scholarly journals Protein conformational entropy is not slaved to water

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Bryan S. Marques ◽  
Matthew A. Stetz ◽  
Christine Jorge ◽  
Kathleen G. Valentine ◽  
A. Joshua Wand ◽  
...  
Keyword(s):  
Protein Function ◽  
Hydration Shell ◽  
Nmr Relaxation ◽  
Bulk Water ◽  
Side Chain ◽  
Conformational Entropy ◽  
Solvent Water ◽  
Protein Molecules ◽  
Protein Functions ◽  
Internal Side

Abstract Conformational entropy can be an important element of the thermodynamics of protein functions such as the binding of ligands. The observed role for conformational entropy in modulating molecular recognition by proteins is in opposition to an often-invoked theory for the interaction of protein molecules with solvent water. The “solvent slaving” model predicts that protein motion is strongly coupled to various aspects of water such as bulk solvent viscosity and local hydration shell dynamics. Changes in conformational entropy are manifested in alterations of fast internal side chain motion that is detectable by NMR relaxation. We show here that the fast-internal side chain dynamics of several proteins are unaffected by changes to the hydration layer and bulk water. These observations indicate that the participation of conformational entropy in protein function is not dictated by the interaction of protein molecules and solvent water under the range of conditions normally encountered.

scholarly journals Structural origins of protein conformational entropy

2021 ◽  
Author(s):  
José A. Caro ◽  
Kathleen G. Valentine ◽  
A. Joshua Wand
Keyword(s):  
Ligand Binding ◽  
Protein Function ◽  
Nmr Relaxation ◽  
Internal Motion ◽  
Side Chains ◽  
Conformational Entropy ◽  
Relaxation Methods ◽  
Detailed Structural Analysis ◽  
Packing Defects ◽  
Single Water Molecule

AbstractThe thermodynamics of molecular recognition by proteins is a central determinant of complex biochemistry. For over a half-century detailed cryogenic structures have provided deep insight into the energetic contributions to ligand binding by proteins1. More recently, a dynamical proxy based on NMR-relaxation methods has revealed an unexpected richness in the contributions of conformational entropy to the thermodynamics of ligand binding2,3,4,5. There remains, however, a discomforting absence of an understanding of the structural origins of fast internal motion and the conformational entropy that this motion represents. Here we report the pressure-dependence of fast internal motion within the ribonuclease barnase and its complex with the protein barstar. Distinctive clustering of the pressure sensitivity correlates with the presence of small packing defects or voids surrounding affected side chains. Prompted by this observation, we performed an analysis of the voids surrounding over 2,500 methyl-bearing side chains having experimentally determined order parameters. We find that changes in unoccupied volume as small as a single water molecule surrounding buried side chains greatly affects motion on the subnanosecond timescale. The discovered relationship begins to permit construction of a united view of the relationship between changes in the internal energy, as exposed by detailed structural analysis, and the conformational entropy, as represented by fast internal motion, in the thermodynamics of protein function.

Conformational dynamics and thermodynamics of protein–ligand binding studied by NMR relaxation

2012 ◽  
Vol 40 (2) ◽  
pp. 419-423 ◽  
Author(s):  
Mikael Akke
Keyword(s):  
Ligand Binding ◽  
Bound States ◽  
Conformational Dynamics ◽  
Nmr Relaxation ◽  
Titration Calorimetry ◽  
Conformational Entropy ◽  
Chain Order ◽  
Ligand Interactions ◽  
Galectin 3 ◽  
Relaxation Experiments

Protein conformational dynamics can be critical for ligand binding in two ways that relate to kinetics and thermodynamics respectively. First, conformational transitions between different substates can control access to the binding site (kinetics). Secondly, differences between free and ligand-bound states in their conformational fluctuations contribute to the entropy of ligand binding (thermodynamics). In the present paper, I focus on the second topic, summarizing our recent results on the role of conformational entropy in ligand binding to Gal3C (the carbohydrate-recognition domain of galectin-3). NMR relaxation experiments provide a unique probe of conformational entropy by characterizing bond-vector fluctuations at atomic resolution. By monitoring differences between the free and ligand-bound states in their backbone and side chain order parameters, we have estimated the contributions from conformational entropy to the free energy of binding. Overall, the conformational entropy of Gal3C increases upon ligand binding, thereby contributing favourably to the binding affinity. Comparisons with the results from isothermal titration calorimetry indicate that the conformational entropy is comparable in magnitude to the enthalpy of binding. Furthermore, there are significant differences in the dynamic response to binding of different ligands, despite the fact that the protein structure is virtually identical in the different protein–ligand complexes. Thus both affinity and specificity of ligand binding to Gal3C appear to depend in part on subtle differences in the conformational fluctuations that reflect the complex interplay between structure, dynamics and ligand interactions.

scholarly journals Dynamics and mechanism of ultrafast water–protein interactions

2016 ◽  
Vol 113 (30) ◽  
pp. 8424-8429 ◽  
Author(s):  
Yangzhong Qin ◽  
Lijuan Wang ◽  
Dongping Zhong
Keyword(s):  
Protein Interactions ◽  
Hydration Shell ◽  
Water Dynamics ◽  
Side Chain ◽  
Water Molecules ◽  
Ultrafast Dynamics ◽  
Hydration Water ◽  
Water Side ◽  
Dynamics And Function ◽  
And Function

Protein hydration is essential to its structure, dynamics, and function, but water–protein interactions have not been directly observed in real time at physiological temperature to our awareness. By using a tryptophan scan with femtosecond spectroscopy, we simultaneously measured the hydration water dynamics and protein side-chain motions with temperature dependence. We observed the heterogeneous hydration dynamics around the global protein surface with two types of coupled motions, collective water/side-chain reorientation in a few picoseconds and cooperative water/side-chain restructuring in tens of picoseconds. The ultrafast dynamics in hundreds of femtoseconds is from the outer-layer, bulk-type mobile water molecules in the hydration shell. We also found that the hydration water dynamics are always faster than protein side-chain relaxations but with the same energy barriers, indicating hydration shell fluctuations driving protein side-chain motions on the picosecond time scales and thus elucidating their ultimate relationship.

