Neighborhood Preference of Amino Acids in Protein Structures and its Applications in Protein Structure Assessment

Liu, Siyuan; Xiang, Xilun; Gao, Xiang; Liu, Haiguang

doi:10.1038/s41598-020-61205-w

Cited by 6 publications

(5 citation statements)

References 57 publications

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“…Local environment of different cysteine side chain oxidation states. (A) Neighboring amino acids (and crystallographic waters) within a cutoff of 3.6 Å around the sulfur atom (SG), compared to the average abundance of amino acids from proteins in the Protein Data Bank (“PROT”, gray) . The amino acids are arranged along the x -axis in decreasing order for the CYS distribution; i.e., an unmodified CYS is most likely to be found near a crystallographic water or another CYS, and least likely to be found near Trp or Lys.…”

Section: Local Environment Of Cysteine Sulfenic Sulfinic and Sulfonic...mentioning

confidence: 99%

Cysteine Oxidation in Proteins: Structure, Biophysics, and Simulation

et al. 2022

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Cysteine side chains can exist in distinct oxidation states depending on the pH and redox potential of the environment, and cysteine oxidation plays important yet complex regulatory roles. Compared with the effects of post-translational modifications such as phosphorylation, the effects of oxidation of cysteine to sulfenic, sulfinic, and sulfonic acid on protein structure and function remain relatively poorly characterized. We present an analysis of the role of cysteine reactivity as a regulatory factor in proteins, emphasizing the interplay between electrostatics and redox potential as key determinants of the resulting oxidation state. A review of current computational approaches suggests underdeveloped areas of research for studying cysteine reactivity through molecular simulations.

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Section: Local Environment Of Cysteine Sulfenic Sulfinic and Sulfonic...mentioning

confidence: 99%

Cysteine Oxidation in Proteins: Structure, Biophysics, and Simulation

et al. 2022

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“…It is based on a scoring system for residue neighborhood preferences deduced from 14,647 PDB structures. It turned out that certain residues exhibit strong preferences for their neighboring residues and their relative positions [ 57 ]. generates with high quality the three-dimensional structure of proteins based on predictions for inter-residue contacts, distances, and residue orientations.…”

Section: Resultsmentioning

confidence: 99%

Rosetta:MSF:NN: Boosting performance of multi-state computational protein design with a neural network

2021

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Rational protein design aims at the targeted modification of existing proteins. To reach this goal, software suites like Rosetta propose sequences to introduce the desired properties. Challenging design problems necessitate the representation of a protein by means of a structural ensemble. Thus, Rosetta multi-state design (MSD) protocols have been developed wherein each state represents one protein conformation. Computational demands of MSD protocols are high, because for each of the candidate sequences a costly three-dimensional (3D) model has to be created and assessed for all states. Each of these scores contributes one data point to a complex, design-specific energy landscape. As neural networks (NN) proved well-suited to learn such solution spaces, we integrated one into the framework Rosetta:MSF instead of the so far used genetic algorithm with the aim to reduce computational costs. As its predecessor, Rosetta:MSF:NN administers a set of candidate sequences and their scores and scans sequence space iteratively. During each iteration, the union of all candidate sequences and their Rosetta scores are used to re-train NNs that possess a design-specific architecture. The enormous speed of the NNs allows an extensive assessment of alternative sequences, which are ranked on the scores predicted by the NN. Costly 3D models are computed only for a small fraction of best-scoring sequences; these and the corresponding 3D-based scores replace half of the candidate sequences during each iteration. The analysis of two sets of candidate sequences generated for a specific design problem by means of a genetic algorithm confirmed that the NN predicted 3D-based scores quite well; the Pearson correlation coefficient was at least 0.95. Applying Rosetta:MSF:NN:enzdes to a benchmark consisting of 16 ligand-binding problems showed that this protocol converges ten-times faster than the genetic algorithm and finds sequences with comparable scores.

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“…The interacting residues are transformed into an internal coordinate system, which allows to detect patterns in pairwise interactions, seen from the perspective of ligand residues. It is defined similarly to Liu et al as follows ( Liu et al , 2020 ):…”

Section: System and Methodsmentioning

confidence: 99%

“…Another important point was investigated by focusing next on positional and orientational information within amino acid-based interactions. For example, Polizzi and DeGrado generalized pairwise interactions by describing connections between amino acids and functional groups in so-called van der Mers ( Polizzi and Degrado, 2020 ), while Liu et al developed the neighborhood-sensitive program NEPRE which is able to assess the quality of protein structures based on amino acid identities ( Liu et al , 2020 ).…”

Section: Introductionmentioning

confidence: 99%

ATLIGATOR: editing protein interactions with an atlas-based approach

2022

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Motivation Recognition of specific molecules by proteins is a fundamental cellular mechanism and relevant for many applications. Being able to modify binding is a key interest and can be achieved by repurposing established interaction motifs. We were specifically interested in a methodology for the design of peptide binding modules. By leveraging interaction data from known protein structures, we plan to accelerate the design of novel protein or peptide binders. Results We developed ATLIGATOR—a computational method to support the analysis and design of a protein’s interaction with a single side chain. Our program enables the building of interaction atlases based on structures from the PDB. From these atlases pocket definitions are extracted that can be searched for frequent interactions. These searches can reveal similarities in unrelated proteins as we show here for one example. Such frequent interactions can then be grafted onto a new protein scaffold as a starting point of the design process. The ATLIGATOR tool is made accessible through a python API as well as a CLI with python scripts. Availability and Implementation Source code can be downloaded at github (https://www.github.com/Hoecker-Lab/atligator), installed from PyPI (“atligator”) and is implemented in Python 3.

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Neighborhood Preference of Amino Acids in Protein Structures and its Applications in Protein Structure Assessment

Cited by 6 publications

References 57 publications

Cysteine Oxidation in Proteins: Structure, Biophysics, and Simulation

Cysteine Oxidation in Proteins: Structure, Biophysics, and Simulation

Rosetta:MSF:NN: Boosting performance of multi-state computational protein design with a neural network

ATLIGATOR: editing protein interactions with an atlas-based approach

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