requisite patterns needed to design sequences. The trained model serves as a potential function defined over the space of amino acid identities and rotamer states, conditioned on the local chemical environment at each residue. While most deep learning based methods for sequence design only produce amino acid sequences, our method generates full-atom structural models, which can be evaluated using established sequence quality metrics. Under these metrics we are able to produce realistic and variable designs with quality comparable to the state-of-the-art. Additionally, we experimentally test designs for a de novo TIM-barrel structure and find designs that fold, demonstrating the algorithm’s generalizability to novel structures. Overall, our results demonstrate that a deep learning model can match state-of-the-art energy functions for guiding protein design.SignificanceProtein design tasks typically depend on carefully modeled and parameterized heuristic energy functions. In this study, we propose a novel machine learning method for fixed-backbone protein sequence design, using a learned neural network potential to not only design the sequence of amino acids but also select their side-chain configurations, or rotamers. Factoring through a structural representation of the protein, the network generates designs on par with the state-of-the-art, despite having been entirely learned from data. These results indicate an exciting future for protein design driven by machine learning.

scholarly journals Protein evolution is structure dependent and non-homogeneous across the tree of life

2020 ◽  
Author(s):  
Akanksha Pandey ◽  
Edward L. Braun
Keyword(s):  
Mixture Models ◽  
Protein Evolution ◽  
Protein Sequence ◽  
Solvent Accessibility ◽  
Tree Of Life ◽  
Supplementary Information ◽  
Amino Acid Side Chain ◽  
Sequence Evolution ◽  
Relative Solvent Accessibility ◽  
Protein Sequence Evolution

AbstractMotivationProtein sequence evolution is a complex process that varies among-sites within proteins and across the tree of life. Comparisons of evolutionary rate matrices for specific taxa (‘clade-specific models’) have the potential to reveal this variation and provide information about the underlying reasons for those changes. To study changes in patterns of protein sequence evolution we estimated and compared clade-specific models in a way that acknowledged variation within proteins due to structure.ResultsClade-specific model fit was able to correctly classify proteins from four specific groups (vertebrates, plants, oomycetes, and yeasts) more than 70% of the time. This was true whether we used mixture models that incorporate relative solvent accessibility or simple models that treat sites as homogeneous. Thus, protein evolution is non-homogeneous over the tree of life. However, a small number of dimensions could explain the differences among models (for mixture models ~50% of the variance reflected relative solvent accessibility and ~25% reflected clade). Relaxed purifying selection in taxa with lower long-term effective population sizes appears to explain much of the among clade variance. Relaxed selection on solvent-exposed sites was correlated with changes in amino acid side-chain volume; other differences among models were more complex. Beyond the information they reveal about protein evolution, our clade-specific models also represent tools for phylogenomic inference.AvailabilityModel files are available from https://github.com/ebraun68/[email protected] informationSupplementary data are appended to this preprint.

RSARF: Prediction of Residue Solvent Accessibility from Protein Sequence Using Random Forest Method

2012 ◽  
Vol 19 (1) ◽  
pp. 50-56 ◽  
Author(s):  
Ganesan Pugalenthi ◽  
Krishna Kumar Kandaswamy ◽  
Kuo-Chen Chou ◽  
Saravanan Vivekanandan ◽  
Prasanna Kolatkar
Keyword(s):  
Random Forest ◽  
Protein Sequence ◽  
Solvent Accessibility ◽  
Random Forest Method

GEMPROT: visualization of the impact on the protein of the genetic variants found on each haplotype

2018 ◽  
Vol 35 (14) ◽  
pp. 2492-2494
Author(s):  
Tania Cuppens ◽  
Thomas E Ludwig ◽  
Pascal Trouvé ◽  
Emmanuelle Genin
Keyword(s):  
Genetic Variants ◽  
Protein Sequence ◽  
Sequence Data ◽  
Protein Sequences ◽  
Supplementary Information ◽  
Analysis Tool ◽  
Functional Protein ◽  
Key Players ◽  
On Line ◽  
The Impact

Abstract Summary When analyzing sequence data, genetic variants are considered one by one, taking no account of whether or not they are found in the same individual. However, variant combinations might be key players in some diseases as variants that are neutral on their own can become deleterious when associated together. GEMPROT is a new analysis tool that allows, from a phased vcf file, to visualize the consequences of the genetic variants on the protein. At the level of an individual, the program shows the variants on each of the two protein sequences and the Pfam functional protein domains. When data on several individuals are available, GEMPROT lists the haplotypes found in the sample and can compare the haplotype distributions between different sub-groups of individuals. By offering a global visualization of the gene with the genetic variants present, GEMPROT makes it possible to better understand the impact of combinations of genetic variants on the protein sequence. Availability and implementation GEMPROT is freely available at https://github.com/TaniaCuppens/GEMPROT. An on-line version is also available at http://med-laennec.univ-brest.fr/GEMPROT/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals VarMap: a web tool for mapping genomic coordinates to protein sequence and structure and retrieving protein structural annotations

