research-article

ProMuteHT: A High Throughput Compute Pipeline for Generating Protein Mutants in silico

Authors:

Erik Andersson,

Filip JagodzinskiAuthors Info & Claims

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

Pages 655 - 660

https://doi.org/10.1145/3107411.3116251

Published: 20 August 2017 Publication History

Abstract

Understanding how an amino acid substitution affects a protein's structure is fundamental to advancing drug design and protein docking studies. Mutagenesis experiments on physical proteins provide a precise assessment of the effects of mutations, but they are time and cost prohibitive. Computational approaches for performing in silico amino acid substitutions are available, but they are not suited for generating large numbers of protein variants needed for high-throughput screening studies. We present ProMuteHT, a program for high throughput in silico generating user-specified sets of mutant protein structures with single or multiple amino acid substitutions. We combine our custom mutation algorithm with side chain homology modeling external libraries, and generate energetically feasible mutant structures. Our efficient command-line invocation syntax requires only a few arguments to specify large datasets of mutant structures. We achieve quick run-times due to our hybrid approach in which we limit the use of costly energy calculations when mutating from a large to a small amino acid. We compare our mutant structures with those generated by FoldX, and report faster run-times. We show that the mutants generated by ProMuteHT are of high quality, as determined via all-atom and mutated residue RMSD measurements for existing mutant structures in the PDB.

References

[1]

Pedro J Ballester and John BO Mitchell 2010. A machine learning approach to predicting protein--ligand binding affinity with applications to molecular docking. Bioinformatics, Vol. 26, 9 (2010), 1169--1175.

Digital Library

[2]

Jeffrey A Bell, Wayne J Becktel, Uwe Sauer, Walter A Baase, and Brian W Matthews 1992. Dissection of helix capping in T4 lysozyme by structural and thermodynamic analysis of six amino acid substitutions at Thr 59. Biochemistry, Vol. 31, 14 (1992), 3590--3596.

[3]

Frances C Bernstein, Thomas F Koetzle, Graheme JB Williams, Edgar F Meyer, Michael D Brice, John R Rodgers, Olga Kennard, Takehiko Shimanouchi, and Mitsuo Tasumi 1977. The protein data bank. European Journal of Biochemistry Vol. 80, 2 (1977), 319--324.

[4]

M J Bower, F E Cohen, and R L Jr Dunbrack 1997. Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: a new homology modeling tool. J Mol Biol, Vol. 267, 5 (1997), 1268--1282.

[5]

Jeffrey R Brender and Yang Zhang 2015. Predicting the effect of mutations on protein-protein binding interactions through structure-based interface profiles. PLoS Comput Biol, Vol. 11, 10 (2015), e1004494.

[6]

Yana Bromberg and Burkhard Rost 2008. Comprehensive in silico mutagenesis highlights functionally important residues in proteins. Bioinformatics, Vol. 24, 16 (2008), i207--i212.

Digital Library

[7]

J. Cheng, A. Randall, and P. Baldi 2006. Prediction of Protein Stability Changes for Single-Site Mutations Using Support Vector Machines. PROTEINS: Structure, Function, and Bioinformatics Vol. 62 (2006), 1125--1132.

[8]

Warren L DeLano. 2002. The PyMOL molecular graphics system. (2002).

[9]

R.L. Jr. Dunbrack and M. Karplus 1994. Conformational analysis of the backbone-dependent rotamer preferences of protein sidechains. Nature Structural Biology Vol. 1 (1994), 334--340. Issue 5.

[10]

D. Gilis and M. Rooman 1997. Predicting Protein Stability Changes Upon Mutation Using Database-dervied Potentials: Solvent Accessibility Determines the Importance of Local Versus Non-Local Interactions Along the Sequence. Journal of Molecular Biology Vol. 272, 2 (1997), 276--290.

[11]

Jeffrey J Gray, Stewart Moughon, Chu Wang, Ora Schueler-Furman, Brian Kuhlman, Carol A Rohl, and David Baker 2003. Protein--protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. Journal of molecular biology Vol. 331, 1 (2003), 281--299.

[12]

Nicolas Guex and Manuel C Peitsch 1997. SWISS-MODEL and the Swiss-Pdb Viewer: an environment for comparative protein modeling. electrophoresis, Vol. 18, 15 (1997), 2714--2723.

[13]

Maximilian Hecht, Yana Bromberg, and Burkhard Rost. 2015. Better prediction of functional effects for sequence variants. BMC genomics, Vol. 16, 8 (2015), S1.

