Partial Least-Squares Functional Mode Analysis: Application to the Membrane Proteins AQP1, Aqy1, and CLC-ec1

Krivobokova, Tatyana; Briones, Rodolfo; Hub, Jochen S.; Munk, Axel; Groot, Bert L. de

doi:10.1016/j.bpj.2012.07.022

Cited by 69 publications

(147 citation statements)

References 35 publications

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“…To test this hypothesis using completely independent data, we 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 assembled a set of 217 distinct ubiquitin conformations from 70 high-resolution crystal structures in the Protein Data Bank (PDB). We used partial least squares (PLS) functional mode analysis (FMA) (25,26) to train a linear model to predict the peptide bond conformation solely from the coordinates of residues that interact with USPs, thus excluding the peptide flip region (Fig. 3B).…”

Section: Significancementioning

confidence: 99%

“…A residue from ubiquitin (PDB ID code 3MHS chain D) was selected for PLS FMA (25,26) if any of its atoms was within 5 Å of a USP (PDB ID code 3MHS chain A) or if both of its adjacent residues were within 5 Å of the USP. This selection included the following 36 residues: Q2-T14, K33-P37, Q40-Q49, and K63-V70.…”

Section: Predicting Rd From MD Snapshots and Principal Component Analmentioning

confidence: 99%

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Allosteric switch regulates protein–protein binding through collective motion

Smith

Ban

Pratihar

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

105

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Many biological processes depend on allosteric communication between different parts of a protein, but the role of internal protein motion in propagating signals through the structure remains largely unknown. Through an experimental and computational analysis of the ground state dynamics in ubiquitin, we identify a collective global motion that is specifically linked to a conformational switch distant from the binding interface. This allosteric coupling is also present in crystal structures and is found to facilitate multispecificity, particularly binding to the ubiquitin-specific protease (USP) family of deubiquitinases. The collective motion that enables this allosteric communication does not affect binding through localized changes but, instead, depends on expansion and contraction of the entire protein domain. The characterization of these collective motions represents a promising avenue for finding and manipulating allosteric networks.allostery | protein dynamics | concerted motion | relaxation dispersion | nuclear magnetic resonance I ntermolecular interactions are one of the key mechanisms by which proteins mediate their biological functions. For many proteins, these interactions are enhanced or suppressed by allosteric networks that couple distant regions together (1). The mechanisms by which these networks function are just starting to be understood (2-4), and many of the important details have yet to be uncovered. In particular, the role of intrinsic protein motion and kinetics remains particularly poorly characterized. A number of structural ensembles representing ubiquitin motion have been recently proposed (5-9). Additionally, it has been suggested that through motion at the binding interface, its free state visits the same conformations found in complex with its many binding partners (5, 10). However, it remains an unanswered question if the dynamics that enable this multispecificity are only clustered around the canonical binding interface or whether this motion is allosterically coupled to the rest of the protein, especially given the presence of motion at distal sites (11). ResultsTo answer this question and to provide a detailed structural picture of the underlying mechanism, we applied recently developed high-power relaxation dispersion (RD) experiments (12, 13) to both the backbone amide proton ( 1 H N ) and nitrogen ( 15 N) nuclei of ubiquitin. This survey yielded a nearly twofold increase in the number of nuclei where RD had been previously observed (11)(12)(13)(14) (from 17 to 31; Fig. 1A and Fig. S1). When fit individually, the full set of backbone and side-chain nuclei shows a consistent time scale of motion [exchange lifetime (τ ex ) = 55 μs; Fig. 1B]. Furthermore, the nuclei showing exchange are spread throughout the structure (Fig. 1C). Put together, these data suggest that the motions are not independent but share a common molecular mechanism.To determine whether the RD data could be modeled using a single collective motion, we developed a computational method to take a set of molecu...

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Section: Significancementioning

confidence: 99%

Section: Predicting Rd From MD Snapshots and Principal Component Analmentioning

confidence: 99%

Allosteric switch regulates protein–protein binding through collective motion

Smith

Ban

Pratihar

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

105

View full text Add to dashboard Cite

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“…Given such a wide distribution of mutation effects, we were interested whether this is related to structural changes in the protease or a different pose of the inhibitor. Partial least-squares (PLS) regression based functional mode analysis, 65 a supervised machine learning technique, allows for correlation of Cartesian input from MD trajectories to a desired functional property. In our case we investigated whether the major collective motions discriminate wildtype and mutant complexes: using Cartesian coordinates as an input, we aimed to predict whether a trajectory was generated by a wildtype or mutant protein.…”

Section: Journal Of Chemical Theory and Computationmentioning

confidence: 99%

“…Partial least-squares regression was performed using the functional mode analysis tool. 65 The following sets of input atoms (excluding hydrogens) were used for the model: backbone, ligand, protein, and side chain. Constants 0 and 1 have been used as response variables for trajectories corresponding to mutant and wildtype protein simulations, respectively.…”

Section: ■ Introductionmentioning

confidence: 99%

Consistent Prediction of Mutation Effect on Drug Binding in HIV-1 Protease Using Alchemical Calculations

Bastys

Gapsys

Doncheva

et al. 2018

J. Chem. Theory Comput.

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Despite a large number of antiretroviral drugs targeting HIV-1 protease for inhibition, mutations in this protein during the course of patient treatment can render them inefficient. This emerging resistance inspired numerous computational studies of the HIV-1 protease aimed at predicting the effect of mutations on drug binding in terms of free binding energy ΔG, as well as in mechanistic terms. In this study, we analyze ten different protease-inhibitor complexes carrying major resistance-associated mutations (RAMs) G48V, I50V, and L90M using molecular dynamics simulations. We demonstrate that alchemical free energy calculations can consistently predict the effect of mutations on drug binding. By explicitly probing different protonation states of the catalytic aspartic dyad, we reveal the importance of the correct choice of protonation state for the accuracy of the result. We also provide insight into how different mutations affect drug binding in their specific ways, with the unifying theme of how all of them affect the crucial drug binding regions of the protease. ■ INTRODUCTIONHIV-1 (human immunodeficiency virus-1, further denoted as HIV) has caused a global epidemic that affects approximately 37 million people worldwide.1 There is no vaccine or cure available against HIV, but antiretroviral therapy (ART) is recommended for every infected individual 1 to suppress the virus. Success of ART has led to a near-normal life expectancy of HIV infected patients.2 Nevertheless, resistance toward drugs remains a major issue, making it necessary to switch therapy during the course of treatment of a single patient. 1One of the main targets of ART is the HIV protease, a protein responsible for cleaving HIV polyproteins during virus maturation ( Figure 1a). HIV protease is a homodimer with two 99 residues long subunits. The major structural components of the binding pocket of this protein are the active site at its bottom, which is followed in sequence by a loop and a short β-sheet (residues 26−32) that constitute the side of the pocket together with the so-called 80s loop (residues 79−84) ( Figure 1a). On the top the pocket is covered by the so-called flap region. In the framework of ART, HIV protease can be inhibited by protease inhibitors (PIs) (Figure 1b), a class of competitive inhibitors which are currently recommended as second-and third-line ART treatment.1 Specific substitutions in 13 different positions in the protease are considered to be major resistanceassociated mutations (RAMs). 3I50V is a RAM toward protease inhibitors Lopinavir (LPV) 4,5 and Darunavir (DRV) 6,7 and has been associated with particularly strong resistance to Amprenavir (APV) 8−10 and correspondingly to its prodrug Fosamprenavir (FPV). It also has been suggested to cause resistance toward Indinavir (IDV), 9,11 and at the same time, there is evidence pointing that this mutation has been associated with sensitivity toward Atazanavir (ATV) 12,13 and Tipranavir (TPV).14 I50 is located in the protease flap region (Figure 1a). This flap is...

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“…A partial least square functional mode analysis (PLS-FMA), consisting of a multivariate linear regression (33), was carried out to identify conformational changes of the VWF A2 domain induced by the mutation G1629E (see details in the Supporting Material). The root mean square fluctuation (RMSF) of the atomic positions was computed by time-averaging concatenated simulations of either the wild-type or the mutant domain.…”

Section: Equilibrium MD Simulationsmentioning

confidence: 99%

Mutation G1629E Increases von Willebrand Factor Cleavage via a Cooperative Destabilization Mechanism

Aponte-Santamaría

Lippok

Mittag

et al. 2017

Biophysical Journal

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The large multimeric glycoprotein von Willebrand Factor (VWF) plays a pivotal adhesive role during primary hemostasis. VWF is cleaved by the protease ADAMTS13 as a down-regulatory mechanism to prevent excessive VWF-mediated platelet aggregation. For each VWF monomer, the ADAMTS13 cleavage site is located deeply buried inside the VWF A2 domain. External forces in vivo or denaturants in vitro trigger the unfolding of this domain, thereby leaving the cleavage site solvent-exposed and ready for cleavage. Mutations in the VWF A2 domain, facilitating the cleavage process, cause a distinct form of von Willebrand disease (VWD), VWD type 2A. In particular, the VWD type 2A Gly1629Glu mutation drastically accelerates the proteolytic cleavage activity, even in the absence of forces or denaturants. However, the effect of this mutation has not yet been quantified, in terms of kinetics or thermodynamics, nor has the underlying molecular mechanism been revealed. In this study, we addressed these questions by using fluorescence correlation spectroscopy, molecular dynamics simulations, and free energy calculations. The measured enzyme kinetics revealed a 20-fold increase in the cleavage rate for the Gly1629Glu mutant compared with the wild-type VWF. Cleavage was found cooperative with a cooperativity coefficient n ¼ 2.3, suggesting that the mutant VWF gives access to multiple cleavage sites of the VWF multimer at the same time. According to our simulations and free energy calculations, the Gly1629Glu mutation causes structural perturbation in the A2 domain and thereby destabilizes the domain by~10 kJ/mol, promoting its unfolding. Taken together, the enhanced proteolytic activity of Gly1629Glu can be readily explained by an increased availability of the ADAMTS13 cleavage site through A2-domain-fold thermodynamic destabilization. Our study puts forward the Gly1629Glu mutant as a very efficient enzyme substrate for ADAMTS13 activity assays.

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Partial Least-Squares Functional Mode Analysis: Application to the Membrane Proteins AQP1, Aqy1, and CLC-ec1

Cited by 69 publications

References 35 publications

Allosteric switch regulates protein–protein binding through collective motion

Allosteric switch regulates protein–protein binding through collective motion

Consistent Prediction of Mutation Effect on Drug Binding in HIV-1 Protease Using Alchemical Calculations

Mutation G1629E Increases von Willebrand Factor Cleavage via a Cooperative Destabilization Mechanism

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