Protein dynamics and motions in relation to their functions: several case studies and the underlying mechanisms

Mechanical Properties of a Solvated Biomolecule: RGD (1FUV) Peptide

The mechanical properties of proteins/peptides play an essential role in their functionalities and implications, as well as their structure and dynamic properties. Understanding mechanical properties is pivotal to our knowledge of protein folding and the molecular basis of diverse cellular processes. Herein, we present a computational approach using ab initio quantum mechanical calculations to determine the mechanical properties-such as bulk modulus, shear modulus, Young's modulus, and Poisson's ratio-of a solvated Arg-Gly-Asp (RGD) peptide model. Since this peptide serves as the RGD-directed integrin recognition site and may participate in cellular adhesion, it is considered a promising small peptide for medicinal applications. This successful approach paves the way for investigating larger and more complex biomolecules.

Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics

Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence have revolutionized our understanding of protein dynamics. Nuclear magnetic resonance spectroscopy provides atomic-resolution insights, while molecular dynamics simulations offer detailed trajectories of protein motions. Computational methods applied to X-ray crystallography and cryo-electron microscopy (cryo-EM) have enabled the exploration of protein dynamics, capturing conformational ensembles that were previously unattainable. The integration of machine learning, exemplified by AlphaFold2, has accelerated structure prediction and dynamics analysis. These approaches have revealed the importance of protein dynamics in allosteric regulation, enzyme catalysis, and intrinsically disordered proteins. The shift towards ensemble representations of protein structures and the application of single-molecule techniques have further enhanced our ability to capture the dynamic nature of proteins. Understanding protein dynamics is essential for elucidating biological mechanisms, designing drugs, and developing novel biocatalysts, marking a significant paradigm shift in structural biology and drug discovery.

Computational Insights into SARS-CoV-2 Main Protease Mutations and Nirmatrelvir Efficacy: The Effects of P132H and P132H-A173V

Nirmatrelvir, a pivotal component of the oral antiviral Paxlovid for COVID-19, targets the SARS-CoV-2 main protease (Mpro) as a covalent inhibitor. Here, we employed combined computational methods to explore how the prevalent Omicron variant mutation P132H, alone and in combination with A173V (P132H-A173V), affects nirmatrelvir's efficacy. Our findings suggest that P132H enhances the noncovalent binding affinity of Mpro for nirmatrelvir, whereas P132H-A173V diminishes it. Although both mutants catalyze the rate-limiting step more efficiently than the wild-type (WT) Mpro, P132H slows the overall rate of covalent bond formation, whereas P132H-A173V accelerates it. Comprehensive analysis of noncovalent and covalent contributions to the overall binding free energy of the covalent complex suggests that P132H likely enhances Mpro sensitivity to nirmatrelvir, while P132H-A173V may confer resistance. Per-residue decompositions of the binding and activation free energies pinpoint key residues that significantly affect the binding affinity and reaction rates, revealing how the mutations modulate these effects. The mutation-induced conformational perturbations alter drug-protein local contact intensities and the electrostatic preorganization of the protein, affecting noncovalent binding affinity and the stability of key reaction states, respectively. Our findings inform the mechanisms of nirmatrelvir resistance and sensitivity, facilitating improved drug design and the detection of resistant strains.

Identifying Oncogenic Missense Single Nucleotide Polymorphisms in Human SAT1 Gene Using Computational Algorithms and Molecular Dynamics Tools

BACKGROUND

The human SAT1 gene encodes spermidine/spermine N1-acetyltransferase 1 (SSAT1), a regulatory biological catalyst of polyamine catabolism. Numerous essential biological processes, such as cellular proliferation, differentiation, and survival, depend on polyamines like spermidine and spermine. Thus, SSAT1 is involved in key cellular activities such as proliferation and survival of cells and mediates various diseases including cancer. A plethora of studies established the involvement of missense single nucleotide polymorphisms (SNPs) in numerous pathological conditions due to their ability to adversely affect the structure and subsequent function of the protein.

AIMS

To date, an in silico study to identify the pathogenic missense SNPs of the human SAT1 gene has not been accomplished yet. This study aimed to filter the missense SNPs that were functionally detrimental and pathogenic.

METHODS AND RESULTS

The rs757435207 (I21N) was ascertained to be the most deleterious and pathogenic by all algorithmic tools. Stability and evolutionary conservation analysis tools also stated that I21N variant decreased the stability and was located in the highly conserved residue. Molecular dynamics simulation revealed that I21N caused substantial alterations in the conformational stability and dynamics of the SSAT1 protein. Consequently, the I21N variant could disrupt the native functional roles of the SSAT1 enzyme.

CONCLUSION

Therefore, the I21N variant was identified and concluded to be an oncogenic missense variant of the human SAT1 gene. Overall, the findings of this study would be a great directory of future experimental research to develop personalized medicine.

Structural and energetic analysis of NS5 protein inhibition by small molecules in Japanese encephalitis virus using machine learning and steered molecular dynamics approach

One of the viral diseases that affect millions of people around the world, particularly in developing countries, is Japanese encephalitis (JE). In this study, the conserved protein of this virus, that is, non-structural protein 5 (NS5), was used as a target protein for this study, and a compound library of 749 antiviral molecules was screened against NS5. The current study employed machine learning-based virtual screening combined with molecular docking. Here, three hits (24360, 123519051 and 213039) had lower binding energies (< -8 kcal/mol) than the control, S-Adenosyl-L-homocysteine (SAH). All the compounds showed significant H-bond interactions with functional residues, which were also observed by the control. Molecular dynamics simulation, MM/GBSA for binding free energy analysis, principal component analysis and free energy landscape were also performed to study the stability of the complex formation. All three compounds had similar root mean square deviation trends, which were comparable to the control, SAH. Post-MD, the 123519051-receptor complex had the highest number of H-bonds (4 to 5) after the control, out of which three exhibited the highest percentage occupancy (50%, 24% and 79%). Both docking and MD, 123519051 showed an H-bond with the residue Gly111, which was also found for the control-protein complex. 123519051 showed the lowest binding free energy with ΔGbind of -89 kJ/mol. Steered molecular dynamics depicted that 123519051 had the maximum magnitude of dissociation (1436.43 kJ/mol/nm), which was more than the control, validating its stable complex formation. This study concluded that 123519051 is a binder and could inhibit the protein NS5 of JE.Communicated by Ramaswamy H. Sarma.

Human Kir2.1 Potassium Channel: Protocols for Cryo-EM Data Processing and Molecular Dynamics Simulations

Kir channels are potassium (K+) channels responsible for the mechanism of inward rectification, which plays a fundamental role in maintaining the resting membrane potential. There are seven Kir subfamilies, and their opening and closing mechanism is regulated by different regulatory factors. Genetically inherited defects in Kir channels are responsible for several rare human diseases, and for most of them, there are currently no effective therapeutic treatments. High-resolution structural information is not available for several members within the Kir subfamilies. Recently, our group achieved a significant breakthrough by utilizing cryo-EM single-particle analysis to elucidate the first structure of the human Kir2.1 channel. We present here the data processing protocol of the cryo-EM data of the human Kir2.1 channel, which is applicable to the structural determination of other ion channels by cryo-EM single-particle analysis. We also introduce a protocol designed to assess the structural heterogeneity within the cryo-EM data, allowing for the identification of other possible protein structure conformations present in the collected data. Moreover, we present a protocol for conducting all-atom molecular dynamics (MD) simulations for K+ channels, which can be incorporated into various membrane models to simulate different environments. We also propose some methods for analyzing the MD simulations, with a particular emphasis on assessing the local mobility of protein residues.

Unveiling clinically significant PPARγ mutations for thiazolidinedione treatment responsiveness through atomistic simulations

In Type 2 diabetes, increased insulin sensitivity is induced by thiazolidinedione activation of the peroxisome proliferator-activated receptor gamma (PPARγ). Recent data indicate a relationship between SNPs in PPARγ and poor drug response. Therefore, understanding the pathogenic consequences of mutations in PPARγ-mediated protein-drug interactions will be prima-facie for establishing personalized medicine. The PPARG gene has 197 missense SNPs, 22 of which were determined to be both deleterious and destabilizing, employing in silico approaches. Molecular docking analysis suggested that the mutation influenced the binding energy of at least seven of the variants. The mutant R316H was identified as the most damaging and deleterious from the observed results. For a better understanding of the dynamic variation upon mutation at the atomic level, molecular dynamics simulations of the wild-type and R316H mutant PPARγ structure were performed. The analysis indicates that the mutation increased protein structural compactness while decreasing flexibility. The reduced dynamics in the mutant structure was further validated by principal component analysis. This mechanistic evaluation of the PPARγ protein variants provides insight into the relationship between genetic variation and interindividual variability of drug responsiveness and will facilitate the future studies for the development of tailored treatment regime for precision medicine.

Unveiling Switching Function of Amino Acids in Proteins Using a Machine Learning Approach

Dynamics of individual amino acids play key roles in the overall properties of proteins. However, the knowledge of protein structural features at the residue level is limited due to the current resolutions of experimental and computational techniques. To address this issue, we designed a novel machine-learning (ML) framework that uses Molecular Dynamics (MD) trajectories to identify the major conformational states of individual amino acids, classify amino acids switching between two distinct modes, and evaluate their degree of dynamic stability. The Random Forest model achieved 96.94% classification accuracy in identifying switch residues within proteins. Additionally, our framework distinguishes between the stable switch (SS) residues, which remain stable in one angular state and jump once to another state during protein dynamics, and unstable switch (US) residues, which constantly fluctuate between the two angular states. This study also illustrates the correlation between the dynamics of SS residues and the protein's global properties.

Comparison of the Molecular Motility of Tubulin Dimeric Isoforms: Molecular Dynamics Simulations and Diffracted X-ray Tracking Study

Tubulin has been recently reported to form a large family consisting of various gene isoforms; however, the differences in the molecular features of tubulin dimers composed of a combination of these isoforms remain unknown. Therefore, we attempted to elucidate the physical differences in the molecular motility of these tubulin dimers using the method of measurable pico-meter-scale molecular motility, diffracted X-ray tracking (DXT) analysis, regarding characteristic tubulin dimers, including neuronal TUBB3 and ubiquitous TUBB5. We first conducted a DXT analysis of neuronal (TUBB3-TUBA1A) and ubiquitous (TUBB5-TUBA1B) tubulin dimers and found that the molecular motility around the vertical axis of the neuronal tubulin dimer was lower than that of the ubiquitous tubulin dimer. The results of molecular dynamics (MD) simulation suggest that the difference in motility between the neuronal and ubiquitous tubulin dimers was probably caused by a change in the major contact of Gln245 in the T7 loop of TUBB from Glu11 in TUBA to Val353 in TUBB. The present study is the first report of a novel phenomenon in which the pico-meter-scale molecular motility between neuronal and ubiquitous tubulin dimers is different.

Exploiting conformational dynamics to modulate the function of designed proteins

With the recent success in calculating protein structures from amino acid sequences using artificial intelligence-based algorithms, an important next step is to decipher how dynamics is encoded by the primary protein sequence so as to better predict function. Such dynamics information is critical for protein design, where strategies could then focus not only on sequences that fold into particular structures that perform a given task, but would also include low-lying excited protein states that could influence the function of the designed protein. Herein, we illustrate the importance of dynamics in modulating the function of C34, a designed α/β protein that captures β-strands of target ligands and is a member of a family of proteins designed to sequester β-strands and β hairpins of aggregation-prone molecules that lead to a variety of pathologies. Using a strategy to "see" regions of apo C34 that are invisible to NMR spectroscopy as a result of pervasive conformational exchange, as well as a mutagenesis approach whereby C34 molecules are stabilized into a single conformer, we determine the structures of the predominant conformations that are sampled by C34 and show that these attenuate the affinity for cognate peptide. Subsequently, the observed motion is exploited to develop an allosterically regulated peptide binder whose binding affinity can be controlled through the addition of a second molecule. Our study emphasizes the unique role that NMR can play in directing the design process and in the construction of new molecules with more complex functionality.

Engineering the Active Site Lid Dynamics to Improve the Catalytic Efficiency of Yeast Cytosine Deaminase

Conformational dynamics is important for enzyme catalysis. However, engineering dynamics to achieve a higher catalytic efficiency is still challenging. In this work, we develop a new strategy to improve the activity of yeast cytosine deaminase (yCD) by engineering its conformational dynamics. Specifically, we increase the dynamics of the yCD C-terminal helix, an active site lid that controls the product release. The C-terminal is extended by a dynamical single α-helix (SAH), which improves the product release rate by up to ~8-fold, and the overall catalytic rate kcat by up to ~2-fold. It is also shown that the kcat increase is due to the favorable activation entropy change. The NMR H/D exchange data indicate that the conformational dynamics of the transition state analog complex increases as the helix is extended, elucidating the origin of the enhanced catalytic entropy. This study highlights a novel dynamics engineering strategy that can accelerate the overall catalysis through the entropy-driven mechanism.

Mechanistic insights into the conformational switch in profilin-1 subject to collective effects of mutation and histidine tautomerism

Mutations and histidine (His) tautomerism in profilin-1 (PFN1) are associated with amyotrophic lateral sclerosis (ALS). The conformational changes in PFN1 caused by the collective effects of G117V mutation and His tautomeric isomers εε, εδ, δε, and δδ were clarified using molecular dynamics (MD) simulations. The predominant structural variations were seen in α-helices, β-sheets, turns, and coils and the His tautomer's unique degree of disruption was seen in these conformations. The content of α-helices was 23.2 % in the εε and δδ isomers, but the observed α-helices in the isomers εδ and δε were 20.3 % and 21.7 % respectively. The percentage of β-sheet was found to be higher (34.1) in the εε isomer than in the εδ, δε, and δδ isomers, and the values were 30.4, 29.7, and 31.9, respectively. Intermolecular water dynamics analysis discloses that His 133 can form an intramolecular H-bond interaction (N^α-H---N^δ), confirming the experimental observations in the simulations of εε, δε, and δδ isomers of G117V PFN1 mutant. It was concluded that these solvent molecules are crucial for aggregation and must be considered in future research on the PFN1 associated with ALS. Overall, the study offers a thorough microscopic understanding of the pathogenic mechanisms behind conformational changes that cause aggregation illnesses like ALS.

Structural insights into the effect of mutations in the spike protein of SARS-CoV-2 on the binding with human furin protein

The SARS COV-2 and its variants are spreading around the world at an alarming speed, due to its higher transmissibility and the conformational changes caused by mutations. The resulting COVID-19 pandemic has imposed severe health consequences on human health. Several countries of the world including Pakistan have studied its genome extensively and provided productive findings. In the current study, the mCSM, DynaMut2, and I-Mutant servers were used to analyze the effect of identified mutations on the structural stability of spike protein however, the molecular docking and simulations approaches were used to evaluate the dynamics of the bonding network between the wild-type and mutant spike proteins with furin. We addressed the mutational modifications that have occurred in the spike protein of SARS-COV-2 that were found in 215 Pakistani's isolates of COVID-19 patients to study the influence of mutations on the stability of the protein and its interaction with the host cell. We found 7 single amino acid substitute mutations in various domains that reside in spike protein. The H49Y, N74K, G181V, and G446V were found in the S1 domain while the D614A, V622F, and Q677H mutations were found in the central helices of the spike protein. Based on the observation, G181V, G446V, D614A, and V622F mutants were found highly destabilizing and responsible for structural perturbation. Protein-protein docking and molecular simulation analysis with that of furin have predicted that all the mutants enhanced the binding efficiency however, the V622F mutant has greatly altered the binding capacity which is further verified by the K_D value (7.1 E^-14) and therefore may enhance the spike protein cleavage by Furin and increase the rate of infectivity by SARS-CoV-2. On the other hand, the total binding energy for each complex was calculated which revealed -50.57 kcal/mol for the wild type, for G181V -52.69 kcal/mol, for G446V -56.44 kcal/mol, for D614A -59.78 kcal/mol while for V622F the TBE was calculated to be -85.84 kcal/mol. Overall, the current finding shows that these mutations have increased the binding of Furin for spike protein and shows that D614A and V622F have significant effects on the binding and infectivity.

Thirty years of molecular dynamics simulations on posttranslational modifications of proteins

Posttranslational modifications (PTMs) are an integral component to how cells respond to perturbation. While experimental advances have enabled improved PTM identification capabilities, the same throughput for characterizing how structural changes caused by PTMs equate to altered physiological function has not been maintained. In this Perspective, we cover the history of computational modeling and molecular dynamics simulations which have characterized the structural implications of PTMs. We distinguish results from different molecular dynamics studies based upon the timescales simulated and analysis approaches used for PTM characterization. Lastly, we offer insights into how opportunities for modern research efforts on in silico PTM characterization may proceed given current state-of-the-art computing capabilities and methodological advancements.

Effect of the R126C mutation on the structure and function of the glucose transporter GLUT1: A molecular dynamics simulation study

Glucose transporter 1 (GLUT1) is responsible for basal glucose uptake and is expressed in most tissues under normal conditions. GLUT1 mutations can cause early-onset absence epilepsy and myoclonus dystonia syndrome (MDS), with MDS potentially lethal. In this study, the effect of the R126C mutation, which is associated with MDS, on structural stability and substrate transport of GLUT1 was investigated. Various bioinformatics tools were used to predict the stability of GLUT1, revealing that the R126C mutation reduces the structural stability of GLUT1. Molecular dynamics (MD) simulations were used to further characterize the effect of the R126C mutation on GLUT1 structural stability. Based on the MD simulations, specific conformational changes and dominant motions of the GLUT1 mutant were characterized by Principal component analysis (PCA). The mutation disrupts hydrogen bonds between substrate-binding residues and glucose, thus likely reducing substrate affinity. The R126C mutation reduces the conformational stability of the protein, and fewer intramolecular hydrogen bonds were present in the mutated GLUT1 when compared with that of wild-type GLUT1. The mutation increased the free energy of glucose transport through GLUT1 significantly, especially at the mutation site, indicating that passage of glucose through the channel is hindered, and this mutant may even release cytoplasmic glucose. This study provides a detailed atomic-level explanation for the reduced structural stability and substrate transport capacity of a GLUT1 mutant. The results aid our understanding of the structure of GLUT1 and provide a framework for developing drugs to treat GLUT1-related diseases, such as MDS.

Recognition in the Domain of Molecular Chirality: From Noncovalent Interactions to Separation of Enantiomers

It is not a coincidence that both chirality and noncovalent interactions are ubiquitous in nature and synthetic molecular systems. Noncovalent interactivity between chiral molecules underlies enantioselective recognition as a fundamental phenomenon regulating life and human activities. Thus, noncovalent interactions represent the narrative thread of a fascinating story which goes across several disciplines of medical, chemical, physical, biological, and other natural sciences. This review has been conceived with the awareness that a modern attitude toward molecular chirality and its consequences needs to be founded on multidisciplinary approaches to disclose the molecular basis of essential enantioselective phenomena in the domain of chemical, physical, and life sciences. With the primary aim of discussing this topic in an integrated way, a comprehensive pool of rational and systematic multidisciplinary information is provided, which concerns the fundamentals of chirality, a description of noncovalent interactions, and their implications in enantioselective processes occurring in different contexts. A specific focus is devoted to enantioselection in chromatography and electromigration techniques because of their unique feature as "multistep" processes. A second motivation for writing this review is to make a clear statement about the state of the art, the tools we have at our disposal, and what is still missing to fully understand the mechanisms underlying enantioselective recognition.

In Search of a Dynamical Vocabulary: A Pipeline to Construct a Basis of Shared Traits in Large-Scale Motions of Proteins

The paradigmatic sequence–structure–dynamics–function relation in proteins is currently well established in the scientific community; in particular, a large effort has been made to probe the first connection, indeed providing convincing evidence of its strength and rationalizing it in a quantitative and general framework. In contrast, however, the role of dynamics as a link between structure and function has eluded a similarly clear-cut verification and description. In this work, we propose a pipeline aimed at building a basis for the quantitative characterization of the large-scale dynamics of a set of proteins, starting from the sole knowledge of their native structures. The method hinges on a dynamics-based clusterization, which allows a straightforward comparison with structural and functional protein classifications. The resulting basis set, obtained through the application to a group of related proteins, is shown to reproduce the salient large-scale dynamical features of the dataset. Most interestingly, the basis set is shown to encode the fluctuation patterns of homologous proteins not belonging to the initial dataset, thus highlighting the general applicability of the pipeline used to build it.

Beyond structure: Deciphering site-specific dynamics in proteins from double histidine-based EPR measurements

Site-specific dynamics in proteins are at the heart of protein function. While electron paramagnetic resonance (EPR) has potential to measure dynamics in large protein complexes, the reliance on flexible nitroxide labels is limitating especially for the accurate measurement of site-specific β-sheet dynamics. Here, we employed EPR spectroscopy to measure site-specific dynamics across the surface of a protein, GB1. Through the use of the double Histidine (dHis) motif, which enables labeling with a Cu(II) - nitrilotriacetic acid (NTA) complex, dynamics information was obtained for both α-helical and β-sheet sites. Spectral simulations of the resulting CW-EPR report unique site-specific fluctuations across the surface of GB1. Additionally, we performed molecular dynamics (MD) simulations to complement the EPR data. The dynamics observed from MD agree with the EPR results. Furthermore, we observe small changes in gǁ values for different sites, which may be due to small differences in coordination geometry and/or local electrostatics of the site. Taken together, this work expands the utility of Cu(II)NTA-based EPR measurements to probe information beyond distance constraints.

Identification of Zinc-Binding Inhibitors of Matrix Metalloproteinase-9 to Prevent Cancer Through Deep Learning and Molecular Dynamics Simulation Approach

The overexpression of matrix metalloproteinase-9 (MMP-9) is associated with tumor development and angiogenesis, and hence, it has been considered an attractive drug target for anticancer therapy. To assist in drug design endeavors for MMP-9 targets, an in silico study was presented to investigate whether our compounds inhibit MMP-9 by binding to the catalytic domain, similar to their inhibitor or not. For that, in the initial stage, a deep-learning algorithm was used for the predictive modeling of the CHEMBL321 dataset of MMP-9 inhibitors. Several regression models were built and evaluated based on R2, MAE MSE, RMSE, and Loss. The best model was utilized to screen the drug bank database containing 9,102 compounds to seek novel compounds as MMP-9 inhibitors. Then top high score compounds were selected for molecular docking based on the comparison between the score of the reference molecule. Furthermore, molecules having the highest docking scores were selected, and interaction mechanisms with respect to S1 pocket and catalytic zinc ion of these compounds were also discussed. Those compounds, involving binding to the catalytic zinc ion and the S1 pocket of MMP-9, were considered preferentially for molecular dynamics studies (100 ns) and an MM-PBSA (last 30 ns) analysis. Based on the results, we proposed several novel compounds as potential candidates for MMP-9 inhibition and investigated their binding properties with MMP-9. The findings suggested that these compounds may be useful in the design and development of MMP-9 inhibitors in the future.

Preproteins couple the intrinsic dynamics of SecA to its ATPase cycle to translocate via a catch and release mechanism

Protein machines undergo conformational motions to interact with and manipulate polymeric substrates. The Sec translocase promiscuously recognizes, becomes activated, and secretes >500 non-folded preprotein clients across bacterial cytoplasmic membranes. Here, we reveal that the intrinsic dynamics of the translocase ATPase, SecA, and of preproteins combine to achieve translocation. SecA possesses an intrinsically dynamic preprotein clamp attached to an equally dynamic ATPase motor. Alternating motor conformations are finely controlled by the γ-phosphate of ATP, while ADP causes motor stalling, independently of clamp motions. Functional preproteins physically bridge these independent dynamics. Their signal peptides promote clamp closing; their mature domain overcomes the rate-limiting ADP release. While repeated ATP cycles shift the motor between unique states, multiple conformationally frustrated prongs in the clamp repeatedly "catch and release" trapped preprotein segments until translocation completion. This universal mechanism allows any preprotein to promiscuously recognize the translocase, usurp its intrinsic dynamics, and become secreted.

Assessment of activity of chalcone compounds as inhibitors of 3-chymotrypsin like protease (3CLPro) of SARS-CoV-2: in silico study

The COVID-19 is still pandemic due to emerging of various variant of concern of SARS-CoV2. Hence, it is devastating the world, causing significant economic as well as social chaos. This needs great effort to search and develop effective alternatives along with vaccination. Therefore, to continue drug discovery endeavors, we used chalcone derivatives to find an effective drug candidate against SARS-CoV2. Chalcone is a common simple scaffold that exists in many diets as well as in traditional medicine. Natural as well as synthetic chalcones have shown numerous interesting biological activities and are also effective in fighting various diseases. Hence, various computational methods were applied to find out potential inhibitors of 3CL^Pro using a library of 3000 compounds of chalcones. Firstly, the screening by structure-based pharmacophore model yielded 84 hits that were subjected to molecular docking. The top 10 docked compounds were characterized for stability by using 100 ns molecular dynamic (MD) simulation approach. Further, the binding free energy calculation by MMPBSA showed that four compounds bind to 3CL^Pro enzyme with high affinity, i.e., − 87.962 (kJ/mol), − 66.125 (kJ/mol), − 59.589 (kJ/mol), and − 66.728 (kJ/mol), respectively. Since chalcone is a common simple scaffold that is present in many diets as well as in traditional medicine, we suggest that screened compounds may emerge as promising drug candidates for SARS-CoV-2. These compounds may be investigated in vitro to evaluate the efficacy against SARS-CoV-2.

Structural modelling of SARS-CoV-2 alpha variant (B.1.1.7) suggests enhanced furin binding and infectivity

The B.1.1.7 SARS-CoV-2 strain that has emerged in the UK in early December presents seven mutations and three deletions on S-protein structure that could lead to a more infective strain. The P681H mutation in the “PRRAR” furin cleavage site might affect the binding affinity to furin enzyme and hence its infectivity.

Therefore, in this study, various structural bioinformatics approaches were used to model the S-protein structure with the B.1.1.7 variant amino acid substitutions and deletions. In addition to modelling the binding of furin to the cleavage site of the wild-type and the B.1.1.7 variant.

Conclusively the B.1.1.7 variant resulted in dynamic stability, conformational changes and variations in binding energies in the S-protein structure, resulting in a more favourable binding of furin enzyme to the SARS-CoV-2 S-protein.

Defining the Energetic Basis for a Conformational Switch Mediating Ligand-Independent Activation of Mutant Estrogen Receptors in Breast Cancer

Although most primary estrogen receptor (ER)-positive breast cancers respond well to endocrine therapies, many relapse later as metastatic disease due to endocrine therapy resistance. Over one third of these are associated with mutations in the ligand-binding domain (LBD) that activate the receptor independent of ligand. We have used an array of advanced computational techniques rooted in molecular dynamics simulations, in concert with and validated by experiments, to characterize the molecular mechanisms by which specific acquired somatic point mutations give rise to ER constitutive activation. By comparing structural and energetic features of constitutively active mutants and ligand-bound forms of ER-LBD with unliganded wild-type (WT) ER, we characterize a spring force originating from strain in the Helix 11-12 loop of WT-ER, opposing folding of Helix 12 into the active conformation and keeping WT-ER off and disordered, with the ligand-binding pocket open for rapid ligand binding. We quantify ways in which this spring force is abrogated by activating mutations that latch (Y537S) or relax (D538G) the folded form of the loop, enabling formation of the active conformation without ligand binding. We also identify a new ligand-mediated hydrogen-bonding network that stabilizes the active, ligand-bound conformation of WT-ER LBD, and similarly stabilizes the active conformation of the ER mutants in the hormone-free state. IMPLICATIONS: Our investigations provide deep insight into the energetic basis for the structural mechanisms of receptor activation through mutation, exemplified here with ER in endocrine-resistant metastatic breast cancers, with potential application to other dysregulated receptor signaling due to driver mutations.

DESP: Deep Enhanced Sampling of Proteins' Conformation Spaces Using AI-Inspired Biasing Forces

The molecular structures (i.e., conformation spaces, CS) of bio-macromolecules and the dynamics that molecules exhibit are crucial to the understanding of the basis of many diseases and in the continuous attempts to retarget known drugs/medications, improve the efficacy of existing drugs, or develop novel drugs. These make a better understanding and the exploration of the CS of molecules a research hotspot. While it is generally easy to computationally explore the CS of small molecules (such as peptides and ligands), the exploration of the CS of a larger biomolecule beyond the local energy well and beyond the initial equilibrium structure of the molecule is generally nontrivial and can often be computationally prohibitive for molecules of considerable size. Therefore, research efforts in this area focus on the development of ways that systematically favor the sampling of new conformations while penalizing the resampling of previously sampled conformations. In this work, we present Deep Enhanced Sampling of Proteins’ Conformation Spaces Using AI-Inspired Biasing Forces (DESP), a technique for enhanced sampling that combines molecular dynamics (MD) simulations and deep neural networks (DNNs), in which biasing potentials for guiding the MD simulations are derived from the KL divergence between the DNN-learned latent space vectors of [a] the most recently sampled conformation and those of [b] the previously sampled conformations. Overall, DESP efficiently samples wide CS and outperforms conventional MD simulations as well as accelerated MD simulations. We acknowledge that this is an actively evolving research area, and we continue to further develop the techniques presented here and their derivatives tailored at achieving DNN-enhanced steered MD simulations and DNN-enhanced targeted MD simulations.

Machine learning reveals hidden stability code in protein native fluorescence

Conformational stability of a protein is usually obtained by spectroscopically measuring the unfolding melting temperature. However, optical spectra under native conditions are considered to contain too little resolution to probe protein stability. Here, we have built and trained a neural network model to take the temperature-dependence of intrinsic fluorescence emission under native-only conditions as inputs, and then predict the spectra at the unfolding transition and denatured state. Application to a therapeutic antibody fragment demonstrates that thermal transitions obtained from the predicted spectra correlate highly with those measured experimentally. Crucially, this work reveals that the temperature-dependence of native fluorescence spectra contains a high-degree of previously hidden information relating native ensemble features to stability. This could lead to rapid screening of therapeutic protein variants and formulations based on spectroscopic measurements under non-denaturing temperatures only.

A nexus of intrinsic dynamics underlies translocase priming

The cytoplasmic ATPase SecA and the membrane-embedded SecYEG channel assemble to form the Sec translocase. How this interaction primes and catalytically activates the translocase remains unclear. We show that priming exploits a nexus of intrinsic dynamics in SecA. Using atomistic simulations, smFRET, and HDX-MS, we reveal multiple dynamic islands that cross-talk with domain and quaternary motions. These dynamic elements are functionally important and conserved. Central to the nexus is a slender stem through which rotation of the preprotein clamp of SecA is biased by ATPase domain motions between open and closed clamping states. An H-bonded framework covering most of SecA enables multi-tier dynamics and conformational alterations with minimal energy input. As a result, cognate ligands select preexisting conformations and alter local dynamics to regulate catalytic activity and clamp motions. These events prime the translocase for high-affinity reception of non-folded preprotein clients. Dynamics nexuses are likely universal and essential in multi-liganded proteins.

Phylogenic analysis of coronavirus genome and molecular studies on potential anti-COVID-19 agents from selected FDA-approved drugs

The emergence of 2019 novel Coronavirus (COVID-19 or 2019-nCoV) has caused significant global morbidity and mortality with no consensus specific treatment. We tested the hypothesis that FDA-approved antiretrovirals, antibiotics, and antimalarials will effectively inhibit COVID-19 two major drug targets, coronavirus nucleocapsid protein (NP) and hemagglutinin-esterase (HE). To test this hypothesis, we carried out a phylogenic analysis of coronavirus genome to understand the origins of NP and HE, and also modeled the proteins before molecular docking, druglikeness, toxicity assessment, molecular dynamics simulation (MDS) and ligand-based pharmacophore modeling of the selected FDA-approved drugs. Our models for NP and HE had over 95% identity with templates 5EPW and 3CL5 respectively in the PDB database, with majority of the amino acids occupying acceptable regions. The active sites of the proteins contained conserved residues that were involved in ligand binding. Lopinavir and ritonavir possessed greater binding affinities for NP and HE relative to remdesivir, while levofloxacin and hydroxychloroquine were the most notable among the other classes of drugs. The Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of gyration (Rg), and binding energy values obtained after 100 ns of MDS revealed good stability of these compounds in the binding sites of the proteins while important pharmacophore features were also identified. The study showed that COVID-19 likely originated from bat, owing to the over 90% genomic similarity observed, and that lopinavir, levofloxacin, and hydroxychloroquine might serve as potential anti-COVID-19 lead molecules for additional optimization and drug development for the treatment of COVID-19.Communicated by Ramaswamy H. Sarma.

Transient Diffusive Interactions with a Protein Crowder Affect Aggregation Processes of Superoxide Dismutase 1 β-Barrel

Aggregate formation of superoxide dismutase 1 (SOD1) inside motor neurons is known as a major factor in onset of amyotrophic lateral sclerosis. The thermodynamic stability of the SOD1 β-barrel has been shown to decrease in crowded environments such as inside a cell, but it remains unclear how the thermodynamics of crowding-induced protein destabilization relate to SOD1 aggregation. Here we have examined the effects of a protein crowder, lysozyme, on fibril aggregate formation of the SOD1 β-barrel. We found that aggregate formation of SOD1 is decelerated even in mildly crowded solutions. Intriguingly, transient diffusive interactions with lysozyme do not significantly affect the static structure of the SOD1 β-barrel but stabilize an alternative excited "invisible" state. The net effect of crowding is to favor species off the aggregation pathway, thereby explaining the decelerated aggregation in the crowded environment. Our observations suggest that the intracellular environment may have a similar negative (inhibitory) effect on fibril formation of other amyloidogenic proteins in living cells. Deciphering how crowded intracellular environments affect aggregation and fibril formation of such disease-associated proteins will probably become central in understanding the exact role of aggregation in the etiology of these enigmatic diseases.

Conformational Strain Indicated by Ramachandran Angles for the Protein Backbone Is Only Weakly Related to the Flexibility

Studies on energy associated with free dipeptides have shown that conformers with unfavorable (ϕ,ψ) torsion angles have higher energy compared to conformers with favorable (ϕ,ψ) angles. It is expected that higher energy confers higher dynamics and flexibility to that part of the protein. Here, we explore a potential relationship between conformational strain in a residue due to unfavorable (ϕ,ψ) angles and its flexibility and dynamics in the context of protein structures. We compared flexibility of strained and relaxed residues, which are recognized based on outlier/allowed and favorable (ϕ,ψ) angles respectively, using normal-mode analysis (NMA). We also performed in-depth analysis on flexibility and dynamics at catalytic residues in protein kinases, which exhibit different strain status in different kinase structures using NMA and molecular dynamics simulations. We underline that strain of a residue, as defined by backbone torsion angles, is almost unrelated to the flexibility and dynamics associated with it. Even the overall trend observed among all high-resolution structures in which relaxed residues tend to have slightly higher flexibility than strained residues is counterintuitive. Consequently, we propose that identifying strained residues based on (ϕ,ψ) values is not an effective way to recognize energetic strain in protein structures.

Valine-279 Deletion-Mutation on Arginine Vasopressin Receptor 2 Causes Obstruction in G-Protein Binding Site: A Clinical Nephrogenic Diabetes Insipidus Case and Its Sub-Molecular Pathogenic Analysis

Congenital nephrogenic diabetes insipidus (CNDI) is a genetic disorder caused by mutations in arginine vasopressin receptor 2 (AVPR2) or aquaporin 2 genes, rendering collecting duct cells insensitive to the peptide hormone arginine vasopressin stimulation for water reabsorption. This study reports a first identified AVPR2 mutation in Taiwan and demonstrates our effort to understand the pathogenesis caused by applying computational structural analysis tools. The CNDI condition of an 8-month-old male patient was confirmed according to symptoms, family history, and DNA sequence analysis. The patient was identified to have a valine 279 deletion-mutation in the AVPR2 gene. Cellular experiments using mutant protein transfected cells revealed that mutated AVPR2 is expressed successfully in cells and localized on cell surfaces. We further analyzed the pathogenesis of the mutation at sub-molecular levels via long-term molecular dynamics (MD) simulations and structural analysis. The MD simulations showed while the structure of the extracellular ligand-binding domain remains unchanged, the mutation alters the direction of dynamic motion of AVPR2 transmembrane helix 6 toward the center of the G-protein binding site, obstructing the binding of G-protein, thus likely disabling downstream signaling. This study demonstrated that the computational approaches can be powerful tools for obtaining valuable information on the pathogenesis induced by mutations in G-protein-coupled receptors. These methods can also be helpful in providing clues on potential therapeutic strategies for CNDI.

Exploring the Cold-Adaptation Mechanism of Serine Hydroxymethyltransferase by Comparative Molecular Dynamics Simulations

Cold-adapted enzymes feature a lower thermostability and higher catalytic activity compared to their warm-active homologues, which are considered as a consequence of increased flexibility of their molecular structures. The complexity of the (thermo)stability-flexibility-activity relationship makes it difficult to define the strategies and formulate a general theory for enzyme cold adaptation. Here, the psychrophilic serine hydroxymethyltransferase (pSHMT) from Psychromonas ingrahamii and its mesophilic counterpart, mSHMT from Escherichia coli, were subjected to μs-scale multiple-replica molecular dynamics (MD) simulations to explore the cold-adaptation mechanism of the dimeric SHMT. The comparative analyses of MD trajectories reveal that pSHMT exhibits larger structural fluctuations and inter-monomer positional movements, a higher global flexibility, and considerably enhanced local flexibility involving the surface loops and active sites. The largest-amplitude motion mode of pSHMT describes the trends of inter-monomer dissociation and enlargement of the active-site cavity, whereas that of mSHMT characterizes the opposite trends. Based on the comparison of the calculated structural parameters and constructed free energy landscapes (FELs) between the two enzymes, we discuss in-depth the physicochemical principles underlying the stability-flexibility-activity relationships and conclude that (i) pSHMT adopts the global-flexibility mechanism to adapt to the cold environment and, (ii) optimizing the protein-solvent interactions and loosening the inter-monomer association are the main strategies for pSHMT to enhance its flexibility.

Weak spots inhibition in the Mycobacterium tuberculosis antigen 85C target for antitubercular drug design through selective irreversible covalent inhibitor-SER124

Mycobacterium tuberculosis (Mtb) encoded secreted antigen 85 enzymes (Ag85A/Ag85B/Ag85C) play that critical roles in the virulence, survival and drug-resistant TB of the pathogen. Ag85 proteins are potential antitubercular drug targets because they are essential in the catalytic synthesis of trehalose moieties and mycolic acid attachment to the Mtb cell wall. Recently, experimental protocols led to the discovery of a selective covalent Ag85 inhibitor, β-isomer monocyclic enolphosphorus Cycliphostin (CyC_8β) compound, which targets the Ag85 serine 124 to exhibit a promising therapeutic activity. For the first time, our study unravelled the structural features among Mtb Ag85C homologs and motions and dynamics of Ag85C when the CyC_8β bound covalently and in open model conformations to the protein using bioinformatics tools and integrated Molecular dynamics simulations. Comparative Ag85C sequence analysis revealed conserved regions; 70% active site, 90% Adeniyi loop L1 and 50% loop L2, which acts as a switch between open and closed conformations. The average C-α atoms RMSD (2.05 Å) and RMSF (0.9 Å) revealed instability and high induced flexibility in the CyC_8β covalent-bound compared to the apo and open model systems, which displayed more stability and lower fluctuations. DSSP showed structural transitions of α-helices to bend and loops to 3₁₀-helices in the bound systems. SASA of CyC_8β covalent bound showed active site hydrophobic residues exposure to huge solvent. Therefore, these findings present the potential opportunity hotspots in Ag85C protein that would aid the structure-based design of novel chemical entities capable of resulting in potent antitubercular drugs.Communicated by Ramaswamy H. Sarma.

Higher binding affinity of furin for SARS-CoV-2 spike (S) protein D614G mutant could be associated with higher SARS-CoV-2 infectivity

Objective

The coronavirus disease 2019 (COVID-19) pandemic has caused an exponential rise in death rates and hospitalizations. The aim of this study was to characterize the D614G substitution in the severe acute respiratory syndome coronavirus 2 (SARS-CoV-2) spike glycoprotein (S protein), which may affect viral infectivity.

Methods

The effect of D614G substitution on the structure and thermodynamic stability of the S protein was analyzed with use of DynaMut and SCooP. HDOCK and PRODIGY were used to model furin protease binding to the S protein RRAR cleavage site and calculate binding affinities. Molecular dynamics simulations were used to predict the S protein apo structure, the S protein–furin complex structure, and the free binding energy of the complex.

Results

The D614G substitution in the G clade of SARS-CoV-2 strains introduced structural mobility and decreased the thermal stability of the S protein (ΔΔG = −0.086 kcal mol⁻¹). The substitution resulted in stronger binding affinity (K_d = 1.6 × 10⁻⁸) for furin, which may enhance S protein cleavage. The results were corroborated by molecular dynamics simulations demonstrating higher binding energy of furin and the S protein D614G mutant (−61.9 kcal mol⁻¹ compared with −56.78 kcal mol⁻¹ for wild-type S protein).

Conclusions

The D614G substitution in the G clade induced flexibility of the S protein, resulting in increased furin binding, which may enhance S protein cleavage and infiltration of host cells. Therefore, the SARS-CoV-2 D614G substitution may result in a more virulent strain.

Signaling Mechanism of Phytochromes in Solution

Phytochrome proteins guide the red/far-red photoresponse of plants, fungi, and bacteria. Crystal structures suggest that the mechanism of signal transduction from the chromophore to the output domains involves refolding of the so-called PHY tongue. It is currently not clear how the two other notable structural features of the phytochrome superfamily, the so-called helical spine and a knot in the peptide chain, are involved in photoconversion. Here, we present solution NMR data of the complete photosensory core module from Deinococcus radiodurans. Photoswitching between the resting and the active states induces changes in amide chemical shifts, residual dipolar couplings, and relaxation dynamics. All observables indicate a photoinduced structural change in the knot region and lower part of the helical spine. This implies that a conformational signal is transduced from the chromophore to the helical spine through the PAS and GAF domains. The discovered pathway underpins functional studies of plant phytochromes and may explain photosensing by phytochromes under biological conditions.

Control of electron transfer by protein dynamics in photosynthetic reaction centers

Trehalose and glycerol are low molecular mass sugars/polyols that have found widespread use in the protection of native protein states, in both short- and long-term storage of biological materials, and as a means of understanding protein dynamics. These myriad uses are often attributed to their ability to form an amorphous glassy matrix. In glycerol, the glass is formed only at cryogenic temperatures, while in trehalose, the glass is formed at room temperature, but only upon dehydration of the sample. While much work has been carried out to elucidate a mechanistic view of how each of these matrices interact with proteins to provide stability, rarely have the effects of these two independent systems been directly compared to each other. This review aims to compile decades of research on how different glassy matrices affect two types of photosynthetic proteins: (i) the Type II bacterial reaction center from Rhodobacter sphaeroides and (ii) the Type I Photosystem I reaction center from cyanobacteria. By comparing aggregate data on electron transfer, protein structure, and protein dynamics, it appears that the effects of these two distinct matrices are remarkably similar. Both seem to cause a "tightening" of the solvation shell when in a glassy state, resulting in severely restricted conformational mobility of the protein and associated water molecules. Thus, trehalose appears to be able to mimic, at room temperature, nearly all of the effects on protein dynamics observed in low temperature glycerol glasses.

Insight into the structural and functional analysis of the impact of missense mutation on cytochrome P450 oxidoreductase

Cytochrome P450 oxidoreductase (POR) is a steroidogenic and drug-metabolizing enzyme. It helps in the NADPH dependent transfer of electrons to cytochrome P450 (CYP) enzymes for their biological activity. In this study, we employed integrative computational approaches to decipher the impact of proline to leucine missense mutation at position 384 (P384L) in the connecting/hinge domain region which is essential for the catalytic activity of POR. Analysis of protein stability using DUET, MUpro, CUPSAT, I-Mutant2.0, iStable and SAAFEC servers predicted that mutation might alter the structural stability of POR. The significant conformational changes induced by the mutation to the POR structure were analyzed by long-range molecular dynamics simulation. The results revealed that missense mutation decreased the conformational stability of POR as compared to wild type (WT). The PCA based FEL analysis described the mutant-specific conformational alterations and dominant motions essential for the biological activity of POR. The LIGPLOT interaction analysis showed the different binding architecture of FMN, FAD, and NADPH as a result of mutation. The increased number of hydrogen bonds in the FEL conformation of WT proved the strong binding of cofactors in the binding pocket as compared to the mutant. The porcupine plot analysis associated with cross-correlation analysis depicted the high-intensity flexible motion exhibited by functionally important FAD and NADPH binding domain regions. The computational findings unravel the impact of mutation at the structural level, which could be helpful in understanding the molecular mechanism of drug metabolism.

Predictive modeling by deep learning, virtual screening and molecular dynamics study of natural compounds against SARS-CoV-2 main protease

The whole world is facing a great challenging time due to Coronavirus disease (COVID-19) caused by SARS-CoV-2. Globally, more than 14.6 M people have been diagnosed and more than 595 K deaths are reported. Currently, no effective vaccine or drugs are available to combat COVID-19. Therefore, the whole world is looking for new drug candidates that can treat the COVID-19. In this study, we conducted a virtual screening of natural compounds using a deep-learning method. A deep-learning algorithm was used for the predictive modeling of a CHEMBL3927 dataset of inhibitors of Main protease (Mpro). Several predictive models were developed and evaluated based on R², MAE MSE, RMSE, and Loss. The best model with R²=0.83, MAE = 1.06, MSE = 1.5, RMSE = 1.2, and loss = 1.5 was deployed on the Selleck database containing 1611 natural compounds for virtual screening. The model predicted 500 hits showing the value score between 6.9 and 3.8. The screened compounds were further enriched by molecular docking resulting in 39 compounds based on comparison with the reference (X77). Out of them, only four compounds were found to be drug-like and three were non-toxic. The complexes of compounds and Mpro were finally subjected to Molecular dynamic (MD) simulation for 100 ns. The MMPBSA result showed that two compounds Palmatine and Sauchinone formed very stable complex with Mpro and had free energy of −71.47 kJ mol⁻¹ and −71.68 kJ mol⁻¹ respectively as compared to X77 (−69.58 kJ mol⁻¹). From this study, we can suggest that the identified natural compounds may be considered for therapeutic development against the SARS-CoV-2.

Communicated by Ramaswamy H. Sarma

Structural analysis of ACE2 variant N720D demonstrates a higher binding affinity to TMPRSS2

Aims

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel member of the betacoronaviruses family affecting the lower respiratory tract mainly through binding to angiotensin converting enzyme 2 (ACE2) via its S-protein. Genetic analysis of (ACE2) gene revealed several variants that have been suggested to regulate the interaction with S protein. This study investigates the N720D variant, positioned in the collectrin-like domain (CLD) at proximity to type II transmembrane serine protease (TMPRSS2) cleavage site.

Main methods

The effect of N720D variant on ACE2 structure and thermodynamic stability was studied by DynaMut. HDOCK was utilised to model TMPRSS2 protease binding to ACE2 WT and D720 variant cleavage site. PRODIGY was used to calculate binding affinities and MD simulation tools calculated the at 100 ns for ACE2 apo structure and the ACE2-TMPRSS2 complex.

Key findings

The N720D variant is a more dynamic structure with a free energy change (ΔΔG): −0.470 kcal/mol. As such, introducing a tighter binding affinity of K_d = 3.2 × 10⁻¹⁰ M between TMPRSS2 and N720D variant. RMSD, RMSF calculations showed the N720D variant is less stable, however, RMSF values of the D720-TMPRSS2 complex reflected a slower dynamic motion.

Significance

The hotspot N720D variant in the CLD of ACE2 affected the stability and flexibility of ACE2 by increasing the level of motion in the loop region, resulting in a more favourable site for TMPRSS2 binding and cleavage. Consequently, this would facilitate S-protein binding and can potentially increase viral entry highlighting the importance of variants affecting the ACE2-TMPRSS2 complex.

Homodimerization of a glycoside hydrolase family GH1 β-glucosidase suggests distinct activity of enzyme different states

In this work, we investigated how activity and oligomeric state are related in a purified GH1 β-glucosidase from Spodoptera frugiperda (Sfβgly). Gel filtration chromatography coupled to a multiple angle light scattering detector allowed separation of the homodimer and monomer states and determination of the dimer dissociation constant (K_D ), which was in the micromolar range. Enzyme kinetic parameters showed that the dimer is on average 2.5-fold more active. Later, we evaluated the kinetics of homodimerization, scanning the changes in the Sfβgly intrinsic fluorescence over time when the dimer dissociates into the monomer after a large dilution. We described how the rate constant of monomerization (k_off ) is affected by temperature, revealing the enthalpic and entropic contributions to the process. We also evaluated how the rate constant (k_obs ) by which equilibrium is reached after dimer dilution behaves when varying the initial Sfβgly concentration. These data indicated that Sfβgly dimerizes through the conformational selection mechanism, in which the monomer undergoes a conformational exchange and then binds to a similar monomer, forming a more active homodimer. Finally, we noted that conformational selection reports and experiments usually rely on a ligand whose concentration is in excess, but for homodimerization, this approach does not hold. Hence, since our approach overcomes this limitation, this study not only is a new contribution to the comprehension of GH1 β-glucosidases, but it can also help to elucidate protein interaction pathways.

A beginner's guide to molecular dynamics simulations and the identification of cross-correlation networks for enzyme engineering

The functional properties of proteins are decided not only by their relatively rigid overall structures, but even more importantly, by their dynamic properties. In a protein, some regions of structure exhibit highly correlated or anti-correlated motions with others, some are highly dynamic but uncorrelated, while other regions are relatively static. The residues with correlated or anti-correlated motions can form a so-called dynamic cross-correlation network, through which information can be transmitted. Such networks have been shown to be critical to allosteric transitions, and ligand binding, and have also been shown to be able to mediate epistatic interactions between mutations. As a result, they are likely to play a significant role in the development of new enzyme engineering strategies. In this chapter, protocols are provided for the assessment of dynamic cross-correlation networks, and for their application in protein engineering. Transketolase from E. coli is used as a model and the software GROMACS is applied for carrying out MD simulations to generate trajectories containing structural ensembles. The trajectory is then used for a dynamic cross correlation analysis using the R package, Bio3D. A matrix of all atom-wise cross-correlation coefficients is finally obtained, which can be displayed in a graphical representation termed a dynamical cross-correlation matrix.

New Insight into Mechanisms of Protein Adaptation to High Temperatures: A Comparative Molecular Dynamics Simulation Study of Thermophilic and Mesophilic Subtilisin-Like Serine Proteases

In high-temperature environments, thermophilic proteins must possess enhanced thermal stability in order to maintain their normal biological functions. However, the physicochemical basis of the structural stability of thermophilic proteins at high temperatures remains elusive. In this study, we performed comparative molecular dynamics simulations on thermophilic serine protease (THM) and its homologous mesophilic counterpart (PRK). The comparative analyses of dynamic structural and geometrical properties suggested that THM adopted a more compact conformation and exhibited more intramolecular interactions and lower global flexibility than PRK, which could be in favor of its thermal stability in high-temperature environments. Comparison between protein solvent interactions and the hydrophobicity of these two forms of serine proteases showed that THM had more burial of nonpolar areas, and less protein solvent hydrogen bonds (HBs), indicating that solvent entropy maximization and mobility may play a significant role in THM's adaption to high temperature environments. The constructed funnel-like free energy landscape (FEL) revealed that, in comparison to PRK, THM had a relatively flat and narrow free energy surface, and a lower minimum free energy level, suggesting that the thermophilic form had lower conformational diversity and flexibility. Combining the FEL theory and our simulation results, we conclude that the solvent (entropy force) plays a significant role in protein adaption at high temperatures.

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

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.

Quantitative Characterization of Binding Pockets and Binding Complementarity by Means of Zernike Descriptors

In this work, we describe the application of the Zernike formalism to quantitatively characterize the binding pockets of two sets of biologically relevant systems. Such an approach, when applied to molecular dynamics trajectories, is able to pinpoint the subtle differences between very similar molecular regions and their impact on the local propensity to ligand binding, allowing us to quantify such differences. The statistical robustness of our procedure suggests that it is very suitable to describe protein binding sites and protein-ligand interactions within a rigorous and well-defined framework.

The Arabidopsis (ASHH2) CW domain binds monomethylated K4 of the histone H3 tail through conformational selection

Chromatin post-translational modifications are thought to be important for epigenetic effects on gene expression. Methylation of histone N-terminal tail lysine residues constitutes one of many such modifications, executed by families of histone lysine methyltransferase (HKMTase). One such protein is ASHH2 from the flowering plant Arabidopsis thaliana, equipped with the interaction domain, CW, and the HKMTase domain, SET. The CW domain of ASHH2 is a selective binder of monomethylation at lysine 4 on histone H3 (H3K4me1) and likely helps the enzyme dock correctly onto chromatin sites. The study of CW and related interaction domains has so far been emphasizing lock-key models, missing important aspects of histone-tail CW interactions. We here present an analysis of the ASHH2 CW-H3K4me1 complex using NMR and molecular dynamics, as well as mutation and affinity studies of flexible coils. β-augmentation and rearrangement of coils coincide with changes in the flexibility of the complex, in particular the η1, η3 and C-terminal coils, but also in the β1 and β2 strands and the C-terminal part of the ligand. Furthermore, we show that mutating residues with outlier dynamic behaviour affect the complex binding affinity despite these not being in direct contact with the ligand. Overall, the binding process is consistent with conformational selection. We propose that this binding mechanism presents an advantage when searching for the correct post-translational modification state among the highly modified and flexible histone tails, and also that the binding shifts the catalytic SET domain towards the nucleosome. DATABASES: Structural data are available in the PDB database under the accession code 6QXZ. Resonance assignments for CW42 in its apo- and holo-forms are available in the BMRB database under the accession code 27251.

Simulated Epidemics in 3D Protein Structures to Detect Functional Properties

The outcome of an epidemic is closely related to the network of interactions between individuals. Likewise, protein functions depend on the 3D arrangement of their residues and the underlying energetic interaction network. Borrowing ideas from the theoretical framework that has been developed to address the spreading of real diseases, we study for the first time the diffusion of a fictitious epidemic inside the protein nonbonded interaction network, aiming to study network features and properties. Our approach allows us to probe the overall stability and the capability of propagating information in complex 3D structures, proving to be very efficient in addressing different problems, from the assessment of thermal stability to the identification of functional sites.

How good are comparative models in the understanding of protein dynamics?

The 3D structure of a protein is essential to understand protein dynamics. If experimentally determined structure is unavailable, comparative models could be used to infer dynamics. However, the effectiveness of comparative models, compared to experimental structures, in inferring dynamics is not clear. To address this, we compared dynamics features of ~800 comparative models with their crystal structures using normal mode analysis. Average similarity in magnitude, direction, and correlation of residue motions is >0.8 (where value 1 is identical) indicating that the dynamics of models and crystal structures are highly similar. Accuracy of 3D structure and dynamics is significantly higher for models built on multiple and/or high sequence identity templates (>40%). Three-dimensional (3D) structure and residue fluctuations of models are closer to that of crystal structures than to templates (TM score 0.9 vs 0.7 and square inner product 0.92 vs 0.88). Furthermore, long-range molecular dynamics simulations on comparative models of RNase 1 and Angiogenin showed significant differences in the conformational sampling of conserved active-site residues that characterize differences in their activity levels. Similar analyses on two EGFR kinase variant models highlight the effect of mutations on the functional state-specific αC helix motions and these results corroborate with the previous experimental observations. Thus, our study adds confidence to the use of comparative models in understanding protein dynamics.

Dynamic signature of ligand binding over a protein surface

We study the motion of Zn^{2+} in the presence of ubiquitin by all-atom molecular-dynamics simulations. We observe that unlike normal diffusive liquid, metal ions show an exponential tail in the self-van Hove function (self-vHf). Moreover, the metal ions are trapped strongly by acidic residues which form a binding pocket over the protein surface. The exponential tail disappears by mutation of trapping residues, suggesting that the tail appears due to trapped motion of the ions. The mean-squared displacements, however, in all the cases show linear dependence on time. Our model establishes that ligand binding generically results in an exponential tail of self-vHf. The self-vHf may give an approach to find binding pockets over a protein surface.

Role of hydration water in the onset of protein structural dynamics

Proteins are the molecular workhorses in a living organism. Their 3D structures are animated by a multitude of equilibrium fluctuations and specific out-of-equilibrium motions that are required for proteins to be biologically active. When studied as a function of temperature, functionally relevant dynamics are observed at and above the so-called protein dynamical transition (~240 K) in hydrated, but not in dry proteins. In this review we present and discuss the main experimental and computational results that provided evidence for the dynamical transition, with a focus on the role of hydration water dynamics in sustaining functional protein dynamics. The coupling and mutual influence of hydration water dynamics and protein dynamics are discussed and the hypotheses illustrated that have been put forward to explain the physical origin of their onsets.