[14]

F. Jagodzinski, B. Akbal-Delibas, and N. Haspel. 2013. An Evolutionary Conservation & Rigidity Analysis Machine Learning Approach for Detecting Critical Protein Residues. In CSBW (Computational Structural Bioinformatics Workshop), in proc. of ACM-BCB (ACM International conference on Bioinformatics and Computational Biology). 780--786.

Digital Library

[15]

J Janin and S Wodak. 1978. Conformation of amino acid side-chains in proteins. J Mol Biol Vol. 125 (1978), 357--386.

[16]

Lei Jia, Ramya Yarlagadda, and Charles C Reed. 2015. Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools. PloS one, Vol. 10, 9 (2015), e0138022.

[17]

Georgii G Krivov, Maxim V Shapovalov, and Roland L Dunbrack. 2009. Improved prediction of protein side-chain conformations with SCWRL4. Proteins: Structure, Function, and Bioinformatics, Vol. 77, 4 (2009), 778--795.

[18]

C. Lee and M. Levitt. 1991. Accurate prediction of the stability and activity effects of site-directed mutagenesis on a protein core. Nature Vol. 352 (1991), 448--451.

[19]

Yunqi Li and Jianwen Fang 2012. PROTS-RF: a robust model for predicting mutation-induced protein stability changes. PloS one, Vol. 7, 10 (2012), e47247.

[20]

Sushil Kumar Mishra, Jan Adam, Michaela Wimmerová, and Jaroslav Koffha 2012. In silico mutagenesis and docking study of Ralstonia solanacearum RSL lectin: performance of docking software to predict saccharide binding. Journal of chemical information and modeling, Vol. 52, 5 (2012), 1250--1261.

[21]

Blaine HM Mooers, Walter A Baase, Jonathan W Wray, and Brian W Matthews 2009. Contributions of all 20 amino acids at site 96 to the stability and structure of T4 lysozyme. Protein Science, Vol. 18, 5 (2009), 871--880.

[22]

James C Phillips, Rosemary Braun, Wei Wang, James Gumbart, Emad Tajkhorshid, Elizabeth Villa, Christophe Chipot, Robert D Skeel, Laxmikant Kale, and Klaus Schulten. 2005. Scalable molecular dynamics with NAMD. Journal of computational chemistry Vol. 26, 16 (2005), 1781--1802.

[23]

J.W. Ponder and F.M. Richards 1987. Tertiary templates for proteins: Use of packing criteria in the enumeration of allowed sequences for different structural classes. Journal Molecular Biology Vol. 193 (1987), 775--791. Issue 4.

[24]

Boris Reva, Yevgeniy Antipin, and Chris Sander. 2011. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Research (2011).

[25]

John A Schellman. 1987. The thermodynamic stability of proteins. Annual review of biophysics and biophysical chemistry, Vol. 16, 1 (1987), 115--137.

[26]

Joost Schymkowitz, Jesper Borg, Francois Stricher, Robby Nys, Frederic Rousseau, and Luis Serrano. 2005. The FoldX web server: an online force field. Nucleic acids research Vol. 33, suppl 2 (2005), W382--W388.

[27]

C.M. Topham, N. Srinivasan, and T. Blundell. 1997. Prediction of the stability of protein mutants based on structural environment-dependent amino acid substitutions and propensity tables. Protein Engineering, Vol. 10, 1 (1997), 7--21.

[28]

C.L. Worth, R. Preissner, and L. Blundell 2011. SDM-a server for predicting effects of mutations on protein stability and malfunction. Nucleic Acids Research Vol. 39, Web Server Issue (2011), W215--W222.

[29]

Lingchong You and John Yin 2002. Dependence of epistasis on environment and mutation severity as revealed by in silico mutagenesis of phage T7. Genetics, Vol. 160, 4 (2002), 1273--1281.

Cited By

Wolfe GBelhoussine ODawson ALisaius MJagodzinski F(2020)Impactful Mutations in Mpro of the SARS-CoV-2 ProteomeProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3388440.3414706(1-3)Online publication date: 21-Sep-2020
https://dl.acm.org/doi/10.1145/3388440.3414706
Thompson EThackray TKalthoff CRapoport RJagodzinski F(2020)Using Energy-Minimization Profiles to Measure Protein Resistance to DrugsProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3388440.3414703(1-6)Online publication date: 21-Sep-2020
https://dl.acm.org/doi/10.1145/3388440.3414703
Dehghanpoor RRicks EHursh KGunderson SFarhoodi RHaspel NHutchinson BJagodzinski F(2018)Predicting the Effect of Single and Multiple Mutations on Protein Structural StabilityMolecules10.3390/molecules2302025123:2(251)Online publication date: 27-Jan-2018
https://doi.org/10.3390/molecules23020251
Show More Cited By

