Accounting for Conformational Entropy in Predicting Binding Free Energies of Protein-Protein Interactions
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Molecular Mechanisms of Protein Thermal Stability
University of Denver Digital Commons @ DU Electronic Theses and Dissertations Graduate Studies 1-1-2016 Molecular Mechanisms of Protein Thermal Stability Lucas Sawle University of Denver Follow this and additional works at: https://digitalcommons.du.edu/etd Part of the Biological and Chemical Physics Commons Recommended Citation Sawle, Lucas, "Molecular Mechanisms of Protein Thermal Stability" (2016). Electronic Theses and Dissertations. 1137. https://digitalcommons.du.edu/etd/1137 This Dissertation is brought to you for free and open access by the Graduate Studies at Digital Commons @ DU. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of Digital Commons @ DU. For more information, please contact [email protected],[email protected]. Molecular Mechanisms of Protein Thermal Stability A Dissertation Presented to the Faculty of Natural Sciences and Mathematics University of Denver in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Lucas Sawle June 2016 Advisor: Dr. Kingshuk Ghosh c Copyright by Lucas Sawle, 2016. All Rights Reserved Author: Lucas Sawle Title: Molecular Mechanisms of Protein Thermal Stability Advisor: Dr. Kingshuk Ghosh Degree Date: June 2016 Abstract Organisms that thrive under extreme conditions, such as high salt concentra- tion, low pH, or high temperature, provide an opportunity to investigate the molec- ular and cellular strategies these organisms have adapted to survive in their harsh environments. Thermophilic proteins, those extracted from organisms that live at high temperature, maintain their structure and function at much higher tempera- tures compared to their mesophilic counterparts, found in organisms that live near room temperature. -
Foldx As Protein Engineering Tool: Better Than Random Based Approaches?
Computational and Structural Biotechnology Journal 16 (2018) 25–33 Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/csbj FoldX as Protein Engineering Tool: Better Than Random Based Approaches? Oliver Buß ⁎,JensRudat,KatrinOchsenreither Institute of Process Engineering in Life Sciences, Section II: Technical Biology, Karlsruhe Institute of Technology, Karlsruhe, Germany article info abstract Article history: Improving protein stability is an important goal for basic research as well as for clinical and industrial applications Received 31 October 2017 but no commonly accepted and widely used strategy for efficient engineering is known. Beside random ap- Received in revised form 21 December 2017 proaches like error prone PCR or physical techniques to stabilize proteins, e.g. by immobilization, in silico ap- Accepted 20 January 2018 proaches are gaining more attention to apply target-oriented mutagenesis. In this review different algorithms Available online 03 February 2018 for the prediction of beneficial mutation sites to enhance protein stability are summarized and the advantages Keywords: and disadvantages of FoldX are highlighted. The question whether the prediction of mutation sites by the algo- FoldX rithm FoldX is more accurate than random based approaches is addressed. Fold-X © 2018 Buß et al. Published by Elsevier B.V. on behalf of the Research Network of Computational and Structural Thermostability Biotechnology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Protein stabilization Protein engineering Enzyme engineering 1. Introduction per unit of product [10–12][13]. For industrial enzymes improved sta- bility against heat, solvents and other relevant process parameters, Increasing protein stability is a desirable goal for different life science e.g. -
The Role of Conformational Entropy in the Determination of Structural-Kinetic Relationships for Helix-Coil Transitions
