Special Issue "Biomolecules In Silico: Contemporary Advances in Computational Approaches to Investigating the Molecular Dynamics of Biological Systems"

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editor

Dr. Thomas R. Caulfield
Website
Guest Editor
1. Dept. of Neuroscience, Dept. of Cancer Biology, Dept. of Health Sciences Research, Dept. of Neurosurgery, Mayo Clinic, Jacksonville, FL 32224, USA
2. Dept. of Clinical Genomics, Mayo Clinic, Rochester, MN 55902, USA
Interests: neurodegeneration drugs and mechanism; cancer mechanisms and drugs (general); brain lymphoma and GBM drugs; dual inhibitors to slow tumorogenesis; drug and technology development; machine learning and deep learning algorithms for drug design; advanced molecular modeling techniques for meso-to-exascale conformational sampling

Special Issue Information

Dear Colleagues,

Molecular dynamics simulations allow us to investigate physically realistic behaviors of biological systems in exquisite spatial and temporal resolution. This information is utilized to elucidate an array of biophysical characteristics, such as stability in different conditions, relative propensity to adopt relevant conformations, discovery of transient binding pockets, etc. Molecular dynamics simulations are employed to garner a fundamental understanding of macromolecules that can be applied from basic science to drug discovery. In this Special Issue, we report current advancements in algorithms, software, and analytical techniques that are related to molecular dynamics simulations of biological systems.

Dr. Thomas R. Caulfield
Guest Editor

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 papers will be 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. Biomolecules is an international peer-reviewed open access monthly 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 2000 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

  • molecular dynamics simulation
  • in silico design
  • algorithms
  • statistical mechanics
  • biothermodynamics
  • enhanced sampling
  • conformational sampling

Published Papers (2 papers)

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Research

Open AccessArticle
Anti-TNF Alpha Antibody Humira with pH-dependent Binding Characteristics: A constant-pH Molecular Dynamics, Gaussian Accelerated Molecular Dynamics, and In Vitro Study
Biomolecules 2021, 11(2), 334; https://doi.org/10.3390/biom11020334 - 23 Feb 2021
Viewed by 147
Abstract
Humira is a monoclonal antibody that binds to TNF alpha, inactivates TNF alpha receptors, and inhibits inflammation. Neonatal Fc receptors can mediate the transcytosis of Humira–TNF alpha complex structures and process them toward degradation pathways, which reduces the therapeutic effect of Humira. Allowing [...] Read more.
Humira is a monoclonal antibody that binds to TNF alpha, inactivates TNF alpha receptors, and inhibits inflammation. Neonatal Fc receptors can mediate the transcytosis of Humira–TNF alpha complex structures and process them toward degradation pathways, which reduces the therapeutic effect of Humira. Allowing the Humira–TNF alpha complex structures to dissociate to Humira and soluble TNF alpha in the early endosome to enable Humira recycling is crucial. We used the cytoplasmic pH (7.4), the early endosomal pH (6.0), and pKa of histidine side chains (6.0–6.4) to mutate the residues of complementarity-determining regions with histidine. Our engineered Humira (W1-Humira) can bind to TNF alpha in plasma at neutral pH and dissociate from the TNF alpha in the endosome at acidic pH. We used the constant-pH molecular dynamics, Gaussian accelerated molecular dynamics, two-dimensional potential mean force profiles, and in vitro methods to investigate the characteristics of W1-Humira. Our results revealed that the proposed Humira can bind TNF alpha with pH-dependent affinity in vitro. The W1-Humira was weaker than wild-type Humira at neutral pH in vitro, and our prediction results were close to the in vitro results. Furthermore, our approach displayed a high accuracy in antibody pH-dependent binding characteristics prediction, which may facilitate antibody drug design. Advancements in computational methods and computing power may further aid in addressing the challenges in antibody drug design. Full article
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Open AccessArticle
Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein–Protein Association
Biomolecules 2020, 10(7), 1056; https://doi.org/10.3390/biom10071056 - 15 Jul 2020
Cited by 1 | Viewed by 518
Abstract
The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can [...] Read more.
The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates. Full article
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