Index Terms

ProMuteHT: A High Throughput Compute Pipeline for Generating Protein Mutants in silico
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
    2. Computational biology
      1. Molecular structural biology

Recommendations

A multidrug efflux protein in Mycobacterium tuberculosis; tap as a potential drug target for drug repurposing
Abstract
Tuberculosis (TB) is a serious communicative disease caused by Mycobacterium tuberculosis. Although there are vaccines and drugs available to treat the disease, they are not efficient, moreover, multidrug-resistant ...
Graphical abstract

Display Omitted
Highlights
- Tuberculosis (TB) is a serious communicative disease caused by M. tuberculosis.
Computational analysis, structural modeling and ligand binding site prediction of Plasmodium falciparum 1-deoxy-d-xylulose-5-phosphate synthase

Display Omitted Plasmodium falciparum 1-deoxy-d-xylulose-5-phosphate synthase (DXP synthase) is predicted to be stable and residues forming pyrimidine binding pocket are evolutionary conserved.Comparative homology based modeling of DXP synthase is ...
In silico SELEX screening and statistical analysis of newly designed 5mer peptide-aptamers as Bcl-xl inhibitors using the Taguchi method
Abstract
Drug development for cancer treatment is a complex process that requires special efforts. Targeting crucial proteins is the most essential purpose of drug design in cancers. Bcl-xl is an anti-apoptotic protein that binds to pro-...
Graphical abstract

Display Omitted
Highlights
- Large libraries of peptide aptamers against Bcl-xl are designed and evaluated using in silico SELEX.

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

August 2017

800 pages

ISBN:9781450347228

DOI:10.1145/3107411

General Chairs:
Nurit Haspel
University of Massachusetts Boston, USA
,
Lenore J. Cowen
Tufts University, USA
,
Program Chairs:
Amarda Shehu
George Mason University, USA
,
Tamer Kahveci
University of Florida, USA
,
Giuseppe Pozzi
Politecnico di Milano, Italy

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGBio: ACM Special Interest Group on Bioinformatics

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

BCB '17

Sponsor:

SIGBio

BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 20 - 23, 2017

Massachusetts, Boston, USA

Acceptance Rates

ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;

Overall Acceptance Rate 254 of 885 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
146
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)1

Reflects downloads up to 26 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wolfe GBelhoussine ODawson ALisaius MJagodzinski F(2020)Impactful Mutations in Mpro of the SARS-CoV-2 ProteomeProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3388440.3414706(1-3)Online publication date: 21-Sep-2020
https://dl.acm.org/doi/10.1145/3388440.3414706
Thompson EThackray TKalthoff CRapoport RJagodzinski F(2020)Using Energy-Minimization Profiles to Measure Protein Resistance to DrugsProceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3388440.3414703(1-6)Online publication date: 21-Sep-2020
https://dl.acm.org/doi/10.1145/3388440.3414703
Dehghanpoor RRicks EHursh KGunderson SFarhoodi RHaspel NHutchinson BJagodzinski F(2018)Predicting the Effect of Single and Multiple Mutations on Protein Structural StabilityMolecules10.3390/molecules2302025123:2(251)Online publication date: 27-Jan-2018
https://doi.org/10.3390/molecules23020251
Majeske NJagodzinski FHutchinson BIslam TShehu AWu CBoucher CLi JLiu HPop M(2018)Low Rank Smoothed Sampling Methods for Identifying Impactful Pairwise MutationsProceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics10.1145/3233547.3233714(681-686)Online publication date: 15-Aug-2018
https://dl.acm.org/doi/10.1145/3233547.3233714
Siderius MJagodzinski F(2018)Mutation Sensitivity Maps: Identifying Residue Substitutions That Impact Protein Structure Via a Rigidity Analysis In Silico Mutation ApproachJournal of Computational Biology10.1089/cmb.2017.016525:1(89-102)Online publication date: Jan-2018
https://doi.org/10.1089/cmb.2017.0165
Farhoodi RShelbourne MHsieh RHaspel NHutchinson BJagodzinski FHaspel NCowen LShehu AKahveci TPozzi G(2017)Predicting the Effect of Point Mutations on Protein Structural StabilityProceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics10.1145/3107411.3107492(247-252)Online publication date: 20-Aug-2017
https://dl.acm.org/doi/10.1145/3107411.3107492
Daw CCruz BMajeske NJagodzinski FIslam THutchinson B(2012)Low Rank Approximation Methods for Identifying Impactful Pairwise Protein MutationsAlgorithms and Methods in Structural Bioinformatics10.1007/978-3-031-05914-8_4(63-87)Online publication date: 24-Feb-2012
https://doi.org/10.1007/978-3-031-05914-8_4

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents