Mechanism for the Unfolding of the TOP7 Protein in Steered Molecular Dynamics Simulations as Revealed by Mutual Information Analysis.

We employed mutual information (MI) analysis to detect motions affecting the mechanical resistance of the human-engineered protein Top7. The results are based on the MI analysis of pair contact correlations measured in steered molecular dynamics (SMD) trajectories and their statistical dependence on global unfolding. This study is the first application of the MI analysis to SMD forced unfolding, and we furnish specific SMD recommendations for the utility of parameters and options in the TimeScapes package. The MI analysis provided a global overview of the effect of perturbation on the stability of the protein. We also employed a more conventional trajectory analysis for a detailed description of the mechanical resistance of Top7. Specifically, we investigated 1) the hydropathy of the interactions of structural segments, 2) the H2O concentration near residues relevant for unfolding, and 3) the changing hydrogen bonding patterns and main chain dihedral angles. The results show that the application of MI in the study of protein mechanical resistance can be useful for the engineering of more resistant mutants when combined with conventional analysis. We propose a novel mutation design based on the hydropathy of residues that would stabilize the unfolding region by mimicking its more stable symmetry mate. The proposed design process does not involve the introduction of covalent crosslinks, so it has the potential to preserve the conformational space and unfolding pathway of the protein.

Recognition of Potential COVID-19 Drug Treatments through the Study of Existing Protein-Drug and Protein-Protein Structures: An Analysis of Kinetically Active Residues.

We report the results of our in silico study of approved drugs as potential treatments for COVID-19. The study is based on the analysis of normal modes of proteins. The drugs studied include chloroquine, ivermectin, remdesivir, sofosbuvir, boceprevir, and α-difluoromethylornithine (DMFO). We applied the tools we developed and standard tools used in the structural biology community. Our results indicate that small molecules selectively bind to stable, kinetically active residues and residues adjoining them on the surface of proteins and inside protein pockets, and that some prefer hydrophobic sites over other active sites. Our approach is not restricted to viruses and can facilitate rational drug design, as well as improve our understanding of molecular interactions, in general.

Sensitive effect of linker histone binding mode and subtype on chromatin condensation.

The complex role of linker histone (LH) on chromatin compaction regulation has been highlighted by recent discoveries of the effect of LH binding variability and isoforms on genome structure and function. Here we examine the effect of two LH variants and variable binding modes on the structure of chromatin fibers. Our mesoscale modeling considers oligonucleosomes with H1C and H1E, bound in three different on and off-dyad modes, and spanning different LH densities (0.5-1.6 per nucleosome), over a wide range of physiologically relevant nucleosome repeat lengths (NRLs). Our studies reveal an LH-variant and binding-mode dependent heterogeneous ensemble of fiber structures with variable packing ratios, sedimentation coefficients, and persistence lengths. For maximal compaction, besides dominantly interacting with parental DNA, LHs must have strong interactions with nonparental DNA and promote tail/nonparental core interactions. An off-dyad binding of H1E enables both; others compromise compaction for bendability. We also find that an increase of LH density beyond 1 is best accommodated in chromatosomes with one on-dyad and one off-dyad LH. We suggest that variable LH binding modes and concentrations are advantageous, allowing tunable levels of chromatin condensation and DNA accessibility/interactions. Thus, LHs add another level of epigenetic regulation of chromatin.

Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues.

Physical interactions between proteins are often difficult to decipher. The aim of this paper is to present an algorithm that is designed to recognize binding patches and supporting structural scaffolds of interacting heterodimer proteins using the Gaussian Network Model (GNM). The recognition is based on the (self) adjustable identification of kinetically hot residues and their connection to possible binding scaffolds. The kinetically hot residues are residues with the lowest entropy, i.e., the highest contribution to the weighted sum of the fastest modes per chain extracted via GNM. The algorithm adjusts the number of fast modes in the GNM's weighted sum calculation using the ratio of predicted and expected numbers of target residues (contact and the neighboring first-layer residues). This approach produces very good results when applied to dimers with high protein sequence length ratios. The protocol's ability to recognize near native decoys was compared to the ability of the residue-level statistical potential of Lu and Skolnick using the Sternberg and Vakser decoy dimers sets. The statistical potential produced better overall results, but in a number of cases its predicting ability was comparable, or even inferior, to the prediction ability of the adjustable GNM approach. The results presented in this paper suggest that in heterodimers at least one protein has interacting scaffold determined by the immovable, kinetically hot residues. In many cases, interacting proteins (especially if being of noticeably different sizes) either behave as a rigid lock and key or, presumably, exhibit the opposite dynamic behavior. While the binding surface of one protein is rigid and stable, its partner's interacting scaffold is more flexible and adaptable.

Dependence of the Linker Histone and Chromatin Condensation on the Nucleosome Environment.

The linker histone (LH), an auxiliary protein that can bind to chromatin and interact with the linker DNA to form stem motifs, is a key element of chromatin compaction. By affecting the chromatin condensation level, it also plays an active role in gene expression. However, the presence and variable concentration of LH in chromatin fibers with different DNA linker lengths indicate that its folding and condensation are highly adaptable and dependent on the immediate nucleosome environment. Recent experimental studies revealed that the behavior of LH in mononucleosomes markedly differs from that in small nucleosome arrays, but the associated mechanism is unknown. Here we report a structural analysis of the behavior of LH in mononucleosomes and oligonucleosomes (2-6 nucleosomes) using mesoscale chromatin simulations. We show that the adapted stem configuration heavily depends on the strength of electrostatic interactions between LH and its parental DNA linkers, and that those interactions tend to be asymmetric in small oligonucleosome systems. Namely, LH in oligonucleosomes dominantly interacts with one DNA linker only, as opposed to mononucleosomes where LH has similar interactions with both linkers and forms a highly stable nucleosome stem. Although we show that the LH condensation depends sensitively on the electrostatic interactions with entering and exiting DNA linkers, other interactions, especially by nonparental cores and nonparental linkers, modulate the structural condensation by softening LH and thus making oligonucleosomes more flexible, in comparison to to mono- and dinucleosomes. We also find that the overall LH/chromatin interactions sensitively depend on the linker length because the linker length determines the maximal nucleosome stem length. For mononucleosomes with DNA linkers shorter than LH, LH condenses fully, while for DNA linkers comparable or longer than LH, the LH extension in mononucleosomes strongly follows the length of DNA linkers, unhampered by neighboring linker histones. Thus, LH is more condensed for mononucleosomes with short linkers, compared to oligonucleosomes, and its orientation is variable and highly environment-dependent. More generally, the work underscores the agility of LH whose folding dynamics critically controls genomic packaging and gene expression.

Parallel targeted next generation sequencing of childhood and adult acute myeloid leukemia patients reveals uniform genomic profile of the disease.

The age-specific differences in the genetic mechanisms of myeloid leukemogenesis have been observed and studied previously. However, NGS technology has provided a possibility to obtain a large amount of mutation data. We analyzed DNA samples from 20 childhood (cAML) and 20 adult AML (aAML) patients, using NGS targeted sequencing. The average coverage of high-quality sequences was 2981 × per amplicon. A total of 412 (207 cAML, 205 aAML) variants in the coding regions were detected; out of which, only 122 (62 cAML and 60 aAML) were potentially protein-changing. Our results confirmed that AML contains small number of genetic alterations (median 3 mutations/patient in both groups). The prevalence of the most frequent single gene AML associated mutations differed in cAML and aAML patient cohorts: IDH1 (0 % cAML, 5 % aAML), IDH2 (0 % cAML, 10 % aAML), NPM1 (10 % cAML, 35 % aAML). Additionally, potentially protein-changing variants were found in tyrosine kinase genes or genes encoding tyrosine kinase associated proteins (JAK3, ABL1, GNAQ, and EGFR) in cAML, while among aAML, the prevalence is directed towards variants in the methylation and histone modifying genes (IDH1, IDH2, and SMARCB1). Besides uniform genomic profile of AML, specific genetic characteristic was exclusively detected in cAML and aAML.

Computational strategies to address chromatin structure problems.

While the genetic information is contained in double helical DNA, gene expression is a complex multilevel process that involves various functional units, from nucleosomes to fully formed chromatin fibers accompanied by a host of various chromatin binding enzymes. The chromatin fiber is a polymer composed of histone protein complexes upon which DNA wraps, like yarn upon many spools. The nature of chromatin structure has been an open question since the beginning of modern molecular biology. Many experiments have shown that the chromatin fiber is a highly dynamic entity with pronounced structural diversity that includes properties of idealized zig-zag and solenoid models, as well as other motifs. This diversity can produce a high packing ratio and thus inhibit access to a majority of the wound DNA. Despite much research, chromatin's dynamic structure has not yet been fully described. Long stretches of chromatin fibers exhibit puzzling dynamic behavior that requires interpretation in the light of gene expression patterns in various tissue and organisms. The properties of chromatin fiber can be investigated with experimental techniques, like in vitro biochemistry, in vivo imagining, and high-throughput chromosome capture technology. Those techniques provide useful insights into the fiber's structure and dynamics, but they are limited in resolution and scope, especially regarding compact fibers and chromosomes in the cellular milieu. Complementary but specialized modeling techniques are needed to handle large floppy polymers such as the chromatin fiber. In this review, we discuss current approaches in the chromatin structure field with an emphasis on modeling, such as molecular dynamics and coarse-grained computational approaches. Combinations of these computational techniques complement experiments and address many relevant biological problems, as we will illustrate with special focus on epigenetic modulation of chromatin structure.

Gene Mutation Profiles in Primary Diffuse Large B Cell Lymphoma of Central Nervous System: Next Generation Sequencing Analyses.

The existence of a potential primary central nervous system lymphoma-specific genomic signature that differs from the systemic form of diffuse large B cell lymphoma (DLBCL) has been suggested, but is still controversial. We investigated 19 patients with primary DLBCL of central nervous system (DLBCL CNS) using the TruSeq Amplicon Cancer Panel (TSACP) for 48 cancer-related genes. Next generation sequencing (NGS) analyses have revealed that over 80% of potentially protein-changing mutations were located in eight genes (CTNNB1, PIK3CA, PTEN, ATM, KRAS, PTPN11, TP53 and JAK3), pointing to the potential role of these genes in lymphomagenesis. TP53 was the only gene harboring mutations in all 19 patients. In addition, the presence of mutated TP53 and ATM genes correlated with a higher total number of mutations in other analyzed genes. Furthermore, the presence of mutated ATM correlated with poorer event-free survival (EFS) (p = 0.036). The presence of the mutated SMO gene correlated with earlier disease relapse (p = 0.023), inferior event-free survival (p = 0.011) and overall survival (OS) (p = 0.017), while mutations in the PTEN gene were associated with inferior OS (p = 0.048). Our findings suggest that the TP53 and ATM genes could be involved in the molecular pathophysiology of primary DLBCL CNS, whereas mutations in the PTEN and SMO genes could affect survival regardless of the initial treatment approach.

On the improvement of free-energy calculation from steered molecular dynamics simulations using adaptive stochastic perturbation protocols.

The potential of mean force (PMF) calculation in single molecule manipulation experiments performed via the steered molecular dynamics (SMD) technique is a computationally very demanding task because the analyzed system has to be perturbed very slowly to be kept close to equilibrium. Faster perturbations, far from equilibrium, increase dissipation and move the average work away from the underlying free energy profile, and thus introduce a bias into the PMF estimate. The Jarzynski equality offers a way to overcome the bias problem by being able to produce an exact estimate of the free energy difference, regardless of the perturbation regime. However, with a limited number of samples and high dissipation the Jarzynski equality also introduces a bias. In our previous work, based on the Brownian motion formalism, we introduced three stochastic perturbation protocols aimed at improving the PMF calculation with the Jarzynski equality in single molecule manipulation experiments and analogous computer simulations. This paper describes the PMF reconstruction results based on full-atom molecular dynamics simulations, obtained with those three protocols. We also want to show that the protocols are applicable with the second-order cumulant expansion formula. Our protocols offer a very noticeable improvement over the simple constant velocity pulling. They are able to produce an acceptable estimate of PMF with a significantly reduced bias, even with very fast perturbation regimes. Therefore, the protocols can be adopted as practical and efficient tools for the analysis of mechanical properties of biological molecules.

Pulling-spring modulation as a method for improving the potential-of-mean-force reconstruction in single-molecule manipulation experiments.

The free-energy calculation is usually limited to close to equilibrium perturbation regimes because faster perturbations introduce a bias into the estimate. The Jarzynski equality offers a solution to this problem by directly connecting the free-energy difference and the external work, regardless how far from equilibrium that work may be. However, a limited sampling coupled to the fast perturbation introduces a slowly converging bias into the Jarzynski free-energy estimate also. In this paper we present two perturbation protocols devised with the intention to overcome the convergence issues of the Jarzynski-based potential of mean force estimation in the single-molecule, constant velocity manipulation experiments. The protocols are designed to improve the convergence issues by increasing the variation of the external work through the modulation of the spring used to pull a molecule. Of the two methods, the one which continuously changes the amplitude of the spring stiffness offers an excellent reconstruction and requires less than one tenth of the samples required by the normal, constant spring pulling to produce the same quality of the reconstruction.

Efficient free-energy-profile reconstruction using adaptive stochastic perturbation protocols.

The Jarzynski-relation-based free-energy calculation is limited by the very slow convergence of the estimate when dissipation is high. We present two novel perturbation protocols able to significantly improve the quality of the potential of mean force (PMF) calculation by reducing the estimate's bias without increasing the number of samples. The protocols are directly applicable with both numerical simulations and real-life experiments. The improvement is achieved through the intentional but controlled widening of the distribution of the external work used to perturb a given system. Our protocols can achieve the same accuracy in PMF estimation as the normal constant velocity pulling with less than 10% of the required samples.

Ligand efficiency indices for an effective mapping of chemico-biological space: the concept of an atlas-like representation.

We propose a numerical framework that permits an effective atlas-like representation of chemico-biological space based on a series of Cartesian planes mapping the ligands with the corresponding targets connected by an affinity parameter (K(i) or related). The numerical framework is derived from the concept of ligand efficiency indices, which provide a natural coordinate system combining the potency toward the target (biological space) with the physicochemical properties of the ligand (chemical space). This framework facilitates navigation in the multidimensional drug discovery space using map-like representations based on pairs of combined variables related to the efficiency of the ligands per Dalton (molecular weight or number of non-hydrogen atoms) and per unit of polar surface area (or number of polar atoms).

Modeling studies of chromatin fiber structure as a function of DNA linker length.

Chromatin fibers encountered in various species and tissues are characterized by different nucleosome repeat lengths (NRLs) of the linker DNA connecting the nucleosomes. While single cellular organisms and rapidly growing cells with high protein production have short NRL ranging from 160 to 189 bp, mature cells usually have longer NRLs ranging between 190 and 220 bp. Recently, various experimental studies have examined the effect of NRL on the internal organization of chromatin fiber. Here, we investigate by mesoscale modeling of oligonucleosomes the folding patterns for different NRL, with and without linker histone (LH), under typical monovalent salt conditions using both one-start solenoid and two-start zigzag starting configurations. We find that short to medium NRL chromatin fibers (173 to 209 bp) with LH condense into zigzag structures and that solenoid-like features are viable only for longer NRLs (226 bp). We suggest that medium NRLs are more advantageous for packing and various levels of chromatin compaction throughout the cell cycle than their shortest and longest brethren; the former (short NRLs) fold into narrow fibers, while the latter (long NRLs) arrays do not easily lead to high packing ratios due to possible linker DNA bending. Moreover, we show that the LH has a small effect on the condensation of short-NRL arrays but has an important condensation effect on medium-NRL arrays, which have linker lengths similar to the LH lengths. Finally, we suggest that the medium-NRL species, with densely packed fiber arrangements, may be advantageous for epigenetic control because their histone tail modifications can have a greater effect compared to other fibers due to their more extensive nucleosome interaction network.

Mechanical signaling on the single protein level studied using steered molecular dynamics.

Efficient communication between the cell and its external environment is of the utmost importance to the function of multicellular organisms. While signaling events can be generally characterized as information exchange by means of controlled energy conversion, research efforts have hitherto mainly been concerned with mechanisms involving chemical and electrical energy transfer. Here, we review recent computational efforts addressing the function of mechanical force in signal transduction. Specifically, we focus on the role of steered molecular dynamics (SMD) simulations in providing details at the atomic level on a group of protein domains, which play a fundamental role in signal exchange by responding properly to mechanical strain. We start by giving a brief introduction to the SMD technique and general properties of mechanically stable protein folds, followed by specific examples illustrating three general regimes of signal transfer utilizing mechanical energy: purely mechanical, mechanical to chemical, and chemical to mechanical. Whenever possible the physiological importance of the example at hand is stressed to highlight the diversity of the processes in which mechanical signaling plays a key role. We also provide an overview of future challenges and perspectives for this rapidly developing field.

Mesoscale simulations of two nucleosome-repeat length oligonucleosomes.

The compaction of chromatin, accessed through coarse-grained modeling and simulation, reveals different folding patterns as a function of the nucleosome repeat length (NRL), the presence of the linker histone, and the ionic strength. Our results indicate that the linker histone has negligible influence on short NRL fibers, whereas for longer NRL fibers it works like, and in tandem with, concentrated positive counterions to condense the chromatin fiber. Longer NRL fibers also exhibit structural heterogeneity, with solenoid-like conformations viable in addition to irregular zigzags. These features of chromatin and associated internucleosomal patterns presented here help interpret structural dependencies of the chromatin fiber on internal and external factors. In particular, we suggest that longer-NRL are more advantageous for packing and achieving various levels of fiber compaction throughout the cell cycle.

Single-molecule force spectroscopy reveals a mechanically stable protein fold and the rational tuning of its mechanical stability.

It is recognized that shear topology of two directly connected force-bearing terminal beta-strands is a common feature among the vast majority of mechanically stable proteins known so far. However, these proteins belong to only two distinct protein folds, Ig-like beta sandwich fold and beta-grasp fold, significantly hindering delineating molecular determinants of mechanical stability and rational tuning of mechanical properties. Here we combine single-molecule atomic force microscopy and steered molecular dynamics simulation to reveal that the de novo designed Top7 fold [Kuhlman B, Dantas G, Ireton GC, Varani G, Stoddard BL, Baker D (2003) Science 302:1364-1368] represents a mechanically stable protein fold that is distinct from Ig-like beta sandwich and beta-grasp folds. Although the two force-bearing beta strands of Top7 are not directly connected, Top7 displays significant mechanical stability, demonstrating that the direct connectivity of force-bearing beta strands in shear topology is not mandatory for mechanical stability. This finding broadens our understanding of the design of mechanically stable proteins and expands the protein fold space where mechanically stable proteins can be screened. Moreover, our results revealed a substructure-sliding mechanism for the mechanical unfolding of Top7 and the existence of two possible unfolding pathways with different height of energy barrier. Such insights enabled us to rationally tune the mechanical stability of Top7 by redesigning its mechanical unfolding pathway. Our study demonstrates that computational biology methods (including de novo design) offer great potential for designing proteins of defined topology to achieve significant and tunable mechanical properties in a rational and systematic fashion.

Improved protein fold assignment using support vector machines.

Because of the relatively large gap of knowledge between number of protein sequences and protein structures, the ability to construct a computational model predicting structure from sequence information has become an important area of research. The knowledge of a protein's structure is crucial in understanding its biological role. In this work, we present a support vector machine based method for recognising a protein's fold from sequence information alone, where this sequence has less similarity with sequences of known structures. We have focused on improving multi-class classification, parameter tuning, descriptor design, and feature selection. The current implementation demonstrates better prediction accuracy than previous similar approaches, and has similar performance when compared with straightforward threading.