Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.4
4.2
Journals
Molecules
Special Issues
Computational Insights into Protein Engineering and Molecular Design
molecules-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Molecules Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Computational Insights into Protein Engineering and Molecular Design

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 9556

Special Issue Editors

Prof. Dr. Jianpeng Ma

E-Mail Website
Guest Editor
Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China
Interests: biophysics; computational biology; structural biology; supra molecular complexes; computer-aided drug designs; structural refinement strategies; X-ray crystallography; cryo-electron microscopy; bioinformatics
Dr. Gang Xu

E-Mail Website
Guest Editor
Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China
Interests: computational biology; bioinformatics; protein structure prediction; protein design; medical image analysis

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers for a forthcoming Special Issue of Molecules, entitled "Computational Insights into Protein Engineering and Molecular Design". As computational biology is taking on an ever-increasingly important role in the broader field of biology, this Special Issue aims to showcase the latest advancements and innovative approaches in the applications of computational methods to protein engineering and molecular design. We invite researchers and practitioners from academia and industry to contribute their original research articles as well as review articles that explore new dimensions and methodologies in this dynamic field. Topics of interest include, but are not limited to, machine learning and AI applications in protein design; computational methods in enzyme engineering; molecular dynamic simulation studies of biomolecular systems; advances in computational docking and drug design; design and development of novel proteins with enhanced functionalities; and case studies as well as applications of computational tools in biotechnology and pharmaceuticals.

Prof. Dr. Jianpeng Ma
Dr. Gang Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • protein engineering
  • protein design
  • protein structure prediction
  • computational molecular docking
  • drug design
  • optimizing small molecules
  • molecular dynamic simulation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

24 pages, 6114 KiB  
Article
De Novo Design of Large Polypeptides Using a Lightweight Diffusion Model Integrating LSTM and Attention Mechanism Under Per-Residue Secondary Structure Constraints
by Sisheng Liao, Gang Xu, Li Jin and Jianpeng Ma
Molecules 2025, 30(5), 1116; https://doi.org/10.3390/molecules30051116 - 28 Feb 2025
Viewed by 686
Abstract
This study presents PolypeptideDesigner (PPD), a novel conditional diffusion-based model for de novo polypeptide sequence design and generation based on per-residue secondary structure conditions. By integrating a lightweight LSTM-attention neural network as the denoiser within a diffusion framework, PPD offers an innovative and [...] Read more.
This study presents PolypeptideDesigner (PPD), a novel conditional diffusion-based model for de novo polypeptide sequence design and generation based on per-residue secondary structure conditions. By integrating a lightweight LSTM-attention neural network as the denoiser within a diffusion framework, PPD offers an innovative and efficient approach to polypeptide generation. Evaluations demonstrate that the PPD model can generate diverse and novel polypeptide sequences across various testing conditions, achieving high pLDDT scores when folded by ESMFold. In comparison to the ProteinDiffusionGenerator B (PDG-B) model, a relevant benchmark in the field, PPD exhibits the ability to produce longer and more diverse polypeptide sequences. This improvement is attributed to PPD’s optimized architecture and expanded training dataset, which enhance its understanding of protein structural pattern. The PPD model shows significant potential for optimizing functional polypeptides with known structures, paving the way for advancements in biomaterial design. Future work will focus on further refining the model and exploring its broader applications in polypeptide engineering. Full article
(This article belongs to the Special Issue Computational Insights into Protein Engineering and Molecular Design)
►▼ Show Figures

Figure 1

29 pages, 4378 KiB  
Article
Sam-Sam Association Between EphA2 and SASH1: In Silico Studies of Cancer-Linked Mutations
by Marian Vincenzi, Flavia Anna Mercurio, Ida Autiero and Marilisa Leone
Molecules 2025, 30(3), 718; https://doi.org/10.3390/molecules30030718 - 5 Feb 2025
Viewed by 787
Abstract
Recently, SASH1 has emerged as a novel protein interactor of a few Eph tyrosine kinase receptors like EphA2. These interactions involve the first N-terminal Sam (sterile alpha motif) domain of SASH1 (SASH1-Sam1) and the Sam domain of Eph receptors. Currently, the functional meaning [...] Read more.
Recently, SASH1 has emerged as a novel protein interactor of a few Eph tyrosine kinase receptors like EphA2. These interactions involve the first N-terminal Sam (sterile alpha motif) domain of SASH1 (SASH1-Sam1) and the Sam domain of Eph receptors. Currently, the functional meaning of the SASH1-Sam1/EphA2-Sam complex is unknown, but EphA2 is a well-established and crucial player in cancer onset and progression. Thus, herein, to investigate a possible correlation between the formation of the SASH1-Sam1/EphA2-Sam complex and EphA2 activity in cancer, cancer-linked mutations in SASH1-Sam1 were deeply analyzed. Our research plan relied first on searching the COSMIC database for cancer-related SASH1 variants carrying missense mutations in the Sam1 domain and then, through a variety of bioinformatic tools and molecular dynamic simulations, studying how these mutations could affect the stability of SASH1-Sam1 alone, leading eventually to a defective fold. Next, through docking studies, with the support of AlphaFold2 structure predictions, we investigated if/how mutations in SASH1-Sam1 could affect binding to EphA2-Sam. Our study, apart from presenting a solid multistep research protocol to analyze structural consequences related to cancer-associated protein variants with the support of cutting-edge artificial intelligence tools, suggests a few mutations that could more likely modulate the interaction between SASH1-Sam1 and EphA2-Sam. Full article
(This article belongs to the Special Issue Computational Insights into Protein Engineering and Molecular Design)
►▼ Show Figures

Graphical abstract

12 pages, 1364 KiB  
Article
Protein A-like Peptide Design Based on Diffusion and ESM2 Models
by Long Zhao, Qiang He, Huijia Song, Tianqian Zhou, An Luo, Zhenguo Wen, Teng Wang and Xiaozhu Lin
Molecules 2024, 29(20), 4965; https://doi.org/10.3390/molecules29204965 - 21 Oct 2024
Cited by 1 | Viewed by 2289
Abstract
Proteins are the foundation of life, and designing functional proteins remains a key challenge in biotechnology. Before the development of AlphaFold2, the focus of design was primarily on structure-centric approaches such as using the well-known open-source software Rosetta3. Following the development of AlphaFold2, [...] Read more.
Proteins are the foundation of life, and designing functional proteins remains a key challenge in biotechnology. Before the development of AlphaFold2, the focus of design was primarily on structure-centric approaches such as using the well-known open-source software Rosetta3. Following the development of AlphaFold2, deep-learning techniques for protein design gained prominence. This study proposes a new method to generate functional proteins using the diffusion model and ESM2 protein language model. Diffusion models, which are widely used in image and natural language generation, are used here for protein design, facilitating the controlled generation of new sequences. The ESM2 model, trained on the basis of large-scale protein sequence data, provides a deep understanding of the context of the sequence, thus improving the model’s ability to generate biologically relevant proteins. In this study, we used the Protein A-like peptide as a model study object, combined the diffusion model and the ESM2 model to generate new peptide sequences from minimal input data, and verified their biological activities through experiments such as the BLI affinity test. In conclusion, we developed a new method for protein design that provides a novel strategy to meet the challenges of generic protein generation. Full article
(This article belongs to the Special Issue Computational Insights into Protein Engineering and Molecular Design)
►▼ Show Figures

Figure 1

19 pages, 6736 KiB  
Article
Structural Insights into Endostatin–Heparan Sulfate Interactions Using Modeling Approaches
by Urszula Uciechowska-Kaczmarzyk, Martin Frank, Sergey A. Samsonov and Martyna Maszota-Zieleniak
Molecules 2024, 29(17), 4040; https://doi.org/10.3390/molecules29174040 - 26 Aug 2024
Viewed by 996
Abstract
Glycosaminoglycans (GAGs) play a key role in a variety of biological processes in the extracellular matrix (ECM) via interactions with their protein targets. Due to their high flexibility, periodicity and electrostatics-driven interactions, GAG-containing complexes are very challenging to characterize both experimentally and in [...] Read more.
Glycosaminoglycans (GAGs) play a key role in a variety of biological processes in the extracellular matrix (ECM) via interactions with their protein targets. Due to their high flexibility, periodicity and electrostatics-driven interactions, GAG-containing complexes are very challenging to characterize both experimentally and in silico. In this study, we, for the first time, systematically analyzed the interactions of endostatin, a proteolytic fragment of collagen XVIII known to be anti-angiogenic and anti-tumoral, with heparin (HP) and representative heparan sulfate (HS) oligosaccharides of various lengths, sequences and sulfation patterns. We first used conventional molecular docking and a docking approach based on a repulsive scaling–replica exchange molecular dynamics technique, as well as unbiased molecular dynamic simulations, to obtain dynamically stable GAG binding poses. Then, the corresponding free energies of binding were calculated and the amino acid residues that contribute the most to GAG binding were identified. We also investigated the potential influence of Zn2+ on endostatin–HP complexes using computational approaches. These data provide new atomistic details of the molecular mechanism of HP’s binding to endostatin, which will contribute to a better understanding of its interplay with proteoglycans at the cell surface and in the extracellular matrix. Full article
(This article belongs to the Special Issue Computational Insights into Protein Engineering and Molecular Design)
►▼ Show Figures

Figure 1

Review

Jump to: Research

40 pages, 6030 KiB  
Review
Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence
by Ahrum Son, Jongham Park, Woojin Kim, Yoonki Yoon, Sangwoon Lee, Yongho Park and Hyunsoo Kim
Molecules 2024, 29(19), 4626; https://doi.org/10.3390/molecules29194626 - 29 Sep 2024
Cited by 5 | Viewed by 4094
Abstract
The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and [...] Read more.
The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology. Full article
(This article belongs to the Special Issue Computational Insights into Protein Engineering and Molecular Design)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop