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Application of Computational Studies for Elucidation of Protein Structure and Function

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Chemical Biology".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 4726

Special Issue Editor

Dr. Takeshi Kikuchi

E-Mail Website
Guest Editor
College of Life Sciences, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu 525-8577, Shiga, Japan
Interests: computational chemistry; biophysics; bioinformatics; protein structure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, computational techniques are widely applied to various problems including biological systems. In particular, many computer simulation techniques are applied to protein dynamics and folding. Bioinformatics techniques are also applied to extract various information from amino acid sequences and evolutional results. Artificial intelligence techniques have been developed in the field of protein structure prediction. Thus, we are planning this Special Issue regarding new applications of various computational techniques to solve problems related to protein structures and functions. It should be noted that pure theoretical works are important to solve protein problems. We are also interested in very complicated protein systems and drug discovery.

Dr. Takeshi Kikuchi
Guest Editor

Manuscript Submission Information

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Keywords

  • simulation
  • artificial intelligence
  • bioinformatics
  • sequence analysis
  • protein assembly
  • drug discovery
  • theory

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Published Papers (6 papers)

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Research

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15 pages, 8484 KiB  
Article
The Dynamical Asymmetry in SARS-CoV2 Protease Reveals the Exchange Between Catalytic Activity and Stability in Homodimers
by Velia Minicozzi, Alessandro Giuliani, Giampiero Mei, Leonardo Domenichelli, Mauro Parise, Almerinda Di Venere and Luisa Di Paola
Molecules 2025, 30(7), 1412; https://doi.org/10.3390/molecules30071412 (registering DOI) - 22 Mar 2025
Viewed by 159
Abstract
The molecular approach to understanding the mechanisms of emerging diseases, like COVID-19, has largely accelerated the search for successful therapeutical strategies. In this work, we present an extensive molecular dynamics (MD) analysis of two forms of the SARS-CoV-2 main protease MPro. [...] Read more.
The molecular approach to understanding the mechanisms of emerging diseases, like COVID-19, has largely accelerated the search for successful therapeutical strategies. In this work, we present an extensive molecular dynamics (MD) analysis of two forms of the SARS-CoV-2 main protease MPro. We analyzed the free form (apo) and compared the results with those coming from the (holo) form bound to the inhibitor Boceprevir, an FDA-approved drug repurposed for COVID-19 therapy. We applied Dynamic Cross Correlation (DCC) analysis to the MD simulations to trace the concerted motion patterns within the protein structure. Although symmetric, the homodimer in the bound form showed clearly asymmetric dynamical behavior. In particular, the presence of concerted motions was detected in the protomer where the expulsion of the substrate from the active site happened. Such behavior was not observed in the same time lapses in the apo form. These results highlight a sort of ‘symmetry breaking’, making a symmetric structure to display functional induced asymmetric behavior in response to a perturbation. This highly coordinated dynamics in response to an external cue confirms the character of ‘complex molecular machines’ of biopolymers. Full article
(This article belongs to the Special Issue Application of Computational Studies for Elucidation of Protein Structure and Function)
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17 pages, 10878 KiB  
Article
Two Methods for Superposing the Structures of Like-Molecule Assemblies: Application to Peptide and Protein Oligomers and Aggregates
by Adam Liwo and Mateusz Leśniewski
Molecules 2025, 30(5), 1156; https://doi.org/10.3390/molecules30051156 - 4 Mar 2025
Viewed by 245
Abstract
Two algorithms are proposed for the superposition of assemblies of like molecules (e.g., peptide and proteins homooligomers and homoaggregates), which do not require examining all permutations of the molecules. Both start from searching the mutual orientation of the two assemblies over a grid [...] Read more.
Two algorithms are proposed for the superposition of assemblies of like molecules (e.g., peptide and proteins homooligomers and homoaggregates), which do not require examining all permutations of the molecules. Both start from searching the mutual orientation of the two assemblies over a grid of quaternion components for the sub-optimal mapping and orientation of the molecules of the second to those of the first assembly. The first one, termed Like-Molecule Assembly Distance Alignment (LMADA), uses Singular Value Decomposition to superpose the two assemblies, given the sub-optimal mapping. The second one, termed Like-Molecule Assembly Gaussian Distance Alignment (LMAGDA), minimizes the negative of the logarithm of the sum of the Gaussian terms in the distances between the corresponding atoms/sites of all pairs of molecules of the two assemblies in quaternion components, starting from those estimated in the first stage. Both algorithms yield as good or nearly as good superposition, in terms of root mean square deviation (RMSD), as examining all permutations to find the lowest RMSD. LMADA results in lower RMSDs, while LMAGDA in a better alignment of the geometrically matching sections of the assemblies. The costs of the proposed algorithms scale only with N2, N being the number of molecules in the assembly, as opposed to N! when examining all permutations. Full article
(This article belongs to the Special Issue Application of Computational Studies for Elucidation of Protein Structure and Function)
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15 pages, 1533 KiB  
Article
Bluues_cplx: Electrostatics at Protein–Protein and Protein–Ligand Interfaces
by Miguel Angel Soler, Rayyan Bassem Adel Yakout, Ozge Ozkilinc, Gennaro Esposito, Walter Rocchia, Christian Klein and Federico Fogolari
Molecules 2025, 30(1), 159; https://doi.org/10.3390/molecules30010159 - 3 Jan 2025
Viewed by 778
Abstract
(1) Background: Electrostatics plays a capital role in protein–protein and protein–ligand interactions. Implicit solvent models are widely used to describe electrostatics and complementarity at interfaces. Electrostatic complementarity at the interface is not trivial, involving surface potentials rather than the charges of surfacial contacting [...] Read more.
