Detecting protein atom correlations using correlation of probability of recurrence: Protein Correlations Using Recurrences

Investigating Pyruvate Dehydrogenase Kinase 3 Inhibitory Potential of Myricetin Using Integrated Computational and Spectroscopic Approaches

Protein kinases are involved in various diseases and currently represent potential targets for drug discovery. These kinases play major roles in regulating the cellular machinery and control growth, homeostasis, and cell signaling. Dysregulation of kinase expression is associated with various disorders such as cancer and neurodegeneration. Pyruvate dehydrogenase kinase 3 (PDK3) is implicated in cancer therapeutics as a potential drug target. In this current study, a molecular docking exhibited a strong binding affinity of myricetin to PDK3. Further, a 100 ns all-atom molecular dynamics (MD) simulation study provided insights into the structural dynamics and stability of the PDK3-myricetin complex, revealing the formation of a stable complex with minimal structural alterations upon ligand binding. Additionally, the actual affinity was ascertained by fluorescence binding studies, and myricetin showed appreciable binding affinity to PDK3. Further, the kinase inhibition assay suggested significant inhibition of PDK3 by myricetin, revealing an excellent inhibitory potential with an IC50 value of 3.3 μM. In conclusion, this study establishes myricetin as a potent PDK3 inhibitor that can be implicated in therapeutic targeting cancer and PDK3-associated diseases. In addition, this study underscores the efficacy of myricetin as a potential lead to drug discovery and provides valuable insights into the inhibition mechanism, enabling advancements in cancer therapeutics.

Identification of high-affinity pyridoxal kinase inhibitors targeting cancer therapy: an integrated docking and molecular dynamics simulation approach

Pyridoxal kinase (PDXK) is a vitamin B6-dependent transferase enzyme encoded by the PDXK gene, crucial for leukemic cell proliferation. Disruption of its activity causes altered metabolism and reduced levels of nucleotides and polyamines. PDXK and pyridoxal 5'-phosphate (PLP) are overexpressed in various carcinomas, making them promising targets for drug design against cancer. Targeting PDXK may hold promise as a therapeutic approach for cancer treatment. This study focused on discovering potential inhibitors that could selectively interrupt the binding of pyridoxal phosphate (PLP) to pyridoxal kinase (PDXK). A commercially available library of 7,28,747 natural and druglike compounds was virtually screened using a molecular docking approach to target the substrate binding pocket of PDXK. Six promising inhibitors were identified, and all-atom molecular dynamics simulations were conducted on the PDXK-ligand complexes for 100 ns to assess their binding conformational stability. The simulation results indicated that the binding of ZINC095099376, ZINC01612996, ZINC049841390, ZINC095098959, ZINC01482077, and ZINC03830976 induced a slight structural change and stabilized the PDXK structure. This analysis provided valuable information about the critical residues involved in the PDXK-PLP complex formation and can be utilized in designing specific and effective PDXK inhibitors. According to this study, these compounds could be developed as anticancer agents targeting PDXK as a potential candidate for further study.Communicated by Ramaswamy H. Sarma.

Dissecting the Structural Dynamics of Authentic Cholesteryl Ester Transfer Protein for the Discovery of Potential Lead Compounds: A Theoretical Study

Current structural and functional investigations of cholesteryl ester transfer protein (CETP) inhibitor design are nearly entirely based on a fully active mutation (CETPMutant) constructed for protein crystallization, limiting the study of the dynamic structural features of authentic CETP involved in lipid transport under physiological conditions. In this study, we conducted comprehensive molecular dynamics (MD) simulations of both authentic CETP (CETPAuthentic) and CETPMutant. Considering the structural differences between the N- and C-terminal domains of CETPAuthentic and CETPMutant, and their crucial roles in lipid transfer, we identified the two domains as binding pockets of the ligands for virtual screening to discover potential lead compounds targeting CETP. Our results revealed that CETPAuthentic displays greater flexibility and pronounced curvature compared to CETPMutant. Employing virtual screening and MD simulation strategies, we found that ZINC000006242926 has a higher binding affinity for the N- and C-termini, leading to reduced N- and C-opening sizes, disruption of the continuous tunnel, and increased curvature of CETP. In conclusion, CETPAuthentic facilitates the formation of a continuous tunnel in the "neck" region, while CETPMutant does not exhibit such characteristics. The ligand ZINC000006242926 screened for binding to the N- and C-termini induces structural changes in the CETP unfavorable to lipid transport. This study sheds new light on the relationship between the structural and functional mechanisms of CETP. Furthermore, it provides novel ideas for the precise regulation of CETP functions.

Identification of Potential Lead Compounds Targeting Novel Druggable Cavity of SARS-CoV-2 Spike Trimer by Molecular Dynamics Simulations

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become an urgent public health problem. Spike (S) protein mediates the fusion between the virus and the host cell membranes, consequently emerging as an important target of drug design. The lack of comparisons of in situ full-length S homotrimer structures in different states hinders understanding the structures and revealing the function, thereby limiting the discovery and development of therapeutic agents. Here, the steady-state structures of the in situ full-length S trimer in closed and open states (Sclosed and Sopen) were modeled with the constraints of density maps, associated with the analysis of the dynamic structural differences. Subsequently, we identified various regions with structure and property differences as potential binding pockets for ligands that promote the formation of inactive trimeric protein complexes. By using virtual screening strategy and a newly defined druggable cavity, five ligands were screened with potential bioactivities. Then molecular dynamic (MD) simulations were performed on apo protein structures and ligand bound complexes to reveal the conformational changes upon ligand binding. Our simulation results revealed that sulforaphane (SFN), which has the best binding affinity, could inhibit the conformational changes of S homotrimer that would occur during the viral membrane fusion. Our results could aid in the understanding of the regulation mechanism of S trimer aggregation and the structure-activity relationship, facilitating the development of potential antiviral agents.

Exploring the mechanism of C473D mutation on CDC25B causing weak binding affinity with CDK2/CyclinA by molecular dynamics study

CDC25B belongs to the CDC25 family, and it plays an important part in regulating the activity of CDK/CyclinA. Studies have shown that CDC25B is closely related to cancer development. When CYS473 on CDC25B is mutated into ASP, the affinity between CDC25B and CDK2/CyclinA weakens, and their dissociation speed is greatly improved. However, the mechanism by which the CDC25BC473D mutant weakens its binding to CDK2/CyclinA is unclear. In order to study the effect of CDC25BC473D mutants on CDK2/CyclinA substrates, we constructed and verified the rationality of the CDC25BWT:CDK2/CyclinA system and CDC25BC473D:CDK2/CyclinA system and conducted molecular dynamics (MD) simulation analysis. In the post-analysis, the fluctuations of residues ARG488-SER499, LYS541-TRP550 on CDC25B and residues ASP206-ASP210 on CDK2 were massive in the mutant CDC25BC473D:CDK2/CyclinA system. And the interactions between residue ARG492 and residue GLU208, residue ARG544 and residue GLU42, residue ARG544 and TRP550 were weakened in the mutant CDC25BC473D:CDK2/CyclinA system. The results showed that when CYS473 on CDC25B was mutated into ASP473, the mutant CDC25BC473D:CDK2/CyclinA system was less stable than the wild-type CDC25BWT:CDK2/CyclinA system. Finally, active site CYS473 of CDC25B was speculated to be the key residue, which had great effects on the binding between CDC25BCYS473 and CDK2 in the CDC25BC473D:CDK2/CyclinA system. Consequently, overall analyses appeared in this study ultimately provided a useful understanding of the weak interactions between CDC25BCYS473D and CDK2/CyclinA.Communicated by Ramaswamy H. Sarma.

Screening and identification of phytochemical drug molecules against mutant BRCA1 receptor of breast cancer using computational approaches

The American Cancer Society claims that breast cancer is the second most significant cause of cancer-related death, with over one million women diagnosed each year. Breast cancer linked to the BRCA1 gene has a significant risk of mortality and recurrence and is susceptible to alteration or over-expression, which can lead to hereditary breast cancer. Given the shortage of effective and possibly curative treatments for breast cancer, the present study combined molecular and computational analysis to find prospective phytochemical substances that can suppress the mutant gene (BRCA1) that causes the disease. Virtual screening and Molecular docking approaches are utilized to find probable phytochemicals from the ZINC database. The 3D structure of mutant BRCA1 protein with the id 3PXB was extracted from the NCBI-PDB. Top 10 phytochemical compounds shortlisted based on molecular docking score between - 11.6 and - 13.0. Following the ADMET properties, only three (ZINC000085490903 = - 12.50, ZINC000085490832 = - 12.44, and ZINC000070454071 = - 11.681) of the 10 selected compounds have drug-like properties. The molecular dynamic simulation study of the top three potential phytochemicals showed stabilized RMSD and RMSF values as compared to the APO form of the BRCA1 receptor. Further, trajectory analysis revealed that approximately similar radius of gyration score tends to the compactness of complex structure, and principal component and cross-correlation analysis suggest that the residues move in a strong correlation. Thermostability of the target complex (B-factor) provides information on the stable energy minimized structure. The findings suggest that the top three ligands show potential as breast cancer inhibitors.

Deciphering binding mechanism of inhibitors to SARS-COV-2 main protease through multiple replica accelerated molecular dynamics simulations and free energy landscapes

The pneumonia outbreak caused by the SARS-CoV-2 virus poses a serious threat to human health and the world economy. Development of safe and highly effective antiviral drugs is of great...

Identification of Phytoconstituents as Potent Inhibitors of Casein Kinase-1 Alpha Using Virtual Screening and Molecular Dynamics Simulations

Casein kinase-1 alpha (CK1α) is a multifunctional protein kinase that belongs to the serine/threonine kinases of the CK1α family. It is involved in various signaling pathways associated with chromosome segregation, cell metabolism, cell cycle progression, apoptosis, autophagy, etc. It has been known to involve in the progression of many diseases, including cancer, neurodegeneration, obesity, and behavioral disorders. The elevated expression of CK1α in diseased conditions facilitates its selective targeting for therapeutic management. Here, we have performed virtual screening of phytoconstituents from the IMPPAT database seeking potential inhibitors of CK1α. First, a cluster of compounds was retrieved based on physicochemical parameters following Lipinski's rules and PAINS filter. Further, high-affinity hits against CK1α were obtained based on their binding affinity score. Furthermore, the ADMET, PAINS, and PASS evaluation was carried out to select more potent hits. Finally, following the interaction analysis, we elucidated three phytoconstituents, Semiglabrinol, Curcusone_A, and Liriodenine, posturing considerable affinity and specificity towards the CK1α binding pocket. The result was further evaluated by molecular dynamics (MD) simulations, dynamical cross-correlation matrix (DCCM), and principal components analysis (PCA), which revealed that binding of the selected compounds, especially Semiglabrinol, stabilizes CK1α and leads to fewer conformational fluctuations. The MM-PBSA analysis suggested an appreciable binding affinity of all three compounds toward CK1α.

Insight into Structure-Function Relationships of β-Lactamase and BLIPs Interface Plasticity using Protein-Protein Interactions

BACKGROUND

Mostly BLIPs are identified in soil bacteria Streptomyces and originally isolated from Streptomyces clavuligerus and can be utilized as a model system for biophysical, structural, mutagenic and computational studies. BLIP possess homology with two proteins viz., BLIP-I (Streptomyces exofoliatus) and BLP (beta-lactamase inhibitory protein like protein from S. clavuligerus). BLIP consists of 165 amino acid, possessing two homologues domains comprising helix-loop-helix motif packed against four stranded beta-sheet resulting into solvent exposed concave surface with extended four stranded beta-sheet. BLIP-I is a 157 amino acid long protein obtained from S. exofoliatus having 37% sequence identity to BLIP and inhibits beta-lactamase.

METHODS

This review is intended to briefly illustrate the beta-lactamase inhibitory activity of BLIP via proteinprotein interaction and aims to open up a new avenue to combat antimicrobial resistance using peptide based inhibition.

RESULTS

D49A mutation in BLIP-I results in a decrease in affinity for TEM-1 from 0.5 nM to 10 nM (Ki). It is capable of inhibiting TEM-1 and bactopenemase and differs from BLIP only in modulating cell wall synthesis enzyme. Whereas, BLP is a 154 amino acid long protein isolated from S. clavuligerus via DNA sequencing analysis of Cephamycin-Clavulanate gene bunch. It shares 32% sequence similarity with BLIP and 42% with BLIP-I. Its biological function is unclear and lacks beta-lactamase inhibitory activity.

CONCLUSION

Protein-protein interactions mediate a significant role in regulation and modulation of cellular developments and processes. Specific biological markers and geometric characteristics are manifested by active site binding clefts of protein surfaces which determines the specificity and affinity for their targets. TEM1.BLIP is a classical model to study protein-protein interaction. β-Lactamase inhibitory proteins (BLIPs) interacts and inhibits various β-lactamases with extensive range of affinities.

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

Tackling the Antibiotic Resistance Caused by Class A β-Lactamases through the Use of β-Lactamase Inhibitory Protein

β-Lactams are the most widely used and effective antibiotics for the treatment of infectious diseases. Unfortunately, bacteria have developed several mechanisms to combat these therapeutic agents. One of the major resistance mechanisms involves the production of β-lactamase that hydrolyzes the β-lactam ring thereby inactivating the drug. To overcome this threat, the small molecule β-lactamase inhibitors (e.g., clavulanic acid, sulbactam and tazobactam) have been used in combination with β-lactams for treatment. However, the bacterial resistance to this kind of combination therapy has evolved recently. Therefore, multiple attempts have been made to discover and develop novel broad-spectrum β-lactamase inhibitors that sufficiently work against β-lactamase producing bacteria. β-lactamase inhibitory proteins (BLIPs) (e.g., BLIP, BLIP-I and BLIP-II) are potential inhibitors that have been found from soil bacterium Streptomyces spp. BLIPs bind and inhibit a wide range of class A β-lactamases from a diverse set of Gram-positive and Gram-negative bacteria, including TEM-1, PC1, SME-1, SHV-1 and KPC-2. To the best of our knowledge, this article represents the first systematic review on β-lactamase inhibitors with a particular focus on BLIPs and their inherent properties that favorably position them as a source of biologically-inspired drugs to combat antimicrobial resistance. Furthermore, an extensive compilation of binding data from β-lactamase–BLIP interaction studies is presented herein. Such information help to provide key insights into the origin of interaction that may be useful for rationally guiding future drug design efforts.

Detecting transitions in protein dynamics using a recurrence quantification analysis based bootstrap method

Background

Proteins undergo conformational transitions over different time scales. These transitions are closely intertwined with the protein’s function. Numerous standard techniques such as principal component analysis are used to detect these transitions in molecular dynamics simulations. In this work, we add a new method that has the ability to detect transitions in dynamics based on the recurrences in the dynamical system. It combines bootstrapping and recurrence quantification analysis. We start from the assumption that a protein has a “baseline” recurrence structure over a given period of time. Any statistically significant deviation from this recurrence structure, as inferred from complexity measures provided by recurrence quantification analysis, is considered a transition in the dynamics of the protein.

Results

We apply this technique to a 132 ns long molecular dynamics simulation of the β-Lactamase Inhibitory Protein BLIP. We are able to detect conformational transitions in the nanosecond range in the recurrence dynamics of the BLIP protein during the simulation. The results compare favorably to those extracted using the principal component analysis technique.

Conclusions

The recurrence quantification analysis based bootstrap technique is able to detect transitions between different dynamics states for a protein over different time scales. It is not limited to linear dynamics regimes, and can be generalized to any time scale. It also has the potential to be used to cluster frames in molecular dynamics trajectories according to the nature of their recurrence dynamics. One shortcoming for this method is the need to have large enough time windows to insure good statistical quality for the recurrence complexity measures needed to detect the transitions.

THz frequency spectrum of protein-solvent interaction energy using a recurrence plot-based Wiener-Khinchin method

The dynamics of a protein and the water surrounding it are coupled via nonbonded energy interactions. This coupling can exhibit a complex, nonlinear, and nonstationary nature. The THz frequency spectrum for this interaction energy characterizes both the vibration spectrum of the water hydrogen bond network, and the frequency range of large amplitude modes of proteins. We use a Recurrence Plot based Wiener-Khinchin method RPWK to calculate this spectrum, and the results are compared to those determined using the classical auto-covariance-based Wiener-Khinchin method WK. The frequency spectra for the total nonbonded interaction energy extracted from molecular dynamics simulations between the β-Lactamase Inhibitory Protein BLIP, and water molecules within a 10 Å distance from the protein surface, are calculated at 150, 200, 250, and 310 K, respectively. Similar calculations are also performed for the nonbonded interaction energy between the residues 49ASP, 53TYR, and 142PHE in BLIP, with water molecules within 10 Å from each residue respectively at 150, 200, 250, and 310 K. A comparison of the results shows that RPWK performs better than WK, and is able to detect some frequency data points that WK fails to detect. This points to the importance of using methods capable of taking the complex nature of the protein-solvent energy landscape into consideration, and not to rely on standard linear methods. In general, RPWK can be a valuable addition to the analysis tools for protein molecular dynamics simulations. Proteins 2016; 84:1549-1557. © 2016 Wiley Periodicals, Inc.

(Dis)similarity Index To Compare Correlated Motions in Molecular Simulations

Molecular dynamics (MD) simulations are widely used to complement or guide experimental studies in the characterization of protein dynamics, thanks to improvements in force-field accuracy, along with in the software and hardware to sample the conformational landscape of proteins. Among the different applications of MD simulations, the study of correlated motions is largely employed for different purposes. Several metrics have been developed to describe correlated motions in the MD ensemble, such as methods based on Pearson Correlation or Mutual Information. Cross-correlation analysis of MD trajectories is indeed appealing not only to identify residues characterized by coupled fluctuations in protein structures but also since it can be used to extrapolate motions along directions in which major conformational changes should occur, for example on longer time scales than the ones that are actually simulated. Nevertheless, most of the MD studies employ average correlation maps and mostly in a qualitative way, even when different systems or different replicates of the same system are compared. The broad application of correlation metrics in the analysis of MD simulations, especially for comparative purposes, requires a step forward toward more quantitative and accurate comparisons. We thus here employed a simple but effective index, which is based on a normalized Frobenius norm of the differences between protein correlation maps, to compare correlated motions. We applied this index for a quantitative comparison of correlated motions from MD simulations of seven proteins of different size and fold. We also employed the index to assess the robustness of correlation description when multi-replicate MD simulations of a same system are used, and we compared our index to metrics for comparison of structural ensembles such as Root Mean Square Inner Product and the Bhattacharyya Coefficient.

Weighted protein residue networks based on joint recurrences between residues

BACKGROUND

Weighted and un-weighted protein residue networks can predict key functional residues in proteins based on the closeness centrality C and betweenness centrality B values for each residue. A static snapshot of the protein structure, and a cutoff distance, are used to define edges between the network nodes. In this work we apply the weighted network approach to study the β-Lactamase Inhibitory Protein (BLIP). Joint recurrences extracted from molecular dynamics MD trajectory positions of the protein residue carbon alpha atoms are used to define edge weights between nodes, and no cutoff distance is used. The results for B and C from our approach are compared with those extracted from an un-weighted network, and a weighted network that uses interatomic contacts to define edge weights between nodes, respectively.

RESULTS

The joint recurrence weighted network approach performs well in pointing out key protein residues. Furthermore, it seems to emphasize residues with medium to high relative solvent accessibility that lie in loop regions between secondary structure elements of the protein.

CONCLUSIONS

Protein residue networks that use joint recurrences extracted from molecular dynamics simulations of a solvated protein perform well in pointing to hotspot residues and hotspot clusters. This approach uses no distance cutoff threshold, and does not exclude any interactions between the residues, including water-mediated interactions.