Trajectory Statistical Learning of the Potential Mean of Force and Diffusion Coefficient from Molecular Dynamics Simulations.

Central to studying the conformational changes of a complex protein is understanding the dynamics and energetics involved. Phenomenologically, structural dynamics can be formulated using an overdamped Langevin model along an observable, e.g., the distance between two residues in the protein. The Langevin model is specified by the deterministic force (the potential of mean force, PMF) and stochastic force (characterized by the diffusion coefficient, D). It is therefore of great interest to be able to extract both PMF and D from an observable time series but under the same computational framework. Here, we approach this challenge in molecular dynamics (MD) simulations by treating it as a missing-data Bayesian estimation problem. An important distinction in our methodology is that the entire MD trajectory, as opposed to the individual data elements, is used as the statistical variable in Bayesian imputation. This idea is implemented through an eigen-decomposition procedure for a time-symmetrized Fokker-Planck equation, followed by maximizing the likelihood for parameter estimation. The mathematical expressions for the functional derivatives used in learning PMF and D also provide new physical insights for the manner by which the information on both the deterministic and stochastic forces is encoded in the dynamics data. An all-atom MD simulation of a nontrivial biomolecule case is used to illustrate the application of this approach. We show that, interestingly, the results of trajectory statistical learning can motivate new order parameters for an improved description of the kinetic bottlenecks in conformational changes. Complementing purely data-driven or black-box methods, this work underscores the advantages of physics-based machine learning in gaining chemical insights from quantitative parameter estimation.

Subdomain dynamics enable chemical chain reactions in non-ribosomal peptide synthetases.

Many peptide-derived natural products are produced by non-ribosomal peptide synthetases (NRPSs) in an assembly-line fashion. Each amino acid is coupled to a designated peptidyl carrier protein (PCP) through two distinct reactions catalysed sequentially by the single active site of the adenylation domain (A-domain). Accumulating evidence suggests that large-amplitude structural changes occur in different NRPS states; yet how these molecular machines orchestrate such biochemical sequences has remained elusive. Here, using single-molecule Förster resonance energy transfer, we show that the A-domain of gramicidin S synthetase I adopts structurally extended and functionally obligatory conformations for alternating between adenylation and thioester-formation structures during enzymatic cycles. Complementary biochemical, computational and small-angle X-ray scattering studies reveal interconversion among these three conformations as intrinsic and hierarchical where intra-A-domain organizations propagate to remodel inter-A-PCP didomain configurations during catalysis. The tight kinetic coupling between structural transitions and enzymatic transformations is quantified, and how the gramicidin S synthetase I A-domain utilizes its inherent conformational dynamics to drive directional biosynthesis with a flexibly linked PCP domain is revealed.

Molecular insight into the specific enzymatic properties of TREX1 revealing the diverse functions in processing RNA and DNA/RNA hybrids.

In various autoimmune diseases, dysfunctional TREX1 (Three prime Repair Exonuclease 1) leads to accumulation of endogenous single-stranded DNA (ssDNA), double-stranded DNA (dsDNA) and DNA/RNA hybrids in the cytoplasm and triggers immune activation through the cGAS-STING pathway. Although inhibition of TREX1 could be a useful strategy for cancer immunotherapy, profiling cellular functions in terms of its potential substrates is a key step. Particularly important is the functionality of processing DNA/RNA hybrids and RNA substrates. The exonuclease activity measurements conducted here establish that TREX1 can digest both ssRNA and DNA/RNA hybrids but not dsRNA. The newly solved structures of TREX1-RNA product and TREX1-nucleotide complexes show that 2'-OH does not impose steric hindrance or specific interactions for the recognition of RNA. Through all-atom molecular dynamics simulations, we illustrate that the 2'-OH-mediated intra-chain hydrogen bonding in RNA would affect the binding with TREX1 and thereby reduce the exonuclease activity. This notion of higher conformational rigidity in RNA leading TREX1 to exhibit weaker catalytic cleavage is further validated by the binding affinity measurements with various synthetic DNA-RNA junctions. The results of this work thus provide new insights into the mechanism by which TREX1 processes RNA and DNA/RNA hybrids and contribute to the molecular-level understanding of the complex cellular functions of TREX1 as an exonuclease.

Mechanical codes of chemical-scale specificity in DNA motifs.

In gene transcription, certain sequences of double-stranded (ds)DNA play a vital role in nucleosome positioning and expression initiation. That dsDNA is deformed to various extents in these processes leads us to ask: Could the genomic DNA also have sequence specificity in its chemical-scale mechanical properties? We approach this question using statistical machine learning to determine the rigidity between DNA chemical moieties. What emerges for the polyA, polyG, TpA, and CpG sequences studied here is a unique trigram that contains the quantitative mechanical strengths between bases and along the backbone. In a way, such a sequence-dependent trigram could be viewed as a DNA mechanical code. Interestingly, we discover a compensatory competition between the axial base-stacking interaction and the transverse base-pairing interaction, and such a reciprocal relationship constitutes the most discriminating feature of the mechanical code. Our results also provide chemical-scale understanding for experimental observables. For example, the long polyA persistence length is shown to have strong base stacking while its complement (polyAc) exhibits high backbone rigidity. The mechanical code concept enables a direct reading of the physical interactions encoded in the sequence which, with further development, is expected to shed new light on DNA allostery and DNA-binding drugs.

Structural basis for water modulating RNA duplex formation in the CUG repeats of myotonic dystrophy type 1.

Secondary structures formed by expanded CUG RNA are involved in the pathobiology of myotonic dystrophy type 1. Understanding the molecular basis of toxic RNA structures can provide insights into the mechanism of disease pathogenesis and accelerate the drug discovery process. Here, we report the crystal structure of CUG repeat RNA containing three U-U mismatches between C-G and G-C base pairs. The CUG RNA crystallizes as an A-form duplex, with the first and third U-U mismatches adopting a water-mediated asymmetric mirror isoform geometry. We found for the first time that a symmetric, water-bridged U-H2O-U mismatch is well tolerated within the CUG RNA duplex, which was previously suspected but not observed. The new water-bridged U-U mismatch resulted in high base-pair opening and single-sided cross-strand stacking interactions, which in turn dominate the CUG RNA structure. Furthermore, we performed molecular dynamics simulations that complemented the structural findings and proposed that the first and third U-U mismatches are interchangeable conformations, while the central water-bridged U-U mismatch represents an intermediate state that modulates the RNA duplex conformation. Collectively, the new structural features provided in this work are important for understanding the recognition of U-U mismatches in CUG repeats by external ligands such as proteins or small molecules.

Statistical learning of protein elastic network from positional covariance matrix.

Positional fluctuation and covariance during protein dynamics are key observables for understanding the molecular origin of biological functions. A frequently employed potential energy function for describing protein structural variation at the coarse-gained level is elastic network model (ENM). A long-standing issue in biomolecular simulation is thus the parametrization of ENM spring constants from the components of positional covariance matrix (PCM). Based on sensitivity analysis of PCM, the direct-coupling statistics of each spring, which is a specific combination of position fluctuation and covariance, is found to exhibit prominent signal of parameter dependence. This finding provides the basis for devising the objective function and the scheme of running through the effective one-dimensional optimization of every spring by self-consistent iteration. Formal derivation of the positional covariance statistical learning (PCSL) method also motivates the necessary data regularization for stable calculations. Robust convergence of PCSL is achieved in taking an all-atom molecular dynamics trajectory or an ensemble of homologous structures as input data. The PCSL framework can also be generalized with mixed objective functions to capture specific property such as the residue flexibility profile. Such physical chemistry-based statistical learning thus provides a useful platform for integrating the mechanical information encoded in various experimental or computational data.

Edge weights in a protein elastic network reorganize collective motions and render long-range sensitivity responses.

The effects of inter-residue interactions on protein collective motions are analyzed by comparing two elastic network models (ENM)-structural contact ENM (SC-ENM) and molecular dynamics (MD)-ENM-with the edge weights computed from an all-atom MD trajectory by structure-mechanics statistical learning. A theoretical framework is devised to decompose the eigenvalues of ENM Hessian into contributions from individual springs and to compute the sensitivities of positional fluctuations and covariances to spring constant variation. Our linear perturbation approach quantifies the response mechanisms as softness modulation and orientation shift. All contacts of Cα positions in SC-ENM have an identical spring constant by fitting the profile of root-of-mean-squared-fluctuation calculated from an all-atom MD simulation, and the same trajectory data are also used to compute the specific spring constant of each contact as an MD-ENM edge weight. We illustrate that the soft-mode reorganization can be understood in terms of gaining weights along the structural contacts of low elastic strengths and loosing magnitude along those of high rigidities. With the diverse mechanical strengths encoded in protein dynamics, MD-ENM is found to have more pronounced long-range couplings and sensitivity responses with orientation shift identified as a key player in driving the specific residues to have high sensitivities. Furthermore, the responses of perturbing the springs of different residues are found to have asymmetry in the action-reaction relationship. In understanding the mutation effects on protein functional properties, such as long-range communications, our results point in the directions of collective motions as a major effector.

Structure-mechanics statistical learning uncovers mechanical relay in proteins.

A protein's adaptive response to its substrates is one of the key questions driving molecular physics and physical chemistry. This work employs the recently developed structure-mechanics statistical learning method to establish a mechanical perspective. Specifically, by mapping all-atom molecular dynamics simulations onto the spring parameters of a backbone-side-chain elastic network model, the chemical moiety specific force constants (or mechanical rigidity) are used to assemble the rigidity graph, which is the matrix of inter-residue coupling strength. Using the S1A protease and the PDZ3 signaling domain as examples, chains of spatially contiguous residues are found to exhibit prominent changes in their mechanical rigidity upon substrate binding or dissociation. Such a mechanical-relay picture thus provides a mechanistic underpinning for conformational changes, long-range communication, and inter-domain allostery in both proteins, where the responsive mechanical hotspots are mostly residues having important biological functions or significant mutation sensitivity.

Methotrexate inhibition of SARS-CoV-2 entry, infection and inflammation revealed by bioinformatics approach and a hamster model.

Background: Drug repurposing is a fast and effective way to develop drugs for an emerging disease such as COVID-19. The main challenges of effective drug repurposing are the discoveries of the right therapeutic targets and the right drugs for combating the disease. Methods: Here, we present a systematic repurposing approach, combining Homopharma and hierarchal systems biology networks (HiSBiN), to predict 327 therapeutic targets and 21,233 drug-target interactions of 1,592 FDA drugs for COVID-19. Among these multi-target drugs, eight candidates (along with pimozide and valsartan) were tested and methotrexate was identified to affect 14 therapeutic targets suppressing SARS-CoV-2 entry, viral replication, and COVID-19 pathologies. Through the use of in vitro (EC50 = 0.4 µM) and in vivo models, we show that methotrexate is able to inhibit COVID-19 via multiple mechanisms. Results: Our in vitro studies illustrate that methotrexate can suppress SARS-CoV-2 entry and replication by targeting furin and DHFR of the host, respectively. Additionally, methotrexate inhibits all four SARS-CoV-2 variants of concern. In a Syrian hamster model for COVID-19, methotrexate reduced virus replication, inflammation in the infected lungs. By analysis of transcriptomic analysis of collected samples from hamster lung, we uncovered that neutrophil infiltration and the pathways of innate immune response, adaptive immune response and thrombosis are modulated in the treated animals. Conclusions: We demonstrate that this systematic repurposing approach is potentially useful to identify pharmaceutical targets, multi-target drugs and regulated pathways for a complex disease. Our findings indicate that methotrexate is established as a promising drug against SARS-CoV-2 variants and can be used to treat lung damage and inflammation in COVID-19, warranting future evaluation in clinical trials.

Mechanical couplings of protein backbone and side chains exhibit scale-free network properties and specific hotspots for function.

A backbone-side-chain elastic network model (bsENM) is devised in this contribution to decipher the network of molecular interactions during protein dynamics. The chemical details in 5 µs all-atom molecular dynamics (MD) simulation are mapped onto the bsENM spring constants by self-consistent iterations. The elastic parameters obtained by this structure-mechanics statistical learning are then used to construct inter-residue rigidity graphs for the chemical components in protein amino acids. A key discovery is that the mechanical coupling strengths of both backbone and side chains exhibit heavy-tailed distributions and scale-free network properties. In both rat trypsin and PDZ3 proteins, the statistically prominent modes of rigidity graphs uncover the sequence-specific coupling patterns and mechanical hotspots. Based on the contributions to graphical modes, our residue rigidity scores in backbone and side chains are found to be very useful metrics for the biological significance. Most functional sites have high residue rigidity scores in side chains while the biologically important glycines are generally next to mechanical hotspots. Furthermore, prominent modes in the rigidity graphs involving side chains oftentimes coincide with the co-evolution patterns due to evolutionary restraints. The bsENM specifically devised to resolve the protein chemical character thus provides useful means for extracting functional information from all-atom MD.

Understanding APE1 cellular functions by the structural preference of exonuclease activities.

Mammalian apurinic/apyrimidinic (AP) endonuclease 1 (APE1) has versatile enzymatic functions, including redox, endonuclease, and exonuclease activities. APE1 is thus broadly associated with pathways in DNA repair, cancer cell growth, and drug resistance. Unlike its AP site-specific endonuclease activity in Base excision repair (BER), the 3'-5' exonucleolytic cleavage of APE1 using the same active site exhibits complex substrate selection patterns, which are key to the biological functions. This work aims to integrate molecular structural information and biocatalytic properties to deduce the substrate recognition mechanism of APE1 as an exonuclease and make connection to its diverse functionalities in the cell. In particular, an induced space-filling model emerges in which a bridge-like structure is formed by Arg177 and Met270 (RM bridge) upon substrate binding, causing the active site to adopt a long and narrow product pocket for hosting the leaving group of an AP site or the 3'-end nucleotide. Rather than distinguishing bases as other exonucleases, the hydrophobicity and steric hindrance due to the APE1 product pocket provides selectivity for substrate structures, such as matched or mismatched blunt-ended dsDNA, recessed dsDNA, gapped dsDNA, and nicked dsDNA with 3'-end overhang shorter than 2 nucleotides. These dsDNAs are similar to the native substrates in BER proofreading, BER for trinucleotide repeats (TNR), Nucleotide incision repair (NIR), DNA single-strand breaks (SSB), SSB with damaged bases, and apoptosis. Integration of in vivo studies, in vitro biochemical assays, and structural analysis is thus essential for linking the APE1 exonuclease activity to the specific roles in cellular functions.

APE1 distinguishes DNA substrates in exonucleolytic cleavage by induced space-filling.

The exonuclease activity of Apurinic/apyrimidinic endonuclease 1 (APE1) is responsible for processing matched/mismatched terminus in various DNA repair pathways and for removing nucleoside analogs associated with drug resistance. To fill in the gap of structural basis for exonucleolytic cleavage, we determine the APE1-dsDNA complex structures displaying end-binding. As an exonuclease, APE1 does not show base preference but can distinguish dsDNAs with different structural features. Integration with assaying enzyme activity and binding affinity for a variety of substrates reveals for the first time that both endonucleolytic and exonucleolytic cleavage can be understood by an induced space-filling model. Binding dsDNA induces RM (Arg176 and Met269) bridge that defines a long and narrow product pocket for exquisite machinery of substrate selection. Our study paves the way to comprehend end-processing of dsDNA in the cell and the drug resistance relating to APE1.

Targeted Covalent Inhibitors Allosterically Deactivate the DEDDh Lassa Fever Virus NP Exonuclease from Alternative Distal Sites.

For using targeted covalent inhibitors (TCIs) as anticancer and antiviral drugs, we establish that the model compounds PCMPS (p-chloromercuriphenyl sulfate) and PCMB (p-chloromercuribenzoate) are inhibitors of the DEDDh family of exonucleases. The underlying mechanism is analyzed by X-ray crystallography, activity/nucleic acid-binding assays, and all-atom molecular dynamics (MD) simulations. The first TCI-complexed structures of a DEDDh enzyme, the Lassa fever virus NP exonuclease (NPexo), are resolved to elucidate that the Cys409 binding site is away from the active site and the RNA-binding lid. The NPexo C409A structures indicate Cys461 as the alternative distal site for obstructing the equally active mutant. All-atom MD simulations of the wild type and mutant NPexos in explicit solvent uncover an allosteric inhibition mechanism that the local perturbation induced by PCMPS sulfonate propagates to impact the RNA-binding lid conformation. Binding assay studies confirm that PCMPS does affect the RNA binding of NPexo. The predicted relative potency between PCMPS and PCMB is also in line with experiments. The structural data and inhibition mechanism established in this work provide an important molecular basis for the drug development of TCIs.

Basal leakage in oscillation: Coupled transcriptional and translational control using feed-forward loops.

The circadian clock is a complex system that plays many important roles in most organisms. Previously, many mathematical models have been used to sharpen our understanding of the Arabidopsis clock, which brought to light the roles of each transcriptional and post-translational regulations. However, the presence of both regulations, instead of either transcription or post-translation, raised curiosity of whether the combination of these two regulations is important for the clock's system. In this study, we built a series of simplified oscillators with different regulations to study the importance of post-translational regulation (specifically, 26S proteasome degradation) in the clock system. We found that a simple transcriptional-based oscillator can already generate sustained oscillation, but the oscillation can be easily destroyed in the presence of transcriptional leakage. Coupling post-translational control with transcriptional-based oscillator in a feed-forward loop will greatly improve the robustness of the oscillator in the presence of basal leakage. Using these general models, we were able to replicate the increased variability observed in the E3 ligase mutant for both plant and mammalian clocks. With this insight, we also predict a plausible regulator of several E3 ligase genes in the plant's clock. Thus, our results provide insights into and the plausible importance in coupling transcription and post-translation controls in the clock system.

Structure-mechanics statistical learning unravels the linkage between local rigidity and global flexibility in nucleic acids.

The mechanical properties of nucleic acids underlie biological processes ranging from genome packaging to gene expression, but tracing their molecular origin has been difficult due to the structural and chemical complexity. We posit that concepts from machine learning can help to tackle this long-standing challenge. Here, we demonstrate the feasibility and advantage of this strategy through developing a structure-mechanics statistical learning scheme to elucidate how local rigidity in double-stranded (ds)DNA and dsRNA may lead to their global flexibility in bend, stretch, and twist. Specifically, the mechanical parameters in a heavy-atom elastic network model are computed from the trajectory data of all-atom molecular dynamics simulation. The results show that the inter-atomic springs for backbone and ribose puckering in dsRNA are stronger than those in dsDNA, but are similar in strengths for base-stacking and base-pairing. Our analysis shows that the experimental observation of dsDNA being easier to bend but harder to stretch than dsRNA comes mostly from the respective B- and A-form topologies. The computationally resolved composition of local rigidity indicates that the flexibility of both nucleic acids is mostly due to base-stacking. But for properties like twist-stretch coupling, backbone springs are shown to play a major role instead. The quantitative connection between local rigidity and global flexibility sets foundation for understanding how local binding and chemical modification of genetic materials effectuate longer-ranged regulatory signals.

Compound Molecular Logic in Accessing the Active Site of Mycobacterium tuberculosis Protein Tyrosine Phosphatase B.

Protein tyrosine phosphatase B (PtpB) from Mycobacterium tuberculosis (Mtb) extends the bacteria's survival in hosts and hence is a potential target for Mtb-specific drugs. To study how Mtb-specific sequence insertions in PtpB may regulate access to its active site through large-amplitude conformational changes, we performed free-energy calculations using an all-atom explicit solvent model. Corroborated by biochemical assays, the results show that PtpB's active site is controlled via an "either/or" compound conformational gating mechanism, an unexpected discovery that Mtb has evolved to bestow a single enzyme with such intricate logical operations. In addition to providing unprecedented insights for its active-site surroundings, the findings also suggest new ways of inactivating PtpB.

An incoherent feed-forward loop switches the Arabidopsis clock rapidly between two hysteretic states.

In higher plants (e.g., Arabidopsis thaliana), the core structure of the circadian clock is mostly governed by a repression process with very few direct activators. With a series of simplified models, we studied the underlying mechanism and found that the Arabidopsis clock consists of type-2 incoherent feed-forward loops (IFFLs), one of them creating a pulse-like expression in PRR9/7. The double-negative feedback loop between CCA1/LHY and PRR5/TOC1 generates a bistable, hysteretic behavior in the Arabidopsis circadian clock. We found that the IFFL involving PRR9/7 breaks the bistability and moves the system forward with a rapid pulse in the daytime, and the evening complex (EC) breaks it in the evening. With this illustration, we can intuitively explain the behavior of the clock under mutant conditions. Thus, our results provide new insights into the underlying network structures of the Arabidopsis core oscillator.

Structural basis for overhang excision and terminal unwinding of DNA duplexes by TREX1.

Three prime repair exonuclease 1 (TREX1) is an essential exonuclease in mammalian cells, and numerous in vivo and in vitro data evidenced its participation in immunity regulation and in genotoxicity remediation. In these very complicated cellular functions, the molecular mechanisms by which duplex DNA substrates are processed are mostly elusive because of the lack of structure information. Here, we report multiple crystal structures of TREX1 complexed with various substrates to provide the structure basis for overhang excision and terminal unwinding of DNA duplexes. The substrates were designed to mimic the intermediate structural DNAs involved in various repair pathways. The results showed that the Leu24-Pro25-Ser26 cluster of TREX1 served to cap the nonscissile 5'-end of the DNA for precise removal of the short 3'-overhang in L- and Y-structural DNA or to wedge into the double-stranded region for further digestion along the duplex. Biochemical assays were also conducted to demonstrate that TREX1 can indeed degrade double-stranded DNA (dsDNA) to a full extent. Overall, this study provided unprecedented knowledge at the molecular level on the enzymatic substrate processing involved in prevention of immune activation and in responses to genotoxic stresses. For example, Arg128, whose mutation in TREX1 was linked to a disease state, were shown to exhibit consistent interaction patterns with the nonscissile strand in all of the structures we solved. Such structure basis is expected to play an indispensable role in elucidating the functional activities of TREX1 at the cellular level and in vivo.

LWD-TCP complex activates the morning gene CCA1 in Arabidopsis.

A double-negative feedback loop formed by the morning genes CIRCADIAN CLOCK ASSOCIATED1 (CCA1)/LATE ELONGATED HYPOCOTYL (LHY) and the evening gene TIMING OF CAB EXPRESSION1 (TOC1) contributes to regulation of the circadian clock in Arabidopsis. A 24-h circadian cycle starts with the peak expression of CCA1 at dawn. Although CCA1 is targeted by multiple transcriptional repressors, including PSEUDO-RESPONSE REGULATOR9 (PRR9), PRR7, PRR5 and CCA1 HIKING EXPEDITION (CHE), activators of CCA1 remain elusive. Here we use mathematical modelling to infer a co-activator role for LIGHT-REGULATED WD1 (LWD1) in CCA1 expression. We show that the TEOSINTE BRANCHED 1-CYCLOIDEA-PCF20 (TCP20) and TCP22 proteins act as LWD-interacting transcriptional activators. The concomitant binding of LWD1 and TCP20/TCP22 to the TCP-binding site in the CCA1 promoter activates CCA1. Our study reveals activators of the morning gene CCA1 and provides an action mechanism that ensures elevated expression of CCA1 at dawn to sustain a robust clock.

Importance of Internal Porosity for Glucan Adsorption in Mesoporous Carbon Materials.

To better understand the adsorption of long-chain poly(1 → 4)-ß-D-glucans on carbon surfaces as well as interactions responsible for this adsorption, we use a comparative study involving mesoporous carbon-silica composite materials that have been etched to varying degrees and all-atom molecular dynamics simulations. The materials synthesized as part of this etching study consist of an as-synthesized composite material (MCN-MSN), MCN-MSN-0.5 (composite materials consisting of 50% carbon by mass), MCN-MSN-0.3 (composite materials consisting of 70% carbon by mass), and MCN, in which silica etching was conducted using an aqueous ethanolic solution of either NaOH or HF. Data for the adsorption of long-chain glucans to these materials from concentrated aqueous HCl (37 wt %) solution demonstrate a direct relationship between the amount of ß-glu adsorption and the magnitude of exposed carbon mesopore surface area, which systematically increases and is also accompanied by an increase in the mesopore size during silica etching. This demonstrates ß-glu adsorption as occurring on internal carbon mesopores rather than exclusively on the external carbon surface. These experimental data on adsorption were corroborated by molecular dynamics (MD) simulations of ß-glu adsorption to a graphene bilayer separated by a distance of 3.2 nm, chosen to correspond to the carbon mesopore diameter of the experimental system. Simulation results using a variety of ß-glu solvent systems demonstrate the rapid adsorption of a ß-glu strand on the graphitic carbon surface via axial coupling and are consistent with experimentally observed trends in fast adsorption kinetics. Solvent-mediated effects such as small-scale hydrophobicity and preferential interactions with ions are shown to play important roles in modulating glucan adsorption to carbon surfaces, whereas experimental data on hydrophobically modified silica demonstrate that hydrophobicity in and of itself is insufficient to cause ß-glu adsorption from concentrated aqueous HCl solution.