2019 ◽  
Vol 35 (22) ◽  
pp. 4854-4856 ◽  
Author(s):  
James D Stephenson ◽  
Roman A Laskowski ◽  
Andrew Nightingale ◽  
Matthew E Hurles ◽  
Janet M Thornton
Keyword(s):  
Protein Sequence ◽  
Structural Information ◽  
Protein Structures ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Web Tool ◽  
Genomic Variants ◽  
Structural Context ◽  
Pathogenic Variants ◽  
Transcript Evidence

Abstract Motivation Understanding the protein structural context and patterning on proteins of genomic variants can help to separate benign from pathogenic variants and reveal molecular consequences. However, mapping genomic coordinates to protein structures is non-trivial, complicated by alternative splicing and transcript evidence. Results Here we present VarMap, a web tool for mapping a list of chromosome coordinates to canonical UniProt sequences and associated protein 3D structures, including validation checks, and annotating them with structural information. Availability and implementation https://www.ebi.ac.uk/thornton-srv/databases/VarMap. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SOLart: a structure-based method to predict protein solubility and aggregation

2019 ◽  
Author(s):  
Qingzhen Hou ◽  
Jean Marc Kwasigroch ◽  
Marianne Rooman ◽  
Fabrizio Pucci
Keyword(s):  
Solvent Accessibility ◽  
Pearson Correlation ◽  
Protein Solubility ◽  
Independent Set ◽  
Supplementary Information ◽  
Training Dataset ◽  
Solubility Prediction ◽  
High Concentration ◽  
Structural Genomic ◽  
Genomic Studies

Abstract Motivation The solubility of a protein is often decisive for its proper functioning. Lack of solubility is a major bottleneck in high-throughput structural genomic studies and in high-concentration protein production, and the formation of protein aggregates causes a wide variety of diseases. Since solubility measurements are time-consuming and expensive, there is a strong need for solubility prediction tools. Results We have recently introduced solubility-dependent distance potentials that are able to unravel the role of residue–residue interactions in promoting or decreasing protein solubility. Here, we extended their construction by defining solubility-dependent potentials based on backbone torsion angles and solvent accessibility, and integrated them, together with other structure- and sequence-based features, into a random forest model trained on a set of Escherichia coli proteins with experimental structures and solubility values. We thus obtained the SOLart protein solubility predictor, whose most informative features turned out to be folding free energy differences computed from our solubility-dependent statistical potentials. SOLart performances are very good, with a Pearson correlation coefficient between experimental and predicted solubility values of almost 0.7 both in cross-validation on the training dataset and in an independent set of Saccharomyces cerevisiae proteins. On test sets of modeled structures, only a limited drop in performance is observed. SOLart can thus be used with both high-resolution and low-resolution structures, and clearly outperforms state-of-art solubility predictors. It is available through a user-friendly webserver, which is easy to use by non-expert scientists. Availability and implementation The SOLart webserver is freely available at http://babylone.ulb.ac.be/SOLART/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SphereCon—a method for precise estimation of residue relative solvent accessible area from limited structural information

2020 ◽  
Vol 36 (11) ◽  
pp. 3372-3378
Author(s):  
Alexander Gress ◽  
Olga V Kalinina
Keyword(s):  
Protein Function ◽  
Structural Information ◽  
Solvent Accessibility ◽  
Three Dimensional ◽  
Structural Data ◽  
Supplementary Information ◽  
Dimensional Structure ◽  
Relative Solvent Accessibility ◽  
Precise Measure ◽  
The Impact

Abstract Motivation In proteins, solvent accessibility of individual residues is a factor contributing to their importance for protein function and stability. Hence one might wish to calculate solvent accessibility in order to predict the impact of mutations, their pathogenicity and for other biomedical applications. A direct computation of solvent accessibility is only possible if all atoms of a protein three-dimensional structure are reliably resolved. Results We present SphereCon, a new precise measure that can estimate residue relative solvent accessibility (RSA) from limited data. The measure is based on calculating the volume of intersection of a sphere with a cone cut out in the direction opposite of the residue with surrounding atoms. We propose a method for estimating the position and volume of residue atoms in cases when they are not known from the structure, or when the structural data are unreliable or missing. We show that in cases of reliable input structures, SphereCon correlates almost perfectly with the directly computed RSA, and outperforms other previously suggested indirect methods. Moreover, SphereCon is the only measure that yields accurate results when the identities of amino acids are unknown. A significant novel feature of SphereCon is that it can estimate RSA from inter-residue distance and contact matrices, without any information about the actual atom coordinates. Availability and implementation https://github.com/kalininalab/spherecon. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document