(1) Background: Electrostatics plays a capital role in protein–protein and protein–ligand interactions. Implicit solvent models are widely used to describe electrostatics and complementarity at interfaces. Electrostatic complementarity at the interface is not trivial, involving surface potentials rather than the charges of surfacial contacting atoms. (2) Results: The program bluues_cplx, here used in conjunction with the software NanoShaper to compute molecular surfaces, has been used to compute the electrostatic properties of 756 protein–protein and 189 protein–ligand complexes along with the corresponding isolated molecules. (3) Methods: The software we make available here uses Generalized Born (GB) radii, computed by a molecular surface integral, to output several descriptors of electrostatics at protein (and in general, molecular) interfaces. We illustrate the usage of the software by analyzing a dataset of protein–protein and protein–ligand complexes, thus extending and refining previous analyses of electrostatic complementarity at protein interfaces. (4) Conclusions: The complete analysis of a molecular complex is performed in tens of seconds on a PC, and the results include the list of surfacial contacting atoms, their charges and Pearson correlation coefficient, the list of contacting surface points with the electrostatic potential (computed for the isolated molecules) and Pearson correlation coefficient, the electrostatic and hydrophobic free energy with different contributions for the isolated molecules, their complex and the difference for all terms. The software is readily usable for any molecular complex in solution. Full article
(This article belongs to the Special Issue Application of Computational Studies for Elucidation of Protein Structure and Function)
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13 pages, 1495 KiB  
Article
A Simple Analysis of the Second (Extra) Disulfide Bridge of VHHs
by Carla Martins, Fabrice Gardebien, Aravindan Arun Nadaradjane, Julien Diharce and Alexandre G. de Brevern
Molecules 2024, 29(20), 4863; https://doi.org/10.3390/molecules29204863 - 14 Oct 2024
Viewed by 982
Abstract
Camelids produce a special type of antibody, known as VHHs, that has lost the VL domain, providing a more optimised VH domain. This particular highly stable antibody domain has interesting properties for biotechnological development. Ordinarily, those molecules possess only [...] Read more.
Camelids produce a special type of antibody, known as VHHs, that has lost the VL domain, providing a more optimised VH domain. This particular highly stable antibody domain has interesting properties for biotechnological development. Ordinarily, those molecules possess only one disulphide bridge, but surprisingly, at least a quarter of these VHHs have a second one. Curiously, this does not seem to be essential for the stability and the function of this domain. In an attempt to characterise precisely the role and impact of this disulphide bridge at the molecular level, several in silico mutants of a VHH were created to disrupt this second disulphide bridge and those systems were submitted to molecular dynamics simulation. The loss of the second disulphide bridge leads to an increase in the flexibility of CDR1 and CDR3 and an unexpected rigidification of CDR2. Local conformational analysis shows local differences in the observed protein conformations. However, in fact, there is no exploration of new conformations but a change in the equilibrium between the different observed conformations. This explains why the interaction of VHHs is not really affected by the presence or absence of this second bridge, but their rigidity is slightly reduced. Therefore, the additional disulphide bridge does not seem to be an essential part of VHHs function. Full article
(This article belongs to the Special Issue Application of Computational Studies for Elucidation of Protein Structure and Function)
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12 pages, 2564 KiB  
Article
Testing the Capability of Embedding-Based Alignments on the GST Superfamily Classification: The Role of Protein Length
by Gabriele Vazzana, Castrense Savojardo, Pier Luigi Martelli and Rita Casadio
Molecules 2024, 29(19), 4616; https://doi.org/10.3390/molecules29194616 - 29 Sep 2024
Viewed by 992
Abstract
In order to shed light on the usage of protein language model-based alignment procedures, we attempted the classification of Glutathione S-transferases (GST; EC 2.5.1.18) and compared our results with the ARBA/UNI rule-based annotation in UniProt. GST is a protein superfamily involved in cellular [...] Read more.
In order to shed light on the usage of protein language model-based alignment procedures, we attempted the classification of Glutathione S-transferases (GST; EC 2.5.1.18) and compared our results with the ARBA/UNI rule-based annotation in UniProt. GST is a protein superfamily involved in cellular detoxification from harmful xenobiotics and endobiotics, widely distributed in prokaryotes and eukaryotes. What is particularly interesting is that the superfamily is characterized by different classes, comprising proteins from different taxa that can act in different cell locations (cytosolic, mitochondrial and microsomal compartments) with different folds and different levels of sequence identity with remote homologs. For this reason, GST functional annotation in a specific class is problematic: unless a structure is released, the protein can be classified only on the basis of sequence similarity, which excludes the annotation of remote homologs. Here, we adopt an embedding-based alignment to classify 15,061 GST proteins automatically annotated by the UniProt-ARBA/UNI rules. Embedding is based on the Meta ESM2-15b protein language. The embedding-based alignment reaches more than a 99% rate of perfect matching with the UniProt automatic procedure. Data analysis indicates that 46% of the UniProt automatically classified proteins do not conserve the typical length of canonical GSTs, whose structure is known. Therefore, 46% of the classified proteins do not conserve the template/s structure required for their family classification. Our approach finds that 41% of 64,207 GST UniProt proteins not yet assigned to any class can be classified consistently with the structural template length. Full article
(This article belongs to the Special Issue Application of Computational Studies for Elucidation of Protein Structure and Function)
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Review

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14 pages, 1448 KiB  
Review
Deep Learning Approaches for the Prediction of Protein Functional Sites
by Borja Pitarch and Florencio Pazos
Molecules 2025, 30(2), 214; https://doi.org/10.3390/molecules30020214 - 7 Jan 2025
Viewed by 877
Abstract
Knowing which residues of a protein are important for its function is of paramount importance for understanding the molecular basis of this function and devising ways of modifying it for medical or biotechnological applications. Due to the difficulty in detecting these residues experimentally, [...] Read more.
Knowing which residues of a protein are important for its function is of paramount importance for understanding the molecular basis of this function and devising ways of modifying it for medical or biotechnological applications. Due to the difficulty in detecting these residues experimentally, prediction methods are essential to cope with the sequence deluge that is filling databases with uncharacterized protein sequences. Deep learning approaches are especially well suited for this task due to the large amounts of protein sequences for training them, the trivial codification of this sequence data to feed into these systems, and the intrinsic sequential nature of the data that makes them suitable for language models. As a consequence, deep learning-based approaches are being applied to the prediction of different types of functional sites and regions in proteins. This review aims to give an overview of the current landscape of methodologies so that interested users can have an idea of which kind of approaches are available for their proteins of interest. We also try to give an idea of how these systems work, as well as explain their limitations and high dependence on the training set so that users are aware of the quality of expected results. Full article
(This article belongs to the Special Issue Application of Computational Studies for Elucidation of Protein Structure and Function)
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