computation Article The Role of Conformational Entropy in the Determination of Structural-Kinetic Relationships for Helix-Coil Transitions Joseph F. Rudzinski * ID and Tristan Bereau ID Max Planck Institute for Polymer Research, Mainz 55128, Germany; [email protected] * Correspondence: [email protected]; Tel.: +49-6131-3790 Received: 21 December 2017; Accepted: 16 February 2018; Published: 26 February 2018 Abstract: Coarse-grained molecular simulation models can provide significant insight into the complex behavior of protein systems, but suffer from an inherently distorted description of dynamical properties. We recently demonstrated that, for a heptapeptide of alanine residues, the structural and kinetic properties of a simulation model are linked in a rather simple way, given a certain level of physics present in the model. In this work, we extend these findings to a longer peptide, for which the representation of configuration space in terms of a full enumeration of sequences of helical/coil states along the peptide backbone is impractical. We verify the structural-kinetic relationships by scanning the parameter space of a simple native-biased model and then employ a distinct transferable model to validate and generalize the conclusions. Our results further demonstrate the validity of the previous findings, while clarifying the role of conformational entropy in the determination of the structural-kinetic relationships. More specifically, while the global, long timescale kinetic properties of a particular class of models with varying energetic parameters but approximately fixed conformational entropy are determined by the overarching structural features of the ensemble, a shift in these kinetic observables occurs for models with a distinct representation of steric interactions. -
Foldx Accurate Structural Protein–DNA Binding
3852–3863 Nucleic Acids Research, 2018, Vol. 46, No. 8 Published online 28 March 2018 doi: 10.1093/nar/gky228 FoldX accurate structural protein–DNA binding prediction using PADA1 (Protein Assisted DNA Assembly 1) Downloaded from https://academic.oup.com/nar/article-abstract/46/8/3852/4955761 by Biblioteca de la Universitat Pompeu Fabra user on 25 November 2019 Javier Delgado Blanco1,†, Leandro Radusky1,†,Hector´ Climente-Gonzalez´ 1 and Luis Serrano1,2,3,* 1Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain, 2Universitat Pompeu Fabra (UPF), Barcelona, Spain and 3Institucio´ Catalana de Recerca i Estudis Avanc¸ats (ICREA), Pg. Lluis Companys 23, 08010 Barcelona, Spain Received January 18, 2018; Revised March 12, 2018; Editorial Decision March 13, 2018; Accepted March 20, 2018 ABSTRACT are involved in numerous processes including DNA repli- cation, DNA repair, gene regulation, recombination, DNA The speed at which new genomes are being se- packing, etc. Currently, although there are >120 000 struc- quenced highlights the need for genome-wide meth- tures deposited in the PDB (3), only ∼5000 of them in- ods capable of predicting protein–DNA interactions. volve DNA–protein complexes. When considering the rate Here, we present PADA1, a generic algorithm that ac- at which new genomes are being sequenced, the resolu- curately models structural complexes and predicts tion of novel DNA–protein structures is relatively scarce. the DNA-binding regions of resolved protein struc- As such, we need to develop methods not only capable of tures. PADA1 relies on a library of protein and double- predicting whether a protein can interact with DNA, but stranded DNA fragment pairs obtained from a train- also capable of determining the protein’s 3D binding re- ing set of 2103 DNA–protein complexes. -
Expanding Molecular Modeling and Design Tools to Nonnatural Sidechains
WWW.C-CHEM.ORG FULL PAPER Expanding Molecular Modeling and Design Tools to Non-Natural Sidechains David Gfeller,[a] Olivier Michielin,*[a,b,c] and Vincent Zoete*[a] Protein–protein interactions encode the wiring diagram of increase peptide ligand binding affinity. Our results obtained on cellular signaling pathways and their deregulations underlie a non-natural mutants of a BCL9 peptide targeting beta-catenin variety of diseases, such as cancer. Inhibiting protein–protein show very good correlation between predicted and interactions with peptide derivatives is a promising way to experimental binding free-energies, indicating that such develop new biological and therapeutic tools. Here, we develop predictions can be used to design new inhibitors. Data a general framework to computationally handle hundreds of generated in this work, as well as PyMOL and UCSF Chimera non-natural amino acid sidechains and predict the effect of plug-ins for user-friendly visualization of non-natural sidechains, inserting them into peptides or proteins. We first generate all are all available at http://www.swisssidechain.ch. Our results structural files (pdb and mol2), as well as parameters and enable researchers to rapidly and efficiently work with hundreds topologies for standard molecular mechanics software of non-natural sidechains. VC 2012 Wiley Periodicals, Inc. (CHARMM and Gromacs). Accurate predictions of rotamer probabilities are provided using a novel combined knowledge and physics based strategy. Non-natural sidechains are useful to DOI: 10.1002/jcc.22982 Introduction are currently expanding peptide screening technologies to include non-natural sidechains. Correct interpretation of these Protein–protein interactions are fundamental to most biologi- results at the atomic level requires structural and biochemical cal and biochemical processes. -
Understanding Protein-RNA Interactions for Alternative Splicing
Understanding Protein-RNA Interactions for Alternative Splicing During Gene Expression Using Molecular Dynamic Simulations Joseph Daou Department of Chemistry & Chemical Engineering/B.S. Chemistry Dequan Xiao Ph.D. Abstract The alternative splicing during gene expression is regulated by a type of protein called splicing factors. Recently, a splicing factor, SRSF2 protein was formed to recognize guanines and cytosines at the binding site of the RNA segment. A series of mutations were performed in experiments on SRSF2 to understand the binding interactions of the protein and RNA. However, the changes of binding interactions at the atomistic level due to the mutations have not been revealed. Here, we used molecular dynamics simulations to investigate the conformational changes and protein-RNA binding free energy changes due to a series of mutations. Through the extrapolation of intermediate states of molecular interactions during a fixed length of time throughout point mutations, we used free energy perturbation methods to compute the binding affinity between the mutated proteins and RNA segments. In addition, we also computed the binding free energy by comparing the free energy difference between the folded and unfolded states of the proteins. Our computed free energy changes due to mutations showed consistency with experimental findings. In addition, by molecular dynamic simulations for the solvated protein-RNA complexes, we revealed here the detailed interactions between the protein residues and the RNA for different mutants. Our results provide new insights on understanding the interaction between the splicing factor proteins and the RNA, which can be linked to understanding the pathological mechanisms of Leukemia and cancers. Introduction the option to solvate the RRM system for an in-vitro model Biophysical researchers have shown, comparable the environment of the protein-RNA complex. -
CHAPTER 4 Proteins: Structure, Function, Folding
Recitation #2 Proteins: Structure, Function, Folding CHAPTER 4 Proteins: Structure, Function, Folding Learning goals: – Structure and properties of the peptide bond – Structural hierarchy in proteins – Structure and function of fibrous proteins – Structure analysis of globular proteins – Protein folding and denaturation Structure of Proteins • Unlike most organic polymers, protein molecules adopt a specific three-dimensional conformation. • This structure is able to fulfill a specific biological function • This structure is called the native fold • The native fold has a large number of favorable interactions within the protein • There is a cost in conformational entropy of folding the protein into one specific native fold Favorable Interactions in Proteins • Hydrophobic effect – Release of water molecules from the structured solvation layer around the molecule as protein folds increases the net entropy • Hydrogen bonds – Interaction of N-H and C=O of the peptide bond leads to local regular structures such as -helices and -sheets • London dispersion – Medium-range weak attraction between all atoms contributes significantly to the stability in the interior of the protein • Electrostatic interactions – Long-range strong interactions between permanently charged groups – Salt-bridges, esp. buried in the hydrophobic environment strongly stabilize the protein • Levels of structure in proteins. The primary structure consists of a sequence of amino acids linked together by peptide bonds • and includes any disulfide bonds. The resulting polypeptide can be arranged into units of secondary structure, such as an α-helix. The helix is a part of the tertiary structure of the folded polypeptide, which is itself one of the subunits that make up the quaternary structure of the multi-subunit protein, in this case hemoglobin. -
Enabling Biomolecular Analysis and Engineering Along Structural Ensembles
bioRxiv preprint doi: https://doi.org/10.1101/2021.08.16.456210; this version posted August 17, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. pyFoldX: enabling biomolecular analysis and engineering along structural ensembles Leandro G. Radusky1 & Luis Serrano1,2,3 1 Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona 08003, Spain 2 Universitat Pompeu Fabra (UPF), Barcelona, Spain 3 ICREA, Pg. Lluis Companys 23, Barcelona 08010, Spain Abstract Recent years have seen an increase in the number of structures available, not only for new proteins but also for the same protein crystallized with different molecules and proteins. While protein design software have proven to be successful in designing and modifying proteins, they can also be overly sensitive to small conformational differences between structures of the same protein. To cope with this, we introduce here pyFoldX, a python library that allows the integrative analysis of structures of the same protein using FoldX, an established forcefield and modeling software. The library offers new functionalities for handling different structures of the same protein, an improved molecular parametrization module, and an easy integration with the data analysis ecosystem of the python programming language. Availability and implementation pyFoldX is an open-source library that uses the FoldX software for energy calculations and modelling. The latter can be downloaded upon registration in http://foldxsuite.crg.eu/ and is free of charge for academics. -
DIP/Dpr Interactions and the Evolutionary Design of Specificity In
ARTICLE https://doi.org/10.1038/s41467-020-15981-8 OPEN DIP/Dpr interactions and the evolutionary design of specificity in protein families Alina P. Sergeeva 1, Phinikoula S. Katsamba2, Filip Cosmanescu2, Joshua J. Brewer3, Goran Ahlsen2, ✉ ✉ Seetha Mannepalli2, Lawrence Shapiro2,3 & Barry Honig1,2,3,4 Differential binding affinities among closely related protein family members underlie many biological phenomena, including cell-cell recognition. Drosophila DIP and Dpr proteins med- 1234567890():,; iate neuronal targeting in the fly through highly specific protein-protein interactions. We show here that DIPs/Dprs segregate into seven specificity subgroups defined by binding preferences between their DIP and Dpr members. We then describe a sequence-, structure- and energy-based computational approach, combined with experimental binding affinity measurements, to reveal how specificity is coded on the canonical DIP/Dpr interface. We show that binding specificity of DIP/Dpr subgroups is controlled by “negative constraints”, which interfere with binding. To achieve specificity, each subgroup utilizes a different com- bination of negative constraints, which are broadly distributed and cover the majority of the protein-protein interface. We discuss the structural origins of negative constraints, and potential general implications for the evolutionary origins of binding specificity in multi- protein families. 1 Department of Systems Biology, Columbia University, New York, NY, USA. 2 Zuckerman Mind, Brain and Behavior Institute, Columbia University, New York, -
Foldx Accurate Structural Protein–DNA Binding Prediction
Nucleic Acids Research, 2018 1 doi: 10.1093/nar/gky228 FoldX accurate structural protein–DNA binding prediction using PADA1 (Protein Assisted DNA Assembly 1) Javier Delgado Blanco1,†, Leandro Radusky1,†,Hector´ Climente-Gonzalez´ 1 and Luis Serrano1,2,3,* 1Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain, 2Universitat Pompeu Fabra (UPF), Barcelona, Spain and 3Institucio´ Catalana de Recerca i Estudis Avanc¸ats (ICREA), Pg. Lluis Companys 23, 08010 Barcelona, Spain Received January 18, 2018; Revised March 12, 2018; Editorial Decision March 13, 2018; Accepted March 20, 2018 ABSTRACT are involved in numerous processes including DNA repli- cation, DNA repair, gene regulation, recombination, DNA The speed at which new genomes are being se- packing, etc. Currently, although there are >120 000 struc- quenced highlights the need for genome-wide meth- tures deposited in the PDB (3), only ∼5000 of them in- ods capable of predicting protein–DNA interactions. volve DNA–protein complexes. When considering the rate Here, we present PADA1, a generic algorithm that ac- at which new genomes are being sequenced, the resolu- curately models structural complexes and predicts tion of novel DNA–protein structures is relatively scarce. the DNA-binding regions of resolved protein struc- As such, we need to develop methods not only capable of tures. PADA1 relies on a library of protein and double- predicting whether a protein can interact with DNA, but stranded DNA fragment pairs obtained from a train- also capable of determining the protein’s 3D binding re- ing set of 2103 DNA–protein complexes. -