A Survey for Predicting ATP Binding Residues of Proteins Using Machine Learning Methods

2021 ◽  
Vol 28 ◽  
Author(s):  
Yu-He Yang ◽  
Jia-Shu Wang ◽  
Shi-Shi Yuan ◽  
Meng-Lu Liu ◽  
Wei Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Function ◽  
Vital Role ◽  
Atp Binding ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Protein Ligand Interactions ◽  
Protein Functions ◽  
Ligand Interactions ◽  
Binding Residues

: Protein-ligand interactions are necessary for majority protein functions. Adenosine-5’-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is cost-ineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.

Optobiochemistry: Genetically Encoded Control of Protein Activity by Light

2021 ◽  
Vol 90 (1) ◽  
Author(s):  
Jihye Seong ◽  
Michael Z. Lin
Keyword(s):  
Protein Function ◽  
Living Cells ◽  
Optical Methods ◽  
Annual Review ◽  
Publication Date ◽  
Protein Activity ◽  
Spatiotemporal Resolution ◽  
Protein Functions ◽  
Protein Classes ◽  
Control Protein

Optobiochemical control of protein activities allows the investigation of protein functions in living cells with high spatiotemporal resolution. Over the last two decades, numerous natural photosensory domains have been characterized and synthetic domains engineered and assembled into photoregulatory systems to control protein function with light.Here, we review the field of optobiochemistry, categorizing photosensory domains by chromophore, describing photoregulatory systems by mechanism of action, and discussing protein classes frequently investigated using optical methods. We also present examples of how spatial or temporal control of proteins in living cells has provided new insights not possible with traditional biochemical or cell biological techniques. Expected final online publication date for the Annual Review of Biochemistry, Volume 90 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Characterization of Internal Protein Dynamics and Conformational Entropy by NMR Relaxation

2019 ◽  
pp. 237-284 ◽  
Author(s):  
Matthew A. Stetz ◽  
José A. Caro ◽  
Sravya Kotaru ◽  
Xuejun Yao ◽  
Bryan S. Marques ◽  
...  
Keyword(s):  
Protein Dynamics ◽  
Nmr Relaxation ◽  
Conformational Entropy ◽  
Internal Protein

scholarly journals DeepGOZero: Improving protein function prediction from sequence and zero-shot learning based on ontology axioms

2022 ◽  
Author(s):  
Maxat Kulmanov ◽  
Robert Hoehndorf
Keyword(s):  
Machine Learning ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Prediction Method ◽  
Function Prediction ◽  
Training Data ◽  
Large Set ◽  
Theoretic Approach ◽  
Machine Learning Model ◽  
Protein Functions

Motivation: Protein functions are often described using the Gene Ontology (GO) which is an ontology consisting of over 50,000 classes and a large set of formal axioms. Predicting the functions of proteins is one of the key challenges in computational biology and a variety of machine learning methods have been developed for this purpose. However, these methods usually require significant amount of training data and cannot make predictions for GO classes which have only few or no experimental annotations. Results: We developed DeepGOZero, a machine learning model which improves predictions for functions with no or only a small number of annotations. To achieve this goal, we rely on a model-theoretic approach for learning ontology embeddings and combine it with neural networks for protein function prediction. DeepGOZero can exploit formal axioms in the GO to make zero-shot predictions, i.e., predict protein functions even if not a single protein in the training phase was associated with that function. Furthermore, the zero-shot prediction method employed by DeepGOZero is generic and can be applied whenever associations with ontology classes need to be predicted. Availability: http://github.com/bio-ontology-research-group/deepgozero

scholarly journals A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Jaehee Jung ◽  
Heung Ki Lee ◽  
Gangman Yi
Keyword(s):  
Protein Function ◽  
Protein Function Prediction ◽  
Controlled Vocabulary ◽  
Functional Annotations ◽  
Functional Homology ◽  
Large Sets ◽  
Unknown Protein ◽  
Protein Functions ◽  
Novel Method ◽  
The Relationship

Automated protein function prediction defines the designation of functions of unknown protein functions by using computational methods. This technique is useful to automatically assign gene functional annotations for undefined sequences in next generation genome analysis (NGS). NGS is a popular research method since high-throughput technologies such as DNA sequencing and microarrays have created large sets of genes. These huge sequences have greatly increased the need for analysis. Previous research has been based on the similarities of sequences as this is strongly related to the functional homology. However, this study aimed to designate protein functions by automatically predicting the function of the genome by utilizing InterPro (IPR), which can represent the properties of the protein family and groups of the protein function. Moreover, we used gene ontology (GO), which is the controlled vocabulary used to comprehensively describe the protein function. To define the relationship between IPR and GO terms, three pattern recognition techniques have been employed under different conditions, such as feature selection and weighted value, instead of a binary one.

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