PyMOL, a cross-platform molecular graphics tool, has been widely used for three-dimensional (3D) visualization of proteins, nucleic acids, small molecules, electron densities, surfaces, and trajectories. It is also capable of editing molecules, ray tracing, and making movies. This Python-based software, alongside many Python plugin tools, has been developed to enhance its utilities and facilitate the drug design in PyMOL. To gain an insightful view of useful drug design tools and their functions in PyMOL, we present an extensive discussion on various molecular modeling modules in PyMOL, covering those for visualization and analysis enhancement, protein–ligand modeling, molecular simulations, and drug screening. This review provides an excellent introduction to present 3D structures visualization and computational drug design in PyMOL.

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Using PyMOL as a platform for computational drug design

The understanding, and even the description of protein folding is impeded by the complexity of the process. Much of this complexity can be described and understood by taking a statistical approach to the energetics of protein conformation, that is, to the energy landscape. The statistical energy landscape approach explains when and why unique behaviors, such as specific folding pathways, occur in some proteins and more generally explains the distinction between folding processes common to all sequences and those peculiar to individual sequences. This approach also gives new, quantitative insights into the interpretation of experiments and simulations of protein folding thermodynamics and kinetics. Specifically, the picture provides simple explanations for folding as a two-state first-order phase transition, for the origin of metastable collapsed unfolded states and for the curved Arrhenius plots observed in both laboratory experiments and discrete lattice simulations. The relation of these quantitative ideas to folding pathways, to uni-exponential {\em vs.} multi-exponential behavior in protein folding experiments and to the effect of mutations on folding is also discussed. The success of energy landscape ideas in protein structure prediction is also described. The use of the energy landscape approach for analyzing data is illustrated with a quantitative analysis of some recent simulations, and a qualitative analysis of experiments on the folding of three proteins. The work unifies several previously proposed ideas concerning the mechanism protein folding and delimits the regions of validity of these ideas under different thermodynamic conditions.

Funnels, Pathways and the Energy Landscape of Protein Folding: A Synthesis

Orthologous proteins of species adapted to different temperatures exhibit differences in stability and function that are interpreted to reflect adaptive variation in structural “flexibility.” However, quantifying flexibility and comparing flexibility across proteins has remained a challenge. To address this issue, we examined temperature effects on cytosolic malate dehydrogenase (cMDH) orthologs from differently thermally adapted congeners of five genera of marine molluscs whose field body temperatures span a range of ∼60 °C. We describe consistent patterns of convergent evolution in adaptation of function [temperature effects on K M of cofactor (NADH)] and structural stability (rate of heat denaturation of activity). To determine how these differences depend on flexibilities of overall structure and of regions known to be important in binding and catalysis, we performed molecular dynamics simulation (MDS) analyses. MDS analyses revealed a significant negative correlation between adaptation temperature and heat-induced increase of backbone atom movements [root mean square deviation (rmsd) of main-chain atoms]. Root mean square fluctuations (RMSFs) of movement by individual amino acid residues varied across the sequence in a qualitatively similar pattern among orthologs. Regions of sequence involved in ligand binding and catalysis—termed mobile regions 1 and 2 (MR1 and MR2), respectively—showed the largest values for RMSF. Heat-induced changes in RMSF values across the sequence and, importantly, in MR1 and MR2 were greatest in cold-adapted species. MDS methods are shown to provide powerful tools for examining adaptation of enzymes by providing a quantitative index of protein flexibility and identifying sequence regions where adaptive change in flexibility occurs.

https://www.pnas.org/content/pnas/115/6/1274.full.pdf

Structural flexibility and protein adaptation to temperature: Molecular dynamics analysis of malate dehydrogenases of marine molluscs

Starting with the enhanced permeability and retention (EPR) effect discovery, nanomedicine has gained a crucial role in cancer treatment. The advances in the field have led to the approval of nanodrugs with improved safety profile and still inspire the ongoing investigations. However, several restrictions, such as high manufacturing costs, technical challenges, and effectiveness below expectations, raised skeptical opinions within the scientific community about the clinical relevance of nanomedicine. In this review, we aim to give an overall vision of the current hurdles encountered by nanotherapeutics along with their design, development, and translation, and we offer a prospective view on possible strategies to overcome such limitations.

Thirty Years of Cancer Nanomedicine: Success, Frustration, and Hope.