Mutational Effects on Stability Are Largely Conserved During Protein Evolution
Mutational effects on stability are largely conserved during protein evolution Orr Ashenberg1, L. Ian Gong1, and Jesse D. Bloom2 Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109 Edited by Andrew J. Roger, Centre for Comparative Genomics and Evolutionary Bioinformatics, Dalhousie University, Halifax, Canada, and accepted bythe Editorial Board November 15, 2013 (received for review August 5, 2013) Protein stability and folding are the result of cooperative inter- approaches used to model protein evolution? The standard way actions among many residues, yet phylogenetic approaches to address this question has been to simulate or analyze protein assume that sites are independent. This discrepancy has engen- sequences, using some computational force field that predicts the dered concerns about large evolutionary shifts in mutational stability of different variants (2–5). Unfortunately, there is no effects that might confound phylogenetic approaches. Here we good a priori reason to believe that the force fields themselves experimentally investigate this issue by introducing the same authentically represent interactions among sites. Even state-of- fl mutations into a set of diverged homologs of the in uenza the-art force fields are at best modestly accurate at predicting fi nucleoprotein and measuring the effects on stability. We nd that mutational effects (6, 7). Furthermore, all tractable force fields mutational effects on stability are largely conserved across the approximate the true multibody quantum–mechanical interactions homologs. We reach qualitatively similar conclusions when we – simulate protein evolution with molecular-mechanics force fields. among protein sites in terms of pairwise interactions (8 10). Our results do not mean that proteins evolve without epistasis, Here we experimentally assess the extent to which mutational which can still arise even when mutational stability effects are effects on stability change during protein evolution. -
Targeting Tmprss2, S-Protein:Ace2, and 3Clpro for Synergetic Inhibitory Engagement Mathew Coban1, Juliet Morrison Phd2, William
Targeting Tmprss2, S-protein:Ace2, and 3CLpro for Synergetic Inhibitory Engagement Mathew Coban1, Juliet Morrison PhD2, William D. Freeman MD3, Evette Radisky PhD1, Karine G. Le Roch PhD4, Thomas R. Caulfield, PhD1,5-8 Affiliations 1 Department of Cancer Biology, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL, 32224 USA 2 Department of Microbiology and Plant Pathology, University of California, Riverside, 900 University, Riverside, CA, 92521 USA 3 Department of Neurology, Mayo Clinic, 4500 San Pablo South, Jacksonville, FL, 32224 USA 4Department of Molecular, Cell and Systems Biology, University of California, Riverside, 900 University, Riverside, CA, 92521 USA 5 Department of Neuroscience, Mayo Clinic, 4500 San Pablo South, Jacksonville, FL, 32224 USA 6 Department of Neurosurgery, Mayo Clinic, 4500 San Pablo South, Jacksonville, FL, 32224 USA 7 Department of Health Science Research (BSI), Mayo Clinic, 4500 San Pablo South, Jacksonville, FL, 32224 USA 8Department of Clinical Genomics (Enterprise), Mayo Clinic, Rochester, MN 55905, USA Correspondence to: Thomas R. Caulfield, PhD, Dept of Neuroscience, Cancer Biology, Neurosurgery, Health Science Research, & Clinical Genomics Mayo Clinic, 4500 San Pablo Road South Jacksonville, FL 32224 Telephone: +1 904-953-6072, E-mail: [email protected] SUMMARY Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 6 months, SARS-CoV-2, the virus responsible for COVID-19, has already infected over 11.6 million (25% located in United States) and killed more than 540K people around the world. As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts.