In Silico Assessment of Chemical Disinfectants on Surface Proteins Unveiled Dissimilarity in Antiviral Efficacy and Suitability towards Pathogenic Viruses.

Viral pathogens pose a substantial threat to public health and necessitate the development of effective remediation and antiviral strategies. This short communication aimed to investigate the antiviral efficacy of disinfectants on the surface proteins of human pathogenic viruses. Using in silico modeling, the ligand-binding energies (LBEs) of selected disinfectants were predicted and combined with their environmental impacts and costs through an eco-pharmaco-economic analysis (EPEA). The results revealed that the binding affinities of chemical disinfectants to viral proteins varied significantly (p < 0.005). Rutin demonstrated promising broad-spectrum antiviral efficacy with an LBE of -8.49 ± 0.92 kcal/mol across all tested proteins. Additionally, rutin showed a superior eco-pharmaco-economic profile compared to the other chemicals, effectively balancing high antiviral effectiveness, moderate environmental impact, and affordability. These findings highlight rutin as a key phytochemical for use in remediating viral contaminants.

Secondary and Topological Structural Merge Prediction of Alpha-Helical Transmembrane Proteins Using a Hybrid Model Based on Hidden Markov and Long Short-Term Memory Neural Networks.

Alpha-helical transmembrane proteins (αTMPs) play essential roles in drug targeting and disease treatments. Due to the challenges of using experimental methods to determine their structure, αTMPs have far fewer known structures than soluble proteins. The topology of transmembrane proteins (TMPs) can determine the spatial conformation relative to the membrane, while the secondary structure helps to identify their functional domain. They are highly correlated on αTMPs sequences, and achieving a merge prediction is instructive for further understanding the structure and function of αTMPs. In this study, we implemented a hybrid model combining Deep Learning Neural Networks (DNNs) with a Class Hidden Markov Model (CHMM), namely HDNNtopss. DNNs extract rich contextual features through stacked attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) networks and Convolutional Neural Networks (CNNs), and CHMM captures state-associative temporal features. The hybrid model not only reasonably considers the probability of the state path but also has a fitting and feature-extraction capability for deep learning, which enables flexible prediction and makes the resulting sequence more biologically meaningful. It outperforms current advanced merge-prediction methods with a Q4 of 0.779 and an MCC of 0.673 on the independent test dataset, which have practical, solid significance. In comparison to advanced prediction methods for topological and secondary structures, it achieves the highest topology prediction with a Q2 of 0.884, which has a strong comprehensive performance. At the same time, we implemented a joint training method, Co-HDNNtopss, and achieved a good performance to provide an important reference for similar hybrid-model training.

Assessment of prediction methods for protein structures determined by NMR in CASP14: Impact of AlphaFold2.

NMR studies can provide unique information about protein conformations in solution. In CASP14, three reference structures provided by solution NMR methods were available (T1027, T1029, and T1055), as well as a fourth data set of NMR-derived contacts for an integral membrane protein (T1088). For the three targets with NMR-based structures, the best prediction results ranged from very good (GDT_TS = 0.90, for T1055) to poor (GDT_TS = 0.47, for T1029). We explored the basis of these results by comparing all CASP14 prediction models against experimental NMR data. For T1027, NMR data reveal extensive internal dynamics, presenting a unique challenge for protein structure prediction methods. The analysis of T1029 motivated exploration of a novel method of "inverse structure determination," in which an AlphaFold2 model was used to guide NMR data analysis. NMR data provided to CASP predictor groups for target T1088, a 238-residue integral membrane porin, was also used to assess several NMR-assisted prediction methods. Most groups involved in this exercise generated similar beta-barrel models, with good agreement with the experimental data. However, as was also observed in CASP13, some pure prediction groups that did not use any NMR data generated models for T1088 that better fit the NMR data than the models generated using these experimental data. These results demonstrate the remarkable power of modern methods to predict structures of proteins with accuracies rivaling solution NMR structures, and that it is now possible to reliably use prediction models to guide and complement experimental NMR data analysis.

SAXSDom: Modeling multidomain protein structures using small-angle X-ray scattering data.

Many proteins are composed of several domains that pack together into a complex tertiary structure. Multidomain proteins can be challenging for protein structure modeling, particularly those for which templates can be found for individual domains but not for the entire sequence. In such cases, homology modeling can generate high quality models of the domains but not for the orientations between domains. Small-angle X-ray scattering (SAXS) reports the structural properties of entire proteins and has the potential for guiding homology modeling of multidomain proteins. In this article, we describe a novel multidomain protein assembly modeling method, SAXSDom that integrates experimental knowledge from SAXS with probabilistic Input-Output Hidden Markov model to assemble the structures of individual domains together. Four SAXS-based scoring functions were developed and tested, and the method was evaluated on multidomain proteins from two public datasets. Incorporation of SAXS information improved the accuracy of domain assembly for 40 out of 46 critical assessment of protein structure prediction multidomain protein targets and 45 out of 73 multidomain protein targets from the ab initio domain assembly dataset. The results demonstrate that SAXS data can provide useful information to improve the accuracy of domain-domain assembly. The source code and tool packages are available at https://github.com/jianlin-cheng/SAXSDom.

Development of a biochemical and biophysical suite for integral membrane protein targets: A review.

The generation of integral membrane proteins (IMPs) in heterologous systems and their characterization remains a major challenge in biomedical research. Significant efforts have been invested both in academia and in the pharmaceutical industry to establish technologies for the expression, isolation and characterization of IMPs. Here we summarize some of the key aspects, which are important to support structure-based drug design (SBDD) in drug discovery projects. We furthermore include timeline estimates and an overview of the target selection and biophysical screening approaches.

Interfacing the Cell with "Biomimetic Membrane Proteins".

Integral membrane proteins mediate a myriad of cellular processes and are the target of many therapeutic drugs. Enhancement and extension of the functional scope of membrane proteins can be realized by membrane incorporation of engineered nanoparticles designed for specific diagnostic and therapeutic applications. In contrast to hydrophobic insertion of small amphiphilic molecules, delivery and membrane incorporation of particles on the nanometric scale poses a crucial barrier for technological development. In this perspective, the transformative potential of biomimetic membrane proteins (BMPs), current state of the art, and the barriers that need to be overcome in order to advance the field are discussed.

Extending hidden Markov models to allow conditioning on previous observations.

Hidden Markov Models (HMMs) are probabilistic models widely used in computational molecular biology. However, the Markovian assumption regarding transition probabilities which dictates that the observed symbol depends only on the current state may not be sufficient for some biological problems. In order to overcome the limitations of the first order HMM, a number of extensions have been proposed in the literature to incorporate past information in HMMs conditioning either on the hidden states, or on the observations, or both. Here, we implement a simple extension of the standard HMM in which the current observed symbol (amino acid residue) depends both on the current state and on a series of observed previous symbols. The major advantage of the method is the simplicity in the implementation, which is achieved by properly transforming the observation sequence, using an extended alphabet. Thus, it can utilize all the available algorithms for the training and decoding of HMMs. We investigated the use of several encoding schemes and performed tests in a number of important biological problems previously studied by our team (prediction of transmembrane proteins and prediction of signal peptides). The evaluation shows that, when enough data are available, the performance increased by 1.8%-8.2% and the existing prediction methods may improve using this approach. The methods, for which the improvement was significant (PRED-TMBB2, PRED-TAT and HMM-TM), are available as web-servers freely accessible to academic users at www.compgen.org/tools/ .

The mutation-dependent pathogenicity of NPHS2 p.R229Q: A guide for clinical assessment.

NPHS2, encoding podocin, is the major gene implicated in steroid-resistant nephrotic syndrome. Its c.686G>A, p.R229Q variant is the first human variant with a mutation-dependent pathogenicity; it is only pathogenic when trans-associated to specific mutations. Secondary to its high allele frequency in the European, South Asian, African, and Latino populations, its benign trans-associations can be accidentally identified in affected patients. Distinguishing pathogenic and benign p.R229Q associations can be challenging. In this paper, we present the currently known pathogenic and benign associations, and show that a rare p.R229Q association can be considered pathogenic if the variant in trans meets the following criteria; it affects the 270-351 residues and alters but does not disrupt the oligomerization, its p.R229Q association is found in a family with slowly progressing focal segmental glomerulosclerosis, but is expected to be rare in the general population (<1:106 ). We show that >15% of the p.R229Q associations identified so far in patients are benign.

FRET Detects the Size of Nanodomains for Coexisting Liquid-Disordered and Liquid-Ordered Phases.

Biomembranes with as few as three lipid components can form coexisting liquid-disordered (Ld) and liquid-ordered (Lo) phases. In the coexistence region of Ld and Lo phases, the lipid mixtures 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC)/1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC)/chol or brain sphingomyelin (bSM)/DOPC/chol form micron-scale domains that are easily visualized with light microscopy. Although large domains are not observed in the mixtures DSPC/1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC)/chol and bSM/POPC/chol, lateral heterogeneity is nevertheless detected using techniques with nanometer-scale spatial resolution. We propose a simple and accessible method to measure domain sizes below optical resolution (∼200 nm). We measured nanodomain size for the latter two mixtures by combining experimental Förster resonance energy transfer data with a Monte-Carlo-based analysis. We found a domain radius of 7.5-10 nm for DSPC/POPC/chol, similar to values obtained previously by neutron scattering, and ∼5 nm for bSM/POPC/chol, slightly smaller than measurable by neutron scattering. These analyses also detect the domain-size transition that is observed by fluorescence microscopy in the four-component lipid mixture bSM/DOPC/POPC/chol. Accurate measurements of fluorescent-probe partition coefficients are especially important for the analysis; therefore, we exploit three different methods to measure the partition coefficient of fluorescent molecules between Ld and Lo phases.

Integrated Structural Biology for α-Helical Membrane Protein Structure Determination.

While great progress has been made, only 10% of the nearly 1,000 integral, α-helical, multi-span membrane protein families are represented by at least one experimentally determined structure in the PDB. Previously, we developed the algorithm BCL::MP-Fold, which samples the large conformational space of membrane proteins de novo by assembling predicted secondary structure elements guided by knowledge-based potentials. Here, we present a case study of rhodopsin fold determination by integrating sparse and/or low-resolution restraints from multiple experimental techniques including electron microscopy, electron paramagnetic resonance spectroscopy, and nuclear magnetic resonance spectroscopy. Simultaneous incorporation of orthogonal experimental restraints not only significantly improved the sampling accuracy but also allowed identification of the correct fold, which is demonstrated by a protein size-normalized transmembrane root-mean-square deviation as low as 1.2 Å. The protocol developed in this case study can be used for the determination of unknown membrane protein folds when limited experimental restraints are available.

Perturbations of Native Membrane Protein Structure in Alkyl Phosphocholine Detergents: A Critical Assessment of NMR and Biophysical Studies.

Membrane proteins perform a host of vital cellular functions. Deciphering the molecular mechanisms whereby they fulfill these functions requires detailed biophysical and structural investigations. Detergents have proven pivotal to extract the protein from its native surroundings. Yet, they provide a milieu that departs significantly from that of the biological membrane, to the extent that the structure, the dynamics, and the interactions of membrane proteins in detergents may considerably vary, as compared to the native environment. Understanding the impact of detergents on membrane proteins is, therefore, crucial to assess the biological relevance of results obtained in detergents. Here, we review the strengths and weaknesses of alkyl phosphocholines (or foscholines), the most widely used detergent in solution-NMR studies of membrane proteins. While this class of detergents is often successful for membrane protein solubilization, a growing list of examples points to destabilizing and denaturing properties, in particular for α-helical membrane proteins. Our comprehensive analysis stresses the importance of stringent controls when working with this class of detergents and when analyzing the structure and dynamics of membrane proteins in alkyl phosphocholine detergents.

Dynamic Scaling Analysis of Molecular Motion within the LAT:Grb2:SOS Protein Network on Membranes.

Biochemical signaling pathways often involve proteins with multiple, modular interaction domains. Signaling activates binding sites, such as by tyrosine phosphorylation, which enables protein recruitment and growth of networked protein assemblies. Although widely observed, the physical properties of the assemblies, as well as the mechanisms by which they function, remain largely unknown. Here we examine molecular mobility within LAT:Grb2:SOS assemblies on supported membranes by single-molecule tracking. Trajectory analysis reveals a discrete temporal transition to subdiffusive motion below a characteristic timescale, indicating that the LAT:Grb2:SOS assembly has the dynamical structure of a loosely entangled polymer. Such dynamical analysis is also applicable in living cells, where it offers another dimension on the characteristics of cellular signaling assemblies.

Ion Channel Conformation and Oligomerization Assessment by Site-Directed Spin Labeling and Pulsed-EPR.

Mechanosensitive (MS) ion channels are multimeric integral membrane proteins that respond to increased lipid bilayer tension by opening their nonselective pores to release solutes and relieve increased cytoplasmic pressure. These systems undergo major conformational changes during gating and the elucidation of their mechanism requires a deep understanding of the interplay between lipids and proteins. Lipids are responsible for transmitting lateral tension to MS channels and therefore play a key role in obtaining a molecular-detail model for mechanosensation. Site-directed spin labeling combined with electron paramagnetic resonance (EPR) spectroscopy is a powerful spectroscopic tool in the study of proteins. The main bottleneck for its use relates to challenges associated with successful isolation of the protein of interest, introduction of paramagnetic labels on desired sites, and access to specialized instrumentation and expertise. The design of sophisticated experiments, which combine a variety of existing EPR methodologies to address a diversity of specific questions, require knowledge of the limitations and strengths, characteristic of each particular EPR method. This chapter is using the MS ion channels as paradigms and focuses on the application of different EPR techniques to ion channels, in order to investigate oligomerization, conformation, and the effect of lipids on their regulation. The methodology we followed, from the initial strategic selection of mutants and sample preparation, including protein purification, spin labeling, reconstitution into lipid mimics to the complete set-up of the pulsed-EPR experiments, is described in detail.

High-Throughput Automated Preparation and Simulation of Membrane Proteins with HTMD.

HTMD is a programmable scientific platform intended to facilitate simulation-based research in molecular systems. This paper presents the functionalities of HTMD for the preparation of a molecular dynamics simulation starting from PDB structures, building the system using well-known force fields, and applying standardized protocols for running the simulations. We demonstrate the framework's flexibility for high-throughput molecular simulations by applying a preparation, building, and simulation protocol with multiple force-fields on all of the seven hundred eukaryotic membrane proteins resolved to-date from the orientation of proteins in membranes (OPM) database. All of the systems are available on www.playmolecule.org .

Predicting Beta Barrel Transmembrane Proteins Using HMMs.

Transmembrane beta-barrels (TMBBs) constitute an important structural class of membrane proteins located in the outer membrane of gram-negative bacteria, and in the outer membrane of chloroplasts and mitochondria. They are involved in a wide variety of cellular functions and the prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes is of great importance as they are promising targets for antimicrobial drugs and vaccines. Several methods have been applied for the prediction of the transmembrane segments and the topology of beta barrel transmembrane proteins utilizing different algorithmic techniques. Hidden Markov Models (HMMs) have been efficiently used in the development of several computational methods used for this task. In this chapter we give a brief review of different available prediction methods for beta barrel transmembrane proteins pointing out sequence and structural features that should be incorporated in a prediction method. We then describe the procedure of the design and development of a Hidden Markov Model capable of predicting the transmembrane beta strands of TMBBs and discriminating them from globular proteins.

Predicting Alpha Helical Transmembrane Proteins Using HMMs.

Alpha helical transmembrane (TM) proteins constitute an important structural class of membrane proteins involved in a wide variety of cellular functions. The prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes, is of great importance for the elucidation of their structure and function. Several methods have been applied for the prediction of the transmembrane segments and the topology of alpha helical transmembrane proteins utilizing different algorithmic techniques. Hidden Markov Models (HMMs) have been efficiently used in the development of several computational methods used for this task. In this chapter we give a brief review of different available prediction methods for alpha helical transmembrane proteins pointing out sequence and structural features that should be incorporated in a prediction method. We then describe the procedure of the design and development of a Hidden Markov Model capable of predicting the transmembrane alpha helices in proteins and discriminating them from globular proteins.

Lipid membrane-mediated attraction between curvature inducing objects.

The interplay of membrane proteins is vital for many biological processes, such as cellular transport, cell division, and signal transduction between nerve cells. Theoretical considerations have led to the idea that the membrane itself mediates protein self-organization in these processes through minimization of membrane curvature energy. Here, we present a combined experimental and numerical study in which we quantify these interactions directly for the first time. In our experimental model system we control the deformation of a lipid membrane by adhering colloidal particles. Using confocal microscopy, we establish that these membrane deformations cause an attractive interaction force leading to reversible binding. The attraction extends over 2.5 times the particle diameter and has a strength of three times the thermal energy (-3.3 kBT). Coarse-grained Monte-Carlo simulations of the system are in excellent agreement with the experimental results and prove that the measured interaction is independent of length scale. Our combined experimental and numerical results reveal membrane curvature as a common physical origin for interactions between any membrane-deforming objects, from nanometre-sized proteins to micrometre-sized particles.

Development of a prediction system for tail-anchored proteins.

BACKGROUND: "Tail-anchored (TA) proteins" is a collective term for transmembrane proteins with a C-terminal transmembrane domain (TMD) and without an N-terminal signal sequence. TA proteins account for approximately 3-5 % of all transmembrane proteins that mediate membrane fusion, regulation of apoptosis, and vesicular transport. The combined use of TMD and signal sequence prediction tools is typically required to predict TA proteins. RESULTS: Here we developed a prediction system named TAPPM that predicted TA proteins solely from target amino acid sequences according to the knowledge of the sequence features of TMDs and the peripheral regions of TA proteins. Manually curated TA proteins were collected from published literature. We constructed hidden markov models of TA proteins as well as three different types of transmembrane proteins with similar structures and compared their likelihoods as TA proteins. CONCLUSIONS: Using the HMM models, we achieved high prediction accuracy; area under the receiver operator curve values reaching 0.963. A command line tool written in Python is available at https://github.com/davecao/tappm_cli .

Coarse-Grained Protein Models and Their Applications.

The traditional computational modeling of protein structure, dynamics, and interactions remains difficult for many protein systems. It is mostly due to the size of protein conformational spaces and required simulation time scales that are still too large to be studied in atomistic detail. Lowering the level of protein representation from all-atom to coarse-grained opens up new possibilities for studying protein systems. In this review we provide an overview of coarse-grained models focusing on their design, including choices of representation, models of energy functions, sampling of conformational space, and applications in the modeling of protein structure, dynamics, and interactions. A more detailed description is given for applications of coarse-grained models suitable for efficient combinations with all-atom simulations in multiscale modeling strategies.

In silico Structural characterization of podocin and assessment of nephrotic syndrome-associated podocin mutants.

Nephrotic syndrome (NS) is manifested by hyperproteinuria, hypoalbuminemia, and edema. NPHS2 that encodes podocin was found to have most mutations among the genes that are involved in the pathophysiology of NS. Podocin, an integral membrane protein belonging to stomatin family, is expressed exclusively in podocytes and is localized to slit-diaphragm (SD). Mutations in podocin are known to be associated with steroid-resistant NS and rapid progression to end-stage renal disease, thus signifying its role in maintaining SD integrity and podocyte function. The structural insights of podocin are not known, and the precise mechanism by which podocin contributes to the architecture of SD is yet to be elucidated. In this study, we deduced a model for human podocin, discussed the details of transmembrane localization and intrinsically unstructured regions, and provide an understanding of how podocin interacts with other SD components. Intraprotein interactions were assessed in wild-type podocin and in some of its mutants that are associated with idiopathic NS. Mutations in podocin alter the innate intraprotein interactions affecting the native structure of podocin and its ability to form critical complex with subpodocyte proteins. © 2016 IUBMB Life, 68(7):578-588, 2016.