MOTIVATION N4-methylcytosine (4mC), an important epigenetic modification formed by the action of specific methyltransferases, plays an essential role in DNA repair, expression and replication. The accurate identification of 4mC sites aids in-depth research to biological functions and mechanisms. Because, experimental identification of 4mC sites is time-consuming and costly, especially given the rapid accumulation of gene sequences. Supplementation with efficient computational methods is urgently needed. RESULTS In this study, we developed a new tool, 4mCPred, for predicting 4mC sites in Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Escherichia coli, Geoalkalibacter subterraneus and Geobacter pickeringii. 4mCPred consists of two independent models, 4mCPred_I and 4mCPred_II, for each species. The predictive results of independent and cross-species tests demonstrated that the performance of 4mCPred_I is a useful tool. To identify position-specific trinucleotide propensity (PSTNP) and electron-ion interaction potential features, we used the F-score method to construct predictive models and to compare their PSTNP features. Compared with other existing predictors, 4mCPred achieved much higher accuracies in rigorous jackknife and independent tests. We also analyzed the importance of different features in detail. AVAILABILITY AND IMPLEMENTATION The web-server 4mCPred is accessible at http://server.malab.cn/4mCPred/index.jsp. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction.

Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

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Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods

Molecular dynamics (MD) simulations of a subtilisin-like serine protease VPR from the psychrophilic marine bacterium Vibrio sp. PA-44 and its mesophilic homologue, proteinase K (PRK), have been performed for 20 ns at four different temperatures (300, 373, 473, and 573 K). The comparative analyses of MD trajectories reveal that at almost all temperatures, VPR exhibits greater structural fluctuations/deviations, more unstable regular secondary structural elements, and higher global flexibility than PRK. Although these two proteases follow similar unfolding pathways at high temperatures, VPR initiates unfolding at a lower temperature and unfolds faster at the same high temperatures than PRK. These observations collectively indicate that VPR is less stable and more heat-labile than PRK. Analyses of the structural/geometrical properties reveal that, when compared to PRK, VPR has larger radius of gyration (Rg), less intramolecular contacts and hydrogen bonds (HBs), more protein-solvent HBs, and smaller burial o...

https://tandfonline.com/doi/pdf/10.1080/07391102.2016.1188155

Comparative thermal unfolding study of psychrophilic and mesophilic subtilisin-like serine proteases by molecular dynamics simulations

The entry of human immunodeficiency virus type I (HIV-1) into host cells is initiated by binding to the cell-surface receptor CD4, which induces a conformational transition of the envelope (Env) glycoprotein gp120 from the closed, unliganded state to the open, CD4-bound state. Despite many available structures in these two states, detailed aspects on the dynamics and thermodynamics of gp120 remain elusive. Here, we performed microsecond-scale (μs-scale) multiple-replica molecular dynamics (MD) simulations to explore the differences in the conformational dynamics, protein motions, and thermodynamics between the unliganded and CD4-bound/complexed forms of gp120. Comparative analyses of MD trajectories reveal that CD4 binding promotes the structural deviations/changes and conformational flexibility, loosens the structural packing, and complicates the molecular motions of gp120. Comparison of the constructed free energy landscapes (FELs) reveals that the CD4-complexed gp120 has more conformational substates, larger conformational entropy, and lower thermostability than the unliganded form. Therefore, the unliganded conformation represents a structurally and energetically stable "ground state" for the full-length gp120. The observed great increase in the mobility of V1/V2 and V3 along with their more versatile movement directions in the CD4-bound gp120 compared to the unliganded form suggests that their orientations with respect to each other and to the structural core determine the differences in the conformational dynamics and thermodynamics between the two gp120 forms. The results presented here provide a basis by which to better understand the functional and immunological properties of gp120 and, furthermore, to deploy appropriate strategies for the development of anti-HIV-1 drugs or vaccines.

/pdf/molecular-dynamics-simulations-reveal-distinct-differences-2f5kpfqd8l.pdf

Molecular dynamics simulations reveal distinct differences in conformational dynamics and thermodynamics between the unliganded and CD4-bound states of HIV-1 gp120

Jing Liang

Papers

Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods

Comparative thermal unfolding study of psychrophilic and mesophilic subtilisin-like serine proteases by molecular dynamics simulations

Molecular dynamics simulations reveal distinct differences in conformational dynamics and thermodynamics between the unliganded and CD4-bound states of HIV-1 gp120